gistlib - ai to generate patterns in conway's game of life in matlab

To generate patterns in Conway's Game of Life using AI in Matlab, you can use a deep learning technique called Generative Adversarial Networks (GANs). Here are the steps to use GANs for pattern generation in Conway's Game of Life:

First, you need to create a dataset of patterns in Conway's Game of Life. You can do this by randomly generating patterns and tracking their evolution over multiple generations.


main.m
% Example code to generate a random initial pattern
initial_pattern = randi([0 1], [100, 100]);
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Next, you can use this dataset to train a neural network to generate new patterns. Here, we will use a GAN with a generator and discriminator network. The generator network takes a random noise vector as input and generates a pattern in the Game of Life. The discriminator network takes a pattern and predicts whether it is real or generated.


main.m
% Example code to define the generator network
generator = [
    imageInputLayer([1 1 100], 'Normalization', 'none')
    fullyConnectedLayer(25*25*5)
    reshapeLayer([25 25 5])
    transposedConv2dLayer([5 5], 32, 'Stride', 2, 'Cropping', 'same', 'WeightsInitializer', 'narrow-normal')
    reluLayer
    transposedConv2dLayer([5 5], 16, 'Stride', 2, 'Cropping', 'same', 'WeightsInitializer', 'narrow-normal')
    reluLayer
    transposedConv2dLayer([5 5], 8, 'Stride', 2, 'Cropping', 'same', 'WeightsInitializer', 'narrow-normal')
    reluLayer
    transposedConv2dLayer([5 5], 1, 'Stride', 2, 'Cropping', 'same', 'WeightsInitializer', 'narrow-normal')
    sigmoidLayer
    ];

% Example code to define the discriminator network
discriminator = [
    imageInputLayer([100 100 1], 'Normalization', 'none')
    convolution2dLayer([5 5], 8, 'Stride', 2, 'Padding', 'same', 'WeightsInitializer', 'narrow-normal')
    leakyReluLayer
    convolution2dLayer([5 5], 16, 'Stride', 2, 'Padding', 'same', 'WeightsInitializer', 'narrow-normal')
    leakyReluLayer
    convolution2dLayer([5 5], 32, 'Stride', 2, 'Padding', 'same', 'WeightsInitializer', 'narrow-normal')
    leakyReluLayer
    convolution2dLayer([5 5], 1, 'Stride', 2, 'Padding', 'same', 'WeightsInitializer', 'narrow-normal')
    sigmoidLayer
    ];

% Example code to train the GAN on the dataset
GAN = gan(generator, discriminator, 'DiscriminatorInputSize', [100 100 1]);
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Once the GAN is trained, you can use the generator network to generate new patterns in the Game of Life. You can initialize the generator with a random noise vector and run it for multiple generations to generate a pattern.


main.m
% Example code to generate a pattern using the GAN
noise = randn(1, 1, 100);
generated_pattern = generator.predict(noise);
for i = 1:50
    generated_pattern = gameOfLife(generated_pattern);
end
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Finally, you can visualize the generated pattern using Matlab's image visualization tools.


main.m
% Example code to visualize the generated pattern
imshow(generated_pattern)
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ai to generate patterns in conway's game of life in matlab

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