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 +1,122 @@
+import numpy as np
+
+
+class RelevanceVectorClassifier(object):
+
+    def __init__(self, kernel, alpha=1.):
+        """
+        construct relevance vector classifier
+
+        Parameters
+        ----------
+        kernel : Kernel
+            kernel function to compute components of feature vectors
+        alpha : float
+            initial precision of prior weight distribution
+        """
+        self.kernel = kernel
+        self.alpha = alpha
+
+    def _sigmoid(self, a):
+        return np.tanh(a * 0.5) * 0.5 + 0.5
+
+    def _map_estimate(self, X, t, w, n_iter=10):
+        for _ in range(n_iter):
+            y = self._sigmoid(X @ w)
+            g = X.T @ (y - t) + self.alpha * w
+            H = (X.T * y * (1 - y)) @ X + np.diag(self.alpha)
+            w -= np.linalg.solve(H, g)
+        return w, np.linalg.inv(H)
+
+    def fit(self, X, t, iter_max=100):
+        """
+        maximize evidence with respect ot hyperparameter
+
+        Parameters
+        ----------
+        X : (sample_size, n_features) ndarray
+            input
+        t : (sample_size,) ndarray
+            corresponding target
+        iter_max : int
+            maximum number of iterations
+
+        Attributes
+        ----------
+        X : (N, n_features) ndarray
+            relevance vector
+        t : (N,) ndarray
+            corresponding target
+        alpha : (N,) ndarray
+            hyperparameter for each weight or training sample
+        cov : (N, N) ndarray
+            covariance matrix of weight
+        mean : (N,) ndarray
+            mean of each weight
+        """
+        if X.ndim == 1:
+            X = X[:, None]
+        assert X.ndim == 2
+        assert t.ndim == 1
+        Phi = self.kernel(X, X)
+        N = len(t)
+        self.alpha = np.zeros(N) + self.alpha
+        mean = np.zeros(N)
+        for _ in range(iter_max):
+            param = np.copy(self.alpha)
+            mean, cov = self._map_estimate(Phi, t, mean, 10)
+            gamma = 1 - self.alpha * np.diag(cov)
+            self.alpha = gamma / np.square(mean)
+            np.clip(self.alpha, 0, 1e10, out=self.alpha)
+            if np.allclose(param, self.alpha):
+                break
+        mask = self.alpha < 1e8
+        self.X = X[mask]
+        self.t = t[mask]
+        self.alpha = self.alpha[mask]
+        Phi = self.kernel(self.X, self.X)
+        mean = mean[mask]
+        self.mean, self.covariance = self._map_estimate(Phi, self.t, mean, 100)
+
+    def predict(self, X):
+        """
+        predict class label
+
+        Parameters
+        ----------
+        X : (sample_size, n_features)
+            input
+
+        Returns
+        -------
+        label : (sample_size,) ndarray
+            predicted label
+        """
+        if X.ndim == 1:
+            X = X[:, None]
+        assert X.ndim == 2
+        phi = self.kernel(X, self.X)
+        label = (phi @ self.mean > 0).astype(np.int)
+        return label
+
+    def predict_proba(self, X):
+        """
+        probability of input belonging class one
+
+        Parameters
+        ----------
+        X : (sample_size, n_features) ndarray
+            input
+
+        Returns
+        -------
+        proba : (sample_size,) ndarray
+            probability of predictive distribution p(C1|x)
+        """
+        if X.ndim == 1:
+            X = X[:, None]
+        assert X.ndim == 2
+        phi = self.kernel(X, self.X)
+        mu_a = phi @ self.mean
+        var_a = np.sum(phi @ self.covariance * phi, axis=1)
+        return self._sigmoid(mu_a / np.sqrt(1 + np.pi * var_a / 8))