{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Рабочая тетрадь No 4" ] }, { "cell_type": "code", "execution_count": 478, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd\n", "from sklearn import metrics\n", "import matplotlib.pyplot as plt\n", "from scipy.optimize import curve_fit\n", "from sklearn.linear_model import LinearRegression\n", "from sklearn.model_selection import train_test_split" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 1.1.3 Задание\n", "\n", "Представьте собственные данные и постройте эктраполяцию полиномами \n", "первой, второй и третьей степени. " ] }, { "cell_type": "code", "execution_count": 479, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[-5.1585996 -4.46639064 -2.55830037 -2.0590825 -0.53638287 0.3553611\n", " 0.60294719 2.45189108 3.28141706 4.08769096 4.92161744]\n", "[24.70826208 16.36113899 9.04877285 4.01427977 1.02983411 0.38854345\n", " 0.74865966 4.11727242 9.13843478 15.83010792 24.60875347]\n" ] } ], "source": [ "delta = 1.0\n", "x = np.linspace(-5, 5, 11)\n", "y = x ** 2 + delta * (np.random.rand(11) - 0.5)\n", "x += delta * (np.random.rand(11) - 0.5)\n", "\n", "print(x)\n", "print(y)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Эктраполяция полиномом первой степени" ] }, { "cell_type": "code", "execution_count": 480, "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "m = np.vstack((x, np.ones(11))).T\n", "s = np.linalg.lstsq(m,y,rcond = None)[0]\n", "\n", "x_prec = np.linspace(-5, 5, 101)\n", "\n", "plt.plot(x, y,'D')\n", "plt.plot(x_prec, s[0] * x_prec + s[1], '-', lw=1)\n", "plt.grid()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Эктраполяция полиномом второй степени" ] }, { "cell_type": "code", "execution_count": 481, "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "m = np.vstack((x**2, x, np.ones(11))).T\n", "s = np.linalg.lstsq(m, y, rcond = None)[0]\n", "\n", "x_prec = np.linspace(-5, 5, 101)\n", "\n", "plt.plot(x, y,'D')\n", "plt.plot(x_prec, s[0] * x_prec**2 + s[1] * x_prec + s[2], '-', lw=2)\n", "plt.grid()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Эктраполяция полиномом третьей степени" ] }, { "cell_type": "code", "execution_count": 482, "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "m = np.vstack((x**3, x**2, x, np.ones(11))).T\n", "s = np.linalg.lstsq(m, y, rcond = None)[0]\n", "\n", "x_prec = np.linspace(-5, 5, 101)\n", "\n", "plt.plot(x, y,'D')\n", "plt.plot(x_prec, s[0] * x_prec**3 + s[1] * x_prec**2 + s[2] * x_prec + s[3], '-', lw=3)\n", "plt.grid()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 1.1.5 Задача\n", "\n", "Необходимо проверить гипотезу, что наши точечно заданная функция ложится \n", "на кривые вида: \n", "1. $f(x,b) = b_0+b_1x$\n", "2. $f(x,b) = b_0+b_1x+b_2x^2$\n", "3. $f(x,b) = b_0+b_1\\ln(x)$\n", "4. $f(x,b) = b_0x^{b_1}$ " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Функция №1" ] }, { "cell_type": "code", "execution_count": 483, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[1.2474115 1.73859228]\n", "0.0001438268762724966\n", "0.12646845774981774\n" ] } ], "source": [ "beta = (1.24, 1.74)\n", "def f(x, b0, b1):\n", " return b0 + b1 * x\n", "\n", "xdata = np.linspace(0,5,50)\n", "y = f(xdata, *beta)\n", "ydata = y + 0.05 * np.random.randn(len(xdata))\n", "\n", "from scipy.optimize import curve_fit\n", "beta_opt, beta_cov = curve_fit(f, xdata, ydata)\n", "print(beta_opt)\n", "\n", "lin_dev = sum(beta_cov[0])\n", "print(lin_dev)\n", "\n", "residuals = ydata - f(xdata, *beta_opt)\n", "fres = sum(residuals**2)\n", "print(fres)" ] }, { "cell_type": "code", "execution_count": 484, "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots()\n", "ax.scatter(xdata, ydata)\n", "ax.plot(xdata, y, 'r', lw=2)\n", "ax.plot(xdata, f(xdata, *beta_opt), 