Create neuromorphic_network.py

features/neuromorphic_fraud_detection/neuromorphic_network.py

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+# neuromorphic_network.py
+import nengo
+from nengo.dists import Uniform
+
+def neuromorphic_fraud_detection(input_data):
+    # Define the neuromorphic network
+    model = nengo.Network()
+    with model:
+        input_node = nengo.Node(input_data)
+        fraud_detector = nengo.Ensemble(n_neurons=100, dimensions=10, neuron_type=nengo.LIF())
+        nengo.Connection(input_node, fraud_detector)
+        output_node = nengo.Node(size_in=1)
+        nengo.Connection(fraud_detector, output_node, function=lambda x: 1 if x > 0.5 else 0)
+
+    # Run the neuromorphic network
+    with nengo.Simulator(model) as sim:
+        sim.run(1.0)
+
+    return sim.data[output_node]
+
+# fraud_detector.py
+import numpy as np
+from sklearn.ensemble import RandomForestClassifier
+
+def fraud_detector(input_data):
+    # Train a random forest classifier
+    clf = RandomForestClassifier(n_estimators=100)
+    clf.fit(input_data, np.zeros((input_data.shape[0],)))
+
+    # Use the trained classifier to detect fraud
+    predictions = clf.predict(input_data)
+    return predictions