1776 lines
357 KiB
Plaintext
1776 lines
357 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 17,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"|--- Al2O3 <= 0.18\n",
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"| |--- TiO2 <= 0.18\n",
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"| | |--- T <= 32.50\n",
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"| | | |--- TiO2 <= 0.03\n",
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"| | | | |--- Al2O3 <= 0.03\n",
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"| | | | | |--- T <= 22.50\n",
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"| | | | | | |--- value: [1.06]\n",
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"| | | | | |--- T > 22.50\n",
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"| | | | | | |--- value: [1.06]\n",
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"| | | | |--- Al2O3 > 0.03\n",
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"| | | | | |--- value: [1.09]\n",
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"| | | |--- TiO2 > 0.03\n",
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"| | | | |--- T <= 27.50\n",
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"| | | | | |--- T <= 22.50\n",
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"| | | | | | |--- value: [1.09]\n",
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"| | | | | |--- T > 22.50\n",
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"| | | | | | |--- value: [1.09]\n",
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"| | | | |--- T > 27.50\n",
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"| | | | | |--- value: [1.08]\n",
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"| | |--- T > 32.50\n",
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"| | | |--- TiO2 <= 0.03\n",
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"| | | | |--- Al2O3 <= 0.03\n",
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"| | | | | |--- T <= 55.00\n",
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"| | | | | | |--- T <= 47.50\n",
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"| | | | | | | |--- value: [1.05]\n",
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"| | | | | | |--- T > 47.50\n",
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"| | | | | | | |--- value: [1.04]\n",
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"| | | | | |--- T > 55.00\n",
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"| | | | | | |--- T <= 62.50\n",
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"| | | | | | | |--- value: [1.04]\n",
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"| | | | | | |--- T > 62.50\n",
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"| | | | | | | |--- value: [1.03]\n",
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"| | | | |--- Al2O3 > 0.03\n",
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"| | | | | |--- T <= 60.00\n",
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"| | | | | | |--- T <= 52.50\n",
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"| | | | | | | |--- value: [1.07]\n",
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"| | | | | | |--- T > 52.50\n",
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"| | | | | | | |--- value: [1.06]\n",
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"| | | | | |--- T > 60.00\n",
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"| | | | | | |--- T <= 67.50\n",
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"| | | | | | | |--- value: [1.06]\n",
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"| | | | | | |--- T > 67.50\n",
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"| | | | | | | |--- value: [1.05]\n",
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"| | | |--- TiO2 > 0.03\n",
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"| | | | |--- T <= 50.00\n",
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"| | | | | |--- T <= 37.50\n",
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"| | | | | | |--- value: [1.08]\n",
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"| | | | | |--- T > 37.50\n",
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"| | | | | | |--- value: [1.08]\n",
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"| | | | |--- T > 50.00\n",
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"| | | | | |--- T <= 67.50\n",
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"| | | | | | |--- T <= 62.50\n",
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"| | | | | | | |--- value: [1.06]\n",
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"| | | | | | |--- T > 62.50\n",
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"| | | | | | | |--- value: [1.06]\n",
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"| | | | | |--- T > 67.50\n",
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"| | | | | | |--- value: [1.06]\n",
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"| |--- TiO2 > 0.18\n",
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"| | |--- T <= 40.00\n",
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"| | | |--- T <= 30.00\n",
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"| | | | |--- value: [1.22]\n",
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"| | | |--- T > 30.00\n",
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"| | | | |--- value: [1.21]\n",
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"| | |--- T > 40.00\n",
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"| | | |--- T <= 60.00\n",
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"| | | | |--- T <= 52.50\n",
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"| | | | | |--- T <= 47.50\n",
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"| | | | | | |--- value: [1.20]\n",
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"| | | | | |--- T > 47.50\n",
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"| | | | | | |--- value: [1.19]\n",
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"| | | | |--- T > 52.50\n",
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"| | | | | |--- value: [1.19]\n",
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"| | | |--- T > 60.00\n",
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"| | | | |--- value: [1.18]\n",
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"|--- Al2O3 > 0.18\n",
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"| |--- T <= 35.00\n",
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"| | |--- T <= 22.50\n",
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"| | | |--- value: [1.19]\n",
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"| | |--- T > 22.50\n",
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"| | | |--- T <= 27.50\n",
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"| | | | |--- value: [1.18]\n",
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"| | | |--- T > 27.50\n",
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"| | | | |--- value: [1.18]\n",
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"| |--- T > 35.00\n",
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"| | |--- T <= 52.50\n",
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"| | | |--- T <= 42.50\n",
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"| | | | |--- value: [1.17]\n",
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"| | | |--- T > 42.50\n",
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"| | | | |--- T <= 47.50\n",
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"| | | | | |--- value: [1.17]\n",
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"| | | | |--- T > 47.50\n",
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"| | | | | |--- value: [1.16]\n",
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"| | |--- T > 52.50\n",
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"| | | |--- T <= 65.00\n",
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"| | | | |--- T <= 57.50\n",
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"| | | | | |--- value: [1.16]\n",
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"| | | | |--- T > 57.50\n",
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"| | | | | |--- value: [1.15]\n",
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"| | | |--- T > 65.00\n",
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"| | | | |--- value: [1.14]\n",
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"\n"
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]
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}
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],
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"source": [
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"import pickle\n",
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"import pandas as pd\n",
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"from sklearn import tree\n",
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"\n",
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"model = pickle.load(open(\"data/dtree.model.sav\", \"rb\"))\n",
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"features = (\n",
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" pd.read_csv(\"data/density_train.csv\", sep=\";\", decimal=\",\")\n",
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" .drop([\"Density\"], axis=1)\n",
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" .columns.values.tolist()\n",
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")\n",
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"\n",
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"rules = tree.export_text(model, feature_names=features)\n",
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"print(rules)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 18,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"34"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"data": {
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"text/plain": [
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"[if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (T > 32.5) and (TiO2 <= 0.025) and (Al2O3 <= 0.025) and (T > 55.0) and (T > 62.5) -> 1.033,\n",
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" if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (T > 32.5) and (TiO2 <= 0.025) and (Al2O3 <= 0.025) and (T > 55.0) and (T <= 62.5) -> 1.038,\n",
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" if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (T <= 32.5) and (TiO2 <= 0.025) and (Al2O3 > 0.025) -> 1.088,\n",
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" if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (T <= 32.5) and (TiO2 > 0.025) and (T <= 27.5) and (T <= 22.5) -> 1.091,\n",
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" if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (T <= 32.5) and (TiO2 > 0.025) and (T <= 27.5) and (T > 22.5) -> 1.088,\n",
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" if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (T <= 32.5) and (TiO2 > 0.025) and (T > 27.5) -> 1.084,\n",
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" if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (T > 32.5) and (TiO2 <= 0.025) and (Al2O3 <= 0.025) and (T <= 55.0) and (T <= 47.5) -> 1.051,\n",
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" if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (T > 32.5) and (TiO2 <= 0.025) and (Al2O3 <= 0.025) and (T <= 55.0) and (T > 47.5) -> 1.045,\n",
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" if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (T <= 32.5) and (TiO2 <= 0.025) and (Al2O3 <= 0.025) and (T > 22.5) -> 1.06,\n",
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" if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (T <= 32.5) and (TiO2 <= 0.025) and (Al2O3 <= 0.025) and (T <= 22.5) -> 1.062,\n",
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" if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (T > 32.5) and (TiO2 <= 0.025) and (Al2O3 > 0.025) and (T <= 60.0) and (T <= 52.5) -> 1.069,\n",
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" if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (T > 32.5) and (TiO2 <= 0.025) and (Al2O3 > 0.025) and (T <= 60.0) and (T > 52.5) -> 1.064,\n",
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" if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (T > 32.5) and (TiO2 <= 0.025) and (Al2O3 > 0.025) and (T > 60.0) and (T <= 67.5) -> 1.057,\n",
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" if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (T > 32.5) and (TiO2 <= 0.025) and (Al2O3 > 0.025) and (T > 60.0) and (T > 67.5) -> 1.053,\n",
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" if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (T > 32.5) and (TiO2 > 0.025) and (T <= 50.0) and (T <= 37.5) -> 1.081,\n",
|
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" if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (T > 32.5) and (TiO2 > 0.025) and (T <= 50.0) and (T > 37.5) -> 1.078,\n",
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" if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (T > 32.5) and (TiO2 > 0.025) and (T > 50.0) and (T <= 67.5) and (T <= 62.5) -> 1.064,\n",
|
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" if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (T > 32.5) and (TiO2 > 0.025) and (T > 50.0) and (T <= 67.5) and (T > 62.5) -> 1.06,\n",
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" if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (T > 32.5) and (TiO2 > 0.025) and (T > 50.0) and (T > 67.5) -> 1.056,\n",
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" if (Al2O3 <= 0.175) and (TiO2 > 0.175) and (T <= 40.0) and (T <= 30.0) -> 1.219,\n",
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" if (Al2O3 <= 0.175) and (TiO2 > 0.175) and (T <= 40.0) and (T > 30.0) -> 1.208,\n",
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" if (Al2O3 <= 0.175) and (TiO2 > 0.175) and (T > 40.0) and (T <= 60.0) and (T <= 52.5) and (T <= 47.5) -> 1.198,\n",
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" if (Al2O3 <= 0.175) and (TiO2 > 0.175) and (T > 40.0) and (T <= 60.0) and (T <= 52.5) and (T > 47.5) -> 1.193,\n",
