 # linear regression

Oct 26th, 2019
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2. print(dataframe)
3.
4. X = dataframe.data[:,:]
5. Y = dataframe.target
6.
7. X_train, X_test, Y_train, Y_test = train_test_split(X, Y,test_size=0.30, random_state=42)
8.
9. train_size = X_train.shape
10. test_size  = X_test.shape
11. dt = dataframe.data.shape
12. #print(X_train)
13.
14. w= np.zeros((1,dt))
15. print(w)
16. b=0
17.
18. alpha=0.000001
19.
20.
21. for i in range (500):
22.     yht = np.dot(X_train, w.T)
23.     yht += b
24.     diff = yht - Y_train.reshape((yht.shape))
25.     b = b - alpha*((1/X_train.shape)*(np.sum(diff)))
26.     m = diff.shape
27.     #print(diff.shape)
28.
29.     for j in range(10):
30.
31.         w[0,j]=w[0,j]-(alpha*((1/m)*(np.dot(X_train[:, j].reshape(1,m), diff))))
32.         #print(w)
33.         df = yht - Y_train.reshape(m,1)
34.         sum=0
35.
36.         for i in range (df.shape):
37.             sum+=df[i, 0]**2
38.
39.        print(sum/2*m)
40.
41.
42. #print(w.shape)
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