Issue 12322

DIRECTORY.md

@@ -832,6 +832,7 @@
   * [Input Data](neural_network/input_data.py)
   * [Simple Neural Network](neural_network/simple_neural_network.py)
   * [Two Hidden Layers Neural Network](neural_network/two_hidden_layers_neural_network.py)
+  * [Radial Basis Function Neural Network](neural_network/radial_basis_function_neural_network.py)
 
 ## Other
   * [Activity Selection](other/activity_selection.py)

neural_network/radial_basis_function_neural_network.py

@@ -0,0 +1,144 @@
+import numpy as np
+
+class RadialBasisFunctionNeuralNetwork:
+    """
+    A simple implementation of a Radial Basis Function Neural Network (RBFNN).
+
+    Attributes:
+        num_centers (int): Number of centers for the radial basis functions.
+        spread (float): Spread of the radial basis functions.
+        centers (np.ndarray): Centers of the radial basis functions.
+        weights (np.ndarray): Weights for the output layer.
+    """
+
+    def __init__(self, num_centers: int, spread: float):
+        """
+        Initialize the RBFNN with the given number of centers and spread.
+
+        Args:
+            num_centers (int): Number of centers for the radial basis functions.
+            spread (float): Spread of the radial basis functions.
+
+        Examples:
+            >>> rbf_nn = RadialBasisFunctionNeuralNetwork(num_centers=3, spread=1.0)
+            >>> rbf_nn.num_centers
+            3
+        """
+        self.num_centers = num_centers
+        self.spread = spread
+        self.centers: np.ndarray = None  # To be initialized during training
+        self.weights: np.ndarray = None  # To be initialized during training
+
+    def _gaussian_rbf(self, input_vector: np.ndarray, center: np.ndarray) -> float:
+        """
+        Calculate the Gaussian radial basis function output for a given input vector and center.
+
+        Args:
+            input_vector (np.ndarray): The input vector for which to calculate the RBF output.
+            center (np.ndarray): The center of the radial basis function.
+
+        Returns:
+            float: The output of the radial basis function evaluated at the input vector.
+
+        Examples:
+            >>> rbf_nn = RadialBasisFunctionNeuralNetwork(num_centers=2, spread=0.5)
+            >>> center = np.array([1, 1])
+            >>> rbf_nn._gaussian_rbf(np.array([0, 0]), center)
+            0.1353352832366127
+        """
+        return np.exp(-(np.linalg.norm(input_vector - center) ** 2) / (2 * self.spread ** 2))
+
+    def _compute_rbf_outputs(self, input_data: np.ndarray) -> np.ndarray:
+        """
+        Compute the outputs of the radial basis functions for the input data.
+
+        Args:
+            input_data (np.ndarray): Input data matrix (num_samples x num_features).
+
+        Returns:
+            np.ndarray: A matrix of shape (num_samples x num_centers) containing the RBF outputs.
+
+        Examples:
+            >>> rbf_nn = RadialBasisFunctionNeuralNetwork(num_centers=2, spread=1.0)
+            >>> rbf_nn.centers = np.array([[0, 0], [1, 1]])
+            >>> rbf_nn._compute_rbf_outputs(np.array([[0, 0], [1, 1]]))
+            array([[1.        , 0.60653066],
+                   [0.60653066, 1.        ]])
+        """
+        rbf_outputs = np.zeros((input_data.shape[0], self.num_centers))
+        for i, center in enumerate(self.centers):
+            for j in range(input_data.shape[0]):
+                rbf_outputs[j, i] = self._gaussian_rbf(input_data[j], center)
+        return rbf_outputs
+
+    def fit(self, input_data: np.ndarray, target_values: np.ndarray):
+        """
+        Train the RBFNN using the provided input data and target values.
+
+        Args:
+            input_data (np.ndarray): Input data matrix (num_samples x num_features).
+            target_values (np.ndarray): Target values (num_samples x output_dim).
+
+        Raises:
+            ValueError: If the number of samples in input_data and target_values do not match.
+
+        Examples:
+            >>> rbf_nn = RadialBasisFunctionNeuralNetwork(num_centers=2, spread=1.0)
+            >>> X = np.array([[0, 0], [1, 0], [0, 1], [1, 1]])
+            >>> y = np.array([[0], [1], [1], [0]])
+            >>> rbf_nn.fit(X, y)
+            >>> rbf_nn.weights is not None
+            True
+        """
+        if input_data.shape[0] != target_values.shape[0]:
+            raise ValueError("Number of samples in input_data and target_values must match.")
+
+        # Initialize centers using random samples from input_data
+        rng = np.random.default_rng()  # Using new random number generator
+        random_indices = rng.choice(input_data.shape[0], self.num_centers, replace=False)
+        self.centers = input_data[random_indices]
+
+        # Compute the RBF outputs for the training data
+        rbf_outputs = self._compute_rbf_outputs(input_data)
+
+        # Calculate weights using the pseudo-inverse
+        self.weights = np.linalg.pinv(rbf_outputs).dot(target_values)
+
+    def predict(self, input_data: np.ndarray) -> np.ndarray:
+        """
+        Predict the output for the given input data using the trained RBFNN.
+
+        Args:
+            input_data (np.ndarray): Input data matrix (num_samples x num_features).
+
+        Returns:
+            np.ndarray: Predicted values (num_samples x output_dim).
+
+        Examples:
+            >>> rbf_nn = RadialBasisFunctionNeuralNetwork(num_centers=2, spread=1.0)
+            >>> rbf_nn.centers = np.array([[0, 0], [1, 1]])
+            >>> rbf_nn.weights = np.array([[0.5], [0.5]])
+            >>> rbf_nn.predict(np.array([[0, 0], [1, 1]]))
+            array([[0.5],
+                   [0.5]])
+        """
+        rbf_outputs = self._compute_rbf_outputs(input_data)
+        return rbf_outputs.dot(self.weights)
+
+
+# Example Usage
+if __name__ == "__main__":
+    import doctest
+    doctest.testmod()
+
+    # Sample dataset for XOR problem
+    X = np.array([[0, 0], [1, 0], [0, 1], [1, 1]])  # 2D input
+    y = np.array([[0], [1], [1], [0]])  # Target output for XOR
+
+    # Create and train the RBFNN
+    rbf_nn = RadialBasisFunctionNeuralNetwork(num_centers=2, spread=0.5)
+    rbf_nn.fit(X, y)
+
+    # Predict using the trained model
+    predictions = rbf_nn.predict(X)
+    print("Predictions:\n", predictions)