Create npae.py

Author: KOSASIH

src/neuromorphic_analytics/npae.py

@@ -0,0 +1,68 @@
+# src/neuromorphic_analytics/npae.py
+
+import numpy as np
+import logging
+from .model import SpikingNeuralNetworkModel
+from .data_pipeline import DataPipeline
+
+# Set up logging for the NPAE
+logger = logging.getLogger(__name__)
+
+class NeuromorphicPredictiveAnalyticsEngine:
+    def __init__(self, model_params, data_sources):
+        """
+        Initialize the Neuromorphic Predictive Analytics Engine.
+
+        Parameters:
+        - model_params (dict): Parameters for the spiking neural network model.
+        - data_sources (list): List of data sources to be processed (e.g., Market Analysis, IoT).
+        """
+        self.model = SpikingNeuralNetworkModel(**model_params)
+        self.data_pipeline = DataPipeline(data_sources)
+        logger.info("Neuromorphic Predictive Analytics Engine initialized.")
+
+    def process_data(self):
+        """
+        Process data from the defined sources and make predictions.
+        """
+        logger.info("Starting data processing...")
+        raw_data = self.data_pipeline.collect_data()
+        preprocessed_data = self.data_pipeline.preprocess_data(raw_data)
+
+        predictions = []
+        for data in preprocessed_data:
+            prediction = self.model.predict(data)
+            predictions.append(prediction)
+            logger.info(f"Processed data: {data}, Prediction: {prediction}")
+
+        return predictions
+
+    def evaluate_model(self, test_data, true_labels):
+        """
+        Evaluate the performance of the model on test data.
+
+        Parameters:
+        - test_data (list): 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.
+        """
+        accuracy = self.model.evaluate(test_data, true_labels)
+        logger.info(f"Model evaluation completed. Accuracy: {accuracy:.2f}")
+        return accuracy
+
+    def run(self):
+        """
+        Run the NPAE to continuously process data and make predictions.
+        """
+        logger.info("Running the Neuromorphic Predictive Analytics Engine...")
+        while True:
+            predictions = self.process_data()
+            # Here you can implement logic to handle predictions, e.g., storing them or triggering alerts
+            # For demonstration, we will just log the predictions
+            logger.info(f"Current predictions: {predictions}")
+
+            # Sleep or wait for the next data collection cycle
+            # In a real implementation, you might want to use a more sophisticated scheduling mechanism
+            break  # Remove this break for continuous operation in a real scenario