'b', lw=2)\n", "ax.set_xlim(0,5)\n", "ax.set_xlabel(r'$x$', fontsize=18)\n", "ax.set_ylabel(r'$f(x, \\beta)$', fontsize=18)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Функция №2" ] }, { "cell_type": "code", "execution_count": 485, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[0.53903101 2.70563694 1.17692555]\n", "0.00014584672644415915\n", "0.12162090612880988\n" ] } ], "source": [ "beta = (0.51, 2.74, 1.17)\n", "def f(x, b0, b1, b2):\n", " return b0 + b1 * x + b2 * x**2\n", "\n", "xdata = np.linspace(0,5,50)\n", "y = f(xdata, *beta)\n", "ydata = y + 0.05 * np.random.randn(len(xdata))\n", "\n", "from scipy.optimize import curve_fit\n", "beta_opt, beta_cov = curve_fit(f, xdata, ydata)\n", "print(beta_opt)\n", "\n", "lin_dev = sum(beta_cov[0])\n", "print(lin_dev)\n", "\n", "residuals = ydata - f(xdata, *beta_opt)\n", "fres = sum(residuals**2)\n", "print(fres)" ] }, { "cell_type": "code", "execution_count": 486, "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots()\n", "ax.scatter(xdata, ydata)\n", "ax.plot(xdata, y, 'r', lw=2)\n", "ax.plot(xdata, f(xdata, *beta_opt), 'b', lw=2)\n", "ax.set_xlim(0,5)\n", "ax.set_xlabel(r'$x$', fontsize=18)\n", "ax.set_ylabel(r'$f(x, \\beta)$', fontsize=18)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Функция №3" ] }, { "cell_type": "code", "execution_count": 487, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[1.36588575 0.48465322]\n", "3.7836263532723186e-05\n", "0.11229032607799314\n" ] } ], "source": [ "beta = (1.37, 0.48)\n", "\n", "def f(x, b0, b1):\n", " return b0 + b1 * np.log(x)\n", "\n", "xdata = np.linspace(0.01,5,50)\n", "y = f(xdata, *beta)\n", "ydata = y + 0.05 * np.random.randn(len(xdata))\n", "\n", "from scipy.optimize import curve_fit\n", "beta_opt, beta_cov = curve_fit(f, xdata, ydata)\n", "print(beta_opt)\n", "\n", "lin_dev = sum(beta_cov[0])\n", "print(lin_dev)\n", "\n", "residuals = ydata - f(xdata, *beta_opt)\n", "fres = sum(residuals**2)\n", "print(fres)" ] }, { "cell_type": "code", "execution_count": 488, "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots()\n", "ax.scatter(xdata, ydata)\n", "ax.plot(xdata, y, 'r', lw=2)\n", "ax.plot(xdata, f(xdata, *beta_opt), 'b', lw=2)\n", "ax.set_xlim(0,5)\n", "ax.set_xlabel(r'$x$', fontsize=18)\n", "ax.set_ylabel(r'$f(x, \\beta)$', fontsize=18)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Функция №4" ] }, { "cell_type": "code", "execution_count": 489, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[0.8817479 1.92063655]\n", "0.00010562358913983954\n", "0.14631327681648387\n" ] } ], "source": [ "beta = (0.89, 1.92)\n", "def f(x, b0, b1):\n", " return b0 + np.float_power(x, b1)\n", "\n", "xdata = np.linspace(0,5,50)\n", "y = f(xdata, *beta)\n", "ydata = y + 0.05 * np.random.randn(len(xdata))\n", "\n", "from scipy.optimize import curve_fit\n", "beta_opt, beta_cov = curve_fit(f, xdata, ydata)\n", "print(beta_opt)\n", "\n", "lin_dev = sum(beta_cov[0])\n", "print(lin_dev)\n", "\n", "residuals = ydata - f(xdata, *beta_opt)\n", "fres = sum(residuals**2)\n", "print(fres)" ] }, { "cell_type": "code", "execution_count": 490, "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots()\n", "ax.scatter(xdata, ydata)\n", "ax.plot(xdata, y, 'r', lw=2)\n", "ax.plot(xdata, f(xdata, *beta_opt), 'b', lw=2)\n", "ax.set_xlim(0,5)\n", "ax.set_xlabel(r'$x$', fontsize=18)\n", "ax.set_ylabel(r'$f(x, \\beta)$', fontsize=18)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 1.2.1 Задание\n", "\n", "Постройте модель линейной регрессии для произвольных данных из двух \n", "столбцов. Для примера можно взять точечную зависимость заработной платы от \n", "опыта работы: \n", "(https://raw.githubusercontent.com/AnnaShestova/salary-years-simple-linear-regression/master/Salary_Data.csv). \n", "Найдите коэффициенты линии регрессии. Постройте прогноз. " ] }, { "cell_type": "code", "execution_count": 491, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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YearsExperienceSalary
