3860 lines
1.6 MiB
3860 lines
1.6 MiB
{
|
||
"cells": [
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 1,
|
||
"id": "initial_id",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Hit:1 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64 InRelease\n",
|
||
"Hit:2 http://archive.ubuntu.com/ubuntu jammy InRelease \n",
|
||
"Hit:3 http://security.ubuntu.com/ubuntu jammy-security InRelease \n",
|
||
"Hit:4 http://archive.ubuntu.com/ubuntu jammy-updates InRelease\n",
|
||
"Hit:5 http://archive.ubuntu.com/ubuntu jammy-backports InRelease\n",
|
||
"Reading package lists... Done\n",
|
||
"Reading package lists... Done\n",
|
||
"Building dependency tree... Done\n",
|
||
"Reading state information... Done\n",
|
||
"graphviz is already the newest version (2.42.2-6ubuntu0.1).\n",
|
||
"0 upgraded, 0 newly installed, 0 to remove and 125 not upgraded.\n",
|
||
"Requirement already satisfied: tensorflow in /usr/local/lib/python3.11/dist-packages (2.14.0)\n",
|
||
"Requirement already satisfied: absl-py>=1.0.0 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (2.0.0)\n",
|
||
"Requirement already satisfied: astunparse>=1.6.0 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (1.6.3)\n",
|
||
"Requirement already satisfied: flatbuffers>=23.5.26 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (23.5.26)\n",
|
||
"Requirement already satisfied: gast!=0.5.0,!=0.5.1,!=0.5.2,>=0.2.1 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (0.5.4)\n",
|
||
"Requirement already satisfied: google-pasta>=0.1.1 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (0.2.0)\n",
|
||
"Requirement already satisfied: h5py>=2.9.0 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (3.9.0)\n",
|
||
"Requirement already satisfied: libclang>=13.0.0 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (16.0.6)\n",
|
||
"Requirement already satisfied: ml-dtypes==0.2.0 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (0.2.0)\n",
|
||
"Requirement already satisfied: numpy>=1.23.5 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (1.26.0)\n",
|
||
"Requirement already satisfied: opt-einsum>=2.3.2 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (3.3.0)\n",
|
||
"Requirement already satisfied: packaging in /usr/local/lib/python3.11/dist-packages (from tensorflow) (23.1)\n",
|
||
"Requirement already satisfied: protobuf!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.20.3 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (4.24.3)\n",
|
||
"Requirement already satisfied: setuptools in /usr/local/lib/python3.11/dist-packages (from tensorflow) (68.2.2)\n",
|
||
"Requirement already satisfied: six>=1.12.0 in /usr/lib/python3/dist-packages (from tensorflow) (1.16.0)\n",
|
||
"Requirement already satisfied: termcolor>=1.1.0 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (2.3.0)\n",
|
||
"Requirement already satisfied: typing-extensions>=3.6.6 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (4.8.0)\n",
|
||
"Requirement already satisfied: wrapt<1.15,>=1.11.0 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (1.14.1)\n",
|
||
"Requirement already satisfied: tensorflow-io-gcs-filesystem>=0.23.1 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (0.37.1)\n",
|
||
"Requirement already satisfied: grpcio<2.0,>=1.24.3 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (1.58.0)\n",
|
||
"Requirement already satisfied: tensorboard<2.15,>=2.14 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (2.14.0)\n",
|
||
"Requirement already satisfied: tensorflow-estimator<2.15,>=2.14.0 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (2.14.0)\n",
|
||
"Requirement already satisfied: keras<2.15,>=2.14.0 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (2.14.0)\n",
|
||
"Requirement already satisfied: wheel<1.0,>=0.23.0 in /usr/local/lib/python3.11/dist-packages (from astunparse>=1.6.0->tensorflow) (0.41.2)\n",
|
||
"Requirement already satisfied: google-auth<3,>=1.6.3 in /usr/local/lib/python3.11/dist-packages (from tensorboard<2.15,>=2.14->tensorflow) (2.23.1)\n",
|
||
"Requirement already satisfied: google-auth-oauthlib<1.1,>=0.5 in /usr/local/lib/python3.11/dist-packages (from tensorboard<2.15,>=2.14->tensorflow) (1.0.0)\n",
|
||
"Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.11/dist-packages (from tensorboard<2.15,>=2.14->tensorflow) (3.4.4)\n",
|
||
"Requirement already satisfied: requests<3,>=2.21.0 in /usr/local/lib/python3.11/dist-packages (from tensorboard<2.15,>=2.14->tensorflow) (2.31.0)\n",
|
||
"Requirement already satisfied: tensorboard-data-server<0.8.0,>=0.7.0 in /usr/local/lib/python3.11/dist-packages (from tensorboard<2.15,>=2.14->tensorflow) (0.7.1)\n",
|
||
"Requirement already satisfied: werkzeug>=1.0.1 in /usr/local/lib/python3.11/dist-packages (from tensorboard<2.15,>=2.14->tensorflow) (2.3.7)\n",
|
||
"Requirement already satisfied: cachetools<6.0,>=2.0.0 in /usr/local/lib/python3.11/dist-packages (from google-auth<3,>=1.6.3->tensorboard<2.15,>=2.14->tensorflow) (5.3.1)\n",
|
||
"Requirement already satisfied: pyasn1-modules>=0.2.1 in /usr/local/lib/python3.11/dist-packages (from google-auth<3,>=1.6.3->tensorboard<2.15,>=2.14->tensorflow) (0.3.0)\n",
|
||
"Requirement already satisfied: rsa<5,>=3.1.4 in /usr/local/lib/python3.11/dist-packages (from google-auth<3,>=1.6.3->tensorboard<2.15,>=2.14->tensorflow) (4.9)\n",
|
||
"Requirement already satisfied: urllib3>=2.0.5 in /usr/local/lib/python3.11/dist-packages (from google-auth<3,>=1.6.3->tensorboard<2.15,>=2.14->tensorflow) (2.0.5)\n",
|
||
"Requirement already satisfied: requests-oauthlib>=0.7.0 in /usr/local/lib/python3.11/dist-packages (from google-auth-oauthlib<1.1,>=0.5->tensorboard<2.15,>=2.14->tensorflow) (1.3.1)\n",
|
||
"Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.11/dist-packages (from requests<3,>=2.21.0->tensorboard<2.15,>=2.14->tensorflow) (3.2.0)\n",
|
||
"Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.11/dist-packages (from requests<3,>=2.21.0->tensorboard<2.15,>=2.14->tensorflow) (3.4)\n",
|
||
"Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.11/dist-packages (from requests<3,>=2.21.0->tensorboard<2.15,>=2.14->tensorflow) (2023.7.22)\n",
|
||
"Requirement already satisfied: MarkupSafe>=2.1.1 in /usr/local/lib/python3.11/dist-packages (from werkzeug>=1.0.1->tensorboard<2.15,>=2.14->tensorflow) (2.1.3)\n",
|
||
"Requirement already satisfied: pyasn1<0.6.0,>=0.4.6 in /usr/local/lib/python3.11/dist-packages (from pyasn1-modules>=0.2.1->google-auth<3,>=1.6.3->tensorboard<2.15,>=2.14->tensorflow) (0.5.0)\n",
|
||
"Requirement already satisfied: oauthlib>=3.0.0 in /usr/lib/python3/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<1.1,>=0.5->tensorboard<2.15,>=2.14->tensorflow) (3.2.0)\n",
|
||
"\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
|
||
"\u001b[0m\n",
|
||
"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n",
|
||
"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n",
|
||
"Requirement already satisfied: numpy in /usr/local/lib/python3.11/dist-packages (1.26.0)\n",
|
||
"\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
|
||
"\u001b[0m\n",
|
||
"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n",
|
||
"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n",
|
||
"Requirement already satisfied: pandas in /usr/local/lib/python3.11/dist-packages (2.2.3)\n",
|
||
"Requirement already satisfied: numpy>=1.23.2 in /usr/local/lib/python3.11/dist-packages (from pandas) (1.26.0)\n",
|
||
"Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.11/dist-packages (from pandas) (2.8.2)\n",
|
||
"Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.11/dist-packages (from pandas) (2024.2)\n",
|
||
"Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.11/dist-packages (from pandas) (2024.2)\n",
|
||
"Requirement already satisfied: six>=1.5 in /usr/lib/python3/dist-packages (from python-dateutil>=2.8.2->pandas) (1.16.0)\n",
|
||
"\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
|
||
"\u001b[0m\n",
|
||
"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n",
|
||
"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n",
|
||
"Requirement already satisfied: keras in /usr/local/lib/python3.11/dist-packages (2.14.0)\n",
|
||
"\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
|
||
"\u001b[0m\n",
|
||
"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n",
|
||
"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n",
|
||
"Requirement already satisfied: scikit-learn in /usr/local/lib/python3.11/dist-packages (1.6.0)\n",
|
||
"Requirement already satisfied: numpy>=1.19.5 in /usr/local/lib/python3.11/dist-packages (from scikit-learn) (1.26.0)\n",
|
||
"Requirement already satisfied: scipy>=1.6.0 in /usr/local/lib/python3.11/dist-packages (from scikit-learn) (1.14.1)\n",
|
||
"Requirement already satisfied: joblib>=1.2.0 in /usr/local/lib/python3.11/dist-packages (from scikit-learn) (1.4.2)\n",
|
||
"Requirement already satisfied: threadpoolctl>=3.1.0 in /usr/local/lib/python3.11/dist-packages (from scikit-learn) (3.5.0)\n",
|
||
"\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
|
||
"\u001b[0m\n",
|
||
"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n",
|
||
"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n",
|
||
"Requirement already satisfied: matplotlib in /usr/local/lib/python3.11/dist-packages (3.8.0)\n",
|
||
"Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (1.1.1)\n",
|
||
"Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (0.11.0)\n",
|
||
"Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (4.42.1)\n",
|
||
"Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (1.4.5)\n",
|
||
"Requirement already satisfied: numpy<2,>=1.21 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (1.26.0)\n",
|
||
"Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (23.1)\n",
|
||
"Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (10.0.1)\n",
|
||
"Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (3.2.0)\n",
|
||
"Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (2.8.2)\n",
|
||
"Requirement already satisfied: six>=1.5 in /usr/lib/python3/dist-packages (from python-dateutil>=2.7->matplotlib) (1.16.0)\n",
|
||
"\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
|
||
"\u001b[0m\n",
|
||
"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n",
|
||
"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n",
|
||
"Requirement already satisfied: joblib in /usr/local/lib/python3.11/dist-packages (1.4.2)\n",
|
||
"\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
|
||
"\u001b[0m\n",
|
||
"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n",
|
||
"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n",
|
||
"Requirement already satisfied: pyarrow in /usr/local/lib/python3.11/dist-packages (18.1.0)\n",
|
||
"\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
|
||
"\u001b[0m\n",
|
||
"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n",
|
||
"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n",
|
||
"Requirement already satisfied: fastparquet in /usr/local/lib/python3.11/dist-packages (2024.11.0)\n",
|
||
"Requirement already satisfied: pandas>=1.5.0 in /usr/local/lib/python3.11/dist-packages (from fastparquet) (2.2.3)\n",
|
||
"Requirement already satisfied: numpy in /usr/local/lib/python3.11/dist-packages (from fastparquet) (1.26.0)\n",
|
||
"Requirement already satisfied: cramjam>=2.3 in /usr/local/lib/python3.11/dist-packages (from fastparquet) (2.9.1)\n",
|
||
"Requirement already satisfied: fsspec in /usr/local/lib/python3.11/dist-packages (from fastparquet) (2024.12.0)\n",
|
||
"Requirement already satisfied: packaging in /usr/local/lib/python3.11/dist-packages (from fastparquet) (23.1)\n",
|
||
"Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.11/dist-packages (from pandas>=1.5.0->fastparquet) (2.8.2)\n",
|
||
"Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.11/dist-packages (from pandas>=1.5.0->fastparquet) (2024.2)\n",
|
||
"Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.11/dist-packages (from pandas>=1.5.0->fastparquet) (2024.2)\n",
|
||
"Requirement already satisfied: six>=1.5 in /usr/lib/python3/dist-packages (from python-dateutil>=2.8.2->pandas>=1.5.0->fastparquet) (1.16.0)\n",
|
||
"\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
|
||
"\u001b[0m\n",
|
||
"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n",
|
||
"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n",
|
||
"Requirement already satisfied: scipy in /usr/local/lib/python3.11/dist-packages (1.14.1)\n",
|
||
"Requirement already satisfied: numpy<2.3,>=1.23.5 in /usr/local/lib/python3.11/dist-packages (from scipy) (1.26.0)\n",
|
||
"\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
|
||
"\u001b[0m\n",
|
||
"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n",
|
||
"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n",
|
||
"Requirement already satisfied: seaborn in /usr/local/lib/python3.11/dist-packages (0.13.2)\n",
|
||
"Requirement already satisfied: numpy!=1.24.0,>=1.20 in /usr/local/lib/python3.11/dist-packages (from seaborn) (1.26.0)\n",
|
||
"Requirement already satisfied: pandas>=1.2 in /usr/local/lib/python3.11/dist-packages (from seaborn) (2.2.3)\n",
|
||
"Requirement already satisfied: matplotlib!=3.6.1,>=3.4 in /usr/local/lib/python3.11/dist-packages (from seaborn) (3.8.0)\n",
|
||
"Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (1.1.1)\n",
|
||
"Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.11/dist-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (0.11.0)\n",
|
||
"Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.11/dist-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (4.42.1)\n",
|
||
"Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (1.4.5)\n",
|
||
"Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.11/dist-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (23.1)\n",
|
||
"Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.11/dist-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (10.0.1)\n",
|
||
"Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (3.2.0)\n",
|
||
"Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.11/dist-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (2.8.2)\n",
|
||
"Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.11/dist-packages (from pandas>=1.2->seaborn) (2024.2)\n",
|
||
"Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.11/dist-packages (from pandas>=1.2->seaborn) (2024.2)\n",
|
||
"Requirement already satisfied: six>=1.5 in /usr/lib/python3/dist-packages (from python-dateutil>=2.7->matplotlib!=3.6.1,>=3.4->seaborn) (1.16.0)\n",
|
||
"\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
|
||
"\u001b[0m\n",
|
||
"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n",
|
||
"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n",
|
||
"Requirement already satisfied: tqdm in /usr/local/lib/python3.11/dist-packages (4.67.1)\n",
|
||
"\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
|
||
"\u001b[0m\n",
|
||
"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n",
|
||
"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n",
|
||
"Requirement already satisfied: pydot in /usr/local/lib/python3.11/dist-packages (3.0.3)\n",
|
||
"Requirement already satisfied: pyparsing>=3.0.9 in /usr/local/lib/python3.11/dist-packages (from pydot) (3.2.0)\n",
|
||
"\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
|
||
"\u001b[0m\n",
|
||
"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n",
|
||
"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n",
|
||
"Requirement already satisfied: tensorflow-io in /usr/local/lib/python3.11/dist-packages (0.37.1)\n",
|
||
"Requirement already satisfied: tensorflow-io-gcs-filesystem==0.37.1 in /usr/local/lib/python3.11/dist-packages (from tensorflow-io) (0.37.1)\n",
|
||
"\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
|
||
"\u001b[0m\n",
|
||
"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n",
|
||
"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n",
|
||
"Requirement already satisfied: tensorflow-addons in /usr/local/lib/python3.11/dist-packages (0.23.0)\n",
|
||
"Requirement already satisfied: packaging in /usr/local/lib/python3.11/dist-packages (from tensorflow-addons) (23.1)\n",
|
||
"Requirement already satisfied: typeguard<3.0.0,>=2.7 in /usr/local/lib/python3.11/dist-packages (from tensorflow-addons) (2.13.3)\n",
|
||
"\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
|
||
"\u001b[0m\n",
|
||
"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n",
|
||
"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"!apt-get update\n",
|
||
"!apt-get install graphviz -y\n",
|
||
"\n",
|
||
"!pip install tensorflow\n",
|
||
"!pip install numpy\n",
|
||
"!pip install pandas\n",
|
||
"\n",
|
||
"!pip install keras\n",
|
||
"!pip install scikit-learn\n",
|
||
"!pip install matplotlib\n",
|
||
"!pip install joblib\n",
|
||
"!pip install pyarrow\n",
|
||
"!pip install fastparquet\n",
|
||
"!pip install scipy\n",
|
||
"!pip install seaborn\n",
|
||
"!pip install tqdm\n",
|
||
"!pip install pydot\n",
|
||
"!pip install tensorflow-io\n",
|
||
"!pip install tensorflow-addons"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 2,
|
||
"id": "a467d3f0dfd9beab",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"2024-12-19 23:39:55.304410: E tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc:9342] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
|
||
"2024-12-19 23:39:55.304484: E tensorflow/compiler/xla/stream_executor/cuda/cuda_fft.cc:609] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
|
||
"2024-12-19 23:39:55.304521: E tensorflow/compiler/xla/stream_executor/cuda/cuda_blas.cc:1518] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
|
||
"2024-12-19 23:39:55.314601: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
|
||
"To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Keras version: 2.14.0\n",
|
||
"TensorFlow version: 2.14.0\n",
|
||
"CUDA available: True\n",
|
||
"GPU devices: [PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]\n",
|
||
"1 Physical GPUs, 1 Logical GPUs\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"2024-12-19 23:39:58.323709: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1886] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 43086 MB memory: -> device: 0, name: NVIDIA L40S, pci bus id: 0000:01:00.0, compute capability: 8.9\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"import os\n",
|
||
"import tensorflow as tf\n",
|
||
"import keras\n",
|
||
"\n",
|
||
"# Print versions and system information\n",
|
||
"print(f\"Keras version: {keras.__version__}\")\n",
|
||
"print(f\"TensorFlow version: {tf.__version__}\")\n",
|
||
"print(f\"CUDA available: {tf.test.is_built_with_cuda()}\")\n",
|
||
"print(f\"GPU devices: {tf.config.list_physical_devices('GPU')}\")\n",
|
||
"\n",
|
||
"# GPU memory configuration\n",
|
||
"gpus = tf.config.experimental.list_physical_devices('GPU')\n",
|
||
"if gpus:\n",
|
||
" try:\n",
|
||
" for gpu in gpus:\n",
|
||
" tf.config.experimental.set_memory_growth(gpu, True)\n",
|
||
"\n",
|
||
" logical_gpus = tf.config.experimental.list_logical_devices('GPU')\n",
|
||
" print(len(gpus), \"Physical GPUs,\", len(logical_gpus), \"Logical GPUs\")\n",
|
||
" except RuntimeError as e:\n",
|
||
" print(e)\n",
|
||
"\n",
|
||
"# Reduce TensorFlow logging verbosity\n",
|
||
"os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'\n",
|
||
"\n",
|
||
"# Set global precision policy\n",
|
||
"tf.keras.mixed_precision.set_global_policy('float32')\n",
|
||
"\n",
|
||
"# Uncomment to set seed for reproducibility\n",
|
||
"#tf.random.set_seed(42)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 3,
|
||
"id": "c0155cde4740b0a3",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"/usr/local/lib/python3.11/dist-packages/tensorflow_addons/utils/tfa_eol_msg.py:23: UserWarning: \n",
|
||
"\n",
|
||
"TensorFlow Addons (TFA) has ended development and introduction of new features.\n",
|
||
"TFA has entered a minimal maintenance and release mode until a planned end of life in May 2024.\n",
|
||
"Please modify downstream libraries to take dependencies from other repositories in our TensorFlow community (e.g. Keras, Keras-CV, and Keras-NLP). \n",
|
||
"\n",
|
||
"For more information see: https://github.com/tensorflow/addons/issues/2807 \n",
|
||
"\n",
|
||
" warnings.warn(\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"from datetime import datetime\n",
|
||
"import os\n",
|
||
"from typing import List\n",
|
||
"\n",
|
||
"import numpy as np\n",
|
||
"import pandas as pd\n",
|
||
"import matplotlib.pyplot as plt\n",
|
||
"from sklearn.preprocessing import StandardScaler\n",
|
||
"import tensorflow_addons as tfa\n",
|
||
"import joblib\n",
|
||
"import re\n",
|
||
"\n",
|
||
"# Set random state value (None for non-deterministic behavior)\n",
|
||
"random_state_value = None\n",
|
||
"\n",
|
||
"# Create execution timestamp for model versioning and logging\n",
|
||
"execute_name = datetime.now().strftime(\"%Y-%m-%d_%H-%M\")\n",
|
||
"\n",
|
||
"# Define directory paths\n",
|
||
"base_project_dir = './'\n",
|
||
"data_dir = '../../sources/'\n",
|
||
"models_project_dir = base_project_dir\n",
|
||
"\n",
|
||
"# Create required directories if they don't exist\n",
|
||
"os.makedirs(base_project_dir, exist_ok=True)\n",
|
||
"os.makedirs(models_project_dir, exist_ok=True)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 4,
|
||
"id": "1347fb59-50cc-4aa8-b805-ca9403037af5",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"def clean_column_name(name: str) -> str:\n",
|
||
" \"\"\"\n",
|
||
" Cleans column names by removing special characters and converting to snake_case with abbreviations.\n",
|
||
"\n",
|
||
" Parameters\n",
|
||
" ----------\n",
|
||
" name : str\n",
|
||
" Column name to clean\n",
|
||
"\n",
|
||
" Returns\n",
|
||
" -------\n",
|
||
" str\n",
|
||
" Cleaned column name\n",
|
||
" \"\"\"\n",
|
||
" # Remove special characters\n",
|
||
" name = re.sub(r'[^a-zA-Z0-9\\s]', '', name)\n",
|
||
"\n",
|
||
" # Convert to snake_case\n",
|
||
" name = name.lower().replace(' ', '_')\n",
|
||
"\n",
|
||
" # Common abbreviations mapping\n",
|
||
" abbreviations = {\n",
|
||
" 'production': 'prod',\n",
|
||
" 'percentage': 'pct',\n",
|
||
" 'hectare': 'ha',\n",
|
||
" 'tonnes': 't',\n",
|
||
" 'litres': 'l',\n",
|
||
" 'minimum': 'min',\n",
|
||
" 'maximum': 'max',\n",
|
||
" 'average': 'avg'\n",
|
||
" }\n",
|
||
"\n",
|
||
" for full, abbr in abbreviations.items():\n",
|
||
" name = name.replace(full, abbr)\n",
|
||
"\n",
|
||
" return name\n",
|
||
"\n",
|
||
"\n",
|
||
"def clean_column_names(df: pd.DataFrame) -> List[str]:\n",
|
||
" \"\"\"\n",
|
||
" Cleans all column names in a DataFrame.\n",
|
||
"\n",
|
||
" Parameters\n",
|
||
" ----------\n",
|
||
" df : pd.DataFrame\n",
|
||
" DataFrame whose columns need cleaning\n",
|
||
"\n",
|
||
" Returns\n",
|
||
" -------\n",
|
||
" list\n",
|
||
" List of cleaned column names\n",
|
||
" \"\"\"\n",
|
||
" new_columns = []\n",
|
||
"\n",
|
||
" for col in df.columns:\n",
|
||
" # Extract variety patterns using regex\n",
|
||
" varieties = re.findall(r'([a-z]+)_([a-z_]+)', col)\n",
|
||
" if varieties:\n",
|
||
" new_columns.append(f\"{varieties[0][0]}_{varieties[0][1]}\")\n",
|
||
" else:\n",
|
||
" new_columns.append(col)\n",
|
||
"\n",
|
||
" return new_columns"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 5,
|
||
"id": "4da1f1bb67343e3e",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"def save_plot(plt, title, output_dir=f'{base_project_dir}/{execute_name}_plots'):\n",
|
||
" os.makedirs(output_dir, exist_ok=True)\n",
|
||
" filename = \"\".join(x for x in title if x.isalnum() or x in [' ', '-', '_']).rstrip()\n",
|
||
" filename = filename.replace(' ', '_').lower()\n",
|
||
" filepath = os.path.join(output_dir, f\"{filename}.png\")\n",
|
||
" plt.savefig(filepath, bbox_inches='tight', dpi=300)\n",
|
||
" print(f\"Plot salvato come: {filepath}\")\n",
|
||
"\n",
|
||
"\n",
|
||
"def encode_techniques(df, mapping_path=f'{data_dir}technique_mapping.joblib'):\n",
|
||
" if not os.path.exists(mapping_path):\n",
|
||
" raise FileNotFoundError(f\"Mapping not found at {mapping_path}. Run create_technique_mapping first.\")\n",
|
||
"\n",
|
||
" technique_mapping = joblib.load(mapping_path)\n",
|
||
"\n",
|
||
" tech_columns = [col for col in df.columns if col.endswith('_tech')]\n",
|
||
"\n",
|
||
" # Mapping apply to all tech columns\n",
|
||
" for col in tech_columns:\n",
|
||
" df[col] = df[col].str.lower().map(technique_mapping).fillna(0).astype(int)\n",
|
||
"\n",
|
||
" return df\n",
|
||
"\n",
|
||
"\n",
|
||
"def decode_single_technique(technique_value, mapping_path=f'{data_dir}technique_mapping.joblib'):\n",
|
||
" if not os.path.exists(mapping_path):\n",
|
||
" raise FileNotFoundError(f\"Mapping not found at {mapping_path}\")\n",
|
||
"\n",
|
||
" technique_mapping = joblib.load(mapping_path)\n",
|
||
" reverse_mapping = {v: k for k, v in technique_mapping.items()}\n",
|
||
" reverse_mapping[0] = ''\n",
|
||
"\n",
|
||
" return reverse_mapping.get(technique_value, '')\n",
|
||
"\n",
|
||
"\n",
|
||
"def prepare_comparison_data(simulated_data, olive_varieties):\n",
|
||
"\n",
|
||
" df = simulated_data.copy()\n",
|
||
"\n",
|
||
" df.columns = clean_column_names(df)\n",
|
||
" df = encode_techniques(df)\n",
|
||
"\n",
|
||
" all_varieties = olive_varieties['Varietà di Olive'].unique()\n",
|
||
" varieties = [clean_column_name(variety) for variety in all_varieties]\n",
|
||
" comparison_data = []\n",
|
||
"\n",
|
||
" for variety in varieties:\n",
|
||
" olive_prod_col = next((col for col in df.columns if col.startswith(f'{variety}_') and col.endswith('_olive_prod')), None)\n",
|
||
" oil_prod_col = next((col for col in df.columns if col.startswith(f'{variety}_') and col.endswith('_avg_oil_prod')), None)\n",
|
||
" tech_col = next((col for col in df.columns if col.startswith(f'{variety}_') and col.endswith('_tech')), None)\n",
|
||
" water_need_col = next((col for col in df.columns if col.startswith(f'{variety}_') and col.endswith('_water_need')), None)\n",
|
||
"\n",
|
||
" if olive_prod_col and oil_prod_col and tech_col and water_need_col:\n",
|
||
" variety_data = df[[olive_prod_col, oil_prod_col, tech_col, water_need_col]]\n",
|
||
" variety_data = variety_data[variety_data[tech_col] != 0] # Esclude le righe dove la tecnica è 0\n",
|
||
"\n",
|
||
" if not variety_data.empty:\n",
|
||
" avg_olive_prod = pd.to_numeric(variety_data[olive_prod_col], errors='coerce').mean()\n",
|
||
" avg_oil_prod = pd.to_numeric(variety_data[oil_prod_col], errors='coerce').mean()\n",
|
||
" avg_water_need = pd.to_numeric(variety_data[water_need_col], errors='coerce').mean()\n",
|
||
" efficiency = avg_oil_prod / avg_olive_prod if avg_olive_prod > 0 else 0\n",
|
||
" water_efficiency = avg_oil_prod / avg_water_need if avg_water_need > 0 else 0\n",
|
||
"\n",
|
||
" comparison_data.append({\n",
|
||
" 'Variety': variety,\n",
|
||
" 'Avg Olive Production (kg/ha)': avg_olive_prod,\n",
|
||
" 'Avg Oil Production (L/ha)': avg_oil_prod,\n",
|
||
" 'Avg Water Need (m³/ha)': avg_water_need,\n",
|
||
" 'Oil Efficiency (L/kg)': efficiency,\n",
|
||
" 'Water Efficiency (L oil/m³ water)': water_efficiency\n",
|
||
" })\n",
|
||
"\n",
|
||
" return pd.DataFrame(comparison_data)\n",
|
||
"\n",
|
||
"\n",
|
||
"def plot_variety_comparison(comparison_data, metric):\n",
|
||
" plt.figure(figsize=(12, 6))\n",
|
||
" bars = plt.bar(comparison_data['Variety'], comparison_data[metric])\n",
|
||
" plt.title(f'Comparison of {metric} across Olive Varieties')\n",
|
||
" plt.xlabel('Variety')\n",
|
||
" plt.ylabel(metric)\n",
|
||
" plt.xticks(rotation=45, ha='right')\n",
|
||
"\n",
|
||
" for bar in bars:\n",
|
||
" height = bar.get_height()\n",
|
||
" plt.text(bar.get_x() + bar.get_width() / 2., height,\n",
|
||
" f'{height:.2f}',\n",
|
||
" ha='center', va='bottom')\n",
|
||
"\n",
|
||
" plt.tight_layout()\n",
|
||
" plt.show()\n",
|
||
" save_plot(plt, f'variety_comparison_{metric.lower().replace(\" \", \"_\").replace(\"/\", \"_\").replace(\"(\", \"\").replace(\")\", \"\")}')\n",
|
||
" plt.close()\n",
|
||
"\n",
|
||
"\n",
|
||
"def plot_efficiency_vs_production(comparison_data):\n",
|
||
" plt.figure(figsize=(10, 6))\n",
|
||
"\n",
|
||
" plt.scatter(comparison_data['Avg Olive Production (kg/ha)'],\n",
|
||
" comparison_data['Oil Efficiency (L/kg)'],\n",
|
||
" s=100)\n",
|
||
"\n",
|
||
" for i, row in comparison_data.iterrows():\n",
|
||
" plt.annotate(row['Variety'],\n",
|
||
" (row['Avg Olive Production (kg/ha)'], row['Oil Efficiency (L/kg)']),\n",
|
||
" xytext=(5, 5), textcoords='offset points')\n",
|
||
"\n",
|
||
" plt.title('Oil Efficiency vs Olive Production by Variety')\n",
|
||
" plt.xlabel('Average Olive Production (kg/ha)')\n",
|
||
" plt.ylabel('Oil Efficiency (L oil / kg olives)')\n",
|
||
" plt.tight_layout()\n",
|
||
" save_plot(plt, 'efficiency_vs_production')\n",
|
||
" plt.close()\n",
|
||
"\n",
|
||
"\n",
|
||
"def plot_water_efficiency_vs_production(comparison_data):\n",
|
||
" plt.figure(figsize=(10, 6))\n",
|
||
"\n",
|
||
" plt.scatter(comparison_data['Avg Olive Production (kg/ha)'],\n",
|
||
" comparison_data['Water Efficiency (L oil/m³ water)'],\n",
|
||
" s=100)\n",
|
||
"\n",
|
||
" for i, row in comparison_data.iterrows():\n",
|
||
" plt.annotate(row['Variety'],\n",
|
||
" (row['Avg Olive Production (kg/ha)'], row['Water Efficiency (L oil/m³ water)']),\n",
|
||
" xytext=(5, 5), textcoords='offset points')\n",
|
||
"\n",
|
||
" plt.title('Water Efficiency vs Olive Production by Variety')\n",
|
||
" plt.xlabel('Average Olive Production (kg/ha)')\n",
|
||
" plt.ylabel('Water Efficiency (L oil / m³ water)')\n",
|
||
" plt.tight_layout()\n",
|
||
" plt.show()\n",
|
||
" save_plot(plt, 'water_efficiency_vs_production')\n",
|
||
" plt.close()\n",
|
||
"\n",
|
||
"\n",
|
||
"def plot_water_need_vs_oil_production(comparison_data):\n",
|
||
" plt.figure(figsize=(10, 6))\n",
|
||
"\n",
|
||
" plt.scatter(comparison_data['Avg Water Need (m³/ha)'],\n",
|
||
" comparison_data['Avg Oil Production (L/ha)'],\n",
|
||
" s=100)\n",
|
||
"\n",
|
||
" for i, row in comparison_data.iterrows():\n",
|
||
" plt.annotate(row['Variety'],\n",
|
||
" (row['Avg Water Need (m³/ha)'], row['Avg Oil Production (L/ha)']),\n",
|
||
" xytext=(5, 5), textcoords='offset points')\n",
|
||
"\n",
|
||
" plt.title('Oil Production vs Water Need by Variety')\n",
|
||
" plt.xlabel('Average Water Need (m³/ha)')\n",
|
||
" plt.ylabel('Average Oil Production (L/ha)')\n",
|
||
" plt.tight_layout()\n",
|
||
" plt.show()\n",
|
||
" save_plot(plt, 'water_need_vs_oil_production')\n",
|
||
" plt.close()\n",
|
||
"\n",
|
||
"\n",
|
||
"def analyze_by_technique(simulated_data, olive_varieties):\n",
|
||
"\n",
|
||
" df = simulated_data.copy()\n",
|
||
"\n",
|
||
" df.columns = clean_column_names(df)\n",
|
||
" df = encode_techniques(df)\n",
|
||
" all_varieties = olive_varieties['Varietà di Olive'].unique()\n",
|
||
" varieties = [clean_column_name(variety) for variety in all_varieties]\n",
|
||
"\n",
|
||
" technique_data = []\n",
|
||
"\n",
|
||
" for variety in varieties:\n",
|
||
" olive_prod_col = next((col for col in df.columns if col.startswith(f'{variety}_') and col.endswith('_olive_prod')), None)\n",
|
||
" oil_prod_col = next((col for col in df.columns if col.startswith(f'{variety}_') and col.endswith('_avg_oil_prod')), None)\n",
|
||
" tech_col = next((col for col in df.columns if col.startswith(f'{variety}_') and col.endswith('_tech')), None)\n",
|
||
" water_need_col = next((col for col in df.columns if col.startswith(f'{variety}_') and col.endswith('_water_need')), None)\n",
|
||
"\n",
|
||
" if olive_prod_col and oil_prod_col and tech_col and water_need_col:\n",
|
||
" variety_data = df[[olive_prod_col, oil_prod_col, tech_col, water_need_col]]\n",
|
||
" variety_data = variety_data[variety_data[tech_col] != 0]\n",
|
||
"\n",
|
||
" if not variety_data.empty:\n",
|
||
" for tech in variety_data[tech_col].unique():\n",
|
||
" tech_data = variety_data[variety_data[tech_col] == tech]\n",
|
||
"\n",
|
||
" avg_olive_prod = pd.to_numeric(tech_data[olive_prod_col], errors='coerce').mean()\n",
|
||
" avg_oil_prod = pd.to_numeric(tech_data[oil_prod_col], errors='coerce').mean()\n",
|
||
" avg_water_need = pd.to_numeric(tech_data[water_need_col], errors='coerce').mean()\n",
|
||
"\n",
|
||
" efficiency = avg_oil_prod / avg_olive_prod if avg_olive_prod > 0 else 0\n",
|
||
" water_efficiency = avg_oil_prod / avg_water_need if avg_water_need > 0 else 0\n",
|
||
"\n",
|
||
" technique_data.append({\n",
|
||
" 'Variety': variety,\n",
|
||
" 'Technique': tech,\n",
|
||
" 'Technique String': decode_single_technique(tech),\n",
|
||
" 'Avg Olive Production (kg/ha)': avg_olive_prod,\n",
|
||
" 'Avg Oil Production (L/ha)': avg_oil_prod,\n",
|
||
" 'Avg Water Need (m³/ha)': avg_water_need,\n",
|
||
" 'Oil Efficiency (L/kg)': efficiency,\n",
|
||
" 'Water Efficiency (L oil/m³ water)': water_efficiency\n",
|
||
" })\n",
|
||
"\n",
|
||
" return pd.DataFrame(technique_data)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 6,
|
||
"id": "9aa4bf176c4affb9",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"def calculate_real_error(model, test_data, test_targets, scaler_y):\n",
|
||
"\n",
|
||
" predictions = model.predict(test_data)\n",
|
||
"\n",
|
||
" # Denormalize predictions and target values\n",
|
||
" predictions_real = scaler_y.inverse_transform(predictions)\n",
|
||
" targets_real = scaler_y.inverse_transform(test_targets)\n",
|
||
"\n",
|
||
" # Calculate percentage error for each target\n",
|
||
" percentage_errors = []\n",
|
||
" absolute_errors = []\n",
|
||
"\n",
|
||
" for i in range(predictions_real.shape[1]):\n",
|
||
" mae = np.mean(np.abs(predictions_real[:, i] - targets_real[:, i]))\n",
|
||
" mape = np.mean(np.abs((predictions_real[:, i] - targets_real[:, i]) / targets_real[:, i])) * 100\n",
|
||
" percentage_errors.append(mape)\n",
|
||
" absolute_errors.append(mae)\n",
|
||
"\n",
|
||
" target_names = ['olive_prod', 'min_oil_prod', 'max_oil_prod', 'avg_oil_prod', 'total_water_need']\n",
|
||
"\n",
|
||
" print(\"\\nErrori per target:\")\n",
|
||
" print(\"-\" * 50)\n",
|
||
" for i, target in enumerate(target_names):\n",
|
||
" print(f\"{target}:\")\n",
|
||
" print(f\"MAE assoluto: {absolute_errors[i]:.2f}\")\n",
|
||
" print(f\"Errore percentuale medio: {percentage_errors[i]:.2f}%\")\n",
|
||
" print(f\"Precisione: {100 - percentage_errors[i]:.2f}%\")\n",
|
||
" print(\"-\" * 50)\n",
|
||
"\n",
|
||
" return percentage_errors, absolute_errors"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 7,
|
||
"id": "b3ba2b96ba678389",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": "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",
|
||
"text/plain": [
|
||
"<Figure size 1200x600 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Plot salvato come: .//2024-12-19_23-39_plots/variety_comparison_avg_olive_production_kg_ha.png\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": "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",
|
||
"text/plain": [
|
||
"<Figure size 1200x600 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Plot salvato come: .//2024-12-19_23-39_plots/variety_comparison_avg_oil_production_l_ha.png\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": "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",
|
||
"text/plain": [
|
||
