-
Notifications
You must be signed in to change notification settings - Fork 31
/
perceptron.py
78 lines (62 loc) · 2.31 KB
/
perceptron.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
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
from scipy.stats import randint
from sklearn.datasets import load_iris
from sklearn.metrics import confusion_matrix
from sklearn.model_selection import train_test_split
class perceptron:
def __init__(self, num_epochs, dim):
self.num_epochs = num_epochs
self.theta0 = 0
self.theta = np.zeros(dim)
def fit(self, X_train, y_train):
n = X_train.shape[0]
dim = X_train.shape[1]
k = 1
for epoch in range(self.num_epochs):
for i in range(n):
#sample random point
idx = randint.rvs(0, n-1, size=1)[0]
#hinge loss
if (y_train[idx] * (np.dot(self.theta, X_train[idx,:]) + self.theta0) <= 0):
#update learning rate
eta = pow(k+1, -1)
k += 1
#print("eta: ", eta)
#update theta
self.theta = self.theta + eta * y_train[idx] * X_train[idx, :]
self.theta0 = self.theta0 + eta * y_train[idx]
#end if
print("epoch: ", epoch)
print("theta: ", self.theta)
print("theta0: ", self.theta0)
#end for
#end for
def predict(self, X_test):
n = X_test.shape[0]
dim = X_test.shape[1]
y_pred = np.zeros(n)
for idx in range(n):
y_pred[idx] = np.sign(np.dot(self.theta, X_test[idx,:]) + self.theta0)
#end for
return y_pred
if __name__ == "__main__":
#load dataset
iris = load_iris()
X = iris.data[:100,:]
y = 2*iris.target[:100] - 1 #map to {+1,-1} labels
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
#perceptron (binary) classifier
clf = perceptron(num_epochs=5, dim=X.shape[1])
clf.fit(X_train, y_train)
y_pred = clf.predict(X_test)
cmt = confusion_matrix(y_test, y_pred)
acc = np.trace(cmt)/np.sum(np.sum(cmt))
print("percepton accuracy: ", acc)
#generate plots
plt.figure()
sns.heatmap(cmt, annot=True, fmt="d")
plt.title("Confusion Matrix"); plt.xlabel("predicted"); plt.ylabel("actual")
#plt.savefig("./figures/perceptron_acc.png")
plt.show()