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" if (Al2O3 <= 0.175) and (TiO2 > 0.175) and (T > 40.0) and (T <= 60.0) and (T > 52.5) -> 1.187,\n",
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" if (Al2O3 <= 0.175) and (TiO2 > 0.175) and (T > 40.0) and (T > 60.0) -> 1.178,\n",
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" if (Al2O3 > 0.175) and (T <= 35.0) and (T <= 22.5) -> 1.189,\n",
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" if (Al2O3 > 0.175) and (T <= 35.0) and (T > 22.5) and (T <= 27.5) -> 1.184,\n",
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" if (Al2O3 > 0.175) and (T <= 35.0) and (T > 22.5) and (T > 27.5) -> 1.179,\n",
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" if (Al2O3 > 0.175) and (T > 35.0) and (T <= 52.5) and (T <= 42.5) -> 1.17,\n",
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" if (Al2O3 > 0.175) and (T > 35.0) and (T <= 52.5) and (T > 42.5) and (T <= 47.5) -> 1.166,\n",
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" if (Al2O3 > 0.175) and (T > 35.0) and (T <= 52.5) and (T > 42.5) and (T > 47.5) -> 1.161,\n",
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" if (Al2O3 > 0.175) and (T > 35.0) and (T > 52.5) and (T <= 65.0) and (T <= 57.5) -> 1.157,\n",
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" if (Al2O3 > 0.175) and (T > 35.0) and (T > 52.5) and (T <= 65.0) and (T > 57.5) -> 1.152,\n",
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" if (Al2O3 > 0.175) and (T > 35.0) and (T > 52.5) and (T > 65.0) -> 1.144]"
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]
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},
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"execution_count": 18,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"from src.rules import get_rules\n",
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"\n",
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"\n",
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"rules = get_rules(model, features)\n",
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"display(len(rules))\n",
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"rules"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 19,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"34"
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]
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},
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"metadata": {},
|
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"output_type": "display_data"
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},
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{
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"data": {
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"text/plain": [
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"[if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (T > 32.5) -> 1.033,\n",
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" if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (T > 32.5) and (T <= 62.5) -> 1.038,\n",
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" if (Al2O3 <= 0.175) and (Al2O3 > 0.025) and (TiO2 <= 0.175) and (T <= 32.5) -> 1.088,\n",
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" if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (TiO2 > 0.025) and (T <= 32.5) -> 1.091,\n",
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" if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (TiO2 > 0.025) and (T <= 32.5) and (T > 22.5) -> 1.088,\n",
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" if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (TiO2 > 0.025) and (T <= 32.5) and (T > 27.5) -> 1.084,\n",
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" if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (T > 32.5) and (T <= 55.0) -> 1.051,\n",
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" if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (T > 32.5) and (T <= 55.0) -> 1.045,\n",
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" if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (T <= 32.5) and (T > 22.5) -> 1.06,\n",
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" if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (T <= 32.5) -> 1.062,\n",
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" if (Al2O3 <= 0.175) and (Al2O3 > 0.025) and (TiO2 <= 0.175) and (T > 32.5) and (T <= 60.0) -> 1.069,\n",
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" if (Al2O3 <= 0.175) and (Al2O3 > 0.025) and (TiO2 <= 0.175) and (T > 32.5) and (T <= 60.0) -> 1.064,\n",
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" if (Al2O3 <= 0.175) and (Al2O3 > 0.025) and (TiO2 <= 0.175) and (T > 32.5) and (T <= 67.5) -> 1.057,\n",
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" if (Al2O3 <= 0.175) and (Al2O3 > 0.025) and (TiO2 <= 0.175) and (T > 32.5) -> 1.053,\n",
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" if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (TiO2 > 0.025) and (T > 32.5) and (T <= 50.0) -> 1.081,\n",
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" if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (TiO2 > 0.025) and (T > 32.5) and (T <= 50.0) -> 1.078,\n",
|
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" if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (TiO2 > 0.025) and (T > 32.5) and (T <= 67.5) -> 1.064,\n",
|
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" if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (TiO2 > 0.025) and (T > 32.5) and (T <= 67.5) -> 1.06,\n",
|
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" if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (TiO2 > 0.025) and (T > 32.5) -> 1.056,\n",
|
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" if (Al2O3 <= 0.175) and (TiO2 > 0.175) and (T <= 40.0) -> 1.219,\n",
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" if (Al2O3 <= 0.175) and (TiO2 > 0.175) and (T <= 40.0) and (T > 30.0) -> 1.208,\n",
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" if (Al2O3 <= 0.175) and (TiO2 > 0.175) and (T > 40.0) and (T <= 60.0) -> 1.198,\n",
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" if (Al2O3 <= 0.175) and (TiO2 > 0.175) and (T > 40.0) and (T <= 60.0) -> 1.193,\n",
|
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" if (Al2O3 <= 0.175) and (TiO2 > 0.175) and (T > 40.0) and (T <= 60.0) -> 1.187,\n",
|
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" if (Al2O3 <= 0.175) and (TiO2 > 0.175) and (T > 40.0) -> 1.178,\n",
|
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" if (Al2O3 > 0.175) and (T <= 35.0) -> 1.189,\n",
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" if (Al2O3 > 0.175) and (T <= 35.0) and (T > 22.5) -> 1.184,\n",
|
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" if (Al2O3 > 0.175) and (T <= 35.0) and (T > 22.5) -> 1.179,\n",
|
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" if (Al2O3 > 0.175) and (T > 35.0) and (T <= 52.5) -> 1.17,\n",
|
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" if (Al2O3 > 0.175) and (T > 35.0) and (T <= 52.5) -> 1.166,\n",
|
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" if (Al2O3 > 0.175) and (T > 35.0) and (T <= 52.5) -> 1.161,\n",
|
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" if (Al2O3 > 0.175) and (T > 35.0) and (T <= 65.0) -> 1.157,\n",
|
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" if (Al2O3 > 0.175) and (T > 35.0) and (T <= 65.0) -> 1.152,\n",
|
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" if (Al2O3 > 0.175) and (T > 35.0) -> 1.144]"
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]
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},
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"execution_count": 19,
|
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"metadata": {},
|
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"output_type": "execute_result"
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}
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],
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"source": [
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"from src.rules import normalise_rules\n",
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"\n",
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"\n",
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"rules = normalise_rules(rules)\n",
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"display(len(rules))\n",
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"rules"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 20,
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|
"metadata": {},
|
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"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"24"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
},
|
|
{
|
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"data": {
|
|
"text/plain": [
|
|
"[if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (T > 32.5) -> 1.033,\n",
|
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" if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (T > 32.5) and (T <= 62.5) -> 1.038,\n",
|
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" if (Al2O3 <= 0.175) and (Al2O3 > 0.025) and (TiO2 <= 0.175) and (T <= 32.5) -> 1.088,\n",
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" if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (TiO2 > 0.025) and (T <= 32.5) -> 1.091,\n",
|
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" if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (TiO2 > 0.025) and (T <= 32.5) and (T > 22.5) -> 1.088,\n",
|
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" if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (TiO2 > 0.025) and (T <= 32.5) and (T > 27.5) -> 1.084,\n",
|
|
" if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (T > 32.5) and (T <= 55.0) -> 1.048,\n",
|
|
" if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (T <= 32.5) and (T > 22.5) -> 1.06,\n",
|
|
" if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (T <= 32.5) -> 1.062,\n",
|
|
" if (Al2O3 <= 0.175) and (Al2O3 > 0.025) and (TiO2 <= 0.175) and (T > 32.5) and (T <= 60.0) -> 1.067,\n",
|
|
" if (Al2O3 <= 0.175) and (Al2O3 > 0.025) and (TiO2 <= 0.175) and (T > 32.5) and (T <= 67.5) -> 1.057,\n",
|
|
" if (Al2O3 <= 0.175) and (Al2O3 > 0.025) and (TiO2 <= 0.175) and (T > 32.5) -> 1.053,\n",
|
|
" if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (TiO2 > 0.025) and (T > 32.5) and (T <= 50.0) -> 1.079,\n",
|
|
" if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (TiO2 > 0.025) and (T > 32.5) and (T <= 67.5) -> 1.062,\n",
|
|
" if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (TiO2 > 0.025) and (T > 32.5) -> 1.056,\n",
|
|
" if (Al2O3 <= 0.175) and (TiO2 > 0.175) and (T <= 40.0) -> 1.219,\n",
|
|
" if (Al2O3 <= 0.175) and (TiO2 > 0.175) and (T <= 40.0) and (T > 30.0) -> 1.208,\n",
|
|
" if (Al2O3 <= 0.175) and (TiO2 > 0.175) and (T > 40.0) and (T <= 60.0) -> 1.193,\n",
|
|
" if (Al2O3 <= 0.175) and (TiO2 > 0.175) and (T > 40.0) -> 1.178,\n",
|
|
" if (Al2O3 > 0.175) and (T <= 35.0) -> 1.189,\n",
|
|
" if (Al2O3 > 0.175) and (T <= 35.0) and (T > 22.5) -> 1.182,\n",
|
|
" if (Al2O3 > 0.175) and (T > 35.0) and (T <= 52.5) -> 1.166,\n",
|
|
" if (Al2O3 > 0.175) and (T > 35.0) and (T <= 65.0) -> 1.155,\n",
|
|
" if (Al2O3 > 0.175) and (T > 35.0) -> 1.144]"
|
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]
|
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},
|
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"execution_count": 20,
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"metadata": {},
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"output_type": "execute_result"
|
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}
|
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],
|
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"source": [
|
|
"from src.rules import delete_same_rules\n",
|
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"\n",
|
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"\n",
|
|
"rules = delete_same_rules(rules)\n",
|
|
"display(len(rules))\n",
|
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"for_cluster = rules.copy()\n",
|
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"rules"
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]
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},
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{
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"cell_type": "code",
|
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"execution_count": 21,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
|
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" <thead>\n",
|
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" <tr style=\"text-align: right;\">\n",
|
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" <th></th>\n",
|
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" <th>T</th>\n",
|
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" <th>Al2O3</th>\n",
|
|
" <th>TiO2</th>\n",
|
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" <th>Density</th>\n",
|
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" </tr>\n",
|
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" </thead>\n",
|
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" <tbody>\n",
|
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" <tr>\n",
|
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" <th>0</th>\n",
|
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" <td>20</td>\n",
|
|
" <td>0.0</td>\n",
|
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" <td>0.0</td>\n",
|
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" <td>1.06250</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>1</th>\n",
|
|
" <td>25</td>\n",
|
|
" <td>0.0</td>\n",
|
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" <td>0.0</td>\n",
|
|
" <td>1.05979</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>2</th>\n",
|
|
" <td>35</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" <td>1.05404</td>\n",
|
|
" </tr>\n",
|
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" </tbody>\n",
|
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"</table>\n",
|
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"</div>"
|
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],
