import numpy as np
import matplotlib.pyplot as plt
from sklearn.linear_model import LinearRegression
from sklearn.linear_model import RidgeCV
from sklearn.cross_validation import train_test_split
‘‘‘‘‘#load data
n=100
x = np.arange(1,100,n)+np.random.randn(n)
y = 4*x - 3 + np.random.randn(n)
plt . figure ()
plt . plot(x, y, ‘r*‘, label=‘X‘)
plt . ylabel (" Y" )
plt . xlabel (" X")
plt . legend(loc="best")
plt . tight_layout()
plt . show()
‘‘‘
data = [‘C:\\Users\\123\\Desktop\\weather\\2015.txt‘,]
w = np. loadtxt ( data [0] , skiprows =1)
y = w[:,7]/10
x = w[:,10]
plt . figure ()
plt . plot(x,y,"b*",label="Atmospheric pressure")
plt . ylabel (" Temperatures" )
plt . xlabel ("Atmospheric pressure " )
plt . title (‘ Temperatures trent chart of Shanghai in year 2015 ‘)
plt . tight_layout()
plt . legend(loc="best")
plt . show()
x = x.reshape(-1, 1)
x_train, x_test, y_train, y_test = train_test_split(x, y, random_state=1)
lr = LinearRegression()
lr . fit ( x_train , y_train)
y_lr = lr.predict ( x_test )
cv = RidgeCV(alphas=np.logspace(-3, 2, 100))
cv . fit ( x_train , y_train)
y_cv = cv.predict ( x_test )
print lr.coef_
print lr.intercept_
print "mes of Linear Regresion squares is", np. mean(( y_lr - y_test ) ** 2)
print "accuracy of Linear regression is",lr.score(x_test,y_test)
print cv.coef_
print cv.intercept_
print "mes of Linear Regresion+Ridge squares is", np. mean(( y_cv - y_test ) ** 2)
print "accuracy of Linear regression is",cv.score(x_test,y_test)
x1 = np.arange(len(x_test))
plt.plot(x1,y_test,"y*-",label="Test")
plt.plot(x1,y_lr,"ro-",label="Predict")
plt.plot(x1,y_cv,"b^-",label="Predict+Ridge")
plt . ylabel (" Temperatures" )
plt . xlabel (" Atmospheric pressure")
plt . title (‘ Predict chart ‘)
plt . legend(loc="best")
plt . tight_layout()
plt . show()
