建立简单线性回归模型

博主偏向于工程应用，这里就不叙述原理了，对于详细的推导过程，这里给出参考文章：

多元线性回归推导：

https://blog.****.net/qq_34734683/article/details/79534504

https://www.cnblogs.com/GuoJiaSheng/p/3928160.html

http://blog.51cto.com/12133258/2051527

---

先给出数据，将数据保存到txt文本，取名liner_regressor.txt

1.1	39343
1.3	46205
1.5	37731
2	43525
2.2	39891
2.9	56642
3	60150
3.2	54445
3.2	64445
3.7	57189
3.9	63218
4	55794
4	56957
4.1	57081
4.5	61111
4.9	67938
5.1	66029
5.3	83088
5.9	81363
6	93940
6.8	91738
7.1	98273
7.9	101302
8.2	113812
8.7	109431
9	105582
9.5	116969
9.6	112635
10.3	122391
10.5	121872

---

一、数据读取

import sys
#读取文件，前面是x，后面是y，现在用空格分开
filename="liner_regressor.txt"
X=[]
Y=[]
with open(filename,'r')as f:
    for line in f.readlines():
        #print line
        xt,yt=[float(i) for i in line.split()]  #若两个数据之间是空格，则括号不填东西，否则就填分割的符号
        X.append(xt)
        Y.append(yt)

---

二、将数据分成训练集、测试集

np.reshape用法参见：https://blog.****.net/DocStorm/article/details/58593682

import numpy as np

num_training=int(0.8*len(X))
num_test=len(X)-num_training

#Training data
#利用分片取出前num_traing并重组数组成列向量
#https://blog.****.net/DocStorm/article/details/58593682
X_train=np.array(X[:num_training]).reshape((num_training,1))
Y_train=np.array(Y[:num_training])

print X_train
#Test data
X_test=np.array(X[num_training:]).reshape((num_test,1))
Y_test=np.array(Y[num_training:])

则X_train为列向量：

[[ 1.1]
 [ 1.3]
 [ 1.5]
 [ 2. ]
 [ 2.2]
 [ 2.9]
 [ 3. ]
 [ 3.2]
 [ 3.2]
 [ 3.7]
 [ 3.9]
 [ 4. ]
 [ 4. ]
 [ 4.1]
 [ 4.5]
 [ 4.9]
 [ 5.1]
 [ 5.3]
 [ 5.9]
 [ 6. ]
 [ 6.8]
 [ 7.1]
 [ 7.9]
 [ 8.2]]

Y_train为一维数组：

[  39343.   46205.   37731.   43525.   39891.   56642.   60150.   54445.
   64445.   57189.   63218.   55794.   56957.   57081.   61111.   67938.
   66029.   83088.   81363.   93940.   91738.   98273.  101302.  113812.]

---

三、创建回归器对象

from sklearn import linear_model

#Create linear regression object
linear_regressor=linear_model.LinearRegression()

#Train the model using the training sets
linear_regressor.fit(X_train,Y_train)  #利用训练集训练模型

---

四、plot可视化输出

  其中plot用到的函数可参见：

 https://blog.****.net/qiu931110/article/details/68130199

import matplotlib.pyplot as plt

#draw the line
#先计算对应X的拟合直线输出值
X_total=np.array(X).reshape((len(X),1))
y_train_pred=linear_regressor.predict(X_total)

plt.figure()
#绘制散点图
plt.scatter(X,Y,color="orange")
#绘制拟合直线
plt.plot(X_total,y_train_pred,color="blue",linewidth=4)
plt.title("train data")
plt.show()

---

 五、全部代码：

#-*-coding:UTF-8 -*-
import sys
import numpy as np
from sklearn import linear_model
import matplotlib.pyplot as plt
#读取文件，前面是x，后面是y，现在用空格分开
filename="liner_regressor.txt"
X=[]
Y=[]
with open(filename,'r')as f:
    for line in f.readlines():
        #print line
        xt,yt=[float(i) for i in line.split()]
        X.append(xt)
        Y.append(yt)

num_training=int(0.8*len(X))
num_test=len(X)-num_training

#Training data
#利用分片取出前num_traing并重组数组成列向量
#https://blog.****.net/DocStorm/article/details/58593682
X_train=np.array(X[:num_training]).reshape((num_training,1))
Y_train=np.array(Y[:num_training])

print X_train
print Y_train
#Test data
X_test=np.array(X[num_training:]).reshape((num_test,1))
Y_test=np.array(Y[num_training:])

#Create linear regression object
linear_regressor=linear_model.LinearRegression()

#Train the model using the training sets
linear_regressor.fit(X_train,Y_train)

#draw the line
#先计算对应X的拟合直线输出值
X_total=np.array(X).reshape((len(X),1))
y_train_pred=linear_regressor.predict(X_total)

plt.figure()
#绘制散点图
plt.scatter(X,Y,color="orange")
#绘制拟合直线
plt.plot(X_total,y_train_pred,color="blue",linewidth=4)
plt.title("train data")
plt.show()