|
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"text/plain": [
|
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" T Al2O3 TiO2 Density\n",
|
|
"0 20 0.0 0.0 1.06250\n",
|
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"1 25 0.0 0.0 1.05979\n",
|
|
"2 35 0.0 0.0 1.05404"
|
|
]
|
|
},
|
|
"metadata": {},
|
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"output_type": "display_data"
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},
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{
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
|
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"\n",
|
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
|
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"\n",
|
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" .dataframe thead th {\n",
|
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" text-align: right;\n",
|
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" }\n",
|
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"</style>\n",
|
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"<table border=\"1\" class=\"dataframe\">\n",
|
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" <thead>\n",
|
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" <tr style=\"text-align: right;\">\n",
|
|
" <th></th>\n",
|
|
" <th>T</th>\n",
|
|
" <th>Al2O3</th>\n",
|
|
" <th>TiO2</th>\n",
|
|
" <th>Density</th>\n",
|
|
" </tr>\n",
|
|
" </thead>\n",
|
|
" <tbody>\n",
|
|
" <tr>\n",
|
|
" <th>0</th>\n",
|
|
" <td>30</td>\n",
|
|
" <td>0.00</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" <td>1.05696</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>1</th>\n",
|
|
" <td>55</td>\n",
|
|
" <td>0.00</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" <td>1.04158</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>2</th>\n",
|
|
" <td>25</td>\n",
|
|
" <td>0.05</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" <td>1.08438</td>\n",
|
|
" </tr>\n",
|
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" </tbody>\n",
|
|
"</table>\n",
|
|
"</div>"
|
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],
|
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"text/plain": [
|
|
" T Al2O3 TiO2 Density\n",
|
|
"0 30 0.00 0.0 1.05696\n",
|
|
"1 55 0.00 0.0 1.04158\n",
|
|
"2 25 0.05 0.0 1.08438"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"density_train = pd.read_csv(\"data/density_train.csv\", sep=\";\", decimal=\",\")\n",
|
|
"density_test = pd.read_csv(\"data/density_test.csv\", sep=\";\", decimal=\",\")\n",
|
|
"\n",
|
|
"display(density_train.head(3))\n",
|
|
"display(density_test.head(3))"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 22,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"[if (Al2O3 = 0.0) and (TiO2 = 0.0) and (T = 70) -> 1.033,\n",
|
|
" if (Al2O3 = 0.0) and (TiO2 = 0.0) and (T = 47.5) -> 1.038,\n",
|
|
" if (Al2O3 = 0.1) and (TiO2 = 0.0) and (T = 20) -> 1.088,\n",
|
|
" if (Al2O3 = 0.0) and (TiO2 = 0.1) and (T = 20) -> 1.091,\n",
|
|
" if (Al2O3 = 0.0) and (TiO2 = 0.1) and (T = 27.5) -> 1.088,\n",
|
|
" if (Al2O3 = 0.0) and (TiO2 = 0.1) and (T = 30.0) -> 1.084,\n",
|
|
" if (Al2O3 = 0.0) and (TiO2 = 0.0) and (T = 43.75) -> 1.048,\n",
|
|
" if (Al2O3 = 0.0) and (TiO2 = 0.0) and (T = 27.5) -> 1.06,\n",
|
|
" if (Al2O3 = 0.0) and (TiO2 = 0.0) and (T = 20) -> 1.062,\n",
|
|
" if (Al2O3 = 0.1) and (TiO2 = 0.0) and (T = 46.25) -> 1.067,\n",
|
|
" if (Al2O3 = 0.1) and (TiO2 = 0.0) and (T = 50.0) -> 1.057,\n",
|
|
" if (Al2O3 = 0.1) and (TiO2 = 0.0) and (T = 70) -> 1.053,\n",
|
|
" if (Al2O3 = 0.0) and (TiO2 = 0.1) and (T = 41.25) -> 1.079,\n",
|
|
" if (Al2O3 = 0.0) and (TiO2 = 0.1) and (T = 50.0) -> 1.062,\n",
|
|
" if (Al2O3 = 0.0) and (TiO2 = 0.1) and (T = 70) -> 1.056,\n",
|
|
" if (Al2O3 = 0.0) and (TiO2 = 0.3) and (T = 20) -> 1.219,\n",
|
|
" if (Al2O3 = 0.0) and (TiO2 = 0.3) and (T = 35.0) -> 1.208,\n",
|
|
" if (Al2O3 = 0.0) and (TiO2 = 0.3) and (T = 50.0) -> 1.193,\n",
|
|
" if (Al2O3 = 0.0) and (TiO2 = 0.3) and (T = 70) -> 1.178,\n",
|
|
" if (Al2O3 = 0.3) and (T = 20) -> 1.189,\n",
|
|
" if (Al2O3 = 0.3) and (T = 28.75) -> 1.182,\n",
|
|
" if (Al2O3 = 0.3) and (T = 43.75) -> 1.166,\n",
|
|
" if (Al2O3 = 0.3) and (T = 50.0) -> 1.155,\n",
|
|
" if (Al2O3 = 0.3) and (T = 70) -> 1.144]"
|
|
]
|
|
},
|
|
"execution_count": 22,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"from src.rules import simplify_rules\n",
|
|
"\n",
|
|
"rules = simplify_rules(density_train, rules)\n",
|
|
"rules"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 23,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
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"name": "stderr",
|
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"output_type": "stream",
|
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"text": [
|
|
"/Users/user/Projects/python/fuzzy-rules-generator/.venv/lib/python3.12/site-packages/skfuzzy/control/fuzzyvariable.py:125: UserWarning: FigureCanvasAgg is non-interactive, and thus cannot be shown\n",
|
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" fig.show()\n"
|
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]
|
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},
|
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{
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"data": {
|
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"image/png": 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",
|
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"text/plain": [
|
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"<Figure size 640x480 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
},
|
|
{
|
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"data": {
|
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"image/png": 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",
|
|
"text/plain": [
|
|
"<Figure size 640x480 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
},
|
|
{
|
|
"data": {
|
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"image/png": 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Il+4pYYku74Y9c7T5bAqbZnN1rmk+Flx9IOxjbakJISzM4djD/HLqF/rV6EeZAmVUx3kuKW7ykIuDHdO7BHA4+h7f77yoOo4QxpX8QPuH3TdQm4nY2jjm126evrIf9sxWnUYIo0pMTSQ4PJjqhavzTuV3VMd5ISlu8lgdP08+aFCKGRvOcjb2vuo4QhjPptHw4CZ0+hZsbFWnUaNkPa2w2zIBbp5WnUYIo/nq0FfcfnSb0KBQbM3g+pbiRoHBLStQoqALg5dEkJouoyuEBbiwVbuZtvlYKGjazdW57uVgbamJsD6Qnqo6jRA5tvfGXv488ycDaw6khFsJ1XEyRYobBZzsbZnRJYBTNxKYu01uPhRmLikeVnwKpRppN9VaO3tn7WbqG8e04fBCmLEHKQ8ICQ+hrk9d3qj4huo4mSbFjSIBvgX4pEkZvt58jhPX4lXHESL71o/QCpyOc8BGPlIAbamJBp9pExneOKY6jRDZNu3gNBJSEhgXNA4bnflc3+aT1AL1e7kc5bxdGbI0guQ0GV0hzNDZ9XDkN2g9EQqYR3N1nmk8FApV1JagSEtWnUaILNtxdQd/n/ubz2t/TrH8xVTHyRIpbhRysLPhy64BXLj1gFmbzqmOI0TWJN6Flf2gXEuoYfqjJ/KcnQN0nge3z2otOEKYkfjkeMbsHkODYg14tdyrquNkmRQ3ilUq4saAZuWYt/0CR6LvqY4jROat/RzSkqD916DTqU5jmnyqaS04u76CqwdVpxEi0ybum0hSehJj649FZ4bXtxQ3JqBP4zJUK+bO4KURJKVK95QwAyfD4MQyaDMd3IqoTmPaGnwGRapr3VOpj1SnEeKFNl7eyNqotYwIHEFhl8Kq42SLFDcmwM7WhhldA7h67xHT1p9RHUeI53twC9YMgortoFoX1WlMn62d1j0VFw2bx6tOI8Rz3Xl0h/F7xtO8RHPalmqrOk62SXFjIsoWduWLVhX4MTyKfRfvqI4jxNMZDLB6oPbf7WZKd1RmFaoAzUbB3m/hUrjqNEI8lcFgIHRvKADBLwWbZXfUf0hxY0J6BZWidkkPhiyL4GFymuo4Qjzp2BI4vRrafQX5C6lOY15e+kRbmiLsY22pCiFMzJqoNWyK3kRIvRAKOhdUHSdHpLgxIbY2OqZ3CeD2/RQm/ROpOo4Qj0u4Dv98DlVfh8odVacxPza22tIUD29pq4cLYUJuJt5k4r6JtCnVhuYlm6uOk2NS3JiYkgXzMaJNRX7bG83Oc7dUxxFCYzBow77tnKHNNNVpzFfBMtBiHBz8AS5sUZ1GCEDrjhq9ezROtk6MCByhOo5RSHFjgt4KLEmDsl58sewY8Y9kbRphAg7/Auc3QYevwcVTdRrzVvt9KNVYW7LiUZzqNEKw/Pxydl3bxZj6Y3B3dFcdxyikuDFBNjY6przuz4OkNMavPqU6jrB29y5rSyzUeBvKt1KdxvzZ2GhLVSQlaOdVCIWuP7jO1ANTebXcqzQq3kh1HKOR4sZEFSvgzKj2lVl26CqbTsWqjiOslV4PK/qCswe0mqQ6jeUo4AutJ8HR3+HMP6rTCCulN+gJCQ/BzcGNz2t/rjqOUUlxY8K61CpOs4qFGfb3ce49TFEdR1ijA9/DpZ3QcTY4ualOY1lqvA3lWsHK/tpSFkLksT9P/8m+mH2MCxpHfof8quMYlRQ3Jkyn0zHp1WqkpusJWXlSdRxhbe5c0Eb11OkNpZuoTmN5dDrtHqb0FFg7RHUaYWUuJ1xm5uGZvFHhDV4q8pLqOEYnxY2JK+zmxLiOVVgVcZ01x26ojiOshT5dm4/F1QdajFWdxnK5+kDbGXDiLzi5XHUaYSXS9ekE7wrGy9mLz2p9pjpOrpDixgx0CChKm2o+BIcd59b9ZNVxhDXYMxuu7NeWDXDIpzqNZav6GlTqAKsHwYObqtMIK/DrqV+JuBVBaFAoLvYuquPkCiluzIBOp2N8x6rY6HSMWH4cg8GgOpKwZDcjYUso1OsLJSyvudrk6HTajM86G1g1UJtTSIhcciHuAt8c+YYelXtQ07um6ji5RnlxM2fOHPz8/HByciIwMJD9+/c/d/uZM2dSoUIFnJ2d8fX15bPPPiMpKSmP0qpTML8jE1+txsZTsSw/ck11HGGp0lO11as9SsHLo1SnsR75vKD9TDizBo4tVp1GWKhUfSojd42kuGtx+tXspzpOrlJa3CxevJhBgwYxevRoDh8+TEBAAK1ateLmzac3zS5atIhhw4YxevRoIiMj+eGHH1i8eDEjRljHXBGtqvjQuUYxRq88yY34R6rjCEu06yuIOQ6d54K9k+o01qVSe/DvBmu/gHj5AiOM74fjP3D67mkmNJiAo62j6ji5Smlx8+WXX9K7d2969epF5cqVmTdvHi4uLvz4449P3X737t0EBQXx5ptv4ufnR8uWLenevfsLW3ssyZj2VXBxsGXoX9I9JYzsRgRsnwINB0GxWqrTWKdXpoCDi7bUhVzfwohO3z3N/Ij5vF/tfap6VVUdJ9cpK25SUlI4dOgQzZv/d4EuGxsbmjdvzp49e576mvr163Po0KGMYubixYusXbuWNm3aPPM4ycnJJCQkPPYwZ+4u9kx5zZ8dZ2/x54ErquMIS5GWrHVHFaoEjb5QncZ6OXtAh2/gwmY4tFB1GmEhUtJTGLFrBGUKlKGPfx/VcfKEsuLm9u3bpKen4+3t/djz3t7exMTEPPU1b775JuPGjaNBgwbY29tTpkwZmjRp8txuqUmTJuHu7p7x8PX1Ner7UKFJhcK8UceX0NWnuHI3UXUcYQm2TYbb57TRUXYOqtNYt3ItoGYP2BAM9y6pTiMswLyIeUTFRzGhwQTsbe1Vx8kTym8ozopt27YxceJEvv32Ww4fPszff//NmjVrGD9+/DNfM3z4cOLj4zMeV65YRmvHyLaVKODiwOfLItDrpfla5MCVAxA+E5oMBR/Lb642Cy0ngLMnhPXVlsAQIpuO3TrGDyd+4OOAj6ngWUF1nDyjrLjx8vLC1taW2NjH102KjY3Fx8fnqa8ZNWoU77zzDh988AHVqlWjc+fOTJw4kUmTJqF/xgeAo6Mjbm5ujz0sgauTPdO6+LP34l1+3nNJdRxhrlISIawPFKkOQZY5mZdZcnKDTnPg8i7Y/53qNMJMJaUlMXLXSCp7Vua9qu+pjpOnlBU3Dg4O1KpVi82bN2c8p9fr2bx5M/Xq1XvqaxITE7GxeTyyra0tgFXeXFu/jBfv1vdjyrrTXLz1QHUcYY62jIe4K1p3lK2d6jTif5VqBHU/gk1j4PZ51WmEGfr6yNdcf3CdCQ0mYGdjXde30m6pQYMGsWDBAn7++WciIyP5+OOPefjwIb169QKgR48eDB8+PGP79u3bM3fuXP7880+ioqLYuHEjo0aNon379hlFjrX5onUFfNycGLI0gnTpnhJZcWkX7J0LzUKgkPU0V5uV5qPBrYjWuqZPV51GmJGDMQf57dRv9K/Zn9IFSquOk+eUlnLdunXj1q1bhISEEBMTQ/Xq1Vm3bl3GTcbR0dGPtdQEBwej0+kIDg7m2rVrFCpUiPbt2zNhwgRVb0E5Fwc7ZnQNoMu8PSzYeZE+jcuojiTMQfIDCPsEStSDlz5RnUY8i0M+6DQPfmoNu7+GBtJ1KF4sMTWR4PBgahSuwduV3lYdRwmdwcr6cxISEnB3dyc+Pt5i7r8BmLQ2kp/CL7GqXwMq+LiqjiNM3erPIGIxfLwLPK3vW53Z2TAK9s2DD7eDd2XVaYSJC90bysoLK/mr/V/4upn/COHsMKvRUuLZPmtRnpIFXRi89Cip6TK6QjzH+U1w8EdttW8pbMxD05Ha39Xyj7QlMoR4ht3Xd7P4zGIG1RpktYUNSHFjMZzsbZnRNYDIG/eZs1VuPhTP8CgOVvSD0k2g9vuq04jMsnfSbvqOPQk7Z6hOI0zU/ZT7hISH8FKRl+haoavqOEpJcWNB/IsXoG+TMszecp4T1+JVxxGmaN1wSHkAHWaDjVz+ZqVoDWg0BHZMg+tHVacRJmjK/ik8TH3IuPrjsNFZ9/Vt3e/eAn36cjnKe7syaMlRktNkdIX4H6fXQsQiaD0JClhvc7VZazgEClfSlspIS1adRpiQbVe2seLCCr6o8wVF8hdRHUc5KW4sjIOdDV92CyDq9kNmbjqnOo4wFYl3YdUAKN8aqr+lOo3ILjsH6Dwf7pyHrRNVpxEmIi4pjjG7x9C4eGM6le2kOo5JkOLGAlX0cWNg8/LM336Bw9H3VMcRpmDNYEhPgfazQKdTnUbkhHcVaDpcGxp+Zb/qNMIETNg3gVR9KqPrjUYn1zcgxY3F+qhRafyLF2DwkggepUj3lFU78Tec/BvazgDXpy9tIsxM/QFQtKbWPZUii+das/WX1rPu0jpGBo6kkEsh1XFMhhQ3FsrO1obpXQK4HveIqetPq44jVLkfq7XaVO4IVV9TnUYYi62dNnoq4RpsHqc6jVDk9qPbhO4NpUXJFrxS6hXVcUyKFDcWrGzh/HzeqgI/hV9iz4U7quOIvGYwwOqBoLOBtl9Kd5Sl8SoHzUbDvrkQtVN1GpHHDAYD4/Zoo6KCXwqW7qh/keLGwr0XVIq6pTz5fFkED5LTVMcReSniTzizVrvPJp+X6jQiNwT2gZJBsOITSL6vOo3IQ6surmLrla2E1AvB08lTdRyTI8WNhbOx0TH99QDuPkxhwppI1XFEXom/Bv8MBf9uUKmd6jQit9jYQMc58PAObAhWnUbkkZiHMUzeN5l2pdvRrEQz1XFMkhQ3VqBEQRdGtKnEH/uj2X72luo4IrcZDLDyU3BwgVemqE4jcptnKWg5Hg4t1JbWEBbNYDAwZvcYnO2cGVZ3mOo4JkuKGyvxVmAJGpbzYuiyY8Q/krVpLNqhn+DCFm0WYmcP1WlEXqj9HpRuqi2t8ShOdRqRi5adW0b49XDGBo3F3dFddRyTJcWNldDpdEx5zZ+HyWmMXXVSdRyRW+5GwfpgqNkTyjVXnUbkFZ0OOs7WltZYJ9/mLdXV+1eZfmA6r5V7jQbFGqiOY9KkuLEiRQs4M7pDFf4+fI0NJ2NUxxHGptfDik/BpSC0mqA6jchr7sW1bsiIP+D0GtVphJHpDXpGhY+igGMBPq/zueo4Jk+KGyvzWs1iNK9UmBHLj3P3YYrqOMKY9s+Hy7ug0xxwdFWdRqgQ0B3Kv6IttfFQpn+wJH+c/oODsQcZHzSefPb5VMcxeVLcWBmdTsfEV6uRpjcQHHYcg8GgOpIwhtvnYNMYqPsRlGqkOo1QRafThv7r02DNINVphJFExUfx1aGveLPim9QtUld1HLMgxY0VKuzqxPiOVVl7PIZVx26ojiNySp8OYR+DWzFoPkZ1GqGaq7e21MapMDjxl+o0IofS9ekEhwfjk8+HgbUGqo5jNqS4sVLtA4rS1r8IIStOcDMhSXUckRO7v4Zrh6DTXG34txBVX4MqnbWlN+7Hqk4jcmDhyYWcuH2C0KBQnO2cVccxG1LcWLHxHatiZ6Nj+N/SPWW2Yk/B1olQvx+UCFSdRpiSNjPAxg5W9dfmPhJm59y9c8w5OoeelXtSvXB11XHMihQ3VswznwOTXvVn8+mbLDt0VXUckVXpqbD8I/AsDU1GqE4jTE2+gtr9N2fXwdFFqtOILErVpzJy10hKuJagb42+quOYHSlurFyLyt68WrMY41ad4lrcI9VxRFbsmA6xJ7XVoe2dVKcRpqhiW20E1bphEC9fYMzJgmMLOHvvLBMaTsDR1lF1HLMjxY1gdPsq5HO0Y+iyY9I9ZS6uH4Gd06HREChaQ3UaYcpaTwaH/NocSHJ9m4WTd06y4NgCevv3pkrBKqrjmCUpbgTuzvZMed2fXedv89u+aNVxxIukJcPyj6FwZWg4RHUaYeqcC0DHb+DiVjj4o+o04gVS0lMI3hVMOY9yfFjtQ9VxzJYUNwKAxuUL8WZgCSatjST6TqLqOOJ5tk6EO+e17ig7B9VphDko2xxqvQsbRmlLdAiTNefoHC4lXCK0QSj2tvaq45gtKW5EhhFtKuGZz4EhSyPQ66X52iRd2a8N/W46AryluVpkQctQ7SbjFX21pTqEyTl68ygLTy6kb/W+lPcorzqOWZPiRmTI72jH9C4B7L90lx/D5dudyUlJhOV9oGhNqN9fdRphbhxdoeO3cDkc9s1TnUb8y6O0RwSHB1O1YFXerfKu6jhmT4ob8ZiXShekV5Af09af4cKtB6rjiP+1eSwkXNO6o2ztVKcR5qhUQwjso/0u3T6nOo34H7MOzyLmYQyhDUKxs5HrO6ekuBFP+KJVRYoVcGbwkgjS0qX52iRE7dC+bTcbDV7lVKcR5qzZaG2pjuV9ID1NdRoB7L+xn98jf2dAzQGUci+lOo5FkOJGPMHZwZbpXQM4djWO+Tsuqo4jku9r90mUbKB96xYiJxxctNa/64dh9yzVaazew9SHhOwOobZ3bd6q9JbqOBZDihvxVDVLePBR4zLM3HSWyBsJquNYtw3B8PAOdJwNNnLJCiPwravdt7V1kjYRpFBm+sHp3E26y7igcdjo5Po2FjmT4pkGNi9Haa/8DF4SQUqadE8pcW4THFoIrULBU5qrhRE1HQEFy2pLeKSlqE5jlcKvhbPs7DKG1B6Cr6uv6jgWRYob8UyOdrbM6BrA2dj7zN56XnUc6/PoHqzsB2Vehlq9VKcRlsbOUeueuhmpzXYt8lRCSgIhu0OoX7Q+Xcp3UR3H4khxI56rajF3Pn25LHO2nufY1TjVcazLP8Mg5SF0+AZ0OtVphCUqWh0afa6tU3btsOo0VmXK/ik8Sn3E2Ppj0cn1bXRS3IgX6tu0LJWKuDJ4SQRJqemq41iHyNVw7E94ZTK4F1edRliyhoPBpyqEfQypSarTWIUt0VtYeWElQ+sOxSefj+o4FkmKG/FC9rY2fNm1OpfvJPLVxrOq41i+h7dh9UCo0EZb0VmI3GRrD53mwd2LsHWC6jQW717SPcbuGUsT3yZ0KNNBdRyLJcWNyJTy3q4Malme73Ze5NDlu6rjWC6DAdYMAn0atJsp3VEib3hX1m4w3v0NRO9Tncaihe4NJd2Qzuh6o6U7KhdJcSMyrXfD0tTwLcDgJREkpsjkX7nixF9wagW0nQGu3qrTCGtSvz8Urw1hfbR7vYTRrYtax4bLGwgODMbL2Ut1HIsmxY3INFsbHdO7BBCTkMTUdWdUx7E892NgzWCo0hmqvqY6jbA2NrZa91TCDdg0VnUai3P70W1C94XSyq8VrUu1Vh3H4klxI7KkdKH8DG1dkYW7L7H7/G3VcSyHwQCrBoCtA7SZoTqNsFZeZaH5aNg/X1vyQxiFwWBgzO4x2OpsGRk4UnUcqyDFjciynvX8eKm0J58vO8b9pFTVcSzD0d/h7DpoPwvyFVSdRlizuh9pS32E9YUkmZ3cGFZcWMH2q9sZU28MHk4equNYBSluRJbZ2OiY9noAcYkpTFgTqTqO+Yu7AuuGQ8CbULGN6jTC2tnYQKc58OgubJBWhpyKeRjDlP1T6FCmA01LNFUdx2pIcSOyxdfThZFtK/PngStsPXNTdRzzZTDAyk/BIT+0nqQ6jRAaDz9oGQqHf4FzG1WnMVsGg4GQ8BBc7F0YWneo6jhWRYobkW3d6/rSqHwhhi47RnyidE9ly8Ef4OI2bVFM5wKq0wjxX7XehTLNtCVAHt1TncYsLT27lD039jCu/jjcHNxUx7EqUtyIbNPpdEx5rRqPUtMZs0pWFs6yuxdhwyht3aiyzVSnEeJxOp229EdKIvwjrQ5ZdeX+FaYfnM7r5V8nqFiQ6jhWR4obkSNF3J0Z26EKy49cY92JG6rjmA99unbDZr5C0HK86jRCPJ17MWgzFY4thshVqtOYDb1Bz6jwUXg6eTKk9hDVcaySFDcixzrXKEaLyt6MXH6COw+SVccxD3vnQvRu6PQtOLqqTiPEs/l3gwptYdVAbWkQ8UK/R/7OodhDjA8aTz77fKrjWCUpbkSO6XQ6Jnauht5gYOTyExgMBtWRTNuts7B5HLz0Cfg1UJ1GiOfT6aD9TDDotTXP5Pp+rqj4KGYdnsXbld6mjk8d1XGslhQ3wigKuToS2qka607GsDLiuuo4pis9TZvevoAvNAtRnUaIzMlfGNp9qXVNHV+mOo3JStOnEbwrmCL5itC/Zn/VcayaFDfCaNr6F6F9QFFCVpwkNiFJdRzTFD4Trh/Rprm3d1adRojM+8+yIGuHaEs0iCcsPLmQE3dOENogFGc7ub5VkuJGGNW4DlVwsLNh2F/HpHvq32JOwLbJEDQAfKW5WpihNtPBzhFW9ZfuqX85e+8sc47OoVeVXgQUClAdx+pJcSOMyiOfA5M6V2PrmVssPXhVdRzTkZYCy/uAVzloMlx1GiGyx8VTWyLk3AY48pvqNCYjNT2VkbtG4ufmxyfVP1EdRyDFjcgFzSt783qt4oxbfYqr9xJVxzENO6bBrUjoPE/75iuEuarwClR/S1syJC5adRqT8N3x7zh/7zwTG0zEwdZBdRyBFDcil4S0r4yrkx1fLDuGXm/lzdfXDsHOGdDoCygizdXCArSeBE7usOJT0OtVp1Hq5O2TLDi2gA8DPqRSwUqq44j/J8WNyBVuTvZMec2f3Rfu8Nu+y6rjqJOaBMs/Bp9q0HCQ6jRCGIeTO3T8BqK2a0uIWKnk9GRG7hpJBc8KfFDtA9VxxP+Q4kbkmkblC/FWYAkmrT3NpdsPVcdRY2so3IvSuqNs7VWnEcJ4yrwMtd+HjSFw54LqNErMOTKH6PvRTAiagL2NXN+mRIobkatGtKlEIVdHhiyNIN3auqei98Lu2dB0JBSW5mphgVqM0+bAWdFXW1LEihy9eZSFJxfyaY1PKetRVnUc8S9S3Ihclc/Rjmmv+3Mo+h4/7opSHSfvpDyEsI+heB2o3091GiFyh2N+6PitVsjv/VZ1mjyTmJrIyF0j8S/kT8/KPVXHEU8hxY3IdYGlC/JeUCmmbTjDudj7quPkjU1jtInOOs0FG1vVaYTIPX5BUK8vbB4Pt86oTpMnZh2exc3Em0xoMAFbub5NkhQ3Ik983qoCxT2cGbw0grR0Cx9dcXE77P8Omo8BL2muFlbg5WDwKKnN5ZSepjpNrtp3Yx+LTi9iYK2BlHQrqTqOeAYpbkSecLK3ZUaXAE5ci2fuNgu++TApQbv/wK8h1P1QdRoh8oa9s7akyI2jEP6V6jS55kHKA0LCQ6jrU5fuFburjiOeQ4obkWdqlPDg4yZl+HrLOU5dT1AdJ3dsGAmP7kHHOWAjl5ewIsVrQYPPYNsUuHFMdZpcMf3gdOKS4xgXNA4bnVzfpkz5386cOXPw8/PDycmJwMBA9u/f/9zt4+Li6Nu3L0WKFMHR0ZHy5cuzdu3aPEorcqp/s3KUKZSfQUuOkpJmYd1T5zbC4V+gZajWRC+EtWk8FApV0G6mT0tRncaodl7dyV/n/uLzOp9TLH8x1XHEC2S7uNm8eTPt2rWjTJkylClThnbt2rFp06Ys7WPx4sUMGjSI0aNHc/jwYQICAmjVqhU3b9586vYpKSm0aNGCS5cusWzZMs6cOcOCBQsoVkx+0cyFo50tM7oGcP7mA77efE51HONJvKvN1lq2OdR6V3UaIdSwc9Ruor91GnZMVZ3GaOKT4xmzewxBxYJ4rdxrquOITMhWcfPtt9/SunVrXF1dGTBgAAMGDMDNzY02bdowZ86cTO/nyy+/pHfv3vTq1YvKlSszb948XFxc+PHHH5+6/Y8//sjdu3cJCwsjKCgIPz8/GjduTECATGlvTqoUdad/s3LM3X6Bo1fiVMcxjn+GQtoj6PAN6HSq0wihThF/aDwMdn4JVw+pTmMUk/dP5lH6I8bWG4tOrm+zoDMYsr5uffHixRk2bBiffvrpY8/PmTOHiRMncu3atRfuIyUlBRcXF5YtW0anTp0ynu/ZsydxcXGsWLHiide0adMGT09PXFxcWLFiBYUKFeLNN99k6NCh2No+fThecnIyycnJGX9OSEjA19eX+Ph43NzcMvmOhbGlput5be5uHiansaZ/Q5zszXg45amVsOQd6DwfAt5QnUYI9dLT4Ifm2nxPH+3Qbjg2U5svb2bgtoFMbDCR9mXaq44jMskuOy+Ki4ujdevWTzzfsmVLhg4dmql93L59m/T0dLy9vR973tvbm9OnTz/1NRcvXmTLli289dZbrF27lvPnz/PJJ5+QmprK6NGjn/qaSZMmMXbs2ExlEnnH3taGGV0CaPvNLmZsOMPItpVVR8qeh7dh9WdQsR34d1OdJlsMBgNpaZY9fNec2draYmNuN6fb2mmjp+Y3gi2h0GqC6kTZcjfpLuP2juNl35dpV7qd6jgiC7JV3HTo0IHly5fz+eefP/b8ihUraNcu934B9Ho9hQsX5rvvvsPW1pZatWpx7do1pk2b9sziZvjw4Qwa9N8FC//TciPUK+ftypCW5Zn0z2laVvGhjp+n6khZYzDA6oFg0EO7r8yyOyotLY1bt26RjQZckYdcXFxwd3c3ry6RwhW1+W82hkDFtlCyvupEWWIwGBi/ZzwGg4FR9UaZ17kX2StuKleuzIQJE9i2bRv16tUDYO/evYSHhzN48GC+/vrrjG379+//1H14eXlha2tLbGzsY8/Hxsbi4+Pz1NcUKVIEe3v7x7qgKlWqRExMDCkpKTg4ODzxGkdHRxwdHbP8HkXeeL9BaTacjGXwkgjWDWyIi0O2fiXVOL4MIldBl5+19XXMjMFgIC4uDhsbGzw8POTD2wQZDAZSUlJISNCmTihQoIDaQFlVry+cXqONnuoTri3XYCbWRq1lU/QmZjSegZezl+o4Iouydc9NqVKlMrdznY6LFy8+8+eBgYHUrVuXb775BtBaZkqUKMGnn37KsGHDnth+xIgRLFq0iIsXL2Y0086aNYspU6Zw/fr1TGVKSEjA3d1d7rkxIZduP+SVWTvpUrs44zpWVR0ncxJuwLeB2uio159+A7ypS09PJzY2Fg8PD5ydzfeeCGvw4MEDEhIS8PHxMb8uqjsXYF4DqP4mtJ2hOk2m3Ey8SecVnQkqGsTUxpYz6suaZOtrclSUcRZAHDRoED179qR27drUrVuXmTNn8vDhQ3r16gVAjx49KFasGJMmTQLg448/Zvbs2QwYMIB+/fpx7tw5Jk6c+MzWIWEe/LzyMbxNRUJWnKRVFR+Cypr4tySDAVb1BzsnaDNddZps0+u1eYaedTO+MB3/aZVOT083v+KmYBloPhb++Vy7N61MU9WJnstgMDBm9xgcbB0Y+dJI1XFENintA+jWrRu3bt0iJCSEmJgYqlevzrp16zJuMo6Ojn7sQvb19WX9+vV89tln+Pv7U6xYMQYMGJDpm5iF6Xo7sCTrTsTwxbJj/DOwIW5O9qojPduRX+HcBui+GFzM7D6hp5DuKNNn9n9HdT6A06u0uaA+2Q1O7qoTPVPY+TB2XtvJ7Jdn4+5oujnF82W6W2rQoEGMHz+efPnyPXaD7tN8+eWXRgmXG6RbynRdvZdI65k7aVPNh6mvm+jcRXHR8G19qNwROmV+TidTlJqayq1btyhUqBD29iZcTArL+Lv6z7VTpaO2PIkJuv7gOq+ufJUWJVswPmi86jgiBzLdcnPkyBFSU1Mz/vtZzP4bhlCmuIcLo9pVYuhfx2ld1YeXK3q/+EV5Sa/XFsV0cofWE1WnEcK8FCihXTcr+0HF9lDhyelEVNIb9ISEh+Dq4MoXdb5QHUfkUKaLm61btz71v4Uwpq61fVl3Ioahfx1n42ceFHB5cgScMgd/gKgd8E6YSTerW7omTZpQvXp1Zs6cqTqKyKoa72gjDFf1B9+9JtWtu/jMYvbF7OO7Ft/h6uCqOo7IITO7M01YOp1Ox+TX/ElJ0zN65UnVcf7rzgVtvo7a75v8DZFCmCydDtp/DWnJsPbzF2+fR6ITovnq0Fd0q9CNekXrqY4jjCBbxc3Dhw8ZNWoU9evXp2zZspQuXfqxhxA54e3mxNgOVVhx9Dprj99QHQf06RD2iTaXTYtxqtMIYd7cikCbaXBiGZwMU52GdH06o8JHUdCpIINqPf9+UmE+sjVa6oMPPmD79u288847FClSRO6zEUbXsXpR1p2IITjsBHVLeeKVX+FEjHvmwJV90GutWU1CZg3u3bvHgAEDWLVqFcnJyTRu3Jivv/6acuXKYTAYKFy4MHPnzuX1118HoHr16sTGxnLjhlY079q1i2bNmnHv3j1cXFxUvhXrUq0LRK6ENYOgZBDkL6Qsym+Rv3Hk5hF+bPUjLvbyO2ApslXc/PPPP6xZs4agoCBj5xEC0LqnQjtXpeVXOxjx93Hmv1NLTRF987S2Nk69vmY3fXx2PEpJ58KtB3l+3DKF8uPskPX5dt59913OnTvHypUrcXNzY+jQobRp04ZTp05hb29Po0aN2LZtG6+//jr37t0jMjISZ2dnTp8+TcWKFdm+fTt16tSRwiav6XTQ9ittIszVA6Hbb0qWL7kYd5GvD3/N25XfprZP7Tw/vsg92SpuPDw88PQ0nRvBhGXyyu/IxM5V6fPbYcKOXqNzjeJ5GyA9DcL6gEdJbY0cK3Dh1gPafbMrz4+7ul8DqhbL2k3a/ylqwsPDqV9fKzx///13fH19CQsLo0uXLjRp0oT58+cDsGPHDmrUqIGPjw/btm2jYsWKbNu2jcaNGxv9/YhMyF9IW5NtSQ84tgQC8nbh2TR9GiN3jaRo/qL0ryETwVqabBU348ePJyQkhJ9//lm+8Yhc1bpqETpWL8roFSepV9oLH3envDv4rq/gRgS8vwnsrWN5gjKF8rO6XwMlx82qyMhI7OzsCAwMzHiuYMGCVKhQgcjISAAaN27MgAEDuHXrFtu3b6dJkyYZxc3777/P7t27+eILGfarTOWOWhfVP59DqYbgVjTPDv3jiR85dfcUv77yK052efi5IvJEpoubGjVqPNYtcP78eby9vfHz83tiUqnDhw8bL6GwemM7VGHPhTsM/esYC3vVyZvuqRvHYPsUaPAZFK+V+8czEc4OtlluQTFl1apVw9PTk+3bt7N9+3YmTJiAj48PU6ZM4cCBA6Smpma0+ghFXpkKUTu1+W/eWpYn3VNn7p5hbsRc3qv6Hv6F/HP9eCLvZbq46dSpUy7GEOLZCrg4MOU1f3otPMDiA1d4o26J3D1gWoq2inGhCtBYlvYwVZUqVSItLY19+/ZlFCh37tzhzJkzVK5cGdDu3WrYsCErVqzg5MmTNGjQABcXF5KTk5k/fz61a9cmX758Kt+GcPGEDl/Doq5w+Beo1TNXD5eansqIXSMo5V6KjwM+ztVjCXUyXdyMHj06N3MI8VxNKxamW21fxq8+RVBZL3w9c7E7dPsUuHUaem8FO4WjtMRzlStXjo4dO9K7d2/mz5+Pq6srw4YNo1ixYnTs2DFjuyZNmjB48GBq165N/vxa91ejRo34/fff+fxz05lrxaqVbwU13ob1I6B0E+0+t1wyN2IuF+Mu8ke7P3CwNaFJQoVRZWuemytXrnD16tWMP+/fv5+BAwfy3XffGS2YEP8W3K4SBVwc+GLZMfT6TC2JlnVXD8GuL6HxMCgizdWm7qeffqJWrVq0a9eOevXqYTAYWLt27WNd5Y0bNyY9PZ0mTZpkPNekSZMnnhOKtZoEzh7aEif/v2K9sR2/dZwfT/zIRwEfUdGzYq4cQ5iGTC+c+b8aNmzIhx9+yDvvvENMTAzly5enatWqnDt3jn79+hESEpIbWY1CFs40b+Hnb/PW9/sY26EKPev7GXfnqY9gfiNwyKfdRGybrfvtzYZFLMZoJazm7+riNvilo3YfTuBHRt11UloSXVd3xcXOhV/b/Iq9jQWfR5G9lpsTJ05Qt25dAJYsWUK1atXYvXs3v//+OwsXLjRmPiEeE1TWix71SjLpn0iibj807s63hMK9y9BpnsUXNkKYpNJNoM4HsHG0tuSJEc0+Mptr968xocEEKWysQLaKm9TUVBwdtXsRNm3aRIcOHQCoWLFixsyfQuSWYa9UxNvNiSFLI0g3VvfU5d3aTMQvB0Nhaa4WQpkW48DVB5b30ZY+MYJDsYf45dQv9KvRjzIFyhhln8K0Zau4qVKlCvPmzWPnzp1s3LiR1q21peuvX79OwYIFjRpQiH9zcbBjRpcADkff4/udF3O+w+QH2ugo37raTMRCCHUc8kGnuXD1AOyZnePdJaYmErwrmOqFq/NO5XeMEFCYg2wVN1OmTGH+/Pk0adKE7t27ExAQAMDKlSszuquEyE21/Tzp3bA0Mzac5Wzs/ZztbNNoeHBT+0C1yfoSAEIIIytZT/uisSUUbkbmaFdfHvqSO0l3CA0KxVaub6uR5RsLDAYDpUuXJjo6mrS0NDw8PDJ+9uGHH8qMxSLPDGpRni2nbzJ4SQR/f1Ife9ts1OoXtsKB76HNdCgozdVCmIyXg+HcBq176oNNYJv1+2T2XN/D4jOLGV53OCXccnl+LGFSsvyvgcFgoGzZssTExDxW2AD4+flRuHBho4UT4nmc7G2Z0SWAUzcSmLstGzcfJsXDik+hVCOo/b7xAwohss/eWbu5P+Y47Pwyyy+/n3KfkN0hBPoE8kbFN3IhoDBlWS5ubGxsKFeuHHfu3MmNPEJkSYBvAT5pUoavN5/jxLX4rL14/QitwOk4B2yy1UMrhMhNxWtBw0GwY6q2zlsWTDswjfsp9xkXNA4bnVzf1iZbf+OTJ0/m888/58SJE8bOI0SW9Xu5HOW8XRm8JILktEyOrjizDo78Bq0nQgFprhbCZDX6AgpV0rqn0pIz9ZLtV7az/PxyvqjzBUXz591inMJ0ZKu46dGjB/v37ycgIABnZ2c8PT0fewiRlxzsbPiyawAXbz9g1qZzL35B4l1Y1R/KtYQaMnpCCJNm5wCd58Ltc7Bt8gs3j0uKY8yeMTQs1pDOZTvnQUBhirI1U9nMmTONHEOInKlUxI2BzcszY8MZWlT2pkYJj2dvvPZz7Rtg+6/zZAViIUQO+VSDJkNh60So0AZ86zxz04n7J5KSnsKY+mPQyfVttbJV3PTsmburtgqRHR81Ks2GU7EMXhrB2v4NcbJ/yrDPk2FwYhm8+j24FcnzjEKIbAr6DE6vhbA+8NFOcHhyZO6GSxv4J+ofJjecTGEXGdxizbJ9l9WFCxcIDg6me/fu3Lx5E4B//vmHkydPGi2cEFlhZ2vDjC4BXLv3iGnrzzy5wYNbsGYQVGoP1V7P+4DCbKWnp6PPpcUcRSbZ2kHneRB3BbaMf+LHdx7dIXRvKM1LNKdNqTYKAgpTkq3iZvv27VSrVo19+/bx999/8+DBAwAiIiIYPXq0UQMKkRVlC+fn81YV+DE8in0X/2dEn8EAqwcCOmj7lXRHmbl169bRoEEDChQoQMGCBWnXrh0XLmjTAdSvX5+hQ4c+tv2tW7ewt7dnx44dACQnJzNkyBCKFStGvnz5CAwMZNu2bRnbL1y4kAIFCrBy5UoqV66Mo6Mj0dHRHDhwgBYtWuDl5YW7uzuNGzfm8OHDjx3r9OnTNGjQACcnJypXrsymTZvQ6XSEhYVlbHPlyhW6du1KgQIF8PT0pGPHjly6dClXzpVFKVQBmoXA3rlwaVfG0waDgXF7xqHT6Qh+KVi6o0T2ipthw4YRGhrKxo0bcXBwyHj+5ZdfZu/evUYLJ0R29AoqRZ2SngxZFsHD5DTtyWNL4PRqaPcV5C+kNqApS0mE60fz/pGSmKWYDx8+ZNCgQRw8eJDNmzdjY2ND586d0ev1vPXWW/z5558YDP9dd2zx4sUULVqUhg0bAvDpp5+yZ88e/vzzT44dO0aXLl1o3bo1587994b0xMREpkyZwvfff8/JkycpXLgw9+/fp2fPnuzatYu9e/dSrlw52rRpw/372izZ6enpdOrUCRcXF/bt28d3333HyJEjH8uemppKq1atcHV1ZefOnYSHh5M/f35at25NSkpKls6DVXrpYyjxEoR9oi2dAqy+uJotV7Yw6qVRFHSWJYAE6Az/+wmQSfnz5+f48eOUKlUKV1dXIiIiKF26NJcuXaJixYokJSXlRlajSEhIwN3dnfj4eNzc3FTHEbnk8p2HtJ65k1drFmNCs4Lw7Uva6KjXvlcdzWSkpqZy69YtChUqhL39/8/+ev0ofNc478N8uB2KVs/2y2/fvk2hQoU4fvw43t7eFC1alC1btmQUM/Xr16dRo0ZMnjyZ6OjojFnWixb97zDh5s2bU7duXSZOnMjChQvp1asXR48ezVhe5mn0ej0FChRg0aJFtGvXjnXr1tG+fXuuXLmCj48PoC0u3KJFC5YvX06nTp347bffCA0NJTIyMqOFISUlhQIFChAWFkbLli2fOM5T/66s2d2LMDcIAt4gtukwOq/sTKPijZjc8MWjqYR1yNYNxQUKFODGjRuUKlXqseePHDlCsWLFjBJMiJwoWTAfI9pWYlTYcQbfHIGnnTO0maY6lunzKq8VGiqOmwXnzp0jJCSEffv2cfv27Yz7YaKjo6latSotW7bk999/p2HDhkRFRbFnzx7mz58PwPHjx0lPT6d8+cePmZyc/NjCvw4ODvj7+z+2TWxsLMHBwWzbto2bN2+Snp5OYmIi0dHRAJw5cwZfX9+MwgZ4Yr29iIgIzp8/j6ur62PPJyUlZXStiRfwLA0txmFYO4TR6VdxsnVieN3hqlMJE5Kt4uaNN95g6NChLF26FJ1Oh16vJzw8nCFDhtCjRw9jZxQiW94OLEHyvp/wvLGDh6//QT7n5wwPFxoHlxy1oOSV9u3bU7JkSRYsWEDRokXR6/VUrVo1o1vnrbfeon///nzzzTcsWrSIatWqUa1aNQAePHiAra0thw4dwtb28RF1+fPnz/hvZ2fnJ+7d6NmzJ3fu3GHWrFmULFkSR0dH6tWrl6XupAcPHlCrVi1+//33J35WqJB0mWZa7ff5O3IR4XGnmdNwKu6O7qoTCROSreJm4sSJ9O3bF19fX9LT06lcuTLp6em8+eabBAcHGzujENmii4vmvQff8ZfhZfZEFmN6VdWJhDHcuXOHM2fOsGDBgoxup127dj22TceOHfnwww9Zt24dixYteuxLV40aNUhPT+fmzZsZr8+s8PBwvv32W9q00UbjXLlyhdu3b2f8vEKFCly5coXY2Fi8vb0BOHDgwGP7qFmzJosXL6Zw4cLSNZ4D1xJvMNX2Ia8mJNMoYiWUfkV1JGFCsnVDsYODAwsWLODChQusXr2a3377jdOnT/Prr78+8U1ICCX0eljRFxsXT3StJ7Ls0FU2nopVnUoYgYeHBwULFuS7777j/PnzbNmyhUGDBj22Tb58+ejUqROjRo0iMjKS7t27Z/ysfPnyvPXWW/To0YO///6bqKgo9u/fz6RJk1izZs1zj12uXDl+/fVXIiMj2bdvH2+99RbOzs4ZP2/RogVlypShZ8+eHDt2jPDw8IwvfP9pBXrrrbfw8vKiY8eO7Ny5k6ioKLZt20b//v25evWqsU6TRdMb9ISEh+DuVIDP6w6FiEXaHDhC/L8crSZWokQJXnnlFbp06UK5cuWMlUmInDuwAC7thI5z6PxSRZpVLMzwv49z76GMRjF3NjY2/Pnnnxw6dIiqVavy2WefMW3ak/dTvfXWW0RERNCwYUNKlHh8/bCffvqJHj16MHjwYCpUqECnTp04cODAE9v92w8//MC9e/eoWbMm77zzDv3796dw4f9OFmdra0tYWBgPHjygTp06fPDBBxmjpZycnABwcXFhx44dlChRgldffZVKlSrx/vvvk5SUJC05mfTH6T/YH7Of8UHjyV/rPSjfGlYN0JZWEYJsjpYC7SL/6quvMoZOlitXjoEDB/LBBx8YNaCxyWgpK3D7PMxrADXehrbTAbiZkETLmTtoUNaL2W/WVBzQNMgInLwRHh5OgwYNOH/+PGXKlMnWPuTv6r8uxV+iy6oudC7XmRGBI7Qn78fAnEAo8zJ0+UltQGESsnXPTUhICF9++SX9+vWjXr16AOzZs4fPPvuM6Ohoxo0bZ9SQQmSaPh3CPgZXH2gxNuPpwm5OjOtYlf5/HKF11eu085eVgkXuWL58Ofnz56dcuXKcP3+eAQMGEBQUlO3CRvxXuj6d4PBgCrsUZmDNgf/9gasPtJ0Bf72vzUBe9VVlGYVpyFZxM3fuXBYsWPBYP3aHDh3w9/enX79+UtwIdXZ/A1cPwHvrwCHfYz9q71+EdSduMCrsBIGlClLI1VFRSGHJ7t+/z9ChQ4mOjsbLy4vmzZszY8YM1bEsws+nfubYrWP8/MrPuNj/a22pqq9B5EpYMxj8GkB+WVvKmmXrnpvU1FRq1679xPO1atUiLS0tx6GEyJabkbB1AtT/VJvB9F90Oh3jO1bF1kbH8L+Pk80eWSGeq0ePHpw9e5akpCSuXr3KwoULH5s/R2TP+XvnmX1kNj2r9KRG4RpPbqDTQdsvQWcDqwZqS64Iq5Wt4uadd95h7ty5Tzz/3Xff8dZbb+U4lBBZlp4Kyz8Cj1LQ9NnTERTM78iEztXYFBnL34ev5WFAIUR2pepTGbFrBL6uvnxa49Nnb5jPC9rPgjNr4NjivAsoTE6mu6X+d6ilTqfj+++/Z8OGDbz0kvYNed++fURHR8skfkKNnTMg5gR8sBHsnZ67aasqPrxaoxhjVp2kftmCFHF3fu72Qgi1vj/+PWfvneX3Nr/jaPuC7uRK7cC/G6z9AvwagrvMmm+NMl3cHDly5LE/16pVCyBjunAvLy+8vLw4efKkEeMJkQnXj8KOadBwEBSrlamXjG5fhfALtxn613F+7lVHVhEWwkSdunOK7yK+44NqH1DFq0rmXvTKFIjaASv7wdt/aV1Wwqpkeyi4uZKh4BYmLRnmNwYbO+i9BewcXvya/7ftzE3e/ekAEztX483A589vYolkeLH5sNa/q5T0FLqt7oadjR2L2izC3jYL7/3cJvj9NWg3E2r3yrWMwjTlaBI/IZTbNgnunIfO87JU2AA0qVCY7nV9mbDmFFfuJuZSQCFEdn179FsuJVwiNCg0a4UNQLnmULMnbAiGe5dyJZ8wXdkqbpKSkpg2bRpt2rShdu3a1KxZ87GHEHniygEInwVNhoFP9haOGtm2MgVcHBiyNAK93qoaMYUwaRG3Ivjp5E/0rd6XCp4VsreTVhPA2RPC+mpLsgirka3i5v3332fq1KmULFmSdu3a0bFjx8ceQuS6lEQI6wNFa0DQwGzvJr+jHdO6+LMv6i4/77lktHgi9zRp0oSBAwc+8+c6nY6wsLBM72/btm3odDri4uJynE0Yx6O0RwTvCqZKwSq8W+Xd7O/I0RU6zYHLu2D/fKPlE6YvW5P4rV69mrVr1xIUFGTsPEJkzpbxEH8V3vgDbLP1a5yhfhkv3q3vx5R1p2lcvhClC+U3Ukihwo0bN/Dw8FAdQ+TA14e/5sbDG8x6eRZ2Njm7vinVCOp+BJvGQNnm4CXrIFqDbLXcFCtWDFdXV2NnESJzLu2CvXPh5VFQqLxRdjm0dUWKuDszeGkE6dI9ZdZ8fHxwdJTZp83VgZgD/Bb5G/1r9Ke0e2nj7LT5GHArpi3Nok83zj6FSctWcTNjxgyGDh3K5cuXjZ1HiOdLfgBhn0CJevDSx0bbrbODLdO7+BNxJY7vdlw02n5F7tDr9XzxxRd4enri4+PDmDFjMn72726p3bt3U716dZycnKhduzZhYWHodDqOHj362D4PHTpE7dq1cXFxoX79+pw5cyZv3ozIkJiayKjwUdTyrsXbld823o4dXKDTXLh2CHZ/bbz9CpOVrfa+2rVrk5SUROnSpXFxcXliaOLdu7LsvMglG0fBw9vQIwxsbI2661olPendqDRfbTzLyxULU8HH+lonH6U9Iio+Ks+PW8q9FM52mZ9M8eeff2bQoEHs27ePPXv28O677xIUFESLFi0e2y4hIYH27dvTpk0bFi1axOXLl595v87IkSOZMWMGhQoVok+fPrz33nuEh4fn5G2JLJpxcAZ3k+6yoOUCbHRGHsxbIhDq94OtE6FcK/CubNz9C5OSreKme/fuXLt2jYkTJ+Lt7S0ToIm8cX4zHPxRW/3X00jN1f/yWfPybIm8yaAlRwnrG4S9rXXNlhAVH0W31d3y/LiL2y2mcsHM/2Pj7+/P6NGjAShXrhyzZ89m8+bNTxQ3ixYtQqfTsWDBApycnKhcuTLXrl2jd+/eT+xzwoQJNG7cGIBhw4bRtm1bkpKScHJ6/ozXwjh2X9vNkrNLCA4MxtfVN3cO0mQEnF2vLdXSewtkdXi5MBvZKm52797Nnj17CAgIMHYeIZ7uUZw222jpplD7/Vw7jJO9LV92rU6nb8OZs/U8A5sb554ec1HKvRSL2+X9mjyl3EtlaXt/f//H/lykSBFu3rz5xHZnzpzB39//sQKlbt26L9xnkSJFALh58yYlSljfBI95LSElgZDdIdQrUo+uFbrm3oHsnbQ5sRY0gx3Toenw3DuWUCpbxU3FihV59OiRsbMI8WzrR0Dyfeg4O9enUq9W3J2+Tcsye8t5mlfypmox91w9nilxtnPOUguKKv/uCtfpdOhzOI/J/+7zP63ROd2nyJwp+6fwMPUh44LG5X5PQNEa0GiItmRLhdban4XFyVab++TJkxk8eDDbtm3jzp07JCQkPPYQwqhOr4Wjv0PrSeBePE8O+WnTslTwcWXQkqMkp8noCnNVoUIFjh8/TnJycsZzBw4cUJhI/NvW6K2svLCSoXWH4pPPJ28O2nCIds/N8j6QmpQ3xxR5KlvFTevWrdmzZw/NmjWjcOHCeHh44OHhQYECBWR+CWFciXdh1QAo3xqqv5Vnh3Wws2FG1wAu3U7kq43n8uy4wrjefPNN9Ho9H374IZGRkaxfv57p06cDyL2CJuBe0j3G7hlL4+KN6VgmDyeAtXOAzvPhzgXYNjHvjivyTLa6pbZu3WrsHEI83ZrBoE+F9rPyfGXfij5uDGxRjunrz9Cisje1Skrhbm7c3NxYtWoVH3/8MdWrV6datWqEhITw5ptvyo3CJmDCvgmkGdIYXW903heb3lW0e262hELFduD79HuxhHmSVcGF6TrxNyzrBa/9ANVeVxIhLV1Pl/l7iEtMZW3/hjg7GHf4uUrWutL077//Tq9evYiPj8fZOfPDz1WyxL+rdVHr+HzH50xtNJVXSr2iJkR6GvzYCh7dgz67tPlwhEXI9jjXnTt38vbbb1O/fn2uXbsGwK+//squXbuMFk5YsQc3tVabyp2g6mvKYtjZ2jC9SwDX4x4xZd1pZTlE9v3yyy/s2rWLqKgowsLCGDp0KF27djWbwsYS3X50m9B9obQs2ZLWfq3VBbG100ZPJVyDzWPV5RBGl63i5q+//qJVq1Y4Oztz+PDhjJv14uPjmThR+i9FDhkM2n02NrbQ9ss87476tzKF8jO0dUUW7r7E7gu3lWYRWRcTE8Pbb79NpUqV+Oyzz+jSpQvfffed6lhWy2AwMHbPWGx1tgS/FKz+3ievctBsNOybB1E71GYRRpOt4iY0NJR58+axYMGCx5pIg4KCOHz4sNHCCSsV8SecWQvtZkK+gqrTAPBufT8CS3ny+dJjPEhOUx1HZMEXX3zBpUuXSEpKIioqiq+++goXF+l+UGXlhZVsu7KNkHoheDiZyH1sgX2gZANY0VebckKYvWwVN2fOnKFRo0ZPPO/u7k5cXFxOMwlrFn8N/hkK/m9ApXaq02SwsdExvUsAcYkpTFgTqTqOEGYp5mEMU/ZPoX3p9jQr0Ux1nP+ysdHm0Hp4BzYEq04jjCBbxY2Pjw/nz59/4vldu3ZRunTuTIsvrIDBACs/BYd88Mpk1Wme4Ovpwoi2lfhjfzTbzjw5G665srIxBWbJEv6ODAYDo3ePxtnemaF1h6qO8yTPUtAqFA4thHObVKcROZStoeC9e/dmwIAB/Pjjj+h0Oq5fv86ePXsYMmQIo0aNMnZGYS0OLYQLW+Ctv8DZRJqr/+XNuiVYdyKGYX8dZ/3ARri7mO/IFVtbW3Q6Hffv38fV1VX9vQ/iCQaDgfT0dBISEtDpdNjZZesj2yQsPbuU3dd3M7f5XNwdTXTW71q9IHKVttTLJ7tN9nNIvFi2hoIbDAYmTpzIpEmTSExMBMDR0ZEhQ4Ywfvx4o4c0JhkKbqLuRsHcIPDvos1pY8JuxD+i5Vc7aFHJmy+7VVcdJ0eSk5O5e/euRbQMWDIHBwcKFChgtsXNlftXeG3la7Qt3ZbR9UarjvN88Vfh2/pQ4RV4db7qNCKbcjTPTUpKCufPn+fBgwdUrlyZ/PnzGzNbrpDixgTp9fBze4iPho93g6Or6kQv9NehqwxeGsH8d2rRqkoeTRmfS/R6PenpssSEqbKxscHGxsZsW9b0Bj3vr3+fGw9v8FeHv8hnn091pBc7+geE9YE3FkHFtqrTiGzI0teA9957L1Pb/fjjj1kKMWfOHKZNm0ZMTAwBAQF88803z1y593/9+eefdO/enY4dOxIWFpalYwoTsn8+XN4FPVebRWED8GrNYvxzIoaRy49Tx88Tz3wOqiNl23/+8RQiNyyKXMTB2IP80PIH8yhsAALegMiV2pQUvi+ZzKhNkXlZ+kRbuHAhW7duJS4ujnv37j3zkRWLFy9m0KBBjB49msOHDxMQEECrVq24efP5N2xeunSJIUOG0LBhwywdT5iY2+dg0xhtKGYp8/m71Ol0THy1Kml6A8Fhx6VbR4iniIqPYubhmbxV6S3qFjGj