"<Figure size 1200x600 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Plot salvato come: .//2024-12-19_23-39_plots/variety_comparison_avg_water_need_m³_ha.png\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": "iVBORw0KGgoAAAANSUhEUgAABKUAAAJOCAYAAABm7rQwAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy81sbWrAAAACXBIWXMAAA9hAAAPYQGoP6dpAAClHElEQVR4nOzdeXhMZ//H8c8kslgjhCyEWBtqCUEeW6NtiFYtpYryBG1pa+mSau1baS1VtVerpShtWlrValMVoouUllJF7TuJPbElIbl/f/hlHiNBojFDvF/XNRe5z33OfM/MmTMznznnPhZjjBEAAAAAAABgR06OLgAAAAAAAAD3HkIpAAAAAAAA2B2hFAAAAAAAAOyOUAoAAAAAAAB2RygFAAAAAAAAuyOUAgAAAAAAgN0RSgEAAAAAAMDuCKUAAAAAAABgd4RSAAAAAAAAsDtCKQAArsNisWjEiBGOLuNfmz9/vgIDA+Xi4qKiRYvm6rJHjBghi8Vi0xYQEKBu3brl6v1cbx3efvttlS9fXs7OzgoKCrrl+9+3b58sFos+/vjjXKvZkXr16qWmTZvmyrI+/vhjWSwW/fHHH7myvJMnT6pgwYL67rvvcmV597pu3bopICDApi2v7Ltu5HbsZ7LanwEAbi9CKQDAde3evVvPPfecypcvL3d3dxUpUkQNGzbU5MmTdfHiRUeXh2z4559/1K1bN1WoUEGzZs3SBx98cNN5fv31Vz3++OPy9vaWm5ubAgIC9Nxzz+nAgQO5WpvFYrnu7fnnn7/pOixfvlyvv/66GjZsqDlz5uitt97K1fruVnv37tWHH36oQYMGWdsyQrcJEyZkaxlTp06Vh4eHLl26lOv1FS9eXM8++6yGDh2a68vOK7799ls1b95cxYsXl7u7uypXrqx+/frp5MmTji4tS19++aUsFos+/PDD6/b58ccfZbFYNGXKFDtWltmFCxc0YsQIxcbGOrQOAMAV+RxdAADgzrRs2TK1b99ebm5uioiIULVq1ZSamqpffvlFr732mrZs2ZKtgONudvHiReXLd3e/VcbGxio9PV2TJ09WxYoVb9p/6tSpeumll1S+fHn17dtXvr6+2rZtmz788ENFRUXpu+++U4MGDaz9hwwZogEDBtxyfU2bNlVERESm9sqVK990HVauXCknJyd99NFHcnV1tbZv375dTk45+92tbNmyunjxolxcXG5hLe4skydPVrly5fTggw/e8jKWLVumZs2a3bbH4/nnn9eUKVO0cuVKPfTQQ7flPu5W/fr10zvvvKOaNWuqf//+KlasmDZs2KBp06bps88+U0xMjO67774bLsPe+64WLVrIw8NDCxcu1LPPPptln4ULF8rZ2VkdO3bMlfu8lde5dCWUGjlypCSpSZMmNtP+7f4MAJBzd/cnbQDAbbF371517NhRZcuW1cqVK+Xr62ud1rt3b+3atUvLli1zYIW3T3p6ulJTU+Xu7i53d3dHl/OvHTt2TJKyddrer7/+qpdfflmNGjVSdHS0ChQoYJ32wgsvqGHDhnriiSe0ZcsWeXp6SpLy5cv3r778Vq5cWV26dLmldTh27Jjy589vE0hJkpubW47rsFgseeL5vnTpkhYsWGBzpFlOXbhwQatXr9Z7772Xi5XZqlKliqpVq6aPP/7Y4aHU5cuXlZ6enmk7coRPP/1U77zzjjp06KAFCxbI2dnZOq1bt2568MEH1b59e23YsOGGrzt7b8tubm564oknNGfOHB05ckR+fn4205OTk/XVV1+padOmKlmy5C3fjzFGycnJyp8//y29zm/m3+7PAAA5x+l7AIBMxo8fr3Pnzumjjz6yCaQyVKxYUS+99JL178uXL2vUqFGqUKGC9XSvQYMGKSUlxWa+gIAAPfbYY4qNjVWdOnWUP39+Va9e3XoaxZdffqnq1avL3d1dwcHB+vPPP23m79atmwoVKqQ9e/YoPDxcBQsWlJ+fn9544w0ZY2z6TpgwQQ0aNFDx4sWVP39+BQcHa9GiRZnWxWKxqE+fPlqwYIHuv/9+ubm5KTo62jrt6nFZzp49q5dfflkBAQFyc3NTyZIl1bRpU23YsMFmmV988YWCg4OVP39+eXl5qUuXLjp8+HCW63L48GG1adNGhQoVUokSJdSvXz+lpaVd55mxNWPGDGvNfn5+6t27t86cOWPzeA8fPlySVKJEiZuOMzNq1ChZLBbNnTvXJpCSpAoVKmj8+PE6evSo3n//fWv77R6D5XrrYLFYNGfOHJ0/f956yl/GeFBZjTVz5swZvfLKK9bnrnTp0oqIiNCJEyckXX9MqX/++UdPPPGEihUrJnd3d9WpU0dLly616ZMx5tKvv/6qyMhIlShRQgULFtTjjz+u48ePZ1qn77//XqGhoSpcuLCKFCmiunXrauHChZKk4cOHy8XFJcv5evbsqaJFiyo5Ofm6j9cvv/yiEydOKCws7IaP643ExMQoJSVFjzzyyHX7nD59WvXq1VPp0qW1fft2a/sXX3yhqlWryt3dXdWqVdNXX32V5ZhH0pWj5L755ptMr91rnTp1Sv369VP16tVVqFAhFSlSRI888og2bdqUqW9ycrJGjBihypUry93dXb6+vmrbtq12794tyfY0xkmTJln3WVu3bpV05ei7xo0bq2DBgipatKhat26tbdu22dxHdvYDO3fuVLt27eTj4yN3d3eVLl1aHTt2VGJi4g3XdeTIkfL09NQHH3xgE0hJUr169dS/f39t3rw5y33Z1a5+rS9atEgWi0WrV6/O1O/999+XxWLR33//bW3LzjaflS5duig9PV2fffZZpmnLli1TYmKiOnfuLEmaM2eOHnroIZUsWVJubm6qWrVqliFoxnvGDz/8YH3PyNj/XO91/vLLL8vf319ubm6qWLGixo0bp/T0dElXnv8SJUpIuvJYZ+w7Mh6r6+3PPvnkE+s+vVixYurYsaMOHjxo0+dWn3MAuNcRSgEAMvnmm29Uvnx5m9O0buTZZ5/VsGHDVLt2bb377rsKDQ3VmDFjsjxNY9euXXrqqafUsmVLjRkzRqdPn1bLli21YMECvfLKK+rSpYtGjhyp3bt368knn7R+mciQlpam5s2by9vbW+PHj1dwcLCGDx9uDS4yTJ48WbVq1dIbb7yht956S/ny5VP79u2zPMJr5cqVeuWVV9ShQwdNnjw5yy/Q0pVTjt577z21a9dOM2bMUL9+/ZQ/f36bL60ff/yxnnzySTk7O2vMmDHq0aOHvvzySzVq1MgmMMpYl/DwcBUvXlwTJkxQaGio3nnnnWydFjlixAj17t1bfn5+euedd9SuXTu9//77atasmXUcoEmTJunxxx+XJL333nuaP3++2rZtm+XyLly4oJiYGDVu3FjlypXLsk+HDh3k5uamb7/99qb1ZVdycrJOnDiR6ZaamnrDdZg/f74aN24sNzc3zZ8/X/Pnz9cDDzyQ5X2cO3dOjRs31tSpU9WsWTNNnjxZzz//vP755x8dOnTourVt2bJF//nPf7Rt2zYNGDBA77zzjgoWLKg2bdroq6++ytS/b9++2rRpk4YPH64XXnhB33zzjfr06WPT5+OPP1aLFi106tQpDRw4UGPHjlVQUJA1CP3vf/+ry5cvKyoqyma+1NRULVq0SO3atbvhUTBr1qyRxWJRrVq1rtvnZr777jsFBwfL29s7y+knTpzQQw89pISEBK1evdp6KtmyZcvUoUMHubi4aMyYMWrbtq2eeeYZrV+/PsvlBAcH68yZM9qyZcsN69mzZ4+WLFmixx57TBMnTtRrr72mzZs3KzQ0VEeOHLH2S0tL02OPPaaRI0cqODhY77zzjl566SUlJibahC7SlVBk6tSp6tmzp9555x0VK1ZMK1asUHh4uI4dO6YRI0YoMjJSa9asUcOGDbVv3z7rvDfbD6Smpio8PFy//fab+vbtq+nTp6tnz57as2dPpn3A1Xbu3Knt27erdevWKlKkSJZ9Mk51zclrsEWLFipUqJA+//zzTNOioqJ0//33q1q1apJyvs1f7YEHHlDp0qWtAevVFi5cqAIFCqhNmzaSrryWy5Ytq0GDBumdd96Rv7+/evXqpenTp2ead/v27erUqZOaNm2qyZMnWy9ocK0LFy4oNDRUn3zyiSIiIjRlyhQ1bNhQAwcOVGRkpKQrwXZG+PX4449b9x3X2y9K0ptvvqmIiAhVqlRJEydO1Msvv6yYmBg98MAD1ufzVp9zAIAkAwDAVRITE40k07p162z137hxo5Fknn32WZv2fv36GUlm5cqV1rayZcsaSWbNmjXWth9++MFIMvnz5zf79++3tr///vtGklm1apW1rWvXrkaS6du3r7UtPT3dtGjRwri6uprjx49b2y9cuGBTT2pqqqlWrZp56KGHbNolGScnJ7Nly5ZM6ybJDB8+3Pq3h4eH6d2793Ufi9TUVFOyZElTrVo1c/HiRWv7t99+aySZYcOGZVqXN954w2YZtWrVMsHBwde9D2OMOXbsmHF1dTXNmjUzaWlp1vZp06YZSWb27NnWtuHDhxtJNo9NVjKex5deeumG/WrUqGGKFSuWaflXK1u2rOnatesNl2PMlcf3erdPP/30puvQtWtXU7BgwUzLvfb+hw0bZiSZL7/8MlPf9PR0Y4wxe/fuNZLMnDlzrNMefvhhU716dZOcnGzTv0GDBqZSpUrWtjlz5hhJJiwszLo8Y4x55ZVXjLOzszlz5owxxpgzZ86YwoULm5CQEJvt4+o6jDGmfv36JiQkxGb6l19+men1kJUuXbqY4sWLZ2rPWL+33377hvMbY0yZMmVstvuM9fv999/N0aNHzf3332/Kly9v9u3bZzNf9erVTenSpc3Zs2etbbGxsUaSKVu2bKb7WbNmjZFkoqKiblhPcnKyzXaesT5ubm42r5/Zs2cbSWbixImZlnHt81ykSBFz7Ngxmz5BQUGmZMmS5uTJk9a2TZs2GScnJxMREWFtu9l+4M8//zSSzBdffHHD9brWkiVLjCTz7rvv3rBfkSJFTO3ata1/d+3aNdPje+2+q1OnTqZkyZLm8uXL1rajR48aJycnm8cwu9v89bz22mtGktm+fbu1LTEx0bi7u5tOnTpZ267dPxtjTHh4uClfvrxNW8Z7RnR0dKb+177OR40aZQoWLGh27Nhh02/AgAHG2dnZHDhwwBhjzPHjxzM9Phmu3Z/t27fPODs7mzfffNOm3+bNm02+fPms7bf6nAMAjOFIKQCAjaSkJElS4cKFs9U/47LuGb9EZ3j11VclKdORSVWrVlX9+vWtf4eEhEiSHnroIZUpUyZT+549ezLd59VHn2ScfpeamqoVK1ZY2/Pnz2/9/+nTp5WYmKjGjRtnOtVOkkJDQ1W1atWbrOmVMY3Wrl1rc3TG1f744w8dO3ZMvXr1sjmapUWLFgoMDMzyKK1rx/5p3Lhxlut8tRUrVig1NVUvv/yyzUC/PXr0UJEiRW5pvK+zZ89KuvnzXrhwYes2khtat26tH3/8MdPt3wzSfa3FixerZs2a1iOurna9Uw9PnTqllStX6sknn9TZs2etR3CdPHlS4eHh2rlzZ6ZTMnv27GmzvMaNGystLU379++XdOXqY2fPntWAAQMyHe109XwRERFau3at9ZQzSVqwYIH8/f0VGhp6w3U9efKkdbyvW/H333/rwIEDatGiRaZphw4dUmhoqC5duqSffvpJZcuWtU47cuSINm/erIiICBUqVMjaHhoaqurVq2d5Xxl1ZpxCeT1ubm7W7TwtLU0nT55UoUKFdN9999m8nhcvXiwvLy/17ds30zKufZ7btWtnPY1Lko4ePaqNGzeqW7duKlasmLW9Ro0aatq0qXU/J918P+Dh4SFJ+uGHH3ThwoUbrtvVbudrsEOHDjp27JjNFecWLVqk9PR0dejQQdKtbfPXyhgf7uqjpRYvXqzk5GTrqXuS7f45MTFRJ06cUGhoqPbs2ZPpdLdy5copPDz8puv4xRdfqHHjxvL09LQ56jIsLExpaWn66aefbrqMa3355ZdKT0/Xk08+abNMHx8fVapUSatWrZJ06885AIDT9wAA18g4bSTjC9LN7N+/X05OTpmu7Obj46OiRYtav5BnuDp4kv73Yd7f3z/L9tOnT9u0Ozk5qXz58jZtGVdqu/oUm2+//Vb/+c9/5O7urmLFillP28hqfI/rna52rfHjx+vvv/+Wv7+/6tWrpxEjRtgESBnrmtWVsQIDAzM9Fu7u7jZfjKUrX9SvXedrXe9+XF1dVb58+Uz3kx0ZX4Rv9ryfPXs224FldpQuXVphYWGZbtc7dexW7N6923p6Unbt2rVLxhgNHTpUJUqUsLllnCqaMQB7hmu37YzQJeP5zAiZblZLxmmSCxYskHTlS/u3336rzp07Z2v8LnOTMZpuZNmyZfL29ladOnUyTfvvf/+rY8eOafXq1SpVqpTNtIxtLqsrPF7vqo8Zdd5sndLT0/Xuu++qUqVKcnNzk5eXl0qUKKG//vrL5vW8e/du3XfffdkaqPra1/yNXrtVqlTRiRMndP78eUk33w+UK1dOkZGR+vDDD+Xl5aXw8HBNnz79pmML3c7XYPPmzeXh4WFzWmhUVJSCgoKs+89b2eavVaNGDVWrVk2ffvqptW3hwoXWxyHDr7/+qrCwMOvYXSVKlNCgQYMkKctQKjt27typ6OjoTLVnjK92s9qvt0xjjCpVqpRpudu2bbMu81afcwAAV98DAFyjSJEi8vPzyzQGy81kd7DrawfvvVn7rXzB/vnnn9WqVSs98MADmjFjhnx9feXi4qI5c+ZkOd7J1b/a38iTTz6pxo0b66uvvtLy5cv19ttva9y4cfryyy9vOCj09VxvnR2hYsWKypcvn/7666/r9klJSdH27duzDCzymoyxzPr163fdozSuDVtyaxv29PTUY489pgULFmjYsGFatGiRUlJSbnqVQkkqXrz4TUPNG/nuu+/UvHnzLF/Pbdu21bx58zR58mSNGTPmlu8jQ0adXl5eN+z31ltvaejQoXr66ac1atQoFStWTE5OTnr55ZczjTmXXdl9zWclO/uBd955R926ddPXX3+t5cuX68UXX9SYMWP022+/qXTp0lkut0qVKpJ0w9fg/v37lZSUlK0jO6/m5uZmHRdqxowZSkhI0K+//qq33nrL2udWtvmsdOnSRQMGDNAff/yh0qVLa9WqVXruueesYeHu3bv18MMPKzAwUBMnTpS/v79cXV313Xff6d133830nGb3uUpPT1fTpk31+uuvZzk9I3zLifT0dFksFn3//fdZvr6vPirwVp5zAAChFAAgC4899pg++OADxcXF2Zxql5WyZcsqPT1dO3futH6pkqSEhASdOXPG5hSf3JCenq49e/bYfMHYsWOHJFkHKF+8eLHc3d31ww8/2Fw2fM6cOf/6/n19fdWrVy/16tVLx44dU+3atfXmm2/qkUcesa7r9u3bM13mfvv27bn2WFx9P1cfNZaamqq9e/fe0pXXChYsqAcffFArV67U/v37s6z1888/V0pKih577LFbL94BKlSokOOQNeNxdXFx+VdXsru2DunKKXI3+3IfERGh1q1b6/fff9eCBQtUq1Yt3X///Te9j8DAQC1YsECJiYnWow2z68yZM1qzZk2mwdkz9O3bVxUrVtSwYcPk4eGhAQMGWKdlbC+7du3KNF9WbZK0d+9eSbLZb2Rl0aJFevDBB/XRRx9lqvfqQKtChQpau3atLl26JBcXlxsu81pXv6au9c8//8jLy0sFCxa0tt1oP5ChevXqql69uoYMGWIdMH3mzJkaPXp0ljVUrlxZlStX1pIlSzR58uQsj4aaN2+eJN3Sa7BDhw6aO3euYmJitG3bNhljrKfuSbm3zXfq1EkDBw7UwoULVbZsWaWlpdmcuvfNN98oJSVFS5cutTm6MONUuFtVoUIFnTt37qa15+RqoRUqVJAxRuXKlctWqJXT5xwAwOl7AIAsvP766ypYsKCeffZZJSQkZJq+e/duTZ48WZL06KOPSrpylbSrTZw4UZKyHJvm35o2bZr1/8YYTZs2TS4uLnr44YclXTlixWKxKC0tzdpv3759WrJkyS3fZ1paWqZTMUqWLCk/Pz+lpKRIkurUqaOSJUtq5syZ1jZJ+v7777Vt27ZceyzCwsLk6uqqKVOm2ByF89FHHykxMfGW72fIkCEyxqhbt266ePGizbS9e/fq9ddfl6+vr5577rl/Vb+9tWvXTps2bcry6mHXO4qpZMmSatKkid5//30dPXo00/Tjx4/nuI5mzZqpcOHCGjNmjJKTk29YxyOPPCIvLy+NGzdOq1evztZRUpJUv359GWOue8W7G1m+fLm1zusZOnSo+vXrp4EDB1qvYiZJfn5+qlatmubNm6dz585Z21evXq3Nmzdnuaz169fLw8PjpmGbs7Nzpsfniy++yDS+Ubt27XTixAmb/UOGmx2t5uvrq6CgIM2dO9fmaml///23li9fbt3PZWc/kJSUpMuXL9v0qV69upycnGz2C1kZNmyYTp8+reeff95m/yVdebzGjRunatWqqV27djdcTlbCwsJUrFgxRUVFKSoqSvXq1bM5NS63tvkyZcqocePGioqK0ieffKJy5crZXMk144ijq5+TxMTEf/2jwZNPPqm4uDj98MMPmaadOXPG+pwUKFDA2nYzbdu2lbOzs0aOHJlpGzLG6OTJk5L+3XMOAPc6jpQCAGRSoUIFLVy4UB06dFCVKlUUERGhatWqKTU1VWvWrNEXX3yhbt26SZJq1qyprl276oMPPtCZM2cUGhqqdevWae7cuWrTpk2uDlgtXRmHKTo6Wl27dlVISIi+//57LVu2TIMGDbKOz9SiRQtNnDhRzZs311NPPaVjx45p+vTpqlix4g1PjbmRs2fPqnTp0nriiSdUs2ZNFSpUSCtWrNDvv/+ud955R9KVIwzGjRun7t27KzQ0VJ06dVJCQoImT56sgIAAvfLKK7nyGJQoUUIDBw7UyJEj1bx5c7Vq1Urbt2/XjBkzVLdu3WwHGNd64IEHNGHCBEVGRqpGjRrq1q2bfH199c8//2jWrFlKT0/Xd999968G0r7Wjh079Mknn2Rq9/b2VtOmTXPlPl577TUtWrRI7du319NPP63g4GCdOnVKS5cu1cyZM1WzZs0s55s+fboaNWqk6tWrq0ePHipfvrwSEhIUFxenQ4cOadOmTTmqo0iRInr33Xf17LPPqm7dunrqqafk6empTZs26cKFC5o7d661r4uLizp27Khp06bJ2dlZnTp1ytZ9NGrUSMWLF9eKFSsyHa0nSTExMZkCMUlq06aNli1bpkaNGt30CKu3335biYmJ6t27twoXLmzd3t566y21bt1aDRs2VPfu3XX69GlNmzZN1apVswmqMvz4449q2bLlTY9ceeyxx/TGG2+oe/fuatCggTZv3qwFCxZkGlsuIiJC8+bNU2RkpNatW6fGjRvr/PnzWrFihXr16qXWrVvfdL0eeeQR1a9fX88884wuXryoqVOnysPDQyNGjJCUvf3AypUr1adPH7Vv316VK1fW5cuXNX/+fDk7O980TOrcubN+//13TZ48WVu3blXnzp3l6empDRs2aPbs2SpevLgWLVqU4yPBpCvbVNu2bfXZZ5/p/PnzmjBhQqY+ubXNd+nSRT179tSRI0c0ePBgm2nNmjWTq6urWrZsqeeee07nzp3TrFmzVLJkySzDsOx67bXXtHTpUj322GPq1q2bgoODdf78eW3evFmLFi3Svn375OXlpfz586tq1aqKiopS5cqVVaxYMVWrVi3Lsd4qVKig0aNHa+DAgdq3b5/atGmjwoULa+/evfrqq6/Us2dP9evX71895wBwz7Pvxf4AAHeTHTt2mB49epiAgADj6upqChcubBo2bGimTp1qc8nwS5cumZEjR5py5coZFxcX4+/vbwYOHGjTx5grl/Bu0aJFpvuRlOkS61ldwr5r166mYMGCZvfu3aZZs2amQIECxtvb2wwfPjzTJeM/+ugjU6lSJePm5mYCAwPNnDlzMl3u+3r3ffW0jMuGp6SkmNdee83UrFnTFC5c2BQsWNDUrFnTzJgxI9N8UVFRplatWsbNzc0UK1bMdO7c2Rw6dMimT8a6XCurGq9n2rRpJjAw0Li4uBhvb2/zwgsvmNOnT2e5vOPHj2drmcYY89NPP5nWrVsbLy8v4+LiYsqUKWN69Ohh9u3bl616r71U+/VIuu4tNDT0putwvccwq/s/efKk6dOnjylVqpRxdXU1pUuXNl27djUnTpwwxvxve5szZ47NfLt37zYRERHGx8fHuLi4mFKlSpnHHnvMLFq0yNpnzpw5RpL5/fffbeZdtWqVkWRWrVpl07506VLToEEDkz9/flOkSBFTr1498+mnn2Zaj3Xr1hlJplmzZtd7CLP04osvmooVK9q0Zazf9W7z5s0zJUuWNOPHj8+0vKzWLy0tzXTq1Mnky5fPLFmyxNr+2WefmcDAQOPm5maqVatmli5datq1a2cCAwNtlrlt2zYjyaxYseKm65OcnGxeffVV4+vra/Lnz28aNmxo4uLiTGhoqM12YowxFy5cMIMHD7bui3x8fMwTTzxhdu/ebfM4XL1fudqKFStMw4YNrc9Ny5YtzdatW63Ts7Mf2LNnj3n66adNhQoVjLu7uylWrJh58MEHs7WuGZYsWWKaNm1qPD09jZubm6lYsaJ59dVXs3wdd+3a1ZQtW9am7ep919V+/PFHI8lYLBZz8ODBLO87O9v8zZw6dcq4ubkZSTaPX4alS5eaGjVqGHd3dxMQEGDGjRtnZs+ebSSZvXv3Wvtd7z0jY9q1r/OzZ8+agQMHmooVKxpXV1fj5eVlGjRoYCZMmGBSU1Ot/dasWWOCg4ONq6urzWN1vf3v4sWLTaNGjUzBggVNwYIFTWBgoOndu7fZvn27MSZ3nnMAuFdZjPkXl2gBAMCOunXrpkWLFmV51AWQ12zatElBQUGaN2+e/vvf/2Z7vj179igwMFDff/+99ZTWm1m3bp1CQkK0ZcuWHA+ifTNBQUEqUaKEfvzxR2vbyy+/rJ9++knr16/P0Rg/AAAgb2FMKQAAgDvQrFmzVKhQIbVt2zZH85UvX17PPPOMxo4dm6P53nrrrX8VSF26dCnTuDqxsbHatGmTmjRpYm07efKkPvzwQ40ePZpACgCAexxjSgEAANxBvvnmG23dulUffPCB+vTpY3PVt+y6ehDy7KhXr57q1auX4/u52uHDhxUWFqYuXbrIz89P//zzj2bOnCkfHx89//zz1n7FixfnaEcAACCJUAoAAOCO0rdvXyUkJOjRRx/VyJEjHV1Otnl6eio4OFgffvihjh8/roIFC6pFixYaO3asihcv7ujyAADAHYgxpQAAAAAAAGB3jCkFAAAAAAAAuyOUAgAAAAAAgN0xplQW0tPTdeTIERUuXJirwgAAAAAAAOSAMUZnz56Vn5+fnJyufzwUoVQWjhw5In9/f0eXAQAAAAAAcNc6ePCgSpcufd3phFJZKFy4sKQrD16RIkUcXA0AAAAAAMDdIykpSf7+/tZ85XoIpbKQccpekSJFCKUAAAAAAABuwc2GRGKgcwAAAAAAANgdoRQAAAAAAADsjlAKAAAAAAAAdkcoBQAAAAAAALsjlAIAALgDTJ8+XQEBAXJ3d1dISIjWrVt33b6zZs1S48aN5enpKU9PT4WFhWXZf9u2bWrVqpU8PDxUsGBB1a1bVwcOHLidqwEAAJBthFIAAAAOFhUVpcjISA0fPlwbNmxQzZo1FR4ermPHjmXZPzY2Vp06ddKqVasUFxcnf39/NWvWTIcPH7b22b17txo1aqTAwEDFxsbqr7/+0tChQ+Xu7m6v1QIAALghizHGOLqIO01SUpI8PDyUmJioIkWKOLocAACQx4WEhKhu3bqaNm2aJCk9PV3+/v7q27evBgwYcNP509LS5OnpqWnTpikiIkKS1LFjR7m4uGj+/Pm3tXYAAIBrZTdX4UgpAAAAB0pNTdX69esVFhZmbXNyclJYWJji4uKytYwLFy7o0qVLKlasmKQrodayZctUuXJlhYeHq2TJkgoJCdGSJUtuxyoAAADcEkIpAAAABzpx4oTS0tLk7e1t0+7t7a34+PhsLaN///7y8/OzBlvHjh3TuXPnNHbsWDVv3lzLly/X448/rrZt22r16tW5vg4AAAC3Ip+jCwAAAMCtGzt2rD777DPFxsZax4tKT0+XJLVu3VqvvPKKJCkoKEhr1qzRzJkzFRoa6rB6AQAAMnCkFAAAgAN5eXnJ2dlZCQkJNu0JCQny8fG54bwTJkzQ2LFjtXz5ctWoUcNmmfny5VPVqlVt+lepUoWr7wEAgDsGoRQAAIADubq6Kjg4WDExMda29PR0xcTEqH79+tedb/z48Ro1apSio6NVp06dTMusW7eutm/fbtO+Y8cOlS1bNndXAAAA4BZx+h4AAICDRUZGqmvXrqpTp47q1aunSZMm6fz58+revbskKSIiQqVKldKYMWMkSePGjdOwYcO0cOFCBQQEWMeeKlSokAoVKiRJeu2119ShQwc98MADevDBBxUdHa1vvvlGsbGxDllHAACAaxFKAQAAOFiHDh10/PhxDRs2TPHx8QoKClJ0dLR18PMDBw7Iyel/B7i/9957Sk1N1RNPPGGznOHDh2vEiBGSpMcff1wzZ87UmDFj9OKLL+q+++7T4sWL1ahRI7utFwAAwI1YjDHG0UXcaZKSkuTh4aHExEQVKVLE0eUAAAAAAADcNbKbqzCmFAAAAAAAAOyOUAoAAAAAAAB2x5hSAAAgzwgYsMzRJeA22De2haNLAAAAtwFHSgEAAAAAAMDuCKUAAAAAAABgd4RSAAAAAAAAsDtCKQAAbsH06dMVEBAgd3d3hYSEaN26ddftO2vWLDVu3Fienp7y9PRUWFhYpv7dunWTxWKxuTVv3vx2rwYAAADgMIRSAADkUFRUlCIjIzV8+HBt2LBBNWvWVHh4uI4dO5Zl/9jYWHXq1EmrVq1SXFyc/P391axZMx0+fNimX/PmzXX06FHr7dNPP7XH6gAAAAAOcUeEUvzaDAC4m0ycOFE9evRQ9+7dVbVqVc2cOVMFChTQ7Nmzs+y/YMEC9erVS0FBQQoMDNSHH36o9PR0xcTE2PRzc3OTj4+P9ebp6WmP1QEAAAAcwuGhFL82AwDuJqmpqVq/fr3CwsKsbU5OTgoLC1NcXFy2lnHhwgVdunRJxYoVs2mPjY1VyZIldd999+mFF17QyZMnc7V2AAAA4E7i8FCKX5sBAHeTEydOKC0tTd7e3jbt3t7eio+Pz9Yy+vfvLz8/P5tgq3nz5po3b55iYmI0btw4rV69Wo888ojS0tJytX4AAADgTpHPkXee8WvzwIEDrW25/Wuzp6enHnroIY0ePVrFixfPchkpKSlKSUmx/p2UlHQLawMAwM2NHTtWn332mWJjY+Xu7m5t79ixo/X/1atXV40aNVShQgXFxsbq4YcfdkSpAAAAwG3l0COl7pRfm8eMGSMPDw/rzd/f/9ZXCgCQp3l5ecnZ2VkJCQk27QkJCfLx8bnhvBMmTNDYsWO1fPly1ahR44Z9y5cvLy8vL+3atetf1wwAAADciRx++t6/kfFr81dffZXp1+ZWrVqpevXqatOmjb799lv9/vvvio2NzXI5AwcOVGJiovV28OBBO60BAOBu4+rqquDgYJvTxjNOI69fv/515xs/frxGjRql6Oho1alT56b3c+jQIZ08eVK+vr65UjcAAABwp3FoKHWn/Nrs5uamIkWK2NwAALieyMhIzZo1S3PnztW2bdv0wgsv6Pz58+revbskKSIiwubU9HHjxmno0KGaPXu2AgICFB8fr/j4eJ07d06SdO7cOb322mv67bfftG/fPsXExKh169aqWLGiwsPDHbKOAAAAwO3m0FCKX5sBAHejDh06aMKECRo2bJiCgoK0ceNGRUdHW09HP3DggI4ePWrt/9577yk1NVVPPPGEfH19rbcJEyZIkpydnfXXX3+pVatWqly5sp555hkFBwfr559/lpubm0PWEQAAALjdLMYY48gCoqKi1LVrV73//vuqV6+eJk2apM8//1z//POPvL29FRERoVKlSmnMmDGSrvzaPGzYMC1cuFANGza0LqdQoUIqVKiQzp07p5EjR6pdu3by8fHR7t279frrr+vs2bPavHlztj7cJyUlycPDQ4mJiRw1BQDAXSRgwDJHl4DbYN/YFo4uAQAA5EB2cxWHXn1PuvJr8/HjxzVs2DDFx8crKCgo06/NTk7/O6Dr6l+brzZ8+HCNGDHC+mvz3LlzdebMGfn5+alZs2YaNWoUvzYDAAAAAADcIRx+pNSdiCOlAAC4O3GkVN7EkVIAANxd7pojpQAA9y4ChLyLEAEAAAA349CBzgEAAAAAAHBvIpQCAAAAAACA3RFKAQAAAAAAwO4IpQAAAAAAAGB3hFIAAAAAAACwO0IpAAAAAAAA2B2hFAAAAAAAAOyOUAoAAAAAAAB2RygFAAAAAAAAuyOUAgAAAAAAgN0RSgEAAAAAAMDuCKUAAAAAAABgd4RSAAAAAAAAsDtCKQAAAAAAANgdoRQAAAAAAADsjlAKAAAAAAAAdkcoBQAAAAAAALsjlAIAAAAAAIDdEUoBAAAAAADA7gilAAAAAAAAYHeEUgAAAAAAALA7QikAAAAAAADYHaEUAAAAAAAA7I5QCgAAAAAAAHZHKAUAAAAAAAC7I5QCAAAAAACA3RFKAQAAAAAAwO4IpQAAAAAAAGB3hFIAAAAAAACwO0Ip3LGmT5+ugIAAubu7KyQkROvWrbtu31mzZqlx48by9PSUp6enwsLCbPpfunRJ/fv3V/Xq1VWwYEH5+fkpIiJCR44csceqAAAAAAD+H9/1kIFQCnekqKgoRUZGavjw4dqwYYNq1qyp8PBwHTt2LMv+sbGx6tSpk1atWqW4uDj5+/urWbNmOnz4sCTpwoUL2rBhg4YOHaoNGzboyy+/1Pbt29WqVSt7rhYAAAAA3NP4roerWYwxxtFF3GmSkpLk4eGhxMREFSlSxNHl3JNCQkJUt25dTZs2TZKUnp4uf39/9e3bVwMGDLjp/GlpafL09NS0adMUERGRZZ/ff/9d9erV0/79+1WmTJlcrR9A9gQMWOboEnCb7BvbwiH3yzaVNzlqewIA5D6+690bspurcKQU7jipqalav369wsLCrG1OTk4KCwtTXFxctpZx4cIFXbp0ScWKFbtun8TERFksFhUtWvTflgwAAAAAuAm+6+FahFK445w4cUJpaWny9va2aff29lZ8fHy2ltG/f3/5+fnZ7OyulpycrP79+6tTp04cDQcAAAAAdsB3PVwrn6MLAHLb2LFj9dlnnyk2Nlbu7u6Zpl+6dElPPvmkjDF67733HFAhAAAAACCn+K6X9xBK4Y7j5eUlZ2dnJSQk2LQnJCTIx8fnhvNOmDBBY8eO1YoVK1SjRo1M0zN2Uvv379fKlStJzgEAAADATviuh2tx+h7uOK6urgoODlZMTIy1LT09XTExMapfv/515xs/frxGjRql6Oho1alTJ9P0jJ3Uzp07tWLFChUvXvy21A8AAAAAyIzvergWR0rhjhQZGamuXbuqTp06qlevniZNmqTz58+re/fukqSIiAiVKlVKY8aMkSSNGzdOw4YN08KFCxUQEGA9H7lQoUIqVKiQLl26pCeeeEIbNmzQt99+q7S0NGufYsWKydXV1TErCgAAAAD3EL7r4WqEUrgjdejQQcePH9ewYcMUHx+voKAgRUdHWwfEO3DggJyc/neg33vvvafU1FQ98cQTNssZPny4RowYocOHD2vp0qWSpKCgIJs+q1atUpMmTW7r+gAAAAAA+K4HWxZjjHF0EXeapKQkeXh4KDExkfNQAeA2ChiwzNEl4DbZN7aFQ+6XbSpvctT2BAAAbk12cxXGlAIAAAAAAIDdEUoBAAAAAADA7hhTKo/jNIa8i1MZAAAAgHsX3/Xyrnvpux5HSgEAAAAAAMDuCKUAAAAAAABgd4RSAAAAAAAAsDtCKQAAAAAAANgdoRQAAAAAAADsjlAKAAAAAAAAdkcoBQAAAAAAALsjlAIAAAAAAIDdEUoBAAAAAADA7gilAAAAAAAAYHeEUgAAAAAAALA7QikAAAAgj5k+fboCAgLk7u6ukJAQrVu37rp9Z82apcaNG8vT01Oenp4KCwvL1N8Yo2HDhsnX11f58+dXWFiYdu7cebtXAwCQxxFKAQAAAHlIVFSUIiMjNXz4cG3YsEE1a9ZUeHi4jh07lmX/2NhYderUSatWrVJcXJz8/f3VrFkzHT582Npn/PjxmjJlimbOnKm1a9eqYMGCCg8PV3Jysr1WCwCQBxFKAQAAAHnIxIkT1aNHD3Xv3l1Vq1bVzJkzVaBAAc2ePTvL/gsWLFCvXr0UFBSkwMBAffjhh0pPT1dMTIykK0dJTZo0SUOGDFHr1q1Vo0YNzZs3T0eOHNGSJUvsuGYAgLyGUAoAAADII1JTU7V+/XqFhYVZ25ycnBQWFqa4uLhsLePChQu6dOmSihUrJknau3ev4uPjbZbp4eGhkJCQbC8TAICsEEoBAAAAecSJEyeUlpYmb29vm3Zvb2/Fx8dnaxn9+/eXn5+fNYTKmO/fLBMAgKzkc3QBAAAAAO4MY8eO1WeffabY2Fi5u7s7uhwAQB7HkVIAAABAHuHl5SVnZ2clJCTYtCckJMjHx+eG806YMEFjx47V8uXLVaNGDWt7xny3skwAAG6EUAoAAADII1xdXRUcHGwdpFySddDy+vXrX3e+8ePHa9SoUYqOjladOnVsppUrV04+Pj42y0xKStLatWtvuEwAAG6GUArAPWP69OkKCAiQu7u7QkJCtG7duuv2nTVrlho3bixPT095enoqLCwsU39jjIYNGyZfX1/lz59fYWFh2rlz5+1eDQAAbigyMlKzZs3S3LlztW3bNr3wwgs6f/68unfvLkmKiIjQwIEDrf3HjRunoUOHavbs2QoICFB8fLzi4+N17tw5SZLFYtHLL7+s0aNHa+nSpdq8ebMiIiLk5+enNm3aOGIVAQB5BKEUgHtCVFSUIiMjNXz4cG3YsEE1a9ZUeHi4jh07lmX/2NhYderUSatWrVJcXJz8/f3VrFkzHT582Npn/PjxmjJlimbOnKm1a9eqYMGCCg8PV3Jysr1WCwCATDp06KAJEyZo2LBhCgoK0saNGxUdHW0dqPzAgQM6evSotf97772n1NRUPfHEE/L19bXeJkyYYO3z+uuvq2/fvurZs6fq1q2rc+fOKTo6mnGnAAD/isUYYxxdxJ0mKSlJHh4eSkxMVJEiRRxdzr8SMGCZo0vAbbJvbAtHl3BXCQkJUd26dTVt2jRJV05l8Pf3V9++fTVgwICbzp+WliZPT09NmzZNERERMsbIz89Pr776qvr16ydJSkxMlLe3tz7++GN17Njxtq5PXsE+Ku9y1D6KbSpv4j0PADLjPS/vygvve9nNVThSCkCel5qaqvXr11svbS1JTk5OCgsLU1xcXLaWceHCBV26dEnFihWTJO3du1fx8fE2y/Tw8FBISEi2lwkAAAAA97I7IpRinBcAt9OJEyeUlpZmPW0hg7e3t+Lj47O1jP79+8vPz88aQmXM92+WCQAAAAD3snyOLiBjnJeZM2cqJCREkyZNUnh4uLZv366SJUtm6p8xzkuDBg3k7u6ucePGqVmzZtqyZYtKlSol6X/jvMydO1flypXT0KFDFR4erq1bt3LeO4AcGzt2rD777DPFxsayDwGAewinxuRdeeHUGADICxx+pNTEiRPVo0cPde/eXVWrVtXMmTNVoEABzZ49O8v+CxYsUK9evRQUFKTAwEB9+OGH1svcSleOkpo0aZKGDBmi1q1bq0aNGpo3b56OHDmiJUuW2HHNANwpvLy85OzsrISEBJv2hIQE+fj43HDeCRMmaOzYsVq+fLlq1Khhbc+Y71aWCQAAAABwcCh1p4zzkpKSoqSkJJsbgLzD1dVVwcHB1vBakjXMrl+//nXnGz9+vEaNGqXo6GjVqVPHZlq5cuXk4+Njs8ykpCStXbv2hssEAAAAAFzh0FDqThnnZcyYMfLw8LDe/P39c7oqAO5wkZGRmjVrlubOnatt27bphRde0Pnz59W9e3dJUkREhAYOHGjtP27cOA0dOlSzZ89WQECA4uPjFR8fr3PnzkmSLBaLXn75ZY0ePVpLly7V5s2bFRERIT8/P7Vp08YRqwgAAAAAdxWHjyn1b+TWOC8DBw5UZGSk9e+kpCSCKSCP6dChg44fP65hw4YpPj5eQUFBio6OtgbYBw4ckJPT/3L69957T6mpqXriiSdsljN8+HCNGDFCkvT666/r/Pnz6tmzp86cOaNGjRopOjqacacAAAAAIBscGkrlxjgvK1asuO44L76+vjbLDAoKynJZbm5ucnNzu8W1AHC36NOnj/r06ZPltNjYWJu/9+3bd9PlWSwWvfHGG3rjjTdyoToAAAAAuLc49PQ9xnkBAAAAAAC4Nzn89L3IyEh17dpVderUUb169TRp0qRM47yUKlVKY8aMkXRlnJdhw4Zp4cKF1nFeJKlQoUIqVKiQzTgvlSpVUrly5TR06FDGeQEAAAAAALiDODyUYpwX4O4SMGCZo0vAbbJvbAtHlwAAAADgHuLwUEpinBcAAAAAAIB7jUPHlAIAAAAAAMC9iVAKAAAAAAAAdkcoBQAAAAAAALsjlAIAAAAAAIDdEUoBAAAAAADA7gilAAAAAAAAYHeEUgAAAAAAALA7QikAAAAAAADYHaEUAAAAAAAA7I5QCgAAAAAAAHZHKAUAAAAAAAC7I5QCAAAAAFzX9OnTFRAQIHd3d4WEhGjdunXX7btlyxa1a9dOAQEBslgsmjRpUqY+aWlpGjp0qMqVK6f8+fOrQoUKGjVqlIwxt3EtANyJCKUAAAAAAFmKiopSZGSkhg8frg0bNqhmzZoKDw/XsWPHsux/4cIFlS9fXmPHjpWPj0+WfcaNG6f33ntP06ZN07Zt2zRu3DiNHz9eU6dOvZ2rAuAORCgFAAAAAMjSxIkT1aNHD3Xv3l1Vq1bVzJkzVaBAAc2ePTvL/nXr1tXbb7+tjh07ys3NLcs+a9asUevWrdWiRQsFBAToiSeeULNmzW54BBaAvIlQCgAAAACQSWpqqtavX6+wsDBrm5OTk8LCwhQXF3fLy23QoIFiYmK0Y8cOSdKmTZv0yy+/6JFHHvnXNQO4u+RzdAEAAAAAgDvPiRMnlJaWJm9vb5t2b29v/fPPP7e83AEDBigpKUmBgYFydnZWWlqa3nzzTXXu3PnflgzgLkMoBQAAAACwm88//1wLFizQwoULdf/992vjxo16+eWX5efnp65duzq6PAB2RCgFAAAAAMjEy8tLzs7OSkhIsGlPSEi47iDm2fHaa69pwIAB6tixoySpevXq2r9/v8aMGUMoBdxjGFMKAAAAAJCJq6urgoODFRMTY21LT09XTEyM6tevf8vLvXDhgpycbL+KOjs7Kz09/ZaXCeDuxJFSAAAAAIAsRUZGqmvXrqpTp47q1aunSZMm6fz58+revbskKSIiQqVKldKYMWMkXRkcfevWrdb/Hz58WBs3blShQoVUsWJFSVLLli315ptvqkyZMrr//vv1559/auLEiXr66acds5IAHIZQCgAAAACQpQ4dOuj48eMaNmyY4uPjFRQUpOjoaOvg5wcOHLA56unIkSOqVauW9e8JEyZowoQJCg0NVWxsrCRp6tSpGjp0qHr16qVjx47Jz89Pzz33nIYNG2bXdQPgeIRSAAAAAIDr6tOnj/r06ZPltIygKUNAQICMMTdcXuHChTVp0iRNmjQplyoEcLdiTCkAAAAAAADYHaEUAAAAAAAA7I7T9wAAAADgNgsYsMzRJeA22Te2haNLAO5aHCkFAAAAAAAAuyOUAgAAAAAAgN0RSgEAAAAAAMDuCKUAAAAAAABgdzka6Dw9PV2rV6/Wzz//rP379+vChQsqUaKEatWqpbCwMPn7+9+uOgEAAAAAAJCHZOtIqYsXL2r06NHy9/fXo48+qu+//15nzpyRs7Ozdu3apeHDh6tcuXJ69NFH9dtvv93umgEAAAAAAHCXy9aRUpUrV1b9+vU1a9YsNW3aVC4uLpn67N+/XwsXLlTHjh01ePBg9ejRI9eLBQAAAAAAQN6QrVBq+fLlqlKlyg37lC1bVgMHDlS/fv104MCBXCkOAAAAAAAAeVO2Tt+7WSB1NRcXF1WoUOGWCwIAAAAAAEDel6OBziXpr7/+yrLdYrHI3d1dZcqUkZub278uDAAAAAAAAHlXjkOpoKAgWSyW6053cXFRhw4d9P7778vd3f1fFQcAAAAAAIC8KVun713tq6++UqVKlfTBBx9o48aN2rhxoz744APdd999WrhwoT766COtXLlSQ4YMuR31AgAAAAAAIA/I8ZFSb775piZPnqzw8HBrW/Xq1VW6dGkNHTpU69atU8GCBfXqq69qwoQJuVosAAAAAAAA8oYcHym1efNmlS1bNlN72bJltXnzZklXTvE7evTov68OAAAAAAAAeVKOQ6nAwECNHTtWqamp1rZLly5p7NixCgwMlCQdPnxY3t7euVclAAAAAAAA8pQcn743ffp0tWrVSqVLl1aNGjUkXTl6Ki0tTd9++60kac+ePerVq1fuVgoAAAAAAIA8I8ehVIMGDbR3714tWLBAO3bskCS1b99eTz31lAoXLixJ+u9//5u7VQIAAAAAACBPyXEolZSUpCJFiuj555/PNG3Xrl2qWLFirhQGAAAAAACAvCvHY0q1aNFCycnJmdq3b9+uJk2a5EZNAAAAAAAAyONyHEoVKlRIbdu21eXLl61t27ZtU5MmTdSuXbtcLQ4AAAAAAAB5U45DqS+//FKJiYnq3LmzjDH6+++/1aRJE3Xq1EmTJ0++HTUCAAAAAAAgj8lxKJU/f34tW7ZM27dv15NPPqmHH35YERERmjhx4u2oDwAAAAAAAHlQtgY6T0pKsvnbyclJUVFRatq0qdq1a6ehQ4da+xQpUiT3qwQAAAAAAECekq1QqmjRorJYLJnajTGaOXOm3n//fRljZLFYlJaWlutFAgAAAAAAIG/JVii1atWq210HAAAAAAAA7iHZCqVCQ0M1e/ZstWzZUiVKlLjdNQEAAAAAACCPy/ZA55988on8/f3VoEEDjRs3Ttu2bbuddQEAAAAAACAPy3YotXLlSh09elS9evXS+vXrFRISokqVKunVV1/VTz/9pPT09NtZJwAAAAAAAPKQbIdSkuTp6akuXbro888/14kTJzR16lRdvHhRnTt3VsmSJRUREaFFixbp/Pnzt6teAAAAAAAA5AE5CqWu5urqqubNm2vGjBk6ePCgfvjhBwUEBGjUqFGaOHFibtYIAAAAAACAPCZbA51nh6enp3777Tdt2rRJly5dyq3FAgAAAAAAIA+65SOlrnX27FnFxMRIklxcXHJrsQAAAAAAAMiDci2UAgAAAAAAALKLUAoAAAAAAAB2RygFAAAAAAAAu8v2QOe1atWSxWK57vQLFy7kSkEAAAAAAADI+7IdSrVp0+Y2lgEAAAAAAIB7SbZDqeHDh9/OOgAAAAAAAHAPYUwpAAAAAAAA2F22QqnmzZvrt99+u2m/s2fPaty4cZo+ffq/LgwAAAAAAAB5V7ZO32vfvr3atWsnDw8PtWzZUnXq1JGfn5/c3d11+vRpbd26Vb/88ou+++47tWjRQm+//fbtrhsAAAAAAAB3sWwdKfXMM89oz549GjRokLZu3aqePXuqcePGqlu3rsLDwzVr1iyVKVNGv//+u6KiolSmTJlsFzB9+nQFBATI3d1dISEhWrdu3XX7btmyRe3atVNAQIAsFosmTZqUqc+IESNksVhsboGBgdmuBwAAAAAAALdftgc6d3NzU5cuXdSlSxdJUmJioi5evKjixYvLxcXllu48KipKkZGRmjlzpkJCQjRp0iSFh4dr+/btKlmyZKb+Fy5cUPny5dW+fXu98sor113u/fffrxUrVlj/zpcv26sJAAAAAAAAO7jlgc49PDzk4+Nzy4GUJE2cOFE9evRQ9+7dVbVqVc2cOVMFChTQ7Nmzs+xft25dvf322+rYsaPc3Nyuu9x8+fLJx8fHevPy8rrlGgEAAAAAAJD7HHb1vdTUVK1fv15hYWH/K8bJSWFhYYqLi/tXy965c6f8/PxUvnx5de7cWQcOHPi35QIAAAAAACAXOSyUOnHihNLS0uTt7W3T7u3trfj4+FtebkhIiD7++GNFR0frvffe0969e9W4cWOdPXv2uvOkpKQoKSnJ5gYAAAAAAIDbJ88NtvTII49Y/1+jRg2FhISobNmy+vzzz/XMM89kOc+YMWM0cuRIe5UIAAAAAABwz3PYkVJeXl5ydnZWQkKCTXtCQoJ8fHxy7X6KFi2qypUra9euXdftM3DgQCUmJlpvBw8ezLX7BwAAAAAAQGY5DqW6du2qn3766V/fsaurq4KDgxUTE2NtS09PV0xMjOrXr/+vl5/h3Llz2r17t3x9fa/bx83NTUWKFLG5AQAAAAAA4PbJcSiVmJiosLAwVapUSW+99ZYOHz58y3ceGRmpWbNmae7cudq2bZteeOEFnT9/Xt27d5ckRUREaODAgdb+qamp2rhxozZu3KjU1FQdPnxYGzdutDkKql+/flq9erX27dunNWvW6PHHH5ezs7M6dep0y3UCAAAAAAAgd+U4lFqyZIkOHz6sF154QVFRUQoICNAjjzyiRYsW6dKlSzlaVocOHTRhwgQNGzZMQUFB2rhxo6Kjo62Dnx84cEBHjx619j9y5Ihq1aqlWrVq6ejRo5owYYJq1aqlZ5991trn0KFD6tSpk+677z49+eSTKl68uH777TeVKFEip6sKAAAAAACA2+SWBjovUaKEIiMjFRkZqQ0bNmjOnDn673//q0KFCqlLly7q1auXKlWqlK1l9enTR3369MlyWmxsrM3fAQEBMsbccHmfffZZtu4XAAAAAAAAjvOvBjo/evSofvzxR/34449ydnbWo48+qs2bN6tq1ap69913c6tGAAAAAAAA5DE5DqUuXbqkxYsX67HHHlPZsmX1xRdf6OWXX9aRI0c0d+5crVixQp9//rneeOON21EvAAAAAAAA8oAcn77n6+ur9PR0derUSevWrVNQUFCmPg8++KCKFi2aC+UBAAAAAAAgL8pxKPXuu++qffv2cnd3v26fokWLau/evf+qMAAAAAAAAORdOT59r1WrVrpw4UKm9lOnTikpKSlXigIAAAAAAEDeluNQqmPHjlle4e7zzz9Xx44dc6UoAAAAAAAA5G05DqXWrl2rBx98MFN7kyZNtHbt2lwpCgAAAAAAAHlbjkOplJQUXb58OVP7pUuXdPHixVwpCgAAAAAAAHlbjkOpevXq6YMPPsjUPnPmTAUHB+dKUQAAAAAAAMjbcnz1vdGjRyssLEybNm3Sww8/LEmKiYnR77//ruXLl+d6gQAAAAAAAMh7cnykVMOGDRUXFyd/f399/vnn+uabb1SxYkX99ddfaty48e2oEQAAAAAAAHlMjo+UkqSgoCAtWLAgt2sBAAAAAADAPeKWQqn09HTt2rVLx44dU3p6us20Bx54IFcKAwAAAAAAQN6V41Dqt99+01NPPaX9+/fLGGMzzWKxKC0tLdeKAwAAAAAAQN6U41Dq+eefV506dbRs2TL5+vrKYrHcjroAAAAAAACQh+U4lNq5c6cWLVqkihUr3o56AAAAAAAAcA/I8dX3QkJCtGvXrttRCwAAAAAAAO4ROT5Sqm/fvnr11VcVHx+v6tWry8XFxWZ6jRo1cq04AAAAAAAA5E05DqXatWsnSXr66aetbRaLRcYYBjoHAAAAAABAtuQ4lNq7d+/tqAMAAAAAAAD3kByHUmXLlr0ddQAAAAAAAOAekuOBziVp/vz5atiwofz8/LR//35J0qRJk/T111/nanEAAAAAAADIm3IcSr333nuKjIzUo48+qjNnzljHkCpatKgmTZqU2/UBAAAAAAAgD8pxKDV16lTNmjVLgwcPlrOzs7W9Tp062rx5c64WBwAAAAAAgLwpx6HU3r17VatWrUztbm5uOn/+fK4UBQAAAAAAgLwtx6FUuXLltHHjxkzt0dHRqlKlSm7UBAAAAAAAgDwux1ffi4yMVO/evZWcnCxjjNatW6dPP/1UY8aM0Ycffng7agQAAAAAAEAek+NQ6tlnn1X+/Pk1ZMgQXbhwQU899ZT8/Pw0efJkdezY8XbUCAAAAAAAgDwmx6GUJHXu3FmdO3fWhQsXdO7cOZUsWTK36wIAAAAAAEAedkuhVIYCBQqoQIECuVULAAAAAAAA7hHZCqVq166tmJgYeXp6qlatWrJYLNftu2HDhlwrDgAAAAAAAHlTtkKp1q1by83NTZLUpk2b21kPAAAAAAAA7gHZCqWGDx+e5f8BAAAAAACAW+GU0xl+//13rV27NlP72rVr9ccff+RKUQAAAAAAAMjbchxK9e7dWwcPHszUfvjwYfXu3TtXigIAAAAAAEDeluNQauvWrapdu3am9lq1amnr1q25UhQAAAAAAADythyHUm5ubkpISMjUfvToUeXLl60hqgAAAAAAAHCPy3Eo1axZMw0cOFCJiYnWtjNnzmjQoEFq2rRprhYHAAAAAACAvCnHhzZNmDBBDzzwgMqWLatatWpJkjZu3Chvb2/Nnz8/1wsEAAAAAABA3pPjUKpUqVL666+/tGDBAm3atEn58+dX9+7d1alTJ7m4uNyOGgEAAAAAAJDH3NIgUAULFlTPnj1zuxYAAAAAAADcI7IVSi1dulSPPPKIXFxctHTp0hv2bdWqVa4UBgAAAAAAgLwrW6FUmzZtFB8fr5IlS6pNmzbX7WexWJSWlpZbtQEAAAAAACCPylYolZ6enuX/AQAAAAAAgFvhlJ1OxYoV04kTJyRJTz/9tM6ePXtbiwIAAAAAAEDelq1QKjU1VUlJSZKkuXPnKjk5+bYWBQAAAAAAgLwtW6fv1a9fX23atFFwcLCMMXrxxReVP3/+LPvOnj07VwsEAAAAAABA3pOtUOqTTz7Ru+++q927d0uSEhMTOVoKAAAAAAAAtyxboZS3t7fGjh0rSSpXrpzmz5+v4sWL39bCAAAAAAAAkHfleKDzBx98UK6urre1KAAAAAAAAORtDHQOAAAAAAAAu2OgcwAAAAAAANhdjgc6t1gsDHQOAAAAAACAf4WBzgEAAAAAAGB32QqlrrZ3797bUQcAAAAAAADuIdka6FySHn30USUmJlr/Hjt2rM6cOWP9++TJk6patWquFgcAAAAAAIC8Kduh1A8//KCUlBTr32+99ZZOnTpl/fvy5cvavn177lYHAAAAAACAPCnboZQx5oZ/AwAAAAAAANmV7VAKAAAAAAAAyC3ZDqUsFossFkumNgAAAAAAACCnsn31PWOMunXrJjc3N0lScnKynn/+eRUsWFCSbMabAgAAAAAAAG4k26FU165dbf7u0qVLpj4RERH/viIAAAAAAADkedkOpebMmXM76wAAAAAAAMA9hIHOAQAAAAAAYHeEUgAAAAAAALA7QikAAAAAAADYHaEUAAAAAAAA7M7hodT06dMVEBAgd3d3hYSEaN26ddftu2XLFrVr104BAQGyWCyaNGnSv14mAAAAAAAA7C9bV99bunRpthfYqlWrbPeNiopSZGSkZs6cqZCQEE2aNEnh4eHavn27SpYsman/hQsXVL58ebVv316vvPJKriwTAAAAAAAA9petUKpNmzbZWpjFYlFaWlq273zixInq0aOHunfvLkmaOXOmli1bptmzZ2vAgAGZ+tetW1d169aVpCyn38oyAQAAAAAAYH/ZOn0vPT09W7ecBFKpqalav369wsLC/leMk5PCwsIUFxeX8zX5F8tMSUlRUlKSzQ0AAAAAAAC3j8PGlDpx4oTS0tLk7e1t0+7t7a34+Hi7LnPMmDHy8PCw3vz9/W/p/gEAAAAAAJA92Tp9b8qUKerZs6fc3d01ZcqUG/Z98cUXc6Uwexo4cKAiIyOtfyclJRFMAQAAAAAA3EbZCqXeffddde7cWe7u7nr33Xev289isWQ7lPLy8pKzs7MSEhJs2hMSEuTj45OtZeTWMt3c3OTm5nZL9wkAAAAAAICcy9bpe3v37lXx4sWt/7/ebc+ePdm+Y1dXVwUHBysmJsbalp6erpiYGNWvXz+Hq3H7lgkAAAAAAIDcl60jpbJy4sQJSVeOTrpVkZGR6tq1q+rUqaN69epp0qRJOn/+vPXKeRERESpVqpTGjBkj6cpA5lu3brX+//Dhw9q4caMKFSqkihUrZmuZAAAAAAAAcLwchVJnzpzR4MGDFRUVpdOnT0uSPD091bFjR40ePVpFixbN0Z136NBBx48f17BhwxQfH6+goCBFR0dbByo/cOCAnJz+dzDXkSNHVKtWLevfEyZM0IQJExQaGqrY2NhsLRMAAAAAAACOl+1Q6tSpU6pfv74OHz6szp07q0qVKpKkrVu36uOPP1ZMTIzWrFkjT0/PHBXQp08f9enTJ8tpGUFThoCAABlj/tUyAQAAAAAA4HjZDqXeeOMNubq6avfu3ZmOOnrjjTfUrFkzvfHGGzccCB0AAAAAAACQsjnQuSQtWbJEEyZMyPI0OB8fH40fP15fffVVrhYHAAAAAACAvCnbodTRo0d1//33X3d6tWrVFB8fnytFAQAAAAAAIG/Ldijl5eWlffv2XXf63r17VaxYsdyoCQAAAAAAAHlctkOp8PBwDR48WKmpqZmmpaSkaOjQoWrevHmuFgcAAAAAAIC8KUcDndepU0eVKlVS7969FRgYKGOMtm3bphkzZiglJUXz58+/nbUCAAAAAAAgj8h2KFW6dGnFxcWpV69eGjhwoIwxkiSLxaKmTZtq2rRp8vf3v22FAgAAAAAAIO/IdiglSeXKldP333+v06dPa+fOnZKkihUrMpYUAAAAAAAAciRHoVQGT09P1atXL7drAQAAAAAAwD0i2wOdAwAAAAAAALmFUAoAAAAAAAB2RygFAAAAAAAAuyOUAgAAAAAAgN0RSgEAAAAAAMDuCKUAAAAAAABgd4RSAAAAAAAAsDtCKQAAAAAAANgdoRQAAAAAAADsjlAKAAAAAAAAdkcoBQAAAAAAALsjlAIAAAAAAIDdEUoBAAAAAADA7gilAAAAAAAAYHeEUgAAAAAAALA7QikAAAAAAADYHaEUAAAAAAAA7I5QCgAAAAAAAHZHKAUAAAAAAAC7I5QCAAAAAACA3RFKAQAAAAAAwO4IpQAAAAAAAGB3hFIAAAAAAACwO0IpAAAAAAAA2B2hFAAAAAAAAOyOUAoAAAAAAAB2RygFAAAAAAAAuyOUAgAAAAAAgN0RSgEAAAAAAMDuCKUAAAAAAABgd4RSAAAAAAAAsDtCKQAAAAAAANgdoRQAAAAAAADsjlAKAAAAAAAAdkcoBQAAAAAAALsjlAIAAAAAAIDdEUoBAAAAAADA7gilAAAAAAAAYHeEUgAAAAAAALA7QikAAAAAAADYHaEUAAAAAAAA7I5QCgAAAAAAAHZHKAUAAAAAAAC7I5QCAAAAAACA3RFKAQAAAAAAwO4IpQAAAAAAAGB3hFIAAAAAAACwO0IpAAAAAAAA2B2hFAAAAAAAAOyOUAoAAAAAAAB2RygFAAAAAAAAuyOUAgAAAAAAgN0RSgEAAAAAAMDuCKUAAAAAAABgd4RSAAAAAAAAsDtCKQAAAAAAANgdoRQAAAAAAADs7o4IpaZPn66AgAC5u7srJCRE69atu2H/L774QoGBgXJ3d1f16tX13Xff2Uzv1q2bLBaLza158+a3cxUAAAAAAACQAw4PpaKiohQZGanhw4drw4YNqlmzpsLDw3Xs2LEs+69Zs0adOnXSM888oz///FNt2rRRmzZt9Pfff9v0a968uY4ePWq9ffrpp/ZYHQAAAAAAAGSDw0OpiRMnqkePHurevbuqVq2qmTNnqkCBApo9e3aW/SdPnqzmzZvrtddeU5UqVTRq1CjVrl1b06ZNs+nn5uYmHx8f683T09MeqwMAAAAAAIBscGgolZqaqvXr1yssLMza5uTkpLCwMMXFxWU5T1xcnE1/SQoPD8/UPzY2ViVLltR9992nF154QSdPnsz9FQAAAAAAAMAtyefIOz9x4oTS0tLk7e1t0+7t7a1//vkny3ni4+Oz7B8fH2/9u3nz5mrbtq3KlSun3bt3a9CgQXrkkUcUFxcnZ2fnTMtMSUlRSkqK9e+kpKR/s1oAAAAAAAC4CYeGUrdLx44drf+vXr26atSooQoVKig2NlYPP/xwpv5jxozRyJEj7VkiAAAAAADAPc2hp+95eXnJ2dlZCQkJNu0JCQny8fHJch4fH58c9Zek8uXLy8vLS7t27cpy+sCBA5WYmGi9HTx4MIdrAgAAAAAAgJxwaCjl6uqq4OBgxcTEWNvS09MVExOj+vXrZzlP/fr1bfpL0o8//njd/pJ06NAhnTx5Ur6+vllOd3NzU5EiRWxuAAAAAAAAuH0cfvW9yMhIzZo1S3PnztW2bdv0wgsv6Pz58+revbskKSIiQgMHDrT2f+mllxQdHa133nlH//zzj0aMGKE//vhDffr0kSSdO3dOr732mn777Tft27dPMTExat26tSpWrKjw8HCHrCMAAAAAAABsOXxMqQ4dOuj48eMaNmyY4uPjFRQUpOjoaOtg5gcOHJCT0/+yswYNGmjhwoUaMmSIBg0apEqVKmnJkiWqVq2aJMnZ2Vl//fWX5s6dqzNnzsjPz0/NmjXTqFGj5Obm5pB1BAAAAAAAgC2Hh1KS1KdPH+uRTteKjY3N1Na+fXu1b98+y/758+fXDz/8kJvlAQAAAAAAIJc5/PQ9AAAAAAAA3HsIpQAAAAAAAGB3hFIAAAAAAACwO0IpAAAAAAAA2B2hFAAAAAAAAOyOUAoAAAAAAAB2RygFAAAAAAAAuyOUAgAAAAAAgN0RSgEAAAAAAMDuCKUAAAAAAABgd4RSAAAAAAAAsDtCKQAAAAAAANgdoRQAAAAAAADsjlAKAAAAAAAAdkcoBQAAAAAAALsjlAIAAAAAAIDdEUoBAAAAAADA7gilAAAAAAAAYHeEUgAAAAAAALA7QikAAAAAAADYHaEUAAAAAAAA7I5QCgAAAAAAAHZHKAUAAAAAAAC7I5QCAAAAAACA3RFKAQAAAAAAwO4IpQAAAAAAAGB3hFIAAAAAAACwO0IpAAAAAAAA2B2hFAAAAAAAAOyOUAoAAAAAAAB2RygFAAAAAAAAuyOUAgAAAAAAgN0RSgEAAAAAAMDuCKUAAAAAAABgd4RSAAAAAAAAsDtCKQAAAAAAANgdoRQAAAAAAADsjlAKAAAAAAAAdkcoBQAAAAAAALsjlAIAAAAAAIDdEUoBAAAAAADA7gilAAAAAAAAYHeEUgAAAAAAALA7QikAAAAAAADYHaEUAAAAAAAA7I5QCgAAAAAAAHZHKAUAAAAAAAC7I5QCAAAAAACA3RFKAQAAAAAAwO4IpQAAAAAAAGB3hFIAAAAAAACwO0IpAAAAAAAA2B2hFAAAAAAAAOyOUAoAAAAAAAB2RygFAAAAAAAAuyOUAgAAAAAAgN0RSgEAAAAAAMDuCKUAAAAAAABgd4RSAAAAAAAAsDtCKQAAAAAAANgdoRQAAAAAAADsjlAKAAAAAAAAdkcoBQAAAAAAALsjlAIAAAAAAIDdEUoBAAAAAADA7gilAAAAAAAAYHeEUgAAAAAAALC7OyKUmj59ugICAuTu7q6QkBCtW7fuhv2/+OILBQYGyt3dXdWrV9d3331nM90Yo2HDhsnX11f58+dXWFiYdu7ceTtXAQAAAAAAADng8FAqKipKkZGRGj58uDZs2KCaNWsqPDxcx44dy7L/mjVr1KlTJz3zzDP6888/1aZNG7Vp00Z///23tc/48eM1ZcoUzZw5U2vXrlXBggUVHh6u5ORke60WAAAAAAAAbsDhodTEiRPVo0cPde/eXVWrVtXMmTNVoEABzZ49O8v+kydPVvPmzfXaa6+pSpUqGjVqlGrXrq1p06ZJunKU1KRJkzRkyBC1bt1aNWrU0Lx583TkyBEtWbLEjmsGAAAAAACA68nnyDtPTU3V+vXrNXDgQGubk5OTwsLCFBcXl+U8cXFxioyMtGkLDw+3Bk579+5VfHy8wsLCrNM9PDwUEhKiuLg4dezYMdMyU1JSlJKSYv07MTFRkpSUlHTL63anSE+54OgScJs4avtkm8q7HLFNsT3lXeyjkJvYnpDbeM9DbmIfhdyWF7KIjHUwxtywn0NDqRMnTigtLU3e3t427d7e3vrnn3+ynCc+Pj7L/vHx8dbpGW3X63OtMWPGaOTIkZna/f39s7cigAN4THJ0Bchr2KaQm9iekJvYnpDb2KaQm9iekNvy0jZ19uxZeXh4XHe6Q0OpO8XAgQNtjr5KT0/XqVOnVLx4cVksFgdWhpxISkqSv7+/Dh48qCJFiji6HNzl2J6Q29imkJvYnpCb2J6Q29imkJvYnu5OxhidPXtWfn5+N+zn0FDKy8tLzs7OSkhIsGlPSEiQj49PlvP4+PjcsH/GvwkJCfL19bXpExQUlOUy3dzc5ObmZtNWtGjRnKwK7iBFihRhZ4Vcw/aE3MY2hdzE9oTcxPaE3MY2hdzE9nT3udERUhkcOtC5q6urgoODFRMTY21LT09XTEyM6tevn+U89evXt+kvST/++KO1f7ly5eTj42PTJykpSWvXrr3uMgEAAAAAAGBfDj99LzIyUl27dlWdOnVUr149TZo0SefPn1f37t0lSRERESpVqpTGjBkjSXrppZcUGhqqd955Ry1atNBnn32mP/74Qx988IEkyWKx6OWXX9bo0aNVqVIllStXTkOHDpWfn5/atGnjqNUEAAAAAADAVRweSnXo0EHHjx/XsGHDFB8fr6CgIEVHR1sHKj9w4ICcnP53QFeDBg20cOFCDRkyRIMGDVKlSpW0ZMkSVatWzdrn9ddf1/nz59WzZ0+dOXNGjRo1UnR0tNzd3e2+frAfNzc3DR8+PNOpmMCtYHtCbmObQm5ie0JuYntCbmObQm5ie8rbLOZm1+cDAAAAAAAAcplDx5QCAAAAAADAvYlQCgAAAAAAAHZHKAUAAAAAAAC7I5QCAAAAAACA3RFKAQAAAAAAwO4IpQAAAAAAAGB3hFIAAAB3GGOMo0sAAAC47QilcMdKT0/P1JaWluaASpDX8eUPuSGrfRaQXRn7oTNnzujixYs6d+6cgyvC3Yz9EYC7EZ/J7035HF0AkJX09HQ5OV3JTPfu3au0tDSVLVtWLi4uDq4MdytjjCwWizZu3Kh//vlHrq6uqlixomrUqCGLxWKdDmRHxvZy4MABnT9/XoUKFZKfn5+jy8JdKmN7WrZsmaZOnaojR46oSpUqevrppxUeHu7o8nCXufoz1JIlS3T8+HFJUseOHVW4cGFJ4j0POXb1dnUttifkVMY2s2/fPl26dEkpKSmqVq0a29E9ymKII3GHufpNb+TIkYqKitLFixdlsVg0e/ZshYSEKH/+/A6uEnejxYsX69lnn1XlypW1c+dOlS5dWk888YSGDRsmiQ9VyJ6M7eTLL7/Uq6++KmdnZx06dEidO3fWf//7XzVp0sTRJeIutHTpUnXq1EmDBw9WqVKltHLlSn377bf6+OOP1bJlS0eXh7vE1e9jAwYM0Mcff6yqVatq48aNql+/vl599VU99NBDmfoCN3L1Z/OvvvpK8fHxMsaoadOmqlSpkiS2J2RfxrayePFiDRw4UMnJybp8+bJCQ0M1YcIElSpVytElwt4McIcaNmyY8fX1NYsXLzanTp0yjRs3NhUqVDCfffaZSU5OdnR5uMts3rzZeHl5mRkzZpjk5GSzfft2M3r0aFOmTBkzatQoR5eHu0BaWpr1/z///LMpWLCgmTJlitmyZYv59NNPTWhoqHnsscfM6tWrHVgl7kY7duwwdevWNTNmzDDGGJOQkGBKly5tAgMDTeHChc2SJUuMMcakp6c7skzcRd59911TunRp88cffxhjjFmwYIGxWCzm4YcfNitWrLD2Y5tCTrz66qvGy8vLPPDAA6Zw4cKmbt26ZsKECdbtiO0J2bV69WpToEAB88EHH5jY2FgTHR1tSpUqZR544AETHx9vjGF7upcwphTuSOvXr9ePP/6ojz/+WG3btlVcXJw2b94sT09PPfPMM1qyZIkuXLjg6DJxF9m5c6dKliypp556Sm5ubqpcubJ69Oihp59+WkuWLNHu3bsdXSLuUCtXrpQkm9MWli9frsaNG6tv376qWrWqOnbsqOHDh+v06dOKioqSxJguyD43NzfVq1dPHTt21KFDh9S4cWM9+uijWrJkiWrUqKGIiAgtWrSIoxCQLWfOnNHevXs1YsQIBQcHa/Hixerdu7dGjx6tAwcOaNCgQfrxxx8liW0K2bZ48WItXLhQ0dHRio2N1ZEjRxQcHKyvv/5a77//viS2J2Rt586dSk1NtWn7+eefFRoaqh49eig0NFTh4eHasGGDduzYoddff10S29O9hFAKd6TChQurW7duatasmVatWqWnn35aY8eO1e+//64aNWpo8ODBioqKyrSDA66nSJEiOnXqlHbt2mVtK1mypB5//HFt3bpVe/bscWB1uFMtWrRIo0ePto7JksFisejs2bNKTU21Dsr54IMP6plnntHHH3+sY8eOXXfsDeBaZcqU0fDhw+Xp6am3335b1atX18SJE3XfffepSpUqcnV1Vb9+/ZSUlMQgsMjk2gC8cOHCat++vVq3bq3NmzdrwIABGjFihAYNGqTx48frzz//1JAhQ7Ru3ToHVYy70b59+1S6dGlVq1ZNxhgVKlRIo0aNko+Pj7744gtHl4c71JIlS3Tffffpu+++06VLlyRdOX1vz549On36tLVfcnKySpYsqWnTpumnn37Snj17eL+7h/CJGQ6X1dEElStXVuvWrSVJM2fO1JNPPqkePXro8uXL8vf317lz57RgwQK5urrau1zcBbJ6E/Px8VHRokX1+eefKyEhwdru7++vwMBAjmpBlho2bKh58+apRIkS2rt3r7W9fPnyWrdundatW2fzS17lypVVtmxZAnNcV8b+6eDBg9qyZYsuX74sSSpRooSSk5O1ceNGlS1bVgULFpR05SiqsWPHasOGDSpSpAi/HMPG1WP9zJ07V9HR0UpLS1ODBg3k5eWlNWvWyMfHR507d5YknT9/Xu3atVP16tVVp04dR5aOu0TGla+dnZ2VkpKiS5cuycnJSZcvX5aXl5cGDRqkVatWacOGDQ6uFHeiNm3aqG3bturRo4e+//57paamymKx6Mknn9TOnTv16aefSpLc3d0lSa6urnJ2dlb+/Pl5v7uHEErBoa7+MBUTE6PVq1db39S8vb2VlJSkvXv3ytfXV05OTsqXL5+cnZ21Zs0aLV++3JGl4w5l/n/wxF9++UXTp0/X1KlTdfnyZd1///3q16+fZsyYobfffls///yzjh49qnHjxik+Pl7333+/o0vHHcYYI19fX5UuXVpbt25Vhw4dNGrUKElS165d1a5dOz3++ONavXq1kpKSJF0ZADZfvnzWQAG4VsbgrqGhoXrwwQcVGhqqr776SsnJyXJ3d1ft2rX19ddfa9asWXrxxRf11Vdf6aGHHlKxYsUcXTruQBmfoV5//XUNGDBA27dv17lz56ztx48fV1JSkuLj43X27Fl9/vnnql+/vj788EM5OTnxgwwyuXabcHZ2liSFh4dr69atGjNmjCQpX74rF3G/dOmS7r//fhUpUsS+heKOl/Gjy6JFi/Twww+rW7duio6O1qVLl1SzZk21bt1aM2bM0MKFCyVJqamp+u233+Th4SE3NzdHlg474+p7cBhz1VU6Xn31VS1YsEDp6ekqXbq0OnbsaD2fuEOHDlq9erUiIiL0yy+/KCkpSZs2bZKzs/MNL0+Le9dXX32lLl26qHLlyjpy5IiKFi2qH3/8UWXKlNGcOXM0efJkHTx4UN7e3jp//ryWLFmiWrVqObps3MEOHz6soUOHaseOHWrZsqX69++v5ORkPfPMM1q8eLHuu+8+FS5cWFu3blVMTAzbE2xc/X63detWPf744+rZs6eCg4P11ltv6fTp03ruuefUtWtX7dy5U2PGjNEvv/yi4sWLa9asWWxPuKEPP/xQgwcP1g8//KBq1apZwwJJ2rVrlxo1aqSCBQsqLS1NHh4e+uOPP+Ti4uLAinGnunpfNXv2bO3cuVOlSpXSI488ogoVKmjevHnq0aOHnnvuObVv316enp7q37+/kpKStHr1aj6TI5PLly9b90kdOnTQ8uXLNXfuXLVq1Up//fWXpk6dqsWLF8vPz09FixbVtm3btGLFCt737jGEUnCIaz+gd+7cWbNnz9bFixe1fPlyzZkzRxEREdajErp06aKTJ0+qaNGimjdvnlxcXAikYCNjm0pJSVGfPn3UqFEjtW/fXgcPHlSPHj108OBBrVixQhUqVNC+fft07NgxnT9/XoGBgfL19XV0+bgLHDx40Dq23ZNPPql+/fpJkr744gsdOXJExhi1bNlSFSpUcHCluFOcOnXK5ginTZs2aeXKlTp48KAmTpwo6coH9i5dumjnzp3q27ev/vvf/8rZ2Vnx8fFyc3OTp6eno8rHXeLFF1/UxYsXNWvWLKWlpWX60W7fvn3Wgc27d++ufPny2XxRBCTbsxcGDBig2bNnq1KlSjp79qzy58+vjz76SNWqVdOSJUvUu3dvWSwW5c+fXz4+Plq5ciWfzZEtTzzxhFasWKF58+apVatWOnHihLZv365vvvlGZcqUUdOmTVWpUiVHlwk7I5SCQ3344YdasWKFSpYsqSlTpkiS4uPj9fHHH2vGjBnq1q2b3njjDUlXxkHIOCWGD1OQpHXr1qlevXrWv3/++Wf16tVLAQEBevPNN1WjRg1J0tGjR9WxY0ft379fK1euVPny5R1VMu4CGQHn+vXr9ffff+vy5ctq0KCBqlSpoqNHj+rNN9/UunXr1K5dO/Xv39/R5eIONWHCBB07dkxvvvmm9TSpxo0ba926dQoLC7M5BT01NVURERHau3evIiIi9PTTTyt//vwOrB53qqt/1JOufB5q0qSJSpcurc8++0zS/8KF1NRU/f3336pdu7bNMjKCKyDD1WHS7t27NXbsWPXu3VtBQUFatWqV3n33Xe3cuVNRUVGqUaOGjh49quPHj+vy5csKCgqyjjHFZ3NI/9tPbdy4Uf/8849cXV1VtmxZBQcHS7INppo3b84YwWBMKdjX1RnopUuXFBcXp5UrV+rAgQPWdh8fH3Xv3l29evXS/Pnz9fLLL0uSNZAyxvCmB0VHRys8PNzmyh2FCxeWk5OTli9fbv1wlZ6eLl9fX0VFRalChQqqXbu29u3b56CqcTewWCxatGiRmjVrpokTJ2r8+PGqWbOmZs6cKV9fXw0ePFh169bVN998oxEjRji6XNyhChQooGeeeUYuLi5KSUmRi4uLvvvuOzVv3lw7d+7Ul19+aR1vw9XVVfPmzVPx4sW1ePFipaSkOLh63InS09OtgdSePXt09uxZ5cuXT23bttWmTZsUExMj6X/jTB04cEBjxozRn3/+abMcAilk+OqrryT9b5v57LPPrGNH+fv7S7pyZdn+/furUqVK6tixozZt2iRfX1/VqFFDtWvXtobufDZHhoyxEx988EG9++67evbZZ9W9e3cNGzZM0pUxpsLCwvTss8/q66+/5uIwkAxgJ9u3bzcXLlwwxhgzYsQI8/fff5s9e/aYl156yRQqVMhMnz7dpn98fLwZNGiQefzxx016erojSsYd7ujRo8YYYw4ePGht27hxo6lWrZqpXbu2OX/+vDHGWLefw4cPmxYtWpidO3fav1jcNTZv3mxKlChhZs+ebc6ePWuSkpLMqFGjTL58+cysWbOMMcYcOHDAREREmLCwMHPy5EkHV4w72c8//2z69Oljtm3bZowx5uTJk6ZRo0amQYMG5ptvvjFpaWnWvikpKebQoUOOKhV3sKu3k2HDhpnHHnvMxMTEGGOMiYuLM40bNzbt2rUzy5YtM8YYs2/fPtOqVSvTsGFDc/nyZYfUjDvbBx98YOrXr2/S0tKs28i8efNMaGio8fT0NIcPH7bp/+uvv5o2bdqYYsWKmd27dzuiZNzBrt7PbN682Xh5eZkZM2aY5ORks337djN69GhTpkwZM3z4cGu/5s2bm4CAAHP27FkHVIw7CaEUbrv09HSzefNmY7FYzIcffmh69+5t8ufPb7Zu3WqMMWb37t3mpZdeMoGBgWbmzJk28546dcoaKBBMISs7duwwFovFvPfee9a2jRs3msDAQFO3bl1rEJqx/fDhHNe6dt8SExNj7r//fnP06FGbaSNGjDAFChSwhppHjx61BqPA1aFBamqq9f+zZs0yFSpUMK+88orZvn27McaYEydOmIYNG5oGDRqYZcuW2cwL3MiAAQNMyZIlzZdffmmOHz9ubV+5cqVp2bKlKV68uPH39zdVq1Y1wcHB1m2RbQzXio+Pt34m+v33363tS5YsMcHBwaZRo0Zm//79NvOsXLnSvP7663yWgtWUKVMy7V++/PJLU7VqVXPmzBlrW0JCghkxYoQJDg62/khjjOGHGBhjCKVgR+PHjzfu7u6mQIECJi4uzhjzvy+DO3bssAZTH3zwQaZ5CaRwI/369TP58+c3H330kbUtI5iqX7++9YgpIEPGB6irP0idOHHCpKenm2+++cY4OTlZfyVOSUkxxhhz5MgRExAQYBYvXmz/gnFX2Ldvnzl37pwxxpivvvrKjBw50hhjzOTJk02tWrXMiy++aBNMhYaGmqpVq5offvjBYTXj7vHTTz+ZMmXKmLVr1xpjjElOTjb79u0z0dHR5tixYyY1NdXExcWZqVOnmqVLl1qDg0uXLjmybNxhBgwYYH1fM8aY2NhYY7FYzOTJk61tixYtMg899JBp0qSJOXDgQJbLIZjCtm3bTL169TKdgbBixQrj4+Nj/vjjD5v2TZs2mfz585vly5fbs0zcBRhTCrddWlqaJCkgIECXLl1ScnKyNm/erKSkJOvYCJUqVVLv3r31yCOP6LXXXtPXX39ts4yrB/XEvc38/7hk69atU1RUlNLT0/X222/r9ddfV8+ePTV79mxJUs2aNRUVFaXdu3erZcuWjiwZdyAnJyft379fQ4YMkSQtXrxYjzzyiE6ePKkHH3xQDRo0UN++fXXs2DHrAJyurq5yd3dn3AxkKTk5WR07dlTdunU1f/58tW3b1nolxhdffFFdu3bVzz//rOnTp2vHjh0qXry4Fi1apDJlyqhy5coOrh53A4vFouLFi6tgwYLauHGjhgwZooceekjPP/+8mjRpom3btuk///mP+vTpo5YtW8rZ2VlpaWnss2C1Z88eTZ8+XQ899JB1HJ9y5cpp4MCBGjFihKZNmyZJateunV544QU5OTmpe/fuWY7FydhkqFixolasWKGKFStq7dq1Sk9Pl3RlfOCiRYvq888/V0JCgrW/v7+/AgMDrf0AK0enYsi7rj2U8/Lly+by5cvmzTffNE5OTmbKlCkmKSnJps+RI0fMxIkT+fUFWco4Ym7RokWmRIkSZsCAAWbz5s3W6cOHDzfOzs42R0xt3rzZ7Nq1y+614s6Wnp5uxo8fb2rWrGlatWpl8uXLZ+bNm2edPmfOHBMaGmpatWpldu3aZXbs2GGGDh1q/Pz8Mp3OAGQ4dOiQ8fPzM25ubub99983xlw5miXDpEmTTK1atcwrr7xiPYWd06qQlayGLvjjjz9MQECAadKkiSlUqJDp0aOHWbhwofnll19MYGCgWbJkiaPKxV1k/fr15r777jP169e3nt556NAhM3ToUFO4cGEzdepUa9/Fixeb6tWrmz59+jiqXNwFjh8/bqpUqWJq165tfU97//33TaFChcyrr75qfvrpJ3PkyBHTv39/4+vrazMWLGAMp+/hNrn6Q/Zvv/1mfvjhBxMbG2ttGzZsmHFycjIzZsywBlPdunWzfkg3hsOCkbU1a9YYDw8P8/7772d5SsLw4cNN/vz5Mw2cD2QlIiLCWCwW07x5c5v29PR0M3fuXNOkSRNjsVhMlSpVTEBAgFm/fr2DKsXd4OjRo8bDw8MUL17c/Oc//7Geynf1qTKTJ082ZcqUMf379zepqamcno5Mrv4Mdfz4cXP27FlreLB69Wrz3nvvmWXLllkHB7548aKpVauW+eqrrxxRLu5C69evN5UqVTL/+c9/rNvWwYMHrcHUtGnTrH1XrVrFZ3Lc0KVLl8w333xjgoKCbC6uMGvWLFOzZk1TrFgxU6VKFVOmTBmzYcMGB1eLO5HFmP8/Fwa4DQYMGKBvvvlGFy9eVMmSJZUvXz79/PPPslgsGjVqlEaPHq3OnTtr+/btOnr0qHbs2MFh5siSMUYWi0VjxoxRXFycli5dap2WlpZmcxh5v379NHfuXO3atUseHh6OKBd3uEuXLslisWjAgAE6ePCgjhw5ouDgYL311lsqUKCAtV96erp++eUXFSpUSL6+vvL19XVg1bgbHDx4UCkpKWrZsqUKFSqk2NhYFSxYUKmpqdZTQT/55BM1aNBA5cuXd3C1uNOkp6fLyenK6Brjx4/X0qVLlZqaKm9vb82fP19FixbV5cuXlS9fPqWkpOjs2bOKiIjQyZMntWbNGk6pQpYyPkNd/feff/6pJ598UiVKlNBPP/0kFxcXHTp0SB988IGmTp2qAQMGqH///tZ5rv2sBVwtJSVFq1atUr9+/VS0aFGtXr1azs7O2rVrl06dOqXz588rMDCQz1HIEqEUcs3VH6QkadKkSRo9erSWLVumevXqaezYsRo8eLCio6PVrFkzSdLUqVO1bt06ubi46P3335eLiwtverihV155RRs2bNCqVatstjfpyjhTderUkZOTk44fP64SJUo4qErcqa79YJ7RNmLECP3www/6z3/+YxNMHT58WL6+vpm2NUD63/Z0/Phx6/tX8eLFlZ6ers2bN6tjx47y8PBQTEyMChYsqHfffVfnz5+3jmUGXO3q/dPgwYP10UcfafTo0SpWrJgGDRokV1dXfffddypdurSSk5P19ttva9WqVbpw4YJ+/vlnPkMhS1d/Pk9PT1dqaqrc3d0lSX/++aeeeOIJlSxZ0hpMHT58WG+//ba2bNmi5cuXS2JsV/xPxn7qjz/+0B9//CGLxaL69eurRo0aNsGUp6enVq9ezecnZAtbCXLF8ePH5eTkZB3UPC0tTX/99ZfefPNNhYSEaOnSpRo7dqzef/99NWvWTGfPnpUk9e3bVx988IFmz54tFxcXXb58mQ9TsMrIzA8dOmT9f6lSpbRly5ZMg24mJydr/vz5+vbbbyWJQAqZZHyQiomJUd++fTVkyBCtWrVKFotF/fv3V/PmzbVu3ToNGDBAiYmJGj58uJ588kldvHjR0aXjDpSxPX377bdq2bKlQkNDVb9+fcXExMjJyUk1a9bUZ599pnPnzqly5crq1KmTXn/9dbVq1crRpeMOs3v3bkn/++K/fPlyfffdd1q8eLGeffZZubi46OjRozp16pQaN26sQ4cOyd3dXWFhYWrRooV+/fVXPkMhS1cHUhMnTtRTTz2lkJAQjR8/XuvXr1etWrW0ePFiHTt2TKGhobp06ZJKlSqlwYMHa/ny5YRRsJHxvvfll1+qVatWmjNnjqKiotSkSRPFxMTIzc1NDz30kCZMmKBz586pVq1aDGqO7LH7CYPIc0aMGGEKFChg9u7da4y5MhZCWlqaadSokXn//fdNdHS0KVSokJkxY4Yx5spYUe+8846ZP3++zXIYVwNXy9gevv76a1O9enUzZ84c67R69eqZ6tWrm+3bt5vz58+b5ORkM3DgQOPv72/27dvnoIpxN/jmm2+Mu7u7adq0qalTp47x9PQ0CxYsMMYYc+HCBTN69GhTo0YNU7ZsWePj42N+++03B1eMO9k333xjChUqZMaNG2d++eUX88wzz5iCBQvavL8lJCSY3r17m+eff978/fffDqwWd6LHH3/cDB061KZtxYoV5o033jDGGPP9998bLy8vM336dLN582bj5eVlgoKCzJ49e2zmYcwfXO3az9QDBgwwxYsXN4MHDzYvvPCCqVy5smnRooX54YcfjDHGbNiwwdx3332mQoUKNuN18tkc1/rpp59MiRIlzAcffGCMuTI+mcViMS4uLmbx4sXGmCvjKH799demfv36fC5HthBK4V/75ZdfTHh4uKlQoYL1Q9Lly5fNwIEDzYMPPmiKFCli3nvvPWv/+Ph48+ijj5opU6Y4qmTcJb799lvj7u5uJk+ebPNlbufOnaZRo0amePHipkaNGiY0NNSUKFGCwRNxQydOnDDTp0+3fpDau3evee2114zFYjGffPKJMebKldJ+++03ExUVlelLH3C1/fv3myZNmph33nnHGGPMgQMHTPny5U3VqlVNvnz5zMcff2wzYHXGYMLA1X799VfrQPjx8fHW9kOHDpmUlBTz8MMPm8GDBxtjjElKSjINGjQwTk5OplWrVsYYQgNcX8b+Z9OmTaZSpUo2FxyKjY01rVq1Mm3atDEHDx406enpJi4uznTo0IGAEzc0cuRIM2TIEGPMlcHxy5QpY55++mnTo0cPky9fPhMdHW2MuRJMZVzsA7gZQincss8//9z6/3Xr1pmmTZuacuXKmd27dxtjjPn9999NqVKlTO3atc2WLVtMWlqaOXz4sHnkkUdM/fr1edPDDZ07d840bdrUDBo06Lp9Zs+ebcaOHWumTp1q3e6ArGzevNkUKVLEVK1a1fqByZgrV7bKCKYyjpgCricjADh9+rRJTU01b775pjl16pQ5cuSICQwMNM8884xJTU017du3N56enuajjz4iNEC2TJkyxbRt29bmx5X9+/ebsmXLmuXLlxtjjDl16pTp0KGD+f33320CTyBD//79zcCBA23atmzZYry9vc3q1att2leuXGmKFi1q3b6uxmd0ZMh4D1u+fLnZsmWL2blzp/n111/N2bNnTf369U3Pnj2NMVeutm6xWIzFYjFLly51ZMm4CzGmFG7JDz/8oA4dOmj06NGSpLp16+rNN99UpUqV9PDDD2vXrl2qU6eOFi5cqCNHjqhLly6qVKmS2rdvr+PHj1uvyJAxBhVwreTkZO3cuVPVqlWTJJtz0s3/jy/VvXt39e/fX3369OEqVrghFxcXtWvXTrt379bp06clXdmOvLy81L9/fw0YMEBdunTR4sWLHVwp7mQWi0VRUVEKDg7WxYsX9fTTT8vT01NTp05V+fLlNXHiRLm4uMjf319OTk7q37+/kpKSHF027kDmmusM+fv767ffftO0adP0119/SZLKlCmjkiVLasCAAfriiy/Utm1bHT58WLVr17YZxxOQpMTERB09evT/2rvv+Brv/o/jr5MlIgkaI0pr1KgVmxZVjda6bbVnGxoxo4hRtXcVrUhTTYlNbSptzZhBEFE1isZepZSINJHz/f3hl3Mnpffd9i4neD//cy2fy+N6nHN5n+/382XLli1MnDjRtv3evXtYLBYuXLhg+zPAG2+8wYsvvsjevXsfuJZ6k0kqi8XC9u3badKkCTExMRQuXJiqVaty/PhxkpKS6NOnDwBZs2alVatWDBkyhMKFC9u5annSKJSSv6VmzZqEhIQwatQoRo8eDdwPpsaMGUPRokV56623OHHiBDVq1GDTpk0MHTqUbt26MWjQIHbv3q2GnPJfeXh44OXlxb59+wDSvYDv27ePsLAw27G/f7kX+b1ixYoxYMAAWrZsiZ+fH5s2bbI1cPXy8qJv374MGzaM4sWL27lSycguXLjA/Pnz6devH56ennh7ewNw7Ngx8ubNi6enJ3B/sY85c+bw448/kjVrVnuWLBlU6ufPnj17SEhIoEmTJoSFhbFx40amTp1KbGwsAJ9++imZMmVi9OjRZMqUic2bN+Pg4IDVatU7lKSTNWtWPvroIypXrkxERARjx44FwMfHh7fffht/f392796Nk5MTADdv3sQYw/PPP2/PsiWDO3PmDBEREQwdOpR27drZtt+4cYMDBw6QkJAAwIIFC7hx4waDBw/Wu5T8dXYdpyVPtN9++82EhIQYR0dHW0NOY+5P5atdu7YpUKCAOXnypDHmwZ4HGhYsaaU+H8nJySYhIcG2vW/fvqZMmTK2xompgoKCzKuvvmpu3LjxOMuUJ0Tq8xQXF2eOHz9uDh06ZNt37Ngx07lzZ5M9e3azcePGdMdrOoz8J/v27TNt27Y1derUMZcvX073PTZs2DCTJUsWM378eNOxY0eTPXt2c/z4cTtWKxlV2s+ZtWvXGh8fHzNlyhSTmJhojDFm3bp15sUXXzSdOnUyR44csR179uzZdN+VImml/TzauHGjadmypSlcuLCt550xxrRu3dq4uLiYoKAgM3LkSFO7dm1TunRpPU/yh44cOWKqVq1qChQoYD7//HNjzL+ftd9++820bNnSWCwWU7FiRePh4WEOHjxoz3LlCWYxRkMM5M8z/78UaKrExERmzZpFr169GDFiBB9++CEA0dHRfPjhh5w6dYqIiAiKFClir5Ilg0t9piIiIpg3bx6xsbH861//ol69elStWpWmTZvyyy+/ULZsWXx8fIiOjmbFihXs2LEDHx8fe5cvGUzq87R69Wo+/PBDbt++jZubG7Vr12bq1KnA/VEtEydOJCIigjlz5lC3bl07Vy1PglGjRjFnzhwSEhL48ccf8fDwIDk5GWdnZ+7cucOQIUOIjIwke/bsTJs2jbJly9q7ZMlgrFYrDg73JynMnz+fQ4cOMXPmTLJnz06/fv3o0qULrq6uRERE0L17d3x9fenevTsVK1Z86DVEfq9fv37Exsbi4ODAwYMHcXNzIyAggIEDBwIwbtw4Nm/eTFJSEgUKFODLL7/E2dmZlJQUjbyTh+rVqxfz5s2jVq1ahIeH4+HhYXvXun79OhEREfz666/UrVtX0/bk77NnIiZPlrS/7iUnJ6cb/TR9+nTj4ODwwIip8uXLm7fffvux1ilPjtRnaPXq1cbNzc18+OGHZt68eaZmzZqmYMGC5vTp0+bmzZtm6NChpmbNmqZ06dKmcePG6Ua+iPxeRESEcXd3NzNmzDA//vijmTFjhrFYLKZbt262Y44dO2aaN29uChYsaO7cuaNm1PJfJSUlmcmTJ5u8efOaDh06mJs3bxpj0o8E/uWXX8ydO3fsVaI8IYYOHWprhD937lxTvXp1U6ZMGTN16lRz9+5dY8z9z7FMmTKZMWPG2LlaeVIsXrzYZMuWzezdu9fcvXvXXLlyxXTq1MlUqFDBTJo0yXbcr7/+mm5klUZKSao/ehfq16+fKVGihBk9erT59ddfH3NV8izQSCn5U9L+Mjdt2jQOHjzIyZMnadasGU2aNKFQoUKEhITQu3dvhg8fbhsxdfToUYoVK6Zf9cQmIiKCfPny4ePjgzGGa9eu0aJFC5o0aUJgYCB3794lf/78tG/fnsmTJ6d7duLj43FxccHFxcWOdyAZ2bVr1+jSpQs1atTg/fff59KlS1StWpWiRYuyc+dOWrdubetHduLECdzd3cmTJ4+dq5aMxvz/r8BXrlyxjYR64YUXuHfvHh9//DErVqygcuXKjBs3Dg8PD+7du2fr0yLyR4wxXLhwgVq1avHhhx/Svn17AO7cuUPXrl2Jjo6mT58+thFTUVFRVK5cWSNY5E+ZNGkSixYtIjo62vZ5dP78eQICAti/fz8DBgygb9++6c4xv5sBIc+u1Gdh7969REVF4eLiQqFChahTpw4AgYGB7Nixg6ZNm9KrVy88PT01clP+MXqK5E9J/cAZNGgQY8eOpVKlSvj6+hIWFoa/vz8JCQn4+fkxffp0xowZQ1BQEADFixe3NeQUuXLlCj179mTatGkcPXoUi8WCm5sb8fHx1KtXj7i4OIoUKUKTJk2YMmUKDg4OfPfdd5w4cQIAd3d3BVKSjjHG1uj+xIkT5MiRgzfffJNGjRpx9epVateuTZ06dVizZg19+/Zl1qxZtv8IFilSRIGUPCD1xXzVqlXUq1ePKlWq8MYbbzBmzBicnJzo378/TZo0Yd++fQwdOpRbt24pkJI/lPa3X4vFQpYsWXBwcLA1B7537x5ZsmRh7ty5ODg4EBISwsyZM/ntt9949dVXtVKx/Fep79i5cuXCarXaVtmzWq3ky5ePIUOGkJCQwKeffkp4eHi6cxVISSqLxcLy5ct56623WLp0KSEhITRo0MD2f7pp06bx6quv8vXXXzNx4kRu376tQEr+MXqS5L9KfaHau3cva9asYe3atfTo0YPq1asTFxdHu3btcHNzI1OmTAQEBDB69GiioqLSvYjpQ0sAcufOzbJlyzh8+DBTpkzh8OHDODo6cvfuXSIjI6lduzb16tXjs88+A+D06dOEh4dz8uRJO1cuGc3t27eB+y9RFouFNWvW8Prrr3PkyBH8/f0pXLgwS5cuJXfu3IwcOZJMmTKRN29eKlSoQFRUlO2lXeT3LBYLGzdupHXr1nTu3JmRI0fSq1cvRo4ciZ+fH46OjvTv359GjRqxfv16xowZoxVA5aHSjkK5du0aAM7Oznh6erJx40YAnJycSElJwcnJiXLlyuHi4sKyZcvYsWOH7ToaKSVp/f6H3tR37EqVKhEXF8cnn3xCQkKCbXtycjKvvfYaffv2pWPHjo+9XnkynDhxgp49ezJhwgR27NjB1q1bCQ8PJzg4mEGDBgEwffp0SpYsSVRUFElJSXauWJ4qdpgyKE+A4cOHm7Vr16bbtmXLFlO8eHFjjDHLli0zHh4e5rPPPjPGGBMfH29WrVpl7ty5Y1JSUmxzktWnRR7mwIEDpnz58sbPz89cvHjRBAcHG4vFYurXr5/uuCFDhphSpUqZs2fP2qlSyYi6du1q3nnnHZOUlGSMMebMmTOmVatWJjQ0NN1xAQEBpnLlyrY/DxgwwIwfPz7dCo8iaaV+ZwUEBJi2bdum27dlyxbj4OBgJk6caIy5v/LQxx9/bOLi4h53mfIESNuHc+XKlaZWrVrmhx9+MMYYs2fPHuPm5mZ69uxp69FptVpNmzZtTEREhClXrpxp0aKFvUqXDCzte3VoaKh5//33zbBhw8yZM2eMMfffzx0dHY2/v79Zt26d+eGHH0zdunVNQECA7VytgC0Ps2vXLlOsWDFz/vz5dNvnzJljMmfObCIjI23bLl++/LjLk6ecxpvLAw4fPsyGDRvYuXMnrq6uvPnmm8D9X/y8vLxYtGgR3bp1Y+LEiXTr1g2A3bt3s3r1akqWLGlbecFonrr8gXLlyhEWFsa7777LsGHDaN26Nf369WPq1Kl89NFHAMTFxTF//ny2bdvGCy+8YOeKJaNYvHgxq1atYv369Tg7OxMTE0NISAgXLlzA19cX+HcPvCZNmjB79myaNm2Ki4sL3333HVFRUWTOnNnOdyEZTer3VUJCAlmyZCEuLo7s2bPb9iUnJ1OzZk1Gjx7NggUL6NSpE7lz5+b999+3c+WSEaXts7J582aWL1/OgQMHGDFiBCNHjqRy5crMnz+fdu3aERMTQ+7cubl48SK//PILCxcuZNeuXWzZskX9WiSdtM/DoEGDmDVrFmXKlOHq1avMmjWLjRs30rx5c9asWcOAAQNYt24djo6O5MiRgzVr1mCxWDDGaOSdPJSzszMnTpzgxIkT5M2b1/a96OvrS548ebh06ZLt2Ny5c9uxUnka6ZtOHlCqVCnGjh2Lq6srkyZN4rvvvgPgjTfe4Ndff6Vdu3aMGzeOgIAAABITE5kyZQq3b9+mUKFCtusokJL/pFy5csyaNYuYmBiWLl1K7dq1mTZtGnPmzGH58uXcvHmTXbt2aVl1SefcuXN4eXlRtmxZvv32Wzp16sT27dvZt28fcXFxwL+nMlStWpXZs2dz584dHBwc2LZtG8WLF7dn+ZIBpb54b9y4kWHDhnH27FkaN27Mli1b2LdvHxaLBWdnZwCyZ8+OxWLB09PTzlVLRpb6GfT+++/Tq1cvnnvuOV577TW2bdvG0KFDOXbsGE2bNiU2NpayZcvi6elJlSpVOHz4MHB/kZhChQppWqikk/pcXb16lYSEBL777js2bNjAwoUL8fHx4ZVXXuHYsWPUr1+f9evXs2nTJpYsWcLevXtxdnbm3r17ejcX4N+tWY4ePcr27duJi4ujfPnyNGzYkBkzZnDw4EHbs5IzZ06yZcum6XrySGn1PUknOTnZ9vK9ePFi5s2bR3x8PCNGjOCNN97g6NGjNG7cGC8vL/z9/bl37x5Llizh8uXLxMTE4OTkpF/25C85cOAA/v7+lC1bllGjRuHt7Y3FYiExMRFXV1d7lycZTHR0NB06dOD5559n69atrF+/nuTkZPr370+hQoUYNmwYFStWTHeO1WolOTmZTJky2alqyehWrFhB+/btGTx4MPXr18fV1ZXBgweTkpLCqFGjqFChAgD9+/dn//79rFmzBg8PDztXLRnZ1q1badWqFStXruTVV18FICwsjPDwcHLnzs2YMWMoXrw4KSkptpErV69e5aOPPiI8PJytW7dSokQJe96CZEDz588nICCAEiVKsGzZMttI8pMnT9KnTx+ioqKIioqiWLFi6c5L+5yJAKxatYoOHTrg7e3NuXPnCAsL4+7duyxatAhPT0/8/f0pUKAAc+bMYfbs2ezZs4cCBQrYu2x5Wtlp2qBkcMOHDzctW7Y0Pj4+xsHBwVSvXt1s3LjRGGPMiRMnTK1atUypUqVMtWrVTKdOnWy9XTRPXf6OAwcOmEqVKplWrVqZw4cPG2PUj0z+WPfu3Y3FYjFVqlSxbVu4cKGpWLGi6dChg9m/f79te9q+LiIPc/z4cVOwYEETEhKSbvuqVatMw4YNjZeXl6lfv76pU6eO8fT0NDExMfYpVJ4omzZtMl5eXrbvtFSffvqpyZQpk2nevLmtx5Qxxpw7d86MGzfOFC1aVM+Y/KHNmzebOnXqGHd3d1sfqdT3pZMnT5qGDRsai8Vizp07Z88yJQNLSUkx169fN9WqVTOff/65OXHihBk9erRxcnIyM2bMMF988YVp1aqVcXBwMC+//LIpXLiwOXDggL3LlqecRkrJA0JCQhg0aBBr166lcOHCREVFERwcjIODA0OHDrX1bfn5559xc3MjS5YswP1ljbUstvxd0dHRDBgwgEWLFpEnTx57lyMZ1N27d2nQoAGFChVi165d+Pj4sGjRIgAWLlzI1KlTKVWqFAEBAVSuXNnO1cqTYOPGjfTo0YP169eTP3/+dKN9jx07xv79+1m/fj358uWjQ4cOvPzyy3auWDIy8//TQXfv3k27du349NNP+de//mV7rlJSUvDx8cHNzY2SJUsyfvx48uTJg9Vq5dKlSzg5OalfiwA8dOaBMYZ9+/YREBDArVu32LlzJzlz5rQ9d8ePHycsLIzx48frnVzSSX1GEhMTMcYwZswY+vfvb+ufOHXqVIKCgpg8eTJt2rTh9u3bJCUl4eXlRa5cuexcvTzt9GklD9i7dy+NGjXi9ddfB+Dtt9/G3d2dfv36MWzYMBwcHKhZsyY5c+a0nWOM0Zef/E8qVarEt99+qyl78h9lzpyZtWvX4ubmxqxZs5g0aRJt27Zl4cKFtG3b1haeu7q6UqZMGU3Zk/8qPj6eu3fvptuWOtXl8uXLVKtWjXbt2tmpOsnofh8cpPZheeWVVyhatCh9+vThhRdewMfHB4DLly9TunRpihcvzty5czly5Ah58uTBwcGBvHnz2uUeJONJ+1ytXLmSixcvYrVaeeutt6hUqRIzZ86kd+/e1KxZky1btpArVy6MMRQrVsy2YIx+LJa0LBYLq1ev5rPPPuPcuXNYrVZatWplC6X69u2LxWIhKCiIq1evMmTIENvAA5FHTY1/5AHPPfcc169fT/eSXrduXTp27Mj+/fsJDAxkz5496c5R40T5JyiQkj/Dzc0NgJYtWzJw4EBiYmJo27YtAK1bt2bChAkEBQUpkJI/pUyZMly7do2ZM2cC95sJp/ZeWbVqFbNnz1aDV3motMHB0qVLGT58ONOnT2fr1q0AREREkDNnTho1asS4ceMIDw+nU6dOxMfHM3z4cIwxfPPNN/a8BcmgUp+roKAgevToQWRkJLNmzaJt27bMmjWL8uXLM2nSJLy8vHjzzTe5fPnyA+/iCqQkrX379tGxY0cKFixI5cqVOXXqFLNmzeLMmTO2YwIDAxk1ahQhISEkJibasVp51iiUkgf4+Piwa9cuNm7cmG7ll9y5c1O1alXefvttKlWqZMcKRUTA3d2dli1bEhQUxPfff0+DBg2A+6M7CxYsaOfq5ElRsGBBgoOD+eijjwgKCuLw4cMcPXqUgQMHMmfOHNq0aYOLi4u9y5QMxhiTLjgIDAxk//79rFy5kgEDBjBv3jwsFgtRUVG8+eabrFu3jvHjx+Pi4sLSpUsByJMnD0WLFrXnbUgGtmjRIhYtWsSaNWtYunQpvXv35ocffiBbtmzA/RVmJ0+eTFJSEv3797dvsZKhnTp1irVr1zJ48GA+++wzZs+ezSeffMLy5csJDQ1NF0wNHDiQn376CS8vLztWLM8aRejygM6dO7Nz507at29PaGgo5cuXx9vbmxUrVuDr68uQIUOwWCxaZU9E7C5Lliy0bNmSxMREwsPDuXDhgqbAyF/WuXNnPDw88Pf3Z9GiRbi6uuLo6MjmzZvVQ0oekPb9Z8aMGXz11VcsX76cV155hZCQEPr27cvw4cNJSEjA39+fsLAwbt68iTHGNlVm2LBhxMXFUatWLXveimRgJ0+epEaNGlSsWJGlS5cSGBjIJ598QrNmzYiPj+fq1atUrlyZZcuWUbx4cXuXKxnUrVu3aN26NadPn+a9996zbQ8ICMBqtTJ+/HgcHR3x8/Oz/aCXGnyKPC5qdC7ppH3R6tmzJ6tWrSIlJQUPDw8cHR35/vvvcXJysjXLExHJCBISEkhOTiZr1qz2LkWeYBcvXuTMmTNYLBYKFiyohtPygLTvP7du3WLIkCEUKFCA/v37s2bNGjp27EifPn04ceIE27ZtY8KECbRv3952/okTJxg2bBhbt25l3bp1lCtXzl63IhnIw37oHTRoEI6OjjRs2JC33nqLjz76iG7dumGMITw8nF9++YXevXvj7OwM/LsXnsjvxcTE0KpVK3LlykVoaCilSpWy7QsNDaVv374MHjyYIUOGaNqn2IVCKXlA2heuqKgorl27xp07d2jRogWOjo760hMREZFnzpYtW7h48SLt2rXD39+f7Nmz06dPH+7evUtKSgr169enR48eBAYGsnLlStq0aYOzszNz586ladOmACQmJrJhwwaKFy9O4cKF7XxHkhGkDaROnTpF5syZyZkzJ9HR0VSvXh2AJUuW0KJFCwDu3LlDs2bNKFWqFB9//LHd6pYny6FDh+jUqROVK1emd+/elCxZ0rbvyy+/pEaNGhQpUsSOFcqzTKGUPNQfTc1TICUiIiLPEmMM8fHxNG/enKSkJDw9Pdm6dSvbt2+3rao3f/58pk+fzvr168maNSvr16/n888/p169erzzzjt6d5KHSvtD8KBBg1i9ejU///wzJUuWtPWz6969O7NmzaJatWrcunWLAQMGcPXqVfbu3atRLfKXxMTE0KVLF8qXL0/fvn0pUaKEvUsSAdTo/JlhtVofuv2PMsnUQOr35+mlSkRERJ4lFosFDw8PFi9ezOXLl/n6668ZMmSILZACcHZ25uzZs2zfvp2EhASmT59OgQIF8PPzs40yF0nLarXaAqnFixczZ84cJkyYwMcff0yVKlUIDAwkOjqaSZMm4efnx6uvvkrHjh1JSkpiz549ODk56bmSv6RcuXKEhYVx6NAhRo8ezbFjx+xdkgigRufPhLSjng4fPkxCQgK5cuWiQIECWCyWPxz9lHZlmWPHjvHiiy/almIXEREReZY4ODjw0ksvkTt3bjZt2kS+fPlo164dACVKlKBGjRp07NiRbNmykSVLFlasWIHFYsEYox/15AGp79iRkZFs2rSJoKAgGjduDNzvV1agQAEGDRrEokWL+OGHHzh37hyenp6UKVMGBwcH7t27p5FS8peVK1eO4OBgBgwYoD6ckmFo+t5TLu2w4A8++IA1a9Zw9uxZqlSpQuXKlRkzZgzw4LS8tOdNnz6d8ePHs2vXLgoUKPDY70FEREQko7h8+TJ+fn7cvXsXPz8/WzB1/Phxjh07xu3bt2nTpg2Ojo4KDuQ/unz5MtWrV+fq1asMHDiQDz74wLbv+vXr+Pn58cILLzB9+vR052kFbPlfJSYm4urqau8yRABN33vqpQZLY8aMISwsjE8++YSTJ0+SN29egoOD6dmzJ0C6oeVpA6nPP/+cESNGMGXKFAVSIiIi8szz9vYmODgYNzc35syZw6xZs0hJSaF79+58//33tG/f3vZepUBK/hNvb29WrFhBrly5WLFiBTExMbZ9Xl5e5MiRg1OnTj1wngIp+V8pkJKMRJ9oT6m0A+COHDnCypUrWbBgAb6+vsTGxvLVV19Ru3Ztvv32WwIDA4H7wVRycnK6QCooKIiZM2fSunVre9yGiIiISIZTsGBBpk+fjoeHB5MnT6ZIkSJcvXqVoKAg2zGasid/ho+PDytWrCAlJYVp06Zx8OBBAG7fvs3Ro0fJly+ffQsUEXnENH3vKZR2pFNsbCw+Pj6EhYXRrFkzDh8+TOvWrRk9ejTvvvsuDRo0YPPmzTRr1oyFCxfarjFz5kyCgoL48ssvad68ub1uRURERCTDunTpEvv37+fKlSt06tQJJycnTdmTvyUmJob27dvzyy+/ULFiRVxcXIiLi2P37t24uLike78XEXmaKJR6yvx+adndu3ezePFicufOjcViISAgACcnJ6ZMmYKzszMDBgwgOjqaEiVKEBwcjIODA2vWrKFJkyYsW7aMZs2a2fmORERERJ4Mf7R4jMifcfjwYRo1akS+fPlo27Yt3bp1AyA5ORlnZ2c7Vyci8mho+t5TJjWQOnbsGFFRUYwdOxZvb2/b9ri4OM6fP4+zszMpKSmcOXOGDh06MGPGDNv89AYNGrBlyxYFUiIiIiJ/gQIp+V+UKlWKFStWkJSUxIEDBzh58iSAAikReapppNRTaPz48URGRuLq6sr8+fPx8PDAarUCMG3aNObNm0eePHm4desWN2/eJDY2FkdHR4wxasopIiIiImJHMTExdOvWjUKFCjF8+HBefvlle5ckIvLIaKTUU6h48eJs2LCBHTt2cPr0aeD+Kh0ODg60adOGjh07kjVrVkqWLElMTIxthRiLxaJASkRERETEjsqVK0dwcDCXLl0ia9as9i5HROSR0kipJ9wfNT3ctGkTtWvXpnPnzrYpfH9EDTlFRERERDKWxMREXF1d7V2GiMgjpZFSTzCr1WoLpK5evcrZs2dt+2rVqsWqVasIDw9nzJgxXLlyJd15qYwxCqRERERERDIYBVIi8ixQKPWEslqttsbko0aNol69elSqVIm6desSGRlJYmIiDRs2ZNWqVYSGhjJ27FguXboEYDsP0NKyIiIiIiIiImIXCqWeQMYYW7A0fPhwQkNDCQwMJCoqip9++omhQ4eydu3adMFUcHAwixYtsnPlIiIiIiIiIiL3ad7WE+To0aMUL17c9uedO3eyevVq5s+fj6+vL9u3b+fChQsYYxg6dCiOjo7Ur1+fBg0asH37dqpUqWLH6kVERERERERE/k0jpZ4QkydPtgVPFosFYwzZs2enZ8+e+Pr6smnTJpo1a8aMGTM4ceIEiYmJTJkyhSVLlpCUlES1atVwcnLi3r179r4VERERERERERGFUk+K0qVLU6NGDfr06WMLpooUKULDhg1JTk5m2rRpdO3alY4dO2KMoUiRIsTGxrJz505cXFxs11FTcxERERERERHJCBRKZXBffPEFAHXq1KF79+4ULlyYXr16sW3bNpydncmdOzdJSUlcu3YNLy8vW6+pF198kcjISEJDQ+1ZvoiIiIiIiIjIQ2nYTAa2ceNG/P39iY2NJTg4mNdffx1jDCEhIfTu3Zvp06fz2muv4eDggJOTE8uWLePWrVts376d69evU65cORwcHEhJScHR0dHetyMiIiIiIiIiYqORUhlYpUqVmDlzJsuWLaNHjx4A1KxZk+7du1O0aFF69epFZGQkmTNnZvny5bi5ubFz5048PDzYt28fDg4OWK1WBVIiIiIiIiIikuFYjDHG3kXIH7t9+zaLFy/mgw8+oEWLFsyYMQOAyMhIQkJC+PHHH5kyZQq+vr4kJiZijMHV1RWLxcK9e/fUQ0pEREREREREMiQlFhmQMQaLxQKAh4cHLVq0AGDIkCEAzJgxg5o1awIQEhLCgAEDGD9+PLVr1053DQVSIiIiIiIiIpJRKbXIYKxWq61ZudVq5d69e2TLlo1OnToBMHjwYODfwZTFYmH06NEsXLgwXSiVGmqJiIiIiIiIiGRECqUykLSB1Mcff0xsbCwHDhzA39+fN954g65duwLwwQcfYLFYbM3PPT09KVOmjD1LFxERERERERH5S9RTKgMaPHgwX375JcOGDSM+Pp6wsDBefvllFi9eTEpKCkuXLmXo0KHUqlWLBQsW2M5LG2qJiIiIiIiIiGRkSjAymL1797Jq1SrWrl1Lz549qV69OmfPnqVly5a4u7uTNWtWOnTowODBg7l58yZWq9V2rgIpEREREREREXlSKMXIYKxWK66urlSpUoWvvvqKevXq8emnn9KxY0fu3LlDREQEAO+99x5ff/01Dg4O6YIpEREREREREZEngUIpO3pYmBQfH09iYiKLFy/mvffeY8KECXTr1g2AXbt2sXDhQs6ePUvmzJmxWCwYYzRCSkRERERERESeOOopZSdp+z+FhoYC2MKnOnXqsGHDBqZPn06PHj0ASExM5O233yZz5swsWbJEQZSIiIiIiIiIPNG0+p6dpIZKAwYMYMmSJXTq1Inz58+TL18+xo0bx6+//srUqVPJmjUrN27cYO3atVy8eJGDBw/apuwpmBIRERERERGRJ5VGStnR/Pnzef/99/nmm2+oUKGCbbvVauXYsWOMGjWK2NhYcuXKRZEiRfjss89wdnbm3r17ODkpTxQRERERERGRJ5dCKTsaMmQIFy5cYM6cOaSkpODo6PhA4HTlyhW8vLxs2xRIiYiIiIiIiMjTQPO/7OjChQvExcUB4OjoiDEGJycnEhMT2bhxIwC5c+e2hVCp+0VEREREREREnnQKpR6Dh62yB1CuXDmuXLnCli1bSEpKwmKxAHDr1i1GjhzJN998k+741P0iIiIiIiIiIk86Td97xNI2JI+OjsZqteLo6EjFihX57bffqFatGgCDBw+mWrVqxMfHExgYyI0bN9i2bRuOjo72LF9ERERERERE5JFQKPUIGWNso5sGDhzIokWLsFgsXLlyhTZt2jBp0iQ8PDxo3LgxFy5c4OTJk5QoUQJnZ2d27NiBs7OzrdeUiIiIiIiIiMjTRKHUYxAcHMzIkSNZvXo1Xl5enDt3jg4dOlClShUWLFiAi4sLR44c4fjx4+TOnZvq1as/tOm5iIiIiIiIiMjTQqHUY9CpUycyZ85MaGiobfTUwYMHqVGjBr169WLs2LEPnKMRUiIiIiIiIiLyNFOj83/Y7zO+5ORkLly4QGJiom1/UlISZcuWZcSIESxdupQbN26QkpKS7jwFUiIiIiIiIiLyNFMo9Q+yWq22HlI//fQTV69exdnZmY4dO7Js2TI2bdqEg4MDzs7OAGTKlIkcOXKQJUsWhVAiIiIiIiIi8kxRKPUPSl1lb8iQITRq1IgSJUoQFBSEu7s77777Lj169ODbb7/FarXy66+/8vXXX5M3b15bSCUiIiIiIiIi8qxQF+1/gNVqtQVSS5cuZe7cuQQHB3Po0CG+/fZbzp49yyuvvELDhg1p0KABhQoVwtHRkUyZMhEdHY3FYkm3Up+IiIiIiIiIyNNOjc7/Qdu2bWP58uWUKVOGd999F4A1a9Ywffp0smfPTteuXcmVKxd79uzB3d2dVq1aaZU9EREREREREXkmKZT6h1y+fJnq1avz888/M3LkSAIDA2371q5dy7Rp0/D09GTw4MFUrlzZtk+r7ImIiIiIiIjIs0g9pf4h3t7erFixAm9vbyIiIvj+++9t+xo2bEi/fv04efIkK1euTHeeAikREREREREReRZppNQ/LDY2lnfeeYeKFSvSp08fSpYsadu3a9cuqlSpoiBKRERERERERJ55CqUegZiYGLp06UKFChUIDAykRIkS6fZryp6IiIiIiIiIPOsUSj0iMTEx+Pv7kz9/fiZNmkTBggXtXZKIiIiIiIiISIahnlKPSLly5QgODsbDw4P8+fPbuxwRERERERERkQxFI6UeMWMMFosFq9WKg4MyQBERERERERERUCj1WKQGUyIiIiIiIiIicp+G7jwGCqRERERERERERNJTKCUiIiIiIiIiIo+dQikREREREREREXnsFEqJiIiIiIiIiMhjp1BKREREREREREQeO4VSIiIiIiIiIiLy2CmUEhEREXnCWCwWVq1aZe8yRERERP4nCqVEREREHoGGDRtSt27dh+7bvn07FouFQ4cO/a1rX7p0iXr16v3p4zt37kyTJk3+1t8lIiIi8qgolBIRERF5BPz8/NiwYQPnz59/YN/s2bOpWLEiPj4+f+maSUlJAHh7e5MpU6Z/pE4RERERe1EoJSIiIvIINGjQgJw5cxIeHp5ue3x8PEuXLqVJkya0adOGvHnz4ubmRunSpVm0aFG6Y2vWrEnPnj0JDAwkR44c1KlTB3hw+t65c+do2bIl2bJl47nnnqNx48acPn0agBEjRjBnzhxWr16NxWLBYrEQGRmJr68vPXv2TPf3/fzzz7i4uLBp06Z//N9DRERE5PcUSomIiIg8Ak5OTnTs2JHw8HCMMbbtS5cuJSUlhfbt21OhQgXWrVvH4cOHee+99+jQoQN79+5Nd505c+bg4uLCzp07CQ0NfeDvSU5Opk6dOnh4eLB9+3Z27tyJu7s7devWJSkpif79+9OyZUvq1q3LpUuXuHTpElWrVqVLly4sXLiQ3377zXat+fPnkzdvXnx9fR/dP4yIiIjI/1MoJSIiIvKIvPvuu5w6dYqtW7fats2ePZvmzZuTP39++vfvT9myZSlUqBC9evWibt26fPXVV+muUaRIESZNmkSxYsUoVqzYA3/HkiVLsFqthIWFUbp0aYoXL87s2bM5e/YskZGRuLu7kzlzZjJlyoS3tzfe3t64uLjQrFkzAFavXm27Vnh4OJ07d8ZisTyifxERERGRf1MoJSIiIvKIvPzyy1StWpVZs2YBcPLkSbZv346fnx8pKSmMHj2a0qVL89xzz+Hu7s53333H2bNn012jQoUK//HviI2N5eTJk3h4eODu7o67uzvPPfcciYmJnDp16g/Pc3V1pUOHDrbaDhw4wOHDh+ncufP/dtMiIiIif5KTvQsQEREReZr5+fnRq1cvZsyYwezZs3nppZd4/fXXmThxIp988gnTpk2jdOnSZMmShcDAQFsz81RZsmT5j9ePj4+nQoUKLFiw4IF9OXPm/I/ndunShbJly3L+/Hlmz56Nr68v+fPn/+s3KSIiIvI3KJQSEREReYRatmxJnz59WLhwIXPnziUgIACLxcLOnTtp3Lgx7du3B8BqtfLjjz9SokSJv3T98uXLs2TJEnLlyoWnp+dDj3FxcSElJeWB7aVLl6ZixYp88cUXLFy4kODg4L9+gyIiIiJ/k6bviYiIiDxC7u7utGrVisGDB3Pp0iXb9LgiRYqwYcMGdu3axdGjR/H39+fKlSt/+frt2rUjR44cNG7cmO3btxMXF0dkZCS9e/fm/PnzABQoUIBDhw5x/Phxrl27RnJysu38Ll26MGHCBIwxNG3a9B+5ZxEREZE/Q6GUiIiIyCPm5+fHjRs3qFOnDs8//zwAQ4cOpXz58tSpU4eaNWvi7e1NkyZN/vK13dzc2LZtGy+++CLNmjWjePHi+Pn5kZiYaBs51bVrV4oVK0bFihXJmTMnO3futJ3fpk0bnJycaNOmDa6urv/I/YqIiIj8GRaTdo1iEREREXmmnD59mpdeeono6GjKly9v73JERETkGaJQSkREROQZlJyczPXr1+nfvz9xcXHpRk+JiIiIPA6aviciIiLyDNq5cyd58uQhOjqa0NBQe5cjIiIizyCNlBIRERERERERkcdOI6VEREREREREROSxUyglIiIiIiIiIiKPnUIpERERERERERF57BRKiYiIiIiIiIjIY6dQSkREREREREREHjuFUiIiIiIiIiIi8tgplBIRERERERERkcdOoZSIiIiIiIiIiDx2CqVEREREREREROSx+z/Ng4n3nxVd9gAAAABJRU5ErkJggg==",
|
||
"text/plain": [
|
||
"<Figure size 1200x600 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Plot salvato come: .//2024-12-19_23-39_plots/variety_comparison_oil_efficiency_l_kg.png\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": "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",
|
||
"text/plain": [
|
||
"<Figure size 1200x600 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Plot salvato come: .//2024-12-19_23-39_plots/variety_comparison_water_efficiency_l_oil_m³_water.png\n",
|
||
"Plot salvato come: .//2024-12-19_23-39_plots/efficiency_vs_production.png\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": "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",
|
||
"text/plain": [
|
||
"<Figure size 1000x600 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Plot salvato come: .//2024-12-19_23-39_plots/water_efficiency_vs_production.png\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": "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",
|
||
"text/plain": [
|
||
"<Figure size 1000x600 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Plot salvato come: .//2024-12-19_23-39_plots/water_need_vs_oil_production.png\n",
|
||
" Variety Technique Technique String \\\n",
|
||
"0 nocellara_delletna 2 superintensiva \n",
|
||
"1 nocellara_delletna 3 tradizionale \n",
|
||
"2 nocellara_delletna 1 intensiva \n",
|
||
"3 leccino 3 tradizionale \n",
|
||
"4 leccino 1 intensiva \n",
|
||
"5 leccino 2 superintensiva \n",
|
||
"6 frantoio 2 superintensiva \n",
|
||
"7 frantoio 1 intensiva \n",
|
||
"8 frantoio 3 tradizionale \n",
|
||
"9 coratina 3 tradizionale \n",
|
||
"10 coratina 1 intensiva \n",
|
||
"11 coratina 2 superintensiva \n",
|
||
"12 taggiasca 1 intensiva \n",
|
||
"13 taggiasca 2 superintensiva \n",
|
||
"14 taggiasca 3 tradizionale \n",
|
||
"15 pendolino 2 superintensiva \n",
|
||
"16 pendolino 3 tradizionale \n",
|
||
"17 pendolino 1 intensiva \n",
|
||
"18 moraiolo 3 tradizionale \n",
|
||
"19 moraiolo 2 superintensiva \n",
|
||
"20 moraiolo 1 intensiva \n",
|
||
"\n",
|
||
" Avg Olive Production (kg/ha) Avg Oil Production (L/ha) \\\n",
|
||
"0 17727.298661 3870.709391 \n",
|
||
"1 9563.406694 2088.211185 \n",
|
||
"2 13667.805614 2984.246320 \n",
|
||
"3 10938.684057 2149.430161 \n",
|
||
"4 16371.651842 3216.842318 \n",
|
||
"5 20544.147125 4037.062548 \n",
|
||
"6 24663.572247 6058.337502 \n",
|
||
"7 20516.024787 5039.266103 \n",
|
||
"8 13673.948225 3358.771782 \n",
|
||
"9 12299.616734 3155.538475 \n",
|
||
"10 16410.475160 4210.501916 \n",
|
||
"11 19156.162270 4914.756321 \n",
|
||
"12 10933.864174 2208.344627 \n",
|
||
"13 16408.233777 3314.030124 \n",
|
||
"14 6833.214251 1380.020941 \n",
|
||
"15 19153.020538 3450.403695 \n",
|
||
"16 10951.390291 1972.852549 \n",
|
||
"17 13704.478410 2468.577580 \n",
|
||
"18 8756.527000 1912.018469 \n",
|
||
"19 17789.470297 3884.014946 \n",
|
||
"20 13119.074364 2864.192536 \n",
|
||
"\n",
|
||
" Avg Water Need (m³/ha) Oil Efficiency (L/kg) \\\n",
|
||
"0 12131.560516 0.218347 \n",
|
||
"1 12158.140744 0.218354 \n",
|
||
"2 12163.565060 0.218341 \n",
|
||
"3 9819.115536 0.196498 \n",
|
||
"4 9801.126450 0.196489 \n",
|
||
"5 9838.437036 0.196507 \n",
|
||
"6 10989.828820 0.245639 \n",
|
||
"7 10968.425693 0.245626 \n",
|
||
"8 10965.353268 0.245633 \n",
|
||
"9 13708.071663 0.256556 \n",
|
||
"10 13715.117102 0.256574 \n",
|
||
"11 13721.617765 0.256563 \n",
|
||
"12 9953.737673 0.201973 \n",
|
||
"13 9958.298782 0.201974 \n",
|
||
"14 9950.943918 0.201958 \n",
|
||
"15 10266.579633 0.180149 \n",
|
||
"16 10277.884893 0.180146 \n",
|
||
"17 10288.302212 0.180129 \n",
|
||
"18 11992.446013 0.218354 \n",
|
||
"19 11993.120349 0.218332 \n",
|
||
"20 11980.845607 0.218323 \n",
|
||
"\n",
|
||
" Water Efficiency (L oil/m³ water) \n",
|
||
"0 0.319061 \n",
|
||
"1 0.171754 \n",
|
||
"2 0.245343 \n",
|
||
"3 0.218903 \n",
|
||
"4 0.328211 \n",
|
||
"5 0.410336 \n",
|
||
"6 0.551268 \n",
|
||
"7 0.459434 \n",
|
||
"8 0.306308 \n",
|
||
"9 0.230196 \n",
|
||
"10 0.306997 \n",
|
||
"11 0.358176 \n",
|
||
"12 0.221861 \n",
|
||
"13 0.332791 \n",
|
||
"14 0.138682 \n",
|
||
"15 0.336081 \n",
|
||
"16 0.191951 \n",
|
||
"17 0.239940 \n",
|
||
"18 0.159435 \n",
|
||
"19 0.323854 \n",
|
||
"20 0.239064 \n",
|
||
"Comparison by Variety:\n",
|
||
" Avg Olive Production (kg/ha) Avg Oil Production (L/ha) \\\n",
|
||
"Variety \n",
|
||
"nocellara_delletna 13661.848435 2983.023191 \n",
|
||
"leccino 15949.725702 3134.097415 \n",
|
||
"frantoio 19617.660163 4818.745399 \n",
|
||
"coratina 15957.184888 4094.052239 \n",
|
||
"taggiasca 11385.861672 2299.605028 \n",
|
||
"pendolino 14600.902596 2630.240053 \n",
|
||
"moraiolo 13223.690511 2887.178517 \n",
|
||
"\n",
|
||
" Avg Water Need (m³/ha) Oil Efficiency (L/kg) \\\n",
|
||
"Variety \n",
|
||
"nocellara_delletna 12151.058320 0.218347 \n",
|
||
"leccino 9819.553079 0.196499 \n",
|
||
"frantoio 10974.527808 0.245633 \n",
|
||
"coratina 13714.938951 0.256565 \n",
|
||
"taggiasca 9954.322217 0.201970 \n",
|
||
"pendolino 10277.592837 0.180142 \n",
|
||
"moraiolo 11988.799794 0.218334 \n",
|
||
"\n",
|
||
" Water Efficiency (L oil/m³ water) \n",
|
||
"Variety \n",
|
||
"nocellara_delletna 0.245495 \n",
|
||
"leccino 0.319169 \n",
|
||
"frantoio 0.439085 \n",
|
||
"coratina 0.298510 \n",
|
||
"taggiasca 0.231016 \n",
|
||
"pendolino 0.255920 \n",
|
||
"moraiolo 0.240823 \n",
|
||
"\n",
|
||
"Best Varieties by Water Efficiency:\n",
|
||
" Variety Avg Olive Production (kg/ha) \\\n",
|
||
"2 frantoio 19617.660163 \n",
|
||
"1 leccino 15949.725702 \n",
|
||
"3 coratina 15957.184888 \n",
|
||
"5 pendolino 14600.902596 \n",
|
||
"0 nocellara_delletna 13661.848435 \n",
|
||
"\n",
|
||
" Avg Oil Production (L/ha) Avg Water Need (m³/ha) Oil Efficiency (L/kg) \\\n",
|
||
"2 4818.745399 10974.527808 0.245633 \n",
|
||
"1 3134.097415 9819.553079 0.196499 \n",
|
||
"3 4094.052239 13714.938951 0.256565 \n",
|
||
"5 2630.240053 10277.592837 0.180142 \n",
|
||
"0 2983.023191 12151.058320 0.218347 \n",
|
||
"\n",
|
||
" Water Efficiency (L oil/m³ water) \n",
|
||
"2 0.439085 \n",
|
||
"1 0.319169 \n",
|
||
"3 0.298510 \n",
|
||
"5 0.255920 \n",
|
||
"0 0.245495 \n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"simulated_data = pd.read_parquet(f\"{data_dir}olive_training_dataset.parquet\")\n",
|
||
"olive_varieties = pd.read_parquet(f\"{data_dir}olive_varieties.parquet\")\n",
|
||
"# Esecuzione dell'analisi\n",
|
||
"comparison_data = prepare_comparison_data(simulated_data, olive_varieties)\n",
|
||
"\n",
|
||
"# Genera i grafici\n",
|
||
"plot_variety_comparison(comparison_data, 'Avg Olive Production (kg/ha)')\n",
|
||
"plot_variety_comparison(comparison_data, 'Avg Oil Production (L/ha)')\n",
|
||
"plot_variety_comparison(comparison_data, 'Avg Water Need (m³/ha)')\n",
|
||
"plot_variety_comparison(comparison_data, 'Oil Efficiency (L/kg)')\n",
|
||
"plot_variety_comparison(comparison_data, 'Water Efficiency (L oil/m³ water)')\n",
|
||
"plot_efficiency_vs_production(comparison_data)\n",
|
||
"plot_water_efficiency_vs_production(comparison_data)\n",
|
||
"plot_water_need_vs_oil_production(comparison_data)\n",
|
||
"\n",
|
||
"technique_data = analyze_by_technique(simulated_data, olive_varieties)\n",
|
||
"\n",
|
||
"print(technique_data)\n",
|
||
"\n",
|
||
"# Stampa un sommario statistico\n",
|
||
"print(\"Comparison by Variety:\")\n",
|
||
"print(comparison_data.set_index('Variety'))\n",
|
||
"print(\"\\nBest Varieties by Water Efficiency:\")\n",
|
||
"print(comparison_data.sort_values('Water Efficiency (L oil/m³ water)', ascending=False).head())"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 8,
|
||
"id": "bbe87b415168368",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"def prepare_transformer_data(df, olive_varieties_df):\n",
|
||
" # Crea una copia del DataFrame per evitare modifiche all'originale\n",
|
||
" df = df.copy()\n",
|
||
"\n",
|
||
" # Definisci le feature\n",
|
||
" temporal_features = ['temp_mean', 'precip_sum', 'solar_energy_sum']\n",
|
||
" static_features = ['ha'] # Feature statiche base\n",
|
||
" target_features = ['olive_prod', 'min_oil_prod', 'max_oil_prod', 'avg_oil_prod', 'total_water_need']\n",
|
||
"\n",
|
||
" # Ottieni le varietà pulite\n",
|
||
" all_varieties = olive_varieties_df['Varietà di Olive'].unique()\n",
|
||
" varieties = [clean_column_name(variety) for variety in all_varieties]\n",
|
||
"\n",
|
||
" # Crea la struttura delle feature per ogni varietà\n",
|
||
" variety_features = [\n",
|
||
" 'tech', 'pct', 'prod_t_ha', 'oil_prod_t_ha', 'oil_prod_l_ha',\n",
|
||
" 'min_yield_pct', 'max_yield_pct', 'min_oil_prod_l_ha', 'max_oil_prod_l_ha',\n",
|
||
" 'avg_oil_prod_l_ha', 'l_per_t', 'min_l_per_t', 'max_l_per_t', 'avg_l_per_t'\n",
|
||
" ]\n",
|
||
"\n",
|
||
" # Prepara dizionari per le nuove colonne\n",
|
||
" new_columns = {}\n",
|
||
"\n",
|
||
" # Prepara le feature per ogni varietà\n",
|
||
" for variety in varieties:\n",
|
||
" # Feature esistenti\n",
|
||
" for feature in variety_features:\n",
|
||
" col_name = f\"{variety}_{feature}\"\n",
|
||
" if col_name in df.columns:\n",
|
||
" if feature != 'tech': # Non includere la colonna tech direttamente\n",
|
||
" static_features.append(col_name)\n",
|
||
"\n",
|
||
" # Feature binarie per le tecniche di coltivazione\n",
|
||
" for technique in ['tradizionale', 'intensiva', 'superintensiva']:\n",
|
||
" col_name = f\"{variety}_{technique}\"\n",
|
||
" new_columns[col_name] = df[f\"{variety}_tech\"].notna() & (\n",
|
||
" df[f\"{variety}_tech\"].str.lower() == technique\n",
|
||
" ).fillna(False)\n",
|
||
" static_features.append(col_name)\n",
|
||
"\n",
|
||
" # Aggiungi tutte le nuove colonne in una volta sola\n",
|
||
" df = pd.concat([df] + [pd.Series(v, name=k) for k, v in new_columns.items()], axis=1)\n",
|
||
"\n",
|
||
" # Prepara X e y\n",
|
||
" X_temporal = df[temporal_features].values\n",
|
||
" X_static = df[static_features].values\n",
|
||
" y = df[target_features].values\n",
|
||
"\n",
|
||
" print(f\"Dataset completo - Temporal: {X_temporal.shape}, Static: {X_static.shape}, Target: {y.shape}\")\n",
|
||
"\n",
|
||
" # Split dei dati (usando indici casuali per una migliore distribuzione)\n",
|
||
" indices = np.random.permutation(len(X_temporal))\n",
|
||
" train_idx = int(len(indices) * 0.65) # 65% training\n",
|
||
" val_idx = int(len(indices) * 0.85) # 20% validation\n",
|
||
" # Il resto rimane 15% test\n",
|
||
"\n",
|
||
" train_indices = indices[:train_idx]\n",
|
||
" val_indices = indices[train_idx:val_idx]\n",
|
||
" test_indices = indices[val_idx:]\n",
|
||
"\n",
|
||
" # Split dei dati\n",
|
||
" X_temporal_train = X_temporal[train_indices]\n",
|
||
" X_temporal_val = X_temporal[val_indices]\n",
|
||
" X_temporal_test = X_temporal[test_indices]\n",
|
||
"\n",
|
||
" X_static_train = X_static[train_indices]\n",
|
||
" X_static_val = X_static[val_indices]\n",
|
||
" X_static_test = X_static[test_indices]\n",
|
||
"\n",
|
||
" y_train = y[train_indices]\n",
|
||
" y_val = y[val_indices]\n",
|
||
" y_test = y[test_indices]\n",
|
||
"\n",
|
||
" # Standardizzazione\n",
|
||
" scaler_temporal = StandardScaler()\n",
|
||
" scaler_static = StandardScaler()\n",
|
||
" scaler_y = StandardScaler()\n",
|
||
"\n",
|
||
" # Standardizzazione dei dati\n",
|
||
" X_temporal_train = scaler_temporal.fit_transform(X_temporal_train)\n",
|
||
" X_temporal_val = scaler_temporal.transform(X_temporal_val)\n",
|
||
" X_temporal_test = scaler_temporal.transform(X_temporal_test)\n",
|
||
"\n",
|
||
" X_static_train = scaler_static.fit_transform(X_static_train)\n",
|
||
" X_static_val = scaler_static.transform(X_static_val)\n",
|
||
" X_static_test = scaler_static.transform(X_static_test)\n",
|
||
"\n",
|
||
" y_train = scaler_y.fit_transform(y_train)\n",
|
||
" y_val = scaler_y.transform(y_val)\n",
|
||
" y_test = scaler_y.transform(y_test)\n",
|
||
"\n",
|
||
" print(\"\\nShape dopo lo split e standardizzazione:\")\n",
|
||
" print(f\"Train - Temporal: {X_temporal_train.shape}, Static: {X_static_train.shape}, Target: {y_train.shape}\")\n",
|
||
" print(f\"Val - Temporal: {X_temporal_val.shape}, Static: {X_static_val.shape}, Target: {y_val.shape}\")\n",
|
||
" print(f\"Test - Temporal: {X_temporal_test.shape}, Static: {X_static_test.shape}, Target: {y_test.shape}\")\n",
|
||
"\n",
|
||
" # Reshape per il transformer (aggiunge la dimensione del sequence length = 1)\n",
|
||
" X_temporal_train = X_temporal_train.reshape(X_temporal_train.shape[0], 1, -1)\n",
|
||
" X_temporal_val = X_temporal_val.reshape(X_temporal_val.shape[0], 1, -1)\n",
|
||
" X_temporal_test = X_temporal_test.reshape(X_temporal_test.shape[0], 1, -1)\n",
|
||
"\n",
|
||
" # Prepara i dizionari di input\n",
|
||
" train_data = {'temporal': X_temporal_train, 'static': X_static_train}\n",
|
||
" val_data = {'temporal': X_temporal_val, 'static': X_static_val}\n",
|
||
" test_data = {'temporal': X_temporal_test, 'static': X_static_test}\n",
|
||
"\n",
|
||
" # Salva gli scaler\n",
|
||
" joblib.dump(scaler_temporal, os.path.join(base_project_dir, f'{execute_name}_scaler_temporal.joblib'))\n",
|
||
" joblib.dump(scaler_static, os.path.join(base_project_dir, f'{execute_name}_scaler_static.joblib'))\n",
|
||
" joblib.dump(scaler_y, os.path.join(base_project_dir, f'{execute_name}_scaler_y.joblib'))\n",
|
||
"\n",
|
||
" return (train_data, y_train), (val_data, y_val), (test_data, y_test), (scaler_temporal, scaler_static, scaler_y)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 9,
|
||
"id": "9c4d5f0f3fafdc2d",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Dataset completo - Temporal: (4080000, 3), Static: (4080000, 113), Target: (4080000, 5)\n",
|
||
"\n",
|
||
"Shape dopo lo split e standardizzazione:\n",
|
||
"Train - Temporal: (2652000, 3), Static: (2652000, 113), Target: (2652000, 5)\n",
|
||
"Val - Temporal: (816000, 3), Static: (816000, 113), Target: (816000, 5)\n",
|
||
"Test - Temporal: (612000, 3), Static: (612000, 113), Target: (612000, 5)\n",
|
||
"Temporal data shape: (2652000, 1, 3)\n",
|
||
"Static data shape: (2652000, 113)\n",
|
||
"Target shape: (2652000, 5)\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"simulated_data = pd.read_parquet(f\"{data_dir}olive_training_dataset.parquet\")\n",
|
||
"olive_varieties = pd.read_parquet(f\"{data_dir}olive_varieties.parquet\")\n",
|
||
"\n",
|
||
"(train_data, train_targets), (val_data, val_targets), (test_data, test_targets), scalers = prepare_transformer_data(simulated_data, olive_varieties)\n",
|
||
"\n",
|
||
"scaler_temporal, scaler_static, scaler_y = scalers\n",
|
||
"\n",
|
||
"print(\"Temporal data shape:\", train_data['temporal'].shape)\n",
|
||
"print(\"Static data shape:\", train_data['static'].shape)\n",
|
||
"print(\"Target shape:\", train_targets.shape)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 10,
|
||
"id": "604c952c7195f40c",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"@keras.saving.register_keras_serializable()\n",
|
||
"class DataAugmentation(tf.keras.layers.Layer):\n",
|
||
" \"\"\"Custom layer per l'augmentation dei dati\"\"\"\n",
|
||
"\n",
|
||
" def __init__(self, noise_stddev=0.03, **kwargs):\n",
|
||
" super().__init__(**kwargs)\n",
|
||
" self.noise_stddev = noise_stddev\n",
|
||
"\n",
|
||
" def call(self, inputs, training=None):\n",
|
||
" if training:\n",
|
||
" return inputs + tf.random.normal(\n",
|
||
" shape=tf.shape(inputs),\n",
|
||
" mean=0.0,\n",
|
||
" stddev=self.noise_stddev\n",
|
||
" )\n",
|
||
" return inputs\n",
|
||
"\n",
|
||
" def get_config(self):\n",
|
||
" config = super().get_config()\n",
|
||
" config.update({\"noise_stddev\": self.noise_stddev})\n",
|
||
" return config\n",
|
||
"\n",
|
||
"\n",
|
||
"@keras.saving.register_keras_serializable()\n",
|
||
"class PositionalEncoding(tf.keras.layers.Layer):\n",
|
||
" \"\"\"Custom layer per l'encoding posizionale\"\"\"\n",
|
||
"\n",
|
||
" def __init__(self, d_model, **kwargs):\n",
|
||
" super().__init__(**kwargs)\n",
|
||
" self.d_model = d_model\n",
|
||
"\n",
|
||
" def build(self, input_shape):\n",
|
||
" _, seq_length, _ = input_shape\n",
|
||
"\n",
|
||
" # Crea la matrice di encoding posizionale\n",
|
||
" position = tf.range(seq_length, dtype=tf.float32)[:, tf.newaxis]\n",
|
||
" div_term = tf.exp(\n",
|
||
" tf.range(0, self.d_model, 2, dtype=tf.float32) *\n",
|
||
" (-tf.math.log(10000.0) / self.d_model)\n",
|
||
" )\n",
|
||
"\n",
|
||
" # Calcola sin e cos\n",
|
||
" pos_encoding = tf.zeros((1, seq_length, self.d_model))\n",
|
||
" pos_encoding_even = tf.sin(position * div_term)\n",
|
||
" pos_encoding_odd = tf.cos(position * div_term)\n",
|
||
"\n",
|
||
" # Assegna i valori alle posizioni pari e dispari\n",
|
||
" pos_encoding = tf.concat(\n",
|
||
" [tf.expand_dims(pos_encoding_even, -1),\n",
|
||
" tf.expand_dims(pos_encoding_odd, -1)],\n",
|
||
" axis=-1\n",
|
||
" )\n",
|
||
" pos_encoding = tf.reshape(pos_encoding, (1, seq_length, -1))\n",
|
||
" pos_encoding = pos_encoding[:, :, :self.d_model]\n",
|
||
"\n",
|
||
" # Salva l'encoding come peso non trainabile\n",
|
||
" self.pos_encoding = self.add_weight(\n",
|
||
" shape=(1, seq_length, self.d_model),\n",
|
||
" initializer=tf.keras.initializers.Constant(pos_encoding),\n",
|
||
" trainable=False,\n",
|
||
" name='positional_encoding'\n",
|
||
" )\n",
|
||
"\n",
|
||
" super().build(input_shape)\n",
|
||
"\n",
|
||
" def call(self, inputs):\n",
|
||
" # Broadcast l'encoding posizionale sul batch\n",
|
||
" batch_size = tf.shape(inputs)[0]\n",
|
||
" pos_encoding_tiled = tf.tile(self.pos_encoding, [batch_size, 1, 1])\n",
|
||
" return inputs + pos_encoding_tiled\n",
|
||
"\n",
|
||
" def get_config(self):\n",
|
||
" config = super().get_config()\n",
|
||
" config.update({\"d_model\": self.d_model})\n",
|
||
" return config\n",
|
||
"\n",
|
||
"\n",
|
||
"@keras.saving.register_keras_serializable()\n",
|
||
"class WarmUpLearningRateSchedule(tf.keras.optimizers.schedules.LearningRateSchedule):\n",
|
||
" \"\"\"Custom learning rate schedule with linear warmup and exponential decay.\"\"\"\n",
|
||
"\n",
|
||
" def __init__(self, initial_learning_rate=1e-3, warmup_steps=500, decay_steps=5000):\n",
|
||
" super().__init__()\n",
|
||
" self.initial_learning_rate = initial_learning_rate\n",
|
||
" self.warmup_steps = warmup_steps\n",
|
||
" self.decay_steps = decay_steps\n",
|
||
"\n",
|
||
" def __call__(self, step):\n",
|
||
" warmup_pct = tf.cast(step, tf.float32) / self.warmup_steps\n",
|
||
" warmup_lr = self.initial_learning_rate * warmup_pct\n",
|
||
" decay_factor = tf.pow(0.1, tf.cast(step, tf.float32) / self.decay_steps)\n",
|
||
" decayed_lr = self.initial_learning_rate * decay_factor\n",
|
||
" return tf.where(step < self.warmup_steps, warmup_lr, decayed_lr)\n",
|
||
"\n",
|
||
" def get_config(self):\n",
|
||
" return {\n",
|
||
" 'initial_learning_rate': self.initial_learning_rate,\n",
|
||
" 'warmup_steps': self.warmup_steps,\n",
|
||
" 'decay_steps': self.decay_steps\n",
|
||
" }\n",
|
||
"\n",
|
||
"\n",
|
||
"def create_olive_oil_transformer(temporal_shape, static_shape, num_outputs,\n",
|
||
" d_model=128, num_heads=8, ff_dim=256,\n",
|
||
" num_transformer_blocks=4, mlp_units=None,\n",
|
||
" dropout=0.2):\n",
|
||
" if mlp_units is None:\n",
|
||
" mlp_units = [256, 128, 64]\n",
|
||
"\n",
|
||
" temporal_input = tf.keras.layers.Input(shape=temporal_shape, name='temporal')\n",
|
||
" static_input = tf.keras.layers.Input(shape=static_shape, name='static')\n",
|
||
"\n",
|
||
" # === TEMPORAL PATH ===\n",
|
||
" x = tf.keras.layers.LayerNormalization(epsilon=1e-6)(temporal_input)\n",
|
||
" x = DataAugmentation()(x)\n",
|
||
"\n",
|
||
" # Temporal projection con dimensione aumentata per compensare la sequenza corta\n",
|
||
" x = tf.keras.layers.Dense(\n",
|
||
" d_model,\n",
|
||
" activation='swish',\n",
|
||
" kernel_regularizer=tf.keras.regularizers.l2(1e-5)\n",
|
||
" )(x)\n",
|
||
" x = tf.keras.layers.Dropout(dropout)(x)\n",
|
||
"\n",
|
||
" # Additional feature extraction prima del transformer\n",
|
||
" x = tf.keras.layers.Dense(\n",
|
||
" d_model * 2,\n",
|
||
" activation='swish',\n",
|
||
" kernel_regularizer=tf.keras.regularizers.l2(1e-5)\n",
|
||
" )(x)\n",
|
||
"\n",
|
||
" x = PositionalEncoding(d_model * 2)(x)\n",
|
||
"\n",
|
||
" skip_connection = x\n",
|
||
" for _ in range(num_transformer_blocks):\n",
|
||
" # Multi-head self-attention con più heads per compensare la sequenza corta\n",
|
||
" attention_output = tf.keras.layers.MultiHeadAttention(\n",
|
||
" num_heads=num_heads * 2,\n",
|
||
" key_dim=d_model // num_heads,\n",
|
||
" value_dim=d_model // num_heads\n",
|
||
" )(x, x)\n",
|
||
" attention_output = tf.keras.layers.Dropout(dropout)(attention_output)\n",
|
||
"\n",
|
||
" # Residual connection con gating mechanism\n",
|
||
" gate = tf.keras.layers.Dense(d_model * 2, activation='sigmoid')(x)\n",
|
||
" x = x + gate * attention_output\n",
|
||
" x = tf.keras.layers.LayerNormalization(epsilon=1e-6)(x)\n",
|
||
"\n",
|
||
" # Feed-forward network potenziato\n",
|
||
" ffn = tf.keras.Sequential([\n",
|
||
" tf.keras.layers.Dense(ff_dim * 2, activation=\"swish\"), # Raddoppiato\n",
|
||
" tf.keras.layers.Dropout(dropout),\n",
|
||
" tf.keras.layers.Dense(ff_dim, activation=\"swish\"),\n",
|
||
" tf.keras.layers.Dropout(dropout),\n",
|
||
" tf.keras.layers.Dense(d_model * 2)\n",
|
||
" ])\n",
|
||
" ffn_output = ffn(x)\n",
|
||
"\n",
|
||
" # Gated residual connection\n",
|
||
" gate = tf.keras.layers.Dense(d_model * 2, activation='sigmoid')(x)\n",
|
||
" x = x + gate * ffn_output\n",
|
||
" x = tf.keras.layers.LayerNormalization(epsilon=1e-6)(x)\n",
|
||
"\n",
|
||
" # Global feature attention\n",
|
||
" x = tf.keras.layers.MultiHeadAttention(\n",
|
||
" num_heads=num_heads,\n",
|
||
" key_dim=d_model // num_heads\n",
|
||
" )(x, x)\n",
|
||
"\n",
|
||
" # Feature pooling\n",
|
||
" x = tf.keras.layers.GlobalAveragePooling1D()(x)\n",
|
||
"\n",
|
||
" # === STATIC PATH ===\n",
|
||
" s = tf.keras.layers.LayerNormalization(epsilon=1e-6)(static_input)\n",
|
||
" for units in [512, 256, 128]: # Aumentate le dimensioni\n",
|
||
" s = tf.keras.layers.Dense(\n",
|
||
" units,\n",
|
||
" activation='swish',\n",
|
||
" kernel_regularizer=tf.keras.regularizers.l2(1e-5)\n",
|
||
" )(s)\n",
|
||
" s = tf.keras.layers.BatchNormalization()(s)\n",
|
||
" s = tf.keras.layers.Dropout(dropout)(s)\n",
|
||
"\n",
|
||
"\n",
|
||
" # === FEATURE FUSION con attention ===\n",
|
||
" # Project features to same dimensionality\n",
|
||
" x = tf.keras.layers.Dense(256)(x)\n",
|
||
" s = tf.keras.layers.Dense(256)(s)\n",
|
||
"\n",
|
||
" # Cross-attention between temporal and static features\n",
|
||
" combined = tf.keras.layers.Concatenate()([x, s])\n",
|
||
" combined = tf.keras.layers.Dense(256, activation='swish')(combined)\n",
|
||
"\n",
|
||
" # MLP head with residual connections\n",
|
||
" for units in mlp_units:\n",
|
||
" skip = combined\n",
|
||
" combined = tf.keras.layers.BatchNormalization()(combined)\n",
|
||
" combined = tf.keras.layers.Dense(units, activation=\"swish\")(combined)\n",
|
||
" combined = tf.keras.layers.Dropout(dropout)(combined)\n",
|
||
" if units == skip.shape[-1]: # Se le dimensioni combaciano\n",
|
||
" combined = combined + skip\n",
|
||
"\n",
|
||
" # Apply final normalization to output\n",
|
||
" outputs = tf.keras.layers.BatchNormalization()(combined)\n",
|
||
" outputs = tf.keras.layers.Dense(\n",
|
||
" num_outputs,\n",
|
||
" activation='linear',\n",
|
||
" kernel_regularizer=tf.keras.regularizers.l2(1e-5)\n",
|
||
" )(outputs)\n",
|
||
"\n",
|
||
" # Create model\n",
|
||
" model = tf.keras.Model(\n",
|
||
" inputs={'temporal': temporal_input, 'static': static_input},\n",
|
||
" outputs=outputs,\n",
|
||
" name='OilTransformer'\n",
|
||
" )\n",
|
||
"\n",
|
||
" return model\n",
|
||
"\n",
|
||
"\n",
|
||
"def create_transformer_callbacks(target_names, val_data, val_targets):\n",
|
||
" \"\"\"\n",
|
||
" Crea i callbacks per il training del modello single-step.\n",
|
||
" \"\"\"\n",
|
||
" class TargetSpecificMetric(tf.keras.callbacks.Callback):\n",
|
||
" def __init__(self, validation_data, target_names):\n",
|
||
" super().__init__()\n",
|
||
" self.validation_data = validation_data\n",
|
||
" self.target_names = target_names\n",
|
||
" self.best_metrics = {name: float('inf') for name in target_names}\n",
|
||
" \n",
|
||
" def on_epoch_end(self, epoch, logs=None):\n",
|
||
" logs = logs or {}\n",
|
||
" \n",
|
||
" # Esegui il calcolo solo ogni 5 epoche\n",
|
||
" if epoch % 5 == 0:\n",
|
||
" x_val, y_val = self.validation_data\n",
|
||
" y_pred = self.model.predict(x_val, verbose=0)\n",
|
||
" \n",
|
||
" # Calcola e logga le metriche per ogni target\n",
|
||
" for i, name in enumerate(self.target_names):\n",
|
||
" mae = np.mean(np.abs(y_val[:, i] - y_pred[:, i]))\n",
|
||
" mape = np.mean(np.abs((y_val[:, i] - y_pred[:, i]) / np.clip(np.abs(y_val[:, i]), 1e-7, None))) * 100\n",
|
||
" logs[f'val_{name}_mae'] = mae\n",
|
||
" logs[f'val_{name}_mape'] = mape\n",
|
||
" \n",
|
||
" # Traccia i migliori risultati\n",
|
||
" if mae < self.best_metrics[name]:\n",
|
||
" self.best_metrics[name] = mae\n",
|
||
" logs[f'best_{name}_mae'] = mae\n",
|
||
"\n",
|
||
"\n",
|
||
" callbacks = [\n",
|
||
" # Early Stopping ottimizzato\n",
|
||
" tf.keras.callbacks.EarlyStopping(\n",
|
||
" monitor='val_loss',\n",
|
||
" patience=25, # Aumentato per dare più chance al modello\n",
|
||
" restore_best_weights=True,\n",
|
||
" min_delta=0.0001, # Più sensibile ai miglioramenti\n",
|
||
" mode='min'\n",
|
||
" ),\n",
|
||
"\n",
|
||
" # Model Checkpoint con monitoraggio multiplo\n",
|
||
" tf.keras.callbacks.ModelCheckpoint(\n",
|
||
" filepath=f'{execute_name}_best_oil_model.h5',\n",
|
||
" monitor='val_loss',\n",
|
||
" save_best_only=True,\n",
|
||
" mode='min',\n",
|
||
" save_weights_only=True\n",
|
||
" ),\n",
|
||
"\n",
|
||
