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 +1,38 @@
+import numpy as np
+from prml import nn
+
+
+class Autoencoder(nn.Network):
+
+    def __init__(self, *args):
+        self.n_unit = len(args)
+        super().__init__()
+        for i in range(self.n_unit - 1):
+            self.parameter[f"w_encode{i}"] = nn.Parameter(np.random.randn(args[i], args[i + 1]))
+            self.parameter[f"b_encode{i}"] = nn.Parameter(np.zeros(args[i + 1]))
+            self.parameter[f"w_decode{i}"] = nn.Parameter(np.random.randn(args[i + 1], args[i]))
+            self.parameter[f"b_decode{i}"] = nn.Parameter(np.zeros(args[i]))
+
+    def transform(self, x):
+        h = x
+        for i in range(self.n_unit - 1):
+            h = nn.tanh(h @ self.parameter[f"w_encode{i}"] + self.parameter[f"b_encode{i}"])
+        return h.value
+
+    def forward(self, x):
+        h = x
+        for i in range(self.n_unit - 1):
+            h = nn.tanh(h @ self.parameter[f"w_encode{i}"] + self.parameter[f"b_encode{i}"])
+        for i in range(self.n_unit - 2, 0, -1):
+            h = nn.tanh(h @ self.parameter[f"w_decode{i}"] + self.parameter[f"b_decode{i}"])
+        x_ = h @ self.parameter["w_decode0"] + self.parameter["b_decode0"]
+        self.px = nn.random.Gaussian(x_, 1., data=x)
+
+    def fit(self, x, n_iter=100, learning_rate=1e-3):
+        optimizer = nn.optimizer.Adam(self.parameter, learning_rate)
+        for _ in range(n_iter):
+            self.clear()
+            self.forward(x)
+            log_likelihood = self.log_pdf()
+            log_likelihood.backward()
+            optimizer.update()