final OzaBoost implementation

adaboost4.py

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+from collections import defaultdict
+from math import log
+from numpy.random import poisson
+
+
+class AdaBooster(object):
+
+    def __init__(self, Learner, M=10):
+        self.M = M
+        self.N = 0
+        self.learners = [Learner() for i in range(self.M)]
+        self.wrongWeight = [0 for i in range(self.M)]
+        self.correctWeight = [0 for i in range(self.M)]
+        self.epsilon = [0 for i in range(self.M)]
+
+    def update(self, features, label):
+        self.N += 1.0
+        lam = 1.0
+        for i, learner in enumerate(self.learners):
+            k = poisson(lam)
+            for _ in range(k):
+                learner.update(features, label)
+
+            if learner.predict(features) == label:
+                self.correctWeight[i] += lam
+                lam *= self.N / (2 * self.correctWeight[i])
+            else:
+                self.wrongWeight[i] += lam
+                lam *= self.N / (2 * self.wrongWeight[i])
+
+    def predict(self, features):
+        # If you haven't been updated, just guess
+        if not self.N:
+            return 1
+
+        label_weights = defaultdict(int)
+
+        for i, learner in enumerate(self.learners):
+
+            def get_classifier_weight(i):
+                epsilon = float(self.wrongWeight[i]) / \
+                    (self.wrongWeight[i] + self.correctWeight[i])
+                if epsilon > 0.5:
+                    return 0.0
+                elif epsilon == 0.0:
+                    epsilon = 0.0001
+
+                beta = epsilon / (1.0 - epsilon)
+                return log(1.0 / beta)
+
+            weight = get_classifier_weight(i)
+            if weight > 0.0:
+                label = learner.predict(features)
+                label_weights[label] += weight
+
+        return max(label_weights.iterkeys(), key=(lambda key: label_weights[key]))