tony/tensorneat-mend
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remove "return state" for functions which will be executed in vmap;
recover randkey as args in mutation methods
This commit is contained in:
wls2002
2024-05-26 15:46:04 +08:00
parent 79d53ea7af
commit cf69b916af
38 changed files with 932 additions and 582 deletions
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1
tensorneat/algorithm/hyperneat/substrate/base.py
View File

@@ -1,5 +1,4 @@
class BaseSubstrate:
def make_nodes(self, query_res):
raise NotImplementedError

1
tensorneat/algorithm/hyperneat/substrate/default.py
View File

@@ -3,7 +3,6 @@ from . import BaseSubstrate
class DefaultSubstrate(BaseSubstrate):
def __init__(self, num_inputs, num_outputs, coors, nodes, conns):
self.inputs = num_inputs
self.outputs = num_outputs

50
tensorneat/algorithm/hyperneat/substrate/full.py
View File

@@ -3,20 +3,16 @@ from .default import DefaultSubstrate
class FullSubstrate(DefaultSubstrate):
def __init__(self,
input_coors=((-1, -1), (0, -1), (1, -1)),
hidden_coors=((-1, 0), (0, 0), (1, 0)),
output_coors=((0, 1),),
):
query_coors, nodes, conns = analysis_substrate(input_coors, output_coors, hidden_coors)
super().__init__(
len(input_coors),
len(output_coors),
query_coors,
nodes,
conns
def __init__(
self,
input_coors=((-1, -1), (0, -1), (1, -1)),
hidden_coors=((-1, 0), (0, 0), (1, 0)),
output_coors=((0, 1),),
):
query_coors, nodes, conns = analysis_substrate(
input_coors, output_coors, hidden_coors
)
super().__init__(len(input_coors), len(output_coors), query_coors, nodes, conns)
def analysis_substrate(input_coors, output_coors, hidden_coors):
@@ -38,22 +34,30 @@ def analysis_substrate(input_coors, output_coors, hidden_coors):
correspond_keys = np.zeros((total_conns, 2))
# connect input to hidden
aux_coors, aux_keys = cartesian_product(input_idx, hidden_idx, input_coors, hidden_coors)
query_coors[0: si * sh, :] = aux_coors
correspond_keys[0: si * sh, :] = aux_keys
aux_coors, aux_keys = cartesian_product(
input_idx, hidden_idx, input_coors, hidden_coors
)
query_coors[0 : si * sh, :] = aux_coors
correspond_keys[0 : si * sh, :] = aux_keys
# connect hidden to hidden
aux_coors, aux_keys = cartesian_product(hidden_idx, hidden_idx, hidden_coors, hidden_coors)
query_coors[si * sh: si * sh + sh * sh, :] = aux_coors
correspond_keys[si * sh: si * sh + sh * sh, :] = aux_keys
aux_coors, aux_keys = cartesian_product(
hidden_idx, hidden_idx, hidden_coors, hidden_coors
)
query_coors[si * sh : si * sh + sh * sh, :] = aux_coors
correspond_keys[si * sh : si * sh + sh * sh, :] = aux_keys
# connect hidden to output
aux_coors, aux_keys = cartesian_product(hidden_idx, output_idx, hidden_coors, output_coors)
query_coors[si * sh + sh * sh:, :] = aux_coors
correspond_keys[si * sh + sh * sh:, :] = aux_keys
aux_coors, aux_keys = cartesian_product(
hidden_idx, output_idx, hidden_coors, output_coors
)
query_coors[si * sh + sh * sh :, :] = aux_coors
correspond_keys[si * sh + sh * sh :, :] = aux_keys
nodes = np.concatenate((input_idx, output_idx, hidden_idx))[..., np.newaxis]
conns = np.zeros((correspond_keys.shape[0], 4), dtype=np.float32) # input_idx, output_idx, enabled, weight
conns = np.zeros(
(correspond_keys.shape[0], 4), dtype=np.float32
) # input_idx, output_idx, enabled, weight
conns[:, 0:2] = correspond_keys
conns[:, 2] = 1 # enabled is True