LSTM.py

# FILE: lstm_optimizer.py

import numpy as np
import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import LSTM, Dense

class LSTMOptimizer:
    def __init__(self, input_shape):
        self.model = self.build_model(input_shape)
    
    def build_model(self, input_shape):
        model = Sequential()
        model.add(LSTM(50, return_sequences=True, input_shape=input_shape))
        model.add(LSTM(50))
        model.add(Dense(4))  # Output layer for x, y, z, rotation
        model.compile(optimizer='adam', loss='mse')
        return model
    
    def train(self, X_train, y_train, epochs=10, batch_size=32):
        self.model.fit(X_train, y_train, epochs=epochs, batch_size=batch_size)
    
    def predict(self, X):
        return self.model.predict(X)

# Example usage:
# optimizer = LSTMOptimizer((10, 4))  # Assuming 10 timesteps and 4 features (x, y, z, rotation)
# optimizer.train(X_train, y_train)
# optimized_signal = optimizer.predict(X_test)