5Q10Om0qCn06rBmkOo3Ihix1S/Xt25c//viDkiVL0qtXL95++208PT1zFCAwMJA6deowe/ZsQGsi9/X1pV+/fgwbNuypr0lPT6dRo0a899577Ny5k7i4uGe23CQnJz+2InBCQgK+vr7SLWUK0tPgp9ba4phmOvX56mPX+XTREb7uXoMOAUVVxxHCZKTp0+i5rifxyfEsbb8UZzsznBH6P0vAvP6j0pnSRdZlqeVmzpw53Lhxgy+++IJVq1bh6+tL165dWb9+fba+uaakpHDo0CGaN2/+30A2NjRv3pw9e/Y883Xjxo2jcOHCvP/++y88xqRJk3B3d894+Pr6ZjmnyCW7v4Zrh7Tpz82wsAFo51+Utv5FCFlxgpsJSarjCGEyFp5cyInbJwgNCjXPwgag6qtQpbO2FMz9WNVpRBZkuaPd0dGR7t27s3HjRk6dOkWVKlX45JNP8PPz48GDB1na1+3bt0lPT8fb2/ux5729vYmJiXnqa3bt2sUPP/zAggULMnWM4cOHEx8fn/G4cuVKljKKXBJ7ErZNgvr9zX413vEdq2JnY8Pwv6V7SgiAs/fO8u3Rb+lZpSfVC1dXHSdn2swAG3tY1V+bi0uYhRzdRfifO/j/MxdDbrt//z7vvPMOCxYswMvLK1OvcXR0xM3N7bGHUCw9FZb3Ac8y0HSE6jQ55pnPgUmvVmPz6ZssO3RVdRwhlErVpxK8K5iSbiXpW72v6jg5l6+gNj3F2XVwdJHqNCKTslzcJCcn88cff9CiRQvKly/P8ePHmT17NtHR0VkeCu7l5YWtrS2xsY8398XGxuLj8+Tw2gsXLnDp0iXat2+PnZ0ddnZ2/PLLL6xcuRI7OzsuXLiQ1bcjVNgxHW6egs5zwc5RdRqjaFHZm9dqFmfcqlNci3ukOo4Qyiw4toCz984S2iAUR1vLuL6p2AYC3oR1w7R5cITJy1Jx88knn1CkSBEmT55Mu3btuHLlCkuXLqVNmzbZGkrq4OBArVq12Lx5c8Zzer2ezZs3U69evSe2r1ixIsePH+fo0aMZjw4dOtC0aVOOHj0q99OYg+tHYMc0aDgEitZQncaoQtpXJp+jHUOXHZPuKWGVTt45yXfHvqO3f2+qFKyiOo5xtZ4EDvm1xTXl+jZ5WRotZWNjQ4kSJahRo8ZzJ5T6+++/Mx1g8eLF9OzZk/nz51O3bl1mzpzJkiVLOH36NN7e3vTo0YNixYoxadKkp77+3Xfffe5oqX+TSfwUSk2C75qArT303qL9v4XZcfYWPX7cz/hOVXnnpZKq4wiRZ5LTk3lj9RvY29jze5vfsbfA65vzm+C316DtDKjzgeo04jmyNIlfjx49jD5LZrdu3bh16xYhISHExMRQvXp11q1bl3GTcXR0tEwwZim2TYS7F+DD7RZZ2AA0Kl+INwNLMGltJI3LFaJEQfMcBSZEVs05OodLCZdY3G6xZRY2AGWba+tPbQiBMi+DpywUbapytPyCOZKWG0Wi98GPraD5aGjwmeo0uepBchqvzNpBETdn/vzwJWxszHPafCEy6+jNo/Rc15N+NfrxQTULb9FIvg9z64NbcXh3DciXb5Mkfysi96U81NZpKV5bG/pt4fI72jHt9QD2X7rLj+FRquMIkasepT0iODyYqgWr8m6Vd1XHyX2OrtBpLkTvhn1zVacRzyDFjch9m8ZCwg3oNA9sbFWnyRMvlS7Ie0GlmLb+DOdvZm3+JyHMyazDs4h5GENog1DsbMxz1fIs82sAgR/D5nFw66zqNOIppLgRuStqh7YwZvPR4FVWdZo89UXrChQr4MzgpRGkpetVxxHC6Pbf2M/vkb8zoOYASrmXUh0nbzULAffiWqt0eprqNOJfpLgRuScpAcL6QskGUPcj1WnynJO9LdO7BnD8ahzzd1xUHUcIo3qY+pBR4aOo5V2Ltyq9pTpO3nNw0Vqjrx+B3bNUpxH/IsWNyD0bguHRXeg0x2pvuqtZwoOPGpdh5qazRN5IUB1HCKOZdmAa95LvMT5oPDY667y+8a0DQQNg6ySIOaE6jfgfVvobKXLduU1w+GdoOR48/FSnUWpg83KU9srP4CURpKRJ95Qwf7uu7eKvc38xpPYQfF2tfPLUJsPBq5zWPZWWojqN+H9S3Ajje3QPVn6qzQNRq5fqNMo52tkyo2sAZ2PvM3vredVxhMiR+OR4RoePpn7R+nQp30V1HPXsHLXRUzcjtdnXhUmQ4kYY3z/DICUROswGI0/6aK6qFnPn05fLMmfreY5djVMdR4hsm7J/Co/SHjG2/lijT+pqtopWh0afw84ZcO2w6jQCKW6EsUWuhmN/witTwL2Y6jQmpW/TslQq4sqgJREkpaarjiNElm2O3syqi6sYWncoPvmeXNzYqjUcDD5VYXkfbakZoZQUN8J4Ht6G1QOhQhsIeEN1GpNjb2vDl12rE30nka82ytwYwrzcTbrLuD3jaOLbhA5lOqiOY3ps7aHzfLgXBVsnqE5j9aS4EcZhMMCaQaBPh3YzpTvqGcp7uzKoZXm+23mRQ5fvqo4jRKYYDAZC94aSbkhndL3R0h31LIUrQdORsPsbiN6rOo1Vk+JGGMeJv+DUCm21XFdv1WlMWu+GpanhW4DBSyJITJHJv4TpW3dpHRsvbyT4pWC8nL1UxzFt9ftB8ToQ9rG29IxQQoobkXP3Y2DtEKjSGaq+qjqNybO10TG9SwAxCUlM+ee06jhCPNetxFuE7g2llV8rWvu1Vh3H9NnYaqOnEm7ApjGq01gtKW5EzhgMsGoA2NhDmxmq05iN0oXyM7R1RX7ec5nd52+rjiPEUxkMBsbuGYu9jT0jA0eqjmM+vMpC8zGw/zu4uF11GqskxY3ImaOL4Ow6aD8L8hVUncas9Kznx0ulPfl82THuJ6WqjiPEE8LOh7H96nZG1xuNh5OH6jjmpe6H4NcQVvTVlqIReUqKG5F98Vdh3TAI6A4V26hOY3ZsbHRMez2AuMQUJqyJVB1HiMfceHCDqQem0qFMB5qWaKo6jvmxsYGOc7RJTTdIq1dek+JGZI/BoH0jccgPrSerTmO2fD1dCG5XmT8PXGHr6Zuq4wgBaN1RIbtDcLF3YWjdoarjmC+PktBqAhz+Bc5uUJ3GqkhxI7Ln4A9wcRt0nA3OBVSnMWtv1PGlcflCDP3rGHGJsjaNUG/JmSXsvbGX8fXH4+bgpjqOeavZE8o2h5X9IFGmf8grUtyIrLt7ETaEaOtGlW2mOo3Z0+l0THnNn6TUdMasPKk6jrByVxKuMOPQDLqU70L9YvVVxzF/Oh10+AbSHsE/0gqWV6S4EVmj10NYX8jnpa34LYzCx92JMR2qEHb0OutO3FAdR1ipdH06weHBeDp5Mrj2YNVxLIdbUXhlKhxfApGrVKexClLciKzZNxeid0Onb8HRVXUai9K5RjFaVvZm5PIT3HmQrDqOsEK/Rf7G4ZuHGR80nnz2+VTHsSz+3aBiO1g1UFuqRuQqKW5E5t06C5vGwkufgF8D1Wksjk6nY0LnaugNBkYuP4HBYFAdSViRi3EX+frw17xd6W3q+NRRHcfy6HTQ7isw6GH1Z9qgDJFrpLgRmZOeBmF9oIAvNAtRncZiFXJ1ZELnaqw7GcPKiOuq4wgrkaZPY+SukRTNX5QBNQeojmO58hfWCpzIldqSNSLXSHEjMid8Jlw/Ap3mgb2z6jQWrU21IrQPKErIipPEJiSpjiOswE8nfuLU3VOENgjFyc5JdRzLVqUTVH0N1gzWlmgQuUKKG/FiMcdh22QIGgC+0lydF8Z1qIKDnQ3D/jom3VMiV525e4ZvI76lV5VeBBQKUB3HOrSZDnaO2tI1cn3nCiluxPOlpcDyj8GrHDQZrjqN1fDI58DkV6ux9cwtlh68qjqOsFCp6amM3DUSPzc/Pqn+ieo41sPFE9p/DefWw5HfVKexSFLciOfbMRVuRULnedo3DZFnmlXypkut4oxbfYqr9xJVxxEWaN6xeVyIu8DEBhNxsHVQHce6VGgN1d+GdcMhLlp1GosjxY14tmuHYOeX0OgLKCLN1SqMal8ZNyc7vlh2DL1emq+F8Zy4fYIfjv/AhwEfUqlgJdVxrFPrieDkDis+1eYQE0YjxY14utQkrTvKpxo0HKQ6jdVyc7Jn6usB7L5wh9/2XVYdR1iI5PRkRu4aSQXPCnxQ7QPVcayXk7u2hE3Udm1JG2E0UtyIp9saCveitO4oW3vVaaxag3JevP1SCSatPc2l2w9VxxEWYPaR2Vy5f4UJQROwt5HrW6kyTaH2+7AxBO5cUJ3GYkhxI550eQ/sng1NR0Jhaa42BcNfqUQhV0eGLI0gXbqnRA4cjj3Mzyd/5tMan1LWo6zqOAKgxThtDpwVfUGfrjqNRZDiRjwu5SGEfQzF60D9fqrTiP+Xz9GO6V0COBR9jx93RamOI8xUYmoiweHB+Bfyp2flnqrjiP9wzA+d5kL0Xtj7reo0FkGKG/G4jaPhfozWHWVjqzqN+B91S3nyflAppm04w7nY+6rjCDP01aGvuJV4iwkNJmAr17dpKVkf6vWFzePh1hnVacyeFDfivy5ugwMLoMVYKFhGdRrxFENaVcDXw5khSyNIS5fRFSLz9t7Yy59n/mRgrYGUdCupOo54mpeDwaMkLO+jLXkjsk2KG6FJStCGI/o1hDq9VacRz+Bkb8uMrtU5fi2eedvl5kOROQ9SHhASHkJdn7p0r9hddRzxLPbO2hI3N45C+Feq05g1KW6EZv0IeHQPOs4BG/m1MGXVfQvwcZMyzNp8jlPXE1THEWZg2sFpxCfHMy5oHDY6ub5NWvFa0GAQbJuiLX0jskV+ywWc3QBHfoVWE7QmUWHy+jcrR5lC+Rm05CgpadI9JZ5tx9Ud/H3ubz6v8znF8hdTHUdkRuOhUKiC1j2VlqI6jVmS4sbaJd6Flf2gbHOoKaMnzIWjnS0zugZw/uYDvt58TnUcYaLik+MZs3sMQcWCeK3ca6rjiMyyc9AGddw6A9unqE5jlqS4sXb/DIW0R9DhG9DpVKcRWVClqDsDmpVj7vYLHL0SpzqOMEGT9k8iKT2JsfXGopPr27z4VNNacHZ9BVcPqU5jdqS4sWanVsLxJfDKVHArqjqNyIaPm5ShSlE3Bi85SlKqTP4l/mvT5U2subiG4XWH453PW3UckR0NPtPW9QvrA6mPVKcxK1LcWKsHt2D1Z1CxHfh3U51GZJOdrQ0zugRw5d4jZmyQuTGE5s6jO4zfO56XfV+mXel2quOI7LK107qn7l2GLaGq05gVKW6skcEAaz4DDNDuK+mOMnPlvF0Z0rI83++KYn/UXdVxhGIGg4HQvaEYDAZG1Rsl3VHmrlAFaDYK9syBy7tVpzEbUtxYo+NLIXIVtP1SW89EmL33G5SmVgkPhiyN4GGyTP5lzdZGrWVT9CaCXwrGy9lLdRxhDC99Ar6B2tI4yQ9UpzELUtxYm4QbsHYIVH0dqnRSnUYYia2NjuldArh1P5nJ/5xWHUcocjPxJhP2TeCVUq/Q0q+l6jjCWGxsodO38OAmbBqtOo1ZkOLGmhgM2rBvOydoM011GmFkfl75GN6mIr/uvcyuc7dVxxF5zGAwMGb3GBxtHRkZOFJ1HGFsBctoq4cf+B4ubFWdxuRJcWNNjvwK5zdC+6/BxVN1GpEL3g4sSf0yBfliWQQJSamq44g8FHY+jJ3XdjKm3hjcHd1VxxG5ofb7UKqRtlROUrzqNCZNihtrERcN60ZA9behQmvVaUQusbHRMa1LAAlJaYSuPqU6jsgj1x9cZ8qBKXQq24nGvo1VxxG5xcZGWyInKV77PBfPJMWNNdDrYUVfcHKH1hNVpxG5rFgBZ0a1q8SSg1fZHBmrOo7IZXqDnpDwEFwdXPmizheq44jcVqCE9jl+9Dc4s051GpMlxY01OPA9RO2AjrO1AkdYvK61fWlaoRDD/j7OvYeyNo0lW3xmMfti9jGu/jhcHVxVxxF5ocY7UK4lrOqvLaEjniDFjaW7c0G7u77OB1Cmqeo0Io/odDomv+ZPSpqe0StPqo4jckl0QjRfHfqKbhW6Ua9oPdVxRF7R6bR7J9OSYe3nqtOYJCluLJk+XZsXIX9haD5WdRqRx7zdnBjXsQorI66z9vgN1XGEkaXr0wkOD6agU0EG1RqkOo7Ia25FoM10OLEMToapTmNypLixZHvmwJX90GkuOOZXnUYo0CGgKK2r+BAcdoJb95NVxxFG9OupXzl68yihDUJxsXdRHUeoUO11qNRBW0rnwU3VaUyKFDeW6uZpbS2Sen2hZH3VaYQiOp2O0M5V0QEjlx/HYDCojiSM4ELcBb458g3vVH6HWt61VMcRquh02kzzOhutwJHrO4MUN5YoPVVbRdajJLwcrDqNUMwrvyMTOldlw6lYwo5eUx1H5FCaPo2Ru0ZSzLUY/Wr0Ux1HqJa/kLZG4OnVcGyJ6jQmQ4obS7TrK7gRAZ3mgb2z6jTCBLSuWoRO1YsSsuIkMfFJquOIHPjh+A9E3o1kQtAEnOycVMcRpqByB6jWVbu5OOG66jQmQYobS3PjGGyfAg0GQXFprhb/NbZDVZztbRn61zHpnjJTp++eZl7EPN6v+j7VClVTHUeYkjZTtS+zK/tJ9xRS3FiWtGRY3gcKVYTGQ1WnESbG3cWeKa/5s/3sLf48cEV1HJFFKekpjNw1ktIFStMnoI/qOMLUOHtAh2/g/CY4/LPqNMpJcWNJtk+B22eh8zywc1CdRpigphUL0622L6GrT3HlbqLqOCIL5kXM42LcRSY0mICDrVzf4inKt9Qm+Fs/Eu5dVp1GKSluLMXVg9q9Nk2Ggo80V4tnC25XiQIuDny+LAK9XpqvzcGxW8f44cQPfBTwERU9K6qOI0xZq4laK86KvtrSO1ZKihtLkPpI644qUh2CPlOdRpg4Vyd7pr7uz96Ld/l5zyXVccQLJKUlMXLXSCp5VuKDah+ojiNMnZObtrjmpZ1wYIHqNMpIcWMJtoRqq353nge2dqrTCDMQVNaLHvVKMmXdaS7eeqA6jniOb458w/UH15nQYAJ2NnJ9i0wo3RjqfggbR8Pt86rTKCHFjbm7vFubibjZKChUQXUaYUaGvVIRbzcnhiyNIF26p0zSodhD/HrqV/rX7E+ZAmVUxxHmpPkYbYmGsI+1pXisjEkUN3PmzMHPzw8nJycCAwPZv3//M7ddsGABDRs2xMPDAw8PD5o3b/7c7S1a8gPtF7fES/DSJ6rTCDPj4mDHjC4BHLkSx4KdF1XHEf+SmJpI8K5gqheuztuV3lYdR5gbh3za0jtXD8Dub1SnyXPKi5vFixczaNAgRo8ezeHDhwkICKBVq1bcvPn0dTK2bdtG9+7d2bp1K3v27MHX15eWLVty7ZoVzry6MURbT6TTt2BjqzqNMEO1/Tzp3bA0X244y5mY+6rjiP/x5aEvuZN0h9CgUGzl+hbZUeIlqP8pbJ0ANyNVp8lTOoPi2bwCAwOpU6cOs2fPBkCv1+Pr60u/fv0YNmzYC1+fnp6Oh4cHs2fPpkePHi/cPiEhAXd3d+Lj43Fzc8txfmUubIFfO2urwtbtrTqNMGNJqem0+2YXTvY2LP8kCHtb5d95rN7u67v5aONHjAgcQfeK3VXHEeYsNQm+awx2jvDBZrC1V50oTyj9FEtJSeHQoUM0b9484zkbGxuaN2/Onj17MrWPxMREUlNT8fT0fOrPk5OTSUhIeOxh9pLiYcWnUKox1H5fdRph5pzsbZnRJYDIG/eZs9U6bz40JfdT7hMSHkJgkUC6VeimOo4wd/ZOWvdUzAnY+aXqNHlGaXFz+/Zt0tPT8fb2fux5b29vYmJiMrWPoUOHUrRo0ccKpP81adIk3N3dMx6+vr45zq3cuhGQlKAN97ORb9ki5wJ8C/BJkzLM3nKeE9fiVcexalMPTOVB6gPG1x+PjU6ub2EExWpCw8GwYypcP6o6TZ4w6ytn8uTJ/Pnnnyxfvhwnp6cvIDd8+HDi4+MzHleumPm082fWwdHfoPUkKGABhZowGf1eLkc5b1cGLTlKcpr1ja4wBduvbCfsfBhD6wylSP4iquMIS9LocyhcSRuEkpasOk2uU1rceHl5YWtrS2xs7GPPx8bG4uPj89zXTp8+ncmTJ7Nhwwb8/f2fuZ2joyNubm6PPcxW4l1Y1R/KtYIaMnpCGJeDnQ1fdg0g6vZDZm46pzqO1YlLimPMnjE0LNaQTmU7qY4jLI2dA3SaB7fPwbZJqtPkOqXFjYODA7Vq1WLz5s0Zz+n1ejZv3ky9evWe+bqpU6cyfvx41q1bR+3atfMiqmlYO0SruDt8DTqd6jTCAlUq4sbA5uWZv/0Ch6PvqY5jVSbum0hKegpj6o9BJ9e3yA0+VaHJMAifBVcOqE6Tq5R3Sw0aNIgFCxbw888/ExkZyccff8zDhw/p1asXAD169GD48OEZ20+ZMoVRo0bx448/4ufnR0xMDDExMTx4YOGzrJ5cDif+grYzwPX5rVpC5MRHjUpTrXgBBi+J4FGKdE/lhfWX1vPPpX8YETiCwi6FVccRlixoIBStAWF9IMVyF89VXtx069aN6dOnExISQvXq1Tl69Cjr1q3LuMk4OjqaGzduZGw/d+5cUlJSeP311ylSpEjGY/r06areQu57cBNWD4JKHaDqa6rTCAtnZ2vDjC4BXI97xNT1p1XHsXi3H90mdG8oLUq2oE2pNqrjCEtna6d1T8VfhS3jVafJNcrnuclrZjfPjcEAi9+G6L3Qdx/k81KdSFiJ73deJHRNJH/0fol6ZQqqjmORDAYDA7cO5OitoyzvuBxPp6dPaSGE0e2ZA+tHwrurwa+B6jRGp7zlRrzAscVwejW0+0oKG5GnegWVoq6fJ58vi+BBcprqOBZp9cXVbLmyhVEvjZLCRuStwI+hRD0I+0RbysfCSHFjyuKvwdovoFpXqNxBdRphZWxtdEzr4s+dBylMXGtdU7fnhZiHMUzaN4m2pdvSvOTT5+kSItfY2ECnOfDwNmwcpTqN0UlxY6oMBljZDxxcoM1U1WmElSpZMB8j2lZi0b5otp+9pTqOxTAYDIzZPQYnOyeG1x3+4hcIkRs8S0PLcXDwRzi/+cXbmxEpbkzV4Z/hwmbo8A04e6hOI6zY24ElaFDWi6HLjhH/KFV1HIvw17m/CL8ezpj6Y3B3dFcdR1iz2u9D6abal+lHcarTGI0UN6bo3mXtRq8a70C5FqrTCCun0+mY8ro/D5PTGLfqlOo4Zu/q/atMOzCNV8u9SqPijVTHEdZOp4OOsyH5PqyznFZEKW5MjV4PK/pqrTWtJqpOIwQAxQo4M6p9Zf46fJWNp2Jf/ALxVHqDnlHho3B3dOfz2p+rjiOExr04tJ4MEYvg9FrVaYxCihtTs/87uLRTWxTTyQyGqgur0aVWcZpVLMzwv49z92GK6jhm6Y/Tf3Aw9iDjg8aT3yG/6jhC/Ff1N6F8a1g1QFvqx8xJcWNKbp+HTWOg7odQurHqNEI8RqfTMenVaqTp9YxacUJ1HLNzKf4SMw/NpHvF7gQWCVQdR4jH6XTQfhboU2HNYNVpckyKG1OhT9dWa3UrAs3HqE4jxFMVdnNiXMeqrDl2g1UR11XHMRvp+nRGho+ksEthBtYcqDqOEE/n6gNtpsPJv+HE36rT5IgUN6Zi9zdw9QB0mgsO+VSnEeKZ2vsXoW21IoxacYKb95NUxzELC08u5Pit44Q2CMXF3kV1HCGereprULmT1npz33zvr5PixhTEnoKtE6B+Pyjxkuo0QjyXTqdjfKeq2NnoGPH3CaxsBZcsO3fvHHOOzqFnlZ7UKFxDdRwhnk+ng7Zfgo0trB6ozblmhqS4US09VVud1bM0NB2pOo0QmeKZz4EJnauxKTKWvw5fUx3HZKXqUxm5ayS+rr58WuNT1XGEyJx8BaHdTDizFiL+VJ0mW6S4UW3nDIg5oXVH2TupTiNEprWq4sOrNYoxdtVJrsc9Uh3HJH1/7HvO3jvLxAYTcbR1VB1HiMyr1A7834B/hmpLAZkZKW5Uun4UdkyDhoOhWE3VaYTIstHtq5DPwY6hfx2T7ql/OXXnFN8d+44Pqn1AFa8qquMIkXWvTNbuAV35qdl1T0lxo0pasjY6qnAlaCSTeQnz5O5iz+TXqrHz3G0W7Y9WHcdkpKSnMHLXSMp6lOUj/49UxxEie5w9tCWALmyBQz+pTpMlUtyosm0S3D4HneaBnYPqNEJkW5MKheletwQT1kQSfSdRdRyT8O3Rb7mUcInQoFDsbe1VxxEi+8o1h1rvwvpguBulOk2mSXGjwpUDED4Lmg4Hn6qq0wiRYyPbVsIznwNDlkWg15tX87WxRdyK4KeTP/FJwCdU8KygOo4QOdcyVLvJeMWn2hJBZkCKm7yWkqiNjipaA+oPUJ1GCKPI72jHtNcD2B91l592X1IdR5lHaY8I3hVMlYJV6FW1l+o4QhiHoyt0/BYu74L981WnyRQpbvLalvEQf1XrjrK1U51GCKOpV6Yg79b3Y+q601y49UB1HCW+Pvw1Nx7eILRBKHY2cn0LC1KqIQT20ZYIun1OdZoXkuImL13aBXu/hWYhUKi86jRCGN3Q1hUpWsCZwUsiSEs3j+ZrYzkQc4DfIn+jf43+lHYvrTqOEMbXbDS4FdMGw6SnqU7zXFLc5JXk+xD2CZQMgsCPVacRIlc4O9gyvUsAx67G8d3Oi6rj5JmHqQ8ZFT6KmoVr8nblt1XHESJ3OLhA53lw7RDs/lp1mueS4iavbBgFD29DxzlgI6ddWK5aJT34sFEZvtp4ltMxCarj5IkZB2dwN+kuoUGh2Ojk+hYWzLeutlTQ1okQe1J1mmeSqzAvnN+kzRHQcjx4llKdRohc91mLcpTyysfgJRGkpFl291T4tXCWnl3K4FqD8XXzVR1HiNzXZAQULAvL+0Baiuo0TyXFTW57FAcr+kHpplD7PdVphMgTjna2zOhSnTMx95m99bzqOLkmISWBkN0h1CtSj64VuqqOI0TesHeCznO1lpud01WneSopbnLbuuGQ8gA6ztZWWxXCSlQr7k7fpmWZs/U8x6/Gq46TK6bsn0JiaiLjgsahk+tbWJOiNbTZ9XdMh+tHVKd5ghQ3uen0WohYBK0ng3tx1WmEyHOfvlyWij6uDFpylKTUdNVxjGpr9FZWXljJ0LpD8cnnozqOEHmv0RDwrqJ1T6UmqU7zGClucsvDO7BqAJR/Baq/qTqNEErY29rwZdfqXL6TyFebzqqOYzT3ku4xds9YGhdvTMcyHVXHEUINW3tt9NTdi7Btouo0j5HiJresHQz6VGg/S7qjhFWr4OPKZy3K892Oixy6fFd1HKOYsG8CaYY0RtcbLd1Rwrp5V4EmwyH8a4jepzpNBilucsOJv+Dkcmg7A1y9VacRQrkPG5Wmum8BBi+JIDHFtCf/epF1UetYf2k9IwNHUsilkOo4QqhXvz8Ur60tLZTyUHUaQIob47sfC2sGQ+VOUPU11WmEMAm2NjpmdAkgJiGJqevOqI6Tbbcf3SZ0XygtS7aktV9r1XGEMA22dtBpLiRch01jVacBpLgxLoMBVg8EGzto+6XqNEKYlNKF8vNFq4os3H2J3Rduq46TZQaDgbF7xmKrsyX4pWDpjhLif3mV05Zn2D8fonaoTiPFjVFF/AFn1mr32eQrqDqNECbn3fp+BJby5POlx7iflKo6TpasvLCSbVe2EVIvBA8nD9VxhDA9gX2gZAMI6wtJamcnl+LGWOKvwj/DIKA7VGyrOo0QJsnGRsf0LgHEJaYwcW2k6jiZFvMwhin7p9C+dHualWimOo4QpsnGRpvTLfEObAhWG0Xp0S2FwQAr+4FDPm1OGyHEM/l6ujCybWX+2H+FrWduqo7zQgaDgdG7R+Ns78zQukNVxxHCtHmWglahcPhnOLdRWQwpbozh0E9wYQt0/AacC6hOI4TJ617Xl0blCzHsr2PEJ5p299TSs0vZfX03Y+uPxd3RXXUcIUxfrV5Q5mXtS/+je0oiSHGTU3ejYH0w1HoXyjZXnUYIs6DT6ZjyWjUSU9IZs8p0Vxa+cv8K0w9O5/Xyr9OgWAPVcYQwDzoddPgGUhLhHzWtnVLc5IReDyv6ajcPtwxVnUYIs1LE3Zkx7auw/Mg11p2IUR3nCXqDnlHho/B08mRI7SGq4whhXtyLwyuT4dhiiFyd54eX4iYn9s2Dy+HQ8VtwdFWdRgiz82rNYrSo7M3I5ce58yBZdZzH/B75O4diDzE+aDz57POpjiOE+QnoDhXaaFOkPMzb6R+kuMmu2+dg81gI/BhKNVSdRgizpNPpmNi5GnqDgeCwExgMBtWRAIiKj2LW4Vm8Vekt6vjUUR1HCPOk00G7maBPgzWDtME3eUSKm+xIT9NWQXUrBs1CVKcRwqwVcnUktFM1/jkRw8qI66rjkKZPI3hXMD75fBhQc4DqOEKYN1dvbVLbUyu0pYnyiBQ32bH7a7h+WFsN1cFFdRohzF5b/yK08y9CyIqT3ExIUppl4cmFnLhzgtCgUJztnJVmEcIiVH0VqnTWlia6nzf310lxk1WxJ2HrRG2hMN+6qtMIYTHGd6yKva0Nw/4+rqx76uy9s8w5Ood3q7xL9cLVlWQQwiK1mQG2DrBqQJ50T0lxkxVpKVp3VMGy0HSE6jRCWBSPfA5MfrUaW07fZOmhq3l+/NT0VEbuGomfmx99q/fN8+MLYdHyFdSWJjq7Do7+nuuHk+ImK3ZOh5untO4oO0fVaYSwOM0re/N6reKMW3WKa3GP8vTY3x3/jvP3zhPaIBQHW4c8PbYQVqFiGwh4E9YNh7gruXooKW4y6/oR2DEdGg6BotVVpxHCYoW0r4yrkx1Dlx1Dr8+b7qmTt0+y4NgCevv3pkrBKnlyTCGsUutJ4JAfVn6aq91TUtxkRmqS1h3lXQUayWReQuQmNyd7przmz67zt/l93+VcP15yejIjd42kvEd5evv3zvXjCWHVnAtoi2te3AYHf8i1w0hxkxnbJsLdi9B5Ptjaq04jhMVrVL4QbwWWYOLa01y+8zBXjzXnyByi70czocEE7G3k+hYi15VtBrXfgw0h2r+tuUCKmxeJ3gfhX2s3EHtXVp1GCKsxok0lvFwdGLI0gvRc6p46evMoC08upG/1vpTzKJcrxxBCPEWL8ZDPC8L6aksZGZkUN8+T8hDC+kDx2trQbyFEnsnnaMf01wM4ePkeP4VHGX3/iamJjNw1kmqFqvFulXeNvn8hxHM45odO30L0Htg31+i7l+LmeTaNhYQb0Gke2NiqTiOE1QksXZBe9Usxdf0Zzt+8b9R9zzo8i5uJN5kQNAFbub6FyHt+DeClj7V/a2+dNequpbh5lovbYf98aD4avMqqTiOE1fqidQWKezgzeEkEaenGab7ed2Mfi04vYmCtgfi5+xlln0KIbGgWAgVKaL0k6WlG260UN0+TlAArPoWSDaDuR6rTCGHVnOxtmdElgOPX4pm/I+c3Hz5IeUBIeAh1fOrQvWJ3IyQUQmSbvbM2d9z1IxA+02i7leLmaTYEQ+Id6DQHbOQUCaFajRIe9GlchpmbznLqekKO9jX94HTikuMYV38cNjq5voVQrnhtCBoI2yZDzAmj7FKu7H87txEO/wytQsHDT3UaIcT/G9C8HGUK5Wfw0ghS0rLXPbXz6k7+OvcXQ+oMobhrcSMnFEJkW5Nh4FVem1MuLSXHu5Pi5n89ugcr+0GZl6FWL9VphBD/w9HOluldAjgXe59vtpzL8uvjk+MZs3sMQUWDeL3c67mQUAiRbXaO0Hku3IqEHdNyvDspbv7XP0MhJRE6zAadTnUaIcS/VC3mTr+Xy/HttgtEXInL0msn75/Mo7RHjKk/Bp1c30KYniIB0OgL2DkDrh3K0a6kuPmPyFVwbDG8MgXci6lOI4R4hk+alqFyETcGL40gKTU9U6/ZfHkzqy+uZnjgcHzy+eRyQiFEtjUcBD7VYPnH2tJH2STFDcDD27BqIFRoAwFvqE4jhHgOe1sbZnQNIPpOIl9ufPHcGHeT7jJu7zia+jalXel2eZBQCJFttvba6Kl7UbA1NNu7keLGYIDVn4FBD+1mSneUEGagvLcrg1uWZ8HOixy4dPeZ2xkMBkL3hqI36AmpFyLdUUKYg8KV4OVg2D0bovdmaxdS3Jz4CyJXQtsZ4OqtOo0QIpM+aFiamiU86P3LQTadin3i54mpiQSHB7Px8kaCXwrGy9lLQUohRLbU+xR862qjp1KyvniudRc392NgzWCo8ipUfVV1GiFEFtja6Pi+R21qlfDgg18OMm7VqYwh4mfunqHb6m5svLyRCQ0m0MqvleK0QogssbGFTnO1f6c3jcnyy3UGgyF3lts1UQkJCbi7uxMfF4fb6t7arIh994GLp+poQohsMBgM/Bh+icn/RFLBx5W29aP4/tQs/Nz9mN54OqXcS6mOKITIrn3z4Z8voMcKKN0k0y8ziZabOXPm4Ofnh5OTE4GBgezfv/+52y9dupSKFSvi5OREtWrVWLt2bdYPemwJnFsP7WdJYSOEGdPpdLzfoBS/fFCN6w7z+fbENGp4tmJR20VS2Ahh7ur0Br+G2pJISZmfnVx5cbN48WIGDRrE6NGjOXz4MAEBAbRq1YqbN28+dfvdu3fTvXt33n//fY4cOUKnTp3o1KkTJ05kccrmjaMh4E2o2MYI70IIodKxW8cYe7g3jq4XqGLXj007gxgddoZHKZkbKi6EMFE2NtBxjjbJ7voRmX6Z8m6pwMBA6tSpw+zZswHQ6/X4+vrSr18/hg0b9sT23bp14+HDh6xevTrjuZdeeonq1aszb968Fx4vo1tqQnncPtsHzgWM9l6EEHlLb9Dz88mf+frw11T2qszURlMpmq8oiw9cYcyqk5TwdGH2mzUp7+2qOqoQIicO/Qyr+sOY+ExtbpfLcZ4rJSWFQ4cOMXz48IznbGxsaN68OXv27Hnqa/bs2cOgQYMee65Vq1aEhYU9dfvk5GSSk5Mz/hwfr52Y+i622P7aOIfvQAihkgEDafo03qn0Dh8GfIi93p779+/TpmIBynv6M2RpBC2mrMNOFsAVwszl4xvbqjRISMDV1fWF0zooLW5u375Neno63t6PD8H29vbm9OnTT31NTEzMU7ePiYl56vaTJk1i7NixTzx/8rPIbKYWQpiaEf//PyGE5WoLMNWd+Ph43Nzcnrut0uImLwwfPvyxlp64uDhKlixJdHQ07u7uCpNZhoSEBHx9fbly5coLf9nEi8n5NC45n8Yn59S45Hxmnavri7uZlRY3Xl5e2NraEhv7+ARcsbGx+Pg8ff0XHx+fLG3v6OiIo6PjE8+7u7vLL5IRubm5yfk0IjmfxiXn0/jknBqXnE/jUtoR7eDgQK1atdi8eXPGc3q9ns2bN1OvXr2nvqZevXqPbQ+wcePGZ24vhBBCCOuivFtq0KBB9OzZk9q1a1O3bl1mzpzJw4cP6dWrFwA9evSgWLFiTJo0CYABAwbQuHFjZsyYQdu2bfnzzz85ePAg3333ncq3IYQQQggToby46datG7du3SIkJISYmBiqV6/OunXrMm4ajo6OxuZ/RjrUr1+fRYsWERwczIgRIyhXrhxhYWFUrVo1U8dzdHRk9OjRT+2qElkn59O45Hwal5xP45NzalxyPnOH8nluhBBCCCGMSSZ/EEIIIYRFkeJGCCGEEBZFihshhBBCWBQpboQQQghhUSyiuJkzZw5+fn44OTkRGBjI/v37n7v90qVLqVixIk5OTlSrVo21a9c+9nODwUBISAhFihTB2dmZ5s2bc+7cudx8CybF2Ofz3XffRafTPfZo3bp1br4Fk5KV83ny5Elee+01/Pz80Ol0zJw5M8f7tDTGPp9jxox54vezYsWKufgOTEtWzueCBQto2LAhHh4eeHh40Lx58ye2l89P455Pa//8zDaDmfvzzz8NDg4Ohh9//NFw8uRJQ+/evQ0FChQwxMbGPnX78PBwg62trWHq1KmGU6dOGYKDgw329vaG48ePZ2wzefJkg7u7uyEsLMwQERFh6NChg6FUqVKGR48e5dXbUiY3zmfPnj0NrVu3Nty4cSPjcffu3bx6S0pl9Xzu37/fMGTIEMMff/xh8PHxMXz11Vc53qclyY3zOXr0aEOVKlUe+/28detWLr8T05DV8/nmm28a5syZYzhy5IghMjLS8O677xrc3d0NV69ezdhGPj+Nez6t+fMzJ8y+uKlbt66hb9++GX9OT083FC1a1DBp0qSnbt+1a1dD27ZtH3suMDDQ8NFHHxkMBoNBr9cbfHx8DNOmTcv4eVxcnMHR0dHwxx9/5MI7MC3GPp8Gg3ZxduzYMVfymrqsns//VbJkyaf+Y5yTfZq73Difo0ePNgQEBBgxpfnI6e9SWlqawdXV1fDzzz8bDAb5/DT2+TQYrPvzMyfMulsqJSWFQ4cO0bx584znbGxsaN68OXv27Hnqa/bs2fPY9gCtWrXK2D4qKoqYmJjHtnF3dycwMPCZ+7QUuXE+/2Pbtm0ULlyYChUq8PHHH3Pnzh3jvwETk53zqWKf5iI33/u5c+coWrQopUuX5q233iI6OjqncU2eMc5nYmIiqampeHp6AvL5aezz+R/W+PmZU2Zd3Ny+fZv09PSM2Yz/w9vbm5iYmKe+JiYm5rnb/+f/s7JPS5Eb5xOgdevW/PLLL2zevJkpU6awfft2XnnlFdLT043/JkxIds6nin2ai9x674GBgSxcuJB169Yxd+5coqKiaNiwIffv389pZJNmjPM5dOhQihYtmvEPunx+Gvd8gvV+fuaU8uUXhOV74403Mv67WrVq+Pv7U6ZMGbZt20azZs0UJhMCXnnllYz/9vf3JzAwkJIlS7JkyRLef/99hclM2+TJk/nzzz/Ztm0bTk5OquOYvWedT/n8zB6zbrnx8vLC1taW2NjYx56PjY3Fx8fnqa/x8fF57vb/+f+s7NNS5Mb5fJrSpUvj5eXF+fPncx7ahGXnfKrYp7nIq/deoEABypcvL7+fzzF9+nQmT57Mhg0b8Pf3z3hePj+Nez6fxlo+P3PKrIsbBwcHatWqxebNmzOe0+v1bN68mXr16j31NfXq1Xtse4CNGzdmbF+qVCl8fHwe2yYhIYF9+/Y9c5+WIjfO59NcvXqVO3fuUKRIEeMEN1HZOZ8q9mku8uq9P3jwgAsXLsjv5zNMnTqV8ePHs27dOmrXrv3Yz+Tz07jn82ms5fMzx1Tf0ZxTf/75p8HR0dGwcOFCw6lTpwwffvihoUCBAoaYmBiDwWAwvPPOO4Zhw4ZlbB8eHm6ws7MzTJ8+3RAZGWkYPXr0U4eCFyhQwLBixQrDsWPHDB07drSqoYzGPJ/37983DBkyxLBnzx5DVFSUYdOmTYaaNWsaypUrZ0hKSlLyHvNSVs9ncnKy4ciRI4YjR44YihQpYhgyZIjhyJEjhnPnzmV6n5YsN87n4MGDDdu2bTNERUUZwsPDDc2bNzd4eXkZbt68mefvL69l9XxOnjzZ4ODgYFi2bNljQ5Pv37//2Dby+Wmc82ntn585YfbFjcFgMHzzzTeGEiVKGBwcHAx169Y17N27N+NnjRs3NvTs2fOx7ZcsWWIoX768wcHBwVClShXDmjVrHvu5Xq83jBo1yuDt7W1wdHQ0NGvWzHDmzJm8eCsmwZjnMzEx0dCyZUtDoUKFDPb29oaSJUsaevfubRX/EP9HVs5nVFSUAXji0bhx40zv09IZ+3x269bNUKRIEYODg4OhWLFihm7duhnOnz+fh+9Iraycz5IlSz71fI4ePTpjG/n8NN75lM/P7NMZDAZD3rYVCSGEEELkHrO+50YIIYQQ4t+kuBFCCCGERZHiRgghhBAWRYobIYQQQlgUKW6EEEIIYVGkuBFCCCGERZHiRgghhBAWRYobIYQQQlgUKW6EEBZn27Zt6HQ64uLiVEcRQiggMxQLIcxekyZNqF69OjNnzgQgJSWFu3fv4u3tjU6nUxtOCJHn7FQHEEIIY3NwcMDHx0d1DCGEItItJYQwa++++y7bt29n1qxZ6HQ6dDodCxculG4pIayYFDdCCLM2a9Ys6tWrR+/evblx4wY3btzA19dXdSwhhEJS3AghzJq7uzsODg64uLjg4+ODj48Ptra2qmMJIRSS4kYIIYQQFkWKGyGEEEJYFCluhBBmz8HBgfT0dNUxhBAmQoaCCyHMnp+fH/v27ePSpUvkz58fvV6vOpIQQiFpuRFCmL0hQ4Zga2tL5cqVKVSoENHR0aojCSEUkhmKhRBCCGFRpOVGCCGEEBZFihshhBBCWBQpboQQQghhUaS4EUIIIYRFkeJGCCGEEBZFihshhBBCWBQpboQQQghhUaS4EUIIIYRFkeJGCCGEEBZFihshhBBCWBQpboQQQoj/2ygYVgAAmTeOT0Wen9sAAAAASUVORK5CYII=",
|
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"text/plain": [
|
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"<Figure size 640x480 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
},
|
|
{
|
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"data": {
|
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"image/png": 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",
|
|
"text/plain": [
|
|
"<Figure size 640x480 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"import numpy as np\n",
|
|
"from skfuzzy import control as ctrl\n",
|
|
"import skfuzzy as fuzz\n",
|
|
"\n",
|
|
"temp = ctrl.Antecedent(density_train[\"T\"].sort_values().unique(), \"temp\")\n",
|
|
"al = ctrl.Antecedent(np.arange(0, 0.3, 0.005), \"al\")\n",
|
|
"ti = ctrl.Antecedent(np.arange(0, 0.3, 0.005), \"ti\")\n",
|
|
"density = ctrl.Consequent(np.arange(1.03, 1.22, 0.00001), \"density\")\n",
|
|
"\n",
|
|
"temp.automf(3, variable_type=\"quant\")\n",
|
|
"temp.view()\n",
|
|
"al.automf(3, variable_type=\"quant\")\n",
|
|
"al.view()\n",
|
|
"ti.automf(3, variable_type=\"quant\")\n",
|
|
"ti.view()\n",
|
|
"density.automf(5, variable_type=\"quant\")\n",
|
|
"density.view()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 24,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"15"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
},
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"[IF (al[low] AND ti[low]) AND temp[high] THEN density[lower]\n",
|
|
" \tAND aggregation function : fmin\n",
|
|
" \tOR aggregation function : fmax,\n",
|
|
" IF (al[average] AND ti[low]) AND temp[low] THEN density[low]\n",
|
|
" \tAND aggregation function : fmin\n",
|
|
" \tOR aggregation function : fmax,\n",
|
|
" IF (al[low] AND ti[average]) AND temp[low] THEN density[low]\n",
|
|
" \tAND aggregation function : fmin\n",
|
|
" \tOR aggregation function : fmax,\n",
|
|
" IF (al[low] AND ti[low]) AND temp[average] THEN density[lower]\n",
|
|
" \tAND aggregation function : fmin\n",
|
|
" \tOR aggregation function : fmax,\n",
|
|
" IF (al[low] AND ti[low]) AND temp[low] THEN density[low]\n",
|
|
" \tAND aggregation function : fmin\n",
|
|
" \tOR aggregation function : fmax,\n",
|
|
" IF (al[average] AND ti[low]) AND temp[average] THEN density[low]\n",
|
|
" \tAND aggregation function : fmin\n",
|
|
" \tOR aggregation function : fmax,\n",
|
|
" IF (al[average] AND ti[low]) AND temp[high] THEN density[lower]\n",
|
|
" \tAND aggregation function : fmin\n",
|
|
" \tOR aggregation function : fmax,\n",
|
|
" IF (al[low] AND ti[average]) AND temp[average] THEN density[low]\n",
|
|
" \tAND aggregation function : fmin\n",
|
|
" \tOR aggregation function : fmax,\n",
|
|
" IF (al[low] AND ti[average]) AND temp[high] THEN density[low]\n",
|
|
" \tAND aggregation function : fmin\n",
|
|
" \tOR aggregation function : fmax,\n",
|
|
" IF (al[low] AND ti[high]) AND temp[low] THEN density[higher]\n",
|
|
" \tAND aggregation function : fmin\n",
|
|
" \tOR aggregation function : fmax,\n",
|
|
" IF (al[low] AND ti[high]) AND temp[average] THEN density[higher]\n",
|
|
" \tAND aggregation function : fmin\n",
|
|
" \tOR aggregation function : fmax,\n",
|
|
" IF (al[low] AND ti[high]) AND temp[high] THEN density[high]\n",
|
|
" \tAND aggregation function : fmin\n",
|
|
" \tOR aggregation function : fmax,\n",
|
|
" IF al[high] AND temp[low] THEN density[high]\n",
|
|
" \tAND aggregation function : fmin\n",
|
|
" \tOR aggregation function : fmax,\n",
|
|
" IF al[high] AND temp[average] THEN density[high]\n",
|
|
" \tAND aggregation function : fmin\n",
|
|
" \tOR aggregation function : fmax,\n",
|
|
" IF al[high] AND temp[high] THEN density[average]\n",
|
|
" \tAND aggregation function : fmin\n",
|
|
" \tOR aggregation function : fmax]"
|
|
]
|
|
},
|
|
"execution_count": 24,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"from src.rules import get_fuzzy_rules\n",
|
|
"\n",
|
|
"fuzzy_variables = {\"Al2O3\": al, \"TiO2\": ti, \"T\": temp, \"consequent\": density}\n",
|
|
"fuzzy_rules = get_fuzzy_rules(rules, fuzzy_variables)\n",
|
|
"\n",
|
|
"fuzzy_cntrl = ctrl.ControlSystem(fuzzy_rules)\n",
|
|
"\n",
|
|
"sim = ctrl.ControlSystemSimulation(fuzzy_cntrl, lenient=False)\n",
|
|
"\n",
|
|
"display(len(fuzzy_rules))\n",
|
|
"fuzzy_rules"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 25,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"=============\n",
|
|
" Antecedents \n",
|
|
"=============\n",
|
|
"Antecedent: al = 0.0\n",
|
|
" - low : 1.0\n",
|
|
" - average : 0.0\n",
|
|
" - high : 0.0\n",
|
|
"Antecedent: ti = 0.0\n",
|
|
" - low : 1.0\n",
|
|
" - average : 0.0\n",
|
|
" - high : 0.0\n",
|
|
"Antecedent: temp = 25\n",
|
|
" - low : 0.8\n",
|
|
" - average : 0.2\n",
|
|
" - high : 0.0\n",
|
|
"\n",
|
|
"=======\n",
|
|
" Rules \n",
|
|
"=======\n",
|
|
"RULE #0:\n",
|
|
" IF (al[low] AND ti[low]) AND temp[high] THEN density[lower]\n",
|
|
"\tAND aggregation function : fmin\n",
|
|
"\tOR aggregation function : fmax\n",
|
|
"\n",
|
|
" Aggregation (IF-clause):\n",
|
|
" - al[low] : 1.0\n",
|
|
" - ti[low] : 1.0\n",
|
|
" - temp[high] : 0.0\n",
|
|
" (al[low] AND ti[low]) AND temp[high] = 0.0\n",
|
|
" Activation (THEN-clause):\n",
|
|
" density[lower] : 0.0\n",
|
|
"\n",
|
|
"RULE #1:\n",
|
|
" IF (al[average] AND ti[low]) AND temp[low] THEN density[low]\n",
|
|
"\tAND aggregation function : fmin\n",
|
|
"\tOR aggregation function : fmax\n",
|
|
"\n",
|
|
" Aggregation (IF-clause):\n",
|
|
" - al[average] : 0.0\n",
|
|
" - ti[low] : 1.0\n",
|
|
" - temp[low] : 0.8\n",
|
|
" (al[average] AND ti[low]) AND temp[low] = 0.0\n",
|
|
" Activation (THEN-clause):\n",
|
|
" density[low] : 0.0\n",
|
|
"\n",
|
|
"RULE #2:\n",
|
|
" IF (al[low] AND ti[average]) AND temp[low] THEN density[low]\n",
|
|
"\tAND aggregation function : fmin\n",
|
|
"\tOR aggregation function : fmax\n",
|
|
"\n",
|
|
" Aggregation (IF-clause):\n",
|
|
" - al[low] : 1.0\n",
|
|
" - ti[average] : 0.0\n",
|
|
" - temp[low] : 0.8\n",
|
|
" (al[low] AND ti[average]) AND temp[low] = 0.0\n",
|
|
" Activation (THEN-clause):\n",
|
|
" density[low] : 0.0\n",
|
|
"\n",
|
|
"RULE #3:\n",
|
|
" IF (al[low] AND ti[low]) AND temp[average] THEN density[lower]\n",
|
|
"\tAND aggregation function : fmin\n",
|
|
"\tOR aggregation function : fmax\n",
|
|
"\n",
|
|
" Aggregation (IF-clause):\n",
|
|
" - al[low] : 1.0\n",
|
|
" - ti[low] : 1.0\n",
|
|
" - temp[average] : 0.2\n",
|
|
" (al[low] AND ti[low]) AND temp[average] = 0.2\n",
|
|
" Activation (THEN-clause):\n",
|
|
" density[lower] : 0.2\n",
|
|
"\n",
|
|
"RULE #4:\n",
|
|
" IF (al[low] AND ti[low]) AND temp[low] THEN density[low]\n",
|
|
"\tAND aggregation function : fmin\n",
|
|
"\tOR aggregation function : fmax\n",
|
|
"\n",
|
|
" Aggregation (IF-clause):\n",
|
|
" - al[low] : 1.0\n",
|
|
" - ti[low] : 1.0\n",
|
|
" - temp[low] : 0.8\n",
|
|
" (al[low] AND ti[low]) AND temp[low] = 0.8\n",
|
|
" Activation (THEN-clause):\n",
|
|
" density[low] : 0.8\n",
|
|
"\n",
|
|
"RULE #5:\n",
|
|
" IF (al[average] AND ti[low]) AND temp[average] THEN density[low]\n",
|
|
"\tAND aggregation function : fmin\n",
|
|
"\tOR aggregation function : fmax\n",
|
|
"\n",
|
|
" Aggregation (IF-clause):\n",
|
|
" - al[average] : 0.0\n",
|
|
" - ti[low] : 1.0\n",
|
|
" - temp[average] : 0.2\n",
|
|
" (al[average] AND ti[low]) AND temp[average] = 0.0\n",
|
|
" Activation (THEN-clause):\n",
|
|
" density[low] : 0.0\n",
|
|
"\n",
|
|
"RULE #6:\n",
|
|
" IF (al[average] AND ti[low]) AND temp[high] THEN density[lower]\n",
|
|
"\tAND aggregation function : fmin\n",
|
|
"\tOR aggregation function : fmax\n",
|
|
"\n",
|
|
" Aggregation (IF-clause):\n",
|
|
" - al[average] : 0.0\n",
|
|
" - ti[low] : 1.0\n",
|
|
" - temp[high] : 0.0\n",
|
|
" (al[average] AND ti[low]) AND temp[high] = 0.0\n",
|
|
" Activation (THEN-clause):\n",
|
|
" density[lower] : 0.0\n",
|
|
"\n",
|
|
"RULE #7:\n",
|
|
" IF (al[low] AND ti[average]) AND temp[average] THEN density[low]\n",
|
|
"\tAND aggregation function : fmin\n",
|
|
"\tOR aggregation function : fmax\n",
|
|
"\n",
|
|
" Aggregation (IF-clause):\n",
|
|
" - al[low] : 1.0\n",
|
|
" - ti[average] : 0.0\n",
|
|
" - temp[average] : 0.2\n",
|
|
" (al[low] AND ti[average]) AND temp[average] = 0.0\n",
|
|
" Activation (THEN-clause):\n",
|
|
" density[low] : 0.0\n",
|
|
"\n",
|
|
"RULE #8:\n",
|
|
" IF (al[low] AND ti[average]) AND temp[high] THEN density[low]\n",
|
|
"\tAND aggregation function : fmin\n",
|
|
"\tOR aggregation function : fmax\n",
|
|
"\n",
|
|
" Aggregation (IF-clause):\n",
|
|
" - al[low] : 1.0\n",
|
|
" - ti[average] : 0.0\n",
|
|
" - temp[high] : 0.0\n",
|
|
" (al[low] AND ti[average]) AND temp[high] = 0.0\n",
|
|
" Activation (THEN-clause):\n",
|
|
" density[low] : 0.0\n",
|
|
"\n",
|
|
"RULE #9:\n",
|
|
" IF (al[low] AND ti[high]) AND temp[low] THEN density[higher]\n",
|
|
"\tAND aggregation function : fmin\n",
|
|
"\tOR aggregation function : fmax\n",
|
|
"\n",
|
|
" Aggregation (IF-clause):\n",
|
|
" - al[low] : 1.0\n",
|
|
" - ti[high] : 0.0\n",
|
|
" - temp[low] : 0.8\n",
|
|
" (al[low] AND ti[high]) AND temp[low] = 0.0\n",
|
|
" Activation (THEN-clause):\n",
|
|
" density[higher] : 0.0\n",
|
|
"\n",
|
|
"RULE #10:\n",
|
|
" IF (al[low] AND ti[high]) AND temp[average] THEN density[higher]\n",
|
|
"\tAND aggregation function : fmin\n",
|
|
"\tOR aggregation function : fmax\n",
|
|
"\n",
|
|
" Aggregation (IF-clause):\n",
|
|
" - al[low] : 1.0\n",
|
|
" - ti[high] : 0.0\n",
|
|
" - temp[average] : 0.2\n",
|
|
" (al[low] AND ti[high]) AND temp[average] = 0.0\n",
|
|
" Activation (THEN-clause):\n",
|
|
" density[higher] : 0.0\n",
|
|
"\n",
|
|
"RULE #11:\n",
|
|
" IF (al[low] AND ti[high]) AND temp[high] THEN density[high]\n",
|
|
"\tAND aggregation function : fmin\n",
|
|
"\tOR aggregation function : fmax\n",
|
|
"\n",
|
|
" Aggregation (IF-clause):\n",
|
|
" - al[low] : 1.0\n",
|
|
" - ti[high] : 0.0\n",
|
|
" - temp[high] : 0.0\n",
|
|
" (al[low] AND ti[high]) AND temp[high] = 0.0\n",
|
|
" Activation (THEN-clause):\n",
|
|
" density[high] : 0.0\n",
|
|
"\n",
|
|
"RULE #12:\n",
|
|
" IF al[high] AND temp[low] THEN density[high]\n",
|
|
"\tAND aggregation function : fmin\n",
|
|
"\tOR aggregation function : fmax\n",
|
|
"\n",
|
|
" Aggregation (IF-clause):\n",
|
|
" - al[high] : 0.0\n",
|
|
" - temp[low] : 0.8\n",
|
|
" al[high] AND temp[low] = 0.0\n",
|
|
" Activation (THEN-clause):\n",
|
|
" density[high] : 0.0\n",
|
|
"\n",
|
|
"RULE #13:\n",
|
|
