機器學習sklearn（2）多元線性回歸

from numpy import genfromtxt

import numpy as np

from sklearn import datasets, linear_model

datapath = r"c:\users\meachine learn\delivery.csv"

#skip_header=0和skip_footer=0，表示不跳過任何行。

deliverydata = genfromtxt(datapath, delimiter=',',skip_header=2, usecols=(1,2,3))

print(deliverydata)

[[100.    4.    9.3]

[ 50.    3.    4.8]
[100.    4.    8.9]
[100.    2.    6.5]
[ 50.    2.    4.2]
[ 80.    2.    6.2]
[ 75.    3.    7.4]
[ 65.    4.    6. ]
[ 90.    3.    7.6]
[ 90.    2.    6.1]]

y = deliverydata[:,-1]

regr = linear_model.linearregression()

regr.fit(x,y)

linearregression(copy_x=true, fit_intercept=true, n_jobs=1, normalize=false)

coefficinet:  [0.0611346  0.92342537]

intercept:  -0.868701466781709

x_pred = [102, 6]

x_pred = np.array(x_pred).reshape(-1,2)   #把列表轉換成矩陣形式

y_pred = regr.predict(x_pred)

print(y_pred)

[10.90757981]

path = r"c:\users\meachine learn\deliverydummydone.csv"

data = genfromtxt(path, delimiter=',')

print(data)

[[100.    4.    1.    9.3]

[ 50.    3.    0.    4.8]
[100.    4.    1.    8.9]
[100.    2.    2.    6.5]
[ 50.    2.    2.    4.2]
[ 80.    2.    1.    6.2]
[ 75.    3.    1.    7.4]
[ 65.    4.    0.    6. ]
[ 90.    3.    0.    7.6]]

from sklearn import preprocessing

enc = preprocessing.onehotencoder()

data_coder = data[:,2]

data_coder = np.array(data_coder).reshape(-1,1)

enc.fit(data_coder)

onehotencoder(categorical_features='all', dtype=,

handle_unknown='error', n_values='auto', sparse=true)

data_1 = np.array(data[:,0:2]).reshape(-1,2)

x = np.hstack((data_1, data_2))  #對特徵量矩陣橫向合併

print(x)

[[100.   4.   0.   1.   0.]

[ 50.   3.   1.   0.   0.]
[100.   4.   0.   1.   0.]
[100.   2.   0.   0.   1.]
[ 50.   2.   0.   0.   1.]
[ 80.   2.   0.   1.   0.]
[ 75.   3.   0.   1.   0.]
[ 65.   4.   1.   0.   0.]
[ 90.   3.   1.   0.   0.]]

regr = linear_model.linearregression()

regr.fit(x,y)

linearregression(copy_x=true, fit_intercept=true, n_jobs=1, normalize=false)

coefficinet:  [ 0.05553544  0.69257631 -0.17013278  0.57040007 -0.40026729]

intercept:  0.19995688911881349

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