count30.00000030.000000
mean5.31333376003.000000
std2.83788827414.429785
min1.10000037731.000000
25%3.20000056720.750000
50%4.70000065237.000000
75%7.700000100544.750000
max10.500000122391.000000
\n", "
" ], "text/plain": [ " YearsExperience Salary\n", "count 30.000000 30.000000\n", "mean 5.313333 76003.000000\n", "std 2.837888 27414.429785\n", "min 1.100000 37731.000000\n", "25% 3.200000 56720.750000\n", "50% 4.700000 65237.000000\n", "75% 7.700000 100544.750000\n", "max 10.500000 122391.000000" ] }, "execution_count": 491, "metadata": {}, "output_type": "execute_result" } ], "source": [ "url = 'https://raw.githubusercontent.com/AnnaShestova/salary-years-simple-linear-regression/master/Salary_Data.csv'\n", "\n", "ds = pd.read_csv(url)\n", "\n", "ds.describe()" ] }, { "cell_type": "code", "execution_count": 492, "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.scatter(ds['YearsExperience'], ds['Salary'])\n", "plt.xlabel('Опыт (годы)')\n", "plt.ylabel('Зарплата')\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 493, "metadata": {}, "outputs": [], "source": [ "X = ds.iloc[:, :-1].values\n", "y = ds.iloc[:, 1].values\n", "\n", "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)" ] }, { "cell_type": "code", "execution_count": 494, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "26780.099150628157\n", "[9312.57512673]\n" ] } ], "source": [ "regressor = LinearRegression()\n", "regressor.fit(X_train, y_train)\n", "\n", "print(regressor.intercept_)\n", "print(regressor.coef_)" ] }, { "cell_type": "code", "execution_count": 495, "metadata": {}, "outputs": [], "source": [ "y_pred = regressor.predict(X_test)\n", "df = pd.DataFrame({'Actual': y_test, 'Predicted': y_pred})" ] }, { "cell_type": "code", "execution_count": 496, "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df.plot(kind='bar')\n", "plt.grid()\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 497, "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.scatter(X_test, y_test)\n", "plt.plot(X_test, y_pred, c='r')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 1.3.2 Задание\n", "\n", "Постройте модель множественной линейной регрессии для произвольных \n", "данных из нескольких столбцов. Для примера можно взять потребления \n", "газа (в миллионах галлонов) в 48 штатах США или набор данных о \n", "качестве красного вина (1) и (2) соответственно. Найдите коэффициенты \n", "множественной регрессии. Постройте прогноз. " ] }, { "cell_type": "code", "execution_count": 498, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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fixed acidityvolatile aciditycitric acidresidual sugarchloridesfree sulfur dioxidetotal sulfur dioxidedensitypHsulphatesalcoholquality
count1599.0000001599.0000001599.0000001599.0000001599.0000001599.0000001599.0000001599.0000001599.0000001599.0000001599.0000001599.000000
mean8.3196370.5278210.2709762.5388060.08746715.87492246.4677920.9967473.3111130.65814910.4229835.636023
std1.7410960.1790600.1948011.4099280.04706510.46015732.8953240.0018870.1543860.1695071.0656680.807569
min4.6000000.1200000.0000000.9000000.0120001.0000006.0000000.9900702.7400000.3300008.4000003.000000
25%7.1000000.3900000.0900001.9000000.0700007.00000022.0000000.9956003.2100000.5500009.5000005.000000
50%7.9000000.5200000.2600002.2000000.07900014.00000038.0000000.9967503.3100000.62000010.2000006.000000
75%9.2000000.6400000.4200002.6000000.09000021.00000062.0000000.9978353.4000000.73000011.1000006.000000