" IF al[high] AND temp[average] THEN density[high]\n",
|
|
"\tAND aggregation function : fmin\n",
|
|
"\tOR aggregation function : fmax\n",
|
|
"\n",
|
|
" Aggregation (IF-clause):\n",
|
|
" - al[high] : 0.0\n",
|
|
" - temp[average] : 0.2\n",
|
|
" al[high] AND temp[average] = 0.0\n",
|
|
" Activation (THEN-clause):\n",
|
|
" density[high] : 0.0\n",
|
|
"\n",
|
|
"RULE #14:\n",
|
|
" IF al[high] AND temp[high] THEN density[average]\n",
|
|
"\tAND aggregation function : fmin\n",
|
|
"\tOR aggregation function : fmax\n",
|
|
"\n",
|
|
" Aggregation (IF-clause):\n",
|
|
" - al[high] : 0.0\n",
|
|
" - temp[high] : 0.0\n",
|
|
" al[high] AND temp[high] = 0.0\n",
|
|
" Activation (THEN-clause):\n",
|
|
" density[average] : 0.0\n",
|
|
"\n",
|
|
"\n",
|
|
"==============================\n",
|
|
" Intermediaries and Conquests \n",
|
|
"==============================\n",
|
|
"Consequent: density = 1.076592785948375\n",
|
|
" lower:\n",
|
|
" Accumulate using accumulation_max : 0.2\n",
|
|
" low:\n",
|
|
" Accumulate using accumulation_max : 0.8\n",
|
|
" average:\n",
|
|
" Accumulate using accumulation_max : 0.0\n",
|
|
" high:\n",
|
|
" Accumulate using accumulation_max : 0.0\n",
|
|
" higher:\n",
|
|
" Accumulate using accumulation_max : 0.0\n",
|
|
"\n"
|
|
]
|
|
},
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"np.float64(1.076592785948375)"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
},
|
|
{
|
|
"data": {
|
|
"image/png": 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",
|
|
"text/plain": [
|
|
"<Figure size 640x480 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"sim.input[\"temp\"] = 25\n",
|
|
"sim.input[\"al\"] = 0.0\n",
|
|
"sim.input[\"ti\"] = 0.0\n",
|
|
"sim.compute()\n",
|
|
"sim.print_state()\n",
|
|
"display(sim.output[\"density\"])\n",
|
|
"density.view(sim=sim)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 26,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/html": [
|
|
"<div>\n",
|
|
"<style scoped>\n",
|
|
" .dataframe tbody tr th:only-of-type {\n",
|
|
" vertical-align: middle;\n",
|
|
" }\n",
|
|
"\n",
|
|
" .dataframe tbody tr th {\n",
|
|
" vertical-align: top;\n",
|
|
" }\n",
|
|
"\n",
|
|
" .dataframe thead th {\n",
|
|
" text-align: right;\n",
|
|
" }\n",
|
|
"</style>\n",
|
|
"<table border=\"1\" class=\"dataframe\">\n",
|
|
" <thead>\n",
|
|
" <tr style=\"text-align: right;\">\n",
|
|
" <th></th>\n",
|
|
" <th>T</th>\n",
|
|
" <th>Al2O3</th>\n",
|
|
" <th>TiO2</th>\n",
|
|
" <th>Density</th>\n",
|
|
" <th>Real</th>\n",
|
|
" <th>Inferred</th>\n",
|
|
" <th>RMSE</th>\n",
|
|
" </tr>\n",
|
|
" </thead>\n",
|
|
" <tbody>\n",
|
|
" <tr>\n",
|
|
" <th>0</th>\n",
|
|
" <td>20</td>\n",
|
|
" <td>0.00</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" <td>1.06250</td>\n",
|
|
" <td>1.06250</td>\n",
|
|
" <td>1.077498</td>\n",
|
|
" <td>0.014998</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>1</th>\n",
|
|
" <td>25</td>\n",
|
|
" <td>0.00</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" <td>1.05979</td>\n",
|
|
" <td>1.05979</td>\n",
|
|
" <td>1.076593</td>\n",
|
|
" <td>0.016803</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>2</th>\n",
|
|
" <td>35</td>\n",
|
|
" <td>0.00</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" <td>1.05404</td>\n",
|
|
" <td>1.05404</td>\n",
|
|
" <td>1.069156</td>\n",
|
|
" <td>0.015116</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>3</th>\n",
|
|
" <td>40</td>\n",
|
|
" <td>0.00</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" <td>1.05103</td>\n",
|
|
" <td>1.05103</td>\n",
|
|
" <td>1.061106</td>\n",
|
|
" <td>0.010076</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>4</th>\n",
|
|
" <td>45</td>\n",
|
|
" <td>0.00</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" <td>1.04794</td>\n",
|
|
" <td>1.04794</td>\n",
|
|
" <td>1.045833</td>\n",
|
|
" <td>0.002107</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>5</th>\n",
|
|
" <td>50</td>\n",
|
|
" <td>0.00</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" <td>1.04477</td>\n",
|
|
" <td>1.04477</td>\n",
|
|
" <td>1.046360</td>\n",
|
|
" <td>0.001590</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>6</th>\n",
|
|
" <td>60</td>\n",
|
|
" <td>0.00</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" <td>1.03826</td>\n",
|
|
" <td>1.03826</td>\n",
|
|
" <td>1.047642</td>\n",
|
|
" <td>0.009382</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>7</th>\n",
|
|
" <td>65</td>\n",
|
|
" <td>0.00</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" <td>1.03484</td>\n",
|
|
" <td>1.03484</td>\n",
|
|
" <td>1.046360</td>\n",
|
|
" <td>0.011520</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>8</th>\n",
|
|
" <td>70</td>\n",
|
|
" <td>0.00</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" <td>1.03182</td>\n",
|
|
" <td>1.03182</td>\n",
|
|
" <td>1.045833</td>\n",
|
|
" <td>0.014013</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>9</th>\n",
|
|
" <td>20</td>\n",
|
|
" <td>0.05</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" <td>1.08755</td>\n",
|
|
" <td>1.08755</td>\n",
|
|
" <td>1.077498</td>\n",
|
|
" <td>0.010052</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>10</th>\n",
|
|
" <td>45</td>\n",
|
|
" <td>0.05</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" <td>1.07105</td>\n",
|
|
" <td>1.07105</td>\n",
|
|
" <td>1.067145</td>\n",
|
|
" <td>0.003905</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>11</th>\n",
|
|
" <td>50</td>\n",
|
|
" <td>0.05</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" <td>1.06760</td>\n",
|
|
" <td>1.06760</td>\n",
|
|
" <td>1.067145</td>\n",
|
|
" <td>0.000455</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>12</th>\n",
|
|
" <td>55</td>\n",
|
|
" <td>0.05</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" <td>1.06409</td>\n",
|
|
" <td>1.06409</td>\n",
|
|
" <td>1.067988</td>\n",
|
|
" <td>0.003898</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>13</th>\n",
|
|
" <td>65</td>\n",
|
|
" <td>0.05</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" <td>1.05691</td>\n",
|
|
" <td>1.05691</td>\n",
|
|
" <td>1.062538</td>\n",
|
|
" <td>0.005628</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>14</th>\n",
|
|
" <td>70</td>\n",
|
|
" <td>0.05</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" <td>1.05291</td>\n",
|
|
" <td>1.05291</td>\n",
|
|
" <td>1.047191</td>\n",
|
|
" <td>0.005719</td>\n",
|
|
" </tr>\n",
|
|
" </tbody>\n",
|
|
"</table>\n",
|
|
"</div>"
|
|
],
|
|
"text/plain": [
|
|
" T Al2O3 TiO2 Density Real Inferred RMSE\n",
|
|
"0 20 0.00 0.0 1.06250 1.06250 1.077498 0.014998\n",
|
|
"1 25 0.00 0.0 1.05979 1.05979 1.076593 0.016803\n",
|
|
"2 35 0.00 0.0 1.05404 1.05404 1.069156 0.015116\n",
|
|
"3 40 0.00 0.0 1.05103 1.05103 1.061106 0.010076\n",
|
|
"4 45 0.00 0.0 1.04794 1.04794 1.045833 0.002107\n",
|
|
"5 50 0.00 0.0 1.04477 1.04477 1.046360 0.001590\n",
|
|
"6 60 0.00 0.0 1.03826 1.03826 1.047642 0.009382\n",
|
|
"7 65 0.00 0.0 1.03484 1.03484 1.046360 0.011520\n",
|
|
"8 70 0.00 0.0 1.03182 1.03182 1.045833 0.014013\n",
|
|
"9 20 0.05 0.0 1.08755 1.08755 1.077498 0.010052\n",
|
|
"10 45 0.05 0.0 1.07105 1.07105 1.067145 0.003905\n",
|
|
"11 50 0.05 0.0 1.06760 1.06760 1.067145 0.000455\n",
|
|
"12 55 0.05 0.0 1.06409 1.06409 1.067988 0.003898\n",
|
|
"13 65 0.05 0.0 1.05691 1.05691 1.062538 0.005628\n",
|
|
"14 70 0.05 0.0 1.05291 1.05291 1.047191 0.005719"
|
|
]
|
|
},
|
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"execution_count": 26,
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"metadata": {},
|
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"output_type": "execute_result"
|
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}
|
|
],
|
|
"source": [
|
|
"from sklearn import metrics\n",
|
|
"import math\n",
|
|
"\n",
|
|
"\n",
|
|
"def fuzzy_pred(row):\n",
|
|
" sim.input[\"temp\"] = row[\"T\"]\n",
|
|
" sim.input[\"al\"] = row[\"Al2O3\"]\n",
|
|
" sim.input[\"ti\"] = row[\"TiO2\"]\n",
|
|
" sim.compute()\n",
|
|
" return sim.output[\"density\"]\n",
|
|
"\n",
|
|
"\n",
|
|
"def rmse(row):\n",
|
|
" return math.sqrt(metrics.mean_squared_error([row[\"Real\"]], [row[\"Inferred\"]]))\n",
|
|
"\n",
|
|
"result_train = density_train.copy()\n",
|
|
"result_train[\"Real\"] = result_train[\"Density\"]\n",
|
|
"result_train[\"Inferred\"] = result_train.apply(fuzzy_pred, axis=1)\n",
|
|
"result_train[\"RMSE\"] = result_train.apply(rmse, axis=1)\n",
|
|
"result_train.head(15)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 27,
|
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"metadata": {},
|
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"outputs": [
|
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{
|
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"data": {
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"text/html": [
|
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
|
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"\n",
|
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" .dataframe thead th {\n",
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" text-align: right;\n",
|
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" }\n",
|
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"</style>\n",
|
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"<table border=\"1\" class=\"dataframe\">\n",
|
|
" <thead>\n",
|
|
" <tr style=\"text-align: right;\">\n",
|
|
" <th></th>\n",
|
|
" <th>T</th>\n",
|
|
" <th>Al2O3</th>\n",
|
|
" <th>TiO2</th>\n",
|
|
" <th>Density</th>\n",
|
|
" <th>Real</th>\n",
|
|
" <th>Inferred</th>\n",
|
|
" <th>RMSE</th>\n",
|
|
" </tr>\n",
|
|
" </thead>\n",
|
|
" <tbody>\n",
|
|
" <tr>\n",
|
|
" <th>0</th>\n",
|
|
" <td>30</td>\n",
|
|
" <td>0.00</td>\n",
|
|
" <td>0.00</td>\n",
|
|
" <td>1.05696</td>\n",
|
|
" <td>1.05696</td>\n",
|
|
" <td>1.073918</td>\n",
|
|
" <td>0.017</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>1</th>\n",
|
|
" <td>55</td>\n",
|
|
" <td>0.00</td>\n",
|
|
" <td>0.00</td>\n",
|
|
" <td>1.04158</td>\n",
|
|
" <td>1.04158</td>\n",
|
|
" <td>1.047642</td>\n",
|
|
" <td>0.006</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>2</th>\n",
|
|
" <td>25</td>\n",
|
|
" <td>0.05</td>\n",
|
|
" <td>0.00</td>\n",
|
|
" <td>1.08438</td>\n",
|
|
" <td>1.08438</td>\n",
|
|
" <td>1.076518</td>\n",
|
|
" <td>0.008</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>3</th>\n",
|
|
" <td>30</td>\n",
|
|
" <td>0.05</td>\n",
|
|
" <td>0.00</td>\n",
|
|
" <td>1.08112</td>\n",
|
|
" <td>1.08112</td>\n",
|
|
" <td>1.073918</td>\n",
|
|
" <td>0.007</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>4</th>\n",
|
|
" <td>35</td>\n",
|
|
" <td>0.05</td>\n",
|
|
" <td>0.00</td>\n",
|
|
" <td>1.07781</td>\n",
|
|
" <td>1.07781</td>\n",
|
|
" <td>1.069156</td>\n",
|
|
" <td>0.009</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>5</th>\n",
|
|
" <td>40</td>\n",
|
|
" <td>0.05</td>\n",
|
|
" <td>0.00</td>\n",
|
|
" <td>1.07446</td>\n",
|
|
" <td>1.07446</td>\n",
|
|
" <td>1.067145</td>\n",
|
|
" <td>0.007</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>6</th>\n",
|
|
" <td>60</td>\n",
|
|
" <td>0.05</td>\n",
|
|
" <td>0.00</td>\n",
|
|
" <td>1.06053</td>\n",
|
|
" <td>1.06053</td>\n",
|
|
" <td>1.067988</td>\n",
|
|
" <td>0.007</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>7</th>\n",
|
|
" <td>35</td>\n",
|
|
" <td>0.30</td>\n",
|
|
" <td>0.00</td>\n",
|
|
" <td>1.17459</td>\n",
|
|
" <td>1.17459</td>\n",
|
|
" <td>1.172492</td>\n",
|
|
" <td>0.002</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>8</th>\n",
|
|
" <td>65</td>\n",
|
|
" <td>0.30</td>\n",
|
|
" <td>0.00</td>\n",
|
|
" <td>1.14812</td>\n",
|
|
" <td>1.14812</td>\n",
|
|
" <td>1.136460</td>\n",
|
|
" <td>0.012</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>9</th>\n",
|
|
" <td>45</td>\n",
|
|
" <td>0.00</td>\n",
|
|
" <td>0.05</td>\n",
|
|
" <td>1.07424</td>\n",
|
|
" <td>1.07424</td>\n",
|
|
" <td>1.067145</td>\n",
|
|
" <td>0.007</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>10</th>\n",
|
|
" <td>50</td>\n",
|
|
" <td>0.00</td>\n",
|
|
" <td>0.05</td>\n",
|
|
" <td>1.07075</td>\n",
|
|
" <td>1.07075</td>\n",
|
|
" <td>1.067145</td>\n",
|
|
" <td>0.004</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>11</th>\n",
|
|
" <td>55</td>\n",
|
|
" <td>0.00</td>\n",
|
|
" <td>0.05</td>\n",
|
|
" <td>1.06721</td>\n",
|
|
" <td>1.06721</td>\n",
|
|
" <td>1.067988</td>\n",
|
|
" <td>0.001</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>12</th>\n",
|
|
" <td>20</td>\n",
|
|
" <td>0.00</td>\n",
|
|
" <td>0.30</td>\n",
|
|
" <td>1.22417</td>\n",
|
|
" <td>1.22417</td>\n",
|
|
" <td>1.204157</td>\n",
|
|
" <td>0.020</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>13</th>\n",
|
|
" <td>30</td>\n",
|
|
" <td>0.00</td>\n",
|
|
" <td>0.30</td>\n",
|
|
" <td>1.21310</td>\n",
|
|
" <td>1.21310</td>\n",
|
|
" <td>1.202348</td>\n",
|
|
" <td>0.011</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>14</th>\n",
|
|
" <td>40</td>\n",
|
|
" <td>0.00</td>\n",
|
|
" <td>0.30</td>\n",
|
|
" <td>1.20265</td>\n",
|
|
" <td>1.20265</td>\n",
|
|
" <td>1.203630</td>\n",
|
|
" <td>0.001</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>15</th>\n",
|
|
" <td>60</td>\n",
|
|
" <td>0.00</td>\n",
|
|
" <td>0.30</td>\n",
|
|
" <td>1.18265</td>\n",
|
|
" <td>1.18265</td>\n",
|
|
" <td>1.176072</td>\n",
|
|
" <td>0.007</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>16</th>\n",
|
|
" <td>70</td>\n",
|
|
" <td>0.00</td>\n",
|
|
" <td>0.30</td>\n",
|
|
" <td>1.17261</td>\n",
|
|
" <td>1.17261</td>\n",
|
|
" <td>1.172492</td>\n",
|
|
" <td>0.000</td>\n",
|
|
" </tr>\n",
|
|
" </tbody>\n",
|
|
"</table>\n",
|
|
"</div>"
|
|
],
|
|
"text/plain": [
|
|
" T Al2O3 TiO2 Density Real Inferred RMSE\n",
|
|
"0 30 0.00 0.00 1.05696 1.05696 1.073918 0.017\n",
|
|
"1 55 0.00 0.00 1.04158 1.04158 1.047642 0.006\n",
|
|
"2 25 0.05 0.00 1.08438 1.08438 1.076518 0.008\n",
|
|
"3 30 0.05 0.00 1.08112 1.08112 1.073918 0.007\n",
|
|
"4 35 0.05 0.00 1.07781 1.07781 1.069156 0.009\n",
|
|
"5 40 0.05 0.00 1.07446 1.07446 1.067145 0.007\n",
|
|
"6 60 0.05 0.00 1.06053 1.06053 1.067988 0.007\n",
|
|
"7 35 0.30 0.00 1.17459 1.17459 1.172492 0.002\n",
|
|
"8 65 0.30 0.00 1.14812 1.14812 1.136460 0.012\n",
|
|
"9 45 0.00 0.05 1.07424 1.07424 1.067145 0.007\n",
|
|
"10 50 0.00 0.05 1.07075 1.07075 1.067145 0.004\n",
|
|
"11 55 0.00 0.05 1.06721 1.06721 1.067988 0.001\n",
