def test_LearnBeginningMode(self):
""" testing learn_beginning mode.
"""
print("\nDyBMTestCase.testLearnFirstMode")
length = 300
period = 60
std = 0
dim = 1
data = NoisySin(length, period, std, dim).to_list()
print("\nlearn_beginning = True (Default mode)")
for d in xrange(10, 20):
model = BatchLinearDyBM(dim, delay=d, learn_beginning=True)
model.fit(data)
result = model.get_predictions(data)
rmse = RMSE(result[d:], data[d:])
print("Training RMSE", rmse)
self.assertLessEqual(rmse, 0.1)
print("\nlearn_beginning = False")
for d in xrange(10, 20):
model = BatchLinearDyBM(dim, delay=d, learn_beginning=False)
model.fit(data)
result = model.get_predictions(data)
rmse = RMSE(result[d:], data[d:])
print("Training RMSE", rmse)
self.assertLessEqual(rmse, 0.1)
def test_GenerativeBatchLinearDyBM(self):
""" testing minimal consistency in learning a sequence
"""
print("\nDyBMTestCase.testGenerativeBatchLinearDyBM")
for delay in [1, 3]:
for SGD in [AdaGrad]:
for GENERATOR in [True]:
model = BatchLinearDyBM(self.in_dim, self.in_dim, delay,
SGD=SGD())
model.set_learning_rate(self.rate)
i = tests.simple.test_real_model(model, self.max_repeat,
GENERATOR)
self.assertLess(i, self.max_repeat)
def test_LearnMultiSequences(self):
""" testing learn_beginning mode.
"""
print("\nDyBMTestCase.testLearnMultiSequences")
seqs = list()
for i in xrange(10):
length = 300
period = 60
std = 0.1 * i
dim = 1
data = NoisySin(length, period, std, dim).to_list()
seqs.append(data)
for d in xrange(10, 20):
model = BatchLinearDyBM(dim, delay=d, learn_beginning=True)
model.fit_multi_seqs(seqs)
result = model.get_predictions(seqs[0])
rmse = RMSE(result[d:], seqs[0][d:])
print("Training RMSE", rmse)
self.assertLessEqual(rmse, 0.2)
def test_LearnSequenceBatch(self):
""" testing learning with Sequence
"""
print("\nBatchDyBMTestCase.testLearnSequenceBatch")
batch = 3
in_mean = 1.0
out_mean = 2.0
d = 0.01
delay = 1
rates = [0.5, 0.8]
L1 = 0.1
L2 = 0.1
print("\n * testing wo_delay")
random = amath.random.RandomState(0)
in_seq = random.uniform(low=in_mean - d,
high=in_mean + d,
size=(batch, self.in_dim))
model = BatchLinearDyBM(self.in_dim,
self.in_dim,
delay=delay,
decay_rates=rates,
L1=L1,
L2=L2,
insert_to_etrace="wo_delay")
model.set_learning_rate(0.1)
model.learn_batch(in_seq)
model.fit(in_seq)
model.init_state()
prediction = model.predict(in_seq)
print("RMSE:", self._compute_rmse(prediction, in_seq))
random = amath.random.RandomState(0)
in_seq = random.uniform(low=in_mean - d,
high=in_mean + d,
size=(batch, self.in_dim))
random = amath.random.RandomState(0)
out_seq = random.uniform(low=out_mean - d,
high=out_mean + d,
size=(batch, self.out_dim))
model = BatchLinearDyBM(self.in_dim,
self.out_dim,
delay=delay,
decay_rates=rates,
L1=L1,
L2=L2,
insert_to_etrace="wo_delay")
model.set_learning_rate(0.1)
model.learn_batch(in_seq, out_seq)
model.fit(in_seq, out_seq)
model.init_state()
prediction = model.predict(in_seq)
print("RMSE:", self._compute_rmse(prediction, out_seq))
print("\n * testing w_delay")
random = amath.random.RandomState(0)
in_seq = random.uniform(low=in_mean - d,
high=in_mean + d,
size=(batch, self.in_dim))
model = BatchLinearDyBM(self.in_dim,
self.in_dim,
delay=delay,
decay_rates=rates,
L1=L1,
L2=L2,
insert_to_etrace="w_delay")
model.set_learning_rate(0.1)
model.learn_batch(in_seq)
model.fit(in_seq)
model.init_state()
prediction = model.predict(in_seq)
print("RMSE:", self._compute_rmse(prediction, in_seq))
random = amath.random.RandomState(0)
in_seq = random.uniform(low=in_mean - d,
high=in_mean + d,
size=(batch, self.in_dim))
random = amath.random.RandomState(0)
out_seq = random.uniform(low=out_mean - d,
high=out_mean + d,
size=(batch, self.out_dim))
model = BatchLinearDyBM(self.in_dim,
self.out_dim,
delay=delay,
decay_rates=rates,
L1=L1,
L2=L2,
insert_to_etrace="w_delay")
model.set_learning_rate(0.1)
model.learn_batch(in_seq, out_seq)
model.fit(in_seq, out_seq)
model.init_state()
prediction = model.predict(in_seq)
print("RMSE:", self._compute_rmse(prediction, out_seq))
print("\n * testing w_delay using Lasso")
random = amath.random.RandomState(0)
in_seq = random.uniform(low=in_mean - d,
high=in_mean + d,
size=(batch, self.in_dim))
model = BatchLinearDyBM(self.in_dim,
self.in_dim,
delay=delay,
decay_rates=rates,
L1=L1,
L2=L2,
insert_to_etrace="w_delay",
batch_method="Lasso")
model.set_learning_rate(0.1)
model.learn_batch(in_seq)
model.fit(in_seq)
model.init_state()
prediction = model.predict(in_seq)
print("RMSE:", self._compute_rmse(prediction, in_seq))
random = amath.random.RandomState(0)
in_seq = random.uniform(low=in_mean - d,
high=in_mean + d,
size=(batch, self.in_dim))
random = amath.random.RandomState(0)
out_seq = random.uniform(low=out_mean - d,
high=out_mean + d,
size=(batch, self.out_dim))
model = BatchLinearDyBM(self.in_dim,
self.out_dim,
delay=delay,
decay_rates=rates,
L1=L1,
L2=L2,
insert_to_etrace="w_delay",
batch_method="Lasso")
model.set_learning_rate(0.1)
model.learn_batch(in_seq, out_seq)
model.fit(in_seq, out_seq)
model.init_state()
prediction = model.predict(in_seq)
print("RMSE:", self._compute_rmse(prediction, out_seq))