def testOneDimensional(self):
eps = 1e-2
period = 10
for Wave in [NoisySin, NoisySawtooth]:
for std in [0.0, 0.1, 1.0]:
print("NoisyWaveTestCase.testOneDimensional " + str(Wave) +
" with std: " + str(std))
length = period * 10
dim = 1
phase = None
reverse = False
if Wave == NoisySin:
angles = 2 * amath.pi * amath.arange(length) / period
target = amath.sin(angles)
elif Wave == NoisySawtooth:
r = 1. * amath.arange(length) / period
target = r % 1
else:
print(Wave)
wave = Wave(length, period, std, dim, phase, reverse)
sequence = list()
for pattern in wave:
sequence.append(pattern[0])
sequence = amath.array(sequence, dtype=float)
diff = target - sequence
rmse = amath.sqrt(amath.dot(diff, diff) / len(diff))
print("RMSE: %f" % rmse)
self.assertLessEqual(amath.abs(rmse - std), std * eps)
def _compute_rmse(self, predictions, targets):
rmse = 0.0
for i, pred in enumerate(predictions):
#print(type(amath.array(pred.to_list())), amath.array(pred.to_list()))
#print(type(amath.array(targets[i])), amath.array(targets[i]))
a = amath.array(pred.to_list(), dtype=float)
b = amath.array(targets[i])
rmse += amath.sqrt(amath.mean((a - b)**2))
return rmse
def _compute_rmse(self, prediction, target):
a = amath.array(prediction.to_list(), dtype=float)
b = amath.array(target)
rmse = amath.sqrt(amath.mean((a - b)**2))
return rmse
def test_complex_model(model, max_repeat=100):
dim = [layer.get_input_dimension() for layer in model.layers]
activations = model.activations
random = amath.random.RandomState(0)
mean = 1.0
batch = np.prod(dim)
d = 0.01
seqs = list()
for dimension, activation in zip(dim, activations):
if activation == "linear":
seq = random.uniform(low=mean - d,
high=mean + d,
size=(batch, dimension))
elif activation == "sigmoid":
seq = amath.array(
[[i % dimension == j % dimension for j in range(dimension)]
for i in range(batch)],
dtype=int)
seq = amath.array(
[[i % dimension == j % dimension for j in range(dimension)]
for i in range(batch)],
dtype=float)
seqs.append(seq)
in_seq = list()
i = 0
for i in xrange(batch):
pattern = [s[i] for s in seqs]
in_seq.append(pattern)
for i in xrange(max_repeat):
model._learn_sequence(in_seq)
predictions = model.get_predictions(in_seq)
predictions = np.concatenate(predictions).reshape(
(len(predictions), len(predictions[0])))
rmse = 0
start = 0
j = 0
for j, (dimension, sequence) in enumerate(zip(dim, seqs)):
sub_prediction = predictions[:, j]
sub_prediction = amath.concatenate(sub_prediction).reshape(
(len(sub_prediction), len(sub_prediction[0])))
start += dimension
rmse += amath.sqrt(amath.mean((sub_prediction - sequence)**2))
if i % 1000 == 0:
print("rmse at %d:\t%1.4f" % (i, rmse))
if rmse < d * sum(dim):
print("Successfully completed in %d iterations with rmse: %f" %
(i + 1, rmse))
break
LL = model.get_LL_sequence(in_seq)
print("LL: ", LL)
return i + 1