" # Metric per target specifici\n",
|
||
" TargetSpecificMetric(\n",
|
||
" validation_data=(val_data, val_targets),\n",
|
||
" target_names=target_names\n",
|
||
" ),\n",
|
||
"\n",
|
||
" # LR reduction ottimizzato per single-step\n",
|
||
" tf.keras.callbacks.ReduceLROnPlateau(\n",
|
||
" monitor='val_loss',\n",
|
||
" factor=0.2, # Riduzione più aggressiva\n",
|
||
" patience=15,\n",
|
||
" min_lr=1e-7,\n",
|
||
" verbose=1,\n",
|
||
" cooldown=5 # Periodo di cool-down per stabilizzazione\n",
|
||
" ),\n",
|
||
"\n",
|
||
" # TensorBoard con più metriche\n",
|
||
" tf.keras.callbacks.TensorBoard(\n",
|
||
" log_dir=f'./logs_{execute_name}',\n",
|
||
" histogram_freq=1,\n",
|
||
" write_graph=True,\n",
|
||
" write_images=True,\n",
|
||
" update_freq='epoch',\n",
|
||
" profile_batch='500,520' # Profile per ottimizzazione\n",
|
||
" )\n",
|
||
" ]\n",
|
||
"\n",
|
||
" return callbacks\n",
|
||
"\n",
|
||
"def compile_model(model, learning_rate=5e-4): # Learning rate ridotto\n",
|
||
" \"\"\"\n",
|
||
" Compila il modello con ottimizzazioni per single-step.\n",
|
||
" \"\"\"\n",
|
||
" lr_schedule = WarmUpLearningRateSchedule(\n",
|
||
" initial_learning_rate=learning_rate,\n",
|
||
" warmup_steps=1000, # Aumentato per stabilità\n",
|
||
" decay_steps=7000 # Aumentato per permettere più esplorazione\n",
|
||
" )\n",
|
||
"\n",
|
||
" @keras.saving.register_keras_serializable()\n",
|
||
" def weighted_huber_loss(y_true, y_pred):\n",
|
||
" # Pesi per diversi output\n",
|
||
" weights = tf.constant([1.0, 0.8, 0.8, 1.0, 0.6], dtype=tf.float32)\n",
|
||
" huber = tf.keras.losses.Huber(delta=1.0)\n",
|
||
" loss = huber(y_true, y_pred)\n",
|
||
" weighted_loss = tf.reduce_mean(loss * weights)\n",
|
||
" return weighted_loss\n",
|
||
"\n",
|
||
" model.compile(\n",
|
||
" optimizer=tf.keras.optimizers.AdamW(\n",
|
||
" learning_rate=lr_schedule,\n",
|
||
" weight_decay=0.01,\n",
|
||
" clipnorm=1.0, # Gradient clipping\n",
|
||
" epsilon=1e-7 # Aumentato per stabilità numerica\n",
|
||
" ),\n",
|
||
" loss=weighted_huber_loss,\n",
|
||
" metrics=['mae', 'mape']\n",
|
||
" )\n",
|
||
"\n",
|
||
" return model\n",
|
||
"\n",
|
||
"def setup_transformer_training(train_data, train_targets, val_data, val_targets):\n",
|
||
" \"\"\"\n",
|
||
" Configura il single-step transformer.\n",
|
||
" \"\"\"\n",
|
||
" # Estrai le shape dai dati\n",
|
||
" temporal_shape = (1, train_data['temporal'].shape[2])\n",
|
||
" static_shape = (train_data['static'].shape[1],)\n",
|
||
" num_outputs = train_targets.shape[1]\n",
|
||
"\n",
|
||
" print(f\"Shape rilevate:\")\n",
|
||
" print(f\"- Temporal shape: {temporal_shape}\")\n",
|
||
" print(f\"- Static shape: {static_shape}\")\n",
|
||
" print(f\"- Numero di output: {num_outputs}\")\n",
|
||
"\n",
|
||
" target_names = ['olive_prod', 'min_oil_prod', 'max_oil_prod', 'avg_oil_prod', 'total_water_need']\n",
|
||
"\n",
|
||
" assert len(target_names) == num_outputs, \\\n",
|
||
" f\"Il numero di target names ({len(target_names)}) non corrisponde al numero di output ({num_outputs})\"\n",
|
||
"\n",
|
||
" # Crea il modello con il nuovo transformer\n",
|
||
" model = create_olive_oil_transformer(\n",
|
||
" temporal_shape=temporal_shape,\n",
|
||
" static_shape=static_shape,\n",
|
||
" num_outputs=num_outputs,\n",
|
||
" d_model=256,\n",
|
||
" num_heads=8,\n",
|
||
" ff_dim=512,\n",
|
||
" num_transformer_blocks=6,\n",
|
||
" dropout=0.1\n",
|
||
" )\n",
|
||
"\n",
|
||
" model = compile_model(model)\n",
|
||
" callbacks = create_transformer_callbacks(target_names, val_data, val_targets)\n",
|
||
"\n",
|
||
" return model, callbacks, target_names\n",
|
||
"\n",
|
||
"def train_transformer(train_data, train_targets, val_data, val_targets, epochs=200, batch_size=128, save_name='final_model'):\n",
|
||
" \"\"\"\n",
|
||
" Training ottimizzato per single-step transformer.\n",
|
||
" \"\"\"\n",
|
||
" # Dataset con augmentation\n",
|
||
" def augment(x, y):\n",
|
||
" # Ottieni il dtype dei dati originali\n",
|
||
" original_dtype = x['temporal'].dtype\n",
|
||
" # Genera il rumore con lo stesso dtype\n",
|
||
" noise = tf.random.normal(\n",
|
||
" tf.shape(x['temporal']), \n",
|
||
" mean=0.0, \n",
|
||
" stddev=0.01,\n",
|
||
" dtype=original_dtype\n",
|
||
" )\n",
|
||
" x['temporal'] += noise\n",
|
||
" return x, y\n",
|
||
"\n",
|
||
" train_dataset = tf.data.Dataset.from_tensor_slices((train_data, train_targets))\\\n",
|
||
" .map(augment, num_parallel_calls=tf.data.AUTOTUNE)\\\n",
|
||
" .cache()\\\n",
|
||
" .shuffle(buffer_size=10000)\\\n",
|
||
" .batch(batch_size)\\\n",
|
||
" .prefetch(tf.data.AUTOTUNE)\n",
|
||
"\n",
|
||
" val_dataset = tf.data.Dataset.from_tensor_slices((val_data, val_targets))\\\n",
|
||
" .cache()\\\n",
|
||
" .batch(batch_size)\\\n",
|
||
" .prefetch(tf.data.AUTOTUNE)\n",
|
||
"\n",
|
||
" strategy = tf.distribute.MirroredStrategy() if len(tf.config.list_physical_devices('GPU')) > 1 else tf.distribute.get_strategy()\n",
|
||
"\n",
|
||
" with strategy.scope():\n",
|
||
" model, callbacks, target_names = setup_transformer_training(\n",
|
||
" train_data, train_targets, val_data, val_targets\n",
|
||
" )\n",
|
||
"\n",
|
||
" model.summary()\n",
|
||
"\n",
|
||
" try:\n",
|
||
" keras.utils.plot_model(model, f\"{execute_name}_{save_name}.png\", show_shapes=True)\n",
|
||
" except Exception as e:\n",
|
||
" print(f\"Warning: Could not create model plot: {e}\")\n",
|
||
"\n",
|
||
" # Training con gestione errori e memory saving\n",
|
||
" try:\n",
|
||
" with tf.device('/GPU:0'): # Forza l'uso della GPU principale\n",
|
||
" history = model.fit(\n",
|
||
" train_dataset,\n",
|
||
" validation_data=val_dataset,\n",
|
||
" epochs=epochs,\n",
|
||
" callbacks=callbacks,\n",
|
||
" verbose=1,\n",
|
||
" workers=8,\n",
|
||
" use_multiprocessing=True\n",
|
||
" )\n",
|
||
" except tf.errors.ResourceExhaustedError:\n",
|
||
" print(\"Memoria GPU esaurita, riprovo con batch size più piccolo...\")\n",
|
||
" batch_size = batch_size // 2\n",
|
||
" train_dataset = train_dataset.unbatch().batch(batch_size)\n",
|
||
" val_dataset = val_dataset.unbatch().batch(batch_size)\n",
|
||
" history = model.fit(\n",
|
||
" train_dataset,\n",
|
||
" validation_data=val_dataset,\n",
|
||
" epochs=epochs,\n",
|
||
" callbacks=callbacks,\n",
|
||
" verbose=1\n",
|
||
" )\n",
|
||
"\n",
|
||
" # Salvataggio modello\n",
|
||
" try:\n",
|
||
" save_path = f'{execute_name}_{save_name}.keras'\n",
|
||
" model.save(save_path, save_format='keras')\n",
|
||
"\n",
|
||
" os.makedirs(f'{execute_name}/weights', exist_ok=True)\n",
|
||
" model.save_weights(f'{execute_name}/weights')\n",
|
||
" print(f\"\\nModello salvato in: {save_path}\")\n",
|
||
"\n",
|
||
" # Salva anche la storia del training\n",
|
||
" with open(f'{execute_name}_training_history.json', 'w') as f:\n",
|
||
" json.dump(history.history, f)\n",
|
||
" except Exception as e:\n",
|
||
" print(f\"Warning: Could not save model: {e}\")\n",
|
||
"\n",
|
||
" return model, history"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 11,
|
||
"id": "35490e902e494c4a",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Shape rilevate:\n",
|
||
"- Temporal shape: (1, 3)\n",
|
||
"- Static shape: (113,)\n",
|
||
"- Numero di output: 5\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"2024-12-19 23:43:47.552446: I tensorflow/tsl/platform/default/subprocess.cc:304] Start cannot spawn child process: No such file or directory\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Model: \"OilTransformer\"\n",
|
||
"__________________________________________________________________________________________________\n",
|
||
" Layer (type) Output Shape Param # Connected to \n",
|
||
"==================================================================================================\n",
|
||
" temporal (InputLayer) [(None, 1, 3)] 0 [] \n",
|
||
" \n",
|
||
" layer_normalization (Layer (None, 1, 3) 6 ['temporal[0][0]'] \n",
|
||
" Normalization) \n",
|
||
" \n",
|
||
" data_augmentation (DataAug (None, 1, 3) 0 ['layer_normalization[0][0]'] \n",
|
||
" mentation) \n",
|
||
" \n",
|
||
" dense (Dense) (None, 1, 256) 1024 ['data_augmentation[0][0]'] \n",
|
||
" \n",
|
||
" dropout (Dropout) (None, 1, 256) 0 ['dense[0][0]'] \n",
|
||
" \n",
|
||
" dense_1 (Dense) (None, 1, 512) 131584 ['dropout[0][0]'] \n",
|
||
" \n",
|
||
" positional_encoding (Posit (None, 1, 512) 512 ['dense_1[0][0]'] \n",
|
||
" ionalEncoding) \n",
|
||
" \n",
|
||
" multi_head_attention (Mult (None, 1, 512) 1050624 ['positional_encoding[0][0]', \n",
|
||
" iHeadAttention) 'positional_encoding[0][0]'] \n",
|
||
" \n",
|
||
" dense_2 (Dense) (None, 1, 512) 262656 ['positional_encoding[0][0]'] \n",
|
||
" \n",
|
||
" dropout_1 (Dropout) (None, 1, 512) 0 ['multi_head_attention[0][0]']\n",
|
||
" \n",
|
||
" tf.math.multiply (TFOpLamb (None, 1, 512) 0 ['dense_2[0][0]', \n",
|
||
" da) 'dropout_1[0][0]'] \n",
|
||
" \n",
|
||
" tf.__operators__.add (TFOp (None, 1, 512) 0 ['positional_encoding[0][0]', \n",
|
||
" Lambda) 'tf.math.multiply[0][0]'] \n",
|
||
" \n",
|
||
" layer_normalization_1 (Lay (None, 1, 512) 1024 ['tf.__operators__.add[0][0]']\n",
|
||
" erNormalization) \n",
|
||
" \n",
|
||
" dense_6 (Dense) (None, 1, 512) 262656 ['layer_normalization_1[0][0]'\n",
|
||
" ] \n",
|
||
" \n",
|
||
" sequential (Sequential) (None, 1, 512) 1312768 ['layer_normalization_1[0][0]'\n",
|
||
" ] \n",
|
||
" \n",
|
||
" tf.math.multiply_1 (TFOpLa (None, 1, 512) 0 ['dense_6[0][0]', \n",
|
||
" mbda) 'sequential[0][0]'] \n",
|
||
" \n",
|
||
" tf.__operators__.add_1 (TF (None, 1, 512) 0 ['layer_normalization_1[0][0]'\n",
|
||
" OpLambda) , 'tf.math.multiply_1[0][0]'] \n",
|
||
" \n",
|
||
" layer_normalization_2 (Lay (None, 1, 512) 1024 ['tf.__operators__.add_1[0][0]\n",
|
||
" erNormalization) '] \n",
|
||
" \n",
|
||
" multi_head_attention_1 (Mu (None, 1, 512) 1050624 ['layer_normalization_2[0][0]'\n",
|
||
" ltiHeadAttention) , 'layer_normalization_2[0][0]\n",
|
||
" '] \n",
|
||
" \n",
|
||
" dense_7 (Dense) (None, 1, 512) 262656 ['layer_normalization_2[0][0]'\n",
|
||
" ] \n",
|
||
" \n",
|
||
" dropout_4 (Dropout) (None, 1, 512) 0 ['multi_head_attention_1[0][0]\n",
|
||
" '] \n",
|
||
" \n",
|
||
" tf.math.multiply_2 (TFOpLa (None, 1, 512) 0 ['dense_7[0][0]', \n",
|
||
" mbda) 'dropout_4[0][0]'] \n",
|
||
" \n",
|
||
" tf.__operators__.add_2 (TF (None, 1, 512) 0 ['layer_normalization_2[0][0]'\n",
|
||
" OpLambda) , 'tf.math.multiply_2[0][0]'] \n",
|
||
" \n",
|
||
" layer_normalization_3 (Lay (None, 1, 512) 1024 ['tf.__operators__.add_2[0][0]\n",
|
||
" erNormalization) '] \n",
|
||
" \n",
|
||
" dense_11 (Dense) (None, 1, 512) 262656 ['layer_normalization_3[0][0]'\n",
|
||
" ] \n",
|
||
" \n",
|
||
" sequential_1 (Sequential) (None, 1, 512) 1312768 ['layer_normalization_3[0][0]'\n",
|
||
" ] \n",
|
||
" \n",
|
||
" tf.math.multiply_3 (TFOpLa (None, 1, 512) 0 ['dense_11[0][0]', \n",
|
||
" mbda) 'sequential_1[0][0]'] \n",
|
||
" \n",
|
||
" tf.__operators__.add_3 (TF (None, 1, 512) 0 ['layer_normalization_3[0][0]'\n",
|
||
" OpLambda) , 'tf.math.multiply_3[0][0]'] \n",
|
||
" \n",
|
||
" layer_normalization_4 (Lay (None, 1, 512) 1024 ['tf.__operators__.add_3[0][0]\n",
|
||
" erNormalization) '] \n",
|
||
" \n",
|
||
" multi_head_attention_2 (Mu (None, 1, 512) 1050624 ['layer_normalization_4[0][0]'\n",
|
||
" ltiHeadAttention) , 'layer_normalization_4[0][0]\n",
|
||
" '] \n",
|
||
" \n",
|
||
" dense_12 (Dense) (None, 1, 512) 262656 ['layer_normalization_4[0][0]'\n",
|
||
" ] \n",
|
||
" \n",
|
||
" dropout_7 (Dropout) (None, 1, 512) 0 ['multi_head_attention_2[0][0]\n",
|
||
" '] \n",
|
||
" \n",
|
||
" tf.math.multiply_4 (TFOpLa (None, 1, 512) 0 ['dense_12[0][0]', \n",
|
||
" mbda) 'dropout_7[0][0]'] \n",
|
||
" \n",
|
||
" tf.__operators__.add_4 (TF (None, 1, 512) 0 ['layer_normalization_4[0][0]'\n",
|
||
" OpLambda) , 'tf.math.multiply_4[0][0]'] \n",
|
||
" \n",
|
||
" layer_normalization_5 (Lay (None, 1, 512) 1024 ['tf.__operators__.add_4[0][0]\n",
|
||
" erNormalization) '] \n",
|
||
" \n",
|
||
" dense_16 (Dense) (None, 1, 512) 262656 ['layer_normalization_5[0][0]'\n",
|
||
" ] \n",
|
||
" \n",
|
||
" sequential_2 (Sequential) (None, 1, 512) 1312768 ['layer_normalization_5[0][0]'\n",
|
||
" ] \n",
|
||
" \n",
|
||
" tf.math.multiply_5 (TFOpLa (None, 1, 512) 0 ['dense_16[0][0]', \n",
|
||
" mbda) 'sequential_2[0][0]'] \n",
|
||
" \n",
|
||
" tf.__operators__.add_5 (TF (None, 1, 512) 0 ['layer_normalization_5[0][0]'\n",
|
||
" OpLambda) , 'tf.math.multiply_5[0][0]'] \n",
|
||
" \n",
|
||
" layer_normalization_6 (Lay (None, 1, 512) 1024 ['tf.__operators__.add_5[0][0]\n",
|
||
" erNormalization) '] \n",
|
||
" \n",
|
||
" multi_head_attention_3 (Mu (None, 1, 512) 1050624 ['layer_normalization_6[0][0]'\n",
|
||
" ltiHeadAttention) , 'layer_normalization_6[0][0]\n",
|
||
" '] \n",
|
||
" \n",
|
||
" dense_17 (Dense) (None, 1, 512) 262656 ['layer_normalization_6[0][0]'\n",
|
||
" ] \n",
|
||
" \n",
|
||
" dropout_10 (Dropout) (None, 1, 512) 0 ['multi_head_attention_3[0][0]\n",
|
||
" '] \n",
|
||
" \n",
|
||
" tf.math.multiply_6 (TFOpLa (None, 1, 512) 0 ['dense_17[0][0]', \n",
|
||
" mbda) 'dropout_10[0][0]'] \n",
|
||
" \n",
|
||
" tf.__operators__.add_6 (TF (None, 1, 512) 0 ['layer_normalization_6[0][0]'\n",
|
||
" OpLambda) , 'tf.math.multiply_6[0][0]'] \n",
|
||
" \n",
|
||
" layer_normalization_7 (Lay (None, 1, 512) 1024 ['tf.__operators__.add_6[0][0]\n",
|
||
" erNormalization) '] \n",
|
||
" \n",
|
||
" dense_21 (Dense) (None, 1, 512) 262656 ['layer_normalization_7[0][0]'\n",
|
||
" ] \n",
|
||
" \n",
|
||
" sequential_3 (Sequential) (None, 1, 512) 1312768 ['layer_normalization_7[0][0]'\n",
|
||
" ] \n",
|
||
" \n",
|
||
" tf.math.multiply_7 (TFOpLa (None, 1, 512) 0 ['dense_21[0][0]', \n",
|
||
" mbda) 'sequential_3[0][0]'] \n",
|
||
" \n",
|
||
" tf.__operators__.add_7 (TF (None, 1, 512) 0 ['layer_normalization_7[0][0]'\n",
|
||
" OpLambda) , 'tf.math.multiply_7[0][0]'] \n",
|
||
" \n",
|
||
" layer_normalization_8 (Lay (None, 1, 512) 1024 ['tf.__operators__.add_7[0][0]\n",
|
||
" erNormalization) '] \n",
|
||
" \n",
|
||
" multi_head_attention_4 (Mu (None, 1, 512) 1050624 ['layer_normalization_8[0][0]'\n",
|
||
" ltiHeadAttention) , 'layer_normalization_8[0][0]\n",
|
||
" '] \n",
|
||
" \n",
|
||
" dense_22 (Dense) (None, 1, 512) 262656 ['layer_normalization_8[0][0]'\n",
|
||
" ] \n",
|
||
" \n",
|
||
" dropout_13 (Dropout) (None, 1, 512) 0 ['multi_head_attention_4[0][0]\n",
|
||
" '] \n",
|
||
" \n",
|
||
" tf.math.multiply_8 (TFOpLa (None, 1, 512) 0 ['dense_22[0][0]', \n",
|
||
" mbda) 'dropout_13[0][0]'] \n",
|
||
" \n",
|
||
" tf.__operators__.add_8 (TF (None, 1, 512) 0 ['layer_normalization_8[0][0]'\n",
|
||
" OpLambda) , 'tf.math.multiply_8[0][0]'] \n",
|
||
" \n",
|
||
" layer_normalization_9 (Lay (None, 1, 512) 1024 ['tf.__operators__.add_8[0][0]\n",
|
||
" erNormalization) '] \n",
|
||
" \n",
|
||
" dense_26 (Dense) (None, 1, 512) 262656 ['layer_normalization_9[0][0]'\n",
|
||
" ] \n",
|
||
" \n",
|
||
" sequential_4 (Sequential) (None, 1, 512) 1312768 ['layer_normalization_9[0][0]'\n",
|
||
" ] \n",
|
||
" \n",
|
||
" tf.math.multiply_9 (TFOpLa (None, 1, 512) 0 ['dense_26[0][0]', \n",
|
||
" mbda) 'sequential_4[0][0]'] \n",
|
||
" \n",
|
||
" tf.__operators__.add_9 (TF (None, 1, 512) 0 ['layer_normalization_9[0][0]'\n",
|
||
" OpLambda) , 'tf.math.multiply_9[0][0]'] \n",
|
||
" \n",
|
||
" layer_normalization_10 (La (None, 1, 512) 1024 ['tf.__operators__.add_9[0][0]\n",
|
||
" yerNormalization) '] \n",
|
||
" \n",
|
||
" multi_head_attention_5 (Mu (None, 1, 512) 1050624 ['layer_normalization_10[0][0]\n",
|
||
" ltiHeadAttention) ', \n",
|
||
" 'layer_normalization_10[0][0]\n",
|
||
" '] \n",
|
||
" \n",
|
||
" static (InputLayer) [(None, 113)] 0 [] \n",
|
||
" \n",
|
||
" dense_27 (Dense) (None, 1, 512) 262656 ['layer_normalization_10[0][0]\n",
|
||
" '] \n",
|
||
" \n",
|
||
" dropout_16 (Dropout) (None, 1, 512) 0 ['multi_head_attention_5[0][0]\n",
|
||
" '] \n",
|
||
" \n",
|
||
" layer_normalization_13 (La (None, 113) 226 ['static[0][0]'] \n",
|
||
" yerNormalization) \n",
|
||
" \n",
|
||
" tf.math.multiply_10 (TFOpL (None, 1, 512) 0 ['dense_27[0][0]', \n",
|
||
" ambda) 'dropout_16[0][0]'] \n",
|
||
" \n",
|
||
" dense_32 (Dense) (None, 512) 58368 ['layer_normalization_13[0][0]\n",
|
||
" '] \n",
|
||
" \n",
|
||
" tf.__operators__.add_10 (T (None, 1, 512) 0 ['layer_normalization_10[0][0]\n",
|
||
" FOpLambda) ', \n",
|
||
" 'tf.math.multiply_10[0][0]'] \n",
|
||
" \n",
|
||
" batch_normalization (Batch (None, 512) 2048 ['dense_32[0][0]'] \n",
|
||
" Normalization) \n",
|
||
" \n",
|
||
" layer_normalization_11 (La (None, 1, 512) 1024 ['tf.__operators__.add_10[0][0\n",
|
||
" yerNormalization) ]'] \n",
|
||
" \n",
|
||
" dropout_19 (Dropout) (None, 512) 0 ['batch_normalization[0][0]'] \n",
|
||
" \n",
|
||
" dense_31 (Dense) (None, 1, 512) 262656 ['layer_normalization_11[0][0]\n",
|
||
" '] \n",
|
||
" \n",
|
||
" sequential_5 (Sequential) (None, 1, 512) 1312768 ['layer_normalization_11[0][0]\n",
|
||
" '] \n",
|
||
" \n",
|
||
" dense_33 (Dense) (None, 256) 131328 ['dropout_19[0][0]'] \n",
|
||
" \n",
|
||
" tf.math.multiply_11 (TFOpL (None, 1, 512) 0 ['dense_31[0][0]', \n",
|
||
" ambda) 'sequential_5[0][0]'] \n",
|
||
" \n",
|
||
" batch_normalization_1 (Bat (None, 256) 1024 ['dense_33[0][0]'] \n",
|
||
" chNormalization) \n",
|
||
" \n",
|
||
" tf.__operators__.add_11 (T (None, 1, 512) 0 ['layer_normalization_11[0][0]\n",
|
||
" FOpLambda) ', \n",
|
||
" 'tf.math.multiply_11[0][0]'] \n",
|
||
" \n",
|
||
" dropout_20 (Dropout) (None, 256) 0 ['batch_normalization_1[0][0]'\n",
|
||
" ] \n",
|
||
" \n",
|
||
" layer_normalization_12 (La (None, 1, 512) 1024 ['tf.__operators__.add_11[0][0\n",
|
||
" yerNormalization) ]'] \n",
|
||
" \n",
|
||
" dense_34 (Dense) (None, 128) 32896 ['dropout_20[0][0]'] \n",
|
||
" \n",
|
||
" multi_head_attention_6 (Mu (None, 1, 512) 525568 ['layer_normalization_12[0][0]\n",
|
||
" ltiHeadAttention) ', \n",
|
||
" 'layer_normalization_12[0][0]\n",
|
||
" '] \n",
|
||
" \n",
|
||
" batch_normalization_2 (Bat (None, 128) 512 ['dense_34[0][0]'] \n",
|
||
" chNormalization) \n",
|
||
" \n",
|
||
" global_average_pooling1d ( (None, 512) 0 ['multi_head_attention_6[0][0]\n",
|
||
" GlobalAveragePooling1D) '] \n",
|
||
" \n",
|
||
" dropout_21 (Dropout) (None, 128) 0 ['batch_normalization_2[0][0]'\n",
|
||
" ] \n",
|
||
" \n",
|
||
" dense_35 (Dense) (None, 256) 131328 ['global_average_pooling1d[0][\n",
|
||
" 0]'] \n",
|
||
" \n",
|
||
" dense_36 (Dense) (None, 256) 33024 ['dropout_21[0][0]'] \n",
|
||
" \n",
|
||
" concatenate (Concatenate) (None, 512) 0 ['dense_35[0][0]', \n",
|
||
" 'dense_36[0][0]'] \n",
|
||
" \n",
|
||
" dense_37 (Dense) (None, 256) 131328 ['concatenate[0][0]'] \n",
|
||
" \n",
|
||
" batch_normalization_3 (Bat (None, 256) 1024 ['dense_37[0][0]'] \n",
|
||
" chNormalization) \n",
|
||
" \n",
|
||
" dense_38 (Dense) (None, 256) 65792 ['batch_normalization_3[0][0]'\n",
|
||
" ] \n",
|
||
" \n",
|
||
" dropout_22 (Dropout) (None, 256) 0 ['dense_38[0][0]'] \n",
|
||
" \n",
|
||
" tf.__operators__.add_12 (T (None, 256) 0 ['dropout_22[0][0]', \n",
|
||
" FOpLambda) 'dense_37[0][0]'] \n",
|
||
" \n",
|
||
" batch_normalization_4 (Bat (None, 256) 1024 ['tf.__operators__.add_12[0][0\n",
|
||
" chNormalization) ]'] \n",
|
||
" \n",
|
||
" dense_39 (Dense) (None, 128) 32896 ['batch_normalization_4[0][0]'\n",
|
||
" ] \n",
|
||
" \n",
|
||
" dropout_23 (Dropout) (None, 128) 0 ['dense_39[0][0]'] \n",
|
||
" \n",
|
||
" batch_normalization_5 (Bat (None, 128) 512 ['dropout_23[0][0]'] \n",
|
||
" chNormalization) \n",
|
||
" \n",
|
||
" dense_40 (Dense) (None, 64) 8256 ['batch_normalization_5[0][0]'\n",
|
||
" ] \n",
|
||
" \n",
|
||
" dropout_24 (Dropout) (None, 64) 0 ['dense_40[0][0]'] \n",
|
||
" \n",
|
||
" batch_normalization_6 (Bat (None, 64) 256 ['dropout_24[0][0]'] \n",
|
||
" chNormalization) \n",
|
||
" \n",
|
||
" dense_41 (Dense) (None, 5) 325 ['batch_normalization_6[0][0]'\n",
|
||
" ] \n",
|
||
" \n",
|
||
"==================================================================================================\n",
|
||
"Total params: 18635373 (71.09 MB)\n",
|
||
"Trainable params: 18631661 (71.07 MB)\n",
|
||
"Non-trainable params: 3712 (14.50 KB)\n",
|
||
"__________________________________________________________________________________________________\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"2024-12-19 23:43:50.363178: I tensorflow/tsl/profiler/lib/profiler_session.cc:104] Profiler session initializing.\n",
|
||
"2024-12-19 23:43:50.363220: I tensorflow/tsl/profiler/lib/profiler_session.cc:119] Profiler session started.\n",
|
||
"2024-12-19 23:43:50.363276: I tensorflow/compiler/xla/backends/profiler/gpu/cupti_tracer.cc:1694] Profiler found 1 GPUs\n",
|
||
"2024-12-19 23:43:50.410241: I tensorflow/tsl/profiler/lib/profiler_session.cc:131] Profiler session tear down.\n",
|
||
"2024-12-19 23:43:50.410423: I tensorflow/compiler/xla/backends/profiler/gpu/cupti_tracer.cc:1828] CUPTI activity buffer flushed\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Epoch 1/200\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"2024-12-19 23:44:15.723844: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x7b5c587c2a20 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n",
|
||
"2024-12-19 23:44:15.723900: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): NVIDIA L40S, Compute Capability 8.9\n",
|
||
"2024-12-19 23:44:15.731034: I tensorflow/compiler/mlir/tensorflow/utils/dump_mlir_util.cc:269] disabling MLIR crash reproducer, set env var `MLIR_CRASH_REPRODUCER_DIRECTORY` to enable.\n",
|
||
"2024-12-19 23:44:15.824204: I tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc:442] Loaded cuDNN version 8905\n",
|
||
"2024-12-19 23:44:16.017696: I ./tensorflow/compiler/jit/device_compiler.h:186] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"81/81 [==============================] - 519s 6s/step - loss: 0.5779 - mae: 1.0754 - mape: 880.2349 - val_loss: 0.3937 - val_mae: 0.8479 - val_mape: 408.4369 - val_olive_prod_mae: 0.7764 - val_olive_prod_mape: 179.0094 - best_olive_prod_mae: 0.7764 - val_min_oil_prod_mae: 1.0417 - val_min_oil_prod_mape: 827.8750 - best_min_oil_prod_mae: 1.0417 - val_max_oil_prod_mae: 0.7703 - val_max_oil_prod_mape: 178.8319 - best_max_oil_prod_mae: 0.7703 - val_avg_oil_prod_mae: 0.8092 - val_avg_oil_prod_mape: 578.2767 - best_avg_oil_prod_mae: 0.8092 - val_total_water_need_mae: 0.8420 - val_total_water_need_mape: 278.1917 - best_total_water_need_mae: 0.8420 - lr: 4.0000e-05\n",
|
||
"Epoch 2/200\n",
|
||
"81/81 [==============================] - 46s 562ms/step - loss: 0.2425 - mae: 0.6015 - mape: 563.2821 - val_loss: 0.4574 - val_mae: 0.9300 - val_mape: 778.7053 - lr: 8.0500e-05\n",
|
||
"Epoch 3/200\n",
|
||
"81/81 [==============================] - 45s 545ms/step - loss: 0.1409 - mae: 0.4210 - mape: 431.1277 - val_loss: 0.2816 - val_mae: 0.6775 - val_mape: 529.1827 - lr: 1.2100e-04\n",
|
||
"Epoch 4/200\n",
|
||
"81/81 [==============================] - 45s 551ms/step - loss: 0.1043 - mae: 0.3407 - mape: 326.2097 - val_loss: 0.1583 - val_mae: 0.4714 - val_mape: 334.0154 - lr: 1.6150e-04\n",
|
||
"Epoch 5/200\n",
|
||
"81/81 [==============================] - 47s 570ms/step - loss: 0.0782 - mae: 0.2827 - mape: 232.1423 - val_loss: 0.0972 - val_mae: 0.3635 - val_mape: 290.3582 - lr: 2.0200e-04\n",
|
||
"Epoch 6/200\n",
|
||
"81/81 [==============================] - 430s 5s/step - loss: 0.0600 - mae: 0.2404 - mape: 192.1100 - val_loss: 0.0521 - val_mae: 0.2606 - val_mape: 219.4938 - val_olive_prod_mae: 0.2230 - val_olive_prod_mape: 268.5168 - best_olive_prod_mae: 0.2230 - val_min_oil_prod_mae: 0.2973 - val_min_oil_prod_mape: 134.6088 - best_min_oil_prod_mae: 0.2973 - val_max_oil_prod_mae: 0.2204 - val_max_oil_prod_mape: 174.5454 - best_max_oil_prod_mae: 0.2204 - val_avg_oil_prod_mae: 0.2480 - val_avg_oil_prod_mape: 278.0671 - best_avg_oil_prod_mae: 0.2480 - val_total_water_need_mae: 0.3143 - val_total_water_need_mape: 241.7285 - best_total_water_need_mae: 0.3143 - lr: 2.4250e-04\n",
|
||
"Epoch 7/200\n",
|
||
"13/81 [===>..........................] - ETA: 28s - loss: 0.0542 - mae: 0.2247 - mape: 202.4973"
|
||
]
|
||
},
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"2024-12-20 00:02:52.728150: I tensorflow/tsl/profiler/lib/profiler_session.cc:104] Profiler session initializing.\n",
|
||
"2024-12-20 00:02:52.728206: I tensorflow/tsl/profiler/lib/profiler_session.cc:119] Profiler session started.\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"33/81 [===========>..................] - ETA: 20s - loss: 0.0527 - mae: 0.2215 - mape: 175.1488"
|
||
]
|
||
},
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"2024-12-20 00:03:01.834386: I tensorflow/tsl/profiler/lib/profiler_session.cc:70] Profiler session collecting data.\n",
|
||
"2024-12-20 00:03:01.926067: I tensorflow/compiler/xla/backends/profiler/gpu/cupti_tracer.cc:1828] CUPTI activity buffer flushed\n",
|
||
"2024-12-20 00:03:07.275024: I tensorflow/compiler/xla/backends/profiler/gpu/cupti_collector.cc:541] GpuTracer has collected 110853 callback api events and 111417 activity events. \n",
|
||
"2024-12-20 00:04:14.089035: I tensorflow/tsl/profiler/lib/profiler_session.cc:131] Profiler session tear down.\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"81/81 [==============================] - 118s 1s/step - loss: 0.0508 - mae: 0.2157 - mape: 185.7417 - val_loss: 0.0406 - val_mae: 0.2162 - val_mape: 198.2257 - lr: 2.8300e-04\n",
|
||
"Epoch 8/200\n",
|
||
"81/81 [==============================] - 45s 552ms/step - loss: 0.0439 - mae: 0.1973 - mape: 161.9557 - val_loss: 0.0259 - val_mae: 0.1641 - val_mape: 199.7436 - lr: 3.2350e-04\n",
|
||
"Epoch 9/200\n",
|
||
"81/81 [==============================] - 49s 583ms/step - loss: 0.0431 - mae: 0.1915 - mape: 164.8879 - val_loss: 0.0709 - val_mae: 0.3176 - val_mape: 337.8962 - lr: 3.6400e-04\n",
|
||
"Epoch 10/200\n",
|
||
"81/81 [==============================] - 43s 529ms/step - loss: 0.0423 - mae: 0.1887 - mape: 156.7106 - val_loss: 0.0464 - val_mae: 0.2383 - val_mape: 294.8042 - lr: 4.0450e-04\n",
|
||
"Epoch 11/200\n",
|
||
"81/81 [==============================] - 425s 5s/step - loss: 0.0412 - mae: 0.1862 - mape: 151.4311 - val_loss: 0.1553 - val_mae: 0.4763 - val_mape: 384.9722 - val_olive_prod_mae: 0.2800 - val_olive_prod_mape: 168.5654 - val_min_oil_prod_mae: 0.5849 - val_min_oil_prod_mape: 494.9827 - val_max_oil_prod_mae: 0.8025 - val_max_oil_prod_mape: 712.3788 - val_avg_oil_prod_mae: 0.2131 - val_avg_oil_prod_mape: 215.8353 - best_avg_oil_prod_mae: 0.2131 - val_total_water_need_mae: 0.5012 - val_total_water_need_mape: 333.0994 - lr: 4.4500e-04\n",
|
||
"Epoch 12/200\n",
|
||
"81/81 [==============================] - 48s 577ms/step - loss: 0.0334 - mae: 0.1657 - mape: 139.6879 - val_loss: 0.0329 - val_mae: 0.1911 - val_mape: 208.6394 - lr: 4.8550e-04\n",
|
||
"Epoch 13/200\n",
|
||
"81/81 [==============================] - 43s 526ms/step - loss: 0.0300 - mae: 0.1565 - mape: 130.8686 - val_loss: 0.0180 - val_mae: 0.1290 - val_mape: 152.9059 - lr: 3.5374e-04\n",
|
||
"Epoch 14/200\n",
|
||
"81/81 [==============================] - 43s 527ms/step - loss: 0.0302 - mae: 0.1576 - mape: 134.6192 - val_loss: 0.0309 - val_mae: 0.1843 - val_mape: 232.8076 - lr: 3.4444e-04\n",
|
||
"Epoch 15/200\n",
|
||
"81/81 [==============================] - 45s 544ms/step - loss: 0.0281 - mae: 0.1504 - mape: 126.4103 - val_loss: 0.0565 - val_mae: 0.2682 - val_mape: 290.2245 - lr: 3.3538e-04\n",
|
||
"Epoch 16/200\n",
|
||
"81/81 [==============================] - 414s 5s/step - loss: 0.0276 - mae: 0.1492 - mape: 126.1187 - val_loss: 0.0210 - val_mae: 0.1440 - val_mape: 214.6380 - val_olive_prod_mae: 0.1530 - val_olive_prod_mape: 196.5764 - best_olive_prod_mae: 0.1530 - val_min_oil_prod_mae: 0.1129 - val_min_oil_prod_mape: 94.3189 - best_min_oil_prod_mae: 0.1129 - val_max_oil_prod_mae: 0.1045 - val_max_oil_prod_mape: 90.8162 - best_max_oil_prod_mae: 0.1045 - val_avg_oil_prod_mae: 0.1553 - val_avg_oil_prod_mape: 514.4499 - best_avg_oil_prod_mae: 0.1553 - val_total_water_need_mae: 0.1945 - val_total_water_need_mape: 177.0285 - best_total_water_need_mae: 0.1945 - lr: 3.2657e-04\n",
|
||
"Epoch 17/200\n",
|
||
"81/81 [==============================] - 44s 536ms/step - loss: 0.0261 - mae: 0.1443 - mape: 121.8932 - val_loss: 0.0324 - val_mae: 0.1903 - val_mape: 286.4989 - lr: 3.1798e-04\n",
|
||
"Epoch 18/200\n",
|
||
"81/81 [==============================] - 44s 539ms/step - loss: 0.0250 - mae: 0.1420 - mape: 119.7674 - val_loss: 0.0193 - val_mae: 0.1254 - val_mape: 136.5804 - lr: 3.0962e-04\n",
|
||
"Epoch 19/200\n",
|
||
"81/81 [==============================] - 45s 545ms/step - loss: 0.0244 - mae: 0.1396 - mape: 123.8952 - val_loss: 0.0137 - val_mae: 0.0964 - val_mape: 107.5592 - lr: 3.0148e-04\n",
|
||
"Epoch 20/200\n",
|
||
"81/81 [==============================] - 45s 550ms/step - loss: 0.0241 - mae: 0.1380 - mape: 119.1140 - val_loss: 0.0136 - val_mae: 0.0996 - val_mape: 104.9675 - lr: 2.9355e-04\n",
|
||
"Epoch 21/200\n",
|
||
"81/81 [==============================] - 405s 5s/step - loss: 0.0232 - mae: 0.1360 - mape: 116.4450 - val_loss: 0.0137 - val_mae: 0.0968 - val_mape: 92.3893 - val_olive_prod_mae: 0.0957 - val_olive_prod_mape: 92.1067 - best_olive_prod_mae: 0.0957 - val_min_oil_prod_mae: 0.1077 - val_min_oil_prod_mape: 92.6856 - best_min_oil_prod_mae: 0.1077 - val_max_oil_prod_mae: 0.1119 - val_max_oil_prod_mape: 93.4440 - val_avg_oil_prod_mae: 0.1049 - val_avg_oil_prod_mape: 141.0929 - best_avg_oil_prod_mae: 0.1049 - val_total_water_need_mae: 0.0639 - val_total_water_need_mape: 42.6167 - best_total_water_need_mae: 0.0639 - lr: 2.8583e-04\n",
|
||
"Epoch 22/200\n",
|
||
"81/81 [==============================] - 43s 521ms/step - loss: 0.0230 - mae: 0.1351 - mape: 117.9255 - val_loss: 0.0173 - val_mae: 0.1206 - val_mape: 113.5945 - lr: 2.7832e-04\n",
|
||
"Epoch 23/200\n",
|
||
"81/81 [==============================] - 44s 529ms/step - loss: 0.0237 - mae: 0.1362 - mape: 119.0401 - val_loss: 0.0263 - val_mae: 0.1418 - val_mape: 103.9691 - lr: 2.7100e-04\n",
|
||
"Epoch 24/200\n",
|
||
"81/81 [==============================] - 44s 535ms/step - loss: 0.0228 - mae: 0.1345 - mape: 122.7791 - val_loss: 0.0140 - val_mae: 0.0997 - val_mape: 78.1515 - lr: 2.6388e-04\n",
|
||
"Epoch 25/200\n",
|
||
"81/81 [==============================] - 46s 566ms/step - loss: 0.0230 - mae: 0.1339 - mape: 115.1308 - val_loss: 0.0230 - val_mae: 0.1624 - val_mape: 188.7469 - lr: 2.5694e-04\n",
|
||
"Epoch 26/200\n",
|
||
"81/81 [==============================] - 416s 5s/step - loss: 0.0214 - mae: 0.1307 - mape: 108.7911 - val_loss: 0.0159 - val_mae: 0.1149 - val_mape: 104.5200 - val_olive_prod_mae: 0.1179 - val_olive_prod_mape: 102.0244 - val_min_oil_prod_mae: 0.1223 - val_min_oil_prod_mape: 86.6709 - val_max_oil_prod_mae: 0.1164 - val_max_oil_prod_mape: 86.5862 - val_avg_oil_prod_mae: 0.1132 - val_avg_oil_prod_mape: 159.2269 - val_total_water_need_mae: 0.1044 - val_total_water_need_mape: 88.0918 - lr: 2.5018e-04\n",
|
||
"Epoch 27/200\n",
|
||
"81/81 [==============================] - 43s 521ms/step - loss: 0.0221 - mae: 0.1313 - mape: 109.7560 - val_loss: 0.0155 - val_mae: 0.1120 - val_mape: 154.6095 - lr: 2.4360e-04\n",
|
||
"Epoch 28/200\n",
|
||
"81/81 [==============================] - 45s 554ms/step - loss: 0.0219 - mae: 0.1317 - mape: 121.4256 - val_loss: 0.0125 - val_mae: 0.0898 - val_mape: 87.1925 - lr: 2.3720e-04\n",
|
||
"Epoch 29/200\n",
|
||
"81/81 [==============================] - 43s 522ms/step - loss: 0.0211 - mae: 0.1291 - mape: 115.6524 - val_loss: 0.0124 - val_mae: 0.0922 - val_mape: 84.2523 - lr: 2.3096e-04\n",
|
||
"Epoch 30/200\n",
|
||
"81/81 [==============================] - 44s 531ms/step - loss: 0.0206 - mae: 0.1279 - mape: 110.3134 - val_loss: 0.0135 - val_mae: 0.0992 - val_mape: 131.3028 - lr: 2.2489e-04\n",
|
||
"Epoch 31/200\n",
|
||
"81/81 [==============================] - 408s 5s/step - loss: 0.0201 - mae: 0.1261 - mape: 114.8836 - val_loss: 0.0122 - val_mae: 0.0904 - val_mape: 83.3467 - val_olive_prod_mae: 0.0966 - val_olive_prod_mape: 90.2318 - val_min_oil_prod_mae: 0.0955 - val_min_oil_prod_mape: 100.6078 - best_min_oil_prod_mae: 0.0955 - val_max_oil_prod_mae: 0.0971 - val_max_oil_prod_mape: 80.8267 - best_max_oil_prod_mae: 0.0971 - val_avg_oil_prod_mae: 0.0919 - val_avg_oil_prod_mape: 99.4094 - best_avg_oil_prod_mae: 0.0919 - val_total_water_need_mae: 0.0706 - val_total_water_need_mape: 45.6571 - lr: 2.1898e-04\n",
|
||
"Epoch 32/200\n",
|
||
"81/81 [==============================] - 43s 531ms/step - loss: 0.0201 - mae: 0.1258 - mape: 112.7482 - val_loss: 0.0135 - val_mae: 0.0985 - val_mape: 113.5038 - lr: 2.1322e-04\n",
|
||
"Epoch 33/200\n",
|
||
"81/81 [==============================] - 45s 543ms/step - loss: 0.0197 - mae: 0.1250 - mape: 104.7758 - val_loss: 0.0133 - val_mae: 0.1006 - val_mape: 91.1548 - lr: 2.0761e-04\n",
|
||
"Epoch 34/200\n",
|
||
"81/81 [==============================] - 44s 541ms/step - loss: 0.0195 - mae: 0.1244 - mape: 109.6190 - val_loss: 0.0122 - val_mae: 0.0907 - val_mape: 80.4084 - lr: 2.0215e-04\n",
|
||
"Epoch 35/200\n",
|
||
"81/81 [==============================] - 44s 536ms/step - loss: 0.0193 - mae: 0.1237 - mape: 111.1468 - val_loss: 0.0121 - val_mae: 0.0875 - val_mape: 78.0447 - lr: 1.9684e-04\n",
|
||
"Epoch 36/200\n",
|
||
"81/81 [==============================] - 416s 5s/step - loss: 0.0196 - mae: 0.1248 - mape: 109.9066 - val_loss: 0.0199 - val_mae: 0.1307 - val_mape: 141.8087 - val_olive_prod_mae: 0.1195 - val_olive_prod_mape: 109.6746 - val_min_oil_prod_mae: 0.1206 - val_min_oil_prod_mape: 136.0026 - val_max_oil_prod_mae: 0.1150 - val_max_oil_prod_mape: 98.9782 - val_avg_oil_prod_mae: 0.1120 - val_avg_oil_prod_mape: 225.4990 - val_total_water_need_mae: 0.1862 - val_total_water_need_mape: 138.8892 - lr: 1.9166e-04\n",
|
||
"Epoch 37/200\n",
|
||
"81/81 [==============================] - 43s 524ms/step - loss: 0.0196 - mae: 0.1242 - mape: 107.2102 - val_loss: 0.0139 - val_mae: 0.1027 - val_mape: 125.0853 - lr: 1.8663e-04\n",
|
||
"Epoch 38/200\n",
|
||
"81/81 [==============================] - 44s 532ms/step - loss: 0.0195 - mae: 0.1247 - mape: 108.7505 - val_loss: 0.0171 - val_mae: 0.1160 - val_mape: 127.9954 - lr: 1.8172e-04\n",
|
||
"Epoch 39/200\n",
|
||
"81/81 [==============================] - 43s 530ms/step - loss: 0.0191 - mae: 0.1233 - mape: 114.1887 - val_loss: 0.0149 - val_mae: 0.1126 - val_mape: 115.3988 - lr: 1.7694e-04\n",
|
||
"Epoch 40/200\n",
|
||
"81/81 [==============================] - 44s 539ms/step - loss: 0.0187 - mae: 0.1224 - mape: 111.9256 - val_loss: 0.0130 - val_mae: 0.1018 - val_mape: 120.4004 - lr: 1.7229e-04\n",
|
||
"Epoch 41/200\n",
|
||
"81/81 [==============================] - 413s 5s/step - loss: 0.0183 - mae: 0.1212 - mape: 107.0688 - val_loss: 0.0111 - val_mae: 0.0833 - val_mape: 85.6888 - val_olive_prod_mae: 0.0903 - val_olive_prod_mape: 93.8519 - best_olive_prod_mae: 0.0903 - val_min_oil_prod_mae: 0.0895 - val_min_oil_prod_mape: 98.7712 - best_min_oil_prod_mae: 0.0895 - val_max_oil_prod_mae: 0.0896 - val_max_oil_prod_mape: 79.7747 - best_max_oil_prod_mae: 0.0896 - val_avg_oil_prod_mae: 0.0869 - val_avg_oil_prod_mape: 111.3278 - best_avg_oil_prod_mae: 0.0869 - val_total_water_need_mae: 0.0602 - val_total_water_need_mape: 44.7183 - best_total_water_need_mae: 0.0602 - lr: 1.6776e-04\n",
|
||
"Epoch 42/200\n",
|
||
"81/81 [==============================] - 43s 522ms/step - loss: 0.0182 - mae: 0.1211 - mape: 103.9119 - val_loss: 0.0115 - val_mae: 0.0915 - val_mape: 79.4720 - lr: 1.6335e-04\n",
|
||
"Epoch 43/200\n",
|
||
"81/81 [==============================] - 43s 520ms/step - loss: 0.0181 - mae: 0.1208 - mape: 110.8668 - val_loss: 0.0127 - val_mae: 0.0985 - val_mape: 107.8702 - lr: 1.5905e-04\n",
|
||
"Epoch 44/200\n",
|
||
"81/81 [==============================] - 44s 534ms/step - loss: 0.0181 - mae: 0.1206 - mape: 106.5163 - val_loss: 0.0120 - val_mae: 0.0901 - val_mape: 86.5637 - lr: 1.5487e-04\n",
|
||
"Epoch 45/200\n",
|
||
"81/81 [==============================] - 42s 512ms/step - loss: 0.0181 - mae: 0.1207 - mape: 109.4113 - val_loss: 0.0118 - val_mae: 0.0911 - val_mape: 85.9042 - lr: 1.5080e-04\n",
|
||
"Epoch 46/200\n",
|
||
"81/81 [==============================] - 418s 5s/step - loss: 0.0178 - mae: 0.1199 - mape: 120.5106 - val_loss: 0.0114 - val_mae: 0.0863 - val_mape: 82.2661 - val_olive_prod_mae: 0.0933 - val_olive_prod_mape: 87.2666 - val_min_oil_prod_mae: 0.0922 - val_min_oil_prod_mape: 97.8242 - val_max_oil_prod_mae: 0.0925 - val_max_oil_prod_mape: 81.9897 - val_avg_oil_prod_mae: 0.0892 - val_avg_oil_prod_mape: 108.5426 - val_total_water_need_mae: 0.0644 - val_total_water_need_mape: 35.7076 - lr: 1.4683e-04\n",
|
||
"Epoch 47/200\n",
|
||
"81/81 [==============================] - 43s 526ms/step - loss: 0.0176 - mae: 0.1193 - mape: 111.5625 - val_loss: 0.0119 - val_mae: 0.0918 - val_mape: 79.6498 - lr: 1.4297e-04\n",
|
||
"Epoch 48/200\n",
|
||
"81/81 [==============================] - 46s 555ms/step - loss: 0.0175 - mae: 0.1190 - mape: 110.9619 - val_loss: 0.0117 - val_mae: 0.0921 - val_mape: 78.5738 - lr: 1.3921e-04\n",
|
||
"Epoch 49/200\n",
|
||
"81/81 [==============================] - 44s 531ms/step - loss: 0.0177 - mae: 0.1193 - mape: 105.3367 - val_loss: 0.0126 - val_mae: 0.0990 - val_mape: 82.5022 - lr: 1.3555e-04\n",
|
||
"Epoch 50/200\n",
|
||
"81/81 [==============================] - 45s 552ms/step - loss: 0.0172 - mae: 0.1179 - mape: 103.1276 - val_loss: 0.0118 - val_mae: 0.0932 - val_mape: 91.8961 - lr: 1.3199e-04\n",
|
||
"Epoch 51/200\n",
|
||
"81/81 [==============================] - 407s 5s/step - loss: 0.0171 - mae: 0.1179 - mape: 109.7217 - val_loss: 0.0116 - val_mae: 0.0920 - val_mape: 99.3956 - val_olive_prod_mae: 0.0969 - val_olive_prod_mape: 92.6791 - val_min_oil_prod_mae: 0.0952 - val_min_oil_prod_mape: 104.2999 - val_max_oil_prod_mae: 0.0940 - val_max_oil_prod_mape: 84.1292 - val_avg_oil_prod_mae: 0.0930 - val_avg_oil_prod_mape: 158.9966 - val_total_water_need_mae: 0.0811 - val_total_water_need_mape: 56.8740 - lr: 1.2852e-04\n",
|
||
"Epoch 52/200\n",
|
||
"81/81 [==============================] - 43s 520ms/step - loss: 0.0169 - mae: 0.1171 - mape: 102.4648 - val_loss: 0.0117 - val_mae: 0.0977 - val_mape: 99.1071 - lr: 1.2514e-04\n",
|
||
"Epoch 53/200\n",
|
||
"81/81 [==============================] - 45s 545ms/step - loss: 0.0167 - mae: 0.1166 - mape: 103.6632 - val_loss: 0.0140 - val_mae: 0.1187 - val_mape: 127.0951 - lr: 1.2185e-04\n",
|
||
"Epoch 54/200\n",
|
||
"81/81 [==============================] - 45s 551ms/step - loss: 0.0167 - mae: 0.1169 - mape: 117.7828 - val_loss: 0.0106 - val_mae: 0.0828 - val_mape: 87.0786 - lr: 1.1865e-04\n",
|
||
"Epoch 55/200\n",
|
||
"81/81 [==============================] - 45s 541ms/step - loss: 0.0164 - mae: 0.1157 - mape: 103.2019 - val_loss: 0.0105 - val_mae: 0.0858 - val_mape: 91.8022 - lr: 1.1553e-04\n",
|
||
"Epoch 56/200\n",
|
||
