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h36.py
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h36.py
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#! /usr/bin/env python3
"""36.
Implement the AdaBoost algorithm, with decision stamp as base learner, by
yourself. Use the dataset in Table 3 for training, and x = (1, M ) for test.
"""
import numpy as np
class AdaBoost:
"""AdaBoost."""
def __init__(self, n_estimators=50, learning_rate=1.0):
"""__init__.
:param n_estimators:
:param learning_rate:
"""
self.clf_num = n_estimators
self.learning_rate = learning_rate
def _G(self, features, labels, weights):
"""_G.
:param features:
:param labels:
:param weights:
"""
m = len(features)
error = float('inf')
best_v = 0.0
features_min = min(features)
features_max = max(features)
n_step = (features_max - features_min +
self.learning_rate) // self.learning_rate
direct, compare_array = None, None
for i in range(1, int(n_step)):
v = features_min + self.learning_rate * i
if v not in features:
compare_array_positive = np.array(
[1 if features[k] > v else -1 for k in range(m)])
weight_error_positive = sum([
weights[k] for k in range(m)
if compare_array_positive[k] != labels[k]
])
compare_array_nagetive = np.array(
[-1 if features[k] > v else 1 for k in range(m)])
weight_error_nagetive = sum([
weights[k] for k in range(m)
if compare_array_nagetive[k] != labels[k]
])
if weight_error_positive < weight_error_nagetive:
weight_error = weight_error_positive
_compare_array = compare_array_positive
direct = 'positive'
else:
weight_error = weight_error_nagetive
_compare_array = compare_array_nagetive
direct = 'nagetive'
if weight_error < error:
error = weight_error
compare_array = _compare_array
best_v = v
return best_v, direct, error, compare_array
def _alpha(self, error):
"""_alpha.
:param error:
"""
return 0.5 * np.log((1 - error) / error)
def _Z(self, weights, a, clf):
"""_Z.
:param weights:
:param a:
:param clf:
"""
return sum([
weights[i] * np.exp(-1 * a * self.Y[i] * clf[i])
for i in range(self.M)
])
def _w(self, a, clf, Z):
"""_w.
:param a:
:param clf:
:param Z:
"""
for i in range(self.M):
self.weights[i] = self.weights[i] * np.exp(
-1 * a * self.Y[i] * clf[i]) / Z
def G(self, x, v, direct):
"""G.
:param x:
:param v:
:param direct:
"""
if direct == 'positive':
return 1 if x > v else -1
else:
return -1 if x > v else 1
def fit(self, X, y):
"""fit.
:param X:
:param y:
"""
self.X = X
self.Y = y
self.M, self.N = X.shape
self.clf_sets = []
self.weights = [1.0 / self.M] * self.M
self.alpha = []
for _ in range(self.clf_num):
best_clf_error, best_v, clf_result = 100000, None, None
for j in range(self.N):
features = self.X[:, j]
v, direct, error, compare_array = self._G(
features, self.Y, self.weights)
if error < best_clf_error:
best_clf_error = error
best_v = v
final_direct = direct
clf_result = compare_array
axis = j
if best_clf_error == 0:
break
a = self._alpha(best_clf_error)
self.alpha.append(a)
self.clf_sets.append((axis, best_v, final_direct))
Z = self._Z(self.weights, a, clf_result)
self._w(a, clf_result, Z)
def predict(self, feature):
"""predict.
:param feature:
"""
result = 0.0
for i in range(len(self.clf_sets)):
axis, clf_v, direct = self.clf_sets[i]
f_input = feature[axis]
result += self.alpha[i] * self.G(f_input, clf_v, direct)
return 1 if result > 0 else -1
S, M, L = 1, 2, 3
X = np.array([[1, S], [1, M], [1, M], [1, S], [1, S], [2, S], [2, M], [2, M], [2, L], [2, L], [3, L]])
y = np.array([-1, -1, 1, 1, -1, -1, -1, 1, 1, 1, 1])
clf = AdaBoost(n_estimators=3, learning_rate=0.5)
clf.fit(X, y)
print(clf.predict([1, M]))