Rick van Hattem
111 lines
8.6 KiB
Plaintext
111 lines
8.6 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "7188f302",
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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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"Coefficients: \n",
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" [938.23786125]\n",
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"Mean squared error: 2548.07\n",
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"Coefficient of determination: 0.47\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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\n",
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"text/plain": [
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"<Figure size 432x288 with 1 Axes>"
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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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"source": [
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"%matplotlib inline\n",
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"\n",
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"import matplotlib.pyplot as plt\n",
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"import numpy as np\n",
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"from sklearn import datasets, linear_model\n",
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"from sklearn.metrics import mean_squared_error, r2_score\n",
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"from skimage import io, data\n",
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"\n",
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"from sklearn import linear_model\n",
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"reg = linear_model.LinearRegression()\n",
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"reg.fit([[0, 0], [1, 1], [2, 2]], [0, 1, 2])\n",
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"\n",
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"reg.coef_\n",
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"\n",
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"\n",
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"# Load the diabetes dataset\n",
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"diabetes_X, diabetes_y = datasets.load_diabetes(return_X_y=True)\n",
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"\n",
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"# Use only one feature\n",
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"diabetes_X = diabetes_X[:, np.newaxis, 2]\n",
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"\n",
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"# Split the data into training/testing sets\n",
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"diabetes_X_train = diabetes_X[:-20]\n",
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"diabetes_X_test = diabetes_X[-20:]\n",
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"\n",
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"# Split the targets into training/testing sets\n",
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"diabetes_y_train = diabetes_y[:-20]\n",
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"diabetes_y_test = diabetes_y[-20:]\n",
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"\n",
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"# Create linear regression object\n",
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"regr = linear_model.LinearRegression()\n",
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"\n",
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"# Train the model using the training sets\n",
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"regr.fit(diabetes_X_train, diabetes_y_train)\n",
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"\n",
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"# Make predictions using the testing set\n",
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"diabetes_y_pred = regr.predict(diabetes_X_test)\n",
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"\n",
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"# The coefficients\n",
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"print('Coefficients: \\n', regr.coef_)\n",
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"# The mean squared error\n",
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"print('Mean squared error: %.2f'\n",
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" % mean_squared_error(diabetes_y_test, diabetes_y_pred))\n",
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"# The coefficient of determination: 1 is perfect prediction\n",
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"print('Coefficient of determination: %.2f'\n",
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" % r2_score(diabetes_y_test, diabetes_y_pred))\n",
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"\n",
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"# Plot outputs\n",
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"plt.scatter(diabetes_X_test, diabetes_y_test, color='black')\n",
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"plt.plot(diabetes_X_test, diabetes_y_pred, color='blue', linewidth=3)\n",
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"\n",
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"plt.xticks(())\n",
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"plt.yticks(())\n",
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"\n",
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"plt.show()"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.9.6"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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