"81/81 [==============================] - 414s 5s/step - loss: 0.0162 - mae: 0.1154 - mape: 106.0157 - val_loss: 0.0107 - val_mae: 0.0879 - val_mape: 76.6214 - val_olive_prod_mae: 0.0940 - val_olive_prod_mape: 89.4869 - val_min_oil_prod_mae: 0.0947 - val_min_oil_prod_mape: 100.1891 - val_max_oil_prod_mae: 0.0949 - val_max_oil_prod_mape: 81.9265 - val_avg_oil_prod_mae: 0.0927 - val_avg_oil_prod_mape: 81.1511 - val_total_water_need_mae: 0.0632 - val_total_water_need_mape: 30.3533 - lr: 1.1249e-04\n",
|
||
"Epoch 57/200\n",
|
||
"81/81 [==============================] - 44s 540ms/step - loss: 0.0161 - mae: 0.1154 - mape: 111.6323 - val_loss: 0.0122 - val_mae: 0.0993 - val_mape: 89.6327 - lr: 1.0953e-04\n",
|
||
"Epoch 58/200\n",
|
||
"81/81 [==============================] - 45s 547ms/step - loss: 0.0163 - mae: 0.1160 - mape: 103.7626 - val_loss: 0.0189 - val_mae: 0.1307 - val_mape: 154.6112 - lr: 1.0665e-04\n",
|
||
"Epoch 59/200\n",
|
||
"81/81 [==============================] - 45s 545ms/step - loss: 0.0163 - mae: 0.1160 - mape: 107.4402 - val_loss: 0.0132 - val_mae: 0.1094 - val_mape: 108.5976 - lr: 1.0385e-04\n",
|
||
"Epoch 60/200\n",
|
||
"81/81 [==============================] - 45s 549ms/step - loss: 0.0158 - mae: 0.1145 - mape: 102.4115 - val_loss: 0.0129 - val_mae: 0.1075 - val_mape: 113.4921 - lr: 1.0112e-04\n",
|
||
"Epoch 61/200\n",
|
||
"81/81 [==============================] - 413s 5s/step - loss: 0.0158 - mae: 0.1146 - mape: 112.1566 - val_loss: 0.0118 - val_mae: 0.0954 - val_mape: 87.6620 - val_olive_prod_mae: 0.0924 - val_olive_prod_mape: 87.6679 - val_min_oil_prod_mae: 0.0952 - val_min_oil_prod_mape: 89.1397 - val_max_oil_prod_mae: 0.0964 - val_max_oil_prod_mape: 80.2136 - val_avg_oil_prod_mae: 0.0914 - val_avg_oil_prod_mape: 81.9330 - val_total_water_need_mae: 0.1017 - val_total_water_need_mape: 99.3555 - lr: 9.8459e-05\n",
|
||
"Epoch 62/200\n",
|
||
"81/81 [==============================] - 45s 544ms/step - loss: 0.0157 - mae: 0.1143 - mape: 100.9561 - val_loss: 0.0125 - val_mae: 0.1035 - val_mape: 105.1631 - lr: 9.5870e-05\n",
|
||
"Epoch 63/200\n",
|
||
"81/81 [==============================] - 44s 541ms/step - loss: 0.0156 - mae: 0.1139 - mape: 99.1020 - val_loss: 0.0109 - val_mae: 0.0919 - val_mape: 86.3423 - lr: 9.3350e-05\n",
|
||
"Epoch 64/200\n",
|
||
"81/81 [==============================] - 45s 554ms/step - loss: 0.0156 - mae: 0.1140 - mape: 102.9145 - val_loss: 0.0150 - val_mae: 0.1133 - val_mape: 117.4506 - lr: 9.0895e-05\n",
|
||
"Epoch 65/200\n",
|
||
"81/81 [==============================] - 45s 549ms/step - loss: 0.0155 - mae: 0.1138 - mape: 99.9012 - val_loss: 0.0107 - val_mae: 0.0902 - val_mape: 78.9081 - lr: 8.8505e-05\n",
|
||
"Epoch 66/200\n",
|
||
"81/81 [==============================] - 426s 5s/step - loss: 0.0154 - mae: 0.1132 - mape: 101.1288 - val_loss: 0.0103 - val_mae: 0.0883 - val_mape: 84.9515 - val_olive_prod_mae: 0.0942 - val_olive_prod_mape: 89.3613 - val_min_oil_prod_mae: 0.0928 - val_min_oil_prod_mape: 90.1885 - val_max_oil_prod_mae: 0.0920 - val_max_oil_prod_mape: 80.9348 - val_avg_oil_prod_mae: 0.0877 - val_avg_oil_prod_mape: 101.2916 - val_total_water_need_mae: 0.0748 - val_total_water_need_mape: 62.9818 - lr: 8.6178e-05\n",
|
||
"Epoch 67/200\n",
|
||
"81/81 [==============================] - 44s 532ms/step - loss: 0.0154 - mae: 0.1134 - mape: 99.7389 - val_loss: 0.0103 - val_mae: 0.0847 - val_mape: 80.2572 - lr: 8.3913e-05\n",
|
||
"Epoch 68/200\n",
|
||
"81/81 [==============================] - 43s 524ms/step - loss: 0.0152 - mae: 0.1126 - mape: 105.7351 - val_loss: 0.0105 - val_mae: 0.0907 - val_mape: 92.2278 - lr: 8.1706e-05\n",
|
||
"Epoch 69/200\n",
|
||
"81/81 [==============================] - 45s 541ms/step - loss: 0.0152 - mae: 0.1127 - mape: 97.2689 - val_loss: 0.0106 - val_mae: 0.0892 - val_mape: 74.3894 - lr: 7.9558e-05\n",
|
||
"Epoch 70/200\n",
|
||
"81/81 [==============================] - 44s 534ms/step - loss: 0.0150 - mae: 0.1125 - mape: 97.9516 - val_loss: 0.0112 - val_mae: 0.0940 - val_mape: 81.9546 - lr: 7.7466e-05\n",
|
||
"Epoch 71/200\n",
|
||
"81/81 [==============================] - 404s 5s/step - loss: 0.0149 - mae: 0.1122 - mape: 101.4992 - val_loss: 0.0101 - val_mae: 0.0850 - val_mape: 81.0987 - val_olive_prod_mae: 0.0941 - val_olive_prod_mape: 91.1528 - val_min_oil_prod_mae: 0.0925 - val_min_oil_prod_mape: 91.4974 - val_max_oil_prod_mae: 0.0936 - val_max_oil_prod_mape: 79.5238 - val_avg_oil_prod_mae: 0.0884 - val_avg_oil_prod_mape: 102.5653 - val_total_water_need_mae: 0.0564 - val_total_water_need_mape: 40.7536 - best_total_water_need_mae: 0.0564 - lr: 7.5430e-05\n",
|
||
"Epoch 72/200\n",
|
||
"81/81 [==============================] - 43s 531ms/step - loss: 0.0149 - mae: 0.1121 - mape: 100.9465 - val_loss: 0.0110 - val_mae: 0.0896 - val_mape: 92.5869 - lr: 7.3446e-05\n",
|
||
"Epoch 73/200\n",
|
||
"81/81 [==============================] - 45s 555ms/step - loss: 0.0149 - mae: 0.1119 - mape: 103.2648 - val_loss: 0.0098 - val_mae: 0.0836 - val_mape: 76.6982 - lr: 7.1515e-05\n",
|
||
"Epoch 74/200\n",
|
||
"81/81 [==============================] - 43s 523ms/step - loss: 0.0147 - mae: 0.1117 - mape: 104.3210 - val_loss: 0.0100 - val_mae: 0.0875 - val_mape: 75.5992 - lr: 6.9635e-05\n",
|
||
"Epoch 75/200\n",
|
||
"81/81 [==============================] - 45s 546ms/step - loss: 0.0149 - mae: 0.1120 - mape: 107.9233 - val_loss: 0.0109 - val_mae: 0.0933 - val_mape: 76.4215 - lr: 6.7804e-05\n",
|
||
"Epoch 76/200\n",
|
||
"81/81 [==============================] - 420s 5s/step - loss: 0.0146 - mae: 0.1111 - mape: 100.7668 - val_loss: 0.0104 - val_mae: 0.0883 - val_mape: 80.8174 - val_olive_prod_mae: 0.0976 - val_olive_prod_mape: 92.8249 - val_min_oil_prod_mae: 0.0958 - val_min_oil_prod_mape: 95.7500 - val_max_oil_prod_mae: 0.0950 - val_max_oil_prod_mape: 79.5825 - val_avg_oil_prod_mae: 0.0928 - val_avg_oil_prod_mape: 106.9812 - val_total_water_need_mae: 0.0605 - val_total_water_need_mape: 28.9489 - lr: 6.6021e-05\n",
|
||
"Epoch 77/200\n",
|
||
"81/81 [==============================] - 44s 536ms/step - loss: 0.0145 - mae: 0.1112 - mape: 99.6902 - val_loss: 0.0102 - val_mae: 0.0891 - val_mape: 78.1710 - lr: 6.4285e-05\n",
|
||
"Epoch 78/200\n",
|
||
"81/81 [==============================] - 46s 559ms/step - loss: 0.0145 - mae: 0.1110 - mape: 97.5761 - val_loss: 0.0097 - val_mae: 0.0835 - val_mape: 72.4018 - lr: 6.2595e-05\n",
|
||
"Epoch 79/200\n",
|
||
"81/81 [==============================] - 45s 543ms/step - loss: 0.0144 - mae: 0.1109 - mape: 99.8900 - val_loss: 0.0095 - val_mae: 0.0828 - val_mape: 77.4501 - lr: 6.0949e-05\n",
|
||
"Epoch 80/200\n",
|
||
"81/81 [==============================] - 45s 549ms/step - loss: 0.0143 - mae: 0.1107 - mape: 101.5637 - val_loss: 0.0096 - val_mae: 0.0839 - val_mape: 75.1728 - lr: 5.9347e-05\n",
|
||
"Epoch 81/200\n",
|
||
"81/81 [==============================] - 415s 5s/step - loss: 0.0144 - mae: 0.1109 - mape: 101.2297 - val_loss: 0.0101 - val_mae: 0.0892 - val_mape: 82.6180 - val_olive_prod_mae: 0.0929 - val_olive_prod_mape: 90.3583 - val_min_oil_prod_mae: 0.0929 - val_min_oil_prod_mape: 98.0599 - val_max_oil_prod_mae: 0.0914 - val_max_oil_prod_mape: 80.4533 - val_avg_oil_prod_mae: 0.0911 - val_avg_oil_prod_mape: 94.1108 - val_total_water_need_mae: 0.0778 - val_total_water_need_mape: 50.1071 - lr: 5.7787e-05\n",
|
||
"Epoch 82/200\n",
|
||
"81/81 [==============================] - 44s 533ms/step - loss: 0.0142 - mae: 0.1104 - mape: 103.8522 - val_loss: 0.0099 - val_mae: 0.0843 - val_mape: 71.8070 - lr: 5.6267e-05\n",
|
||
"Epoch 83/200\n",
|
||
"81/81 [==============================] - 44s 537ms/step - loss: 0.0142 - mae: 0.1105 - mape: 99.8903 - val_loss: 0.0099 - val_mae: 0.0863 - val_mape: 75.7442 - lr: 5.4788e-05\n",
|
||
"Epoch 84/200\n",
|
||
"81/81 [==============================] - 44s 538ms/step - loss: 0.0142 - mae: 0.1103 - mape: 100.6964 - val_loss: 0.0097 - val_mae: 0.0853 - val_mape: 71.8215 - lr: 5.3347e-05\n",
|
||
"Epoch 85/200\n",
|
||
"81/81 [==============================] - 44s 535ms/step - loss: 0.0141 - mae: 0.1100 - mape: 101.9498 - val_loss: 0.0096 - val_mae: 0.0851 - val_mape: 73.2126 - lr: 5.1945e-05\n",
|
||
"Epoch 86/200\n",
|
||
"81/81 [==============================] - 424s 5s/step - loss: 0.0140 - mae: 0.1101 - mape: 99.0340 - val_loss: 0.0100 - val_mae: 0.0888 - val_mape: 71.7597 - val_olive_prod_mae: 0.0976 - val_olive_prod_mape: 89.1973 - val_min_oil_prod_mae: 0.0948 - val_min_oil_prod_mape: 92.7337 - val_max_oil_prod_mae: 0.0945 - val_max_oil_prod_mape: 79.1431 - val_avg_oil_prod_mae: 0.0921 - val_avg_oil_prod_mape: 68.7220 - val_total_water_need_mae: 0.0650 - val_total_water_need_mape: 29.0024 - lr: 5.0579e-05\n",
|
||
"Epoch 87/200\n",
|
||
"81/81 [==============================] - 44s 535ms/step - loss: 0.0139 - mae: 0.1094 - mape: 98.9774 - val_loss: 0.0095 - val_mae: 0.0838 - val_mape: 77.6027 - lr: 4.9249e-05\n",
|
||
"Epoch 88/200\n",
|
||
"81/81 [==============================] - 44s 537ms/step - loss: 0.0139 - mae: 0.1099 - mape: 103.9017 - val_loss: 0.0094 - val_mae: 0.0836 - val_mape: 79.2909 - lr: 4.7954e-05\n",
|
||
"Epoch 89/200\n",
|
||
"81/81 [==============================] - 43s 529ms/step - loss: 0.0138 - mae: 0.1095 - mape: 95.7289 - val_loss: 0.0091 - val_mae: 0.0812 - val_mape: 73.1377 - lr: 4.6693e-05\n",
|
||
"Epoch 90/200\n",
|
||
"81/81 [==============================] - 43s 524ms/step - loss: 0.0138 - mae: 0.1095 - mape: 105.6472 - val_loss: 0.0092 - val_mae: 0.0832 - val_mape: 77.7363 - lr: 4.5466e-05\n",
|
||
"Epoch 91/200\n",
|
||
"81/81 [==============================] - 419s 5s/step - loss: 0.0138 - mae: 0.1094 - mape: 102.3868 - val_loss: 0.0094 - val_mae: 0.0854 - val_mape: 78.2091 - val_olive_prod_mae: 0.0919 - val_olive_prod_mape: 90.3287 - val_min_oil_prod_mae: 0.0909 - val_min_oil_prod_mape: 97.2435 - val_max_oil_prod_mae: 0.0911 - val_max_oil_prod_mape: 80.7007 - val_avg_oil_prod_mae: 0.0883 - val_avg_oil_prod_mape: 91.3950 - val_total_water_need_mae: 0.0646 - val_total_water_need_mape: 31.3778 - lr: 4.4270e-05\n",
|
||
"Epoch 92/200\n",
|
||
"81/81 [==============================] - 42s 513ms/step - loss: 0.0138 - mae: 0.1095 - mape: 100.2383 - val_loss: 0.0095 - val_mae: 0.0859 - val_mape: 74.0313 - lr: 4.3106e-05\n",
|
||
"Epoch 93/200\n",
|
||
"81/81 [==============================] - 43s 524ms/step - loss: 0.0137 - mae: 0.1094 - mape: 98.0624 - val_loss: 0.0092 - val_mae: 0.0824 - val_mape: 75.3717 - lr: 4.1973e-05\n",
|
||
"Epoch 94/200\n",
|
||
"81/81 [==============================] - 42s 509ms/step - loss: 0.0137 - mae: 0.1092 - mape: 97.3056 - val_loss: 0.0094 - val_mae: 0.0847 - val_mape: 72.2782 - lr: 4.0869e-05\n",
|
||
"Epoch 95/200\n",
|
||
"81/81 [==============================] - 42s 516ms/step - loss: 0.0136 - mae: 0.1090 - mape: 99.6563 - val_loss: 0.0093 - val_mae: 0.0832 - val_mape: 71.3101 - lr: 3.9795e-05\n",
|
||
"Epoch 96/200\n",
|
||
"81/81 [==============================] - 416s 5s/step - loss: 0.0135 - mae: 0.1088 - mape: 98.3289 - val_loss: 0.0093 - val_mae: 0.0830 - val_mape: 73.6735 - val_olive_prod_mae: 0.0907 - val_olive_prod_mape: 88.2918 - val_min_oil_prod_mae: 0.0899 - val_min_oil_prod_mape: 92.6629 - val_max_oil_prod_mae: 0.0888 - val_max_oil_prod_mape: 81.5504 - best_max_oil_prod_mae: 0.0888 - val_avg_oil_prod_mae: 0.0862 - val_avg_oil_prod_mape: 79.1624 - best_avg_oil_prod_mae: 0.0862 - val_total_water_need_mae: 0.0594 - val_total_water_need_mape: 26.7003 - lr: 3.8749e-05\n",
|
||
"Epoch 97/200\n",
|
||
"81/81 [==============================] - 43s 527ms/step - loss: 0.0135 - mae: 0.1087 - mape: 93.9776 - val_loss: 0.0091 - val_mae: 0.0827 - val_mape: 74.7657 - lr: 3.7730e-05\n",
|
||
"Epoch 98/200\n",
|
||
"81/81 [==============================] - 43s 528ms/step - loss: 0.0134 - mae: 0.1088 - mape: 95.6354 - val_loss: 0.0091 - val_mae: 0.0811 - val_mape: 76.5250 - lr: 3.6738e-05\n",
|
||
"Epoch 99/200\n",
|
||
"81/81 [==============================] - 45s 541ms/step - loss: 0.0135 - mae: 0.1088 - mape: 105.4989 - val_loss: 0.0093 - val_mae: 0.0863 - val_mape: 75.0138 - lr: 3.5772e-05\n",
|
||
"Epoch 100/200\n",
|
||
"81/81 [==============================] - 44s 534ms/step - loss: 0.0134 - mae: 0.1085 - mape: 102.6167 - val_loss: 0.0091 - val_mae: 0.0833 - val_mape: 74.0674 - lr: 3.4831e-05\n",
|
||
"Epoch 101/200\n",
|
||
"81/81 [==============================] - 405s 5s/step - loss: 0.0134 - mae: 0.1088 - mape: 99.3198 - val_loss: 0.0094 - val_mae: 0.0860 - val_mape: 71.5030 - val_olive_prod_mae: 0.0935 - val_olive_prod_mape: 90.6842 - val_min_oil_prod_mae: 0.0939 - val_min_oil_prod_mape: 93.7380 - val_max_oil_prod_mae: 0.0917 - val_max_oil_prod_mape: 79.1902 - val_avg_oil_prod_mae: 0.0904 - val_avg_oil_prod_mape: 66.9787 - val_total_water_need_mae: 0.0603 - val_total_water_need_mape: 26.9237 - lr: 3.3916e-05\n",
|
||
"Epoch 102/200\n",
|
||
"81/81 [==============================] - 43s 526ms/step - loss: 0.0134 - mae: 0.1082 - mape: 96.7114 - val_loss: 0.0091 - val_mae: 0.0839 - val_mape: 73.3150 - lr: 3.3024e-05\n",
|
||
"Epoch 103/200\n",
|
||
"81/81 [==============================] - 44s 543ms/step - loss: 0.0132 - mae: 0.1078 - mape: 96.8080 - val_loss: 0.0090 - val_mae: 0.0836 - val_mape: 76.6545 - lr: 3.2156e-05\n",
|
||
"Epoch 104/200\n",
|
||
"81/81 [==============================] - ETA: 0s - loss: 0.0132 - mae: 0.1080 - mape: 103.5235\n",
|
||
"Epoch 104: ReduceLROnPlateau reducing learning rate to 6.262018723646179e-06.\n",
|
||
"81/81 [==============================] - 44s 541ms/step - loss: 0.0132 - mae: 0.1080 - mape: 103.5235 - val_loss: 0.0092 - val_mae: 0.0835 - val_mape: 74.9676 - lr: 3.1310e-05\n",
|
||
"Epoch 105/200\n",
|
||
"81/81 [==============================] - 45s 550ms/step - loss: 0.0132 - mae: 0.1080 - mape: 99.4297 - val_loss: 0.0088 - val_mae: 0.0815 - val_mape: 71.3449 - lr: 3.0487e-05\n",
|
||
"Epoch 106/200\n",
|
||
"81/81 [==============================] - 405s 5s/step - loss: 0.0133 - mae: 0.1075 - mape: 97.3356 - val_loss: 0.0088 - val_mae: 0.0813 - val_mape: 71.0418 - val_olive_prod_mae: 0.0902 - val_olive_prod_mape: 88.2805 - best_olive_prod_mae: 0.0902 - val_min_oil_prod_mae: 0.0891 - val_min_oil_prod_mape: 90.8285 - best_min_oil_prod_mae: 0.0891 - val_max_oil_prod_mae: 0.0890 - val_max_oil_prod_mape: 76.4075 - val_avg_oil_prod_mae: 0.0854 - val_avg_oil_prod_mape: 73.6663 - best_avg_oil_prod_mae: 0.0854 - val_total_water_need_mae: 0.0525 - val_total_water_need_mape: 26.0260 - best_total_water_need_mae: 0.0525 - lr: 2.9685e-05\n",
|
||
"Epoch 107/200\n",
|
||
"81/81 [==============================] - 46s 556ms/step - loss: 0.0131 - mae: 0.1077 - mape: 95.6784 - val_loss: 0.0086 - val_mae: 0.0803 - val_mape: 76.2398 - lr: 2.8905e-05\n",
|
||
"Epoch 108/200\n",
|
||
"81/81 [==============================] - 43s 520ms/step - loss: 0.0130 - mae: 0.1076 - mape: 97.3676 - val_loss: 0.0087 - val_mae: 0.0815 - val_mape: 72.6132 - lr: 2.8145e-05\n",
|
||
"Epoch 109/200\n",
|
||
"81/81 [==============================] - 44s 532ms/step - loss: 0.0130 - mae: 0.1074 - mape: 93.8073 - val_loss: 0.0088 - val_mae: 0.0831 - val_mape: 73.7144 - lr: 2.7405e-05\n",
|
||
"Epoch 110/200\n",
|
||
"81/81 [==============================] - 45s 547ms/step - loss: 0.0130 - mae: 0.1074 - mape: 98.0165 - val_loss: 0.0085 - val_mae: 0.0794 - val_mape: 74.5856 - lr: 2.6684e-05\n",
|
||
"Epoch 111/200\n",
|
||
"81/81 [==============================] - 421s 5s/step - loss: 0.0130 - mae: 0.1075 - mape: 98.1536 - val_loss: 0.0087 - val_mae: 0.0821 - val_mape: 72.5281 - val_olive_prod_mae: 0.0913 - val_olive_prod_mape: 89.8939 - val_min_oil_prod_mae: 0.0896 - val_min_oil_prod_mape: 93.7233 - val_max_oil_prod_mae: 0.0898 - val_max_oil_prod_mape: 76.0811 - val_avg_oil_prod_mae: 0.0865 - val_avg_oil_prod_mape: 75.4671 - val_total_water_need_mae: 0.0534 - val_total_water_need_mape: 27.4751 - lr: 2.5983e-05\n",
|
||
"Epoch 112/200\n",
|
||
"81/81 [==============================] - 45s 554ms/step - loss: 0.0130 - mae: 0.1073 - mape: 96.7342 - val_loss: 0.0087 - val_mae: 0.0815 - val_mape: 71.0408 - lr: 2.5300e-05\n",
|
||
"Epoch 113/200\n",
|
||
"81/81 [==============================] - 45s 550ms/step - loss: 0.0129 - mae: 0.1073 - mape: 93.0413 - val_loss: 0.0087 - val_mae: 0.0812 - val_mape: 70.8242 - lr: 2.4634e-05\n",
|
||
"Epoch 114/200\n",
|
||
"81/81 [==============================] - 44s 534ms/step - loss: 0.0129 - mae: 0.1073 - mape: 97.2687 - val_loss: 0.0089 - val_mae: 0.0836 - val_mape: 74.5174 - lr: 2.3987e-05\n",
|
||
"Epoch 115/200\n",
|
||
"81/81 [==============================] - 46s 563ms/step - loss: 0.0129 - mae: 0.1072 - mape: 97.5502 - val_loss: 0.0084 - val_mae: 0.0801 - val_mape: 70.5831 - lr: 2.3356e-05\n",
|
||
"Epoch 116/200\n",
|
||
"81/81 [==============================] - 410s 5s/step - loss: 0.0128 - mae: 0.1069 - mape: 99.3510 - val_loss: 0.0085 - val_mae: 0.0805 - val_mape: 70.4120 - val_olive_prod_mae: 0.0892 - val_olive_prod_mape: 89.7023 - best_olive_prod_mae: 0.0892 - val_min_oil_prod_mae: 0.0880 - val_min_oil_prod_mape: 91.7272 - best_min_oil_prod_mae: 0.0880 - val_max_oil_prod_mae: 0.0877 - val_max_oil_prod_mape: 75.6732 - best_max_oil_prod_mae: 0.0877 - val_avg_oil_prod_mae: 0.0848 - val_avg_oil_prod_mape: 69.0733 - best_avg_oil_prod_mae: 0.0848 - val_total_water_need_mae: 0.0528 - val_total_water_need_mape: 25.8832 - lr: 2.2742e-05\n",
|
||
"Epoch 117/200\n",
|
||
"81/81 [==============================] - 43s 518ms/step - loss: 0.0129 - mae: 0.1074 - mape: 101.2562 - val_loss: 0.0088 - val_mae: 0.0843 - val_mape: 70.6695 - lr: 2.2144e-05\n",
|
||
"Epoch 118/200\n",
|
||
"81/81 [==============================] - 45s 549ms/step - loss: 0.0128 - mae: 0.1069 - mape: 100.3844 - val_loss: 0.0087 - val_mae: 0.0824 - val_mape: 76.1144 - lr: 2.1562e-05\n",
|
||
"Epoch 119/200\n",
|
||
"81/81 [==============================] - 46s 557ms/step - loss: 0.0128 - mae: 0.1069 - mape: 99.9334 - val_loss: 0.0085 - val_mae: 0.0805 - val_mape: 73.0375 - lr: 2.0995e-05\n",
|
||
"Epoch 120/200\n",
|
||
"81/81 [==============================] - 45s 549ms/step - loss: 0.0128 - mae: 0.1070 - mape: 100.1810 - val_loss: 0.0085 - val_mae: 0.0815 - val_mape: 76.3325 - lr: 2.0443e-05\n",
|
||
"Epoch 121/200\n",
|
||
"81/81 [==============================] - 408s 5s/step - loss: 0.0128 - mae: 0.1069 - mape: 94.3632 - val_loss: 0.0086 - val_mae: 0.0817 - val_mape: 72.1149 - val_olive_prod_mae: 0.0906 - val_olive_prod_mape: 87.6058 - val_min_oil_prod_mae: 0.0886 - val_min_oil_prod_mape: 91.1811 - val_max_oil_prod_mae: 0.0890 - val_max_oil_prod_mape: 75.9044 - val_avg_oil_prod_mae: 0.0861 - val_avg_oil_prod_mape: 79.9283 - val_total_water_need_mae: 0.0544 - val_total_water_need_mape: 25.9553 - lr: 1.9905e-05\n",
|
||
"Epoch 122/200\n",
|
||
"81/81 [==============================] - 44s 539ms/step - loss: 0.0127 - mae: 0.1067 - mape: 95.9895 - val_loss: 0.0083 - val_mae: 0.0775 - val_mape: 74.3044 - lr: 1.9382e-05\n",
|
||
"Epoch 123/200\n",
|
||
"81/81 [==============================] - 45s 547ms/step - loss: 0.0127 - mae: 0.1068 - mape: 103.0387 - val_loss: 0.0084 - val_mae: 0.0787 - val_mape: 70.4848 - lr: 1.8872e-05\n",
|
||
"Epoch 124/200\n",
|
||
"81/81 [==============================] - 44s 534ms/step - loss: 0.0127 - mae: 0.1070 - mape: 101.5992 - val_loss: 0.0084 - val_mae: 0.0790 - val_mape: 76.9380 - lr: 1.8376e-05\n",
|
||
"Epoch 125/200\n",
|
||
"81/81 [==============================] - 43s 530ms/step - loss: 0.0127 - mae: 0.1066 - mape: 97.1413 - val_loss: 0.0084 - val_mae: 0.0793 - val_mape: 71.0964 - lr: 1.7893e-05\n",
|
||
"Epoch 126/200\n",
|
||
"81/81 [==============================] - 405s 5s/step - loss: 0.0127 - mae: 0.1067 - mape: 99.8651 - val_loss: 0.0083 - val_mae: 0.0798 - val_mape: 72.0929 - val_olive_prod_mae: 0.0891 - val_olive_prod_mape: 89.7311 - best_olive_prod_mae: 0.0891 - val_min_oil_prod_mae: 0.0881 - val_min_oil_prod_mape: 90.9031 - val_max_oil_prod_mae: 0.0879 - val_max_oil_prod_mape: 76.2289 - val_avg_oil_prod_mae: 0.0848 - val_avg_oil_prod_mape: 77.5775 - val_total_water_need_mae: 0.0491 - val_total_water_need_mape: 26.0246 - best_total_water_need_mae: 0.0491 - lr: 1.7423e-05\n",
|
||
"Epoch 127/200\n",
|
||
"81/81 [==============================] - 44s 535ms/step - loss: 0.0126 - mae: 0.1068 - mape: 95.1028 - val_loss: 0.0081 - val_mae: 0.0781 - val_mape: 71.2596 - lr: 1.6965e-05\n",
|
||
"Epoch 128/200\n",
|
||
"81/81 [==============================] - 45s 552ms/step - loss: 0.0127 - mae: 0.1067 - mape: 93.2160 - val_loss: 0.0084 - val_mae: 0.0812 - val_mape: 69.7919 - lr: 1.6518e-05\n",
|
||
"Epoch 129/200\n",
|
||
"81/81 [==============================] - 46s 564ms/step - loss: 0.0127 - mae: 0.1067 - mape: 95.8274 - val_loss: 0.0082 - val_mae: 0.0793 - val_mape: 71.5809 - lr: 1.6084e-05\n",
|
||
"Epoch 130/200\n",
|
||
"81/81 [==============================] - 43s 525ms/step - loss: 0.0125 - mae: 0.1064 - mape: 94.9175 - val_loss: 0.0084 - val_mae: 0.0789 - val_mape: 72.1971 - lr: 1.5661e-05\n",
|
||
"Epoch 131/200\n",
|
||
"81/81 [==============================] - 414s 5s/step - loss: 0.0126 - mae: 0.1064 - mape: 101.2890 - val_loss: 0.0083 - val_mae: 0.0800 - val_mape: 71.2394 - val_olive_prod_mae: 0.0891 - val_olive_prod_mape: 91.4833 - best_olive_prod_mae: 0.0891 - val_min_oil_prod_mae: 0.0879 - val_min_oil_prod_mape: 92.5499 - best_min_oil_prod_mae: 0.0879 - val_max_oil_prod_mae: 0.0879 - val_max_oil_prod_mape: 76.0341 - val_avg_oil_prod_mae: 0.0850 - val_avg_oil_prod_mape: 69.7731 - val_total_water_need_mae: 0.0500 - val_total_water_need_mape: 26.3569 - lr: 1.5250e-05\n",
|
||
"Epoch 132/200\n",
|
||
"81/81 [==============================] - 46s 557ms/step - loss: 0.0125 - mae: 0.1063 - mape: 97.2383 - val_loss: 0.0082 - val_mae: 0.0788 - val_mape: 70.9983 - lr: 1.4849e-05\n",
|
||
"Epoch 133/200\n",
|
||
"81/81 [==============================] - 42s 517ms/step - loss: 0.0125 - mae: 0.1065 - mape: 100.5532 - val_loss: 0.0083 - val_mae: 0.0797 - val_mape: 70.9532 - lr: 1.4458e-05\n",
|
||
"Epoch 134/200\n",
|
||
"81/81 [==============================] - 45s 549ms/step - loss: 0.0125 - mae: 0.1062 - mape: 91.1623 - val_loss: 0.0082 - val_mae: 0.0794 - val_mape: 70.6925 - lr: 1.4078e-05\n",
|
||
"Epoch 135/200\n",
|
||
"81/81 [==============================] - 46s 565ms/step - loss: 0.0125 - mae: 0.1062 - mape: 100.0002 - val_loss: 0.0083 - val_mae: 0.0803 - val_mape: 71.6639 - lr: 1.3708e-05\n",
|
||
"Epoch 136/200\n",
|
||
"81/81 [==============================] - 408s 5s/step - loss: 0.0125 - mae: 0.1062 - mape: 98.6318 - val_loss: 0.0083 - val_mae: 0.0795 - val_mape: 71.3979 - val_olive_prod_mae: 0.0890 - val_olive_prod_mape: 89.4010 - best_olive_prod_mae: 0.0890 - val_min_oil_prod_mae: 0.0877 - val_min_oil_prod_mape: 91.2028 - best_min_oil_prod_mae: 0.0877 - val_max_oil_prod_mae: 0.0877 - val_max_oil_prod_mape: 75.7092 - val_avg_oil_prod_mae: 0.0847 - val_avg_oil_prod_mape: 75.1019 - best_avg_oil_prod_mae: 0.0847 - val_total_water_need_mae: 0.0482 - val_total_water_need_mape: 25.5753 - best_total_water_need_mae: 0.0482 - lr: 1.3347e-05\n",
|
||
"Epoch 137/200\n",
|
||
"81/81 [==============================] - 46s 564ms/step - loss: 0.0125 - mae: 0.1065 - mape: 94.3227 - val_loss: 0.0081 - val_mae: 0.0783 - val_mape: 75.9415 - lr: 1.2997e-05\n",
|
||
"Epoch 138/200\n",
|
||
"81/81 [==============================] - 46s 554ms/step - loss: 0.0125 - mae: 0.1063 - mape: 97.7030 - val_loss: 0.0082 - val_mae: 0.0791 - val_mape: 72.1331 - lr: 1.2655e-05\n",
|
||
"Epoch 139/200\n",
|
||
"81/81 [==============================] - 43s 526ms/step - loss: 0.0125 - mae: 0.1061 - mape: 98.8603 - val_loss: 0.0083 - val_mae: 0.0795 - val_mape: 72.5747 - lr: 1.2322e-05\n",
|
||
"Epoch 140/200\n",
|
||
"81/81 [==============================] - 43s 522ms/step - loss: 0.0124 - mae: 0.1061 - mape: 96.8847 - val_loss: 0.0083 - val_mae: 0.0793 - val_mape: 69.8753 - lr: 1.1998e-05\n",
|
||
"Epoch 141/200\n",
|
||
"81/81 [==============================] - 398s 5s/step - loss: 0.0124 - mae: 0.1062 - mape: 95.8760 - val_loss: 0.0082 - val_mae: 0.0790 - val_mape: 71.8602 - val_olive_prod_mae: 0.0878 - val_olive_prod_mape: 91.0793 - best_olive_prod_mae: 0.0878 - val_min_oil_prod_mae: 0.0876 - val_min_oil_prod_mape: 92.5032 - best_min_oil_prod_mae: 0.0876 - val_max_oil_prod_mae: 0.0871 - val_max_oil_prod_mape: 76.3926 - best_max_oil_prod_mae: 0.0871 - val_avg_oil_prod_mae: 0.0844 - val_avg_oil_prod_mape: 73.0280 - best_avg_oil_prod_mae: 0.0844 - val_total_water_need_mae: 0.0480 - val_total_water_need_mape: 26.2976 - best_total_water_need_mae: 0.0480 - lr: 1.1683e-05\n",
|
||
"Epoch 142/200\n",
|
||
"81/81 [==============================] - ETA: 0s - loss: 0.0124 - mae: 0.1059 - mape: 93.7385\n",
|
||
"Epoch 142: ReduceLROnPlateau reducing learning rate to 2.2750975404051134e-06.\n",
|
||
"81/81 [==============================] - 43s 521ms/step - loss: 0.0124 - mae: 0.1059 - mape: 93.7385 - val_loss: 0.0081 - val_mae: 0.0781 - val_mape: 71.1219 - lr: 1.1375e-05\n",
|
||
"Epoch 143/200\n",
|
||
"81/81 [==============================] - 43s 524ms/step - loss: 0.0124 - mae: 0.1062 - mape: 96.3209 - val_loss: 0.0082 - val_mae: 0.0806 - val_mape: 71.3129 - lr: 1.1076e-05\n",
|
||
"Epoch 144/200\n",
|
||
"81/81 [==============================] - 43s 530ms/step - loss: 0.0124 - mae: 0.1062 - mape: 95.7396 - val_loss: 0.0082 - val_mae: 0.0809 - val_mape: 71.7618 - lr: 1.0785e-05\n",
|
||
"Epoch 145/200\n",
|
||
"81/81 [==============================] - 45s 547ms/step - loss: 0.0123 - mae: 0.1058 - mape: 101.4618 - val_loss: 0.0081 - val_mae: 0.0782 - val_mape: 70.8179 - lr: 1.0502e-05\n",
|
||
"Epoch 146/200\n",
|
||
"81/81 [==============================] - 385s 5s/step - loss: 0.0124 - mae: 0.1063 - mape: 97.9897 - val_loss: 0.0082 - val_mae: 0.0797 - val_mape: 70.4109 - val_olive_prod_mae: 0.0886 - val_olive_prod_mape: 89.7785 - val_min_oil_prod_mae: 0.0879 - val_min_oil_prod_mape: 92.4329 - val_max_oil_prod_mae: 0.0875 - val_max_oil_prod_mape: 76.0445 - val_avg_oil_prod_mae: 0.0847 - val_avg_oil_prod_mape: 67.6560 - val_total_water_need_mae: 0.0498 - val_total_water_need_mape: 26.1428 - lr: 1.0225e-05\n",
|
||
"Epoch 147/200\n",
|
||
"81/81 [==============================] - 45s 545ms/step - loss: 0.0123 - mae: 0.1060 - mape: 99.9797 - val_loss: 0.0081 - val_mae: 0.0788 - val_mape: 70.8160 - lr: 9.9566e-06\n",
|
||
"Epoch 148/200\n",
|
||
"81/81 [==============================] - 43s 527ms/step - loss: 0.0123 - mae: 0.1059 - mape: 97.9168 - val_loss: 0.0081 - val_mae: 0.0787 - val_mape: 70.3717 - lr: 9.6949e-06\n",
|
||
"Epoch 149/200\n",
|
||
"81/81 [==============================] - 43s 520ms/step - loss: 0.0123 - mae: 0.1061 - mape: 91.6191 - val_loss: 0.0081 - val_mae: 0.0788 - val_mape: 72.8542 - lr: 9.4400e-06\n",
|
||
"Epoch 150/200\n",
|
||
"81/81 [==============================] - 44s 537ms/step - loss: 0.0124 - mae: 0.1060 - mape: 95.8393 - val_loss: 0.0082 - val_mae: 0.0798 - val_mape: 72.8548 - lr: 9.1918e-06\n",
|
||
"Epoch 151/200\n",
|
||
"81/81 [==============================] - 400s 5s/step - loss: 0.0123 - mae: 0.1058 - mape: 101.8415 - val_loss: 0.0083 - val_mae: 0.0796 - val_mape: 72.3389 - val_olive_prod_mae: 0.0884 - val_olive_prod_mape: 88.7991 - val_min_oil_prod_mae: 0.0875 - val_min_oil_prod_mape: 91.5470 - best_min_oil_prod_mae: 0.0875 - val_max_oil_prod_mae: 0.0873 - val_max_oil_prod_mape: 76.2444 - val_avg_oil_prod_mae: 0.0844 - val_avg_oil_prod_mape: 79.9107 - best_avg_oil_prod_mae: 0.0844 - val_total_water_need_mae: 0.0503 - val_total_water_need_mape: 25.1932 - lr: 8.9501e-06\n",
|
||
"Epoch 152/200\n",
|
||
"81/81 [==============================] - 44s 531ms/step - loss: 0.0122 - mae: 0.1058 - mape: 96.1754 - val_loss: 0.0081 - val_mae: 0.0793 - val_mape: 71.8548 - lr: 8.7148e-06\n",
|
||
"\n",
|
||
"Modello salvato in: 2024-12-19_23-39_final_model.keras\n",
|
||
"Warning: Could not save model: name 'json' is not defined\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"model, history = train_transformer(train_data, train_targets, val_data, val_targets, 200, 32768)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 12,
|
||
"id": "3e2fb5a5341dac92",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"25500/25500 [==============================] - 378s 15ms/step\n",
|
||
"\n",
|
||
"Errori per target:\n",
|
||
"--------------------------------------------------\n",
|
||
"olive_prod:\n",
|
||
"MAE assoluto: 1438.01\n",
|
||
"Errore percentuale medio: 5.63%\n",
|
||
"Precisione: 94.37%\n",
|
||
"--------------------------------------------------\n",
|
||
"min_oil_prod:\n",
|
||
"MAE assoluto: 296.21\n",
|
||
"Errore percentuale medio: 5.78%\n",
|
||
"Precisione: 94.22%\n",
|
||
"--------------------------------------------------\n",
|
||
"max_oil_prod:\n",
|
||
"MAE assoluto: 356.48\n",
|
||
"Errore percentuale medio: 5.76%\n",
|
||
"Precisione: 94.24%\n",
|
||
"--------------------------------------------------\n",
|
||
"avg_oil_prod:\n",
|
||
"MAE assoluto: 313.20\n",
|
||
"Errore percentuale medio: 5.53%\n",
|
||
"Precisione: 94.47%\n",
|
||
"--------------------------------------------------\n",
|
||
"total_water_need:\n",
|
||
"MAE assoluto: 511.21\n",
|
||
"Errore percentuale medio: 3.23%\n",
|
||
"Precisione: 96.77%\n",
|
||
"--------------------------------------------------\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"percentage_errors, absolute_errors = calculate_real_error(model, val_data, val_targets, scaler_y)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 13,
|
||
"id": "4af58aa9bbc156f5",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"def evaluate_model_performance(model, data, targets, set_name=\"\"):\n",
|
||
" \"\"\"\n",
|
||
" Valuta le performance del modello su un set di dati specifico.\n",
|
||
" \"\"\"\n",
|
||
" predictions = model.predict(data, verbose=0)\n",
|
||
"\n",
|
||
" target_names = ['olive_prod', 'min_oil_prod', 'max_oil_prod', 'avg_oil_prod', 'total_water_need']\n",
|
||
" metrics = {}\n",
|
||
"\n",
|
||
" for i, name in enumerate(target_names):\n",
|
||
" mae = np.mean(np.abs(targets[:, i] - predictions[:, i]))\n",
|
||
" mse = np.mean(np.square(targets[:, i] - predictions[:, i]))\n",
|
||
" rmse = np.sqrt(mse)\n",
|
||
" mape = np.mean(np.abs((targets[:, i] - predictions[:, i]) / (targets[:, i] + 1e-7))) * 100\n",
|
||
"\n",
|
||
" metrics[f\"{name}_mae\"] = mae\n",
|
||
" metrics[f\"{name}_rmse\"] = rmse\n",
|
||
" metrics[f\"{name}_mape\"] = mape\n",
|
||
"\n",
|
||
" if set_name:\n",
|
||
" print(f\"\\nPerformance sul set {set_name}:\")\n",
|
||
" for metric, value in metrics.items():\n",
|
||
" print(f\"{metric}: {value:.4f}\")\n",
|
||
"\n",
|
||
" return metrics\n",
|
||
"\n",
|
||
"\n",
|
||
"def retrain_model(base_model, train_data, train_targets,\n",
|
||
" val_data, val_targets,\n",
|
||
" test_data, test_targets,\n",
|
||
" epochs=50, batch_size=128):\n",
|
||
" \"\"\"\n",
|
||
" Implementa il retraining del modello con i dati combinati.\n",
|
||
" \"\"\"\n",
|
||
" print(\"Valutazione performance iniziali del modello...\")\n",
|
||
" initial_metrics = {\n",
|
||
" 'train': evaluate_model_performance(base_model, train_data, train_targets, \"training\"),\n",
|
||
" 'val': evaluate_model_performance(base_model, val_data, val_targets, \"validazione\"),\n",
|
||
" 'test': evaluate_model_performance(base_model, test_data, test_targets, \"test\")\n",
|
||
" }\n",
|
||
"\n",
|
||
" # Combina i dati per il retraining\n",
|
||
" combined_data = {\n",
|
||
" 'temporal': np.concatenate([train_data['temporal'], val_data['temporal'], test_data['temporal']]),\n",
|
||
" 'static': np.concatenate([train_data['static'], val_data['static'], test_data['static']])\n",
|
||
" }\n",
|
||
" combined_targets = np.concatenate([train_targets, val_targets, test_targets])\n",
|
||
"\n",
|
||
" # Crea una nuova suddivisione per la validazione\n",
|
||
" indices = np.arange(len(combined_targets))\n",
|
||
" np.random.shuffle(indices)\n",
|
||
"\n",
|
||
" split_idx = int(len(indices) * 0.9)\n",
|
||
" train_idx, val_idx = indices[:split_idx], indices[split_idx:]\n",
|
||
"\n",
|
||
" # Prepara i dati per il retraining\n",
|
||
" retrain_data = {k: v[train_idx] for k, v in combined_data.items()}\n",
|
||
" retrain_targets = combined_targets[train_idx]\n",
|
||
" retrain_val_data = {k: v[val_idx] for k, v in combined_data.items()}\n",
|
||
" retrain_val_targets = combined_targets[val_idx]\n",
|
||
"\n",
|
||
" # Configura callbacks\n",
|
||
" callbacks = [\n",
|
||
" tf.keras.callbacks.EarlyStopping(\n",
|
||
" monitor='val_loss',\n",
|
||
" patience=10,\n",
|
||
" restore_best_weights=True,\n",
|
||
" min_delta=0.0001\n",
|
||
" ),\n",
|
||
" tf.keras.callbacks.ReduceLROnPlateau(\n",
|
||
" monitor='val_loss',\n",
|
||
" factor=0.2,\n",
|
||
" patience=5,\n",
|
||
" min_lr=1e-6,\n",
|
||
" verbose=1\n",
|
||
" ),\n",
|
||
" tf.keras.callbacks.ModelCheckpoint(\n",
|
||
" filepath=f'{execute_name}_retrained_best_oil_model.h5',\n",
|
||
" monitor='val_loss',\n",
|
||
" save_best_only=True,\n",
|
||
" mode='min',\n",
|
||
" save_weights_only=True\n",
|
||
" )\n",
|
||
" ]\n",
|
||
"\n",
|
||
" # Imposta learning rate per il fine-tuning\n",
|
||
" optimizer = tf.keras.optimizers.AdamW(\n",
|
||
" learning_rate=tf.keras.optimizers.schedules.ExponentialDecay(\n",
|
||
" initial_learning_rate=1e-4,\n",
|
||
" decay_steps=1000,\n",
|
||
" decay_rate=0.9\n",
|
||
" ),\n",
|
||
" weight_decay=0.01\n",
|
||
" )\n",
|
||
"\n",
|
||
" # Ricompila il modello con il nuovo optimizer\n",
|
||
" base_model.compile(\n",
|
||
" optimizer=optimizer,\n",
|
||
" loss=tf.keras.losses.Huber(),\n",
|
||
" metrics=['mae']\n",
|
||
" )\n",
|
||
"\n",
|
||
" print(\"\\nAvvio retraining...\")\n",
|
||
" history = base_model.fit(\n",
|
||
" retrain_data,\n",
|
||
" retrain_targets,\n",
|
||
" validation_data=(retrain_val_data, retrain_val_targets),\n",
|
||
" epochs=epochs,\n",
|
||
" batch_size=batch_size,\n",
|
||
" callbacks=callbacks,\n",
|
||
" verbose=1\n",
|
||
" )\n",
|
||
"\n",
|
||
" print(\"\\nValutazione performance finali...\")\n",
|
||
" final_metrics = {\n",
|
||
" 'train': evaluate_model_performance(base_model, train_data, train_targets, \"training\"),\n",
|
||
" 'val': evaluate_model_performance(base_model, val_data, val_targets, \"validazione\"),\n",
|
||
" 'test': evaluate_model_performance(base_model, test_data, test_targets, \"test\")\n",
|
||
" }\n",
|
||
"\n",
|
||
" # Salva il modello finale\n",
|
||
" save_path = f'{execute_name}_retrained_model.keras'\n",
|
||
" os.makedirs(f'{execute_name}_retrained/weights', exist_ok=True)\n",
|
||
" \n",
|
||
" base_model.save_weights(f'{execute_name}_retrained/weights')\n",
|
||
" base_model.save(save_path, save_format='keras')\n",
|
||
" print(f\"\\nModello riaddestrato salvato in: {save_path}\")\n",
|
||
"\n",
|
||
" # Report miglioramenti\n",
|
||
" print(\"\\nMiglioramenti delle performance:\")\n",
|
||
" for dataset in ['train', 'val', 'test']:\n",
|
||
" print(f\"\\nSet {dataset}:\")\n",
|
||
" for metric in initial_metrics[dataset].keys():\n",
|
||
" initial = initial_metrics[dataset][metric]\n",
|
||
" final = final_metrics[dataset][metric]\n",
|
||
" improvement = ((initial - final) / initial) * 100\n",
|
||
" print(f\"{metric}: {improvement:.2f}% di miglioramento\")\n",
|
||
"\n",
|
||
" return base_model, history, final_metrics\n",
|
||
"\n",
|
||
"\n",
|
||
"def start_retraining(model_path, train_data, train_targets,\n",
|
||
" val_data, val_targets,\n",
|
||
" test_data, test_targets,\n",
|
||
" epochs=50, batch_size=128):\n",
|
||
" \"\"\"\n",
|
||
" Avvia il processo di retraining in modo sicuro.\n",
|
||
" \"\"\"\n",
|
||
" try:\n",
|
||
" print(\"Caricamento del modello...\")\n",
|
||
" base_model = tf.keras.models.load_model(model_path, compile=False)\n",
|
||
" print(\"Modello caricato con successo!\")\n",
|
||
"\n",
|
||
" return retrain_model(\n",
|
||
" base_model=base_model,\n",
|
||
" train_data=train_data,\n",
|
||
" train_targets=train_targets,\n",
|
||
" val_data=val_data,\n",
|
||
" val_targets=val_targets,\n",
|
||
" test_data=test_data,\n",
|
||
" test_targets=test_targets,\n",
|
||
" epochs=epochs,\n",
|
||
" batch_size=batch_size\n",
|
||
" )\n",
|
||
" except Exception as e:\n",
|
||
" print(f\"Errore durante il retraining: {str(e)}\")\n",
|
||
" raise"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 14,
|
||
"id": "588c7e49371f4a0c",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Caricamento del modello...\n",
|
||
"Modello caricato con successo!\n",
|
||
"Valutazione performance iniziali del modello...\n",
|
||
"\n",
|
||
"Performance sul set training:\n",
|
||
"olive_prod_mae: 0.0875\n",
|
||
"olive_prod_rmse: 0.1181\n",
|
||
"olive_prod_mape: 92.6990\n",
|
||
"min_oil_prod_mae: 0.0867\n",
|
||
"min_oil_prod_rmse: 0.1208\n",
|
||
"min_oil_prod_mape: 81.7229\n",
|
||
"max_oil_prod_mae: 0.0862\n",
|
||
"max_oil_prod_rmse: 0.1200\n",
|
||
"max_oil_prod_mape: 83.8850\n",
|
||
"avg_oil_prod_mae: 0.0830\n",
|
||
"avg_oil_prod_rmse: 0.1148\n",
|
||
"avg_oil_prod_mape: 74.0966\n",
|
||
"total_water_need_mae: 0.0467\n",
|
||
"total_water_need_rmse: 0.0635\n",
|
||
"total_water_need_mape: 37.3349\n",
|
||
"\n",
|
||
"Performance sul set validazione:\n",
|
||
"olive_prod_mae: 0.0875\n",
|
||
"olive_prod_rmse: 0.1181\n",
|
||
"olive_prod_mape: 94.3014\n",
|
||
"min_oil_prod_mae: 0.0868\n",
|
||
"min_oil_prod_rmse: 0.1209\n",
|
||
"min_oil_prod_mape: 91.2537\n",
|
||
"max_oil_prod_mae: 0.0862\n",
|
||
"max_oil_prod_rmse: 0.1199\n",
|
||
"max_oil_prod_mape: 75.9864\n",
|
||
"avg_oil_prod_mae: 0.0831\n",
|
||
"avg_oil_prod_rmse: 0.1148\n",
|
||
"avg_oil_prod_mape: 70.9473\n",
|
||
"total_water_need_mae: 0.0467\n",
|
||
"total_water_need_rmse: 0.0635\n",
|
||
"total_water_need_mape: 25.8122\n",
|
||
"\n",
|
||
"Performance sul set test:\n",
|
||
"olive_prod_mae: 0.0874\n",
|
||
"olive_prod_rmse: 0.1181\n",
|
||
"olive_prod_mape: 95.5103\n",
|
||
"min_oil_prod_mae: 0.0866\n",
|
||
"min_oil_prod_rmse: 0.1208\n",
|
||
"min_oil_prod_mape: 228.7476\n",
|
||
"max_oil_prod_mae: 0.0862\n",
|
||
"max_oil_prod_rmse: 0.1201\n",
|
||
"max_oil_prod_mape: 222.9631\n",
|
||
"avg_oil_prod_mae: 0.0830\n",
|
||
"avg_oil_prod_rmse: 0.1148\n",
|
||
"avg_oil_prod_mape: 92.3280\n",
|
||
"total_water_need_mae: 0.0467\n",
|
||
"total_water_need_rmse: 0.0635\n",
|
||
"total_water_need_mape: 43.6031\n",
|
||
"\n",
|
||
"Avvio retraining...\n",
|
||
"Epoch 1/100\n",
|
||
"225/225 [==============================] - 90s 220ms/step - loss: 0.0151 - mae: 0.1089 - val_loss: 0.0104 - val_mae: 0.0893 - lr: 9.7668e-05\n",
|
||
"Epoch 2/100\n",
|
||
"225/225 [==============================] - 46s 204ms/step - loss: 0.0147 - mae: 0.1077 - val_loss: 0.0104 - val_mae: 0.0918 - lr: 9.5379e-05\n",
|
||
"Epoch 3/100\n",
|
||
"225/225 [==============================] - 42s 185ms/step - loss: 0.0146 - mae: 0.1073 - val_loss: 0.0109 - val_mae: 0.0958 - lr: 9.3145e-05\n",
|
||
"Epoch 4/100\n",
|
||
"225/225 [==============================] - 46s 206ms/step - loss: 0.0143 - mae: 0.1070 - val_loss: 0.0094 - val_mae: 0.0827 - lr: 9.0963e-05\n",
|
||
"Epoch 5/100\n",
|
||
"225/225 [==============================] - 46s 204ms/step - loss: 0.0140 - mae: 0.1062 - val_loss: 0.0097 - val_mae: 0.0887 - lr: 8.8832e-05\n",
|
||
"Epoch 6/100\n",
|
||
"225/225 [==============================] - 47s 209ms/step - loss: 0.0146 - mae: 0.1089 - val_loss: 0.0100 - val_mae: 0.0885 - lr: 8.6751e-05\n",
|
||