max15.9000001.5800001.00000015.5000000.61100072.000000289.0000001.0036904.0100002.00000014.9000008.000000
\n", "
" ], "text/plain": [ " fixed acidity volatile acidity citric acid residual sugar \n", "count 1599.000000 1599.000000 1599.000000 1599.000000 \\\n", "mean 8.319637 0.527821 0.270976 2.538806 \n", "std 1.741096 0.179060 0.194801 1.409928 \n", "min 4.600000 0.120000 0.000000 0.900000 \n", "25% 7.100000 0.390000 0.090000 1.900000 \n", "50% 7.900000 0.520000 0.260000 2.200000 \n", "75% 9.200000 0.640000 0.420000 2.600000 \n", "max 15.900000 1.580000 1.000000 15.500000 \n", "\n", " chlorides free sulfur dioxide total sulfur dioxide density \n", "count 1599.000000 1599.000000 1599.000000 1599.000000 \\\n", "mean 0.087467 15.874922 46.467792 0.996747 \n", "std 0.047065 10.460157 32.895324 0.001887 \n", "min 0.012000 1.000000 6.000000 0.990070 \n", "25% 0.070000 7.000000 22.000000 0.995600 \n", "50% 0.079000 14.000000 38.000000 0.996750 \n", "75% 0.090000 21.000000 62.000000 0.997835 \n", "max 0.611000 72.000000 289.000000 1.003690 \n", "\n", " pH sulphates alcohol quality \n", "count 1599.000000 1599.000000 1599.000000 1599.000000 \n", "mean 3.311113 0.658149 10.422983 5.636023 \n", "std 0.154386 0.169507 1.065668 0.807569 \n", "min 2.740000 0.330000 8.400000 3.000000 \n", "25% 3.210000 0.550000 9.500000 5.000000 \n", "50% 3.310000 0.620000 10.200000 6.000000 \n", "75% 3.400000 0.730000 11.100000 6.000000 \n", "max 4.010000 2.000000 14.900000 8.000000 " ] }, "execution_count": 498, "metadata": {}, "output_type": "execute_result" } ], "source": [ "url = 'https://raw.githubusercontent.com/aniruddhachoudhury/Red-Wine-Quality/master/winequality-red.csv'\n", "\n", "ds = pd.read_csv(url)\n", "\n", "ds.describe()" ] }, { "cell_type": "code", "execution_count": 499, "metadata": {}, "outputs": [], "source": [ "X = ds[['fixed acidity', 'volatile acidity', 'citric acid', 'residual sugar', 'chlorides', 'free sulfur dioxide', 'total sulfur dioxide', 'density', 'pH', 'sulphates', 'alcohol']]\n", "y = ds['quality']\n", "\n", "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)" ] }, { "cell_type": "code", "execution_count": 500, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "34.99871062872134\n", "[ 4.12835075e-02 -1.14952802e+00 -1.77927063e-01 2.78700036e-02\n", " -1.87340739e+00 2.68362616e-03 -2.77748370e-03 -3.15166657e+01\n", " -2.54486051e-01 9.24040106e-01 2.67797417e-01]\n" ] } ], "source": [ "regressor = LinearRegression()\n", "regressor.fit(X_train, y_train)\n", "\n", "print(regressor.intercept_)\n", "print(regressor.coef_)" ] }, { "cell_type": "code", "execution_count": 501, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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Coefficient
fixed acidity0.041284
volatile acidity-1.149528
citric acid-0.177927
residual sugar0.027870
chlorides-1.873407
free sulfur dioxide0.002684
total sulfur dioxide-0.002777
density-31.516666
pH-0.254486
sulphates0.924040
alcohol0.267797
\n", "
" ], "text/plain": [ " Coefficient\n", "fixed acidity 0.041284\n", "volatile acidity -1.149528\n", "citric acid -0.177927\n", "residual sugar 0.027870\n", "chlorides -1.873407\n", "free sulfur dioxide 0.002684\n", "total sulfur dioxide -0.002777\n", "density -31.516666\n", "pH -0.254486\n", "sulphates 0.924040\n", "alcohol 0.267797" ] }, "execution_count": 501, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pd.DataFrame(regressor.coef_, X.columns, columns=['Coefficient'])" ] }, { "cell_type": "code", "execution_count": 502, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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ActualPredicted
110965.782930
103255.036193
100276.596989
48765.339126
97955.939529
.........
