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 +1,62 @@
+import numpy as np
+
+
+class SigmoidalFeature(object):
+    """
+    Sigmoidal features
+
+    1 / (1 + exp((m - x) @ c)
+    """
+
+    def __init__(self, mean, coef=1):
+        """
+        construct sigmoidal features
+
+        Parameters
+        ----------
+        mean : (n_features, ndim) or (n_features,) ndarray
+            center of sigmoid function
+        coef : (ndim,) ndarray or int or float
+            coefficient to be multplied with the distance
+        """
+        if mean.ndim == 1:
+            mean = mean[:, None]
+        else:
+            assert mean.ndim == 2
+        if isinstance(coef, int) or isinstance(coef, float):
+            if np.size(mean, 1) == 1:
+                coef = np.array([coef])
+            else:
+                raise ValueError("mismatch of dimension")
+        else:
+            assert coef.ndim == 1
+            assert np.size(mean, 1) == len(coef)
+        self.mean = mean
+        self.coef = coef
+
+    def _sigmoid(self, x, mean):
+        return np.tanh((x - mean) @ self.coef * 0.5) * 0.5 + 0.5
+
+    def transform(self, x):
+        """
+        transform input array with sigmoidal features
+
+        Parameters
+        ----------
+        x : (sample_size, ndim) or (sample_size,) ndarray
+            input array
+
+        Returns
+        -------
+        output : (sample_size, n_features) ndarray
+            sigmoidal features
+        """
+        if x.ndim == 1:
+            x = x[:, None]
+        else:
+            assert x.ndim == 2
+        assert np.size(x, 1) == np.size(self.mean, 1)
+        basis = [np.ones(len(x))]
+        for m in self.mean:
+            basis.append(self._sigmoid(x, m))
+        return np.asarray(basis).transpose()