update week 11

Author: mhjensen

doc/pub/week11/html/week11-bs.html

@@ -2,67 +2,51 @@
 
 class BinaryBinaryRBM:
     def __init__(self, n_visible, n_hidden, learning_rate=0.1):
-        """
-        Initialize the RBM with given parameters.
-        """
+        # Initialize the RBM with given parameters.
         self.n_visible = n_visible
         self.n_hidden = n_hidden
         self.learning_rate = learning_rate
-
         # Initialize weights and biases
         self.weights = np.random.normal(0, 0.01, (n_visible, n_hidden))  # Weights
         self.visible_bias = np.zeros(n_visible)  # Bias for visible units
         self.hidden_bias = np.zeros(n_hidden)  # Bias for hidden units
 
     def sigmoid(self, x):
-        """
-        Sigmoid activation function.
-        """
+        # Sigmoid activation function.
         return 1 / (1 + np.exp(-x))
 
     def sample_hidden(self, v):
-        """
-        Sample hidden units given visible units.
-        """
+        # Sample hidden units given visible units.
         activation = np.dot(v, self.weights) + self.hidden_bias
         prob_h = self.sigmoid(activation)
         return np.random.binomial(n=1, p=prob_h), prob_h
 
     def sample_visible(self, h):
-        """
-        Sample visible units given hidden units.
-        """
+        # Sample visible units given hidden units.
         activation = np.dot(h, self.weights.T) + self.visible_bias
         prob_v = self.sigmoid(activation)
         return np.random.binomial(n=1, p=prob_v), prob_v
 
     def contrastive_divergence(self, v0, k=1):
-        """
-        Contrastive Divergence (CD-k) algorithm for training the RBM.
-        """
+        # Contrastive Divergence (CD-k) algorithm for training the RBM, Gibbs sampling
         # Positive phase
         h0, prob_h0 = self.sample_hidden(v0)
         pos_associations = np.outer(v0, prob_h0)
-
         # Gibbs Sampling (k steps)
         v_k = v0
         for _ in range(k):
             h_k, _ = self.sample_hidden(v_k)
             v_k, _ = self.sample_visible(h_k)
-
         # Negative phase
         h_k, prob_h_k = self.sample_hidden(v_k)
         neg_associations = np.outer(v_k, prob_h_k)
-
         # Update weights and biases
         self.weights += self.learning_rate * (pos_associations - neg_associations)
         self.visible_bias += self.learning_rate * (v0 - v_k)
         self.hidden_bias += self.learning_rate * (prob_h0 - prob_h_k)
 
     def train(self, data, epochs=1000, batch_size=10):
-        """
-        Train the RBM using mini-batch gradient descent.
-        """
+        # Train the RBM using mini-batch gradient descent.
         n_samples = data.shape[0]
         for epoch in range(epochs):
             np.random.shuffle(data)
@@ -75,9 +59,7 @@ def train(self, data, epochs=1000, batch_size=10):
                 print(f"Epoch {epoch + 1}/{epochs} - Reconstruction Error: {error:.4f}")
 
     def reconstruction_error(self, data):
-        """
-        Compute reconstruction error for the dataset.
-        """
+        # Compute reconstruction error for the dataset.
         error = 0
         for v in data:
             _, prob_h = self.sample_hidden(v)
@@ -86,9 +68,7 @@ def reconstruction_error(self, data):
         return error / len(data)
 
     def reconstruct(self, v):
-        """
-        Reconstruct a visible vector after one pass through hidden units.
-        """
+        # Reconstruct a visible vector after one pass through hidden units.
         _, prob_h = self.sample_hidden(v)
         _, prob_v = self.sample_visible(prob_h)
         return prob_v