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147 lines
3.1 KiB
147 lines
3.1 KiB
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Fit 3rd order Polynomial to graph data using scikit-learn, more infos\n",
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"https://www.datatechnotes.com/2018/06/polynomial-regression-fitting-in-python.html"
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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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"outputs": [],
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"source": [
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"from matplotlib import pyplot as plt\n",
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"plt.rcParams[\"font.size\"] = 20\n",
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"\n",
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"import numpy as np\n",
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"\n",
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"from sklearn.linear_model import LinearRegression\n",
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"from sklearn.preprocessing import PolynomialFeatures"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"data"
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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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"outputs": [],
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"source": [
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"x = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10], dtype='d')\n",
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"dx = np.array([0.1,0.1,0.5,0.1,0.5,0.1,0.5,0.1,0.5,0.1], dtype='d')\n",
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"y = np.array([1.1 ,2.3 ,2.7 ,3.2 ,3.1 ,2.4 ,1.7 ,1.5 ,1.5 ,1.7 ], dtype='d')\n",
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"dy = np.array([0.15,0.22,0.29,0.39,0.31,0.21,0.13,0.15,0.19,0.13], dtype='d')"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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" building polynomial model"
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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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"outputs": [],
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"source": [
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"polyModel = PolynomialFeatures(degree = 3)\n",
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"xpol = polyModel.fit_transform(x.reshape(-1, 1))\n",
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"preg = polyModel.fit(xpol,y)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Building linear model"
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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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"outputs": [],
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"source": [
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"linearModel = LinearRegression(fit_intercept = True)\n",
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"linearModel.fit(xpol, y[:, np.newaxis])"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Plotting\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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"outputs": [],
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"source": [
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"x_plot = np.linspace(0.1,10.1,200)\n",
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"polyfit = linearModel.predict(preg.fit_transform(x_plot.reshape(-1, 1)))"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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" plot data"
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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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"outputs": [],
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"source": [
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"plt.figure()\n",
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"plt.errorbar(x, y, dy , dx, fmt=\"o\")\n",
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"plt.plot(x_plot, polyfit )\n",
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"plt.title(\"scikit-learn Fit Test\")\n",
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"plt.xlim(-0.1, 10.1)\n",
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"plt.show()"
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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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"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 (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": "3.8.16"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 4
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}
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