Create advanced_ai_pilot.py

advanced_ai_pilot/advanced_ai_pilot.py

@@ -0,0 +1,71 @@
+import numpy as np
+from tensorflow.keras.models import Model
+from tensorflow.keras.layers import Input, Dense, LSTM
+
+class AdvancedAIPilot:
+    def __init__(self, num_inputs, num_outputs):
+        self.num_inputs = num_inputs
+        self.num_outputs = num_outputs
+
+    def create_neural_network(self):
+        """
+Create a neural network using Keras.
+
+        Returns:
+            Model: The created neural network.
+        """
+        inputs = Input(shape=(self.num_inputs,))
+        x = Dense(64, activation="relu")(inputs)
+        x = LSTM(64)(x)
+        outputs = Dense(self.num_outputs, activation="softmax")(x)
+        model = Model(inputs=inputs, outputs=outputs)
+        return model
+
+    def train_neural_network(self, model, X_train, y_train):
+        """
+        Train a neural network using Keras.
+
+        Args:
+            model (Model): The neural network to train.
+            X_train (numpy array): The training input data.
+            y_train (numpy array): The training output data.
+        """
+        model.compile(loss="categorical_crossentropy", optimizer="adam", metrics=["accuracy"])
+        model.fit(X_train, y_train, epochs=100, batch_size=128)
+
+    def use_neural_network(self, model, input_data):
+        """
+        Use a trained neural network to make predictions.
+
+        Args:
+            model (Model): The trained neural network.
+            input_data (numpy array): The input data to make predictions on.
+
+        Returns:
+            numpy array: The predicted output data.
+        """
+        predictions = model.predict(input_data)
+        return predictions
+
+    def optimize_neural_network(self, model, X_train, y_train):
+        """
+        Optimize a neural network using a genetic algorithm.
+
+        Args:
+            model (Model): The neural network to optimize.
+            X_train (numpy array): The training input data.
+            y_train (numpy array): The training output data.
+
+        Returns:
+            Model: The optimized neural network.
+        """
+        from scipy.optimize import differential_evolution
+        def objective_function(params):
+            model.set_weights(params)
+            loss = model.evaluate(X_train, y_train)
+            return loss
+        bounds = [(0, 1) for _ in range(model.count_params())]
+        result = differential_evolution(objective_function, bounds)
+        optimized_weights = result.x
+        model.set_weights(optimized_weights)
+        return model