Artificial-Intelligence-Computer-Vision/step_function_model_executables

1import numpy as np2 3 4class LocalPredictionModel:5    """Simplified local version of the prediction model"""6 7    def __init__(self, model_path="bellande_step_model.h5", state_space=10):8        self.model_path = model_path9        self.state_space = state_space10        self.num_actions = 311 12        # Initialize mock model weights for demonstration13        self.model_weights = self._initialize_mock_weights()14 15    def _initialize_mock_weights(self):16        """Initialize random weights to simulate a trained model"""17        return {18            "layer1": np.random.randn(self.state_space, 64) * 0.1,19            "layer2": np.random.randn(64, 32) * 0.1,20            "output": np.random.randn(32, self.num_actions) * 0.1,21        }22 23    def predict(self, state):24        """25        Predict Q-values for given state26        Returns: array of Q-values for each action27        """28        # Ensure state is 2D: (batch_size, state_space)29        if state.ndim == 1:30            state = state.reshape(1, -1)31 32        # Simple feedforward prediction (mock DQN)33        x = state34        x = np.tanh(x @ self.model_weights["layer1"])  # Hidden layer 135        x = np.tanh(x @ self.model_weights["layer2"])  # Hidden layer 236        q_values = x @ self.model_weights["output"]  # Output layer37 38        return q_values39 40    def get_q_values(self, state):41        """Get the best action based on Q-values"""42        q_values = self.predict(state)43        return np.argmax(q_values[0])44 45    def reset(self):46        """Reset environment to initial state"""47        return np.random.randn(self.state_space)48 49    def step(self, action):50        """51        Take a step in the environment52        Returns: (reward, next_state, done)53        """54        # Mock environment step55        reward = np.random.randn()56        next_state = np.random.randn(self.state_space)57        done = False58        return reward, next_state, done59 60    def predict_action_state(self, state):61        """62        Predict next action and state63        Returns: (next_state, action)64        """65        # Uncomment to use model predictions instead of random66        # action = self.get_q_values(state)67        action = np.random.randint(0, self.num_actions)68        _, next_state, _ = self.step(action)69        return next_state, action70 71 72class DisplayActionsState(LocalPredictionModel):73    """Display actions and states for visualization"""74 75    def __init__(self, state_space=10, num_steps=10):76        super().__init__(state_space=state_space)77        self.num_steps = num_steps78        self.starting_state = self.reset()79        self.run_demonstration()80 81    def run_demonstration(self):82        """Run and print predictions for demonstration"""83        state = self.starting_state84 85        print("=" * 80)86        print("Running Prediction Demonstration")87        print("=" * 80)88 89        for i in range(self.num_steps):90            # Get predictions91            state_reshaped = state.reshape(-1, *state.shape)92            q_values = self.predict(state_reshaped)[0]93 94            # Get next state and action95            next_state, action = self.predict_action_state(state)96 97            # Display results98            print(f"\nStep {i + 1}:")99            print(f"  Q-values: {q_values}")100            print(f"  Action: {action}")101            print(f"  Next State: {next_state[:5]}...")  # Show first 5 values102 103            # Update state for next iteration104            state = next_state105 106        print("\n" + "=" * 80)107 108 109# Example usage110if __name__ == "__main__":111    # Create and run the display system112    display_system = DisplayActionsState(state_space=10, num_steps=10)113