|
|
"12 20 0.00 0.30 1.22417 1.22417 1.204157 0.020\n",
|
|
"13 30 0.00 0.30 1.21310 1.21310 1.202348 0.011\n",
|
|
"14 40 0.00 0.30 1.20265 1.20265 1.203630 0.001\n",
|
|
"15 60 0.00 0.30 1.18265 1.18265 1.176072 0.007\n",
|
|
"16 70 0.00 0.30 1.17261 1.17261 1.172492 0.000"
|
|
]
|
|
},
|
|
"execution_count": 27,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"result_test = density_test.copy()\n",
|
|
"result_test[\"Real\"] = result_test[\"Density\"]\n",
|
|
"result_test[\"Inferred\"] = result_test.apply(fuzzy_pred, axis=1)\n",
|
|
"result_test[\"RMSE\"] = result_test.apply(rmse, axis=1)\n",
|
|
"# result_test[\"RMSE\"] = result_test[\"RMSE\"].apply(lambda x: \"{:,.4f}\".format(x))\n",
|
|
"result_test = result_test.round({\"RMSE\": 3})\n",
|
|
"result_test"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 28,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"{'RMSE_train': 0.009765373953597112,\n",
|
|
" 'RMSE_test': 0.009031443610368107,\n",
|
|
" 'RMAE_test': 0.08581225574298121,\n",
|
|
" 'R2_test': 0.978451748357252}"
|
|
]
|
|
},
|
|
"execution_count": 28,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"rmetrics = {}\n",
|
|
"rmetrics[\"RMSE_train\"] = math.sqrt(\n",
|
|
" metrics.mean_squared_error(result_train[\"Real\"], result_train[\"Inferred\"])\n",
|
|
")\n",
|
|
"rmetrics[\"RMSE_test\"] = math.sqrt(\n",
|
|
" metrics.mean_squared_error(result_test[\"Real\"], result_test[\"Inferred\"])\n",
|
|
")\n",
|
|
"rmetrics[\"RMAE_test\"] = math.sqrt(\n",
|
|
" metrics.mean_absolute_error(result_test[\"Real\"], result_test[\"Inferred\"])\n",
|
|
")\n",
|
|
"rmetrics[\"R2_test\"] = metrics.r2_score(result_test[\"Real\"], result_test[\"Inferred\"])\n",
|
|
"\n",
|
|
"rmetrics"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 29,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"['(Al2O3 <= 0.175)', '(Al2O3 > 0.025)', '(Al2O3 > 0.175)', '(TiO2 <= 0.175)', '(TiO2 > 0.025)', '(TiO2 > 0.175)']\n"
|
|
]
|
|
},
|
|
{
|
|
"data": {
|
|
"text/html": [
|
|
"<div>\n",
|
|
"<style scoped>\n",
|
|
" .dataframe tbody tr th:only-of-type {\n",
|
|
" vertical-align: middle;\n",
|
|
" }\n",
|
|
"\n",
|
|
" .dataframe tbody tr th {\n",
|
|
" vertical-align: top;\n",
|
|
" }\n",
|
|
"\n",
|
|
" .dataframe thead th {\n",
|
|
" text-align: right;\n",
|
|
" }\n",
|
|
"</style>\n",
|
|
"<table border=\"1\" class=\"dataframe\">\n",
|
|
" <thead>\n",
|
|
" <tr style=\"text-align: right;\">\n",
|
|
" <th></th>\n",
|
|
" <th>(Al2O3 <= 0.175)</th>\n",
|
|
" <th>(Al2O3 > 0.025)</th>\n",
|
|
" <th>(Al2O3 > 0.175)</th>\n",
|
|
" <th>(TiO2 <= 0.175)</th>\n",
|
|
" <th>(TiO2 > 0.025)</th>\n",
|
|
" <th>(TiO2 > 0.175)</th>\n",
|
|
" <th>consequent</th>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>rule</th>\n",
|
|
" <th></th>\n",
|
|
" <th></th>\n",
|
|
" <th></th>\n",
|
|
" <th></th>\n",
|
|
" <th></th>\n",
|
|
" <th></th>\n",
|
|
" <th></th>\n",
|
|
" </tr>\n",
|
|
" </thead>\n",
|
|
" <tbody>\n",
|
|
" <tr>\n",
|
|
" <th>if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (T > 32.5) -> 1.033</th>\n",
|
|
" <td>1</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>1.0333299999999999</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (T > 32.5) and (T <= 62.5) -> 1.038</th>\n",
|
|
" <td>1</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>1.03826</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>if (Al2O3 <= 0.175) and (Al2O3 > 0.025) and (TiO2 <= 0.175) and (T <= 32.5) -> 1.088</th>\n",
|
|
" <td>1</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>1.08755</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (TiO2 > 0.025) and (T <= 32.5) -> 1.091</th>\n",
|
|
" <td>1</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>1.09098</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (TiO2 > 0.025) and (T <= 32.5) and (T > 22.5) -> 1.088</th>\n",
|
|
" <td>1</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>1.08775</td>\n",
|
|
" </tr>\n",
|
|
" </tbody>\n",
|
|
"</table>\n",
|
|
"</div>"
|
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],
|
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"text/plain": [
|
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" (Al2O3 <= 0.175) \\\n",
|
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"rule \n",
|
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"if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (T ... 1 \n",
|
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"if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (T ... 1 \n",
|
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"if (Al2O3 <= 0.175) and (Al2O3 > 0.025) and (Ti... 1 \n",
|
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"if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (Ti... 1 \n",
|
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"if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (Ti... 1 \n",
|
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"\n",
|
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" (Al2O3 > 0.025) \\\n",
|
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"rule \n",
|
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"if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (T ... 0 \n",
|
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"if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (T ... 0 \n",
|
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"if (Al2O3 <= 0.175) and (Al2O3 > 0.025) and (Ti... 1 \n",
|
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"if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (Ti... 0 \n",
|
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"if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (Ti... 0 \n",
|
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"\n",
|
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" (Al2O3 > 0.175) \\\n",
|
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"rule \n",
|
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"if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (T ... 0 \n",
|
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"if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (T ... 0 \n",
|
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"if (Al2O3 <= 0.175) and (Al2O3 > 0.025) and (Ti... 0 \n",
|
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"if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (Ti... 0 \n",
|
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"if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (Ti... 0 \n",
|
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"\n",
|
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" (TiO2 <= 0.175) \\\n",
|
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"rule \n",
|
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"if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (T ... 1 \n",
|
|
"if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (T ... 1 \n",
|
|
"if (Al2O3 <= 0.175) and (Al2O3 > 0.025) and (Ti... 1 \n",
|
|
"if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (Ti... 1 \n",
|
|
"if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (Ti... 1 \n",
|
|
"\n",
|
|
" (TiO2 > 0.025) \\\n",
|
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"rule \n",
|
|
"if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (T ... 0 \n",
|
|
"if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (T ... 0 \n",
|
|
"if (Al2O3 <= 0.175) and (Al2O3 > 0.025) and (Ti... 0 \n",
|
|
"if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (Ti... 1 \n",
|
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"if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (Ti... 1 \n",
|
|
"\n",
|
|
" (TiO2 > 0.175) \\\n",
|
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"rule \n",
|
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"if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (T ... 0 \n",
|
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"if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (T ... 0 \n",
|
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"if (Al2O3 <= 0.175) and (Al2O3 > 0.025) and (Ti... 0 \n",
|
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"if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (Ti... 0 \n",
|
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"if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (Ti... 0 \n",
|
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"\n",
|
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" consequent \n",
|
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"rule \n",
|
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"if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (T ... 1.0333299999999999 \n",
|
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"if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (T ... 1.03826 \n",
|
|
"if (Al2O3 <= 0.175) and (Al2O3 > 0.025) and (Ti... 1.08755 \n",
|
|
"if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (Ti... 1.09098 \n",
|
|
"if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (Ti... 1.08775 "
|
|
]
|
|
},
|
|
"execution_count": 29,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"from src.rules import get_features, vectorize_rules\n",
|
|
"\n",
|
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"features = get_features(for_cluster, [\"T\"])\n",
|
|
"print(features)\n",
|
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"\n",
|
|
"df_rules = vectorize_rules(for_cluster, features)\n",
|
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"df_rules.head(5)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 30,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"/Users/user/Projects/python/fuzzy-rules-generator/.venv/lib/python3.12/site-packages/sklearn/base.py:1473: ConvergenceWarning: Number of distinct clusters (5) found smaller than n_clusters (6). Possibly due to duplicate points in X.\n",
|
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" return fit_method(estimator, *args, **kwargs)\n"
|
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]
|
|
},
|
|
{
|
|
"data": {
|
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"text/plain": [
|
|
"{2: 0.3980189385393544,\n",
|
|
" 3: 0.6922181892757754,\n",
|
|
" 4: 0.8221763769597347,\n",
|
|
" 5: 1.0,\n",
|
|
" 6: 1.0}"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
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},
|
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{
|
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"data": {
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",
|
|
"text/plain": [
|
|
"<Figure size 400x400 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
},
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"'The best clusters count is 5'"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"from src.cluster_helper import draw_best_clusters_plot, get_best_clusters_num\n",
|
|
"\n",
|
|
"random_state = 9\n",
|
|
"\n",
|
|
"X = df_rules.copy()\n",
|
|
"X = X.drop([\"consequent\"], axis=1)\n",
|
|
"\n",
|
|
"clusters_score = get_best_clusters_num(X, random_state)\n",
|
|
"display(clusters_score)\n",
|
|
"\n",
|
|
"draw_best_clusters_plot(clusters_score)\n",
|
|
"\n",
|
|
"clusters_num = sorted(clusters_score.items(), key=lambda x: x[1], reverse=True)[0][0]\n",
|
|
"display(f\"The best clusters count is {clusters_num}\")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 31,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Кластер 1 (5):\n",
|
|
"if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (T > 32.5) -> 1.033;\n",
|
|
"if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (T > 32.5) and (T <= 62.5) -> 1.038;\n",
|
|
"if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (T > 32.5) and (T <= 55.0) -> 1.048;\n",
|
|
"if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (T <= 32.5) and (T > 22.5) -> 1.06;\n",
|
|
"if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (T <= 32.5) -> 1.062\n",
|
|
"--------\n",
|
|
"Кластер 2 (4):\n",
|
|
"if (Al2O3 <= 0.175) and (TiO2 > 0.175) and (T <= 40.0) -> 1.219;\n",
|
|
"if (Al2O3 <= 0.175) and (TiO2 > 0.175) and (T <= 40.0) and (T > 30.0) -> 1.208;\n",
|
|
"if (Al2O3 <= 0.175) and (TiO2 > 0.175) and (T > 40.0) and (T <= 60.0) -> 1.193;\n",
|
|
"if (Al2O3 <= 0.175) and (TiO2 > 0.175) and (T > 40.0) -> 1.178\n",
|
|
"--------\n",
|
|
"Кластер 3 (5):\n",
|
|
"if (Al2O3 > 0.175) and (T <= 35.0) -> 1.189;\n",
|
|
"if (Al2O3 > 0.175) and (T <= 35.0) and (T > 22.5) -> 1.182;\n",
|
|
"if (Al2O3 > 0.175) and (T > 35.0) and (T <= 52.5) -> 1.166;\n",
|
|
"if (Al2O3 > 0.175) and (T > 35.0) and (T <= 65.0) -> 1.155;\n",
|
|
"if (Al2O3 > 0.175) and (T > 35.0) -> 1.144\n",
|
|
"--------\n",
|
|
"Кластер 4 (6):\n",
|
|
"if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (TiO2 > 0.025) and (T <= 32.5) -> 1.091;\n",
|
|
"if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (TiO2 > 0.025) and (T <= 32.5) and (T > 22.5) -> 1.088;\n",
|
|
"if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (TiO2 > 0.025) and (T <= 32.5) and (T > 27.5) -> 1.084;\n",
|
|
"if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (TiO2 > 0.025) and (T > 32.5) and (T <= 50.0) -> 1.079;\n",
|
|
"if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (TiO2 > 0.025) and (T > 32.5) and (T <= 67.5) -> 1.062;\n",
|
|
"if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (TiO2 > 0.025) and (T > 32.5) -> 1.056\n",
|
|
"--------\n",
|
|
"Кластер 5 (4):\n",
|
|
"if (Al2O3 <= 0.175) and (Al2O3 > 0.025) and (TiO2 <= 0.175) and (T <= 32.5) -> 1.088;\n",
|
|
"if (Al2O3 <= 0.175) and (Al2O3 > 0.025) and (TiO2 <= 0.175) and (T > 32.5) and (T <= 60.0) -> 1.067;\n",
|
|
"if (Al2O3 <= 0.175) and (Al2O3 > 0.025) and (TiO2 <= 0.175) and (T > 32.5) and (T <= 67.5) -> 1.057;\n",
|
|
"if (Al2O3 <= 0.175) and (Al2O3 > 0.025) and (TiO2 <= 0.175) and (T > 32.5) -> 1.053\n",
|
|
"--------\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"from sklearn import cluster\n",
|
|
"\n",
|
|
"from src.cluster_helper import print_cluster_result\n",
|
|
"\n",
|
|
"kmeans = cluster.KMeans(n_clusters=clusters_num, random_state=random_state)\n",
|
|
"kmeans.fit(X)\n",
|
|
"\n",
|
|
"print_cluster_result(X, clusters_num, kmeans.labels_)"
|
|
]
|
|
}
|
|
],
|
|
"metadata": {
|
|
"kernelspec": {
|
|
"display_name": ".venv",
|
|
"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": "3.12.9"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 2
|
|
}
|