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 +1,71 @@
+import numpy as np
+from prml.rv.rv import RandomVariable
+
+
+class Uniform(RandomVariable):
+    """
+    Uniform distribution
+    p(x|a, b)
+    = 1 / ((b_0 - a_0) * (b_1 - a_1)) if a <= x <= b else 0
+    """
+
+    def __init__(self, low, high):
+        """
+        construct uniform distribution
+
+        Parameters
+        ----------
+        low : int, float, or np.ndarray
+            lower boundary
+        high : int, float, or np.ndarray
+            higher boundary
+        """
+        super().__init__()
+        low = np.asarray(low)
+        high = np.asarray(high)
+        assert low.shape == high.shape
+        assert (low <= high).all()
+        self.low = low
+        self.high = high
+        self.value = 1 / np.prod(high - low)
+
+    @property
+    def low(self):
+        return self.parameter["low"]
+
+    @low.setter
+    def low(self, low):
+        self.parameter["low"] = low
+
+    @property
+    def high(self):
+        return self.parameter["high"]
+
+    @high.setter
+    def high(self, high):
+        self.parameter["high"] = high
+
+    @property
+    def ndim(self):
+        return self.low.ndim
+
+    @property
+    def size(self):
+        return self.low.size
+
+    @property
+    def shape(self):
+        return self.low.shape
+
+    @property
+    def mean(self):
+        return 0.5 * (self.low + self.high)
+
+    def _pdf(self, X):
+        higher = np.logical_and.reduce(X >= self.low, 1)
+        lower = np.logical_and.reduce(X <= self.high, 1)
+        return self.value * np.logical_and(higher, lower)
+
+    def _draw(self, sample_size=1):
+        u01 = np.random.uniform(size=(sample_size,) + self.shape)
+        return u01 * (self.high - self.low) + self.low