使用python进行多元线性回归 [英] Multiple linear regression with python
本文介绍了使用python进行多元线性回归的处理方法，对大家解决问题具有一定的参考价值，需要的朋友们下面随着小编来一起学习吧！
问题描述
我想用python计算多元线性回归. 我找到了用于简单线性回归的代码
I would like to calculate multiple linear regression with python. I found this code for simple linear regression
import numpy as np
from matplotlib.pyplot import *
x = np.array([1, 2, 3, 4, 5])
y = np.array([2, 3, 4, 4, 5])
n = np.max(x.shape)
X = np.vstack([np.ones(n), x]).T
a = np.linalg.lstsq(X, y)[0]
所以a是系数，但我不明白[0]是什么意思?
So, a is the coefficient, but I don't see what [0] means ?
如何更改代码以获得多个线性回归?
And how can I change the code to obtain multiple linear regressions ?
推荐答案
要使用python实现多重线性回归，您可以使用以下任一选项:
To implement multiple linear regression with python you can use any of the following options:
1)使用正态方程法(使用矩阵求逆)
2)Numpy的最小二乘numpy.linalg.lstsq工具
3)Numpy的np.linalg.solve工具
1) Use normal equation method (that uses matrix inverse)
2) Numpy's least-squares numpy.linalg.lstsq tool
3) Numpy's np.linalg.solve tool
对于法线方程方法，您可以使用以下公式:
在上面的公式中， X 是特征矩阵， y 是标签矢量.
For normal equations method you can use this formula:
In above formula X is feature matrix and y is label vector.
对于Numpy的 numpy.linalg.lstsq 或 np.linalg.solve 工具，您可以直接使用它们.
As for Numpy's numpy.linalg.lstsq or np.linalg.solve tools you just use them out of the box.
在这里，我提供了可用于测试的示例数据的链接: https://drive.google.com/file/d/0BzzUvSbpsTAvN1UxTkxXd2U0eVE/view
Here I provide a link for sample data that you can use for tests: https://drive.google.com/file/d/0BzzUvSbpsTAvN1UxTkxXd2U0eVE/view
替代: https://www.dropbox.com/s /e3pd7fp0rfm1cfs/DB2.csv?dl=0
数据准备代码:
import pandas as pd
import numpy as np
path = 'DB2.csv'
data = pd.read_csv(path, header=None, delimiter=";")
data.insert(0, 'Ones', 1)
cols = data.shape[1]
X = data.iloc[:,0:cols-1]
y = data.iloc[:,cols-1:cols]
IdentitySize = X.shape[1]
IdentityMatrix= np.zeros((IdentitySize, IdentitySize))
np.fill_diagonal(IdentityMatrix, 1)
对于最小二乘方法，请使用Numpy的 numpy.linalg.lstsq .这是Python代码:
For least squares method you use Numpy's numpy.linalg.lstsq. Here is Python code:
lamb = 1
th = np.linalg.lstsq(X.T.dot(X) + lamb * IdentityMatrix, X.T.dot(y))[0]
您还可以使用numpy的 np.linalg.solve 工具:
Also you can use np.linalg.solve tool of numpy:
lamb = 1
XtX_lamb = X.T.dot(X) + lamb * IdentityMatrix
XtY = X.T.dot(y)
x = np.linalg.solve(XtX_lamb, XtY);
对于正态方程方法，请使用:
lamb = 1
xTx = X.T.dot(X) + lamb * IdentityMatrix
XtX = np.linalg.inv(xTx)
XtX_xT = XtX.dot(X.T)
theta = XtX_xT.dot(y)
在所有方法中均使用正则化.以下是结果(θ系数)，以了解这三种方法之间的差异:
In all methods regularization is used. Here is results (theta coefficients) to see difference between these three approaches:
Normal equation: np.linalg.lstsq np.linalg.solve
[-27551.99918303] [-27551.95276154] [-27551.9991855]
[-940.27518383] [-940.27520138] [-940.27518383]
[-9332.54653964] [-9332.55448263] [-9332.54654461]
[-3149.02902071] [-3149.03496582] [-3149.02900965]
[-1863.25125909] [-1863.2631435] [-1863.25126344]
[-2779.91105618] [-2779.92175308] [-2779.91105347]
[-1226.60014026] [-1226.61033117] [-1226.60014192]
[-920.73334259] [-920.74331432] [-920.73334194]
[-6278.44238081] [-6278.45496955] [-6278.44237847]
[-2001.48544938] [-2001.49566981] [-2001.48545349]
[-715.79204971] [-715.79664124] [-715.79204921]
[ 4039.38847472] [ 4039.38302499] [ 4039.38847515]
[-2362.54853195] [-2362.55280478] [-2362.54853139]
[-12730.8039209] [-12730.80866036] [-12730.80392076]
[-24872.79868125] [-24872.80203459] [-24872.79867954]
