59 lines
1.6 KiB
Python
59 lines
1.6 KiB
Python
import jax, jax.numpy as jnp
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from .. import BaseGene
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class BaseNodeGene(BaseGene):
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"Base class for node genes."
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fixed_attrs = ["index"]
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def __init__(self):
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super().__init__()
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def forward(self, state, attrs, inputs, is_output_node=False):
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raise NotImplementedError
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def input_transform(self, state, attrs, inputs):
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"""
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make transformation in the input node.
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default: do nothing
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"""
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return inputs
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def update_by_batch(self, state, attrs, batch_inputs, is_output_node=False):
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# default: do not update attrs, but to calculate batch_res
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return (
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jax.vmap(self.forward, in_axes=(None, None, 0, None))(
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state, attrs, batch_inputs, is_output_node
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),
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attrs,
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)
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def update_input_transform(self, state, attrs, batch_inputs):
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"""
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update the attrs for transformation in the input node.
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default: do nothing
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"""
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return (
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jax.vmap(self.input_transform, in_axes=(None, None, 0))(
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state, attrs, batch_inputs
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),
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attrs,
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)
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def repr(self, state, node, precision=2, idx_width=3, func_width=8):
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idx = node[0]
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idx = int(idx)
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return "{}(idx={:<{idx_width}})".format(
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self.__class__.__name__, idx, idx_width=idx_width
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)
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def to_dict(self, state, node):
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idx = node[0]
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return {
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"idx": int(idx),
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}
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def sympy_func(self, state, node_dict, inputs, is_output_node=False, precision=None):
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raise NotImplementedError
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