The whole NEAT algorithm is written into functional programming.
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@@ -65,55 +65,6 @@ def initialize_genomes(N: int, C: int, config: Dict) -> Tuple[NDArray, NDArray]:
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return pop_nodes, pop_cons
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def expand_single(nodes: NDArray, cons: NDArray, new_N: int, new_C: int) -> Tuple[NDArray, NDArray]:
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"""
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Expand a single genome to accommodate more nodes or connections.
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:param nodes: (N, 5)
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:param cons: (C, 4)
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:param new_N:
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:param new_C:
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:return: (new_N, 5), (new_C, 4)
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"""
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old_N, old_C = nodes.shape[0], cons.shape[0]
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new_nodes = np.full((new_N, 5), np.nan)
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new_nodes[:old_N, :] = nodes
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new_cons = np.full((new_C, 4), np.nan)
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new_cons[:old_C, :] = cons
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return new_nodes, new_cons
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def expand(pop_nodes: NDArray, pop_cons: NDArray, new_N: int, new_C: int) -> Tuple[NDArray, NDArray]:
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"""
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Expand the population to accommodate more nodes or connections.
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:param pop_nodes: (pop_size, N, 5)
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:param pop_cons: (pop_size, C, 4)
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:param new_N:
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:param new_C:
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:return: (pop_size, new_N, 5), (pop_size, new_C, 4)
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"""
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pop_size, old_N, old_C = pop_nodes.shape[0], pop_nodes.shape[1], pop_cons.shape[1]
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new_pop_nodes = np.full((pop_size, new_N, 5), np.nan)
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new_pop_nodes[:, :old_N, :] = pop_nodes
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new_pop_cons = np.full((pop_size, new_C, 4), np.nan)
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new_pop_cons[:, :old_C, :] = pop_cons
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return new_pop_nodes, new_pop_cons
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@jit
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def count(nodes: NDArray, cons: NDArray) -> Tuple[NDArray, NDArray]:
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"""
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Count how many nodes and connections are in the genome.
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"""
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node_cnt = jnp.sum(~jnp.isnan(nodes[:, 0]))
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cons_cnt = jnp.sum(~jnp.isnan(cons[:, 0]))
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return node_cnt, cons_cnt
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@jit
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def add_node(nodes: NDArray, cons: NDArray, new_key: int,
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bias: float = 0.0, response: float = 1.0, act: int = 0, agg: int = 0) -> Tuple[NDArray, NDArray]:
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