def _catch_unhashable(x):
if hasattr(x, '__getitem__'):
res = list(x)
for i, value in enumerate(x):
if isinstance(value, np.ndarray):
res[i] = _hash_numpy_array(value)
else:
res[i] = value
return tuple(res)
elif isinstance(x, np.ndarray):
return _hash_numpy_array(x)
return x
def _compute_eigendecomposition(self, neig):
""" Conducts the eigenvalue decomposition and stores k eigenvalues, left and right eigenvectors """
from msmtools.analysis import rdl_decomposition
self._p_id = _hash_numpy_array(self.transition_matrix)
if self.reversible:
self._R, self._D, self._L = rdl_decomposition(
self.transition_matrix,
norm='reversible',
k=neig,
ncv=self.ncv)
# everything must be real-valued
self._R = self._R.real
self._D = self._D.real
self._L = self._L.real
else:
self._R, self._D, self._L = rdl_decomposition(
self.transition_matrix, k=neig, norm='standard', ncv=self.ncv)
# if the imaginary parts are zero, discard them.
if _np.all(self._R.imag == 0):
self._R = _np.real(self._R)
if _np.all(self._D.imag == 0):
self._D = _np.real(self._D)
if _np.all(self._L.imag == 0):
self._L = _np.real(self._L)
self._eigenvalues = _np.diag(self._D)
def _ensure_eigendecomposition(self, neig=None):
"""Ensures that eigendecomposition has been performed with at least neig eigenpairs
neig : int
number of eigenpairs needed. If not given the default value will
be used - see __init__()
"""
if neig is None:
neig = self.neig
# ensure that eigenvalue decomposition with k components is done.
try:
m = self._D.shape[0] # this will raise and exception if self._D doesn't exist yet.
if m < neig or self._p_id != _hash_numpy_array(self.P):
# not enough eigenpairs present
# or eigendecomposition computed for an outdated transition matrix - recompute.
self._compute_eigendecomposition(neig)
except AttributeError:
# no eigendecomposition yet - compute:
self._compute_eigendecomposition(neig)
def _hash_dtrajs(dtraj_list):
from pyemma.util.numeric import _hash_numpy_array
x = _hash_numpy_array(dtraj_list[0])
for d in dtraj_list[1:]:
x ^= _hash_numpy_array(d)
return x
def _hash_dtrajs(dtraj_list):
x = 0
from pyemma.util.numeric import _hash_numpy_array
for d in dtraj_list:
x ^= _hash_numpy_array(d)
return x