integrated_model.py

import numpy as np
import tensorflow as tf
from tensorflow.keras.datasets import mnist
from tensorflow.keras.models import Model

# Import custom modules
from kolmogorov_arnold_network import kolmogorov_arnold_network
from wae_mnist import WAE, build_encoder, build_decoder

def load_preprocess_data():
    # Load MNIST data
    (x_train, _), (x_test, _) = mnist.load_data()
    x_train = np.expand_dims(x_train, -1).astype('float32') / 255.0
    x_test = np.expand_dims(x_test, -1).astype('float32') / 255.0
    return x_train, x_test

class IntegratedModel:
    def __init__(self, encoder, decoder, V, W):
        self.encoder = encoder
        self.decoder = decoder
        self.V = V
        self.W = W

    def preprocess_with_ka_network(self, x):
        # Flatten the images for processing with K-A network
        flat_x = np.reshape(x, (x.shape[0], -1))
        processed_x = np.array([kolmogorov_arnold_network(xi, self.V, self.W) for xi in flat_x])
        return processed_x.reshape(x.shape)

    def predict(self, x):
        preprocessed_x = self.preprocess_with_ka_network(x)
        z_mean, _ = self.encoder.predict(preprocessed_x)
        reconstructed = self.decoder.predict(z_mean)
        return reconstructed

# Setup
encoder = build_encoder()
decoder = build_decoder()
V = np.random.rand(784, 784)  # Assuming flattening the 28x28 images
W = np.random.rand(784)

# Load and preprocess data
x_train, x_test = load_preprocess_data()

# Create integrated model instance
integrated_model = IntegratedModel(encoder, decoder, V, W)

# Use the integrated model to process and reconstruct images
reconstructed_images = integrated_model.predict(x_test[:10])

# Simple visualization of results (considering implementing a visualization function)
print("Reconstructed shapes:", reconstructed_images.shape)