43 lines
1005 B
Python
43 lines
1005 B
Python
import jax, jax.numpy as jnp
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from tensorneat.common import ACT
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from algorithm.neat import *
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import numpy as np
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def main():
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node_path = "../examples/brax/nan_node.npy"
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conn_path = "../examples/brax/nan_conn.npy"
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nodes = np.load(node_path)
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conns = np.load(conn_path)
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nodes, conns = jax.device_put([nodes, conns])
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genome = DefaultGenome(
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num_inputs=8,
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num_outputs=2,
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max_nodes=20,
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max_conns=20,
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node_gene=DefaultNodeGene(
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activation_options=(ACT.tanh,),
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activation_default=ACT.tanh,
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),
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)
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transformed = genome.transform(nodes, conns)
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seq, nodes, conns = transformed
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print(seq)
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exit(0)
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# print(*transformed, sep='\n')
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key = jax.random.key(0)
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dummy_input = jnp.zeros((8,))
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output = genome.forward(dummy_input, transformed)
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print(output)
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if __name__ == "__main__":
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a = jnp.array([1, 3, 5, 6, 8])
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b = jnp.array([1, 2, 3])
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print(jnp.isin(a, b))
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# main()
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