"Epoch 7/100\n",
|
||
"225/225 [==============================] - 44s 196ms/step - loss: 0.0138 - mae: 0.1061 - val_loss: 0.0093 - val_mae: 0.0823 - lr: 8.4718e-05\n",
|
||
"Epoch 8/100\n",
|
||
"225/225 [==============================] - 48s 216ms/step - loss: 0.0136 - mae: 0.1057 - val_loss: 0.0094 - val_mae: 0.0845 - lr: 8.2734e-05\n",
|
||
"Epoch 9/100\n",
|
||
"225/225 [==============================] - 44s 194ms/step - loss: 0.0135 - mae: 0.1056 - val_loss: 0.0096 - val_mae: 0.0851 - lr: 8.0795e-05\n",
|
||
"Epoch 10/100\n",
|
||
"225/225 [==============================] - 43s 192ms/step - loss: 0.0134 - mae: 0.1048 - val_loss: 0.0094 - val_mae: 0.0835 - lr: 7.8903e-05\n",
|
||
"Epoch 11/100\n",
|
||
"225/225 [==============================] - 46s 204ms/step - loss: 0.0132 - mae: 0.1045 - val_loss: 0.0090 - val_mae: 0.0827 - lr: 7.7054e-05\n",
|
||
"Epoch 12/100\n",
|
||
"225/225 [==============================] - 46s 205ms/step - loss: 0.0130 - mae: 0.1042 - val_loss: 0.0090 - val_mae: 0.0809 - lr: 7.5249e-05\n",
|
||
"Epoch 13/100\n",
|
||
"225/225 [==============================] - 44s 194ms/step - loss: 0.0129 - mae: 0.1040 - val_loss: 0.0093 - val_mae: 0.0840 - lr: 7.3486e-05\n",
|
||
"Epoch 14/100\n",
|
||
"225/225 [==============================] - 46s 202ms/step - loss: 0.0128 - mae: 0.1037 - val_loss: 0.0089 - val_mae: 0.0801 - lr: 7.1764e-05\n",
|
||
"Epoch 15/100\n",
|
||
"225/225 [==============================] - 45s 201ms/step - loss: 0.0126 - mae: 0.1033 - val_loss: 0.0088 - val_mae: 0.0819 - lr: 7.0083e-05\n",
|
||
"Epoch 16/100\n",
|
||
"225/225 [==============================] - 45s 202ms/step - loss: 0.0125 - mae: 0.1030 - val_loss: 0.0088 - val_mae: 0.0833 - lr: 6.8441e-05\n",
|
||
"Epoch 17/100\n",
|
||
"225/225 [==============================] - 45s 199ms/step - loss: 0.0124 - mae: 0.1029 - val_loss: 0.0083 - val_mae: 0.0797 - lr: 6.6838e-05\n",
|
||
"Epoch 18/100\n",
|
||
"225/225 [==============================] - 43s 189ms/step - loss: 0.0123 - mae: 0.1027 - val_loss: 0.0089 - val_mae: 0.0835 - lr: 6.5272e-05\n",
|
||
"Epoch 19/100\n",
|
||
"225/225 [==============================] - 43s 192ms/step - loss: 0.0121 - mae: 0.1024 - val_loss: 0.0090 - val_mae: 0.0844 - lr: 6.3743e-05\n",
|
||
"Epoch 20/100\n",
|
||
"225/225 [==============================] - 44s 194ms/step - loss: 0.0121 - mae: 0.1025 - val_loss: 0.0089 - val_mae: 0.0839 - lr: 6.2250e-05\n",
|
||
"Epoch 21/100\n",
|
||
"225/225 [==============================] - 45s 201ms/step - loss: 0.0119 - mae: 0.1019 - val_loss: 0.0086 - val_mae: 0.0809 - lr: 6.0791e-05\n",
|
||
"Epoch 22/100\n",
|
||
"225/225 [==============================] - ETA: 0s - loss: 0.0118 - mae: 0.1017\n",
|
||
"Epoch 22: ReduceLROnPlateau reducing learning rate to 1.1873427138198168e-05.\n",
|
||
"225/225 [==============================] - 46s 205ms/step - loss: 0.0118 - mae: 0.1017 - val_loss: 0.0082 - val_mae: 0.0789 - lr: 5.9367e-05\n",
|
||
"Epoch 23/100\n",
|
||
"225/225 [==============================] - 46s 205ms/step - loss: 0.0117 - mae: 0.1014 - val_loss: 0.0081 - val_mae: 0.0794 - lr: 5.7976e-05\n",
|
||
"Epoch 24/100\n",
|
||
"225/225 [==============================] - 44s 197ms/step - loss: 0.0116 - mae: 0.1011 - val_loss: 0.0080 - val_mae: 0.0783 - lr: 5.6618e-05\n",
|
||
"Epoch 25/100\n",
|
||
"225/225 [==============================] - 46s 202ms/step - loss: 0.0115 - mae: 0.1009 - val_loss: 0.0080 - val_mae: 0.0773 - lr: 5.5292e-05\n",
|
||
"Epoch 26/100\n",
|
||
"225/225 [==============================] - 46s 206ms/step - loss: 0.0115 - mae: 0.1008 - val_loss: 0.0080 - val_mae: 0.0775 - lr: 5.3996e-05\n",
|
||
"Epoch 27/100\n",
|
||
"225/225 [==============================] - 45s 199ms/step - loss: 0.0114 - mae: 0.1005 - val_loss: 0.0077 - val_mae: 0.0763 - lr: 5.2731e-05\n",
|
||
"Epoch 28/100\n",
|
||
"225/225 [==============================] - 45s 201ms/step - loss: 0.0113 - mae: 0.1004 - val_loss: 0.0082 - val_mae: 0.0779 - lr: 5.1496e-05\n",
|
||
"Epoch 29/100\n",
|
||
"225/225 [==============================] - 47s 206ms/step - loss: 0.0112 - mae: 0.1002 - val_loss: 0.0077 - val_mae: 0.0758 - lr: 5.0290e-05\n",
|
||
"Epoch 30/100\n",
|
||
"225/225 [==============================] - 45s 201ms/step - loss: 0.0111 - mae: 0.1000 - val_loss: 0.0078 - val_mae: 0.0775 - lr: 4.9111e-05\n",
|
||
"Epoch 31/100\n",
|
||
"225/225 [==============================] - 45s 199ms/step - loss: 0.0111 - mae: 0.0998 - val_loss: 0.0077 - val_mae: 0.0769 - lr: 4.7961e-05\n",
|
||
"Epoch 32/100\n",
|
||
"225/225 [==============================] - ETA: 0s - loss: 0.0110 - mae: 0.0997\n",
|
||
"Epoch 32: ReduceLROnPlateau reducing learning rate to 9.367457096232101e-06.\n",
|
||
"225/225 [==============================] - 45s 200ms/step - loss: 0.0110 - mae: 0.0997 - val_loss: 0.0077 - val_mae: 0.0761 - lr: 4.6837e-05\n",
|
||
"Epoch 33/100\n",
|
||
"225/225 [==============================] - 44s 195ms/step - loss: 0.0109 - mae: 0.0994 - val_loss: 0.0076 - val_mae: 0.0760 - lr: 4.5740e-05\n",
|
||
"Epoch 34/100\n",
|
||
"225/225 [==============================] - 45s 202ms/step - loss: 0.0109 - mae: 0.0993 - val_loss: 0.0076 - val_mae: 0.0767 - lr: 4.4668e-05\n",
|
||
"Epoch 35/100\n",
|
||
"225/225 [==============================] - 45s 200ms/step - loss: 0.0108 - mae: 0.0992 - val_loss: 0.0077 - val_mae: 0.0781 - lr: 4.3622e-05\n",
|
||
"Epoch 36/100\n",
|
||
"225/225 [==============================] - 45s 199ms/step - loss: 0.0108 - mae: 0.0991 - val_loss: 0.0079 - val_mae: 0.0787 - lr: 4.2600e-05\n",
|
||
"Epoch 37/100\n",
|
||
"225/225 [==============================] - 45s 201ms/step - loss: 0.0107 - mae: 0.0989 - val_loss: 0.0074 - val_mae: 0.0758 - lr: 4.1602e-05\n",
|
||
"Epoch 38/100\n",
|
||
"225/225 [==============================] - 43s 192ms/step - loss: 0.0107 - mae: 0.0987 - val_loss: 0.0076 - val_mae: 0.0770 - lr: 4.0627e-05\n",
|
||
"Epoch 39/100\n",
|
||
"225/225 [==============================] - 43s 192ms/step - loss: 0.0106 - mae: 0.0986 - val_loss: 0.0074 - val_mae: 0.0770 - lr: 3.9676e-05\n",
|
||
"Epoch 40/100\n",
|
||
"225/225 [==============================] - 46s 204ms/step - loss: 0.0106 - mae: 0.0985 - val_loss: 0.0075 - val_mae: 0.0762 - lr: 3.8746e-05\n",
|
||
"Epoch 41/100\n",
|
||
"225/225 [==============================] - 46s 204ms/step - loss: 0.0105 - mae: 0.0983 - val_loss: 0.0072 - val_mae: 0.0759 - lr: 3.7838e-05\n",
|
||
"Epoch 42/100\n",
|
||
"225/225 [==============================] - 45s 200ms/step - loss: 0.0105 - mae: 0.0983 - val_loss: 0.0075 - val_mae: 0.0761 - lr: 3.6952e-05\n",
|
||
"Epoch 43/100\n",
|
||
"225/225 [==============================] - 43s 190ms/step - loss: 0.0104 - mae: 0.0982 - val_loss: 0.0072 - val_mae: 0.0748 - lr: 3.6086e-05\n",
|
||
"Epoch 44/100\n",
|
||
"225/225 [==============================] - 44s 195ms/step - loss: 0.0104 - mae: 0.0981 - val_loss: 0.0073 - val_mae: 0.0770 - lr: 3.5241e-05\n",
|
||
"Epoch 45/100\n",
|
||
"225/225 [==============================] - 42s 187ms/step - loss: 0.0104 - mae: 0.0982 - val_loss: 0.0074 - val_mae: 0.0769 - lr: 3.4415e-05\n",
|
||
"Epoch 46/100\n",
|
||
"225/225 [==============================] - ETA: 0s - loss: 0.0104 - mae: 0.0984\n",
|
||
"Epoch 46: ReduceLROnPlateau reducing learning rate to 6.721798854414374e-06.\n",
|
||
"225/225 [==============================] - 46s 204ms/step - loss: 0.0104 - mae: 0.0984 - val_loss: 0.0072 - val_mae: 0.0740 - lr: 3.3609e-05\n",
|
||
"Epoch 47/100\n",
|
||
"225/225 [==============================] - 45s 202ms/step - loss: 0.0103 - mae: 0.0978 - val_loss: 0.0072 - val_mae: 0.0745 - lr: 3.2822e-05\n",
|
||
"Epoch 48/100\n",
|
||
"225/225 [==============================] - 44s 197ms/step - loss: 0.0103 - mae: 0.0978 - val_loss: 0.0071 - val_mae: 0.0740 - lr: 3.2053e-05\n",
|
||
"Epoch 49/100\n",
|
||
"225/225 [==============================] - 47s 211ms/step - loss: 0.0103 - mae: 0.0977 - val_loss: 0.0070 - val_mae: 0.0746 - lr: 3.1302e-05\n",
|
||
"Epoch 50/100\n",
|
||
"225/225 [==============================] - 44s 196ms/step - loss: 0.0102 - mae: 0.0976 - val_loss: 0.0072 - val_mae: 0.0752 - lr: 3.0568e-05\n",
|
||
"Epoch 51/100\n",
|
||
"225/225 [==============================] - 43s 189ms/step - loss: 0.0102 - mae: 0.0975 - val_loss: 0.0070 - val_mae: 0.0742 - lr: 2.9852e-05\n",
|
||
"Epoch 52/100\n",
|
||
"225/225 [==============================] - 44s 197ms/step - loss: 0.0102 - mae: 0.0974 - val_loss: 0.0069 - val_mae: 0.0735 - lr: 2.9153e-05\n",
|
||
"Epoch 53/100\n",
|
||
"225/225 [==============================] - 46s 201ms/step - loss: 0.0102 - mae: 0.0974 - val_loss: 0.0070 - val_mae: 0.0738 - lr: 2.8470e-05\n",
|
||
"Epoch 54/100\n",
|
||
"225/225 [==============================] - 45s 198ms/step - loss: 0.0101 - mae: 0.0972 - val_loss: 0.0071 - val_mae: 0.0742 - lr: 2.7803e-05\n",
|
||
"Epoch 55/100\n",
|
||
"225/225 [==============================] - 45s 201ms/step - loss: 0.0101 - mae: 0.0972 - val_loss: 0.0070 - val_mae: 0.0744 - lr: 2.7152e-05\n",
|
||
"Epoch 56/100\n",
|
||
"225/225 [==============================] - 45s 202ms/step - loss: 0.0101 - mae: 0.0971 - val_loss: 0.0069 - val_mae: 0.0739 - lr: 2.6516e-05\n",
|
||
"Epoch 57/100\n",
|
||
"225/225 [==============================] - ETA: 0s - loss: 0.0100 - mae: 0.0970\n",
|
||
"Epoch 57: ReduceLROnPlateau reducing learning rate to 5.178878927836195e-06.\n",
|
||
"225/225 [==============================] - 50s 222ms/step - loss: 0.0100 - mae: 0.0970 - val_loss: 0.0068 - val_mae: 0.0738 - lr: 2.5894e-05\n",
|
||
"Epoch 58/100\n",
|
||
"225/225 [==============================] - 45s 198ms/step - loss: 0.0100 - mae: 0.0969 - val_loss: 0.0069 - val_mae: 0.0742 - lr: 2.5288e-05\n",
|
||
"Epoch 59/100\n",
|
||
"225/225 [==============================] - 47s 208ms/step - loss: 0.0100 - mae: 0.0969 - val_loss: 0.0070 - val_mae: 0.0744 - lr: 2.4695e-05\n",
|
||
"Epoch 60/100\n",
|
||
"225/225 [==============================] - 45s 199ms/step - loss: 0.0100 - mae: 0.0968 - val_loss: 0.0071 - val_mae: 0.0755 - lr: 2.4117e-05\n",
|
||
"Epoch 61/100\n",
|
||
"225/225 [==============================] - 43s 193ms/step - loss: 0.0100 - mae: 0.0968 - val_loss: 0.0069 - val_mae: 0.0738 - lr: 2.3552e-05\n",
|
||
"Epoch 62/100\n",
|
||
"225/225 [==============================] - ETA: 0s - loss: 0.0099 - mae: 0.0967\n",
|
||
"Epoch 62: ReduceLROnPlateau reducing learning rate to 4.600007378030569e-06.\n",
|
||
"225/225 [==============================] - 43s 192ms/step - loss: 0.0099 - mae: 0.0967 - val_loss: 0.0070 - val_mae: 0.0750 - lr: 2.3000e-05\n",
|
||
"\n",
|
||
"Valutazione performance finali...\n",
|
||
"\n",
|
||
"Performance sul set training:\n",
|
||
"olive_prod_mae: 0.0839\n",
|
||
"olive_prod_rmse: 0.1158\n",
|
||
"olive_prod_mape: 90.9233\n",
|
||
"min_oil_prod_mae: 0.0825\n",
|
||
"min_oil_prod_rmse: 0.1166\n",
|
||
"min_oil_prod_mape: 82.2691\n",
|
||
"max_oil_prod_mae: 0.0821\n",
|
||
"max_oil_prod_rmse: 0.1164\n",
|
||
"max_oil_prod_mape: 82.9904\n",
|
||
"avg_oil_prod_mae: 0.0798\n",
|
||
"avg_oil_prod_rmse: 0.1124\n",
|
||
"avg_oil_prod_mape: 74.0703\n",
|
||
"total_water_need_mae: 0.0392\n",
|
||
"total_water_need_rmse: 0.0563\n",
|
||
"total_water_need_mape: 36.0925\n",
|
||
"\n",
|
||
"Performance sul set validazione:\n",
|
||
"olive_prod_mae: 0.0839\n",
|
||
"olive_prod_rmse: 0.1157\n",
|
||
"olive_prod_mape: 95.2416\n",
|
||
"min_oil_prod_mae: 0.0825\n",
|
||
"min_oil_prod_rmse: 0.1166\n",
|
||
"min_oil_prod_mape: 88.1669\n",
|
||
"max_oil_prod_mae: 0.0820\n",
|
||
"max_oil_prod_rmse: 0.1162\n",
|
||
"max_oil_prod_mape: 75.9403\n",
|
||
"avg_oil_prod_mae: 0.0798\n",
|
||
"avg_oil_prod_rmse: 0.1123\n",
|
||
"avg_oil_prod_mape: 69.5427\n",
|
||
"total_water_need_mae: 0.0392\n",
|
||
"total_water_need_rmse: 0.0562\n",
|
||
"total_water_need_mape: 23.9373\n",
|
||
"\n",
|
||
"Performance sul set test:\n",
|
||
"olive_prod_mae: 0.0839\n",
|
||
"olive_prod_rmse: 0.1157\n",
|
||
"olive_prod_mape: 96.0586\n",
|
||
"min_oil_prod_mae: 0.0824\n",
|
||
"min_oil_prod_rmse: 0.1165\n",
|
||
"min_oil_prod_mape: 210.2095\n",
|
||
"max_oil_prod_mae: 0.0821\n",
|
||
"max_oil_prod_rmse: 0.1164\n",
|
||
"max_oil_prod_mape: 223.1585\n",
|
||
"avg_oil_prod_mae: 0.0798\n",
|
||
"avg_oil_prod_rmse: 0.1123\n",
|
||
"avg_oil_prod_mape: 95.1335\n",
|
||
"total_water_need_mae: 0.0392\n",
|
||
"total_water_need_rmse: 0.0563\n",
|
||
"total_water_need_mape: 38.0699\n",
|
||
"\n",
|
||
"Modello riaddestrato salvato in: 2024-12-19_23-39_retrained_model.keras\n",
|
||
"\n",
|
||
"Miglioramenti delle performance:\n",
|
||
"\n",
|
||
"Set train:\n",
|
||
"olive_prod_mae: 4.03% di miglioramento\n",
|
||
"olive_prod_rmse: 1.98% di miglioramento\n",
|
||
"olive_prod_mape: 1.92% di miglioramento\n",
|
||
"min_oil_prod_mae: 4.90% di miglioramento\n",
|
||
"min_oil_prod_rmse: 3.47% di miglioramento\n",
|
||
"min_oil_prod_mape: -0.67% di miglioramento\n",
|
||
"max_oil_prod_mae: 4.75% di miglioramento\n",
|
||
"max_oil_prod_rmse: 3.00% di miglioramento\n",
|
||
"max_oil_prod_mape: 1.07% di miglioramento\n",
|
||
"avg_oil_prod_mae: 3.86% di miglioramento\n",
|
||
"avg_oil_prod_rmse: 2.06% di miglioramento\n",
|
||
"avg_oil_prod_mape: 0.04% di miglioramento\n",
|
||
"total_water_need_mae: 16.11% di miglioramento\n",
|
||
"total_water_need_rmse: 11.38% di miglioramento\n",
|
||
"total_water_need_mape: 3.33% di miglioramento\n",
|
||
"\n",
|
||
"Set val:\n",
|
||
"olive_prod_mae: 4.06% di miglioramento\n",
|
||
"olive_prod_rmse: 2.06% di miglioramento\n",
|
||
"olive_prod_mape: -1.00% di miglioramento\n",
|
||
"min_oil_prod_mae: 4.96% di miglioramento\n",
|
||
"min_oil_prod_rmse: 3.54% di miglioramento\n",
|
||
"min_oil_prod_mape: 3.38% di miglioramento\n",
|
||
"max_oil_prod_mae: 4.84% di miglioramento\n",
|
||
"max_oil_prod_rmse: 3.11% di miglioramento\n",
|
||
"max_oil_prod_mape: 0.06% di miglioramento\n",
|
||
"avg_oil_prod_mae: 3.94% di miglioramento\n",
|
||
"avg_oil_prod_rmse: 2.17% di miglioramento\n",
|
||
"avg_oil_prod_mape: 1.98% di miglioramento\n",
|
||
"total_water_need_mae: 16.07% di miglioramento\n",
|
||
"total_water_need_rmse: 11.40% di miglioramento\n",
|
||
"total_water_need_mape: 7.26% di miglioramento\n",
|
||
"\n",
|
||
"Set test:\n",
|
||
"olive_prod_mae: 4.08% di miglioramento\n",
|
||
"olive_prod_rmse: 2.05% di miglioramento\n",
|
||
"olive_prod_mape: -0.57% di miglioramento\n",
|
||
"min_oil_prod_mae: 4.94% di miglioramento\n",
|
||
"min_oil_prod_rmse: 3.54% di miglioramento\n",
|
||
"min_oil_prod_mape: 8.10% di miglioramento\n",
|
||
"max_oil_prod_mae: 4.82% di miglioramento\n",
|
||
"max_oil_prod_rmse: 3.09% di miglioramento\n",
|
||
"max_oil_prod_mape: -0.09% di miglioramento\n",
|
||
"avg_oil_prod_mae: 3.92% di miglioramento\n",
|
||
"avg_oil_prod_rmse: 2.16% di miglioramento\n",
|
||
"avg_oil_prod_mape: -3.04% di miglioramento\n",
|
||
"total_water_need_mae: 16.15% di miglioramento\n",
|
||
"total_water_need_rmse: 11.39% di miglioramento\n",
|
||
"total_water_need_mape: 12.69% di miglioramento\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"model_path = f'{execute_name}_final_model.keras'\n",
|
||
"\n",
|
||
"retrained_model, retrain_history, final_metrics = start_retraining(\n",
|
||
" model_path=model_path,\n",
|
||
" train_data=train_data,\n",
|
||
" train_targets=train_targets,\n",
|
||
" val_data=val_data,\n",
|
||
" val_targets=val_targets,\n",
|
||
" test_data=test_data,\n",
|
||
" test_targets=test_targets,\n",
|
||
" epochs=100,\n",
|
||
" batch_size=16384\n",
|
||
")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 15,
|
||
"id": "a95606bc-c4bc-418a-acdb-2e24c30dfa81",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"25500/25500 [==============================] - 393s 15ms/step\n",
|
||
"\n",
|
||
"Errori per target:\n",
|
||
"--------------------------------------------------\n",
|
||
"olive_prod:\n",
|
||
"MAE assoluto: 1379.61\n",
|
||
"Errore percentuale medio: 4.99%\n",
|
||
"Precisione: 95.01%\n",
|
||
"--------------------------------------------------\n",
|
||
"min_oil_prod:\n",
|
||
"MAE assoluto: 281.52\n",
|
||
"Errore percentuale medio: 5.05%\n",
|
||
"Precisione: 94.95%\n",
|
||
"--------------------------------------------------\n",
|
||
"max_oil_prod:\n",
|
||
"MAE assoluto: 339.24\n",
|
||
"Errore percentuale medio: 5.04%\n",
|
||
"Precisione: 94.96%\n",
|
||
"--------------------------------------------------\n",
|
||
"avg_oil_prod:\n",
|
||
"MAE assoluto: 300.87\n",
|
||
"Errore percentuale medio: 4.88%\n",
|
||
"Precisione: 95.12%\n",
|
||
"--------------------------------------------------\n",
|
||
"total_water_need:\n",
|
||
"MAE assoluto: 429.07\n",
|
||
"Errore percentuale medio: 2.51%\n",
|
||
"Precisione: 97.49%\n",
|
||
"--------------------------------------------------\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"percentage_errors, absolute_errors = calculate_real_error(retrained_model, val_data, val_targets, scaler_y)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 16,
|
||
"id": "e6f357cb-56a4-4f19-a4e8-77b73a28329d",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"import numpy as np\n",
|
||
"import tensorflow as tf\n",
|
||
"import matplotlib.pyplot as plt\n",
|
||
"from typing import List, Dict, Tuple, Union\n",
|
||
"\n",
|
||
"def analyze_feature_importance(model: tf.keras.Model, \n",
|
||
" test_data: dict, \n",
|
||
" feature_names: List[str]) -> Dict[str, float]:\n",
|
||
" \"\"\"\n",
|
||
" Analizza l'importanza delle feature usando perturbazione.\n",
|
||
" \n",
|
||
" Args:\n",
|
||
" model: Modello TensorFlow addestrato\n",
|
||
" test_data: Dizionario con chiavi 'temporal' e 'static' contenenti i dati\n",
|
||
" feature_names: Lista dei nomi delle feature\n",
|
||
" \n",
|
||
" Returns:\n",
|
||
" dict: Dizionario con l'importanza relativa di ogni feature\n",
|
||
" \"\"\"\n",
|
||
" # Estrai i dati temporali e statici\n",
|
||
" temporal_data = test_data['temporal']\n",
|
||
" static_data = test_data['static']\n",
|
||
" \n",
|
||
" # Ottieni la predizione base\n",
|
||
" base_prediction = model.predict(test_data)\n",
|
||
" feature_importance = {}\n",
|
||
" \n",
|
||
" # Per ogni feature temporale\n",
|
||
" for i, feature in enumerate(feature_names):\n",
|
||
" if feature in ['temp_mean', 'precip_sum', 'solar_energy_sum']:\n",
|
||
" # Crea copia perturbata dei dati\n",
|
||
" perturbed_data = {\n",
|
||
" 'temporal': temporal_data.copy(),\n",
|
||
" 'static': static_data.copy()\n",
|
||
" }\n",
|
||
" \n",
|
||
" # Trova l'indice della feature temporale\n",
|
||
" temp_idx = ['temp_mean', 'precip_sum', 'solar_energy_sum'].index(feature)\n",
|
||
" \n",
|
||
" # Crea rumore per la feature temporale\n",
|
||
" feature_values = temporal_data[..., temp_idx]\n",
|
||
" noise = np.random.normal(0, np.std(feature_values) * 0.1, \n",
|
||
" size=feature_values.shape)\n",
|
||
" \n",
|
||
" # Applica il rumore alla feature temporale\n",
|
||
" perturbed_temporal = perturbed_data['temporal'].copy()\n",
|
||
" perturbed_temporal[..., temp_idx] = feature_values + noise\n",
|
||
" perturbed_data['temporal'] = perturbed_temporal\n",
|
||
" \n",
|
||
" else: # Feature statiche\n",
|
||
" # Crea copia perturbata dei dati\n",
|
||
" perturbed_data = {\n",
|
||
" 'temporal': temporal_data.copy(),\n",
|
||
" 'static': static_data.copy()\n",
|
||
" }\n",
|
||
" \n",
|
||
" # Trova l'indice della feature statica\n",
|
||
" static_idx = ['ha'].index(feature)\n",
|
||
" \n",
|
||
" # Crea rumore per la feature statica\n",
|
||
" feature_values = static_data[..., static_idx]\n",
|
||
" noise = np.random.normal(0, np.std(feature_values) * 0.1, \n",
|
||
" size=feature_values.shape)\n",
|
||
" \n",
|
||
" # Applica il rumore alla feature statica\n",
|
||
" perturbed_static = perturbed_data['static'].copy()\n",
|
||
" perturbed_static[..., static_idx] = feature_values + noise\n",
|
||
" perturbed_data['static'] = perturbed_static\n",
|
||
" \n",
|
||
" # Calcola nuova predizione\n",
|
||
" perturbed_prediction = model.predict(perturbed_data)\n",
|
||
" \n",
|
||
" # Calcola impatto della perturbazione\n",
|
||
" impact = np.mean(np.abs(perturbed_prediction - base_prediction))\n",
|
||
" feature_importance[feature] = float(impact)\n",
|
||
" \n",
|
||
" # Normalizza le importanze\n",
|
||
" total_importance = sum(feature_importance.values())\n",
|
||
" feature_importance = {k: v/total_importance \n",
|
||
" for k, v in feature_importance.items()}\n",
|
||
" \n",
|
||
" return feature_importance\n",
|
||
"\n",
|
||
"class ProbabilityFunctions:\n",
|
||
" @staticmethod\n",
|
||
" def calculate_statistics(data: Union[np.ndarray, tf.Tensor]) -> Dict[str, float]:\n",
|
||
" \"\"\"\n",
|
||
" Calcola statistiche di base usando TensorFlow.\n",
|
||
" \n",
|
||
" Args:\n",
|
||
" data: Tensor o array dei dati\n",
|
||
" \n",
|
||
" Returns:\n",
|
||
" dict: Dizionario con le statistiche\n",
|
||
" \"\"\"\n",
|
||
" if not isinstance(data, tf.Tensor):\n",
|
||
" data = tf.convert_to_tensor(data, dtype=tf.float32)\n",
|
||
" \n",
|
||
" mean = tf.reduce_mean(data)\n",
|
||
" # Calcola varianza manualmente\n",
|
||
" squared_deviations = tf.square(data - mean)\n",
|
||
" variance = tf.reduce_mean(squared_deviations)\n",
|
||
" std = tf.sqrt(variance)\n",
|
||
" \n",
|
||
" # Ordina il tensor per il calcolo della mediana\n",
|
||
" sorted_data = tf.sort(data)\n",
|
||
" size = tf.size(data)\n",
|
||
" mid_index = size // 2\n",
|
||
" median = sorted_data[mid_index]\n",
|
||
" \n",
|
||
" return {\n",
|
||
" 'mean': mean.numpy(),\n",
|
||
" 'variance': variance.numpy(),\n",
|
||
" 'std': std.numpy(),\n",
|
||
" 'min': tf.reduce_min(data).numpy(),\n",
|
||
" 'max': tf.reduce_max(data).numpy(),\n",
|
||
" 'median': median.numpy()\n",
|
||
" }\n",
|
||
"\n",
|
||
" @staticmethod\n",
|
||
" def calculate_pmf(data: np.ndarray, bins: int = 50) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:\n",
|
||
" \"\"\"\n",
|
||
" Calcola la Probability Mass Function (PMF) dei dati.\n",
|
||
" \n",
|
||
" Args:\n",
|
||
" data: Array di dati\n",
|
||
" bins: Numero di bin per l'istogramma\n",
|
||
" \n",
|
||
" Returns:\n",
|
||
" tuple: (bin_centers, pmf, bin_edges)\n",
|
||
" \"\"\"\n",
|
||
" # Calcola l'istogramma\n",
|
||
" hist, bin_edges = np.histogram(data, bins=bins, density=True)\n",
|
||
" \n",
|
||
" # Calcola i centri dei bin\n",
|
||
" bin_centers = (bin_edges[:-1] + bin_edges[1:]) / 2\n",
|
||
" \n",
|
||
" # Normalizza per ottenere la PMF\n",
|
||
" pmf = hist / np.sum(hist)\n",
|
||
" \n",
|
||
" return bin_centers, pmf, bin_edges\n",
|
||
"\n",
|
||
" @staticmethod\n",
|
||
" def calculate_cmf(pmf: np.ndarray) -> np.ndarray:\n",
|
||
" \"\"\"\n",
|
||
" Calcola la Cumulative Mass Function (CMF) dalla PMF.\n",
|
||
" \n",
|
||
" Args:\n",
|
||
" pmf: Probability Mass Function\n",
|
||
" \n",
|
||
" Returns:\n",
|
||
" array: Cumulative Mass Function\n",
|
||
" \"\"\"\n",
|
||
" return np.cumsum(pmf)\n",
|
||
"\n",
|
||
" def plot_distributions(self, data: np.ndarray, \n",
|
||
" bins: int = 50, \n",
|
||
" title: str = \"Distribuzione\") -> Tuple[np.ndarray, np.ndarray, np.ndarray]:\n",
|
||
" \"\"\"\n",
|
||
" Calcola e visualizza PMF e CMF delle distribuzioni.\n",
|
||
" \n",
|
||
" Args:\n",
|
||
" data: Array di dati da analizzare\n",
|
||
" bins: Numero di bin per l'istogramma\n",
|
||
" title: Titolo del grafico\n",
|
||
" \n",
|
||
" Returns:\n",
|
||
" tuple: (bin_centers, pmf, cmf)\n",
|
||
" \"\"\"\n",
|
||
" # Calcola PMF e CMF\n",
|
||
" bin_centers, pmf, bin_edges = self.calculate_pmf(data, bins)\n",
|
||
" cmf = self.calculate_cmf(pmf)\n",
|
||
" \n",
|
||
" # Crea il plot\n",
|
||
" fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(10, 8))\n",
|
||
" \n",
|
||
" # Plot PMF\n",
|
||
" width = np.diff(bin_edges)\n",
|
||
" ax1.bar(bin_centers, pmf, width=width, alpha=0.5, label='PMF')\n",
|
||
" ax1.set_title('Probability Mass Function')\n",
|
||
" ax1.set_ylabel('Probability')\n",
|
||
" ax1.grid(True, alpha=0.3)\n",
|
||
" ax1.legend()\n",
|
||
" \n",
|
||
" # Plot CMF\n",
|
||
" ax2.plot(bin_centers, cmf, 'r-', label='CMF')\n",
|
||
" ax2.set_title('Cumulative Mass Function')\n",
|
||
" ax2.set_xlabel('Value')\n",
|
||
" ax2.set_ylabel('Cumulative Probability')\n",
|
||
" ax2.grid(True, alpha=0.3)\n",
|
||
" ax2.legend()\n",
|
||
" \n",
|
||
" # Imposta il titolo generale\n",
|
||
" fig.suptitle(title, y=1.02)\n",
|
||
" plt.tight_layout()\n",
|
||
" plt.show()\n",
|
||
" \n",
|
||
" return bin_centers, pmf, cmf\n",
|
||
"\n",
|
||
"def analyze_model_predictions(model: tf.keras.Model, \n",
|
||
" test_data: np.ndarray,\n",
|
||
" test_targets: np.ndarray,\n",
|
||
" scaler_y) -> None:\n",
|
||
" \"\"\"\n",
|
||
" Analizza le distribuzioni di probabilità delle predizioni del modello.\n",
|
||
" \n",
|
||
" Args:\n",
|
||
" model: Modello TensorFlow addestrato\n",
|
||
" test_data: Dati di test\n",
|
||
" test_targets: Target di test\n",
|
||
" scaler_y: Scaler usato per denormalizzare i target\n",
|
||
" \"\"\"\n",
|
||
" # Ottieni le predizioni\n",
|
||
" predictions = model.predict(test_data)\n",
|
||
" \n",
|
||
" # Denormalizza predizioni e target\n",
|
||
" predictions_real = scaler_y.inverse_transform(predictions)\n",
|
||
" targets_real = scaler_y.inverse_transform(test_targets)\n",
|
||
" \n",
|
||
" # Inizializza la classe per l'analisi delle probabilità\n",
|
||
" prob = ProbabilityFunctions()\n",
|
||
" \n",
|
||
" # Analizza ogni target\n",
|
||
" target_names = ['olive_prod', 'min_oil_prod', 'max_oil_prod', \n",
|
||
" 'avg_oil_prod', 'total_water_need']\n",
|
||
" \n",
|
||
" for i, target in enumerate(target_names):\n",
|
||
" print(f\"\\nAnalisi per {target}\")\n",
|
||
" print(\"-\" * 50)\n",
|
||
" \n",
|
||
" # Calcola errori\n",
|
||
" errors = predictions_real[:, i] - targets_real[:, i]\n",
|
||
" \n",
|
||
" # Calcola statistiche degli errori\n",
|
||
" error_stats = prob.calculate_statistics(errors)\n",
|
||
" print(\"\\nStatistiche degli Errori:\")\n",
|
||
" for key, value in error_stats.items():\n",
|
||
" print(f\"{key}: {value:.3f}\")\n",
|
||
" \n",
|
||
" # Visualizza le distribuzioni degli errori\n",
|
||
" bin_centers, pmf, cmf = prob.plot_distributions(\n",
|
||
" errors, \n",
|
||
" bins=50,\n",
|
||
" title=f\"Distribuzione degli Errori - {target}\"\n",
|
||
" )\n",
|
||
" \n",
|
||
" # Calcola intervalli di confidenza\n",
|
||
" confidence_levels = [0.68, 0.95, 0.99] # 1σ, 2σ, 3σ\n",
|
||
" for level in confidence_levels:\n",
|
||
" lower_idx = np.searchsorted(cmf, (1 - level) / 2)\n",
|
||
" upper_idx = np.searchsorted(cmf, (1 + level) / 2)\n",
|
||
" \n",
|
||
" print(f\"\\nIntervallo di Confidenza {level*100}%:\")\n",
|
||
" print(f\"Range: [{bin_centers[lower_idx]:.2f}, {bin_centers[upper_idx]:.2f}]\")\n",
|
||
"\n",
|
||
"def run_comprehensive_analysis(retrained_model, test_data, test_targets, scaler_y):\n",
|
||
" \"\"\"\n",
|
||
" Esegue un'analisi completa del modello includendo errori,\n",
|
||
" importanza delle feature e distribuzioni.\n",
|
||
" \"\"\"\n",
|
||
" print(\"=== ANALISI COMPLETA DEL MODELLO ===\")\n",
|
||
" \n",
|
||
" # 1. Analisi degli errori\n",
|
||
" print(\"\\n1. ANALISI DEGLI ERRORI\")\n",
|
||
" print(\"-\" * 50)\n",
|
||
" analyze_model_predictions(retrained_model, test_data, test_targets, scaler_y)\n",
|
||
" \n",
|
||
" # 2. Analisi dell'importanza delle feature\n",
|
||
" print(\"\\n2. IMPORTANZA DELLE FEATURE\")\n",
|
||
" print(\"-\" * 50)\n",
|
||
" \n",
|
||
" # Definisci i nomi delle feature\n",
|
||
" temporal_features = ['temp_mean', 'precip_sum', 'solar_energy_sum']\n",
|
||
" static_features = ['ha']\n",
|
||
" \n",
|
||
" all_features = temporal_features + static_features\n",
|
||
" importance = analyze_feature_importance(retrained_model, test_data, all_features)\n",
|
||
" \n",
|
||
" print(\"\\nImportanza relativa delle feature:\")\n",
|
||
" for feature, imp in sorted(importance.items(), key=lambda x: x[1], reverse=True):\n",
|
||
" print(f\"{feature}: {imp:.4f}\")\n",
|
||
" \n",
|
||
" # 3. Analisi distribuzionale\n",
|
||
" print(\"\\n3. ANALISI DISTRIBUZIONALE\")\n",
|
||
" print(\"-\" * 50)\n",
|
||
" \n",
|
||
" prob = ProbabilityFunctions()\n",
|
||
" predictions = retrained_model.predict(test_data)\n",
|
||
" predictions_real = scaler_y.inverse_transform(predictions)\n",
|
||
" targets_real = scaler_y.inverse_transform(test_targets)\n",
|
||
" \n",
|
||
" target_names = ['olive_prod', 'min_oil_prod', 'max_oil_prod', \n",
|
||
" 'avg_oil_prod', 'total_water_need']\n",
|
||
" \n",
|
||
" for i, target in enumerate(target_names):\n",
|
||
" print(f\"\\nAnalisi distribuzionale per {target}\")\n",
|
||
" \n",
|
||
" # Statistiche\n",
|
||
" stats_pred = prob.calculate_statistics(predictions_real[:, i])\n",
|
||
" stats_true = prob.calculate_statistics(targets_real[:, i])\n",
|
||
" \n",
|
||
" print(\"\\nStatistiche Predizioni:\")\n",
|
||
" for key, value in stats_pred.items():\n",
|
||
" print(f\"{key}: {value:.3f}\")\n",
|
||
" \n",
|
||
" print(\"\\nStatistiche Target Reali:\")\n",
|
||
" for key, value in stats_true.items():\n",
|
||
" print(f\"{key}: {value:.3f}\")\n",
|
||
" \n",
|
||
" # Visualizza distribuzioni\n",
|
||
" prob.plot_distributions(predictions_real[:, i], bins=50,\n",
|
||
" title=f\"Distribuzione Predizioni - {target}\")\n",
|
||
" prob.plot_distributions(targets_real[:, i], bins=50,\n",
|
||
" title=f\"Distribuzione Target Reali - {target}\")\n",
|
||
"\n",
|
||
"def analyze_model_predictions(model, test_data, test_targets, scaler_y):\n",
|
||
" \"\"\"\n",
|
||
" Analizza le distribuzioni di probabilità delle predizioni del modello.\n",
|
||
" \n",
|
||
" Args:\n",
|
||
" model: Modello TensorFlow addestrato\n",
|
||
" test_data: Dati di test\n",
|
||
" test_targets: Target di test\n",
|
||
" scaler_y: Scaler usato per denormalizzare i target\n",
|
||
" \"\"\"\n",
|
||
" # Ottieni le predizioni\n",
|
||
" predictions = model.predict(test_data)\n",
|
||
" \n",
|
||
" # Denormalizza predizioni e target\n",
|
||
" predictions_real = scaler_y.inverse_transform(predictions)\n",
|
||
" targets_real = scaler_y.inverse_transform(test_targets)\n",
|
||
" \n",
|
||
" # Inizializza la classe per l'analisi delle probabilità\n",
|
||
" prob = ProbabilityFunctions()\n",
|
||
" \n",
|
||
" # Analizza ogni target\n",
|
||
" target_names = ['olive_prod', 'min_oil_prod', 'max_oil_prod', \n",
|
||
" 'avg_oil_prod', 'total_water_need']\n",
|
||
" \n",
|
||
" for i, target in enumerate(target_names):\n",
|
||
" print(f\"\\nAnalisi per {target}\")\n",
|
||
" print(\"-\" * 50)\n",
|
||
" \n",
|
||
" # Calcola errori\n",
|
||
" errors = predictions_real[:, i] - targets_real[:, i]\n",
|
||
" \n",
|
||
" # Calcola statistiche degli errori\n",
|
||
" error_stats = prob.calculate_statistics(errors)\n",
|
||
" print(\"\\nStatistiche degli Errori:\")\n",
|
||
" for key, value in error_stats.items():\n",
|
||
" print(f\"{key}: {value:.3f}\")\n",
|
||
" \n",
|
||
" # Visualizza le distribuzioni degli errori\n",
|
||
" bin_centers, pmf, cmf = prob.plot_distributions(\n",
|
||
" errors, \n",
|
||
" bins=50,\n",
|
||
" title=f\"Distribuzione degli Errori - {target}\"\n",
|
||
" )\n",
|
||
" \n",
|
||
" # Calcola intervalli di confidenza\n",
|
||
" confidence_levels = [0.80,0.85, 0.90, 0.95, 0.99] # 1σ, 2σ, 3σ\n",
|
||
" for level in confidence_levels:\n",
|
||
" lower_idx = np.searchsorted(cmf, (1 - level) / 2)\n",
|
||
" upper_idx = np.searchsorted(cmf, (1 + level) / 2)\n",
|
||
" \n",
|
||
" print(f\"\\nIntervallo di Confidenza {level*100}%:\")\n",
|
||
" print(f\"Range: [{bin_centers[lower_idx]:.2f}, {bin_centers[upper_idx]:.2f}]\")\n",
|
||
"\n",
|
||
"class ProbabilityFunctions:\n",
|
||
" @staticmethod\n",
|
||
" def calculate_statistics(data):\n",
|
||
" \"\"\"\n",
|
||
" Calcola statistiche di base usando TensorFlow.\n",
|
||
" \n",
|
||
" Args:\n",
|
||
" data: Tensor dei dati\n",
|
||
" \n",
|
||
" Returns:\n",
|
||
" dict: Dizionario con le statistiche\n",
|
||
" \"\"\"\n",
|
||
" if not isinstance(data, tf.Tensor):\n",
|
||
" data = tf.convert_to_tensor(data, dtype=tf.float32)\n",
|
||
" \n",
|
||
" mean = tf.reduce_mean(data)\n",
|
||
" # Calculate variance manually\n",
|
||
" squared_deviations = tf.square(data - mean)\n",
|
||
" variance = tf.reduce_mean(squared_deviations)\n",
|
||
" std = tf.sqrt(variance)\n",
|
||
" \n",
|
||
" # Sort the tensor for median calculation\n",
|
||
" sorted_data = tf.sort(data)\n",
|
||
" size = tf.size(data)\n",
|
||
" mid_index = size // 2\n",
|
||
" median = sorted_data[mid_index]\n",
|
||
" \n",
|
||
" return {\n",
|
||
" 'mean': mean.numpy(),\n",
|
||
" 'variance': variance.numpy(),\n",
|
||
" 'std': std.numpy(),\n",
|
||
" 'min': tf.reduce_min(data).numpy(),\n",
|
||
" 'max': tf.reduce_max(data).numpy(),\n",
|
||
" 'median': median.numpy()\n",
|
||
" }\n",
|
||
"\n",
|
||
" @staticmethod\n",
|
||
" def calculate_pmf(data, bins=50):\n",
|
||
" \"\"\"\n",
|
||
" Calcola la Probability Mass Function (PMF) dei dati.\n",
|
||
" \n",
|
||
" Args:\n",
|
||
" data: Array di dati\n",
|
||
" bins: Numero di bin per l'istogramma\n",
|
||
" \n",
|
||
" Returns:\n",
|
||
" tuple: (bin_centers, pmf, bin_edges)\n",
|
||
" \"\"\"\n",
|
||
" # Calcola l'istogramma\n",
|
||
" hist, bin_edges = np.histogram(data, bins=bins, density=True)\n",
|
||
" \n",
|
||
" # Calcola i centri dei bin\n",
|
||
" bin_centers = (bin_edges[:-1] + bin_edges[1:]) / 2\n",
|
||
" \n",
|
||
" # Normalizza per ottenere la PMF\n",
|
||
" pmf = hist / np.sum(hist)\n",
|
||
" \n",
|
||
" return bin_centers, pmf, bin_edges\n",
|
||
"\n",
|
||
" @staticmethod\n",
|
||
" def calculate_cmf(pmf):\n",
|
||
" \"\"\"\n",
|
||
" Calcola la Cumulative Mass Function (CMF) dalla PMF.\n",
|
||
" \n",
|
||
" Args:\n",
|
||
" pmf: Probability Mass Function\n",
|
||
" \n",
|
||
" Returns:\n",
|
||
" array: Cumulative Mass Function\n",
|
||
" \"\"\"\n",
|
||
" return np.cumsum(pmf)\n",
|
||
"\n",
|
||
" def plot_distributions(self, data, bins=50, title=\"Distribuzione\"):\n",
|
||
" \"\"\"\n",
|
||
" Calcola e visualizza PMF e CMF delle distribuzioni.\n",
|
||
" \n",
|
||
" Args:\n",
|
||
" data: Array di dati da analizzare\n",
|
||
" bins: Numero di bin per l'istogramma\n",
|
||
" title: Titolo del grafico\n",
|
||
" \n",
|
||
" Returns:\n",
|
||
" tuple: (bin_centers, pmf, cmf)\n",
|
||
" \"\"\"\n",
|
||
" # Calcola PMF e CMF\n",
|
||
" bin_centers, pmf, bin_edges = self.calculate_pmf(data, bins)\n",
|
||
" cmf = self.calculate_cmf(pmf)\n",
|
||
" \n",
|
||
" # Crea il plot\n",
|
||
" fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(10, 8))\n",
|
||
" \n",
|
||
" # Plot PMF\n",
|
||
" width = np.diff(bin_edges)\n",
|
||
" ax1.bar(bin_centers, pmf, width=width, alpha=0.5, label='PMF')\n",
|
||
" ax1.set_title('Probability Mass Function')\n",
|
||
" ax1.set_ylabel('Probability')\n",
|
||
" ax1.grid(True, alpha=0.3)\n",
|
||
" ax1.legend()\n",
|
||
" \n",
|
||
" # Plot CMF\n",
|
||
" ax2.plot(bin_centers, cmf, 'r-', label='CMF')\n",
|
||
" ax2.set_title('Cumulative Mass Function')\n",
|
||
" ax2.set_xlabel('Value')\n",
|
||
" ax2.set_ylabel('Cumulative Probability')\n",
|
||
" ax2.grid(True, alpha=0.3)\n",
|
||
" ax2.legend()\n",
|
||
" \n",
|
||
" # Set overall title\n",
|
||
" fig.suptitle(title, y=1.02)\n",
|
||
" plt.tight_layout()\n",
|
||
" plt.show()\n",
|
||
" \n",
|
||
" return bin_centers, pmf, cmf"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 17,
|
||
"id": "b98f2a45-ba6f-4519-acaa-0138b4ee7812",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"=== ANALISI COMPLETA DEL MODELLO ===\n",
|
||
"\n",
|
||
"1. ANALISI DEGLI ERRORI\n",
|
||
"--------------------------------------------------\n",
|
||
"19125/19125 [==============================] - 297s 16ms/step\n",
|
||
"\n",
|
||
"Analisi per olive_prod\n",
|
||
"--------------------------------------------------\n",
|
||
"\n",
|
||
"Statistiche degli Errori:\n",
|
||
"mean: -153.802\n",
|
||
"variance: 3593289.250\n",
|
||
"std: 1895.597\n",
|
||
"min: -16577.840\n",
|
||
"max: 13799.312\n",
|
||
"median: 42.076\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": "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",
|
||
"text/plain": [
|
||
"<Figure size 1000x800 with 2 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"\n",
|
||
"Intervallo di Confidenza 80.0%:\n",
|
||
"Range: [-2300.58, 1952.22]\n",
|
||
"\n",
|
||
"Intervallo di Confidenza 85.0%:\n",
|
||
"Range: [-2908.12, 1952.22]\n",
|
||
"\n",
|
||
"Intervallo di Confidenza 90.0%:\n",
|
||
"Range: [-3515.66, 2559.77]\n",
|
||
"\n",
|
||
"Intervallo di Confidenza 95.0%:\n",
|
||
"Range: [-4730.75, 3167.31]\n",
|
||
"\n",
|
||
"Intervallo di Confidenza 99.0%:\n",
|
||
"Range: [-6553.38, 4989.94]\n",
|
||
"\n",
|
||
"Analisi per min_oil_prod\n",
|
||
"--------------------------------------------------\n",
|
||
"\n",
|
||
"Statistiche degli Errori:\n",
|
||
"mean: -35.659\n",
|
||
"variance: 156634.391\n",
|
||
"std: 395.771\n",
|
||
"min: -3838.121\n",
|
||
"max: 3470.118\n",
|
||
"median: 2.335\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": "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",
|
||
"text/plain": [
|
||
"<Figure size 1000x800 with 2 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"\n",
|
||
"Intervallo di Confidenza 80.0%:\n",
|
||
"Range: [-549.41, 327.58]\n",
|
||
"\n",
|
||
"Intervallo di Confidenza 85.0%:\n",
|
||
"Range: [-549.41, 473.74]\n",
|
||
"\n",
|
||
"Intervallo di Confidenza 90.0%:\n",
|
||
"Range: [-695.58, 619.91]\n",
|
||
"\n",
|
||
"Intervallo di Confidenza 95.0%:\n",
|
||
"Range: [-987.91, 766.07]\n",
|
||
"\n",
|
||
"Intervallo di Confidenza 99.0%:\n",
|
||
"Range: [-1426.40, 1058.40]\n",
|
||
"\n",
|
||
"Analisi per max_oil_prod\n",
|
||
"--------------------------------------------------\n",
|
||
"\n",
|
||
"Statistiche degli Errori:\n",
|
||
"mean: -35.914\n",
|
||
"variance: 230541.562\n",
|
||
"std: 480.147\n",
|
||
"min: -5331.337\n",
|
||
"max: 4020.382\n",
|
||
"median: 11.369\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": "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",
|
||
"text/plain": [
|
||
"<Figure size 1000x800 with 2 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"\n",
|
||
"Intervallo di Confidenza 80.0%:\n",
|
||
"Range: [-561.96, 560.25]\n",
|
||
"\n",
|
||
"Intervallo di Confidenza 85.0%:\n",
|
||
"Range: [-748.99, 560.25]\n",
|
||
"\n",
|
||
"Intervallo di Confidenza 90.0%:\n",
|
||
"Range: [-936.03, 747.28]\n",
|
||
"\n",
|
||
"Intervallo di Confidenza 95.0%:\n",
|
||
"Range: [-1123.06, 934.31]\n",
|
||
"\n",
|
||
"Intervallo di Confidenza 99.0%:\n",
|
||
"Range: [-1871.20, 1308.38]\n",
|
||
"\n",
|
||
"Analisi per avg_oil_prod\n",
|
||
"--------------------------------------------------\n",
|
||
"\n",
|
||
"Statistiche degli Errori:\n",
|
||
"mean: -35.829\n",
|
||
"variance: 178113.297\n",
|
||
"std: 422.035\n",
|
||
"min: -4341.829\n",
|
||
"max: 3468.752\n",
|
||
"median: 5.723\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": "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",
|
||
"text/plain": [
|
||
"<Figure size 1000x800 with 2 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"\n",
|
||
"Intervallo di Confidenza 80.0%:\n",
|
||
"Range: [-514.64, 422.63]\n",
|
||
"\n",
|
||
"Intervallo di Confidenza 85.0%:\n",
|
||
"Range: [-670.86, 422.63]\n",
|
||
"\n",
