def update_h(self):
# assign samples to best matching centres ...
m = 1.75
tmp_dist = dist.pdist(self.W, self.data, metric='l2') + self._EPS
self.H[:,:] = 0.0
for i in range(self._num_bases):
for k in range(self._num_bases):
self.H[i,:] += (tmp_dist[i,:]/tmp_dist[k,:])**(2.0/(m-1))
self.H = np.where(self.H>0, 1.0/self.H, 0)
def _update_h(self):
# assign samples to best matching centres ...
m = 1.75
tmp_dist = dist.pdist(self.W, self.data, metric='l2') + self._EPS
self.H[:,:] = 0.0
for i in range(self._num_bases):
for k in range(self._num_bases):
self.H[i,:] += (tmp_dist[i,:]/tmp_dist[k,:])**(2.0/(m-1))
self.H = np.where(self.H>0, 1.0/self.H, 0)
def update_w(self):
def h(sel, D, k):
# compute the volume for a selection of sel columns
# and a k-1 simplex (-> k columns have to be selected)
mv = np.max(D)
# fill the remaining distance by the maximal overall found distance
d = np.zeros((k,k)) + mv
for i in range(k):
d[i,i] = 0.0
for idx_i,i in enumerate(sel):
for idx_j,j in enumerate(sel):
d[idx_i,idx_j] = D[i, j]
return d
# compute distance matrix -> required for the volume
D = pdist(self.data, self.data)
Openset = {}
for i in range(self._num_samples):
# compute volume for temp selection
d = h([i],D,self._num_bases)
Openset[tuple([i])] = cmdet(d)
Closedset = {}
finished = False
self._v = []
self.init_sivm()
next_sel = np.array([self.select[0]])
niter = 0
while not finished:
# add the current selection to closedset
Closedset[(tuple(next_sel))] = []
for i in range(D.shape[0]):
# create a temp selection
tmp_sel = np.array(next_sel).flatten()
tmp_sel = np.concatenate((tmp_sel, [i]),axis=0)
tmp_sel = np.unique(tmp_sel)
tmp_sel = list(tmp_sel)
hkey = tuple(tmp_sel)
if len(tmp_sel) > len(next_sel) and (
not Closedset.has_key(hkey)) and (
not Openset.has_key(hkey)):
# compute volume for temp selection
d = h(tmp_sel, D, self._num_bases)
# add to openset
Openset[hkey] = cmdet(d)
# get next best tuple
vmax = 0.0
for (k,v) in Openset.iteritems():
if v > vmax:
next_sel = k
vmax = v
self._logger.info('Iter:' + str(niter))
self._logger.info('Current selection:' + str(next_sel))
self._logger.info('Current volume:' + str(vmax))
self._v.append(vmax)
# remove next_sel from openset
Openset.pop(next_sel)
if len(list(next_sel)) == self._num_bases:
finished = True
niter += 1
# update some values ...
self.select = list(next_sel)
self.W = self.data[:, self.select]
def _update_w(self):
def h(sel, D, k):
# compute the volume for a selection of sel columns
# and a k-1 simplex (-> k columns have to be selected)
mv = np.max(D)
# fill the remaining distance by the maximal overall found distance
d = np.zeros((k, k)) + mv
for i in range(k):
d[i, i] = 0.0
for idx_i, i in enumerate(sel):
for idx_j, j in enumerate(sel):
d[idx_i, idx_j] = D[i, j]
return d
# compute distance matrix -> required for the volume
D = pdist(self.data, self.data)
Openset = {}
for i in range(self._num_samples):
# compute volume for temp selection
d = h([i], D, self._num_bases)
Openset[tuple([i])] = cmdet(d)
Closedset = {}
finished = False
self._v = []
self.init_sivm()
next_sel = np.array([self.select[0]])
niter = 0
while not finished:
# add the current selection to closedset
Closedset[(tuple(next_sel))] = []
for i in range(D.shape[0]):
# create a temp selection
tmp_sel = np.array(next_sel).flatten()
tmp_sel = np.concatenate((tmp_sel, [i]), axis=0)
tmp_sel = np.unique(tmp_sel)
tmp_sel = list(tmp_sel)
hkey = tuple(tmp_sel)
if len(tmp_sel) > len(next_sel) and (
not Closedset.has_key(hkey)) and (
not Openset.has_key(hkey)):
# compute volume for temp selection
d = h(tmp_sel, D, self._num_bases)
# add to openset
Openset[hkey] = cmdet(d)
# get next best tuple
vmax = 0.0
for (k, v) in Openset.iteritems():
if v > vmax:
next_sel = k
vmax = v
self._logger.info('Iter:' + str(niter))
self._logger.info('Current selection:' + str(next_sel))
self._logger.info('Current volume:' + str(vmax))
self._v.append(vmax)
# remove next_sel from openset
Openset.pop(next_sel)
if len(list(next_sel)) == self._num_bases:
finished = True
niter += 1
# update some values ...
self.select = list(next_sel)
self.W = self.data[:, self.select]
