-
Notifications
You must be signed in to change notification settings - Fork 0
/
adaboost.py
52 lines (45 loc) · 1.62 KB
/
adaboost.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
from __future__ import division
import numpy as np
class AdaBoost:
def __init__(self, training_set):
self.training_set = training_set
self.N = len(self.training_set)
self.weights = np.ones(self.N)/self.N
self.RULES = []
self.ALPHA = []
def set_rule(self, func, test=False):
errors = np.array([t[1]!=func(t[0]) for t in self.training_set])
print(errors)
e = (errors*self.weights).sum()
if test: return e
alpha = 0.5 * np.log((1-e)/e)
print ('e=%.2f a=%.2f'%(e, alpha))
w = np.zeros(self.N)
for i in range(self.N):
if errors[i] == 1: w[i] = self.weights[i] * np.exp(alpha)
else: w[i] = self.weights[i] * np.exp(-alpha)
self.weights = w / w.sum()
self.RULES.append(func)
self.ALPHA.append(alpha)
def evaluate(self):
NR = len(self.RULES)
for (x,l) in self.training_set:
hx = [self.ALPHA[i]*self.RULES[i](x) for i in range(NR)]
print (x, np.sign(l) == np.sign(sum(hx)))
if __name__ == '__main__':
examples = []
examples.append(((1, 2 ), 1))
examples.append(((1, 4 ), 1))
examples.append(((2.5,5.5), 1))
examples.append(((3.5,6.5), 1))
examples.append(((4, 5.4), 1))
examples.append(((2, 1 ),-1))
examples.append(((2, 4 ),-1))
examples.append(((3.5,3.5),-1))
examples.append(((5, 2 ),-1))
examples.append(((5, 5.5),-1))
m = AdaBoost(examples)
m.set_rule(lambda x: 2*(x[0] < 1.5)-1)
m.set_rule(lambda x: 2*(x[0] < 4.5)-1)
m.set_rule(lambda x: 2*(x[1] > 5)-1)
m.evaluate()