79466.559757
81346.017576
132256.251291
70445.163926
102366.367205
\n", "

320 rows × 2 columns

\n", "
" ], "text/plain": [ " Actual Predicted\n", "1109 6 5.782930\n", "1032 5 5.036193\n", "1002 7 6.596989\n", "487 6 5.339126\n", "979 5 5.939529\n", "... ... ...\n", "794 6 6.559757\n", "813 4 6.017576\n", "1322 5 6.251291\n", "704 4 5.163926\n", "1023 6 6.367205\n", "\n", "[320 rows x 2 columns]" ] }, "execution_count": 502, "metadata": {}, "output_type": "execute_result" } ], "source": [ "y_pred = regressor.predict(X_test)\n", "df = pd.DataFrame({'Actual': y_test, 'Predicted': y_pred})\n", "\n", "df" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Средняя квадратичная ошибка" ] }, { "cell_type": "code", "execution_count": 503, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.38447119782012573" ] }, "execution_count": 503, "metadata": {}, "output_type": "execute_result" } ], "source": [ "metrics.mean_squared_error(y_test,y_pred)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 1.3.2 Задание*\n", "\n", "Экспериментально получены N − значений величины Y при \n", "различных значениях величины X. Построить полиномы первой и второй \n", "степени, аппроксимирующие результаты эксперимента, с применением \n", "метода наименьших квадратов. Результаты выводятся в виде таблиц \n", "значений и графиков, полученных полиномов. \n", "\n", "*Вариант:* 2" ] }, { "cell_type": "code", "execution_count": 504, "metadata": {}, "outputs": [], "source": [ "# Линейная функция\n", "def linear_func(x, b0, b1):\n", " return b0 + b1 * x\n", "\n", "# Квадратичная функция\n", "def quadratic_func(x, b0, b1, b2):\n", " return b0 + b1 * x + b2 * x**2" ] }, { "cell_type": "code", "execution_count": 505, "metadata": {}, "outputs": [], "source": [ "xdata = np.linspace(0, 1, 6)\n", "ydata = np.asarray([5.0, 5.0, 4.0, 4.0, 6.0, 6.0])" ] }, { "cell_type": "code", "execution_count": 506, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[4.42857143 1.14285715]\n", " x y\n", "0 0.0 4.428571\n", "1 0.2 4.657143\n", "2 0.4 4.885714\n", "3 0.6 5.114286\n", "4 0.8 5.342857\n", "5 1.0 5.571429\n" ] } ], "source": [ "beta_opt, beta_cov = curve_fit(linear_func, xdata, ydata, method='lm')\n", "print(beta_opt)\n", "df = pd.DataFrame({\"x\": xdata, \"y\": linear_func(xdata, *beta_opt)});\n", "#df.columns=['xdata', 'ydata']\n", "print(df)" ] }, { "cell_type": "code", "execution_count": 507, "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots()\n", "ax.scatter(xdata, ydata)\n", "ax.plot(xdata, linear_func(xdata, *beta_opt), 'b', lw=2)\n", "plt.xlabel('x')\n", "plt.ylabel('linear')\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 508, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[ 5.14285714 -4.2142857 5.35714285]\n", " x y\n", "0 0.0 5.142857\n", "1 0.2 4.514286\n", "2 0.4 4.314286\n", "3 0.6 4.542857\n", "4 0.8 5.200000\n", "5 1.0 6.285714\n" ] } ], "source": [ "beta_opt, beta_cov = curve_fit(quadratic_func, xdata, ydata, method='lm')\n", "print(beta_opt)\n", "df = pd.DataFrame({\"x\": xdata, \"y\": quadratic_func(xdata, *beta_opt)});\n", "print(df)" ] }, { "cell_type": "code", "execution_count": 509, "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots()\n", "ax.scatter(xdata, ydata)\n", "ax.plot(xdata, quadratic_func(xdata, *beta_opt), 'b', lw=2)\n", "plt.xlabel('x')\n", "plt.ylabel('linear')\n", "plt.show()" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.11.0" } }, "nbformat": 4, "nbformat_minor": 2 }