def __init__(self, data, mfmethod, nsub=20, show_progress=True, mapW=False, base_sel=2,
num_bases=3 , niterH=1, compute_h=True, compute_w=True, sstrategy='rand'):
NMF.__init__(self, data, num_bases=num_bases, compute_h=compute_h, show_progress=show_progress, compute_w=compute_w)
self._niterH = niterH
self._nsub = nsub
self.data = data
self._mfmethod = mfmethod
self._mapW = mapW
self._sstrategy = sstrategy
self._base_sel = base_sel
# assign the correct distance function
if self._sstrategy == 'cur':
self._subfunc = self.curselect
elif self._sstrategy == 'kmeans':
self._subfunc = self.kmeansselect
elif self._sstrategy == 'hull':
self._subfunc = self.hullselect
elif self._sstrategy == 'laesa':
self._subfunc = self.laesaselect
elif self._sstrategy == 'sivm':
self._subfunc = self.sivmselect
else:
self._subfunc = self.randselect
def __init__(self, data, mfmethod, nsub=20, show_progress=True, mapW=False, base_sel=2,
num_bases=3 , niterH=1, compute_h=True, compute_w=True, sstrategy='rand'):
NMF.__init__(self, data, num_bases=num_bases, compute_h=compute_h, show_progress=show_progress, compute_w=compute_w)
self._niterH = niterH
self._nsub = nsub
self.data = data
self._mfmethod = mfmethod
self._mapW = mapW
self._sstrategy = sstrategy
self._base_sel = base_sel
# assign the correct distance function
if self._sstrategy == 'cur':
self._subfunc = self.curselect
elif self._sstrategy == 'kmeans':
self._subfunc = self.kmeansselect
elif self._sstrategy == 'hull':
self._subfunc = self.hullselect
elif self._sstrategy == 'laesa':
self._subfunc = self.laesaselect
elif self._sstrategy == 'sivm':
self._subfunc = self.sivmselect
else:
self._subfunc = self.randselect
def __init__(self,
data,
num_bases=0,
niter=1,
show_progress=False,
compW=True,
center_mean=True):
NMF.__init__(self,
data,
num_bases=num_bases,
niter=niter,
show_progress=show_progress,
compW=compW)
# center the data around the mean first
self._center_mean = center_mean
if self._center_mean:
# copy the data before centering it -> arrays
# are passed by reference ...
self._data_orig = data
self._meanv = self._data_orig[:, :].mean(axis=1).reshape(
data.shape[0], -1)
self.data = self._data_orig - self._meanv
else:
self.data = data
def __init__(self,
data,
num_bases=4,
niter=100,
show_progress=False,
compW=True):
""" Inits Nmf class:
sampleNmf = Nmf(data, num_bases=4, niter=100, show_progress=True, compW=True)
Args:
data (required) : d x n data matrix [d - dimension, n -number of samples]
num_bases : number of basis vectors for W (default: 4)
niter : number of iterations (default: 100)
show_progress : (default: True)
compW : set to True if W and H should be optimized, set to False
if only H should be optimized. This is usefull if W is
computed somewhere or if new data should be mapped on a
given set of basis vectors W.
"""
# data can be either supplied by conventional numpy arrays or
# as a numpy array within a pytables table (should be preferred for large data sets)
NMF.__init__(self,
data,
num_bases=num_bases,
niter=niter,
show_progress=show_progress,
compW=compW)
def __init__(self,
data_1,
data_2,
lambd=0.5,
num_bases=4,
niter=100,
show_progress=False,
compH=True,
compW=True):
# generate a new data set data using a weighted
# combination of data_1 and data_2
self._data_1 = data_1
self._data_2 = data_2
self._lambd = lambd
data = np.concatenate(
(lambd * self._data_1, (1.0 - lambd) * self._data_2), axis=0)
NMF.__init__(self,
data,
num_bases=num_bases,
niter=niter,
show_progress=show_progress,
compW=compW)
def __init__(self,
data,
num_bases=4,
niter=10,
show_progress=False,
compW=True):
NMF.__init__(self,
data,
num_bases=num_bases,
niter=niter,
show_progress=show_progress,
compW=compW)
def __init__(self, data, num_bases=0, center_mean=True):
NMF.__init__(self, data, num_bases=num_bases)
# center the data around the mean first
self._center_mean = center_mean
if self._center_mean:
# copy the data before centering it
self._data_orig = data
self._meanv = self._data_orig[:,:].mean(axis=1).reshape(data.shape[0],-1)
self.data = self._data_orig - self._meanv
else:
self.data = data
def __init__(self,
data,
num_bases=4,
niter=100,
show_progress=False,
compW=True):
# call inherited method
NMF.__init__(self,
data,
num_bases=num_bases,
niter=niter,
show_progress=show_progress,
compW=compW)
def __init__(self, data, num_bases=0, center_mean=True, **kwargs):
NMF.__init__(self, data, num_bases=num_bases)
# center the data around the mean first
self._center_mean = center_mean
if self._center_mean:
# copy the data before centering it
self._data_orig = data
self._meanv = self._data_orig[:,:].mean(axis=1).reshape(data.shape[0],-1)
self.data = self._data_orig - self._meanv
else:
self.data = data
def __init__(self,
data,
num_bases=4,
niter=100,
show_progress=False,
compW=True):
# data can be either supplied by conventional numpy arrays or
# as a numpy array within a pytables table (should be preferred for large data sets)
NMF.__init__(self,
data,
num_bases=num_bases,
niter=niter,
show_progress=show_progress,
compW=compW)
def __init__(self,
data,
num_bases=4,
niter=100,
show_progress=False,
compW=True):
# data can be either supplied by conventional numpy arrays or
# as a numpy array within a pytables table (should be preferred for large data sets)
NMF.__init__(self,
data,
num_bases=num_bases,
niter=niter,
show_progress=show_progress,
compW=compW)
# controls how fast lambda should increase:
# this influence convergence to binary values during the update. A value
# <1 will result in non-binary decompositions as the update rule effectively
# is a conventional nmf update rule. Values >1 give more weight to making the
# factorization binary with increasing iterations.
# setting either W or H to 0 results make the resulting matrix non binary.
self._lamb_increase_W = 1.1
self._lamb_increase_H = 1.1
def __init__(self, data, num_bases=4, lamb=2.0):
# call inherited method
NMF.__init__(self, data, num_bases=num_bases)
self._lamb = lamb
def __init__(self, data, k=-1, num_bases=4):
# call inherited method
NMF.__init__(self, data, num_bases=num_bases)
self._k = k
if self._k == -1:
self._k = num_bases
def __init__(self, data, k=-1, num_bases=4):
# call inherited method
NMF.__init__(self, data, num_bases=num_bases)
self._k = k
if self._k == -1:
self._k = num_bases
def __init__(self, data, num_bases=4, lamb=2.0):
# call inherited method
NMF.__init__(self, data, num_bases=num_bases)
self._lamb = lamb