[-3402.50791863] [-3402.5140501] [-3402.50793382]
[ 253.47894001] [ 253.47177732] [ 253.47892472]
[-5998.2045186] [-5998.20513905] [-5998.2045184]
[ 198.40560401] [ 198.4049081] [ 198.4056042]
[ 4368.97581411] [ 4368.97175688] [ 4368.97581426]
[-2885.68026222] [-2885.68154407] [-2885.68026205]
[ 1218.76602731] [ 1218.76562838] [ 1218.7660275]
[-1423.73583813] [-1423.7369068] [-1423.73583793]
[ 173.19125007] [ 173.19086525] [ 173.19125024]
[-3560.81709538] [-3560.81650156] [-3560.8170952]
[-142.68135768] [-142.68162508] [-142.6813575]
[-2010.89489111] [-2010.89601322] [-2010.89489092]
[-4463.64701238] [-4463.64742877] [-4463.64701219]
[ 17074.62997704] [ 17074.62974609] [ 17074.62997723]
[ 7917.75662561] [ 7917.75682048] [ 7917.75662578]
[-4234.16758492] [-4234.16847544] [-4234.16758474]
[-5500.10566329] [-5500.106558] [-5500.10566309]
[-5997.79002683] [-5997.7904842] [-5997.79002634]
[ 1376.42726683] [ 1376.42629704] [ 1376.42726705]
[ 6056.87496151] [ 6056.87452659] [ 6056.87496175]
[ 8149.0123667] [ 8149.01209157] [ 8149.01236827]
[-7273.3450484] [-7273.34480382] [-7273.34504827]
[-2010.61773247] [-2010.61839251] [-2010.61773225]
[-7917.81185096] [-7917.81223606] [-7917.81185084]
[ 8247.92773739] [ 8247.92774315] [ 8247.92773722]
[ 1267.25067823] [ 1267.24677734] [ 1267.25067832]
[ 2557.6208133] [ 2557.62126916] [ 2557.62081337]
[-5678.53744654] [-5678.53820798] [-5678.53744647]
[ 3406.41697822] [ 3406.42040997] [ 3406.41697836]
[-8371.23657044] [-8371.2361594] [-8371.23657035]
[ 15010.61728285] [ 15010.61598236] [ 15010.61728304]
[ 11006.21920273] [ 11006.21711213] [ 11006.21920284]
[-5930.93274062] [-5930.93237071] [-5930.93274048]
[-5232.84459862] [-5232.84557665] [-5232.84459848]
[ 3196.89304277] [ 3196.89414431] [ 3196.8930428]
[ 15298.53309912] [ 15298.53496877] [ 15298.53309919]
[ 4742.68631183] [ 4742.6862601] [ 4742.68631172]
[ 4423.14798495] [ 4423.14765013] [ 4423.14798546]
[-16153.50854089] [-16153.51038489] [-16153.50854123]
[-22071.50792741] [-22071.49808389] [-22071.50792408]
[-688.22903323] [-688.2310229] [-688.22904006]
[-1060.88119863] [-1060.8829114] [-1060.88120546]
[-101.75750066] [-101.75776411] [-101.75750831]
[ 4106.77311898] [ 4106.77128502] [ 4106.77311218]
[ 3482.99764601] [ 3482.99518758] [ 3482.99763924]
[-1100.42290509] [-1100.42166312] [-1100.4229119]
[ 20892.42685103] [ 20892.42487476] [ 20892.42684422]
[-5007.54075789] [-5007.54265501] [-5007.54076473]
[ 11111.83929421] [ 11111.83734144] [ 11111.83928704]
[ 9488.57342568] [ 9488.57158677] [ 9488.57341883]
[-2992.3070786] [-2992.29295891] [-2992.30708529]
[ 17810.57005982] [ 17810.56651223] [ 17810.57005457]
[-2154.47389712] [-2154.47504319] [-2154.47390285]
[-5324.34206726] [-5324.33913623] [-5324.34207293]
[-14981.89224345] [-14981.8965674] [-14981.89224973]
[-29440.90545197] [-29440.90465897] [-29440.90545704]
[-6925.31991443] [-6925.32123144] [-6925.31992383]
[ 104.98071593] [ 104.97886085] [ 104.98071152]
[-5184.94477582] [-5184.9447972] [-5184.94477792]
[ 1555.54536625] [ 1555.54254362] [ 1555.5453638]
[-402.62443474] [-402.62539068] [-402.62443718]
[ 17746.15769322] [ 17746.15458093] [ 17746.15769074]
[-5512.94925026] [-5512.94980649] [-5512.94925267]
[-2202.8589276] [-2202.86226244] [-2202.85893056]
[-5549.05250407] [-5549.05416936] [-5549.05250669]
[-1675.87329493] [-1675.87995809] [-1675.87329255]
[-5274.27756529] [-5274.28093377] [-5274.2775701]
