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import numpy as np | |
from sklearn.linear_model import LinearRegression | |
import matplotlib.pyplot as plt | |
from sklearn.model_selection import train_test_split | |
# loading data | |
x_raw,y_raw=np.loadtxt('data.csv',delimiter=',') | |
# reshape the data | |
x=x_raw.reshape(-1,1) | |
y=y_raw.reshape(-1,1) | |
# split the model into test/train sets | |
x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=.3, | |
random_state=1337, | |
shuffle=True) | |
# fit the linear model | |
model=LinearRegression() | |
model.fit(x_train,y_train) | |
# get the R2 score | |
R2=model.score(x_test,y_test) | |
print ('The R2 score is {0:.2f}'.format (R2)) | |
#get model parameters | |
coef=model.intercept_,*model.coef_ | |
print (', '.join('a{0}={1:.2f}'.format(i,*a) for i,a in enumerate(coef))) | |
# get the model predictions & | |
# visualize the model | |
predictions=model.predict(x) | |
fig1=plt.figure('Linear Regression') | |
plt.plot(x,predictions,color='b',label='Linear model | R2={0:.2f}'.format(R2)) | |
plt.scatter(x_raw,y_raw,color='r',marker='.',label='Input data') | |
plt.xlabel('x') | |
plt.ylabel('f(x)') | |
plt.grid() | |
plt.legend() | |
plt.title('Linear Regression Using sklearn') |
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