def test_RBF_activate_high_distance_scale_by_similarity():
"""RBF may return nan if sum of similarities == 0 and it scales by similarity."""
from learning import SOM
random.seed(0)
numpy.random.seed(0)
clustering_model = SOM(1, 2, neighborhood=0)
clustering_model.logging = False
model = model = rbf.RBF(
1,
2,
1,
variance=1.0,
scale_by_similarity=True,
clustering_model=clustering_model)
model._pre_train(numpy.array([[0], [1]]), numpy.array([[0], [1]]))
assert helpers.approx_equal(model._clustering_model.activate([0]), [0, 1])
assert not numpy.isnan(model.activate(numpy.array([1000.]))).any()
assert helpers.approx_equal(model._similarity_tensor, [0.5, 0.5])
assert not numpy.isnan(model.activate(numpy.array([[0.], [1000.]]))).any()
assert helpers.approx_equal(model._similarity_tensor,
[[0.73105858, 0.26894142], [0.5, 0.5]])
def test_rbf_convergence():
# Run until convergence
# assert that network can converge
model = rbf.RBF(2, 4, 2, scale_by_similarity=True)
dataset = datasets.get_xor()
model.train(*dataset, retries=5, error_break=0.002)
assert validation.get_error(model, *dataset) <= 0.02
def test_rbf():
# Run for a couple of iterations
# assert that new error is less than original
model = rbf.RBF(2, 4, 2, scale_by_similarity=True)
dataset = datasets.get_xor()
error = validation.get_error(model, *dataset)
model.train(*dataset, iterations=10)
assert validation.get_error(model, *dataset) < error
def test_rbf_obj_and_obj_jac_match():
"""obj and obj_jac functions should return the same obj value."""
attrs = random.randint(1, 10)
outs = random.randint(1, 10)
model = rbf.RBF(attrs, random.randint(1, 10), outs)
dataset = datasets.get_random_regression(10, attrs, outs)
# Don't use exactly the same parameters, to ensure obj functions are actually
# using the given parameters
parameters = random.uniform(-1.0, 1.0) * model._weight_matrix.ravel()
assert helpers.approx_equal(
model._get_obj(parameters, dataset[0], dataset[1]),
model._get_obj_jac(parameters, dataset[0], dataset[1])[0])
def test_RBF_reset():
attrs = random.randint(1, 10)
neurons = random.randint(1, 10)
outs = random.randint(1, 10)
model = rbf.RBF(attrs, neurons, outs)
model_2 = rbf.RBF(attrs, neurons, outs)
# Resetting different with the same seed should give the same model
prev_seed = random.randint(0, 2**32-1)
try:
random.seed(0)
numpy.random.seed(0)
model.reset()
random.seed(0)
numpy.random.seed(0)
model_2.reset()
assert model.serialize() == model_2.serialize()
finally:
random.seed(prev_seed)
numpy.random.seed(prev_seed)
def test_rbf_jacobian():
_check_jacobian(
lambda a, n, o: rbf.RBF(a, n, o, scale_by_similarity=False))