mhjensen
67 lines
1.4 KiB
Plaintext
67 lines
1.4 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 2,
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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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"[ 126.84247142 125.01176842]\n"
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]
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}
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],
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"source": [
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"from sklearn.preprocessing import PolynomialFeatures\n",
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"from sklearn import linear_model\n",
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"import matplotlib.pyplot as plt \n",
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"\n",
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"X = [[0.44, 0.68], [0.99, 0.23]]\n",
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"vector = [109.85, 155.72]\n",
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"predict= [[0.49, 0.18], [0.47, 0.22]]\n",
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"\n",
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"poly = PolynomialFeatures(degree=2)\n",
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"X_ = poly.fit_transform(X)\n",
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"predict_ = poly.fit_transform(predict)\n",
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"\n",
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"clf = linear_model.LinearRegression()\n",
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"clf.fit(X_, vector)\n",
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"y=clf.predict(predict_)\n",
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"print (clf.predict(predict_))\n"
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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": null,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": []
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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",
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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": "3.6.3"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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