def hullselect(self):
def selectHullPoints(data, n=20):
""" 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, dist.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()
idx = selectHullPoints(pcamodel.H, n=self._base_sel)
# set the number of subsampled data
self.nsub = len(idx)
return idx
def hullselect(self):
def selectHullPoints(data, n=20):
""" 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, dist.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()
idx = selectHullPoints(pcamodel.H, n=self._base_sel)
# set the number of subsampled data
self.nsub = len(idx)
return idx
def update_w(self):
""" compute new W """
def select_hull_points(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 using either PCA or random
# projections
method = 'randomprojection'
if method == 'pca':
pcamodel = PCA(self.data)
pcamodel.factorize(show_progress=False)
proj = pcamodel.H
else:
R = np.random.randn(self._base_sel, self._data_dimension)
proj = np.dot(R, self.data)
self._hull_idx = select_hull_points(proj, n=self._base_sel)
aa_mdl = AA(self.data[:, self._hull_idx], num_bases=self._num_bases)
# determine W
aa_mdl.factorize(niter=50,
compute_h=True,
compute_w=True,
compute_err=True,
show_progress=False)
self.W = aa_mdl.W
self._map_w_to_data()
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
def update_w(self):
""" compute new W """
def select_hull_points(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 using either PCA or random
# projections
method = 'randomprojection'
if method == 'pca':
pcamodel = PCA(self.data)
pcamodel.factorize(show_progress=False)
proj = pcamodel.H
else:
R = np.random.randn(self._base_sel, self._data_dimension)
proj = np.dot(R, self.data)
self._hull_idx = select_hull_points(proj, n=self._base_sel)
aa_mdl = AA(self.data[:, self._hull_idx], num_bases=self._num_bases)
# determine W
aa_mdl.factorize(niter=50, compute_h=True, compute_w=True,
compute_err=True, show_progress=False)
self.W = aa_mdl.W
self._map_w_to_data()
def _choose_rule_pca(self, data, show_progress):
"""Our projections are onto the PCA vectors"""
pca = PCA(data, num_bases=1)
pca.factorize(show_progress)
primary_vec = pca.W.reshape(self._data_dimension)
return self._choose_rule_vecproject(data, primary_vec)
