ee1a2a8271
1. Add origin_node and origin_conn. 2. Change the behavior of crossover and mutation. Now, TensorNEAT will use all fix_attrs(include historical marker if it has one) as identifier for gene in crossover and distance calculation. 3. Other slightly change. 4. Add two related examples: xor_origin and hopper_origin 5. Add related test file.
56 lines
1.4 KiB
Python
56 lines
1.4 KiB
Python
from tensorneat.pipeline import Pipeline
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from tensorneat import algorithm, genome, problem
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from tensorneat.genome import OriginNode, OriginConn
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from tensorneat.common import ACT
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"""
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Solving XOR-3d problem with OriginGene
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See https://github.com/EMI-Group/tensorneat/issues/11
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"""
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algorithm = algorithm.NEAT(
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pop_size=10000,
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species_size=20,
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survival_threshold=0.01,
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genome=genome.DefaultGenome(
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node_gene=OriginNode(),
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conn_gene=OriginConn(),
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num_inputs=3,
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num_outputs=1,
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max_nodes=7,
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output_transform=ACT.sigmoid,
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),
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)
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problem = problem.XOR3d()
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pipeline = Pipeline(
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algorithm,
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problem,
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generation_limit=200,
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fitness_target=-1e-6,
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seed=42,
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)
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state = pipeline.setup()
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# run until terminate
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state, best = pipeline.auto_run(state)
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# show result
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pipeline.show(state, best)
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# visualize the best individual
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network = algorithm.genome.network_dict(state, *best)
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print(algorithm.genome.repr(state, *best))
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# algorithm.genome.visualize(network, save_path="./imgs/xor_network.svg")
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# transform the best individual to latex formula
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from tensorneat.common.sympy_tools import to_latex_code, to_python_code
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sympy_res = algorithm.genome.sympy_func(
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state, network, sympy_output_transform=ACT.obtain_sympy(ACT.sigmoid)
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)
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latex_code = to_latex_code(*sympy_res)
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print(latex_code)
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# transform the best individual to python code
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python_code = to_python_code(*sympy_res)
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print(python_code)
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