def test_iterator(self):
chunksize = 1000
for itraj, chunk in self.tica_obj.iterator(chunk=chunksize):
assert types.is_int(itraj)
assert types.is_float_matrix(chunk)
self.assertLessEqual(chunk.shape[0], (chunksize + self.lag))
assert chunk.shape[1] == self.tica_obj.dimension()
def test_get_output(self):
O = self.inp.get_output()
assert types.is_list(O)
assert len(O) == 1
assert types.is_float_matrix(O[0])
assert O[0].shape[0] == 100
assert O[0].shape[1] == self.inp.dimension()
def test_get_output(self):
O = self.pca_obj.get_output()
assert types.is_list(O)
assert len(O) == 1
assert types.is_float_matrix(O[0])
assert O[0].shape[0] == self.T
assert O[0].shape[1] == self.pca_obj.dimension()
def test_iterator(self):
self.inp.chunksize = 100
assert self.inp.chunksize == 100
for itraj, chunk in self.inp:
assert types.is_int(itraj)
assert types.is_float_matrix(chunk)
assert chunk.shape[0] == self.inp.chunksize
assert chunk.shape[1] == self.inp.dimension()
def test(self):
# make it deterministic
with numpy_random_seed(0):
data = np.random.randn(100, 10)
tica_obj = api.tica(data=data, lag=10, dim=1)
Y = tica_obj._transform_array(data)
# right shape
assert types.is_float_matrix(Y)
assert Y.shape[0] == 100
assert Y.shape[1] == 1, Y.shape[1]
def test(self):
np.random.seed(0)
data = np.random.randn(100, 10)
tica_obj = api.tica(data=data, lag=10, dim=1)
tica_obj.parametrize()
Y = tica_obj._map_array(data)
# right shape
assert types.is_float_matrix(Y)
assert Y.shape[0] == 100
assert Y.shape[1] == 1
def test(self):
np.random.seed(0)
data = np.random.randn(100, 10)
tica_obj = api.tica(data=data, lag=10, dim=1)
tica_obj.parametrize()
Y = tica_obj._map_array(data)
# right shape
assert types.is_float_matrix(Y)
assert Y.shape[0] == 100
assert Y.shape[1] == 1
def test(self):
# FIXME: this ugly workaround is necessary...
np.random.seed(0)
data = np.random.randn(100, 10)
tica_obj = api.tica(data=data, lag=10, dim=1)
tica_obj.parametrize()
Y = tica_obj._transform_array(data)
# right shape
assert types.is_float_matrix(Y)
assert Y.shape[0] == 100
assert Y.shape[1] == 1
def set_model_params(self,
P=None,
pobs=None,
pi=None,
reversible=None,
dt_model='1 step',
neig=None):
"""
Parameters
----------
P : ndarray(m,m)
coarse-grained or hidden transition matrix
pobs : ndarray (m,n)
observation probability matrix from hidden to observable discrete
states
pi : ndarray(m), optional, default=None
stationary or distribution. Can be optionally given in case if
it was already computed, e.g. by the estimator.
reversible : bool, optional, default=None
whether P is reversible with respect to its stationary distribution.
If None (default), will be determined from P
dt_model : str, optional, default='1 step'
time step of the model
neig : int or None
The number of eigenvalues / eigenvectors to be kept. If set to
None, all eigenvalues will be used.
"""
_MSM.set_model_params(self,
P=P,
pi=pi,
reversible=reversible,
dt_model=dt_model,
neig=neig)
# set P and derived quantities if available
if pobs is not None:
# check and save copy of output probability
assert _types.is_float_matrix(
pobs), 'pobs is not a matrix of floating numbers'
assert _np.allclose(pobs.sum(axis=1),
1), 'pobs is not a stochastic matrix'
self._nstates_obs = pobs.shape[1]
self.update_model_params(pobs=pobs)
def test_iterator(self):
for itraj, chunk in self.pca_obj:
assert types.is_int(itraj)
assert types.is_float_matrix(chunk)
assert chunk.shape[1] == self.pca_obj.dimension()
def test_iterator(self):
for itraj, chunk in self.pca_obj:
assert types.is_int(itraj)
assert types.is_float_matrix(chunk)
assert chunk.shape[0] <= self.pca_obj.chunksize + self.lag
assert chunk.shape[1] == self.pca_obj.dimension()
