wehub-resource-sync
78 lines
2.0 KiB
Python
Executable File
78 lines
2.0 KiB
Python
Executable File
import numpy as np
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from prml.linear.classifier import Classifier
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class LogisticRegression(Classifier):
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"""
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Logistic regression model
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y = sigmoid(X @ w)
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t ~ Bernoulli(t|y)
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"""
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@staticmethod
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def _sigmoid(a):
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return np.tanh(a * 0.5) * 0.5 + 0.5
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def fit(self, X:np.ndarray, t:np.ndarray, max_iter:int=100):
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"""
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maximum likelihood estimation of logistic regression model
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Parameters
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----------
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X : (N, D) np.ndarray
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training data independent variable
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t : (N,) np.ndarray
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training data dependent variable
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binary 0 or 1
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max_iter : int, optional
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maximum number of paramter update iteration (the default is 100)
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"""
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w = np.zeros(np.size(X, 1))
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for _ in range(max_iter):
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w_prev = np.copy(w)
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y = self._sigmoid(X @ w)
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grad = X.T @ (y - t)
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hessian = (X.T * y * (1 - y)) @ X
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try:
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w -= np.linalg.solve(hessian, grad)
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except np.linalg.LinAlgError:
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break
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if np.allclose(w, w_prev):
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break
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self.w = w
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def proba(self, X:np.ndarray):
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"""
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compute probability of input belonging class 1
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Parameters
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----------
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X : (N, D) np.ndarray
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training data independent variable
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Returns
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-------
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(N,) np.ndarray
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probability of positive
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"""
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return self._sigmoid(X @ self.w)
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def classify(self, X:np.ndarray, threshold:float=0.5):
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"""
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classify input data
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Parameters
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----------
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X : (N, D) np.ndarray
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independent variable to be classified
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threshold : float, optional
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threshold of binary classification (default is 0.5)
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Returns
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-------
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(N,) np.ndarray
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binary class for each input
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"""
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return (self.proba(X) > threshold).astype(np.int)
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