def test_uslstm_iter_fit():
raise SkipTest()
X = np.random.standard_normal((10, 5, 2)).astype(theano.config.floatX)
rnn = UnsupervisedLstm(2, 10, 3, loss=lambda x: T.log(x), max_iter=10)
for i, info in enumerate(rnn.iter_fit(X)):
if i >= 10:
break
def test_uslstm_iter_fit():
raise SkipTest()
X = np.random.standard_normal((10, 5, 2))
rnn = UnsupervisedLstm(2, 10, 3, loss=lambda x: T.log(x), max_iter=10)
for i, info in enumerate(rnn.iter_fit(X)):
if i >= 10:
break
def test_uslstm_transform():
raise SkipTest()
X = np.random.standard_normal((10, 5, 2)).astype(theano.config.floatX)
rnn = UnsupervisedLstm(2, 10, 3, loss=lambda x: T.log(x), max_iter=10)
rnn.transform(X)
def test_uslstm_transform():
raise SkipTest()
X = np.random.standard_normal((10, 5, 2))
rnn = UnsupervisedLstm(2, 10, 3, loss=lambda x: T.log(x), max_iter=10)
rnn.transform(X)
def test_uslstm_fit():
raise SkipTest()
X = np.random.standard_normal((10, 5, 2)).astype(theano.config.floatX)
rnn = UnsupervisedLstm(2, 10, 3, loss=lambda x: T.log(x), max_iter=10)
rnn.fit(X)
