In [10]:

    

'''http://wenku.baidu.com/link?
url=Rkiuci3Z4Ox0grmTiq6cYIBaEUsH10XjwS0vY0VANS9uZx5wo5T7
g_AASZ2fyA2_mx7YZ-mQEjAeyTX8R9c2_NbZNoDH-vciZ5XnwTR5W17
'''
import matplotlib.pyplot as plt
import numpy as np
from sklearn import datasets


    

In [11]:

    

class LinearRegression():
    def __init__(self):
        self.w = None
        
    def fit(self,X,y):
        print X.shape
        X = np.insert(X,0,1,axis=1)
        print X.shape
        X_ = np.linalg.inv(X.T.dot(X))
        self.w = X_.dot(X.T).dot(y)
        
    def predict(self,X):
        X = np.insert(X,0,1,axis=1)
        y_pred = X.dot(self.w)
        return y_pred


    

In [12]:

    

def mean_squared_error(y_true,y_pred):
    mse = np.mean(np.power(y_true - y_pred,2))
    return mse
#print help(np.power)


    

In [19]:

    

def main():
    diabetes = datasets.load_diabetes()  #加载数据集
    X = diabetes.data[:,np.newaxis,2]    #np.newaxis行向量转化为列向量
    print X.shape
    x_train,x_test = X[:-20],X[-20:]
    y_train,y_test = diabetes.target[:-20],diabetes.target[-20:]
    
    clf = LinearRegression()
    clf.fit(x_train,y_train)
    y_pred = clf.predict(x_test)
    
    print 'Mean Squared Error:',mean_squared_error(y_test,y_pred)
    
    plt.scatter(x_test[:,0],y_test,color='black')
    plt.plot(x_test[:,0],y_pred,color='blue',linewidth=3)
    plt.show()


    

In [20]:

    

main()


    

    




(442, 1)
(422, 1)
(422, 2)
Mean Squared Error: 2548.07239873






    

In [52]:

    

import matplotlib.pyplot as plt
import numpy as np
from sklearn import datasets


    

In [53]:

    

class LinearRegression_1:
    def __init__(self):
        self.w = None
        
    def fit (self,X,y):
        print X.shape
        X = np.insert(X,0,1,axis=1)
        print X.shape
        X_ = np.linalg.inv(X.T.dot(X))
        self.w = X_.dot(X.T).dot(y)
        
    def predict(self,X):
        X = np.insert(X,0,1,axis=1)
        y_pred = X.dot(self.w)
        return y_pred


    

In [54]:

    

def mean_square_error(y_true,y_pred):
    mse = np.mean(np.power(y_true - y_pred,2))
    print 'Mean Square Error:%.2f' % mse 
    #return mse


    

In [55]:

    

def main_1():
    diabetes = datasets.load_diabetes();
    diabetes_X = diabetes.data[:,np.newaxis,2]
    X_train,X_test = diabetes_X[:-20],diabetes_X[-20:]
    y_train,y_test = diabetes.target[:-20],diabetes.target[-20:]
    
    lr = LinearRegression_1()
    lr.fit(X_train,y_train)
    y_pred = lr.predict(X_test)
    
    mean_square_error(y_test,y_pred)
    #print 'Mean Square Error:%.2f' % mean_square_error(y_true,y_pred) 
    
    plt.scatter(X_test,y_test,color='black')
    plt.plot(X_test,y_pred,color='red',linewidth = 5)
    
    plt.grid()
    plt.show()


    

In [56]:

    

main_1()


    

    




(422, 1)
(422, 2)
Mean Square Error:2548.07






    

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