def factorize(self, niter=10, compute_w=True, compute_h=True,
show_progress=False, compute_err=True):
""" Factorize s.t. WH = data
Parameters
----------
niter : int
number of iterations.
show_progress : bool
print some extra information to stdout.
compute_h : bool
iteratively update values for H.
compute_w : bool
iteratively update values for W.
compute_err : bool
compute Frobenius norm |data-WH| after each update and store
it to .ferr[k].
Updated Values
--------------
.W : updated values for W.
.H : updated values for H.
.ferr : Frobenius norm |data-WH| for each iteration.
"""
# init some learning parameters
self._lamb_W = 1.0/niter
self._lamb_H = 1.0/niter
NMF.factorize(self, niter=niter, compute_w=compute_w,
compute_h=compute_h, show_progress=show_progress,
compute_err=compute_err)
def factorize(self, show_progress=False, compute_w=True, compute_h=True,
compute_err=True, niter=1):
""" Factorize s.t. WH = data
Parameters
----------
show_progress : bool
print some extra information to stdout.
compute_h : bool
iteratively update values for H.
compute_w : bool
iteratively update values for W.
compute_err : bool
compute Frobenius norm |data-WH| after each update and store
it to .ferr[k].
Updated Values
--------------
.W : updated values for W.
.H : updated values for H.
.ferr : Frobenius norm |data-WH|.
"""
NMF.factorize(self, niter=1, show_progress=show_progress,
compute_w=compute_w, compute_h=compute_h,
compute_err=compute_err)
def factorize(self, show_progress=False, compute_w=True, compute_h=True,
compute_err=True, niter=1):
""" Factorize s.t. WH = data
Parameters
----------
show_progress : bool
print some extra information to stdout.
compute_h : bool
iteratively update values for H.
compute_w : bool
iteratively update values for W.
compute_err : bool
compute Frobenius norm |data-WH| after each update and store
it to .ferr[k].
Updated Values
--------------
.W : updated values for W.
.H : updated values for H.
.ferr : Frobenius norm |data-WH|.
"""
NMF.factorize(self, niter=1, show_progress=show_progress,
compute_w=compute_w, compute_h=compute_h,
compute_err=compute_err)
def factorize(self,
niter=1,
show_progress=False,
compute_w=True,
compute_h=True,
compute_err=True):
# enforce certain default values, otherwise it won't work
NMF.factorize(self,
niter=1,
show_progress=show_progress,
compute_w=True,
compute_h=True,
compute_err=compute_err)
def update_h(self):
print self._method
if self._method == 'pca':
self.H = np.dot(pinv(self.W), self.data)
if self._method == 'nmf':
mdl = NMF(self.data, num_bases=self._num_bases)
mdl.W = self.W
mdl.factorize(compute_w=False, niter=50)
self.H = mdl.H.copy()
if self._method == 'aa':
mdl = AA(self.data, num_bases=self._num_bases)
mdl.W = self.W
mdl.factorize(compute_w=False)
self.H = mdl.H.copy()
def update_h(self):
print self._method
if self._method == 'pca':
self.H = np.dot(pinv(self.W), self.data)
if self._method == 'nmf':
mdl = NMF(self.data, num_bases=self._num_bases)
mdl.W = self.W
mdl.factorize(compute_w=False, niter=50)
self.H = mdl.H.copy()
if self._method == 'aa':
mdl = AA(self.data, num_bases=self._num_bases)
mdl.W = self.W
mdl.factorize(compute_w=False)
self.H = mdl.H.copy()
def factorize(self, niter=1, show_progress=False,
compute_w=True, compute_h=True, compute_err=True):
# enforce certain default values, otherwise it won't work
NMF.factorize(self, niter=1, show_progress=show_progress,
compute_w=True, compute_h=True, compute_err=compute_err)
