def sivmselect(self):
sivmmdl = SIVM(self.data, num_bases=self._nsub, compute_w=True, compute_h=False, dist_measure='cosine')
sivmmdl.initialization()
sivmmdl.factorize()
idx = sivmmdl.select
return idx
def sample(self, A, c):
# for optimizing the volume of the submatrix, set init to 'origin' (otherwise the volume of
# the ordinary simplex would be optimized)
sivm_mdl = SIVM(A, num_bases=c, dist_measure=self._dist_measure,
init=self.init)
sivm_mdl.factorize(show_progress=False, compute_w=True, niter=1,
compute_h=False, compute_err=False)
return sivm_mdl.select
def sample(self, A, c):
# for optimizing the volume of the submatrix, set init to 'origin' (otherwise the volume of
# the ordinary simplex would be optimized)
sivm_mdl = SIVM(A, num_bases=c, dist_measure=self._dist_measure,
init=self.init)
sivm_mdl.factorize(show_progress=False, compute_w=True, niter=1,
compute_h=False, compute_err=False)
return sivm_mdl.select
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) SIVM.__init__(self, data, num_bases=num_bases, niter=niter, show_progress=show_progress, compW=compW)
def sivmselect(self):
sivmmdl = SIVM(self.data, num_bases=self._nsub, compute_w=True, compute_h=False, dist_measure='cosine')
sivmmdl.initialization()
sivmmdl.factorize()
idx = sivmmdl.select
return idx