def test_get_individual_size(self, ctrnn_config):
in_space = Box(-1, 1, shape=[3])
out_space = Box(-1, 1, shape=[3])
ContinuousTimeRNN.generate_and_set_class_state(config=ctrnn_config, input_space=in_space,
output_space=out_space, )
ind_size = ContinuousTimeRNN.get_individual_size(config=ctrnn_config, input_space=in_space,
output_space=out_space, )
assert ind_size == 22
def test_get_individual_size(self, ctrnn_config):
in_space = Box(-1, 1, shape=[3])
out_space = Box(-1, 1, shape=[3])
ContinuousTimeRNN.set_masks_globally(
config=ctrnn_config,
input_space=in_space,
output_space=out_space,
)
ind_size = ContinuousTimeRNN.get_individual_size(
config=ctrnn_config,
input_space=in_space,
output_space=out_space,
)
assert ind_size == 22
def __init__(self, input_space: Space, output_space: Space,
individual: np.ndarray, config: CnnCtrnnCfg):
super().__init__(input_space, output_space, individual, config)
assert len(individual) == self.get_individual_size(
config, input_space, output_space)
paramter_usage_dict = self.get_free_parameter_usage(
config, input_space, output_space)
cnn_output_space = paramter_usage_dict[self._usage_key_cnn_output]
cnn_size = Cnn.get_individual_size(config.cnn_conf, input_space,
cnn_output_space)
ctrnn_size = ContinuousTimeRNN.get_individual_size(
config.ctrnn_conf, cnn_output_space, output_space)
ind_cnn = individual[0:cnn_size]
ind_ctrnn = individual[cnn_size:cnn_size + ctrnn_size]
self.cnn = Cnn(input_space=input_space,
output_space=cnn_output_space,
config=config.cnn_conf,
individual=ind_cnn)
self.ctrnn = ContinuousTimeRNN(input_space=cnn_output_space,
output_space=output_space,
config=config.ctrnn_conf,
individual=ind_ctrnn)