chore: import upstream snapshot with attribution

2026-07-13 13:30:25 +08:00
commit f19b2512d7
562 changed files with 38082 additions and 0 deletions
@@ -0,0 +1,77 @@
+import numpy as np
+from prml.linear.classifier import Classifier
+
+
+class LogisticRegression(Classifier):
+    """
+    Logistic regression model
+
+    y = sigmoid(X @ w)
+    t ~ Bernoulli(t|y)
+    """
+
+    @staticmethod
+    def _sigmoid(a):
+        return np.tanh(a * 0.5) * 0.5 + 0.5
+
+    def fit(self, X:np.ndarray, t:np.ndarray, max_iter:int=100):
+        """
+        maximum likelihood estimation of logistic regression model
+
+        Parameters
+        ----------
+        X : (N, D) np.ndarray
+            training data independent variable
+        t : (N,) np.ndarray
+            training data dependent variable
+            binary 0 or 1
+        max_iter : int, optional
+            maximum number of paramter update iteration (the default is 100)
+        """
+        w = np.zeros(np.size(X, 1))
+        for _ in range(max_iter):
+            w_prev = np.copy(w)
+            y = self._sigmoid(X @ w)
+            grad = X.T @ (y - t)
+            hessian = (X.T * y * (1 - y)) @ X
+            try:
+                w -= np.linalg.solve(hessian, grad)
+            except np.linalg.LinAlgError:
+                break
+            if np.allclose(w, w_prev):
+                break
+        self.w = w
+
+    def proba(self, X:np.ndarray):
+        """
+        compute probability of input belonging class 1
+
+        Parameters
+        ----------
+        X : (N, D) np.ndarray
+            training data independent variable
+
+        Returns
+        -------
+        (N,) np.ndarray
+            probability of positive
+        """
+        return self._sigmoid(X @ self.w)
+
+    def classify(self, X:np.ndarray, threshold:float=0.5):
+        """
+        classify input data
+
+        Parameters
+        ----------
+        X : (N, D) np.ndarray
+            independent variable to be classified
+        threshold : float, optional
+            threshold of binary classification (default is 0.5)
+
+        Returns
+        -------
+        (N,) np.ndarray
+            binary class for each input
+        """
+        return (self.proba(X) > threshold).astype(np.int)