def generate_and_set_class_state(cls, config: CnnCtrnnCfg,
input_space: Space, output_space: Space):
cnn_output_space = Cnn.get_output_shape(config=config.cnn_conf,
input_space=input_space)
ContinuousTimeRNN.generate_and_set_class_state(config.ctrnn_conf,
cnn_output_space,
output_space)
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_wrong_size(self, ctrnn_config, box2d, brain_param_identity):
ContinuousTimeRNN.generate_and_set_class_state(config=ctrnn_config, input_space=box2d,
output_space=box2d, )
ind = self.param_to_genom(brain_param_identity)
ContinuousTimeRNN(input_space=box2d, output_space=box2d,
individual=np.append(ind[:-1], [1]), config=ctrnn_config)
with pytest.raises(AssertionError):
ContinuousTimeRNN(input_space=box2d, output_space=box2d,
individual=ind[:-1], config=ctrnn_config)
def test_clipping_legacy(self, ctrnn_config, brain_param_identity, box2d):
bp = brain_param_identity
ContinuousTimeRNN.generate_and_set_class_state(config=ctrnn_config, input_space=box2d,
output_space=Box(-1, 1, shape=[2]), )
brain = ContinuousTimeRNN(input_space=box2d, output_space=box2d, individual=self.param_to_genom(bp),
config=ctrnn_config)
ob = np.array([1, 1])
res = brain.step(ob * 1000)
# due to tanh the maximum output is 1.0
assert np.allclose(res, np.ones(2))
# with legacy-clipping everything is clipped to the lowest max-value, which is 10 in this genome
assert np.allclose(brain.y, np.ones(2) * 10)
def test_clipping_per_neuron(self, ctrnn_config, brain_param_identity, box2d):
ctrnn_config = evolve(ctrnn_config, optimize_state_boundaries="per_neuron")
bp = evolve(brain_param_identity, clip_max=np.array([2, 3]), clip_min=np.array([-4, -5]))
ContinuousTimeRNN.generate_and_set_class_state(config=ctrnn_config, input_space=box2d,
output_space=box2d, )
brain = ContinuousTimeRNN(input_space=box2d, output_space=box2d,
individual=self.param_to_genom(bp), config=ctrnn_config)
ob = np.array([1, 1])
brain.step(ob * 100000)
assert np.allclose(brain.y, bp.clip_max)
brain.step(ob * -100000)
assert np.allclose(brain.y, bp.clip_min)
def test_individual(self, ctrnn_config, brain_param_simple, box2d):
ctrnn_config = evolve(ctrnn_config, set_principle_diagonal_elements_of_W_negative=False)
ContinuousTimeRNN.generate_and_set_class_state(config=ctrnn_config, input_space=box2d,
output_space=Box(-1, 1, shape=[2]), )
bp = brain_param_simple
brain = ContinuousTimeRNN(input_space=box2d, output_space=box2d, individual=self.param_to_genom(bp),
config=ctrnn_config)
assert np.array_equal(bp.V, brain.V.toarray())
assert np.array_equal(bp.W, brain.W.toarray())
assert np.array_equal(bp.T, brain.T.toarray())
assert np.array_equal(bp.y0, brain.y0)
assert np.array_equal(bp.y0, brain.y)
def test_step(self, ctrnn_config, brain_param_identity, box2d):
ctrnn_config = evolve(ctrnn_config, set_principle_diagonal_elements_of_W_negative=False)
bp = brain_param_identity
ContinuousTimeRNN.generate_and_set_class_state(config=ctrnn_config, input_space=box2d,
output_space=Box(-1, 1, shape=[2]), )
brain = ContinuousTimeRNN(input_space=box2d, output_space=box2d, individual=self.param_to_genom(bp),
config=ctrnn_config)
brain.delta_t = 1.0
ob = np.array([1, 1])
assert np.allclose(brain.y, np.zeros([2, 2]))
res = brain.step(ob)
# due to identity matrices after one iteration the internal state is now exactly the observersion
assert np.allclose(brain.y, ob)
# due to identity matrices after one iteration the output is just the input, but with tanh.
assert np.allclose(res, np.tanh(ob))
brain.step(ob)
assert np.allclose(brain.y, np.tanh(ob) + ob + ob)
def test_generating_masks_log(self, ctrnn_config, box2d):
ContinuousTimeRNN.generate_and_set_class_state(
config=evolve(ctrnn_config, v_mask="logarithmic", w_mask="logarithmic", t_mask="logarithmic",
v_mask_param=2.5, w_mask_param=5, t_mask_param=1.1),
input_space=box2d,
output_space=box2d, )
def test_generating_masks_dense(self, ctrnn_config, box2d):
ContinuousTimeRNN.generate_and_set_class_state(
config=evolve(ctrnn_config, v_mask="dense", t_mask="dense", w_mask="dense"), input_space=box2d,
output_space=box2d, )
def test_generating_masks_random(self, ctrnn_config, box2d):
ContinuousTimeRNN.generate_and_set_class_state(
config=evolve(ctrnn_config, v_mask="random", t_mask="random", w_mask="random", v_mask_param=0.5,
w_mask_param=0.5, t_mask_param=0.5), input_space=box2d,
output_space=box2d, )