|
||
"Intervallo di Confidenza 90.0%:\n",
|
||
"Range: [-827.07, 578.84]\n",
|
||
"\n",
|
||
"Intervallo di Confidenza 95.0%:\n",
|
||
"Range: [-983.28, 735.05]\n",
|
||
"\n",
|
||
"Intervallo di Confidenza 99.0%:\n",
|
||
"Range: [-1608.13, 1047.47]\n",
|
||
"\n",
|
||
"Analisi per total_water_need\n",
|
||
"--------------------------------------------------\n",
|
||
"\n",
|
||
"Statistiche degli Errori:\n",
|
||
"mean: -25.087\n",
|
||
"variance: 379237.156\n",
|
||
"std: 615.822\n",
|
||
"min: -5668.621\n",
|
||
"max: 5323.238\n",
|
||
"median: 45.572\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": "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",
|
||
"text/plain": [
|
||
"<Figure size 1000x800 with 2 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"\n",
|
||
"Intervallo di Confidenza 80.0%:\n",
|
||
"Range: [-722.28, 596.74]\n",
|
||
"\n",
|
||
"Intervallo di Confidenza 85.0%:\n",
|
||
"Range: [-942.12, 596.74]\n",
|
||
"\n",
|
||
"Intervallo di Confidenza 90.0%:\n",
|
||
"Range: [-1161.96, 816.58]\n",
|
||
"\n",
|
||
"Intervallo di Confidenza 95.0%:\n",
|
||
"Range: [-1381.80, 1036.41]\n",
|
||
"\n",
|
||
"Intervallo di Confidenza 99.0%:\n",
|
||
"Range: [-2480.98, 2135.60]\n",
|
||
"\n",
|
||
"2. IMPORTANZA DELLE FEATURE\n",
|
||
"--------------------------------------------------\n",
|
||
"19125/19125 [==============================] - 287s 15ms/step\n",
|
||
"19125/19125 [==============================] - 292s 15ms/step\n",
|
||
"19125/19125 [==============================] - 290s 15ms/step\n",
|
||
"19125/19125 [==============================] - 295s 15ms/step\n",
|
||
"19125/19125 [==============================] - 297s 16ms/step\n",
|
||
"\n",
|
||
"Importanza relativa delle feature:\n",
|
||
"ha: 0.8669\n",
|
||
"precip_sum: 0.0575\n",
|
||
"solar_energy_sum: 0.0420\n",
|
||
"temp_mean: 0.0336\n",
|
||
"\n",
|
||
"3. ANALISI DISTRIBUZIONALE\n",
|
||
"--------------------------------------------------\n",
|
||
"19125/19125 [==============================] - 300s 16ms/step\n",
|
||
"\n",
|
||
"Analisi distribuzionale per olive_prod\n",
|
||
"\n",
|
||
"Statistiche Predizioni:\n",
|
||
"mean: 29677.232\n",
|
||
"variance: 255279328.000\n",
|
||
"std: 15977.463\n",
|
||
"min: 3532.861\n",
|
||
"max: 87733.969\n",
|
||
"median: 27822.436\n",
|
||
"\n",
|
||
"Statistiche Target Reali:\n",
|
||
"mean: 29831.037\n",
|
||
"variance: 270305376.000\n",
|
||
"std: 16440.967\n",
|
||
"min: 2156.451\n",
|
||
"max: 98292.078\n",
|
||
"median: 27885.184\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": "iVBORw0KGgoAAAANSUhEUgAAA90AAAM0CAYAAACmhUHMAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy81sbWrAAAACXBIWXMAAA9hAAAPYQGoP6dpAADSPElEQVR4nOzdeVzU1f7H8fcMCLgBLiyiKKS4pCY3TUQtMyncUstyLZdMu5UrbWKWWhmV5Zam127XuqVpeovKuhqi3epK5pItN5cs0VJRTAUFWef8/vDH5AgoIuMgvJ6Pxzxyzvd8z/fznZkT85nz/Z5jMcYYAQAAAACAMmd1dQAAAAAAAFRUJN0AAAAAADgJSTcAAAAAAE5C0g0AAAAAgJOQdAMAAAAA4CQk3QAAAAAAOAlJNwAAAAAATkLSDQAAAACAk5B0AwAAAADgJCTdAFBOTZ8+XRaL5Yoc6+abb9bNN99sf/7555/LYrFo9erVV+T4Bd58801ZLBYlJydf0eNeTZKTk2WxWPTmm2/ay0rzWSmqHWcICQnRiBEjnHqM4pz/ub5S53w1sFgsmj59uqvDAIBKgaQbAK6AgmSy4OHl5aWgoCBFR0dr/vz5OnXqVJkc59ChQ5o+fbp27NhRJu2hsBEjRji8l97e3mrTpo1eeeUVZWdnuzo8AABQzri7OgAAqEyeeeYZhYaGKjc3VykpKfr88881ceJEzZ49Wx999JGuu+46e92pU6dq8uTJl9T+oUOHNGPGDIWEhCg8PLzE+3322WeXdBxnuffeezVo0CB5enq6OpQL8vT01N///ndJ0smTJ/Wvf/1Ljz76qLZs2aIVK1Zc8XhK81lp1KiRzpw5oypVqjgpqrN2794tq7V8/MZ/pc4ZAIBzkXQDwBXUo0cPtWvXzv48NjZWGzZsUO/evdWnTx/t3LlTVatWlSS5u7vL3d25/5vOzMxUtWrV5OHh4dTjlJSbm5vc3NxcHcZFubu765577rE/f+ihhxQREaGVK1dq9uzZCgoKKrSPMUZZWVn297es47nUz0rBFRfOVp5+QLlS5+xMGRkZql69uqvDAABcgvLx0zMAVGK33HKLnnrqKe3fv1/vvPOOvbyo+3QTEhLUuXNn+fr6qkaNGmrWrJmmTJki6ex92DfccIMkaeTIkfbLnwvuX7355pvVqlUrbdu2TTfddJOqVatm3/f8e18L5Ofna8qUKQoMDFT16tXVp08f/fbbbw51irtn9/w2Q0JCHC7LPvfx+eefSyr+nu7XXntNLVu2lKenp4KCgvTwww/r5MmThY7XqlUr/fTTT+ratauqVaum+vXr66WXXioUW3Z2tqZNm6YmTZrI09NTwcHBevzxx0t9ebjVarWfa0HsISEh6t27t9atW6d27dqpatWq+tvf/ibp7Oj4xIkTFRwcLE9PTzVp0kQvvviibDabQ7snT57UiBEj5OPjI19fXw0fPrzQeUuFPyvnXwJ/7qPgPt7i7m/esGGDbrzxRlWvXl2+vr7q27evdu7cWeTx9u7dqxEjRsjX11c+Pj4aOXKkMjMzHeo6457uvLw8Pfvss2rcuLE8PT0VEhKiKVOmXPT9O/+cX375ZVksFu3fv79Q3djYWHl4eOjEiRP2ss2bN6t79+7y8fFRtWrV1KVLF/33v/+9pNgLYnj55Zc1Z84cNWrUSFWrVlWXLl30448/OtQdMWKEatSooV9++UU9e/ZUzZo1NXToUElnk+9HHnnE/hlq1qyZXn75ZRljHNrIzs7WpEmT5Ofnp5o1a6pPnz76/fffLylmAMDlYaQbAMqBe++9V1OmTNFnn32m0aNHF1nnf//7n3r37q3rrrtOzzzzjDw9PbV37177l/4WLVromWee0dNPP60xY8boxhtvlCR17NjR3sYff/yhHj16aNCgQbrnnnsUEBBwwbhmzpwpi8WiJ554QkePHtXcuXMVFRWlHTt2XPKI7dy5c3X69GmHsjlz5mjHjh2qU6dOsftNnz5dM2bMUFRUlB588EHt3r1bixYt0pYtW/Tf//7X4VLhEydOqHv37rrzzjs1YMAArV69Wk888YRat26tHj16SJJsNpv69Omjr776SmPGjFGLFi30ww8/aM6cOdqzZ4/i4+Mv6bwK/PLLL5LkcC67d+/W4MGD9cADD2j06NFq1qyZMjMz1aVLFx08eFAPPPCAGjZsqE2bNik2NlaHDx/W3LlzJZ0dGe/bt6+++uor/fWvf1WLFi30wQcfaPjw4ReN5YEHHlBUVJRD2dq1a7Vs2TL5+/sXu9/69evVo0cPXXPNNZo+fbrOnDmjV199VZ06ddL27dsVEhLiUH/AgAEKDQ1VXFyctm/frr///e/y9/fXiy++WMJXrXTuv/9+vfXWW7rrrrv0yCOPaPPmzYqLi9POnTv1wQcflLidAQMG6PHHH9d7772nxx57zGHbe++9p9tuu021atWSdPbHiB49eqht27aaNm2arFarli5dqltuuUVffvml2rdvf0nn8M9//lOnTp3Sww8/rKysLM2bN0+33HKLfvjhB4d+mZeXp+joaHXu3Fkvv/yyqlWrJmOM+vTpo40bN2rUqFEKDw/XunXr9Nhjj+ngwYOaM2eOw2v1zjvvaMiQIerYsaM2bNigXr16XVKsAIDLZAAATrd06VIjyWzZsqXYOj4+PuYvf/mL/fm0adPMuf+bnjNnjpFkUlNTi21jy5YtRpJZunRpoW1dunQxkszixYuL3NalSxf7840bNxpJpn79+iY9Pd1e/t577xlJZt68efayRo0ameHDh1+0zfMVtPXMM8/Yywpep3379hljjDl69Kjx8PAwt912m8nPz7fXW7BggZFk/vGPfxQ6v3/+85/2suzsbBMYGGj69+9vL3v77beN1Wo1X375pUM8ixcvNpLMf//732JjNsaY4cOHm+rVq5vU1FSTmppq9u7da55//nljsVjMdddd5/C6SDJr16512P/ZZ5811atXN3v27HEonzx5snFzczMHDhwwxhgTHx9vJJmXXnrJXicvL8/ceOONhd7j8z8r5/v555+Nj4+PufXWW01eXp4xxph9+/YVaic8PNz4+/ubP/74w1723XffGavVaoYNG1boePfdd5/Dce644w5Tp04dh7LiPh+ltWPHDiPJ3H///Q7ljz76qJFkNmzYYC87/zNY1DlHRkaatm3bOrT1zTffOHyWbDabCQsLM9HR0cZms9nrZWZmmtDQUHPrrbeWOP6CGKpWrWp+//13e/nmzZuNJDNp0iR72fDhw40kM3nyZIc2Cj4bzz33nEP5XXfdZSwWi9m7d68x5s/X6qGHHnKoN2TIECPJTJs2rcRxAwBKj8vLAaCcqFGjxgVnMff19ZUkffjhh4UuQy4pT09PjRw5ssT1hw0bppo1a9qf33XXXapXr54+/fTTUh2/wE8//aT77rtPffv21dSpU4utt379euXk5GjixIkOk3GNHj1a3t7e+uSTTxzq16hRw+Feaw8PD7Vv316//vqrvWzVqlVq0aKFmjdvrmPHjtkft9xyiyRp48aNF40/IyNDfn5+8vPzU5MmTTRlyhRFRkYWGmUNDQ1VdHS0Q9mqVat04403qlatWg7Hj4qKUn5+vr744gtJ0qeffip3d3c9+OCD9n3d3Nw0bty4i8Z3fqx33HGHatWqpXfffbfYe+YPHz6sHTt2aMSIEapdu7a9/LrrrtOtt95a5Hv+17/+1eH5jTfeqD/++EPp6emXFOOlKIgjJibGofyRRx6RpEKfiYsZOHCgtm3bZr9SQZJWrlwpT09P9e3bV5K0Y8cO/fzzzxoyZIj++OMP+3uWkZGhbt266YsvvrjkPtmvXz/Vr1/f/rx9+/aKiIgo8nU+9zMgnX0N3NzcNH78eIfyRx55RMYY/fvf/7bXk1So3sSJEy8pVgDA5eHycgAoJ06fPn3BS38HDhyov//977r//vs1efJkdevWTXfeeafuuuuuEs8OXb9+/UuaNC0sLMzhucViUZMmTS5rHe309HTdeeedql+/vv75z39ecH3pgnttmzVr5lDu4eGha665ptC9uA0aNCjUXq1atfT999/bn//888/auXOn/Pz8ijzm0aNHL3oOXl5e+vjjjyWd/SEjNDRUDRo0KFQvNDS0UNnPP/+s77///qLH379/v+rVq6caNWo4bD//tbiY0aNH65dfftGmTZsueBl/ca+1dPbWhXXr1hWaxKthw4YO9QouxT5x4oS8vb1LHGNqaqry8/Ptz2vUqFHovM+N02q1qkmTJg7lgYGB8vX1LfL+7Au5++67FRMTo5UrV2rKlCkyxmjVqlXq0aOH/Rx+/vlnSbrgpf1paWn28y+J8/uWJDVt2lTvvfeeQ5m7u3uhz9b+/fsVFBTk8IOYdPZ9Kthe8F+r1arGjRs71LvUzxAA4PKQdANAOfD7778rLS2tUCJxrqpVq+qLL77Qxo0b9cknn2jt2rVauXKlbrnlFn322WclmvXbGTNnF5c05+fnFxnTiBEjdOjQIX3zzTeXlJiVRHGvgTlncimbzabWrVtr9uzZRdYNDg4u0XHOv2e6KEW93jabTbfeeqsef/zxIvdp2rTpRdstqXnz5undd9/VO++8c0lLyJVUSV7vkrjhhhsckuVp06bZJ3wrzoV+rLkUQUFBuvHGG/Xee+9pypQp+vrrr3XgwAGH+9ILRrFnzZpV7OtY3I8El8vT07PcLLkGACgdkm4AKAfefvttSSp0KfL5rFarunXrpm7dumn27Nl6/vnn9eSTT2rjxo2Kiooqs0SkQMEIXwFjjPbu3euwnnitWrWKnFF7//79uuaaaxzKXnjhBcXHx+v9999X8+bNL3r8Ro0aSTo7Idm5beXk5Gjfvn0lSnzP17hxY3333Xfq1q1bmb9eJT3+6dOnLxp7o0aNlJiYqNOnTzskdLt37y7Rcb788ks9+uijmjhxon3G64sdr7j2d+3apbp16zptqaply5bpzJkz9ufnf27O1ahRI9lsNv3888/2kV1JOnLkiE6ePGk/j0sxcOBAPfTQQ9q9e7dWrlypatWq6fbbb7dvLxgp9vb2LtVnrijn9y1J2rNnT6HJ6orSqFEjrV+/XqdOnXIY7d61a5d9e8F/bTabfvnlF4fR7ZJ+hgAAZYOfTgHAxTZs2KBnn31WoaGhF0yOjh8/XqisYNStYKmkgqSoqCS4NApmWC6wevVqHT582D4TuHQ2Ifn666+Vk5NjL1uzZk2hpcXWr1+vqVOn6sknn1S/fv1KdPyoqCh5eHho/vz5DqOnb7zxhtLS0ko1C/OAAQN08OBBvf7664W2nTlzRhkZGZfc5qUePykpSevWrSu07eTJk8rLy5Mk9ezZU3l5eVq0aJF9e35+vl599dWLHuPw4cMaMGCAOnfurFmzZpUornr16ik8PFxvvfWWw+fnxx9/1GeffaaePXuWqJ3S6NSpk6KiouyPCyXdBXEUzPJeoODKhdJ8Jvr37y83Nze9++67WrVqlXr37u3wA0Pbtm3VuHFjvfzyy4Vm4JfOXh5/qeLj43Xw4EH782+++UabN2926FvF6dmzp/Lz87VgwQKH8jlz5shisdjbKPjv/PnzHeqd/9oBAJyLkW4AuIL+/e9/a9euXcrLy9ORI0e0YcMGJSQkqFGjRvroo4/k5eVV7L7PPPOMvvjiC/Xq1UuNGjXS0aNH9dprr6lBgwbq3LmzpLMJsK+vrxYvXqyaNWuqevXqioiIKPLe4pKoXbu2OnfurJEjR+rIkSOaO3eumjRp4rCs2f3336/Vq1ere/fuGjBggH755Re98847he4jHTx4sPz8/BQWFuawHrkk3XrrrUUuX+bn56fY2FjNmDFD3bt3V58+fbR792699tpruuGGGxwmTSupe++9V++9957++te/auPGjerUqZPy8/O1a9cuvffee/Z1tZ3lscce00cffaTevXtrxIgRatu2rTIyMvTDDz9o9erVSk5OVt26dXX77berU6dOmjx5spKTk3Xttdfq/fffV1pa2kWPMX78eKWmpurxxx/XihUrHLZdd911DlcqnGvWrFnq0aOHIiMjNWrUKPuSYT4+Phe93PtKadOmjYYPH64lS5bo5MmT6tKli7755hu99dZb6tevn7p27XrJbfr7+6tr166aPXu2Tp06pYEDBzpst1qt+vvf/64ePXqoZcuWGjlypOrXr6+DBw9q48aN8vb2tt/jX1JNmjRR586d9eCDDyo7O1tz585VnTp1ir3t4Fy33367unbtqieffFLJyclq06aNPvvsM3344YeaOHGive+Fh4dr8ODBeu2115SWlqaOHTsqMTFRe/fuvaRYAQCXyZVTpwNAZVGwFFbBw8PDwwQGBppbb73VzJs3z2FZrgLnLwOVmJho+vbta4KCgoyHh4cJCgoygwcPLrT01IcffmiuvfZa4+7u7rA8UpcuXUzLli2LjK+4JcPeffddExsba/z9/U3VqlVNr169zP79+wvt/8orr5j69esbT09P06lTJ7N169ZCbZ57/uc/Nm7c6PA6FSwZVmDBggWmefPmpkqVKiYgIMA8+OCD5sSJE4XOoajzGz58uGnUqJFDWU5OjnnxxRdNy5Ytjaenp6lVq5Zp27atmTFjhklLSyvyNTq3verVq1+wjjFnl8rq1atXkdtOnTplYmNjTZMmTYyHh4epW7eu6dixo3n55ZdNTk6Ovd4ff/xh7r33XuPt7W18fHzMvffea7799tuLLhlWsHxaUY+CZaKKWj7LGGPWr19vOnXqZKpWrWq8vb3N7bffbn766SeHOgXHO3/5uqLev7JeMswYY3Jzc82MGTNMaGioqVKligkODjaxsbEmKyvLoV5Jlgwr8PrrrxtJpmbNmubMmTNFHvfbb781d955p6lTp47x9PQ0jRo1MgMGDDCJiYkljr0ghlmzZplXXnnFBAcHG09PT3PjjTea7777zqHuhT5rp06dMpMmTTJBQUGmSpUqJiwszMyaNcthSTNjjDlz5owZP368qVOnjqlevbq5/fbbzW+//caSYQBwBVmMucTZTgAAAFAqycnJCg0N1axZs/Too4+6OhwAwBXAPd0AAAAAADgJ93QDAABcpvz8/ItOqOasZcUAAOUbSTcAAMBl+u233y46YeG0adM0YsSIKxMQAKDc4J5uAACAy5SVlaWvvvrqgnWuueaaCy6HBgComEi6AQAAAABwEiZSAwAAAADASUi6AQAAAABwEpJuAAAAAACchKQbAAAAAAAnIekGAAAAAMBJSLoBAAAAAHASkm4AAAAAAJyEpBsAAAAAACch6QYAAAAAwElIugEAAAAAcBKSbgAAAAAAnISkGwAAAAAAJyHpBgAAAADASUi6AQAAAABwEpJuAAAAAACchKQbAAAAAAAnIekGAAAAAMBJSLoBAAAAAHASkm4AAAAAAJyEpBsAAAAAACch6QYAAAAAwElIugEAAAAAcBKSbgAAAAAAnISkGwAAAAAAJyHpBgAAAADASUi6AQAAAABwEpJuAAAAAACchKQbAAAAAAAnIekGAAAAAMBJSLoBAAAAAHASkm4AAAAAAJyEpBsAAAAAACch6QYAAAAAwElIugEAAAAAcBKSbgAAAAAAnISkGwAAAAAAJyHpBgAAAADASUi6AQAAAABwEpJuAAAAAACchKQbAAAAAAAnIekGAAAAAMBJSLoBAAAAAHASkm4AAAAAAJyEpBsAAAAAACch6QYAQJLFYtHYsWPLrL0333xTFotFW7duvWjdm2++WTfffLP9eXJysiwWi95880172fTp02WxWMosPpQf57//AICKhaQbAFBuFSSuBQ8vLy81bdpUY8eO1ZEjR1wdnss9//zzio+PL9M2P//8c/vr/c477xRZp1OnTrJYLGrVqlWZHrssnPt5OfcRGBjo0rh++uknTZ8+XcnJyS6NAwBw5bm7OgAAAC7mmWeeUWhoqLKysvTVV19p0aJF+vTTT/Xjjz+qWrVqrg7vsn322WcXrTN16lRNnjzZoez555/XXXfdpX79+pV5TF5eXlq+fLnuueceh/Lk5GRt2rRJXl5eZX7MsnLrrbdq2LBhDmVVq1Z1UTRn/fTTT5oxY4ZuvvlmhYSEOGwryfsPALh6kXQDAMq9Hj16qF27dpKk+++/X3Xq1NHs2bP14YcfavDgwUXuk5GRoerVq1/JMEvNw8PjonXc3d3l7n7l/mz37NlTH330kY4dO6a6devay5cvX66AgACFhYXpxIkTVyyeS9G0adNCPxaUZyV5/wEAVy8uLwcAXHVuueUWSdK+ffskSSNGjFCNGjX0yy+/qGfPnqpZs6aGDh0q6Wzy/cgjjyg4OFienp5q1qyZXn75ZRljimx72bJlatasmby8vNS2bVt98cUXDtv379+vhx56SM2aNVPVqlVVp04d3X333cVeNpyZmakHHnhAderUkbe3t4YNG1YoWS3JPb3n39NtsViUkZGht956y34J9YgRI7Rx40ZZLBZ98MEHhdpYvny5LBaLkpKSLngsSerbt688PT21atWqQm0MGDBAbm5uhfZZunSpbrnlFvn7+8vT01PXXnutFi1aVKje1q1bFR0drbp166pq1aoKDQ3Vfffd51BnxYoVatu2rWrWrClvb2+1bt1a8+bNu2jcFzNixIhCI81S0ffMF9znHx8fr1atWsnT01MtW7bU2rVrC+1/8OBBjRo1SkFBQfL09FRoaKgefPBB5eTk6M0339Tdd98tSeratav9/fr8888lFf3+Hz16VKNGjVJAQIC8vLzUpk0bvfXWWw51Cu79f/nll7VkyRI1btxYnp6euuGGG7Rly5bSv0gAgDLFSDcA4Krzyy+/SJLq1KljL8vLy1N0dLQ6d+6sl19+WdWqVZMxRn369NHGjRs1atQohYeHa926dXrsscd08OBBzZkzx6Hd//znP1q5cqXGjx8vT09Pvfbaa+revbu++eYb+/3LW7Zs0aZNmzRo0CA1aNBAycnJWrRokW6++Wb99NNPhS53Hzt2rHx9fTV9+nTt3r1bixYt0v79++33TpfW22+/rfvvv1/t27fXmDFjJEmNGzdWhw4dFBwcrGXLlumOO+5w2GfZsmVq3LixIiMjL9p+tWrV1LdvX7377rt68MEHJUnfffed/ve//+nvf/+7vv/++0L7LFq0SC1btlSfPn3k7u6ujz/+WA899JBsNpsefvhhSWeTydtuu01+fn6aPHmyfH19lZycrPfff9/eTkJCggYPHqxu3brpxRdflCTt3LlT//3vfzVhwoSLxp6VlaVjx445lNWsWVOenp4X3fd8X331ld5//3099NBDqlmzpubPn6/+/fvrwIED9s/foUOH1L59e508eVJjxoxR8+bNdfDgQa1evVqZmZm66aabNH78eM2fP19TpkxRixYtJMn+3/OdOXNGN998s/bu3auxY8cqNDRUq1at0ogRI3Ty5MlCr8Hy5ct16tQpPfDAA7JYLHrppZd055136tdff1WVKlUu+ZwBAGXMAABQTi1dutRIMuvXrzepqanmt99+MytWrDB16tQxVatWNb///rsxxpjhw4cbSWby5MkO+8fHxxtJ5rnnnnMov+uuu4zFYjF79+61l0kykszWrVvtZfv37zdeXl7mjjvusJdlZmYWijMpKclIMv/85z8Lxd62bVuTk5NjL3/ppZeMJPPhhx/ay7p06WK6dOlif75v3z4jySxdutReNm3aNHP+n+3q1aub4cOHF4onNjbWeHp6mpMnT9rLjh49atzd3c20adMK1T/Xxo0bjSSzatUqs2bNGmOxWMyBAweMMcY89thj5pprrrHH3LJlS4d9i3ptoqOj7fsYY8wHH3xgJJktW7YUG8OECROMt7e3ycvLu2CsRSl4H89/FLyWw4cPN40aNSq0X1GvryTj4eHh8Dn57rvvjCTz6quv2suGDRtmrFZrkedks9mMMcasWrXKSDIbN24sVOf893/u3LlGknnnnXfsZTk5OSYyMtLUqFHDpKenG2P+/JzUqVPHHD9+3F73ww8/NJLMxx9/XPwLBQC4Yri8HABQ7kVFRcnPz0/BwcEaNGiQatSooQ8++ED169d3qFcwIlvg008/lZubm8aPH+9Q/sgjj8gYo3//+98O5ZGRkWrbtq39ecOGDdW3b1+tW7dO+fn5khwn5MrNzdUff/yhJk2ayNfXV9u3by8U+5gxYxxGGx988EG5u7vr008/vcRXoeSGDRum7OxsrV692l62cuVK5eXlXdK9zrfddptq166tFStWyBijFStWFHsPveT42qSlpenYsWPq0qWLfv31V6WlpUmSfH19JUlr1qxRbm5uke34+voqIyNDCQkJJY71XH379lVCQoLDIzo6ulRtRUVFqXHjxvbn1113nby9vfXrr79Kkmw2m+Lj43X77bfb5x04V2muZvj0008VGBjo8FpXqVJF48eP1+nTp/Wf//zHof7AgQNVq1Yt+/Mbb7xRkuwxAgBci8vLAQDl3sKFC9W0aVO5u7srICBAzZo1k9Xq+Luxu7u7GjRo4FC2f/9+BQUFqWbNmg7lBZf17t+/36E8LCys0LGbNm2qzMxMpaamKjAwUGfOnFFcXJyWLl2qgwcPOtwbXpBYXqjNGjVqqF69ek5dOqp58+a64YYbtGzZMo0aNUrS2UvLO3TooCZNmpS4nSpVqujuu+/W8uXL1b59e/32228aMmRIsfX/+9//atq0aUpKSlJmZqbDtrS0NPn4+KhLly7q37+/ZsyYoTlz5ujmm29Wv379NGTIEPvl3w899JDee+899ejRQ/Xr19dtt92mAQMGqHv37iWKu0GDBoqKiirxeV5Iw4YNC5XVqlXLfl9+amqq0tPTy3T5tP379yssLKzQZ7y4z+35MRYk4OV1ojsAqGwY6QYAlHvt27dXVFSUbr75ZrVo0aJQMiJJnp6eRZaXtXHjxmnmzJkaMGCA3nvvPX322WdKSEhQnTp1ZLPZnH78kho2bJj+85//6Pfff9cvv/yir7/+ulQzeg8ZMkQ7duzQ9OnT1aZNG1177bVF1vvll1/UrVs3HTt2TLNnz9Ynn3yihIQETZo0SZLsr43FYtHq1auVlJSksWPH6uDBg7rvvvvUtm1bnT59WpLk7++vHTt26KOPPrLfk9+jRw8NHz68lK/Gn4obeS64kuF8RU0YJ6nYifhc4WqIEQAqM5JuAECF1ahRIx06dEinTp1yKN+1a5d9+7l+/vnnQm3s2bNH1apVk5+fnyRp9erVGj58uF555RXddddduvXWW9W5c2edPHmyyBjOb/P06dM6fPhwkTNoX6oLXbo8aNAgubm56d1339WyZctUpUoVDRw48JKP0blzZzVs2FCff/75BUe5P/74Y2VnZ+ujjz7SAw88oJ49eyoqKqrY9bE7dOigmTNnauvWrVq2bJn+97//acWKFfbtHh4euv322/Xaa6/pl19+0QMPPKB//vOf2rt37yWfw7lq1apV5Ht1/uhxSfn5+cnb21s//vjjBetdymXmjRo10s8//1zoR5ziPrcAgPKNpBsAUGH17NlT+fn5WrBggUP5nDlzZLFY1KNHD4fypKQkh/uyf/vtN3344Ye67bbb7KOJbm5uhUYQX3311WJHSpcsWeJw7/KiRYuUl5dX6NilUb169WKT/bp166pHjx565513tGzZMnXv3t1hve2Sslgsmj9/vqZNm6Z777232HoFr8/5l9svXbrUod6JEycKvX7h4eGSpOzsbEnSH3/84bDdarXquuuuc6hTWo0bN1ZaWprD7OuHDx8ucom1krBarerXr58+/vhjbd26tdD2gnMtWDO+uPfrXD179lRKSopWrlxpL8vLy9Orr76qGjVqqEuXLqWKFQDgGtzTDQCosG6//XZ17dpVTz75pJKTk9WmTRt99tln+vDDDzVx4kSHCbIkqVWrVoqOjnZYMkySZsyYYa/Tu3dvvf322/Lx8dG1116rpKQkrV+/3mH5snPl5OSoW7duGjBggHbv3q3XXntNnTt3Vp8+fS77/Nq2bav169dr9uzZCgoKUmhoqCIiIuzbhw0bprvuukuS9Oyzz5b6OH379lXfvn0vWOe2226zj04/8MADOn36tF5//XX5+/vr8OHD9npvvfWWXnvtNd1xxx1q3LixTp06pddff13e3t7q2bOnJOn+++/X8ePHdcstt6hBgwbav3+/Xn31VYWHhxe7zFZJDRo0SE888YTuuOMOjR8/XpmZmVq0aJGaNm1a5ER4JfH888/rs88+U5cuXTRmzBi1aNFChw8f1qpVq/TVV1/J19dX4eHhcnNz04svvqi0tDR5enra1zQ/35gxY/S3v/1NI0aM0LZt2xQSEqLVq1frv//9r+bOnVtojgIAQPlG0g0AqLCsVqs++ugjPf3001q5cqWWLl2qkJAQzZo1S4888kih+l26dFFkZKRmzJihAwcO6Nprr9Wbb75pH2WVpHnz5snNzU3Lli1TVlaWOnXqpPXr1xc7O/aCBQu0bNkyPf3008rNzdXgwYM1f/78y1qju8Ds2bM1ZswYTZ06VWfOnNHw4cMdku7bb79dtWrVks1mK5Mk/0KaNWum1atXa+rUqXr00UcVGBioBx98UH5+frrvvvvs9bp06aJvvvlGK1as0JEjR+Tj46P27dtr2bJlCg0NlSTdc889WrJkiV577TWdPHlSgYGBGjhwoKZPn37Z9+3XqVNHH3zwgWJiYvT4448rNDRUcXFx+vnnn0uddNevX1+bN2/WU089pWXLlik9PV3169dXjx497Ou2BwYGavHixYqLi9OoUaOUn5+vjRs3Fpl0V61aVZ9//rkmT56st956S+np6WrWrJmWLl2qESNGXM7pAwBcwGKYZQMAgAopLy9PQUFBuv322/XGG2+4OhwAACol7ukGAKCCio+PV2pqqoYNG+bqUAAAqLQY6QYAoILZvHmzvv/+ez377LOqW7duqS+bBgAAl4+RbgAAKphFixbpwQcflL+/v/75z3+6OhwAACo1RroBAAAAAHASRroBAAAAAHASkm4AAAAAAJyEdbpLyWaz6dChQ6pZs2aZrLUKAAAAALh6GGN06tQpBQUFyWotfjybpLuUDh06pODgYFeHAQAAAABwod9++00NGjQodnu5SLoXLlyoWbNmKSUlRW3atNGrr76q9u3bF1t/1apVeuqpp5ScnKywsDC9+OKL6tmzZ5F1//rXv+pvf/ub5syZo4kTJ9rLjx8/rnHjxunjjz+W1WpV//79NW/ePNWoUaNEMdesWVPS2RfY29vbXm6z2ZSamio/P78L/toBVHT0BeAs+gJAPwAK0BcqlvT0dAUHB9tzw+K4POleuXKlYmJitHjxYkVERGju3LmKjo7W7t275e/vX6j+pk2bNHjwYMXFxal3795avny5+vXrp+3bt6tVq1YOdT/44AN9/fXXCgoKKtTO0KFDdfjwYSUkJCg3N1cjR47UmDFjtHz58hLFXXBJube3d6GkOysrS97e3nQkVGr0BeAs+gJAPwAK0BcqpovdbuzyJcMiIiJ0ww03aMGCBZLOfhCDg4M1btw4TZ48uVD9gQMHKiMjQ2vWrLGXdejQQeHh4Vq8eLG97ODBg4qIiNC6devUq1cvTZw40T7SvXPnTl177bXasmWL2rVrJ0lau3atevbsqd9//73IJD07O1vZ2dn25wW/apw4cYKRbqAI9AXgLPoCQD8ACtAXKpb09HTVqlVLaWlpDjnh+Vw60p2Tk6Nt27YpNjbWXma1WhUVFaWkpKQi90lKSlJMTIxDWXR0tOLj4+3PbTab7r33Xj322GNq2bJlkW34+vraE25JioqKktVq1ebNm3XHHXcU2icuLk4zZswoVJ6amqqsrCyHY6elpckYQ0dCpUZfAM6iLwD0A6AAfaFiOXXqVInquTTpPnbsmPLz8xUQEOBQHhAQoF27dhW5T0pKSpH1U1JS7M9ffPFFubu7a/z48cW2cf6l6+7u7qpdu7ZDO+eKjY11SPYLRrr9/PwKjXRbLBZ+vUKlR18AzqIvAPQDoAB9oWLx8vIqUT2X39Nd1rZt26Z58+Zp+/btZbqUl6enpzw9PQuVW63WQh3GYrEUWQ5UNvQF4Cz6AkA/AAq4ui/k5+crNzfXJce+2ri5ucnd3b3YvLKk76FLk+66devKzc1NR44ccSg/cuSIAgMDi9wnMDDwgvW//PJLHT16VA0bNrRvz8/P1yOPPKK5c+cqOTlZgYGBOnr0qEMbeXl5On78eLHHBQAAAICr2enTp/X777/LxdN6XVWqVaumevXqycPDo9RtuDTp9vDwUNu2bZWYmKh+/fpJOnvJRWJiosaOHVvkPpGRkUpMTHRY/ishIUGRkZGSpHvvvVdRUVEO+0RHR+vee+/VyJEj7W2cPHlS27ZtU9u2bSVJGzZskM1mU0RERBmfJQAAAAC4Vn5+vn7//XdVq1ZNfn5+ZXpVcEVkjFFOTo5SU1O1b98+hYWFlfrqBJdfXh4TE6Phw4erXbt2at++vebOnauMjAx7gjxs2DDVr19fcXFxkqQJEyaoS5cueuWVV9SrVy+tWLFCW7du1ZIlSyRJderUUZ06dRyOUaVKFQUGBqpZs2aSpBYtWqh79+4aPXq0Fi9erNzcXI0dO1aDBg0qcuZyAAAAALia5ebmyhgjPz8/Va1a1dXhXBWqVq2qKlWqaP/+/crJySnxPdznc3nSPXDgQKWmpurpp59WSkqKwsPDtXbtWvtkaQcOHHD4RaFjx45avny5pk6dqilTpigsLEzx8fGF1ui+mGXLlmns2LHq1q2brFar+vfvr/nz55fpuQEAAABAecII96Upi3vvXb5O99UqPT1dPj4+hdZks9lsOnr0qPz9/ZkoBJUafQE4i74A0A+AAq7sC1lZWdq3b59CQ0NLPWJbGV3odSsuJzwf/9cDAAAAAMBJXH55OQAAAADANeYk7Lmix5t0a9MrerzygKQb5d7l/o+gMnZsAAAAoCIYMWKE3nrrLUlnJ8hu2LChhg0bpilTpuirr75S165d5evrq8OHDztc/r1lyxa1b99ekuxLpH3++efq2rVroWM8+eSTeu6555x2DiTdwBXADwcAAABA6XTv3l1Lly5Vdna2Pv30Uz388MOqUqWKfdnomjVr6oMPPtDgwYPt+7zxxhtq2LChDhw4UKi93bt3O9yDXaNGDafGT9KNCq8sLpkh6QUAAABcw9PTU4GBgZKkBx98UB988IE++ugje9I9fPhw/eMf/7An3WfOnNGKFSs0fvx4Pfvss4Xa8/f3l6+v7xWLn6QbKIErfa8LAAAAgKJVrVpVf/zxh/35vffeq1mzZunAgQNq2LCh/vWvfykkJETXX3+9C6P8E0k3cBVgtB4AAACVnTFGiYmJWrduncaNG2cv9/f3V48ePfTmm2/q6aef1j/+8Q/dd999xbbToEEDh+f79+9XnTp1nBY3STcAAAAAoNxas2aNatSoodzcXNlsNg0ZMkTTp0/Xli1b7HXuu+8+TZgwQffcc4+SkpK0atUqffnll0W29+WXX6pmzZr257Vq1XJq/CTdAAAAAIByq2vXrlq0aJE8PDwUFBQkd/fCaWyPHj00ZswYjRo1SrfffvsFR65DQ0O5pxtA2WMGdQAAAFyNqlevriZNmlywjru7u4YNG6aXXnpJ//73v69QZCVD0g2gRC45aTdG1fJPK9MtTbJYJJG4AwAAwHmeffZZPfbYY069P7s0SLoBAAAAoJKqSIMiHh4eqlu3rqvDKISkG8AVwyXuAAAAuBRvvvlmsdtuvvlmGWOK3d6vXz+H7Rer7ywk3QCuGiTtAAAAuNpYXR0AAAAAAAAVFUk3AAAAAABOwuXlcLrLvSQYKCtl8VnkEnUAAABcCka6AQAAAKCScMVEYlezsni9SLoBAAAAoIJzc3OTJOXk5Lg4kqtLZmamJKlKlSqlboPLywEAAACggnN3d1e1atWUmpqqKlWqyGpl/PVCjDHKzMzU0aNH5evra//RojRIugEAAACggrNYLKpXr5727dun/fv3uzqcq4avr68CAwMvqw2SbgAAAACoBDw8PBQWFsYl5iVUpUqVyxrhLkDSDQCX4HJnQGf2cwAA4EpWq1VeXl6uDqNS4UJ+AAAAAACchJFuALiCWCscAACgcmGkGwAAAAAAJykXI90LFy7UrFmzlJKSojZt2ujVV19V+/bti62/atUqPfXUU0pOTlZYWJhefPFF9ezZ0759+vTpWrFihX777Td5eHiobdu2mjlzpiIiIux1QkJCCs3aFxcXp8mTJ5f9CQJAGeK+cgAAgKuHy0e6V65cqZiYGE2bNk3bt29XmzZtFB0draNHjxZZf9OmTRo8eLBGjRqlb7/9Vv369VO/fv30448/2us0bdpUCxYs0A8//KCvvvpKISEhuu2225SamurQ1jPPPKPDhw/bH+PGjXPquQIAAAAAKheXJ92zZ8/W6NGjNXLkSF177bVavHixqlWrpn/84x9F1p83b566d++uxx57TC1atNCzzz6r66+/XgsWLLDXGTJkiKKionTNNdeoZcuWmj17ttLT0/X99987tFWzZk0FBgbaH9WrV3fquQIAAAAAKheXXl6ek5Ojbdu2KTY21l5mtVoVFRWlpKSkIvdJSkpSTEyMQ1l0dLTi4+OLPcaSJUvk4+OjNm3aOGx74YUX9Oyzz6phw4YaMmSIJk2aJHf3ol+S7OxsZWdn25+np6dLkmw2m2w2m73cZrPJGONQVukZ4+oI4ArG/PlAuTLns92Xtf+EqLAyiqRy4O8CQD8ACtAXKpaSvo8uTbqPHTum/Px8BQQEOJQHBARo165dRe6TkpJSZP2UlBSHsjVr1mjQoEHKzMxUvXr1lJCQoLp169q3jx8/Xtdff71q166tTZs2KTY2VocPH9bs2bOLPG5cXJxmzJhRqDw1NVVZWVn25zabTWlpaTLGyGp1+YUE5UK1/NOuDgEuYeRpsiSbJFlcHQzKUHG3/6Bo/F0A6AdAAfpCxXLq1KkS1SsXE6k5Q9euXbVjxw4dO3ZMr7/+ugYMGKDNmzfL399fkhxGy6+77jp5eHjogQceUFxcnDw9PQu1Fxsb67BPenq6goOD5efnJ29vb3u5zWaTxWKRn58fHen/ZbqluToEuIIxkpEyrTUkC0l3RVLw/1GUDH8XAPoBUIC+ULF4eXmVqJ5Lk+66devKzc1NR44ccSg/cuSIAgMDi9wnMDCwRPWrV6+uJk2aqEmTJurQoYPCwsL0xhtvOFzKfq6IiAjl5eUpOTlZzZo1K7Td09OzyGTcarUW6jAWi6XI8kqLhKvyslj+fKDC4P9tl46/CwD9AChAX6g4SvoeuvSdLljOKzEx0V5ms9mUmJioyMjIIveJjIx0qC9JCQkJxdY/t91z78k+344dO2S1WhnBAQAAAACUGZdfXh4TE6Phw4erXbt2at++vebOnauMjAyNHDlSkjRs2DDVr19fcXFxkqQJEyaoS5cueuWVV9SrVy+tWLFCW7du1ZIlSyRJGRkZmjlzpvr06aN69erp2LFjWrhwoQ4ePKi7775b0tnJ2DZv3qyuXbuqZs2aSkpK0qRJk3TPPfeoVq1arnkhAAAAAAAVjsuT7oEDByo1NVVPP/20UlJSFB4errVr19onSztw4IDDsH3Hjh21fPlyTZ06VVOmTFFYWJji4+PVqlUrSZKbm5t27dqlt956S8eOHVOdOnV0ww036Msvv1TLli0lnb1UfMWKFZo+fbqys7MVGhqqSZMmFZoVHQAAAACAy2ExhvV8SiM9PV0+Pj5KS0srNJHa0aNH5e/vz30a/29Owh5XhwBXMEbV8k8r042J1CqaSbc2dXUIVxX+LgD0A6AAfaFiKS4nPJ/LR7oBAFeXsvghjcQdAABUFvy8AgAAAACAk5B0AwAAAADgJCTdAAAAAAA4CUk3AAAAAABOQtINAAAAAICTkHQDAAAAAOAkJN0AAAAAADgJSTcAAAAAAE5C0g0AAAAAgJO4uzoAAEDlMydhz2XtP+nWpmUUCQAAgHMx0g0AAAAAgJOQdAMAAAAA4CQk3QAAAAAAOAlJNwAAAAAATkLSDQAAAACAk5B0AwAAAADgJCTdAAAAAAA4CUk3AAAAAABOQtINAAAAAICTuLs6AAAALtWchD2Xtf+kW5uWUSQAAAAXxkg3AAAAAABOQtINAAAAAICTcHk5AKDSudzL0yUuUQcAACXDSDcAAAAAAE5C0g0AAAAAgJOQdAMAAAAA4CTlIuleuHChQkJC5OXlpYiICH3zzTcXrL9q1So1b95cXl5eat26tT799FOH7dOnT1fz5s1VvXp11apVS1FRUdq8ebNDnePHj2vo0KHy9vaWr6+vRo0apdOnT5f5uQEAAAAAKi+XJ90rV65UTEyMpk2bpu3bt6tNmzaKjo7W0aNHi6y/adMmDR48WKNGjdK3336rfv36qV+/fvrxxx/tdZo2baoFCxbohx9+0FdffaWQkBDddtttSk1NtdcZOnSo/ve//ykhIUFr1qzRF198oTFjxjj9fAEAAAAAlYfFGGNcGUBERIRuuOEGLViwQJJks9kUHByscePGafLkyYXqDxw4UBkZGVqzZo29rEOHDgoPD9fixYuLPEZ6erp8fHy0fv16devWTTt37tS1116rLVu2qF27dpKktWvXqmfPnvr9998VFBRUqI3s7GxlZ2c7tBkcHKwTJ07I29vbXm6z2ZSamio/Pz9ZrS7/TaNcmLf+Z1eHAFcwRtXyTyvTrYZksbg6GqDMTYgKK1E9/i4A9AOgAH2hYklPT1etWrWUlpbmkBOez6VLhuXk5Gjbtm2KjY21l1mtVkVFRSkpKanIfZKSkhQTE+NQFh0drfj4+GKPsWTJEvn4+KhNmzb2Nnx9fe0JtyRFRUXJarVq8+bNuuOOOwq1ExcXpxkzZhQqT01NVVZWlv25zWZTWlqajDF0pP9XLZ/L9isnI0+TJdkkiaQbFU9xV2Sdj78LAP0AKEBfqFhOnTpVonouTbqPHTum/Px8BQQEOJQHBARo165dRe6TkpJSZP2UlBSHsjVr1mjQoEHKzMxUvXr1lJCQoLp169rb8Pf3d6jv7u6u2rVrF2qnQGxsrEOyXzDS7efnV2ik22Kx8OvVOTLd0lwdAlzBGMlImVZGulExnf93pDj8XQDoB0AB+kLF4uXlVaJ6Lk26nalr167asWOHjh07ptdff10DBgzQ5s2bS/wl6Xyenp7y9PQsVG61Wgt1GIvFUmR5pUXCVXlZLH8+gArmUv4fz98FgH4AFKAvVBwlfQ9d+k7XrVtXbm5uOnLkiEP5kSNHFBgYWOQ+gYGBJapfvXp1NWnSRB06dNAbb7whd3d3vfHGG/Y2zr8sMC8vT8ePHy/2uAAAAAAAXCqXJt0eHh5q27atEhMT7WU2m02JiYmKjIwscp/IyEiH+pKUkJBQbP1z2y2YCC0yMlInT57Utm3b7Ns3bNggm82miIiI0p4OAAAAAAAOXH55eUxMjIYPH6527dqpffv2mjt3rjIyMjRy5EhJ0rBhw1S/fn3FxcVJkiZMmKAuXbrolVdeUa9evbRixQpt3bpVS5YskSRlZGRo5syZ6tOnj+rVq6djx45p4cKFOnjwoO6++25JUosWLdS9e3eNHj1aixcvVm5ursaOHatBgwYVOXM5AAAAAACl4fKke+DAgUpNTdXTTz+tlJQUhYeHa+3atfbJ0g4cOOBwrXzHjh21fPlyTZ06VVOmTFFYWJji4+PVqlUrSZKbm5t27dqlt956S8eOHVOdOnV0ww036Msvv1TLli3t7Sxbtkxjx45Vt27dZLVa1b9/f82fP//KnjwAAAAAoEJz+TrdV6uCtb/PX5PNZrPp6NGj8vf3Z3KE/zcnYY+rQ4ArsE43KrhJtzYtUT3+LgD0A6AAfaFiKS4nPB/vNAAAAAAATkLSDQAAAACAk5B0AwAAAADgJCTdAAAAAAA4CUk3AAAAAABOQtINAAAAAICTuHydbgAArkYlXg7RvnxemsPyeSVdcgwAAFzdGOkGAAAAAMBJSLoBAAAAAHASkm4AAAAAAJyEpBsAAAAAACch6QYAAAAAwElIugEAAAAAcBKSbgAAAAAAnISkGwAAAAAAJyHpBgAAAADASUi6AQAAAABwEpJuAAAAAACcxN3VAQAAUBnNSdhz2W1MurVpGUQCAACciZFuAAAAAACchKQbAAAAAAAnIekGAAAAAMBJSLoBAAAAAHASkm4AAAAAAJyEpBsAAAAAACch6QYAAAAAwEnKRdK9cOFChYSEyMvLSxEREfrmm28uWH/VqlVq3ry5vLy81Lp1a3366af2bbm5uXriiSfUunVrVa9eXUFBQRo2bJgOHTrk0EZISIgsFovD44UXXnDK+QEAAAAAKid3VwewcuVKxcTEaPHixYqIiNDcuXMVHR2t3bt3y9/fv1D9TZs2afDgwYqLi1Pv3r21fPly9evXT9u3b1erVq2UmZmp7du366mnnlKbNm104sQJTZgwQX369NHWrVsd2nrmmWc0evRo+/OaNWs6/XwBACgrcxL2XNb+k25tWkaRAACA4liMMcaVAUREROiGG27QggULJEk2m03BwcEaN26cJk+eXKj+wIEDlZGRoTVr1tjLOnTooPDwcC1evLjIY2zZskXt27fX/v371bBhQ0lnR7onTpyoiRMnliru9PR0+fj4KC0tTd7e3vZym82mo0ePyt/fX1ZrubiQ4LJc7hc6VGLGqFr+aWW61ZAsFldHA7hOOe4LJN24Uira9yOgtOgLFUtxOeH5XDrSnZOTo23btik2NtZeZrVaFRUVpaSkpCL3SUpKUkxMjENZdHS04uPjiz1OWlqaLBaLfH19HcpfeOEFPfvss2rYsKGGDBmiSZMmyd296JckOztb2dnZ9ufp6emSznYcm81mL7fZbDLGOJRd1Vz7mwyuZsb8+QAqs3LcFyrM3yqUexXu+xFQSvSFiqWk76NLk+5jx44pPz9fAQEBDuUBAQHatWtXkfukpKQUWT8lJaXI+llZWXriiSc0ePBgh18fxo8fr+uvv161a9fWpk2bFBsbq8OHD2v27NlFthMXF6cZM2YUKk9NTVVWVpb9uc1mU1pamowxFeLXq2r5p10dAq5aRp4mS7JJUvka3QOurPLbF44ePerqEFBJVLTvR0Bp0RcqllOnTpWonsvv6Xam3NxcDRgwQMYYLVq0yGHbuaPl1113nTw8PPTAAw8oLi5Onp6ehdqKjY112Cc9PV3BwcHy8/MrdHm5xWKRn59fhehImW5prg4BVytjJCNlWsvfJbXAFVWO+0JRc6cAzlDRvh8BpUVfqFi8vLxKVK9USffGjRvVtWvX0uzqoG7dunJzc9ORI0ccyo8cOaLAwMAi9wkMDCxR/YKEe//+/dqwYcMFr7GXzt5bnpeXp+TkZDVr1qzQdk9PzyKTcavVWqjDWCyWIsuvSuXsCyKuMhbLnw+gMiunfaFC/J3CVaNCfT8CLgN9oeIo6XtYqne6e/fuaty4sZ577jn99ttvpWlCkuTh4aG2bdsqMTHRXmaz2ZSYmKjIyMgi94mMjHSoL0kJCQkO9QsS7p9//lnr169XnTp1LhrLjh07ZLVa+dUfAAAAAFBmSpV0Hzx4UGPHjtXq1at1zTXXKDo6Wu+9955ycnIuua2YmBi9/vrreuutt7Rz5049+OCDysjI0MiRIyVJw4YNc5hobcKECVq7dq1eeeUV7dq1S9OnT9fWrVs1duxYSWcT7rvuuktbt27VsmXLlJ+fr5SUFKWkpNjjS0pK0ty5c/Xdd9/p119/1bJlyzRp0iTdc889qlWrVmleEgAAAAAACilV0l23bl1NmjRJO3bs0ObNm9W0aVM99NBDCgoK0vjx4/Xdd9+VuK2BAwfq5Zdf1tNPP63w8HDt2LFDa9eutU+WduDAAR0+fNhev2PHjlq+fLmWLFmiNm3aaPXq1YqPj1erVq0knf1B4KOPPtLvv/+u8PBw1atXz/7YtGmTpLOXiq9YsUJdunRRy5YtNXPmTE2aNElLliwpzcsBAAAAAECRymSd7kOHDmnJkiV64YUX5O7urqysLEVGRmrx4sVq2bJlWcRZ7rBON3AR5XhtYuCKKsd9gXW6caVUtO9HQGnRFyqWkq7TXep3Ojc3V6tXr1bPnj3VqFEjrVu3TgsWLNCRI0e0d+9eNWrUSHfffXdpmwcAAAAA4KpXqtnLx40bp3fffVfGGN1777166aWX7Jd3S1L16tX18ssvKygoqMwCBQAAAADgalOqpPunn37Sq6++qjvvvLPIZbSks/d9b9y48bKCAwAAAADgalaqpHvatGnq2LGj3N0dd8/Ly9OmTZt00003yd3dXV26dCmTIAEAQNm73Hk7uCccAICLK9U93V27dtXx48cLlaelpalr166XHRQAAAAAABVBqZJuY4wsRczA+scff6h69eqXHRQAAAAAABXBJV1efuedd0qSLBaLRowY4XA/d35+vr7//nt17NixbCMEAAAAAOAqdUlJt4+Pj6SzI901a9ZU1apV7ds8PDzUoUMHjR49umwjBAAAAADgKnVJSffSpUslSSEhIXr00Ue5lBwAAAAAgAso9ezlAAAAAADgwkqcdF9//fVKTExUrVq19Je//KXIidQKbN++vUyCAwAAAADgalbipLtv3772idP69evnrHgAAAAAAKgwSpx0n3tJOZeXAwAAAABwcaVapxsAAAAAAFxciUe6a9WqdcH7uM91/PjxUgcEAACuDnMS9lx2G5NubVoGkQAAUH6VOOmeO3euE8MAAAAAAKDiKXHSPXz4cGfGAQAAAABAhVPipDs9PV3e3t72f19IQT0AAAAAACqzS7qn+/Dhw/L395evr2+R93cbY2SxWJSfn1+mQQIAAAAAcDUqcdK9YcMG1a5dW5K0ceNGpwUEAAAAAEBFUeKku0uXLkX+GwAAAAAAFK3ESff5Tpw4oTfeeEM7d+6UJF177bUaOXKkfTQcAAAAAIDKzlqanb744guFhIRo/vz5OnHihE6cOKH58+crNDRUX3zxRVnHCAAAAADAValUI90PP/ywBg4cqEWLFsnNzU2SlJ+fr4ceekgPP/ywfvjhhzINEgAAAACAq1GpRrr37t2rRx55xJ5wS5Kbm5tiYmK0d+/eMgsOAAAAAICrWamS7uuvv95+L/e5du7cqTZt2lx2UAAAAAAAVAQlvrz8+++/t/97/PjxmjBhgvbu3asOHTpIkr7++mstXLhQL7zwQtlHCQAAAADAVajEI93h4eH6y1/+ovDwcA0ePFi//fabHn/8cd1000266aab9Pjjj2v//v0aMmTIJQexcOFChYSEyMvLSxEREfrmm28uWH/VqlVq3ry5vLy81Lp1a3366af2bbm5uXriiSfUunVrVa9eXUFBQRo2bJgOHTrk0Mbx48c1dOhQeXt7y9fXV6NGjdLp06cvOXYAAAAAAIpT4pHuffv2OSWAlStXKiYmRosXL1ZERITmzp2r6Oho7d69W/7+/oXqb9q0SYMHD1ZcXJx69+6t5cuXq1+/ftq+fbtatWqlzMxMbd++XU899ZTatGmjEydOaMKECerTp4+2bt1qb2fo0KE6fPiwEhISlJubq5EjR2rMmDFavny5U84TAAAUNidhz2XtP+nWpmUUCQAAzmExxhhXBhAREaEbbrhBCxYskCTZbDYFBwdr3Lhxmjx5cqH6AwcOVEZGhtasWWMv69Chg8LDw7V48eIij7Flyxa1b99e+/fvV8OGDbVz505de+212rJli9q1aydJWrt2rXr27Knff/9dQUFBhdrIzs5Wdna2/Xl6erqCg4N14sQJeXt728ttNptSU1Pl5+cnq7VUt8yXK/PW/+zqEHC1MkbV8k8r062GZLG4OhrAdegLTjUhKszVIaAEKtr3I6C06AsVS3p6umrVqqW0tDSHnPB8pVoyrMBPP/2kAwcOKCcnx6G8T58+Jdo/JydH27ZtU2xsrL3MarUqKipKSUlJRe6TlJSkmJgYh7Lo6GjFx8cXe5y0tDRZLBb5+vra2/D19bUn3JIUFRUlq9WqzZs364477ijURlxcnGbMmFGoPDU1VVlZWfbnNptNaWlpMsZUiI5ULZ9L7lFaRp4mS7JJEokGKjP6gjMdPXrU1SGgBCra9yOgtOgLFcupU6dKVK9USfevv/6qO+64Qz/88IMsFosKBsst//8Lfn5+fonaOXbsmPLz8xUQEOBQHhAQoF27dhW5T0pKSpH1U1JSiqyflZWlJ554QoMHD7b/+pCSklLo0nV3d3fVrl272HZiY2Mdkv2CkW4/P79CI90Wi6XC/HqV6Zbm6hBwtTJGMlKmldE9VHL0Bacq6lY0lD8V7fsRUFr0hYrFy8urRPVKlXRPmDBBoaGhSkxMVGhoqL755hv98ccfeuSRR/Tyyy+XpkmnyM3N1YABA2SM0aJFiy6rLU9PT3l6ehYqt1qthTqMxWIpsvyqxBdEXA6L5c8HUJnRF5ymQvytrSQq1Pcj4DLQFyqOkr6HpUq6k5KStGHDBtWtW9f+gencubPi4uI0fvx4ffvttyVqp27dunJzc9ORI0ccyo8cOaLAwMAi9wkMDCxR/YKEe//+/dqwYYPDaHRgYGChy9Hy8vJ0/PjxYo8LAAAAAMClKtXPK/n5+apZs6aks4lzwXJcjRo10u7du0vcjoeHh9q2bavExER7mc1mU2JioiIjI4vcJzIy0qG+JCUkJDjUL0i4f/75Z61fv1516tQp1MbJkye1bds2e9mGDRtks9kUERFR4vgBAAAAALiQUo10t2rVSt99951CQ0MVERGhl156SR4eHlqyZImuueaaS2orJiZGw4cPV7t27dS+fXvNnTtXGRkZGjlypCRp2LBhql+/vuLi4iSdvbS9S5cueuWVV9SrVy+tWLFCW7du1ZIlSySdTbjvuusubd++XWvWrFF+fr79Pu3atWvLw8NDLVq0UPfu3TV69GgtXrxYubm5Gjt2rAYNGlTkzOUAAAAAAJRGqZLuqVOnKiMjQ5L0zDPPqHfv3rrxxhtVp04drVy58pLaGjhwoFJTU/X0008rJSVF4eHhWrt2rX2ytAMHDjhcK9+xY0ctX75cU6dO1ZQpUxQWFqb4+Hi1atVKknTw4EF99NFHkqTw8HCHY23cuFE333yzJGnZsmUaO3asunXrJqvVqv79+2v+/PmleTkAAAAAAChSma3Tffz4cdWqVcs+g3lFl56eLh8fn0JrstlsNh09elT+/v4VYnKEOQl7XB0CrlasTQycRV9wqkm3NnV1CCiBivb9CCgt+kLFUlxOeL7LWqdbkn777TdJUnBw8OU2BQAAAABAhVKqn1fy8vL01FNPycfHRyEhIQoJCZGPj4+mTp2q3Nzcso4RAAAAAICrUqlGuseNG6f3339fL730kn3W8KSkJE2fPl1//PHHZa+JDQAAAABARVCqpHv58uVasWKFevToYS+77rrrFBwcrMGDB5N0AwAAAACgUl5e7unpqZCQkELloaGh8vDwuNyYAAAAAACoEEqVdI8dO1bPPvussrOz7WXZ2dmaOXOmxo4dW2bBAQAAAABwNSvx5eV33nmnw/P169erQYMGatOmjSTpu+++U05Ojrp161a2EQIAAAAAcJUqcdLt4+Pj8Lx///4Oz1kyDAAAAAAARyVOupcuXerMOAAAAAAAqHBKNXt5gdTUVO3evVuS1KxZM/n5+ZVJUAAAAAAAVASlmkgtIyND9913n+rVq6ebbrpJN910k4KCgjRq1ChlZmaWdYwAAAAAAFyVSjXSHRMTo//85z/6+OOP1alTJ0nSV199pfHjx+uRRx5hnW4AAHBFzEnYc9ltTLq1aRlEAgBA0UqVdP/rX//S6tWrdfPNN9vLevbsqapVq2rAgAEk3QAAAAAAqJSXl2dmZiogIKBQub+/P5eXAwAAAADw/0qVdEdGRmratGnKysqyl505c0YzZsxQZGRkmQUHAAAAAMDVrFSXl8+dO1fdu3dXgwYN1KZNG0nSd999Jy8vL61bt65MAwQAAAAA4GpVqqS7devW+vnnn7Vs2TLt2rVLkjR48GANHTpUVatWLdMAAQAAAAC4Wl1y0p2bm6vmzZtrzZo1Gj16tDNiAgAAAACgQrjke7qrVKnicC83AAAAAAAoWqkmUnv44Yf14osvKi8vr6zjAQAAAACgwijVPd1btmxRYmKiPvvsM7Vu3VrVq1d32P7++++XSXAAAAAAAFzNSpV0+/r6qn///mUdCwAAAAAAFcolJd02m02zZs3Snj17lJOTo1tuuUXTp09nxnIAAAAAAIpwSfd0z5w5U1OmTFGNGjVUv359zZ8/Xw8//LCzYgMAAAAA4Kp2SUn3P//5T7322mtat26d4uPj9fHHH2vZsmWy2WzOig8AAAAAgKvWJV1efuDAAfXs2dP+PCoqShaLRYcOHVKDBg3KPDgAAABnm5Ow57L2n3Rr0zKKBABQEV3SSHdeXp68vLwcyqpUqaLc3NxSB7Bw4UKFhITIy8tLERER+uabby5Yf9WqVWrevLm8vLzUunVrffrppw7b33//fd12222qU6eOLBaLduzYUaiNm2++WRaLxeHx17/+tdTnAAAAAABAUS5ppNsYoxEjRsjT09NelpWVpb/+9a8Oy4aVdMmwlStXKiYmRosXL1ZERITmzp2r6Oho7d69W/7+/oXqb9q0SYMHD1ZcXJx69+6t5cuXq1+/ftq+fbtatWolScrIyFDnzp01YMAAjR49uthjjx49Ws8884z9ebVq1UoUMwAAAAAAJXVJSffw4cMLld1zzz2lPvjs2bM1evRojRw5UpK0ePFiffLJJ/rHP/6hyZMnF6o/b948de/eXY899pgk6dlnn1VCQoIWLFigxYsXS5LuvfdeSVJycvIFj12tWjUFBgaWOnYAAAAAAC7mkpLupUuXltmBc3JytG3bNsXGxtrLrFaroqKilJSUVOQ+SUlJiomJcSiLjo5WfHz8JR9/2bJleueddxQYGKjbb79dTz311AVHu7Ozs5WdnW1/np6eLunsMmrnTiRns9lkjKk4k8sZ4+oIcLUy5s8HUJnRFyq8CvM334kq3PcjoJToCxVLSd/HS0q6y9KxY8eUn5+vgIAAh/KAgADt2rWryH1SUlKKrJ+SknJJxx4yZIgaNWqkoKAgff/993riiSe0e/fuC14WHxcXpxkzZhQqT01NVVZWlv25zWZTWlqajDGyWi/plvlyqVr+aVeHgKuWkafJkmySZHF1MIAL0RcquqNHj7o6hHKvon0/AkqLvlCxnDp1qkT1XJZ0u9KYMWPs/27durXq1aunbt266ZdfflHjxo2L3Cc2NtZhlD09PV3BwcHy8/OTt7e3vdxms8liscjPz69CdKRMtzRXh4CrlTGSkTKtNSQLiQYqMfpChVfUPDRwVNG+HwGlRV+oWM6fZLw4Lku669atKzc3Nx05csSh/MiRI8Xeax0YGHhJ9UsqIiJCkrR3795ik25PT0+HCeQKWK3WQh3GYrEUWX5V4gsiLofF8ucDqMzoCxXavMS9l7V/ZVlyrEJ9PwIuA32h4ijpe+iyd9rDw0Nt27ZVYmKivcxmsykxMVGRkZFF7hMZGelQX5ISEhKKrV9SBcuK1atX77LaAQAAAADgXC69vDwmJkbDhw9Xu3bt1L59e82dO1cZGRn22cyHDRum+vXrKy4uTpI0YcIEdenSRa+88op69eqlFStWaOvWrVqyZIm9zePHj+vAgQM6dOiQJGn37t2Szo6SBwYG6pdfftHy5cvVs2dP1alTR99//70mTZqkm266Sdddd90VfgUAAAAAABWZS5PugQMHKjU1VU8//bRSUlIUHh6utWvX2idLO3DggMOQfceOHbV8+XJNnTpVU6ZMUVhYmOLj4+1rdEvSRx99ZE/aJWnQoEGSpGnTpmn69Ony8PDQ+vXr7Ql+cHCw+vfvr6lTp16hswYAAAAAVBYWY1jDpDTS09Pl4+OjtLS0QhOpHT16VP7+/hXiPo05CXtcHQKuVsaoWv5pZboxeRQqOfoCLqIy3NNd0b4fAaVFX6hYissJz8c7DQAAAACAk5B0AwAAAADgJCTdAAAAAAA4iUsnUgMAAKjsymL+lMpwXzgAXK0Y6QYAAAAAwElIugEAAAAAcBKSbgAAAAAAnISkGwAAAAAAJyHpBgAAAADASUi6AQAAAABwEpYMAwAAuMpd7rJjLDkGAM7DSDcAAAAAAE5C0g0AAAAAgJOQdAMAAAAA4CQk3QAAAAAAOAlJNwAAAAAATkLSDQAAAACAk5B0AwAAAADgJCTdAAAAAAA4iburAwAAAIBrzUnYc1n7T7q1aRlFAgAVDyPdAAAAAAA4CUk3AAAAAABOQtINAAAAAICTkHQDAAAAAOAkJN0AAAAAADgJSTcAAAAAAE5C0g0AAAAAgJO4POleuHChQkJC5OXlpYiICH3zzTcXrL9q1So1b95cXl5eat26tT799FOH7e+//75uu+021alTRxaLRTt27CjURlZWlh5++GHVqVNHNWrUUP/+/XXkyJGyPC0AAAAAAFybdK9cuVIxMTGaNm2atm/frjZt2ig6OlpHjx4tsv6mTZs0ePBgjRo1St9++6369eunfv366ccff7TXycjIUOfOnfXiiy8We9xJkybp448/1qpVq/Sf//xHhw4d0p133lnm5wcAAAAAqNwsxhjjqoNHRETohhtu0IIFCyRJNptNwcHBGjdunCZPnlyo/sCBA5WRkaE1a9bYyzp06KDw8HAtXrzYoW5ycrJCQ0P17bffKjw83F6elpYmPz8/LV++XHfddZckadeuXWrRooWSkpLUoUOHEsWenp4uHx8fpaWlydvb215us9l09OhR+fv7y2p1+YUEl21Owh5Xh4CrlTGqln9amW41JIvF1dEArkNfQCUw6damF9xe0b4fAaVFX6hYissJz+d+BWNykJOTo23btik2NtZeZrVaFRUVpaSkpCL3SUpKUkxMjENZdHS04uPjS3zcbdu2KTc3V1FRUfay5s2bq2HDhhdMurOzs5WdnW1/np6eLulsx7HZbPZym80mY4xD2VXNdb/J4GpnzJ8PoDKjL6ASuNj3ngr3/QgoJfpCxVLS99FlSfexY8eUn5+vgIAAh/KAgADt2rWryH1SUlKKrJ+SklLi46akpMjDw0O+vr6X1E5cXJxmzJhRqDw1NVVZWVn25zabTWlpaTLGVIhfr6rln3Z1CLhqGXmaLMkmSYzuoTKjL6DiK+7WwAIV7fsRUFr0hYrl1KlTJarnsqT7ahMbG+swyp6enq7g4GD5+fkVurzcYrHIz8+vQnSkTLc0V4eAq5UxkpEyrVxSi0qOvoBKwN/f/4LbK9r3I6C06AsVi5eXV4nquSzprlu3rtzc3ArNGn7kyBEFBgYWuU9gYOAl1S+ujZycHJ08edJhtPti7Xh6esrT07NQudVqLdRhLBZLkeVXJb4g4nJYLH8+gMqMvoAKriTfeSrU9yPgMtAXKo6Svocue6c9PDzUtm1bJSYm2stsNpsSExMVGRlZ5D6RkZEO9SUpISGh2PpFadu2rapUqeLQzu7du3XgwIFLagcAAAAAgItx6eXlMTExGj58uNq1a6f27dtr7ty5ysjI0MiRIyVJw4YNU/369RUXFydJmjBhgrp06aJXXnlFvXr10ooVK7R161YtWbLE3ubx48d14MABHTp0SNLZhFo6O8IdGBgoHx8fjRo1SjExMapdu7a8vb01btw4RUZGlnjmcgAAAAAASsKlSffAgQOVmpqqp59+WikpKQoPD9fatWvtk6UdOHDAYci+Y8eOWr58uaZOnaopU6YoLCxM8fHxatWqlb3ORx99ZE/aJWnQoEGSpGnTpmn69OmSpDlz5shqtap///7Kzs5WdHS0XnvttStwxgAAAACAysSl63RfzVinG7gI1iYGzqIvACXqBxdb6xuoCCparlDZlXSdbt5pAAAAAACchKQbAAAAAAAnIekGAAAAAMBJSLoBAAAAAHASkm4AAAAAAJzEpUuGAQAAANLlr5jC7OcAyitGugEAAAAAcBKSbgAAAAAAnISkGwAAAAAAJyHpBgAAAADASZhIrYK73ElJAAAAAAClx0g3AAAAAABOQtINAAAAAICTkHQDAAAAAOAkJN0AAAAAADgJSTcAAAAAAE7C7OUAAAC46pXFii2Tbm1aBpEAgCNGugEAAAAAcBKSbgAAAAAAnISkGwAAAAAAJyHpBgAAAADASUi6AQAAAABwEpJuAAAAAACchKQbAAAAAAAnIekGAAAAAMBJSLoBAAAAAHASkm4AAAAAAJzE3dUBSNLChQs1a9YspaSkqE2bNnr11VfVvn37YuuvWrVKTz31lJKTkxUWFqYXX3xRPXv2tG83xmjatGl6/fXXdfLkSXXq1EmLFi1SWFiYvU5ISIj279/v0G5cXJwmT55c9icIAACAcm9Owp7L2n/SrU3LKBIAFYnLR7pXrlypmJgYTZs2Tdu3b1ebNm0UHR2to0ePFll/06ZNGjx4sEaNGqVvv/1W/fr1U79+/fTjjz/a67z00kuaP3++Fi9erM2bN6t69eqKjo5WVlaWQ1vPPPOMDh8+bH+MGzfOqecKAAAAAKhcLMYY48oAIiIidMMNN2jBggWSJJvNpuDgYI0bN67IUeeBAwcqIyNDa9assZd16NBB4eHhWrx4sYwxCgoK0iOPPKJHH31UkpSWlqaAgAC9+eabGjRokKSzI90TJ07UxIkTSxRndna2srOz7c/T09MVHBysEydOyNvb215us9mUmpoqPz8/Wa0u/01D89b/7OoQUFkZo2r5p5XpVkOyWFwdDeA69AWg0vSDCVFhF6+ESq285Qq4POnp6apVq5bS0tIccsLzufTy8pycHG3btk2xsbH2MqvVqqioKCUlJRW5T1JSkmJiYhzKoqOjFR8fL0nat2+fUlJSFBUVZd/u4+OjiIgIJSUl2ZNuSXrhhRf07LPPqmHDhhoyZIgmTZokd/eiX5K4uDjNmDGjUHlqaqrDCLrNZlNaWpqMMeWiI1XLP+3qEFBpGXmaLMkmSRX3CxZwcfQFoLL0g+Ku1AQKlLdcAZfn1KlTJarn0qT72LFjys/PV0BAgEN5QECAdu3aVeQ+KSkpRdZPSUmxby8oK66OJI0fP17XX3+9ateurU2bNik2NlaHDx/W7NmzizxubGysQ7JfMNLt5+dXaKTbYrGUm1+vMt3SXB0CKitjJCNlWiv2qAZwUfQFoNL0A39/f1eHgHKuvOUKuDxeXl4lqlcuJlJzhXMT6Ouuu04eHh564IEHFBcXJ09Pz0L1PT09iyy3Wq2FOozFYimy3CUq8B82XAUslj8fQGVGXwAqRT8oF9/9UO6Vq1wBl6Wk76FLk+66devKzc1NR44ccSg/cuSIAgMDi9wnMDDwgvUL/nvkyBHVq1fPoU54eHixsURERCgvL0/Jyclq1qxZaU4HAAAAlRiznwMoikt/XvHw8FDbtm2VmJhoL7PZbEpMTFRkZGSR+0RGRjrUl6SEhAR7/dDQUAUGBjrUSU9P1+bNm4ttU5J27Nghq9XKZUEAAAAAgDLj8svLY2JiNHz4cLVr107t27fX3LlzlZGRoZEjR0qShg0bpvr16ysuLk6SNGHCBHXp0kWvvPKKevXqpRUrVmjr1q1asmSJpLOXa0ycOFHPPfecwsLCFBoaqqeeekpBQUHq16+fpLOTsW3evFldu3ZVzZo1lZSUpEmTJumee+5RrVq1XPI6AAAAAAAqHpcn3QMHDlRqaqqefvpppaSkKDw8XGvXrrVPhHbgwAGHa+U7duyo5cuXa+rUqZoyZYrCwsIUHx+vVq1a2es8/vjjysjI0JgxY3Ty5El17txZa9eutd/o7unpqRUrVmj69OnKzs5WaGioJk2aVGhWdAAAAAAALofL1+m+WqWnp8vHx6fQmmw2m01Hjx6Vv79/uZgc4XLvLQJKrZKsyQpcFH0BoB+UEPd0V3zlLVfA5SkuJzwf7zQAAAAAAE5C0g0AAAAAgJOQdAMAAAAA4CQk3QAAAAAAOAlJNwAAAAAATuLyJcMAAAAAlM2qM8yADpQ/jHQDAAAAAOAkJN0AAAAAADgJSTcAAAAAAE5C0g0AAAAAgJMwkRoAAABQQVzuZGxMxAaUPUa6AQAAAABwEpJuAAAAAACchKQbAAAAAAAnIekGAAAAAMBJmEgNAAAAgKTLn4hNYjI24HyMdAMAAAAA4CQk3QAAAAAAOAlJNwAAAAAATkLSDQAAAACAkzCRGgAAAIAyc7mTsTERGyoaRroBAAAAAHASkm4AAAAAAJyEy8sBAAAAlBtcno6KhqQbAAAAQIVxuUm7ROKOskXSDQAAAADnYLQdZalcJN0LFy7UrFmzlJKSojZt2ujVV19V+/bti62/atUqPfXUU0pOTlZYWJhefPFF9ezZ077dGKNp06bp9ddf18mTJ9WpUyctWrRIYWFh9jrHjx/XuHHj9PHHH8tqtap///6aN2+eatSo4dRzBQAAAFCxFZu0G6Nq+aeV6ZYmWSxXNqhLxA8HZcflSffKlSsVExOjxYsXKyIiQnPnzlV0dLR2794tf3//QvU3bdqkwYMHKy4uTr1799by5cvVr18/bd++Xa1atZIkvfTSS5o/f77eeusthYaG6qmnnlJ0dLR++ukneXl5SZKGDh2qw4cPKyEhQbm5uRo5cqTGjBmj5cuXX9HzBwAAAIDyhsv0y47FGGNcGUBERIRuuOEGLViwQJJks9kUHByscePGafLkyYXqDxw4UBkZGVqzZo29rEOHDgoPD9fixYtljFFQUJAeeeQRPfroo5KktLQ0BQQE6M0339SgQYO0c+dOXXvttdqyZYvatWsnSVq7dq169uyp33//XUFBQReNOz09XT4+PkpLS5O3t7e93Gaz6ejRo/L395fV6vrJ4cuiswClYv8lt0a5/yUXcCr6AkA/AArQFy5JeU/ai8sJz+fSke6cnBxt27ZNsbGx9jKr1aqoqCglJSUVuU9SUpJiYmIcyqKjoxUfHy9J2rdvn1JSUhQVFWXf7uPjo4iICCUlJWnQoEFKSkqSr6+vPeGWpKioKFmtVm3evFl33HFHoeNmZ2crOzvb/jwtLU2SdPLkSdlsNnu5zWZTenq6PDw8ykXSnXX6lKtDQGVljCy208qyGv6ooHKjLwD0A6AAfeGSnDx50tUhXFB6erqks7c3X4hLk+5jx44pPz9fAQEBDuUBAQHatWtXkfukpKQUWT8lJcW+vaDsQnXOv3Td3d1dtWvXttc5X1xcnGbMmFGovFGjRsWdHgAAAACglKa4OoASOnXqlHx8fIrd7vJ7uq8WsbGxDiPsNptNx48fV506dWQ551eq9PR0BQcH67fffrvgJQZARUdfAM6iLwD0A6AAfaFiMcbo1KlTF7092aVJd926deXm5qYjR444lB85ckSBgYFF7hMYGHjB+gX/PXLkiOrVq+dQJzw83F7n6NGjDm3k5eXp+PHjxR7X09NTnp6eDmW+vr7Fnpu3tzcdCRB9AShAXwDoB0AB+kLFcaER7gIuvenYw8NDbdu2VWJior3MZrMpMTFRkZGRRe4TGRnpUF+SEhIS7PVDQ0MVGBjoUCc9PV2bN2+214mMjNTJkye1bds2e50NGzbIZrMpIiKizM4PAAAAAFC5ufzy8piYGA0fPlzt2rVT+/btNXfuXGVkZGjkyJGSpGHDhql+/fqKi4uTJE2YMEFdunTRK6+8ol69emnFihXaunWrlixZIkmyWCyaOHGinnvuOYWFhdmXDAsKClK/fv0kSS1atFD37t01evRoLV68WLm5uRo7dqwGDRpUopnLAQAAAAAoCZcn3QMHDlRqaqqefvpppaSkKDw8XGvXrrVPhHbgwAGHWcA7duyo5cuXa+rUqZoyZYrCwsIUHx9vX6Nbkh5//HFlZGRozJgxOnnypDp37qy1a9fa1+iWpGXLlmns2LHq1q2brFar+vfvr/nz51/2+Xh6emratGmFLkUHKhv6AnAWfQGgHwAF6AuVk8vX6QYAAAAAoKJy/ULSAAAAAABUUCTdAAAAAAA4CUk3AAAAAABOQtINAAAAAICTkHSXsYULFyokJEReXl6KiIjQN9984+qQgBKJi4vTDTfcoJo1a8rf31/9+vXT7t27HepkZWXp4YcfVp06dVSjRg31799fR44ccahz4MAB9erVS9WqVZO/v78ee+wx5eXlOdT5/PPPdf3118vT01NNmjTRm2++WSge+hLKgxdeeMG+FGUB+gEqi4MHD+qee+5RnTp1VLVqVbVu3Vpbt261bzfG6Omnn1a9evVUtWpVRUVF6eeff3Zo4/jx4xo6dKi8vb3l6+urUaNG6fTp0w51vv/+e914443y8vJScHCwXnrppUKxrFq1Ss2bN5eXl5dat26tTz/91DknDZwnPz9fTz31lEJDQ1W1alU1btxYzz77rM6di5q+gIsyKDMrVqwwHh4e5h//+If53//+Z0aPHm18fX3NkSNHXB0acFHR0dFm6dKl5scffzQ7duwwPXv2NA0bNjSnT5+21/nrX/9qgoODTWJiotm6davp0KGD6dixo317Xl6eadWqlYmKijLffvut+fTTT03dunVNbGysvc6vv/5qqlWrZmJiYsxPP/1kXn31VePm5mbWrl1rr0NfQnnwzTffmJCQEHPdddeZCRMm2MvpB6gMjh8/bho1amRGjBhhNm/ebH799Vezbt06s3fvXnudF154wfj4+Jj4+Hjz3XffmT59+pjQ0FBz5swZe53u3bubNm3amK+//tp8+eWXpkmTJmbw4MH27WlpaSYgIMAMHTrU/Pjjj+bdd981VatWNX/729/sdf773/8aNzc389JLL5mffvrJTJ061VSpUsX88MMPV+bFQKU2c+ZMU6dOHbNmzRqzb98+s2rVKlOjRg0zb948ex36Ai6GpLsMtW/f3jz88MP25/n5+SYoKMjExcW5MCqgdI4ePWokmf/85z/GGGNOnjxpqlSpYlatWmWvs3PnTiPJJCUlGWOM+fTTT43VajUpKSn2OosWLTLe3t4mOzvbGGPM448/blq2bOlwrIEDB5ro6Gj7c/oSXO3UqVMmLCzMJCQkmC5dutiTbvoBKosnnnjCdO7cudjtNpvNBAYGmlmzZtnLTp48aTw9Pc27775rjDHmp59+MpLMli1b7HX+/e9/G4vFYg4ePGiMMea1114ztWrVsveNgmM3a9bM/nzAgAGmV69eDsePiIgwDzzwwOWdJFACvXr1Mvfdd59D2Z133mmGDh1qjKEvoGS4vLyM5OTkaNu2bYqKirKXWa1WRUVFKSkpyYWRAaWTlpYmSapdu7Ykadu2bcrNzXX4jDdv3lwNGza0f8aTkpLUunVrBQQE2OtER0crPT1d//vf/+x1zm2joE5BG/QllAcPP/ywevXqVeizSj9AZfHRRx+pXbt2uvvuu+Xv76+//OUvev311+3b9+3bp5SUFIfPqI+PjyIiIhz6gq+vr9q1a2evExUVJavVqs2bN9vr3HTTTfLw8LDXiY6O1u7du3XixAl7nQv1F8CZOnbsqMTERO3Zs0eS9N133+mrr75Sjx49JNEXUDLurg6gojh27Jjy8/MdvmRJUkBAgHbt2uWiqIDSsdlsmjhxojp16qRWrVpJklJSUuTh4SFfX1+HugEBAUpJSbHXKaoPFGy7UJ309HSdOXNGJ06coC/BpVasWKHt27dry5YthbbRD1BZ/Prrr1q0aJFiYmI0ZcoUbdmyRePHj5eHh4eGDx9u/ywX9Rk993Pu7+/vsN3d3V21a9d2qBMaGlqojYJttWrVKra/FLQBONPkyZOVnp6u5s2by83NTfn5+Zo5c6aGDh0qSfQFlAhJN4BCHn74Yf3444/66quvXB0KcEX99ttvmjBhghISEuTl5eXqcACXsdlsateunZ5//nlJ0l/+8hf9+OOPWrx4sYYPH+7i6IAr57333tOyZcu0fPlytWzZUjt27NDEiRMVFBREX0CJcXl5Galbt67c3NwKzWB75MgRBQYGuigq4NKNHTtWa9as0caNG9WgQQN7eWBgoHJycnTy5EmH+ud+xgMDA4vsAwXbLlTH29tbVatWpS/BpbZt26ajR4/q+uuvl7u7u9zd3fWf//xH8+fPl7u7uwICAugHqBTq1auna6+91qGsRYsWOnDggKQ/P8sX+owGBgbq6NGjDtvz8vJ0/PjxMukv9AVcCY899pgmT56sQYMGqXXr1rr33ns1adIkxcXFSaIvoGRIusuIh4eH2rZtq8TERHuZzWZTYmKiIiMjXRgZUDLGGI0dO1YffPCBNmzYUOgSp7Zt26pKlSoOn/Hdu3frwIED9s94ZGSkfvjhB4c/LAkJCfL29rZ/eYuMjHRoo6BOQRv0JbhSt27d9MMPP2jHjh32R7t27TR06FD7v+kHqAw6depUaNnIPXv2qFGjRpKk0NBQBQYGOnxG09PTtXnzZoe+cPLkSW3bts1eZ8OGDbLZbIqIiLDX+eKLL5Sbm2uvk5CQoGbNmqlWrVr2OhfqL4AzZWZmymp1TJnc3Nxks9kk0RdQQq6eya0iWbFihfH09DRvvvmm+emnn8yYMWOMr6+vwwy2QHn14IMPGh8fH/P555+bw4cP2x+ZmZn2On/9619Nw4YNzYYNG8zWrVtNZGSkiYyMtG8vWCrptttuMzt27DBr1641fn5+RS6V9Nhjj5mdO3eahQsXFrlUEn0J5cW5s5cbQz9A5fDNN98Yd3d3M3PmTPPzzz+bZcuWmWrVqpl33nnHXueFF14wvr6+5sMPPzTff/+96du3b5HLJP3lL38xmzdvNl999ZUJCwtzWCbp5MmTJiAgwNx7773mxx9/NCtWrDDVqlUrtEySu7u7efnll83OnTvNtGnTWCYJV8zw4cNN/fr17UuGvf/++6Zu3brm8ccft9ehL+BiSLrL2KuvvmoaNmxoPDw8TPv27c3XX3/t6pCAEpFU5GPp0qX2OmfOnDEPPfSQqVWrlqlWrZq54447zOHDhx3aSU5ONj169DBVq1Y1devWNY888ojJzc11qLNx40YTHh5uPDw8zDXXXONwjAL0JZQX5yfd9ANUFh9//LFp1aqV8fT0NM2bNzdLlixx2G6z2cxTTz1lAgICjKenp+nWrZvZvXu3Q50//vjDDB482NSoUcN4e3ubkSNHmlOnTjnU+e6770znzp2Np6enqV+/vnnhhRcKxfLee++Zpk2bGg8PD9OyZUvzySeflP0JA0VIT083EyZMMA0bNjReXl7mmmuuMU8++aTD0l70BVyMxRhjXDnSDgAAAABARcU93QAAAAAAOAlJNwAAAAAATkLSDQAAAACAk5B0AwAAAADgJCTdAAAAAAA4CUk3AAAAAABOQtINAAAAAICTkHQDAAAAAOAkJN0AAAAAADgJSTcAAAAAAE5C0g0AAAAAgJOQdAMAAAAA4CQk3QAAAAAAOAlJNwAAAAAATkLSDQAAAACAk5B0AwAAAADgJCTdAAAAAAA4CUk3AABlbMSIEQoJCSnTNt98801ZLBYlJyeXabsof0JCQjRixAhXhwEAKCMk3QCAcumXX37RAw88oGuuuUZeXl7y9vZWp06dNG/ePJ05c8bV4TnN888/r/j4eFeHYVeQ7FssFn311VeFthtjFBwcLIvFot69e7sgwuIlJyfbYz//0aFDB5fGtmnTJk2fPl0nT550aRwAAOdzd3UAAACc75NPPtHdd98tT09PDRs2TK1atVJOTo6++uorPfbYY/rf//6nJUuWuDpMp3j++ed11113qV+/fg7l9957rwYNGiRPT0+XxOXl5aXly5erc+fODuX/+c9/9Pvvv7ssrpIYPHiwevbs6VDm5+fnomjO2rRpk2bMmKERI0bI19fXYdvu3btltTIuAgAVBUk3AKBc2bdvnwYNGqRGjRppw4YNqlevnn3bww8/rL179+qTTz5xYYSu4ebmJjc3N5cdv2fPnlq1apXmz58vd/c/vz4sX75cbdu21bFjx1wW28Vcf/31uueee1wdRomV5x8wAACXjp9RAQDlyksvvaTTp0/rjTfecEi4CzRp0kQTJkyQ9Oflw2+++WahehaLRdOnT7c/nz59uiwWi/bs2aN77rlHPj4+8vPz01NPPSVjjH777Tf17dtX3t7eCgwM1CuvvOLQXnH3VH/++eeyWCz6/PPPL3heL7/8sjp27Kg6deqoatWqatu2rVavXl0o5oyMDL311lv2y6AL7u09//i9e/fWNddcU+SxIiMj1a5dO4eyd955R23btlXVqlVVu3ZtDRo0SL/99tsFYz7X4MGD9ccffyghIcFelpOTo9WrV2vIkCGlPmdJSkhIUOfOneXr66saNWqoWbNmmjJlikOdV199VS1btlS1atVUq1YttWvXTsuXLy9x/MW5+eabdfPNNxcqP/++/ILP2ssvv6wlS5aocePG8vT01A033KAtW7YU2n/Xrl0aMGCA/Pz8VLVqVTVr1kxPPvmkpLOfxccee0ySFBoaan+vC97bou7p/vXXX3X33Xerdu3aqlatmjp06FDox6eCz+J7772nmTNnqkGDBvLy8lK3bt20d+/e0r9IAIDLQtINAChXPv74Y11zzTXq2LGjU9ofOHCgbDabXnjhBUVEROi5557T3Llzdeutt6p+/fp68cUX1aRJEz366KP64osvyuy48+bN01/+8hc988wzev755+Xu7q67777bIXF6++235enpqRtvvFFvv/223n77bT3wwAPFnse+ffsKJXz79+/X119/rUGDBtnLZs6cqWHDhiksLEyzZ8/WxIkTlZiYqJtuuqnE9xSHhIQoMjJS7777rr3s3//+t9LS0hyOdann/L///U+9e/dWdna2nnnmGb3yyivq06eP/vvf/9rrvP766xo/fryuvfZazZ07VzNmzFB4eLg2b95cotgzMzN17Ngxh0dubm6J9j3f8uXLNWvWLD3wwAN67rnnlJycrDvvvNOhve+//14RERHasGGDRo8erXnz5qlfv376+OOPJUl33nmnBg8eLEmaM2eO/b0u7pL3I0eOqGPHjlq3bp0eeughzZw5U1lZWerTp48++OCDQvVfeOEFffDBB3r00UcVGxurr7/+WkOHDi3V+QIAyoABAKCcSEtLM5JM3759S1R/3759RpJZunRpoW2SzLRp0+zPp02bZiSZMWPG2Mvy8vJMgwYNjMViMS+88IK9/MSJE6Zq1apm+PDh9rKlS5caSWbfvn0Ox9m4caORZDZu3GgvGz58uGnUqJFDvczMTIfnOTk5plWrVuaWW25xKK9evbrDcYs7flpamvH09DSPPPKIQ72XXnrJWCwWs3//fmOMMcnJycbNzc3MnDnTod4PP/xg3N3dC5UXd9wtW7aYBQsWmJo1a9rP5e677zZdu3Y1xhjTqFEj06tXr0s+5zlz5hhJJjU1tdgY+vbta1q2bHnBOItS8Pko6lHwfnXp0sV06dKl0L7nv4cFbdWpU8ccP37cXv7hhx8aSebjjz+2l910002mZs2a9veggM1ms/971qxZRX6ejDn7Wp77GZg4caKRZL788kt72alTp0xoaKgJCQkx+fn5xpg/P4stWrQw2dnZ9rrz5s0zkswPP/xwwdcLAOAcjHQDAMqN9PR0SVLNmjWddoz777/f/m83Nze1a9dOxhiNGjXKXu7r66tmzZrp119/LbPjVq1a1f7vEydOKC0tTTfeeKO2b99eqva8vb3Vo0cPvffeezLG2MtXrlypDh06qGHDhpKk999/XzabTQMGDHAY6Q0MDFRYWJg2btxY4mMOGDBAZ86c0Zo1a3Tq1CmtWbOm2EvLpZKdc8EkYh9++KFsNluR7fj6+ur3338v8jLukhgzZowSEhIcHm3atClVWwMHDlStWrXsz2+88UZJsn9WUlNT9cUXX+i+++6zvwcFLBZLqY756aefqn379g6T2NWoUUNjxoxRcnKyfvrpJ4f6I0eOlIeHR7ExAgCuLCZSAwCUG97e3pKkU6dOOe0Y5ydCPj4+8vLyUt26dQuV//HHH2V23DVr1ui5557Tjh07lJ2dbS8vbSImnU0A4+PjlZSUpI4dO+qXX37Rtm3bNHfuXHudn3/+WcYYhYWFFdlGlSpVSnw8Pz8/RUVFafny5crMzFR+fr7uuuuuYuuX5JwHDhyov//977r//vs1efJkdevWTXfeeafuuusu+wzeTzzxhNavX6/27durSZMmuu222zRkyBB16tSpRHGHhYUpKiqqxOd5Ied/fgoS8BMnTkj6M7Ft1apVmRxPOnvLQERERKHyFi1a2Lefe7yLxQgAuLJIugEA5Ya3t7eCgoL0448/lqh+cQlrfn5+sfsUNQN4cbOCnzuCXJpjFfjyyy/Vp08f3XTTTXrttddUr149ValSRUuXLr2sycBuv/12VatWTe+99546duyo9957T1arVXfffbe9js1mk8Vi0b///e8iz7NGjRqXdMwhQ4Zo9OjRSklJUY8ePQotd1WgpOdctWpVffHFF9q4caM++eQTrV27VitXrtQtt9yizz77TG5ubmrRooV2796tNWvWaO3atfrXv/6l1157TU8//bRmzJhxSfGfz2KxOLzPBYp7X0vyWXG1qyFGAKhMSLoBAOVK7969tWTJEiUlJSkyMvKCdQtG8M6fDGz//v1lHtflHOtf//qXvLy8tG7dOofloJYuXVqo7qWMfFevXl29e/fWqlWrNHv2bK1cuVI33nijgoKC7HUaN24sY4xCQ0PVtGnTErddnDvuuEMPPPCAvv76a61cubLYepdyzlarVd26dVO3bt00e/ZsPf/883ryySe1ceNG+wh19erVNXDgQA0cOFA5OTm68847NXPmTMXGxsrLy6vU51OrVq0iL7su7WeoYEb5i/1wdCnvc6NGjbR79+5C5bt27bJvBwCUX9zTDQAoVx5//HFVr15d999/v44cOVJo+y+//KJ58+ZJOjsyXrdu3UKzjL/22mtlHlfjxo0lyeFY+fn5WrJkyUX3dXNzk8VicRg9TU5OVnx8fKG61atXL/GM4tLZy7MPHTqkv//97/ruu+80cOBAh+133nmn3NzcNGPGjEIjncaYS76EvkaNGlq0aJGmT5+u22+/vdh6JT3n48ePF9o3PDxckuyXpJ8fo4eHh6699loZY0o9C3mBxo0ba9euXUpNTbWXfffddw6zp18KPz8/3XTTTfrHP/6hAwcOOGw79/WvXr26pMI/4hSlZ8+e+uabb5SUlGQvy8jI0JIlSxQSEqJrr722VLECAK4MRroBAOVK48aNtXz5cg0cOFAtWrTQsGHD1KpVK+Xk5GjTpk1atWqVwxrG999/v1544QXdf//9ateunb744gvt2bOnzONq2bKlOnTooNjYWB0/fly1a9fWihUrlJeXd9F9e/XqpdmzZ6t79+4aMmSIjh49qoULF6pJkyb6/vvvHeq2bdtW69ev1+zZsxUUFKTQ0NAi7+ct0LNnT9WsWVOPPvqo3Nzc1L9/f4ftjRs31nPPPafY2FglJyerX79+qlmzpvbt26cPPvhAY8aM0aOPPnpJr8Xw4cPL7JyfeeYZffHFF+rVq5caNWqko0eP6rXXXlODBg3sE4fddtttCgwMVKdOnRQQEKCdO3dqwYIF6tWr12VPunffffdp9uzZio6O1qhRo3T06FEtXrxYLVu2tE/sd6nmz5+vzp076/rrr9eYMWMUGhqq5ORkffLJJ9qxY4eks++zJD355JMaNGiQqlSpottvv92ejJ9r8uTJevfdd9WjRw+NHz9etWvX1ltvvaV9+/bpX//6l/3edwBAOeWaSdMBALiwPXv2mNGjR5uQkBDj4eFhatasaTp16mReffVVk5WVZa+XmZlpRo0aZXx8fEzNmjXNgAEDzNGjR4tdMuz8pamGDx9uqlevXuj4Xbp0KbRM1S+//GKioqKMp6enCQgIMFOmTDEJCQklWjLsjTfeMGFhYcbT09M0b97cLF261B7TuXbt2mVuuukmU7VqVSPJvnRUcUuWGWPM0KFDjSQTFRVV7Ov5r3/9y3Tu3NlUr17dVK9e3TRv3tw8/PDDZvfu3cXuc+5xt2zZcsF6RS0ZVpJzTkxMNH379jVBQUHGw8PDBAUFmcGDB5s9e/bY6/ztb38zN910k6lTp47x9PQ0jRs3No899phJS0u7YEwFy3zNmjXrgvXeeecdc8011xgPDw8THh5u1q1bV+ySYUW1df5nzRhjfvzxR3PHHXcYX19f4+XlZZo1a2aeeuophzrPPvusqV+/vrFarQ7v7flLhhlz9rN311132dtr3769WbNmjUOdgiXDVq1aVeTrUNTSegAA57MYw6waAAAAAAA4A9cjAQAAAADgJCTdAAAAAAA4CUk3AAAAAABOQtINAAAAAICTkHQDAAAAAOAkJN0AAAAAADiJu6sDuFrZbDYdOnRINWvWlMVicXU4AAAAAIAryBijU6dOKSgoSFZr8ePZJN2ldOjQIQUHB7s6DAAAAACAC/32229q0KBBsdtJukupZs2aks6+wN7e3i6OBnAtm82m1NRU+fn5XfBXPqAyoV8ARaNvAEWjb1x90tPTFRwcbM8Ni0PSXUoFl5R7e3uTdKPSs9lsysrKkre3N38kgP9HvwCKRt8AikbfuHpd7HZj3k0AAAAAAJyEpBsAAAAAACch6QYAAAAAwEm4p9uJjDHKy8tTfn6+q0O5alSpUkVubm6uDgMAAAAAygRJt5Pk5OTo8OHDyszMdHUoVxWLxaIGDRqoRo0arg4FAAAAAC4bSbcT2Gw27du3T25ubgoKCpKHh8dFZ7TD2SsDUlNT9fvvvyssLIwRbwAAAABXPZJuJ8jJyZHNZlNwcLCqVavm6nCuKn5+fkpOTlZubi5JNwAAAICrXoWYSO2LL77Q7bffrqCgIFksFsXHx190n88//1zXX3+9PD091aRJE7355ptlHhfr6106rggAAAAAUJFUiKwwIyNDbdq00cKFC0tUf9++ferVq5e6du2qHTt2aOLEibr//vu1bt06J0cKAAAAAKhMKsTl5T169FCPHj1KXH/x4sUKDQ3VK6+8Iklq0aKFvvrqK82ZM0fR0dHOChMAAABAZWaMZLP9+cjP//PfubmyHD8uFVz5eW69y3mcf8zS1D+37FL+bcyf513wuJTnkvToo9JVfstuhUi6L1VSUpKioqIcyqKjozVx4sRi98nOzlZ2drb9eXp6uqSzk6bZbDaHujabTcYY+wMlV/CaFfW6ovwq+MzzngF/ol8ARaNvVGD/nzgqN1fKybnwv88vy8sr+pGff/ZRRLmluLqX+jg3AT637Nz/FvfvC5UVkVhbLpAbWCUFXLl366phe/BBycvL1WEUqaT/H6uUSXdKSooCAhw/0gEBAUpPT9eZM2dUtWrVQvvExcVpxowZhcpTU1OVlZXlUJabmyubzaa8vDzl5eWVbfBXQEpKil544QX9+9//1sGDB+Xv76/rrrtO48eP1y233KKwsDDt379fb7/9tgYOHOiwb5s2bbRz5079/e9/17BhwyTJXv9c9evX1759+wodOy8vTzabTX/88YeqVKnivJNEmbLZbEpLS5MxhrkMgP9HvwCKRt+4gvLzZTlzRpbMzAs/srJkyc6WsrLO/rvgUVCWne1QroLn55QrN1eW/HxXn3GFYSwWyWr982GxyJz7/AJlslhk3NzO/ru4uv9fT1Zr0eUFbZxX16Gd88rtbRWUSX/WOfff///cnPe8yP9aLDqVni5TTj9bp06dKlG9Spl0l0ZsbKxiYmLsz9PT0xUcHCw/Pz95e3s71M3KytKpU6fk7u4ud/er6yVOTk5W586d5evrq5deekmtW7dWbm6u1q1bpwkTJmjnzp2SpODgYL399tsaOnSofd+vv/5aR44cUfXq1WW1Wh3OfcaMGRo9erT9uZub2/+1d+/xOdf/H8ef1zY7mRl2wMwhOR9yXqOj1BSFTpIiScgpo1g5RDmUQtiXIqQTKulAyk9OOeacnHLKIdtobHMcuz6/Pz7tYu2i0XZ9dm2P++123ezzvt7X9Xldzbvt6f35vN9O/9t4eXnJw8NDJUqUkG8e/RctZGW322Wz2RQSEsIvUMDfGBeAc4yNa7DbpdOnpZQU54/UVCklRbZ/tp09K505k+Vh+8fEkBWMQoWkQoUkb2/nf2Z87eVlfu3lZQY2L6/MD0/Pfz/++2Fc8bXTh4fHtZ//Z59/+/pqbdf5tV3S8aQkhYSGyiObu/hcbQni/LQ0cV5OBNnNK+6VCHNIyZIllZCQkKktISFBgYGBTme5JcnHx0c+Pj5Z2j08PLL8wPDw8JDNZnM8ZBjm/wyt4O9/+V+LsqFHjx6y2Wxav369Chcu7GivWbOmOnfu7FhdvH379ho3bpyOHDmiiIgISdKMGTPUvn17zZo16/Jn/1tgYKBKlSr1r+fPeJ2z/67I2/i+AVkxLgDn8v3YuHhR+usv6cQJ8+Hs67/+Mh//DNC5wWYzfycsXNj5w9/fvHzXz+/G/vT1NYPzPwO1l5clO9O4beC022Xz9JSHp2f+HRv5THa/TwUydEdFRWnhwoWZ2hYvXqyoqKjcOeHZs1JAQO689785fdr8n2k2JCUladGiRRoxYkSmwJ0hKCjI8XVYWJiio6P14YcfatCgQTp79qzmzJmj5cuXa9asWTlVPQAAgPXOn5cSErI+rhao/17754Z5eUlFi0qBgf/+KFLEeYi+8tjP77omYQDkrHwRuk+fPq29e/c6jg8cOKAtW7aoePHiKlu2rGJjY3X06FFHGOzWrZsmTZqkl19+Wc8++6x++uknzZ07VwsWLLDqI+QJe/fulWEYqlq1arb6P/vss+rXr59effVVffHFF6pYsaLq1KnjtO+AAQM0aNAgx/HIkSPVu3fvnCgbAADg+p054zxIO3vcSIi22aQSJcxHcLD5+OfXJUo4D9c+PoRkIB/JF6F7w4YNuvvuux3HGfded+zYUTNnztSxY8d06NAhx/MVKlTQggUL1LdvX7377rsqU6aMpk2blnvbhfn7mzPOVriO5fWvd6X1Fi1aqGvXrlqxYoWmT5+uZ5999qp9X3rpJT3zzDOO4+Dg4Os6FwAAQLadOycdOSIdOiQdPpz5kdF2vZdyFyokhYVlfoSEOA/TwcFSUJB5ry6AAi9fhO677rrrmoFx5syZTl+zefPmXKzqCjZbti/xtlKlSpVks9m0a9eubPX38vLS008/raFDh2rdunX66quvrto3ODhYN998c06VCgAACqpLl6Q//8waqK88PnEie+/l65s1SF/tERTE7DOAG5IvQjdyRvHixRUdHa24uDj17t07y33dp06dynRft2ReYv7222+rbdu2KlasmAurBQAA+daFC9KBA9LevdK+fZn/PHjQXKjs3xQuLEVEZH2ULWv+Wbq0eT80QRpALiN0I5O4uDg1adJEjRo10vDhw1W7dm1dunRJixcv1uTJkx1bhmWoVq2aTpw4If/ruIwdAABAp09nDdYZXx8+bO7+cjWFCkllymQO0f8M1sxMA8gjCN3I5KabbtKmTZs0YsQI9evXT8eOHVNISIjq16+vyZMnO31NiRIlXFwlAABwC2lpZojevdvxsO3Zo5A9e+Rx/Pi1XxsQIN18s1SxovnnlV+Hh5v7GwOAGyB0I4tSpUpp0qRJmjRpktPnDx48eM3Xnzp16rr6AwAAN2YY5grfVwRrx+PAASk9PVN3myTH8mIlSlw9WIeEMFMNIF8gdAMAAODfXbpkBukdO7KG62ttqRUQIFWp4njYb75ZSSVKqHjDhvIoXtx19QOARQjdAAAAyOz8eenXX6VNm6TNm83Htm1muzM2m1S+fKZw7XiULp15xtpu16XERPOeawAoAAjdAAAABVlKirRlixmsM0L2jh1ZLguXZM5a16iRNVjffLO5/RYAIAtCNwAAQEGRmHh55jojYO/d67xvcLBUr55Ut675qFfPvN+aBcwA4LoQunORca2tLuAU/80AAMghp0+bwXrdOmn9evPPw4ed942IuBywM/4MD2chMwDIAYTuXFCoUCFJ0tmzZ+Xn52dxNe4lLS1NkuTp6fkvPQEAgEN6uvTbb5fD9fr10vbtkt2euZ/NJlWunHn2uk4dc1YbAJArCN25wNPTU0FBQUpMTJQk+fv7y8a/FP8ru92u48ePy9/fX15e/NUEAMApw5COHLkcsNetkzZulM6cydq3TBkpMlJq1Mj8s149qUgR19cMAAUYySaXlCxZUpIcwRvZ4+HhobJly/KPFAAAZDhzRvrlF2nNmsuz2MeOZe1XpIjUsOHlgN2okblyOADAUoTuXGKz2VSqVCmFhobq4sWLVpfjNry9veXBAi0AgILs8GFp9Wpp1Srzzy1bsq4k7ukp1a59OWBHRpqriHN7FgDkOYTuXObp6cn9yQAAwLmLF6WtWzOH7CNHsvYLD5caN5ZuvdUM2vXqSf7+rq8XAHDdCN0AAACukpRkXiaeEbLXr5fOncvcx9PTXNyscWPz0aSJubo4AMAtEboBAAByy6FD0rJl0ooVZtDeuTNrn6CgywG7cWNzJrtwYVdXCgDIJYRuAACAnGAY0sGD0vLlZtBevtw8/qcqVTKH7KpVJdYzAYB8i9ANAABwIwxD2rcvc8g+fDhzH09PqX596c47pdtvl6Ki2BMbAAoYQjcAAEB2GIa0Z0/mkP3nn5n7eHmZ23bddZcZtBs3Zl9sACjgCN0AAADOGIa0d6+0ZIm0dKl5X3Z8fOY+hQqZ23VlhOyoKO7HBgBkQugGAADIcOyYGbIzHv+8XNzHx9y26847zaB9662Sn58lpQIA3AOhGwAAFFzJyeal4hkhe8eOzM97e5uz102bmiG7USPJ19eKSgEAborQDQAACo7z582tuzJC9i+/SHb75edtNqluXalZM+mee6TbbpP8/a2rFwDg9gjdAAAg/0pPlzZtuhyyf/7ZDN5XqlTpcsi+6y6pRAlLSgUA5E+EbgAAkL+cPCn98IO0YIG0aJF04kTm50uWvByy77lHioiwpk4AQIFA6AYAAO7NMKTffjND9oIF5uXj6emXnw8MlO6++3LIrlbNvIwcAAAXIHQDAAD3c/asuY1XRtA+dCjz89WrSy1bSi1amHtle/ErDwDAGvwEAgAA7uGPPy6H7J9+ynxvtq+vOZvdooX5KF/esjIBALgSoRsAAORN6enmpeLffWcG7d9+y/x8RMTlkN20KauMAwDyJEI3AADIO+x2adUqac4c6YsvpISEy895eJiXimcE7Zo1uTcbAJDnEboBAIC1DENat84M2p9/Lh09evm5YsWkBx4wQ3Z0tFS8uHV1AgBwAwjdAADA9QzD3D97zhxp7lzzfu0MgYFS69ZS27bm1l7e3paVCQDAf0XoBgAArmEY0q+/mkF7zhxp377LzwUESA89ZAbt6GjJx8e6OgEAyEGEbgAAkLt27rwctHftutzu52du69W2rXkJuZ+fdTUCAJBLCN0AACDn/fGH9Mkn0uzZ5ux2Bh8f6f77zaDdsqU5ww0AQD5G6AYAADkjNVX68kvpww+lZcsutxcqJN13nxm0W7Uy79kGAKCAIHQDAIAbl54uLV1qBu1586SzZ812m026+26pfXupTRtzFXIAAAogQjcAALh+u3ebQfujj6QjRy63V6okdewoPf20VLasdfUBAJBHELoBAED2JCWZ92h/+KG0fv3l9qAg6YknzLAdGWnOcgMAAEmEbgAAcC0XL0rffy/NmiV9+62Ulma2e3qaC6J16CA9+KDk62ttnQAA5FGEbgAAkNWWLdLMmdKnn0rHj19uv+UWc0b7ySelsDCrqgMAwG0QugEAgCk1VfrsM2nqVGnDhsvtYWHmgmgdOpihGwAAZBuhGwCAgswwpF9+MYP2Z59JZ86Y7YUKmdt7PfOMFB0tefErAwAAN4KfoAAAFESnTkmffCK9/760bdvl9sqVpeefN2e1Q0IsKw8AgPyC0A0AQEFhGNLq1eas9ty50rlzZruPj/TYY1KXLtLtt7P6OAAAOcjD6gJySlxcnMqXLy9fX19FRkZq/ZVbmTgxfvx4ValSRX5+foqIiFDfvn11/vx5F1ULAIAL/fWXNH68VLOmdNtt5pZf586ZxxMmSMeOmftt33EHgRsAgByWL2a658yZo5iYGE2ZMkWRkZEaP368oqOjtXv3boWGhmbp/+mnn2rgwIGaPn26GjdurD179uiZZ56RzWbT2LFjLfgEAADkMMOQli0zZ7W//PLyVl/+/uae2l26sKc2AAAukC9C99ixY9WlSxd16tRJkjRlyhQtWLBA06dP18CBA7P0X716tZo0aaInn3xSklS+fHm1a9dO69atu+o5Lly4oAsXLjiOU1JSJEl2u112uz0nPw7gdux2uwzDYCwAV7BsXJw8Kc2YIdv778v2+++OZqNePRmdO5tbfQUG/t1omA/AhfiZATjH2HA/2f1euX3oTktL08aNGxUbG+to8/DwULNmzbRmzRqnr2ncuLE+/vhjrV+/Xo0aNdL+/fu1cOFCPf3001c9z6hRozRs2LAs7cePH+eydBR4drtdycnJMgxDHh755q4V4D9x9bjw2rlT/h98IL8vv5Tt759L9oAAnW/TRmfbt9eljK2+zp83H4BF+JkBOMfYcD+pqanZ6uf2ofvEiRNKT09XWFhYpvawsDDt2rXL6WuefPJJnThxQrfddpsMw9ClS5fUrVs3vfLKK1c9T2xsrGJiYhzHKSkpioiIUEhIiAIzZgyAAsput8tmsykkJIQfEsDfXDIuLl2S5s+X7X//k235ckezUbu2jBdekNq1k29AgHxz5+zADeFnBuAcY8P9+Ppm7yes24fuG7Fs2TKNHDlS//vf/xQZGam9e/eqT58+ev311zV48GCnr/Hx8ZGPj0+Wdg8PDwYFIMlmszEegH/ItXFx/Lh5r/bkydKRI2abp6f08MNSr16y3XabbNyrjTyMnxmAc4wN95Ld75Pbh+7g4GB5enoqISEhU3tCQoJKlizp9DWDBw/W008/reeee06SVKtWLZ05c0bPP/+8Xn31Vf6SAwDypg0bpIkTpdmzLy+MFhIide1qPsqUsbY+AACQhdunS29vb9WvX19LlixxtNntdi1ZskRRUVFOX3P27NkswdrT01OSZLCgDAAgL0lLkz79VIqKkho2lGbNMtsyvj58WHr9dQI3AAB5lNvPdEtSTEyMOnbsqAYNGqhRo0YaP368zpw541jNvEOHDgoPD9eoUaMkSQ8++KDGjh2runXrOi4vHzx4sB588EFH+AYAwFJ//im99575yLiaq1AhqW1bqWdPc7svAACQ51kSus+cOaPChQvn2Pu1bdtWx48f15AhQxQfH686depo0aJFjsXVDh06lGlme9CgQbLZbBo0aJCOHj2qkJAQPfjggxoxYkSO1QQAwA1Zt04aN87cW/vSJbOtdGmpWzfp+eelfywcCgAA8jabYcH11AEBAXr88cf17LPP6rbbbnP16XNESkqKihYtquTkZFYvR4Fnt9uVmJio0NBQ1kQA/nZd4+LvVcg1dqx05XaXt90m9eoltWljznID+QA/MwDnGBvuJ7uZ0JLv5scff6ykpCQ1bdpUlStX1ujRo/Xnn39aUQoAANZJSTFntStVkh57zAzc3t7SM89ImzZJK1dKjz9O4AYAwI1ZErpbt26t+fPn6+jRo+rWrZs+/fRTlStXTi1bttS8efN0KeNyOgAA8qODB6V+/czFz2JizOMSJaTBg6U//pBmzJDq1rW6SgAAkAMsvW4hJCREMTEx2rZtm8aOHav/+7//06OPPqrSpUtryJAhOnv2rJXlAQCQs9auNWeuK1Y0LyVPTZWqVpXef99chXz4cOkq210CAAD3ZOnq5QkJCfrwww81c+ZM/fHHH3r00UfVuXNnHTlyRG+++abWrl2rH3/80coSAQD4by5dkr7+2gzZa9debr/3XqlvXyk6WuLePQAA8i1LQve8efM0Y8YM/fDDD6pevbpeeOEFPfXUUwoKCnL0ady4sapVq2ZFeQAA/HcpKfJ/7z3ZZswwLxmXzPu127c3w3atWtbWBwAAXMKS0N2pUyc98cQTWrVqlRo2bOi0T+nSpfXqq6+6uDIAAP6jgwelCRNkmzZNgampZltwsPTCC1L37lw+DgBAAWNJ6D527Jj8/f2v2cfPz09Dhw51UUUAAPxH27dLo0dLs2dL6emySbpUqZI8+vWTR4cOkp+f1RUCAAALWHITWZEiRZSYmJil/a+//pKnp6cFFQEAcINWr5YefNC8XPyTT6T0dKlZM9kXLNCJZcukLl0I3AAAFGCWzHQbhuG0/cKFC/L29nZxNQAAXCfDkH74QRo1Slqxwmyz2aRHH5UGDpTq1ZPsdsnJPzADAICCxaWhe8KECZIkm82madOmKSAgwPFcenq6VqxYoapVq7qyJAAAsi89XfriC/My8i1bzLZChaQOHaSXX5YqV7a0PAAAkPe4NHSPGzdOkjnTPWXKlEyXknt7e6t8+fKaMmWKK0sCAODfXbggzZolvfWWtHev2Va4sNS1q7kSeZky1tYHAADyLJeG7gMHDkiS7r77bs2bN0/FihVz5ekBALg+qanS+++be2z/+afZVry41Lu31LOnVKKEtfUBAIA8z5J7upcuXWrFaQEAyJ4TJ6QJE6RJk6STJ8228HCpf39zYbTCha2tDwAAuA2Xhe6YmBi9/vrrKly4sGJiYq7Zd+zYsS6qCgCAKxw5Ir39tjR1qnT2rNlWubI0YID01FMSi30CAIDr5LLQvXnzZl28eNHx9dXYbDZXlQQAgCkhwVyJfPJkKS3NbKtXT4qNldq0kdjOEgAA3CCXhe4rLynn8nIAQJ6QlGQujjZx4uWZ7TvukAYNkpo1M7cBAwAA+A8suacbAABLpaRI48aZC6SlpJhtkZHSiBFS06aEbQAAkGNcFroffvjhbPedN29eLlYCACiwzpyR4uKkN980Z7kl6ZZbpNdfl1q2JGwDAIAc57LQXbRoUVedCgCAzC5cMLf+GjHCvH9bkqpUkYYPlx59VPLwsLY+AACQb7ksdM+YMcNVpwIAwHTxojRzpjmTffiw2Va+vPTaa1L79pIXd1kBAIDcxW8bAID8Jz1d+uwzM1zv22e2lS4tDR4sPfssW38BAACXcVnorlevnpYsWaJixYqpbt2619wabNOmTa4qCwCQn9jt0ldfSUOGSDt2mG0hIebWX926SX5+1tYHAAAKHJeF7latWsnHx0eS1Lp1a1edFgBQEBiGtGiR9Oqr0ubNZltQkPTSS1Lv3lJAgKXlAQCAgstloXvo0KFOvwYA4D9ZuVJ65RXp55/N44AA6cUXpX79zOANAABgIUvv6d6wYYN27twpSapevbrq169vZTkAAHeyaZM5s71okXns4yP16CENHGheUg4AAJAHWBK6jxw5onbt2mnVqlUK+nsW4tSpU2rcuLFmz56tMmXKWFEWAMAd7Nxp3rP9xRfmsaen9Nxz0qBBEj8/AABAHmPJxqTPPfecLl68qJ07dyopKUlJSUnauXOn7Ha7nnvuOStKAgDkdQcPSp06STVrmoHbZjO3/dq1S5oyhcANAADyJEtmupcvX67Vq1erSpUqjrYqVapo4sSJuv32260oCQCQV8XHSyNGSO+9Z+67LUmtWpl7b9eqZW1tAAAA/8KS0B0REaGLGb84XSE9PV2lS5e2oCIAQJ5z8qT01lvShAnS2bNm2z33mAE8MtLa2gAAALLJksvLx4wZo169emnDhg2Otg0bNqhPnz56++23rSgJAJBXnD4tjRwpVaggjR5tBu7ISOn//s98ELgBAIAbcdlMd7FixWSz2RzHZ86cUWRkpLy8zBIuXbokLy8vPfvss+zjDQAF0YUL5iXkI0ZIiYlmW82a5vGDD5r3cAMAALgZl4Xu8ePHu+pUAAB3kp4uffKJNHiwdOiQ2VaxojR8uNS2rbk6OQAAgJtyWeju2LGjq04FAHAHhiH9+KM0YIC0davZFh5ubgfWqZNUqJC19QEAAOQASxZSu9L58+eVlpaWqS0wMNCiagAALrF5s/Tyy+Y92pJUtKj0yitSr16Sn5+1tQEAAOQgSxZSO3PmjHr27KnQ0FAVLlxYxYoVy/QAAORTBw9KTz0l1atnBm5vbykmRtq3zwzhBG4AAJDPWBK6X375Zf3000+aPHmyfHx8NG3aNA0bNkylS5fWrFmzrCgJAJCbkpKk/v2lKlXM+7clqX17afdu6Z13pBIlrK0PAAAgl1hyefm3336rWbNm6a677lKnTp10++236+abb1a5cuX0ySefqH379laUBQDIaefPSxMnmluAnTpltjVtau6/Xb++paUBAAC4giUz3UlJSbrpppskmfdvJyUlSZJuu+02rVixwoqSAAA5KT1dmjVLqlzZvGz81Cmpdm3p++/Ny8oJ3AAAoICwJHTfdNNNOnDggCSpatWqmjt3riRzBjwoKMiKkgAAOeXHH81Q3bGjdPiwFBEhzZwpbdokNW/OftsAAKBAsSR0d+rUSVv/3h5m4MCBiouLk6+vr/r27auXXnrJipIAAP/V5s3SvfdK0dHmFmBFi0pvvmnet92xI/ttAwCAAsmSe7r79u3r+LpZs2bauXOnNm3apJtvvlm1a9e2oiQAwI06fNjc7uvjj81jb2+pZ0+zjQXSAABAAWf5Pt2SVL58eZUvX97qMgAA1+PMGXNBtDFjpHPnzLb27aU33pD4fzoAAIAkiy4vl6QlS5aoZcuWqlixoipWrKiWLVvq//7v/6wqBwCQXXb75UXShg83A/ftt0sbNpiz3QRuAAAAB0tC9//+9z81b95cRYoUUZ8+fdSnTx8FBgbqgQceUFxcnBUlAQCyY9UqKTLSvEf7zz+lChWkL76Qli9nRXIAAAAnLLm8fOTIkRo3bpx69uzpaOvdu7eaNGmikSNHqkePHlaUBQC4moMHpQEDpL93m1CRItKrr0p9+ki+vpaWBgAAkJdZMtN96tQpNW/ePEv7fffdp+Tk5Bt6z7i4OJUvX16+vr6KjIzU+vXr/7WGHj16qFSpUvLx8VHlypW1cOHCGzo3AORbqanmgmhVq5qB28ND6tJF+v13M4QTuAEAAK7JktD90EMP6auvvsrS/vXXX6tly5bX/X5z5sxRTEyMhg4dqk2bNumWW25RdHS0EhMTnfZPS0vTvffeq4MHD+qLL77Q7t27NXXqVIWHh1/3uQEgX0pPlz74QKpUSRo1SrpwQWra1Nxr+/33pbAwqysEAABwCy67vHzChAmOr6tXr64RI0Zo2bJlioqKkiStXbtWq1atUr9+/a77vceOHasuXbqoU6dOkqQpU6ZowYIFmj59ugYOHJil//Tp05WUlKTVq1erUKFCkvSvq6dfuHBBFy5ccBynpKRIkux2u+x2+3XXDOQndrtdhmEwFvKLpUtl699fti1bJEnGzTfLGDNGevBByWYzF1LDv2JcAM4xNgDnGBvuJ7vfK5thGEYu1yJJqlChQrb62Ww27d+/P9vvm5aWJn9/f33xxRdq3bq1o71jx446deqUvv766yyveeCBB1S8eHH5+/vr66+/VkhIiJ588kkNGDBAnp6eTs/z2muvadiwYVna9+zZoyJFimS7XiA/stvtSk5OVtGiReXhYdmmCPiPPA8cUJHhw+W7aJEkyR4YqNMxMTrbqZO59zauC+MCcI6xATjH2HA/qampqly5spKTkxUYGHjVfi6b6T5w4ECuvO+JEyeUnp6usH9c6hgWFqZdu3Y5fc3+/fv1008/qX379lq4cKH27t2rF154QRcvXtTQoUOdviY2NlYxMTGO45SUFEVERCgkJOSa/4GBgsBut8tmsykkJIQfEu4oOVm2ESOkCRNku3hRhqen1LWrNHSoAoKDFWB1fW6KcQE4x9gAnGNsuB/fbK5tY8nq5VfKmGi32WwuO6fdbldoaKjef/99eXp6qn79+jp69KjGjBlz1dDt4+MjHx+fLO0eHh4MCkDmGGY8uBm7XZoxQ4qNlY4fN9uio2UbO1aqXl2u+79y/sW4AJxjbADOMTbcS3a/T5Z9N2fNmqVatWrJz89Pfn5+ql27tj766KPrfp/g4GB5enoqISEhU3tCQoJKlizp9DWlSpVS5cqVM11KXq1aNcXHxystLe26awAAt7N2rbnf9nPPmYG7ShVp4UJp0SKpenWrqwMAAMg3LAndY8eOVffu3fXAAw9o7ty5mjt3rpo3b65u3bpp3Lhx1/Ve3t7eql+/vpYsWeJos9vtWrJkiWORtn9q0qSJ9u7dm+nG9z179qhUqVLy5r5FAPlZfLz0zDNSVJS0YYO53/Y770jbtkn33291dQAAAPmOJZeXT5w4UZMnT1aHDh0cbQ899JBq1Kih1157TX379r2u94uJiVHHjh3VoEEDNWrUSOPHj9eZM2ccq5l36NBB4eHhGjVqlCSpe/fumjRpkvr06aNevXrp999/18iRI9W7d++c+5AAkJekpUkTJ0rDhpl7b0tm+B41SrrKVUEAAAD47ywJ3ceOHVPjxo2ztDdu3FjHjh277vdr27atjh8/riFDhig+Pl516tTRokWLHIurHTp0KNP19hEREfrhhx/Ut29f1a5dW+Hh4erTp48GDBhw4x8KAPKqH36Q+vSRdu82jxs2NAN4ZKS1dQEAABQAloTum2++WXPnztUrr7ySqX3OnDmqVKnSDb1nz5491bNnT6fPLVu2LEtbVFSU1q5de0PnAgC3sH+/FBMjZWydGBIijR5tznCzQAsAAIBLWBK6hw0bprZt22rFihVq0qSJJGnVqlVasmSJ5s6da0VJAJB/nDljXjb+9tvShQuSp6fUu7c0ZIgUFGR1dQAAAAWKJaH7kUce0fr16zV27FjNnz9fkrl6+Pr161W3bl0rSgIA92cY0ty5Uv/+0pEjZluzZtK777IiOQAAgEVcHrovXryorl27avDgwfr4449dfXoAyJ+2bTNns5cvN4/Ll5fGjpVat5Zs7LgNAABgFZff1FeoUCF9+eWXrj4tAORPSUlSz55S3bpm4Pbzk4YPl3bskNq0IXADAABYzJKVdFq3bu24rBwAcAPS06WpU6UqVaS4OMlulx57TNq5Uxo82AzfAAAAsJwl93RXqlRJw4cP16pVq1S/fn0VLlw40/Pslw0A17BunTm7vWGDeVyjhjRhgtS0qbV1AQAAIAtLQvcHH3ygoKAgbdy4URs3bsz0nM1mI3QDgDOJiVJsrDR9unkcGGheSv7CC1KhQtbWBgAAAKcsCd0HDhyw4rQA4J4uXZImTzYvG09ONtueecbcczsszNLSAAAAcG0uD91r167Vt99+q7S0NN1zzz1q3ry5q0sAAPexYoV5Kfmvv5rH9epJkyZJUVHW1gUAAIBscelCal988YWaNGmid999V9OmTVOLFi309ttvu7IEAHAPR49KTz4p3XmnGbiLF5emTJHWrydwAwAAuBGXhu5Ro0apS5cuSk5O1smTJ/XGG29o5MiRriwBAPK2tDTprbfMVck/+8zc8qtbN2nPHqlrV8nT0+oKAQAAcB1cGrp3796t/v37y/PvXxr79eun1NRUJSYmurIMAMibfvxRqlVLGjBAOnPGnNHesMG8n7tECaurAwAAwA1waeg+e/asAgMDHcfe3t7y9fXV6dOnXVkGAOQtBw9KDz8sRUebM9phYdKHH0o//2zeww0AAAC35fKF1KZNm6aAgADH8aVLlzRz5kwFBwc72tgyDECBcO6cNGaMNGqUdP68eel4797S0KFS0aJWVwcAAIAc4NLQXbZsWU2dOjVTW8mSJfXRRx85jtmnG0CB8O23Up8+UsYWinffLU2cKNWoYW1dAAAAyFEuDd0HDx505ekAIO/Zt88M2wsWmMfh4dI770iPP24umgYAAIB8xaX3dANAgXX2rDRkiDmTvWCBVKiQuWDarl1S27YEbgAAgHzK5fd0A0CBYhjS119LL74o/fGH2XbvvdKECVLVqpaWBgAAgNxH6AaA3PL77+bCaIsWmccREdK4ceZK5cxsAwAAFAhcXg4AOe3MGenVV6WaNc3A7e0tvfKKtHOn9MgjBG4AAIAChJluAMgphiHNmyf17SsdPmy2NW8uvfuuVLmytbUBAADAEpbNdO/bt0+DBg1Su3btlJiYKEn6/vvv9dtvv1lVEgDcuN27peho6dFHzcBdrpz01VfSwoUEbgAAgALMktC9fPly1apVS+vWrdO8efN0+vRpSdLWrVs1dOhQK0oCgBtz+rQ0cKBUq5a0eLHk4yMNHizt2CG1bs2l5AAAAAWcJaF74MCBeuONN7R48WJ5e3s72ps2baq1a9daURIAXB/DkObONVcgf/NN6eJFqUULaft2afhwyd/f6goBAACQB1gSun/99Ve1adMmS3toaKhOnDhhQUUAcB127TK3/WrbVjp6VKpQQfrmG+m776Sbb7a6OgAAAOQhloTuoKAgHTt2LEv75s2bFR4ebkFFAJANGZeS164tLVliXko+dKj022/Sgw9aXR0AAADyIEtC9xNPPKEBAwYoPj5eNptNdrtdq1atUv/+/dWhQwcrSgKAqzMM6YsvpGrVMl9KvmOH9Nprkp+f1RUCAAAgj7IkdI8cOVJVq1ZVRESETp8+rerVq+uOO+5Q48aNNWjQICtKAgDnMlYlf+wx6cgRqXz5y5eS33ST1dUBAAAgj7Nkn25vb29NnTpVgwcP1vbt23X69GnVrVtXlSpVsqIcAMjqzBlpxAjp7bfNmW0fH2nAAPPycma2AQAAkE2WhO6ff/5Zt912m8qWLauyZctaUQIAOGcY0vz50osvSocOmW333y9NmMAiaQAAALhullxe3rRpU1WoUEGvvPKKduzYYUUJAJDV779LDzwgPfywGbjLlTMD+IIFBG4AAADcEEtC959//ql+/fpp+fLlqlmzpurUqaMxY8boyJEjVpQDoKA7e1YaPFiqWVNatEjy9pYGDTIXSmvVSrLZrK4QAAAAbsqS0B0cHKyePXtq1apV2rdvnx577DF9+OGHKl++vJo2bWpFSQAKIsOQvv5aql5deuMNKS3NXDRt+3bp9dclf3+rKwQAAICbsyR0X6lChQoaOHCgRo8erVq1amn58uVWlwSgINi3T2rZUmrdWvrjD6lsWWnePOn77yUWdQQAAEAOsTR0r1q1Si+88IJKlSqlJ598UjVr1tSCBQusLAlAfnfhgjmrXbOmtHChVKiQ9Mor5qXkbdpwKTkAAABylCWrl8fGxmr27Nn6888/de+99+rdd99Vq1at5M+lnABy008/SS+8YO69LUnNmklxcVLlytbWBQAAgHzLktC9YsUKvfTSS3r88ccVHBxsRQkACpKEBKl/f+njj83jkiWlceOktm2Z2QYAAECusiR0r1q1yorTAiho7Hbp/fel2Fjp1CkzYPfoYV5eXrSo1dUBAACgAHBZ6P7mm290//33q1ChQvrmm2+u2fehhx5yUVUA8q3Nm6Vu3aT1683j+vWlKVOkBg2srQsAAAAFistCd+vWrRUfH6/Q0FC1bt36qv1sNpvS09NdVRaA/CY1VRoyRJowwZzpDgyURoyQuneXPD2trg4AAAAFjMtCt91ud/o1AOQIw5C+/FLq00f680+z7YknpLFjpVKlrK0NAAAABZYlW4bNmjVLFy5cyNKelpamWbNmWVARALe2b5/0wAPSY4+ZgbtiRemHH6TPPiNwAwAAwFKWhO5OnTopOTk5S3tqaqo6depkQUUA3NKVe24vWiR5e0tDh0rbt0v33Wd1dQAAAIA1q5cbhiGbk216jhw5oqKsKAwgO5YuNe/TZs9tAAAA5GEuDd1169aVzWaTzWbTPffcIy+vy6dPT0/XgQMH1Lx5c1eWBMDdHD9u7rmdcStKWJi55/YTT7DnNgAAAPIcl4bujFXLt2zZoujoaAUEBDie8/b2Vvny5fXII4/c0HvHxcVpzJgxio+P1y233KKJEyeqUaNG//q62bNnq127dmrVqpXmz59/Q+cG4AJ2uzRjhvTyy1JSkhmwu3c3VyYPCrK6OgAAAMApl4buoUOHSpLKly+vtm3bytfXN0fed86cOYqJidGUKVMUGRmp8ePHKzo6Wrt371ZoaOhVX3fw4EH1799ft99+e47UASCX7Nhh7rm9cqV5fMst0vvvS9n4hzUAAADASjbDMAyri/ivIiMj1bBhQ02aNEmSuSVZRESEevXqpYEDBzp9TXp6uu644w49++yzWrlypU6dOnXNme4LFy5kWnE9JSVFEREROnnypAIDA3P08wDuxm636/jx4woJCZGHRw6uz3junGwjR0pjxsh28aIMf38Zw4dLvXpJXpYsSQFkW66NC8DNMTYA5xgb7iclJUXFihVTcnLyNTOhJb+1pqena9y4cZo7d64OHTqktLS0TM8nJSVl+73S0tK0ceNGxcbGOto8PDzUrFkzrVmz5qqvGz58uEJDQ9W5c2etzJg9u4ZRo0Zp2LBhWdqPHz+u8+fPZ7teID+y2+1KTk6WYRg59kPCe/lyBQ4cKK+DByVJ5++7TylvvCF7RIR5eTmQx+XGuADyA8YG4Bxjw/2kpqZmq58loXvYsGGaNm2a+vXrp0GDBunVV1/VwYMHNX/+fA0ZMuS63uvEiRNKT09XWFhYpvawsDDt2rXL6Wt+/vlnffDBB9qyZUu2zxMbG6uYmBjHccZMd0hICDPdKPDsdrtsNlvO/MtsQoJs/frJ9tlnkiQjPFzGu+/Ku3VrBbNQGtxIjo4LIB9hbADOMTbcT3Zvl7YkdH/yySeaOnWqWrRooddee03t2rVTxYoVVbt2ba1du1a9e/fOtXOnpqbq6aef1tSpUxUcHJzt1/n4+MjHxydLu4eHB4MCkGSz2f7beLDbpWnTpAEDpFOnJA8PqVcv2V5/XbYiRXK0VsBV/vO4APIpxgbgHGPDvWT3+2RJ6I6Pj1etWrUkSQEBAUpOTpYktWzZUoMHD76u9woODpanp6cSEhIytSckJKhkyZJZ+u/bt08HDx7Ugw8+6Giz2+2SJC8vL+3evVsVK1a8rhoA/Efbt0tdu0qrV5vH9eqZC6XVr29tXQAAAMB/ZMk/oZQpU0bHjh2TJFWsWFE//vijJOmXX35xOpt8Ld7e3qpfv76WLFniaLPb7VqyZImioqKy9K9atap+/fVXbdmyxfF46KGHdPfdd2vLli2KiIj4D58MwHU5e1aKjZXq1jUDd0CANH68tG4dgRsAAAD5giUz3W3atNGSJUsUGRmpXr166amnntIHH3ygQ4cOqW/fvtf9fjExMerYsaMaNGigRo0aafz48Tpz5ow6deokSerQoYPCw8M1atQo+fr6qmbNmpleH/T3Hr//bAeQi77/XurRQzpwwDxu00aaMEEqU8baugAAAIAcZEnoHj16tOPrtm3bqmzZslqzZo0qVaqU6bLv7Grbtq2OHz+uIUOGKD4+XnXq1NGiRYsci6sdOnSI+yKAvOLYMalvX2nOHPM4IkKaNEl66CFr6wIAAAByQb7Yp9sKKSkpKlq06L/uyQYUBHa7XYmJiQoNDb36P3DZ7dJ770kDB0opKZKnp9SnjzRsmHlZOZDPZGtcAAUQYwNwjrHhfrKbCV020/3NN99ku+9DzHgB+cu2beZCaWvXmscNG5oBvG5da+sCAAAAcpnLQnfr1q2z1c9msyk9PT13iwHgGmfOSMOHS++8I6WnS0WKSCNHSt27mzPdAAAAQD7nstCdsS0XgAJi4UJzobSDB83jRx81VyYPD7eyKgAAAMClLFlIDUA+duyYea/255+bx2XLSnFxUsuW1tYFAAAAWMCS0D18+PBrPj9kyBAXVQIgx9jt0uTJ0iuvXF4orW9faehQFkoDAABAgWVJ6P7qq68yHV+8eFEHDhyQl5eXKlasSOgG3M3WrSr+3HPy2LTJPG7UyFworU4dS8sCAAAArGZJ6N68eXOWtpSUFD3zzDNq06aNBRUBuCFnzkjDhsk2dqy809NlFCki26hRUrduLJQGAAAASMozG8AFBgZq2LBhGjx4sNWlAMiOhQulGjWkMWNkS0/X+ZYtZezYYS6eRuAGAAAAJOWxhdSSk5OVnJxsdRkAruXPP6UXX8y0UJp94kSdatRIoaGhlpYGAAAA5DWWhO4JEyZkOjYMQ8eOHdNHH32k+++/34qSAPwbu12aMkWKjc28UNprr0l+flJiotUVAgAAAHmOJaF73LhxmY49PDwUEhKijh07KjY21oqSAFzLr79Kzz8vrV1rHjdsKL3//uWF0ux2y0oDAAAA8jJLQveBAwesOC2A63XunDR8uPT229KlS1KRItLIkVL37ty3DQAAAGRDnrqnG0AesnixuQr5/v3mcZs20oQJUpky1tYFAAAAuBFLQvf58+c1ceJELV26VImJibL/49LUTRl7/QJwvcREKSZG+uQT8zg8XIqLk1q1srYuAAAAwA1ZEro7d+6sH3/8UY8++qgaNWokm81mRRkArmQY0owZUv/+0smTks0m9eolvfGGeVk5AAAAgOtmSej+7rvvtHDhQjVp0sSK0wP4p927pa5dpeXLzeM6dcyF0ho2tLQsAAAAwN15WHHS8PBwFWHmDLDehQvSsGFS7dpm4Pb3l8aMkX75hcANAAAA5ABLQvc777yjAQMG6I8//rDi9AAkM2Tfcou5z3ZamnT//dJvv5mXl3uxxiIAAACQEyz5zbpBgwY6f/68brrpJvn7+6tQoUKZnk9KSrKiLKBgSEqSXnpJmj7dPA4Lk959V3r8cfM+bgAAAAA5xpLQ3a5dOx09elQjR45UWFgYC6kBrmAY0qefSn37SsePm23PPy+NHi0VK2ZtbQAAAEA+ZUnoXr16tdasWaNbbrnFitMDBc/+/VL37tKPP5rH1atL770n3XabtXUBAAAA+Zwl93RXrVpV586ds+LUQMFy8aL05ptSzZpm4PbxkV5/Xdq8mcANAAAAuIAloXv06NHq16+fli1bpr/++kspKSmZHgBywPr1UoMG0sCB0rlz0t13S9u2SYMGSd7eVlcHAAAAFAiWXF7evHlzSdI999yTqd0wDNlsNqWnp1tRFpA/pKSYwXrSJPM+7uLFpXfekTp2ZKE0AAAAwMUsCd1Lly614rRA/jd/vtSzp3T0qHn89NNm4A4JsbQsAAAAoKCyJHTfeeedVpwWyL+OHJF69TJDtyRVrChNmSI1a2ZpWQAAAEBBZ0noXrFixTWfv+OOO1xUCeDm0tOlyZOlV16RUlMlLy9zD+7BgyU/P6urAwAAAAo8S0L3XXfdlaXtyr26uacbyIZt28x9ttetM49vvVV6/32pVi1r6wIAAADgYMnq5SdPnsz0SExM1KJFi9SwYUP9mLGPMADnzp41VySvX98M3IGBUlyctGoVgRsAAADIYyyZ6S5atGiWtnvvvVfe3t6KiYnRxo0bLagKcAOLF0vdukn795vHDz8sTZgghYdbWxcAAAAApyyZ6b6asLAw7d692+oygLznxAmpQwfpvvvMwF2mjPT119KXXxK4AQAAgDzMkpnubdu2ZTo2DEPHjh3T6NGjVadOHStKAvImw5A+/VR68UUzeNts5irlb7whFSlidXUAAAAA/oUlobtOnTqy2WwyDCNT+6233qrp06dbURKQ9xw8aF5K/sMP5nGtWtLUqVJkpKVlAQAAAMg+S0L3gQMHMh17eHgoJCREvr6+VpQD5C3p6eZ92oMGmYum+fhIQ4aYW4EVKmR1dQAAAACugyWhu1y5clacFsj7tm6VunSRfvnFPL7jDnMbsCpVrK0LAAAAwA1x6UJqP/30k6pXr66UlJQszyUnJ6tGjRpauXKlK0sC8oZz56RXXpEaNDADd9GiZtheupTADQAAALgxl4bu8ePHq0uXLgoMDMzyXNGiRdW1a1eNHTvWlSUB1lu6VKpdWxo1Srp0SXrkEWnnTnPG2yNPbTAAAAAA4Dq59Df6rVu3qnnz5ld9/r777mOPbhQcJ0+awbppU2nvXql0aemrr6QvvpBKlbK6OgAAAAA5wKWhOyEhQYWusRCUl5eXjh8/7sKKAAsYhhmsq1WTpk0z27p1k3bskFq3trQ0AAAAADnLpaE7PDxc27dvv+rz27ZtUylm+JCfHTliBuvHHpMSEqSqVaUVK6TJk837uAEAAADkKy4N3Q888IAGDx6s8+fPZ3nu3LlzGjp0qFq2bOnKkgDXsNvNYF29uvTNN+bWX0OGSFu2SLffbnV1AAAAAHKJS7cMGzRokObNm6fKlSurZ8+eqvL3qsy7du1SXFyc0tPT9eqrr7qyJCD37dpl3rv988/m8a23mpeV16hhbV0AAAAAcp1LQ3dYWJhWr16t7t27KzY2VoZhSJJsNpuio6MVFxensLAwV5YE5J6LF6UxY6Rhw6S0NCkgQBo5UnrhBcnT0+rqAAAAALiAS0O3JJUrV04LFy7UyZMntXfvXhmGoUqVKqlYsWKuLgXIPRs2SM89J23dah43by5NmSKVK2dtXQAAAABcyuWhO0OxYsXUsGFDq04P5I6zZ6XXXpPeece8j7tECWn8eKl9e8lms7o6AAAAAC7m0oXUclNcXJzKly8vX19fRUZGav369VftO3XqVN1+++0qVqyYihUrpmbNml2zP5AtS5dKtWubl5Tb7dITT5jbgD31FIEbAAAAKKDyReieM2eOYmJiNHToUG3atEm33HKLoqOjlZiY6LT/smXL1K5dOy1dulRr1qxRRESE7rvvPh09etTFlSNfOHXKXCitaVNp3z4pPFz69lvps8+k0FCrqwMAAABgIZuRsZqZG4uMjFTDhg01adIkSZLdbldERIR69eqlgQMH/uvr09PTVaxYMU2aNEkdOnRw2ufChQu6cOGC4zglJUURERE6efKkAgMDc+aDwP3Mny9bz56yHTsmSTK6dZMxapRUwP5O2O12HT9+XCEhIfLwyBf/lgf8Z4wLwDnGBuAcY8P9pKSkqFixYkpOTr5mJrTsnu6ckpaWpo0bNyo2NtbR5uHhoWbNmmnNmjXZeo+zZ8/q4sWLKl68+FX7jBo1SsOGDcvSfvz4caf7jiN/80hMVOCrr8r3u+8kSZcqVlTymDG6GBUlnT9vPgoQu92u5ORkGYbBDwngb4wLwDnGBuAcY8P9pKamZquf24fuEydOKD09PctWY2FhYdq1a1e23mPAgAEqXbq0mjVrdtU+sbGxiomJcRxnzHSHhIQw012QGIb04Yey9e8v28mTMjw9pZdeksfgwSrm62t1dZax2+2y2Wz8yyxwBcYF4BxjA3COseF+fLP5+7/bh+7/avTo0Zo9e7aWLVt2zf9oPj4+8vHxydLu4eHBoCgoDhyQunaVFi82j+vVk+2DD6Q6dcQyaZLNZmM8AP/AuACcY2wAzjE23Et2v09u/90MDg6Wp6enEhISMrUnJCSoZMmS13zt22+/rdGjR+vHH39U7dq1c7NMuLP0dGncOKlmTTNw+/pKb74prVsn1aljdXUAAAAA8jC3D93e3t6qX7++lixZ4miz2+1asmSJoqKirvq6t956S6+//roWLVqkBg0auKJUuKNt26SoKCkmxtyD+847zbaXX5a8CvyFIgAAAAD+hduHbkmKiYnR1KlT9eGHH2rnzp3q3r27zpw5o06dOkmSOnTokGmhtTfffFODBw/W9OnTVb58ecXHxys+Pl6nT5+26iMgrzl/Xnr1Val+femXX6SiRaX33pN++kmqVMnq6gAAAAC4iXwxVde2bVsdP35cQ4YMUXx8vOrUqaNFixY5Flc7dOhQpuvtJ0+erLS0ND366KOZ3mfo0KF67bXXXFk68qLly6Xnn5f27DGPH3lEmjhRKlXK2roAAAAAuJ18sU+3FVJSUlS0aNF/3ZMNbuTUKfOy8alTzeNSpaS4OKlNG0vLcgd2u12JiYkKDQ1l4Q/gb4wLwDnGBuAcY8P9ZDcT8t0EJGnePKlatcuBu2tXaccOAjcAAACA/yRfXF4O3LCjR6WePaX5883jypXN4H3HHZaWBQAAACB/YKYbBZPdbi6MVr26Gbi9vKRBg6StWwncAAAAAHIMM90oeHbvlrp0kVauNI8bNZKmTZNq1bK2LgAAAAD5DjPdKDjS0qQRI6Tatc3AXbiwNH68tHo1gRsAAABArmCmGwXDunXSc89J27ebx82bS5MnS+XLW1oWAAAAgPyNmW7kb6dPS337SlFRZuAODpY++URauJDADQAAACDXMdON/OuHH8ytv/74wzx+6ilp3DgzeAMAAACACzDTjfznxAmpQwfzEvI//pDKlZO+/1766CMCNwAAAACXInQj/zAM6bPPzG3APvpIstmkF180Lytv3tzq6gAAAAAUQFxejvzh0CHphRekBQvM45o1zW3AIiOtrQsAAABAgcZMN9yb3S5NmiTVqGEGbm9vafhwaeNGAjcAAAAAyzHTDfe1c6e5Ddjq1eZxkybS1KlStWrW1gUAAAAAf2OmG+4nLc2cza5TxwzcAQHmbPeKFQRuAAAAAHkKM91wL2vXmrPbv/1mHrdoIU2eLEVEWFsXAAAAADjBTDfcw+nT5krkjRubgTskxFyp/NtvCdwAAAAA8ixmupH3/fCD1LWruee2ZO7BPXasVKKEtXUBAAAAwL8gdCPv+usvqW9fc89tSSpfXnrvPem++ywtCwAAAACyi8vLkfcYhjRnjrko2kcfSTabeWn5r78SuAEAAAC4FWa6kbccOSK98IJ5r7Zk7r/9wQfsuQ0AAADALTHTjbzBbjcvHa9RwwzchQpJw4ZJmzYRuAEAAAC4LWa6Yb3ff5e6dJGWLzePb71VmjbNDOAAAAAA4MaY6YZ1Ll2S3nxTql3bDNz+/tL48dLPPxO4AQAAAOQLzHTDGps3S507m39K0r33mpeXV6hgbV0AAAAAkIOY6YZrnTsnxcZKDRuagbtYMWnmTHMvbgI3AAAAgHyGmW64zsqV0nPPSXv2mMePPSZNmCCVLGltXQAAAACQS5jpRu5LSTG3AbvjDjNwlyolffWVNHcugRsAAABAvsZMN3LXt9+agfvIEfO4SxfprbekoCBLywIAAAAAVyB0I3ccPiz16WPOaEtSxYrS1KnS3XdbWxcAAAAAuBCXlyNnXbwovfOOVK2aGbi9vKSXXpK2bSNwAwAAAChwmOlGzlmzRurWzQzYktSkiTR5slSrlrV1AQAAAIBFmOnGf5eUJHXtKjVubAbu4sWlDz6QVqwgcAMAAAAo0Jjpxo0zDOnjj6V+/aTjx822Tp3MhdKCg62tDQAAAADyAEI3bsyuXVL37tKyZeZx9ermpeR33GFpWQAAAACQl3B5Oa7PuXPS4MFS7dpm4Pbzk0aNkjZvJnADAAAAwD8w043sW7RI6tFD2r/fPH7gAWnSJKlCBWvrAgAAAIA8iplu/LujR6XHH5fuv98M3GXKSF9+KX33HYEbAAAAAK6B0I2ru3hRmjDB3HP7888lT08pJkbasUN6+GHJZrO6QgAAAADI07i8HFnFx0tTp0pTpkh//mm2RUaax3XqWFoaAAAAALgTQjdMhiGtXWveo/355+YstySFhkrDhknPPy95cGEEAAAAAFwPQndBd+6c9NlnZtjevPlye1SU1LOn9Mgjko+PdfUBAAAAgBsjdBdU+/eb+2pPny4lJZltvr7Sk0+aK5TXq2dtfQAAAACQDxC6CxK7XVq82JzVXrDAvKRcksqXl154QXr2WalECUtLBAAAAID8hNBdEJw6Jc2cKcXFSXv3Xm6PjjYvIb//fnNlcgAAAABAjiJ052e//moG7Y8+ks6eNdsCA6VOncyZ7cqVra0PAAAAAPK5fLMcdVxcnMqXLy9fX19FRkZq/fr11+z/+eefq2rVqvL19VWtWrW0cOFCF1XqInFxUu3a0nvvmYG7Zk3zHu6jR6Xx4wncAAAAAOAC+SJ0z5kzRzExMRo6dKg2bdqkW265RdHR0UpMTHTaf/Xq1WrXrp06d+6szZs3q3Xr1mrdurW2b9/u4spz0QMPSN7e0qOPSsuWSdu2Sd26SQEBVlcGAAAAAAWGzTAyVtNyX5GRkWrYsKEmTZokSbLb7YqIiFCvXr00cODALP3btm2rM2fO6LvvvnO03XrrrapTp46mTJni9BwXLlzQhQsXHMcpKSmKiIjQyZMnFRgYmMOfKIckJUnFi1tdBQoAu92u48ePKyQkRB7s5w5IYlwAV8PYAJxjbLiflJQUFStWTMnJydfMhG5/T3daWpo2btyo2NhYR5uHh4eaNWumNWvWOH3NmjVrFBMTk6ktOjpa8+fPv+p5Ro0apWHDhmVpP378uM6fP39jxbvCVWb7gZxkt9uVnJwswzD4IQH8jXEBOMfYAJxjbLif1NTUbPVz+9B94sQJpaenKywsLFN7WFiYdu3a5fQ18fHxTvvHx8df9TyxsbGZgnrGTHdISEjenekGXMRut8tms/Evs8AVGBeAc4wNwDnGhvvx9fXNVj+3D92u4uPjIx8fnyztHh4eDApAks1mYzwA/8C4AJxjbADOMTbcS3a/T27/3QwODpanp6cSEhIytSckJKhkyZJOX1OyZMnr6g8AAAAAwI1w+9Dt7e2t+vXra8mSJY42u92uJUuWKCoqyulroqKiMvWXpMWLF1+1PwAAAAAANyJfXF4eExOjjh07qkGDBmrUqJHGjx+vM2fOqFOnTpKkDh06KDw8XKNGjZIk9enTR3feeafeeecdtWjRQrNnz9aGDRv0/vvvW/kxAAAAAAD5TL4I3W3bttXx48c1ZMgQxcfHq06dOlq0aJFjsbRDhw5lut6+cePG+vTTTzVo0CC98sorqlSpkubPn6+aNWta9REAAAAAAPlQvtin2wopKSkqWrTov+7JBhQEdrtdiYmJCg0NZeEP4G+MC8A5xgbgHGPD/WQ3E/LdBAAAAAAglxC6AQAAAADIJYRuAAAAAABySb5YSM0KGbfCp6SkWFwJYD273a7U1FT5+vpyDxLwN8YF4BxjA3COseF+MrLgvy2TRui+QampqZKkiIgIiysBAAAAAFglNTVVRYsWverzrF5+g+x2u/78808VKVJENpvN6nIAS6WkpCgiIkKHDx9mNX/gb4wLwDnGBuAcY8P9GIah1NRUlS5d+ppXJzDTfYM8PDxUpkwZq8sA8pTAwEB+SAD/wLgAnGNsAM4xNtzLtWa4M3CzAAAAAAAAuYTQDQAAAABALiF0A/jPfHx8NHToUPn4+FhdCpBnMC4A5xgbgHOMjfyLhdQAAAAAAMglzHQDAAAAAJBLCN0AAAAAAOQSQjcAAAAAALmE0A0AAAAAQC4hdAMF0KhRo9SwYUMVKVJEoaGhat26tXbv3p2pz/nz59WjRw+VKFFCAQEBeuSRR5SQkJCpz6FDh9SiRQv5+/srNDRUL730ki5dupSpz7Jly1SvXj35+Pjo5ptv1syZM7PUExcXp/Lly8vX11eRkZFav359jn9m4HqNHj1aNptNL774oqONcYGC6ujRo3rqqadUokQJ+fn5qVatWtqwYYPjecMwNGTIEJUqVUp+fn5q1qyZfv/990zvkZSUpPbt2yswMFBBQUHq3LmzTp8+nanPtm3bdPvtt8vX11cRERF66623stTy+eefq2rVqvL19VWtWrW0cOHC3PnQwL9IT0/X4MGDVaFCBfn5+alixYp6/fXXdeU61YwNSJIMAAVOdHS0MWPGDGP79u3Gli1bjAceeMAoW7ascfr0aUefbt26GREREcaSJUuMDRs2GLfeeqvRuHFjx/OXLl0yatasaTRr1szYvHmzsXDhQiM4ONiIjY119Nm/f7/h7+9vxMTEGDt27DAmTpxoeHp6GosWLXL0mT17tuHt7W1Mnz7d+O2334wuXboYQUFBRkJCgmv+YwBOrF+/3ihfvrxRu3Zto0+fPo52xgUKoqSkJKNcuXLGM888Y6xbt87Yv3+/8cMPPxh79+519Bk9erRRtGhRY/78+cbWrVuNhx56yKhQoYJx7tw5R5/mzZsbt9xyi7F27Vpj5cqVxs0332y0a9fO8XxycrIRFhZmtG/f3ti+fbvx2WefGX5+fsZ7773n6LNq1SrD09PTeOutt4wdO3YYgwYNMgoVKmT8+uuvrvmPAVxhxIgRRokSJYzvvvvOOHDggPH5558bAQEBxrvvvuvow9iAYRgGoRuAkZiYaEgyli9fbhiGYZw6dcooVKiQ8fnnnzv67Ny505BkrFmzxjAMw1i4cKHh4eFhxMfHO/pMnjzZCAwMNC5cuGAYhmG8/PLLRo0aNTKdq23btkZ0dLTjuFGjRkaPHj0cx+np6Ubp0qWNUaNG5fwHBbIhNTXVqFSpkrF48WLjzjvvdIRuxgUKqgEDBhi33XbbVZ+32+1GyZIljTFjxjjaTp06Zfj4+BifffaZYRiGsWPHDkOS8csvvzj6fP/994bNZjOOHj1qGIZh/O9//zOKFSvmGCsZ565SpYrj+PHHHzdatGiR6fyRkZFG165d/9uHBG5AixYtjGeffTZT28MPP2y0b9/eMAzGBi7j8nIASk5OliQVL15ckrRx40ZdvHhRzZo1c/SpWrWqypYtqzVr1kiS1qxZo1q1aiksLMzRJzo6WikpKfrtt98cfa58j4w+Ge+RlpamjRs3Zurj4eGhZs2aOfoArtajRw+1aNEiy99dxgUKqm+++UYNGjTQY489ptDQUNWtW1dTp051PH/gwAHFx8dn+jtbtGhRRUZGZhobQUFBatCggaNPs2bN5OHhoXXr1jn63HHHHfL29nb0iY6O1u7du3Xy5ElHn2uNH8CVGjdurCVLlmjPnj2SpK1bt+rnn3/W/fffL4mxgcu8rC4AgLXsdrtefPFFNWnSRDVr1pQkxcfHy9vbW0FBQZn6hoWFKT4+3tHnymCR8XzGc9fqk5KSonPnzunkyZNKT0932mfXrl059hmB7Jo9e7Y2bdqkX375JctzjAsUVPv379fkyZMVExOjV155Rb/88ot69+4tb29vdezY0fF329nf2Sv/3oeGhmZ63svLS8WLF8/Up0KFClneI+O5YsWKXXX8ZLwH4EoDBw5USkqKqlatKk9PT6Wnp2vEiBFq3769JDE24EDoBgq4Hj16aPv27fr555+tLgWw1OHDh9WnTx8tXrxYvr6+VpcD5Bl2u10NGjTQyJEjJUl169bV9u3bNWXKFHXs2NHi6gDrzJ07V5988ok+/fRT1ahRQ1u2bNGLL76o0qVLMzaQCZeXAwVYz5499d1332np0qUqU6aMo71kyZJKS0vTqVOnMvVPSEhQyZIlHX3+uWpzxvG/9QkMDJSfn5+Cg4Pl6enptE/GewCusnHjRiUmJqpevXry8vKSl5eXli9frgkTJsjLy0thYWGMCxRIpUqVUvXq1TO1VatWTYcOHZJ0+e/2tf7OlixZUomJiZmev3TpkpKSknJk/DA2YIWXXnpJAwcO1BNPPKFatWrp6aefVt++fTVq1ChJjA1cRugGCiDDMNSzZ0999dVX+umnn7JcslS/fn0VKlRIS5YscbTt3r1bhw4dUlRUlCQpKipKv/76a6YfFIsXL1ZgYKDjl7OoqKhM75HRJ+M9vL29Vb9+/Ux97Ha7lixZ4ugDuMo999yjX3/9VVu2bHE8GjRooPbt2zu+ZlygIGrSpEmWbSX37NmjcuXKSZIqVKigkiVLZvo7m5KSonXr1mUaG6dOndLGjRsdfX766SfZ7XZFRkY6+qxYsUIXL1509Fm8eLGqVKmiYsWKOfpca/wArnT27Fl5eGSOU56enrLb7ZIYG7iC1Su5AXC97t27G0WLFjWWLVtmHDt2zPE4e/aso0+3bt2MsmXLGj/99JOxYcMGIyoqyoiKinI8n7E10n333Wds2bLFWLRokRESEuJ0a6SXXnrJ2LlzpxEXF+d0ayQfHx9j5syZxo4dO4znn3/eCAoKyrT6M2CVK1cvNwzGBQqm9evXG15eXsaIESOM33//3fjkk08Mf39/4+OPP3b0GT16tBEUFGR8/fXXxrZt24xWrVo53Rapbt26xrp164yff/7ZqFSpUqZtkU6dOmWEhYUZTz/9tLF9+3Zj9uzZhr+/f5Ztkby8vIy3337b2LlzpzF06FC2RYJlOnbsaISHhzu2DJs3b54RHBxsvPzyy44+jA0YBluGAQWSJKePGTNmOPqcO3fOeOGFF4xixYoZ/v7+Rps2bYxjx45lep+DBw8a999/v+Hn52cEBwcb/fr1My5evJipz9KlS406deoY3t7exk033ZTpHBkmTpxolC1b1vD29jYaNWpkrF27Njc+NnDd/hm6GRcoqL799lujZs2aho+Pj1G1alXj/fffz/S83W43Bg8ebISFhRk+Pj7GPffcY+zevTtTn7/++sto166dERAQYAQGBhqdOnUyUlNTM/XZunWrcdtttxk+Pj5GeHi4MXr06Cy1zJ0716hcubLh7e1t1KhRw1iwYEHOf2AgG1JSUow+ffoYZcuWNXx9fY2bbrrJePXVVzNt7cXYgGEYhs0wDMPKmXYAAAAAAPIr7ukGAAAAACCXELoBAAAAAMglhG4AAAAAAHIJoRsAAAAAgFxC6AYAAAAAIJcQugEAAAAAyCWEbgAAAAAAcgmhGwAAAACAXELoBgAA1+Wuu+7Siy++aHUZAAC4BUI3AAAFyIMPPqjmzZs7fW7lypWy2Wzatm2bi6sCACD/InQDAFCAdO7cWYsXL9aRI0eyPDdjxgw1aNBAtWvXtqAyAADyJ0I3AAAFSMuWLRUSEqKZM2dmaj99+rQ+//xztW7dWu3atVN4eLj8/f1Vq1YtffbZZ9d8T5vNpvnz52dqCwoKynSOw4cP6/HHH1dQUJCKFy+uVq1a6eDBgznzoQAAyMMI3QAAFCBeXl7q0KGDZs6cKcMwHO2ff/650tPT9dRTT6l+/fpasGCBtm/frueff15PP/201q9ff8PnvHjxoqKjo1WkSBGtXLlSq1atUkBAgJo3b660tLSc+FgAAORZhG4AAAqYZ599Vvv27dPy5csdbTNmzNAjjzyicuXKqX///qpTp45uuukm9erVS82bN9fcuXNv+Hxz5syR3W7XtGnTVKtWLVWrVk0zZszQoUOHtGzZshz4RAAA5F2EbgAACpiqVauqcePGmj59uiRp7969WrlypTp37qz09HS9/vrrqlWrlooXL66AgAD98MMPOnTo0A2fb+vWrdq7d6+KFCmigIAABQQEqHjx4jp//rz27duXUx8LAIA8ycvqAgAAgOt17txZvXr1UlxcnGbMmKGKFSvqzjvv1Jtvvql3331X48ePV61atVS4cGG9+OKL17wM3GazZbpUXTIvKc9w+vRp1a9fX5988kmW14aEhOTchwIAIA8idAMAUAA9/vjj6tOnjz799FPNmjVL3bt3l81m06pVq9SqVSs99dRTkiS73a49e/aoevXqV32vkJAQHTt2zHH8+++/6+zZs47jevXqac6cOQoNDVVgYGDufSgAAPIgLi8HAKAACggIUNu2bRUbG6tjx47pmWeekSRVqlRJixcv1urVq7Vz50517dpVCQkJ13yvpk2batKkSdq8ebM2bNigbt26qVChQo7n27dvr+DgYLVq1UorV67UgQMHtGzZMvXu3dvp1mUAAOQnhG4AAAqozp076+TJk4qOjlbp0qUlSYMGDVK9evUUHR2tu+66SyVLllTr1q2v+T7vvPOOIiIidPvtt+vJJ59U//795e/v73je399fK1asUNmyZfXwww+rWrVq6ty5s86fP8/MNwAg37MZ/7wJCwAAAAAA5AhmugEAAAAAyCWEbgAAAAAAcgmhGwAAAACAXELoBgAAAAAglxC6AQAAAADIJYRuAAAAAAByCaEbAAAAAIBcQugGAAAAACCXELoBAAAAAMglhG4AAAAAAHIJoRsAAAAAgFzy/1LMYaEviP5ZAAAAAElFTkSuQmCC",
|
||
"text/plain": [
|
||
"<Figure size 1000x800 with 2 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": "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",
|
||
"text/plain": [
|
||
"<Figure size 1000x800 with 2 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"\n",
|
||
"Analisi distribuzionale per min_oil_prod\n",
|
||
"\n",
|
||
"Statistiche Predizioni:\n",
|
||
"mean: 5875.726\n",
|
||
"variance: 11020948.000\n",
|
||
"std: 3319.781\n",
|
||
"min: 624.859\n",
|
||
"max: 19463.191\n",
|
||
"median: 5412.554\n",
|
||
"\n",
|
||
"Statistiche Target Reali:\n",
|
||
"mean: 5911.384\n",
|
||
"variance: 11648010.000\n",
|
||
"std: 3412.918\n",
|
||
"min: 385.506\n",
|
||
"max: 22119.781\n",
|
||
"median: 5416.575\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": "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",
|
||
"text/plain": [
|
||
"<Figure size 1000x800 with 2 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": "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",
|
||
"text/plain": [
|
||
"<Figure size 1000x800 with 2 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"\n",
|
||
"Analisi distribuzionale per max_oil_prod\n",
|
||
"\n",
|
||
"Statistiche Predizioni:\n",
|
||
"mean: 7108.558\n",
|
||
"variance: 16190243.000\n",
|
||
"std: 4023.710\n",
|
||
"min: 693.562\n",
|
||
"max: 24302.926\n",
|
||
"median: 6544.735\n",
|
||
"\n",
|
||
"Statistiche Target Reali:\n",
|
||
"mean: 7144.473\n",
|
||
"variance: 17129654.000\n",
|
||
"std: 4138.798\n",
|
||
"min: 467.800\n",
|
||
"max: 28135.092\n",
|
||
"median: 6546.016\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": "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",
|
||
"text/plain": [
|
||
"<Figure size 1000x800 with 2 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": "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",
|
||
"text/plain": [
|
||
"<Figure size 1000x800 with 2 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"\n",
|
||
"Analisi distribuzionale per avg_oil_prod\n",
|
||
"\n",
|
||
"Statistiche Predizioni:\n",
|
||
"mean: 6492.088\n",
|
||
"variance: 13472738.000\n",
|
||
"std: 3670.523\n",
|
||
"min: 659.898\n",
|
||
"max: 21879.734\n",
|
||
"median: 5980.175\n",
|
||
"\n",
|
||
"Statistiche Target Reali:\n",
|
||
"mean: 6527.917\n",
|
||
"variance: 14225926.000\n",
|
||
"std: 3771.727\n",
|
||
"min: 426.118\n",
|
||
"max: 24928.848\n",
|
||
"median: 5983.750\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": "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",
|
||
"text/plain": [
|
||
"<Figure size 1000x800 with 2 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": "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",
|
||
"text/plain": [
|
||
"<Figure size 1000x800 with 2 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"\n",
|
||
"Analisi distribuzionale per total_water_need\n",
|
||
"\n",
|
||
"Statistiche Predizioni:\n",
|
||
"mean: 22511.547\n",
|
||
"variance: 114444504.000\n",
|
||
"std: 10697.874\n",
|
||
"min: 4104.441\n",
|
||
"max: 50439.238\n",
|
||
"median: 22263.105\n",
|
||
"\n",
|
||
"Statistiche Target Reali:\n",
|
||
"mean: 22536.635\n",
|
||
"variance: 120029880.000\n",
|
||
"std: 10955.814\n",
|
||
"min: 3238.847\n",
|
||
"max: 53757.914\n",
|
||
"median: 22304.934\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": "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",
|
||
"text/plain": [
|
||
"<Figure size 1000x800 with 2 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": "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",
|
||
"text/plain": [
|
||
"<Figure size 1000x800 with 2 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"run_comprehensive_analysis(retrained_model, test_data, test_targets, scaler_y)"
|
||
]
|
||
}
|
||
],
|
||
"metadata": {
|
||
"kernelspec": {
|
||
"display_name": "Python 3 (ipykernel)",
|
||
"language": "python",
|
||
"name": "python3"
|
||
},
|
||
"language_info": {
|
||
"codemirror_mode": {
|
||
"name": "ipython",
|
||
"version": 3
|
||
},
|
||
"file_extension": ".py",
|
||
"mimetype": "text/x-python",
|
||
"name": "python",
|
||
"nbconvert_exporter": "python",
|
||
"pygments_lexer": "ipython3",
|
||
"version": "3.11.0rc1"
|
||
}
|
||
},
|
||
"nbformat": 4,
|
||
"nbformat_minor": 5
|
||
}
|