def mds_decomp(self, noise=False):
matrix = self.get_dm(noise)
dbc = double_centre(matrix)
return eigen(dbc)
def mds_decomp(self, noise=False):
matrix = self.get_dm(noise)
dbc = double_centre(matrix)
return eigen(dbc)
def spectral_decomp(self,
prune=None,
local_scale=None,
noise=False,
verbosity=0,
logic='or',
**kwargs):
""" Use prune to remove links between distant points:
prune is None: no pruning
prune={int > 0}: prunes links beyond `prune` nearest neighbours
prune='estimate': searches for the smallest value that retains a fully
connected graph
"""
matrix = self.get_dm(noise)
kp, mask, est_scale = binsearch_mask(matrix,
logic=logic) # prune anyway,
# get local scale estimate
ks = kp # ks and kp are the scaling and pruning parameters
est_ks = ks
# ADJUST MASK
if prune is None: # change mask to all
kp = len(matrix) - 1
mask = np.ones(matrix.shape, dtype=bool)
elif isinstance(prune, int) and prune > 0:
kp = prune
mask = kmask(matrix, prune, logic=logic)
else:
if not prune == 'estimate':
raise ValueError(
"'prune' should be None, a positive integer value, or 'estimate', not {}"
.format(prune))
# ADJUST SCALE
if local_scale is not None:
if local_scale == 'median':
ks = 'median'
dist = np.median(matrix, axis=1)
scale = np.outer(dist, dist)
elif isinstance(local_scale, int):
ks = local_scale
scale = kscale(matrix, local_scale)
else:
scale = est_scale
else:
scale = est_scale
# ZeroDivisionError safety check
if not (scale > 1e-5).all():
if verbosity > 0:
print('Rescaling to avoid zero-div error')
scale = est_scale
ks = est_ks
assert (scale > 1e-5).all()
aff = affinity(matrix, mask, scale)
laplacian = laplace(aff, **kwargs)
if verbosity > 0:
print('Pruning parameter: {0}'.format(kp))
print('Scaling parameter: {0}'.format(ks))
print('Mask, scale, affinity matrix and laplacian:')
print(mask)
print(scale)
print(aff)
print(laplace)
return eigen(laplacian) # vectors are in columns
def spectral_decomp(
self,
prune=None,
local_scale=None,
noise=False,
verbosity=0,
logic='or',
**kwargs):
""" Use prune to remove links between distant points:
prune is None: no pruning
prune={int > 0}: prunes links beyond `prune` nearest neighbours
prune='estimate': searches for the smallest value that retains a fully
connected graph
"""
matrix = self.get_dm(noise)
kp, mask, est_scale = binsearch_mask(matrix, logic=logic) # prune anyway,
# get local scale estimate
ks = kp # ks and kp are the scaling and pruning parameters
est_ks = ks
# ADJUST MASK
if prune is None: # change mask to all
kp = len(matrix) - 1
mask = np.ones(matrix.shape, dtype=bool)
elif isinstance(prune, int) and prune > 0:
kp = prune
mask = kmask(matrix, prune, logic=logic)
else:
if not prune=='estimate':
raise ValueError("'prune' should be None, a positive integer value, or 'estimate', not {}".format(prune))
# ADJUST SCALE
if local_scale is not None:
if local_scale == 'median':
ks = 'median'
dist = np.median(matrix, axis=1)
scale = np.outer(dist, dist)
elif isinstance(local_scale, int):
ks = local_scale
scale = kscale(matrix, local_scale)
else:
scale = est_scale
else:
scale = est_scale
# ZeroDivisionError safety check
if not (scale > 1e-5).all():
if verbosity > 0:
print('Rescaling to avoid zero-div error')
scale = est_scale
ks = est_ks
assert (scale > 1e-5).all()
aff = affinity(matrix, mask, scale)
laplacian = laplace(aff, **kwargs)
if verbosity > 0:
print('Pruning parameter: {0}'.format(kp))
print('Scaling parameter: {0}'.format(ks))
print('Mask, scale, affinity matrix and laplacian:')
print(mask)
print(scale)
print(aff)
print(laplace)
return eigen(laplacian) # vectors are in columns
