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@tristanwietsma
Created April 30, 2013 01:13
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AdaBoost Python implementation of the AdaBoost (Adaptive Boosting) classification algorithm.
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()
@qxh5696
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qxh5696 commented Dec 2, 2017

I'm totally new to adaboosting and still haven't found a way to make much sense of it. I'm not following on what the purpose of the function "set_rule" does and attributes RULES and ALPHA, as well as what the examples themselves are representing. Are they just coordinates on a graph and the values at those locations are either 1 or -1?

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