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AdaBoost Python implementation of the AdaBoost (Adaptive Boosting) classification algorithm.
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| from __future__ import division | |
| from numpy import * | |
| class AdaBoost: | |
| def __init__(self, training_set): | |
| self.training_set = training_set | |
| self.N = len(self.training_set) | |
| self.weights = ones(self.N)/self.N | |
| self.RULES = [] | |
| self.ALPHA = [] | |
| def set_rule(self, func, test=False): | |
| errors = array([t[1]!=func(t[0]) for t in self.training_set]) | |
| e = (errors*self.weights).sum() | |
| if test: return e | |
| alpha = 0.5 * log((1-e)/e) | |
| print 'e=%.2f a=%.2f'%(e, alpha) | |
| w = zeros(self.N) | |
| for i in range(self.N): | |
| if errors[i] == 1: w[i] = self.weights[i] * exp(alpha) | |
| else: w[i] = self.weights[i] * 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, sign(l) == 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() |
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