This example shows how adaboost works

adaboost_example.py

#!usr/bin/env python3
# -*- coding: utf-8 -*-

import numpy as np

def get_alpha(error):
    return 0.5 * np.log((1 - error) / error)
def update_weights(y, weights, g, alpha):
    return weights * np.exp(-alpha * g * y) / sum(weights * np.exp(-alpha * g * y))

# 1 feature, 10 samples
X = np.arange(10)
# label
y = np.array([1, 1, 1, -1, -1, -1, 1, 1, 1, -1])

# set equal weights for each sample
weights1 = np.empty(X.shape[0], dtype=np.float64)
weights1[:] = 1. / X.shape[0]

# when X < 2.5, we will get the smallest error
g1 = np.where(X < 2.5, 1, -1)
e1 = np.sum((y != g1) * weights)
alpha1 = get_alpha(e1)

# adjust weights
weights2 = update_weights(y, weights1, g1, alpha1)

g2 = np.where(X < 8.5, 1, -1)
e2 = np.sum((y != g2) * weights2)
alpha2 = get_alpha(e2)

# 
weights3 = update_weights(y, weights2, g2, alpha2)
g3 = np.where(X > 5.5, 1, -1)
e3 = np.sum((y != g3) * weights3)
alpha3 = get_alpha(e3)

# final model
f = alpha1 * g1 + alpha2 * g2 + alpha3 * g3
np.sign(f) == y