def initialization(self):
# Fastmap like initialization
# set the starting index for fastmap initialization
cur_p = 0
# after 3 iterations the first "real" index is found
for i in range(3):
d = self._distance(cur_p)
cur_p = np.argmax(d)
self.select = []
self.select.append(cur_p)
if self._compH:
self.H = np.zeros((self._num_bases, self._num_samples))
if self._compW:
AA.initialization(self)
def updateW(self):
def selectHullPoints(data, n=3):
""" select data points for pairwise projections of the first n
dimensions """
# iterate over all projections and select data points
idx = np.array([])
# iterate over some pairwise combinations of dimensions
for i in combinations(range(n), 2):
# sample convex hull points in 2D projection
convex_hull_d = quickhull(data[i, :].T)
# get indices for convex hull data points
idx = np.append(idx, vq(data[i, :], convex_hull_d.T))
idx = np.unique(idx)
return np.int32(idx)
# determine convex hull data points only if the total
# amount of available data is >50
#if self.data.shape[1] > 50:
pcamodel = PCA(self.data, show_progress=self._show_progress)
pcamodel.factorize()
self._hull_idx = selectHullPoints(pcamodel.H, n=self._base_sel)
#else:
# self._hull_idx = range(self.data.shape[1])
aa_mdl = AA(self.data[:, self._hull_idx],
num_bases=self._num_bases,
niter=self._niter,
show_progress=self._show_progress,
compW=True)
# initialize W, H, and beta
aa_mdl.initialization()
# determine W
aa_mdl.factorize()
self.W = aa_mdl.W
