Create model.py

Author: KOSASIH

src/neuromorphic_analytics/model.py

@@ -0,0 +1,70 @@
+# src/neuromorphic_analytics/model.py
+
+import numpy as np
+import logging
+
+# Set up logging for the Spiking Neural Network Model
+logger = logging.getLogger(__name__)
+
+class SpikingNeuralNetworkModel:
+    def __init__(self, num_neurons, threshold=1.0, decay=0.9):
+        """
+        Initialize the Spiking Neural Network Model.
+
+        Parameters:
+        - num_neurons (int): Number of neurons in the network.
+        - threshold (float): The threshold for neuron firing.
+        - decay (float): The decay factor for the neuron's membrane potential.
+        """
+        self.num_neurons = num_neurons
+        self.threshold = threshold
+        self.decay = decay
+        self.membrane_potential = np.zeros(num_neurons)  # Initialize membrane potential
+        logger.info("Spiking Neural Network Model initialized with %d neurons.", num_neurons)
+
+    def predict(self, input_data):
+        """
+        Make a prediction based on the input data.
+
+        Parameters:
+        - input_data (list or np.ndarray): The input data for prediction.
+
+        Returns:
+        - list: The output spikes from the network.
+        """
+        logger.info("Making prediction with input data: %s", input_data)
+        input_data = np.array(input_data)
+        self.membrane_potential += input_data  # Update membrane potential with input
+
+        # Check for spikes
+        spikes = self.membrane_potential >= self.threshold
+        self.membrane_potential[spikes] = 0  # Reset potential for neurons that spiked
+
+        # Apply decay to the membrane potential
+        self.membrane_potential *= self.decay
+
+        logger.debug("Membrane potential after prediction: %s", self.membrane_potential)
+        return spikes.astype(int).tolist()  # Return spikes as a list of 0s and 1s
+
+    def evaluate(self, test_data, true_labels):
+        """
+        Evaluate the model's performance on test data.
+
+        Parameters:
+        - test_data (list of lists or np.ndarray): The data to test the model on.
+        - true_labels (list): The true labels for the test data.
+
+        Returns:
+        - float: The accuracy of the model on the test data.
+        """
+        correct_predictions = 0
+        total_predictions = len(test_data)
+
+        for data, true_label in zip(test_data, true_labels):
+            prediction = self.predict(data)
+            if prediction == true_label:
+                correct_predictions += 1
+
+        accuracy = correct_predictions / total_predictions if total_predictions > 0 else 0
+        logger.info("Model evaluation completed. Accuracy: %.2f", accuracy)
+        return accuracy