boost.py 文件源码

def init_estimator(self):
        indices = [i for i in np.random.choice(X.shape[0], self.n_samples, p=self.weights)]
        X_tree = np.array([X[i, :] for i in indices])
        y_tree = np.array([y[i] for i in indices])

        print "%s / %s" % (self.count, self.n_estimators)

        while True:
            t1 = time.time()
            tree = Tree(X_tree, y_tree)
            t2 = time.time()

            print "tree generation time: %s" % (t2 - t1)

            predictions = tree.predict(self.X)
            accuracy = accuracy_score(self.y, predictions)
            print "accuracy: %s" % accuracy
            if accuracy != 0.50:
                self.estimators.append(tree)
                break

        return tree, predictions