[-5424.10246845] [-5424.10658526] [-5424.10247326]
[-1014.70864363] [-1014.71145066] [-1014.70864845]
[ 12936.59360437] [ 12936.59168749] [ 12936.59359954]
[ 2912.71566077] [ 2912.71282628] [ 2912.71565599]
[ 6489.36648506] [ 6489.36538259] [ 6489.36648021]
[ 12025.06991281] [ 12025.07040848] [ 12025.06990358]
[ 17026.57841531] [ 17026.56827742] [ 17026.57841044]
[ 2220.1852193] [ 2220.18531961] [ 2220.18521579]
[-2886.39219026] [-2886.39015388] [-2886.39219394]
[-18393.24573629] [-18393.25888463] [-18393.24573872]
[-17591.33051471] [-17591.32838012] [-17591.33051834]
[-3947.18545848] [-3947.17487999] [-3947.18546459]
[ 7707.05472816] [ 7707.05577227] [ 7707.0547217]
[ 4280.72039079] [ 4280.72338194] [ 4280.72038435]
[-3137.48835901] [-3137.48480197] [-3137.48836531]
[ 6693.47303443] [ 6693.46528167] [ 6693.47302811]
[-13936.14265517] [-13936.14329336] [-13936.14267094]
[ 2684.29594641] [ 2684.29859601] [ 2684.29594183]
[-2193.61036078] [-2193.63086307] [-2193.610366]
[-10139.10424848] [-10139.11905454] [-10139.10426049]
[ 4475.11569903] [ 4475.12288711] [ 4475.11569421]
[-3037.71857269] [-3037.72118246] [-3037.71857265]
[-5538.71349798] [-5538.71654224] [-5538.71349794]
[ 8008.38521357] [ 8008.39092739] [ 8008.38521361]
[-1433.43859633] [-1433.44181824] [-1433.43859629]
[ 4212.47144667] [ 4212.47368097] [ 4212.47144686]
[ 19688.24263706] [ 19688.2451694] [ 19688.2426368]
[ 104.13434091] [ 104.13434349] [ 104.13434091]
[-654.02451175] [-654.02493111] [-654.02451174]
[-2522.8642551] [-2522.88694451] [-2522.86424254]
[-5011.20385919] [-5011.22742915] [-5011.20384655]
[-13285.64644021] [-13285.66951459] [-13285.64642763]
[-4254.86406891] [-4254.88695873] [-4254.86405637]
[-2477.42063206] [-2477.43501057] [-2477.42061727]
[ 0.] [ 1.23691279e-10] [ 0.]
[-92.79470071] [-92.79467095] [-92.79470071]
[ 2383.66211583] [ 2383.66209637] [ 2383.66211583]
[-10725.22892185] [-10725.22889937] [-10725.22892185]
[ 234.77560283] [ 234.77560254] [ 234.77560283]
[ 4739.22119578] [ 4739.22121432] [ 4739.22119578]
[ 43640.05854156] [ 43640.05848841] [ 43640.05854157]
[ 2592.3866707] [ 2592.38671547] [ 2592.3866707]
[-25130.02819215] [-25130.05501178] [-25130.02819515]
[ 4966.82173096] [ 4966.7946407] [ 4966.82172795]
[ 14232.97930665] [ 14232.9529959] [ 14232.97930363]
[-21621.77202422] [-21621.79840459] [-21621.7720272]
[ 9917.80960029] [ 9917.80960571] [ 9917.80960029]
[ 1355.79191536] [ 1355.79198092] [ 1355.79191536]
[-27218.44185748] [-27218.46880642] [-27218.44185719]
[-27218.04184348] [-27218.06875423] [-27218.04184318]
[ 23482.80743869] [ 23482.78043029] [ 23482.80743898]
[ 3401.67707434] [ 3401.65134677] [ 3401.67707463]
[ 3030.36383274] [ 3030.36384909] [ 3030.36383274]
[-30590.61847724] [-30590.63933424] [-30590.61847706]
[-28818.3942685] [-28818.41520495] [-28818.39426833]
[-25115.73726772] [-25115.7580278] [-25115.73726753]
[ 77174.61695995] [ 77174.59548773] [ 77174.61696016]
[-20201.86613672] [-20201.88871113] [-20201.86613657]
[ 51908.53292209] [ 51908.53446495] [ 51908.53292207]
[ 7710.71327865] [ 7710.71324194] [ 7710.71327865]
[-16206.9785119] [-16206.97851993] [-16206.9785119]
您可以看到法线方程，最小二乘和np.linalg.solve工具方法在某种程度上给出不同的结果.
As you can see normal equation, least squares and np.linalg.solve tool methods give to some extent different results.
这篇关于使用python进行多元线性回归的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！
查看全文
