-
Notifications
You must be signed in to change notification settings - Fork 1
/
Copy pathRideg2_Q1_P3.py
134 lines (120 loc) · 4.24 KB
/
Rideg2_Q1_P3.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
# Loading dataset from pandas lib
X_tr = pd.read_csv("housing_X_train.csv", header=None)
y_tr = pd.read_csv("housing_y_train.csv", header=None)
X_tr = np.array(X_tr)
X_tr = X_tr.T
y_tr = np.array(y_tr)
y_tr = np.reshape(y_tr,-1)
X_te = pd.read_csv("housing_X_test.csv", header=None)
y_te = pd.read_csv("housing_y_test.csv", header=None)
X_te = np.array(X_te)
X_te = X_te.T
y_te = np.array(y_te)
y_te = np.reshape(y_te,-1)
n,d = X_tr.shape
nt,dt = X_te.shape
# Ridge Regression Algorithms
max_pass = int(1e7)
tol = 1e-7
step = 1e-4
# Lambda = 10 ##########################################################
w = np.zeros(d)
b = 0
one = np.ones(n)
one_t = np.ones(nt)
lam = 10
training_error = []
loss_error = []
test_error = []
B = []
W = []
for i in range(max_pass):
b = 1/(np.dot(one,one)) * (np.dot(one ,(y_tr - np.dot(X_tr,w))))
del_delb = 1/n * (np.dot(one ,(np.dot(X_tr,w) + b*one - y_tr)))
del_delb = 1/n * (np.dot(one ,(np.dot(X_tr,w) + b*one - y_tr)))
del_delw = 1/n * (np.dot(X_tr.T,(np.dot(X_tr,w) + b*one - y_tr))) + 2*lam*w
B.append(b)
W.append(w)
w_pre = w
w = w - step*del_delw
train_e = 1/(2*n) * np.linalg.norm(np.dot(X_tr,w) + b*one - y_tr)**2
training_error.append(train_e)
train_l = 1/(2*n) * np.linalg.norm(np.dot(X_tr,w) + b*one - y_tr)**2 + lam*np.linalg.norm(w)**2
loss_error.append(train_l)
test_e = 1/(2*nt) * np.linalg.norm(np.dot(X_te,w) + b*one_t - y_te)**2
test_error.append(test_e)
if np.linalg.norm(w - w_pre)<= tol:
break
test_l = 1/(2*nt) * np.linalg.norm(np.dot(X_te,w) + b*one_t - y_te)**2 + lam*np.linalg.norm(w)**2
# Figures
plt.figure()
plt.plot(range(np.array(training_error).shape[0]), np.array(training_error), linewidth = 4, label = 'Training_error')
plt.plot(range(np.array(loss_error).shape[0]), np.array(loss_error), label = 'Loss_error')
plt.plot(range(np.array(test_error).shape[0]), np.array(test_error), 'g', label = 'Test_error')
plt.legend()
plt.xlabel('Number of iterations')
plt.ylabel('Error')
plt.title("Step size=1e-4, lambda=10, Tol=1e-7")
plt.show()
# plt.figure()
# plt.plot(range(np.array(B).shape[0]), np.array(B), linewidth = 2)
# plt.legend()
# plt.xlabel('Number of iterations')
# plt.ylabel('b')
# plt.title("Step size=1e-4, lambda=10")
# plt.show()
del W
del B
del training_error
del loss_error
del test_error
# Ridge Regression Algorithms
# Lambda = 0 ############################################
w = np.zeros(d)
b = 0
one = np.ones(n)
one_t = np.ones(nt)
lam = 0
training_error = []
loss_error = []
test_error = []
B = []
W = []
for i in range(max_pass):
b = 1/(np.dot(one,one)) * (np.dot(one ,(y_tr - np.dot(X_tr,w))))
del_delb = 1/n * (np.dot(one ,(np.dot(X_tr,w) + b*one - y_tr)))
del_delb = 1/n * (np.dot(one ,(np.dot(X_tr,w) + b*one - y_tr)))
del_delw = 1/n * (np.dot(X_tr.T,(np.dot(X_tr,w) + b*one - y_tr))) + 2*lam*w
B.append(b)
W.append(w)
w_pre = w
w = w - step*del_delw
train_e = 1/(2*n) * np.linalg.norm(np.dot(X_tr,w) + b*one - y_tr)**2
training_error.append(train_e)
train_l = 1/(2*n) * np.linalg.norm(np.dot(X_tr,w) + b*one - y_tr)**2 + lam*np.linalg.norm(w)**2
loss_error.append(train_l)
test_e = 1/(2*nt) * np.linalg.norm(np.dot(X_te,w) + b*one_t - y_te)**2
test_error.append(test_e)
if np.linalg.norm(w - w_pre)<= tol:
break
test_l = 1/(2*nt) * np.linalg.norm(np.dot(X_te,w) + b*one_t - y_te)**2 + lam*np.linalg.norm(w)**2
# Figures
plt.figure()
plt.plot(range(np.array(training_error).shape[0]), np.array(training_error), linewidth = 4, label = 'Training_error')
plt.plot(range(np.array(loss_error).shape[0]), np.array(loss_error), label = 'Loss_error')
plt.plot(range(np.array(test_error).shape[0]), np.array(test_error), 'g', label = 'Test_error')
plt.legend()
plt.xlabel('Number of iterations')
plt.ylabel('Error')
plt.title("Step size=1e-4, lambda=0, Tol=1e-7")
plt.show()
# plt.figure()
# plt.plot(range(np.array(B).shape[0]), np.array(B), linewidth = 2)
# plt.legend()
# plt.xlabel('Number of iterations')
# plt.ylabel('b')
# plt.title("Step size=1e-4, lambda=0")
# plt.show()