1. w_target.shape:  (3,) [ 1.17881511 -5.13265596 -6.55556511]
2. Pre_Logistic_function 
3.  <class 'function'>
4. Product_x_function 
5.  [1.         0.10262954 0.43893794]
6. data_x 
7.  (300, 3) [[ 1.         -0.15378708  0.9615284 ]
8.  [ 1.          0.36965948 -0.0381362 ]
9.  [ 1.         -0.21576496 -0.31364397]
10.  [ 1.          0.45809941 -0.12285551]
11.  [ 1.         -0.88064421 -0.20391149]]

1. def gradient_descent(data_x, data_y, w_h=None, eta=1.0, max_iterations=10000, epsilon=0.001):
2. if w_h == None:
3.         w_h = np.array([0.0 for i in range(data_x.shape[1])])
4.     w_h_i = [np.copy(w_h)]  
5. 
6. for i in range(max_iterations):
7.         subset_indices = range(data_x.shape[0])
8.         grad_E_in = np.mean(np.tile(- data_y[subset_indices] /
9.                                         ( 1.0 + np.exp(data_y[subset_indices] * w_h.dot(data_x[subset_indices].T)) ),
10.                                     (data_x.shape[1], 1)).T *   data_x[subset_indices], axis=0)
11.         w_h -= eta * grad_E_in
12.         w_h_i.append(np.copy(w_h))
13. if np.linalg.norm(grad_E_in) <= np.linalg.norm(w_h) * epsilon:
14. break
15. return np.array(w_h_i)
16. 
17. 
18. LoR = linear_model.LogisticRegression()
19. LoR.fit(data_x,data_y)
20. y_train=LoR.predict(data_x)
21. 
22. LoRpp_function = lambda z: LoR.predict_proba(z)[:,0]
23. BG_Grid_BaseLoR = apply_to_fill(z_grid, LoRpp_function)
24. 
25. full_N_fig = plot_dataset_and_hypothesis(3,data_x, data_y, xy_1, xy_2, BG_Grid_BaseLoR,title=r'LoR: Hypothesis, $N={:}$'.format(N))
26. 
27. 
28. 
29. SVM_Linear = svm.SVC(kernel='linear')
30. SVM_Linear.fit(data_x,data_y)
31. 
32. SVM_LinearPre_function = lambda z: SVM_Linear.predict(z)
33. BG_Grid_BaseSVM_Linear = apply_to_fill(z_grid, SVM_LinearPre_function)
34. 
35. full_N_fig = plot_dataset_and_hypothesis(5,data_x, data_y, xy_1, xy_2, BG_Grid_BaseSVM_Linear, title=r'SVM_Linear: Hypothesis, $N={:}$'.format(N))
36. 
37. 
38. 
39. 
40. 
41. 
42.