def drawspec(self):
f = plt.figure(self.fig)
f.clear()
img = self.means
mean_idxs = arange(self.nm)
weights = array(np.sum(equal(mean_idxs[:,newaxis],self.labels[newaxis,:]),1),float)
weights /= (sum(self.means * self.priors,1))
ax = f.add_axes([.05,.05,.9,.9])
activity = mycolors.imgcolor(img,BW = True)
ax.imshow(activity,aspect = 'auto')
activity = mycolors.imgcolor(img*weights[:,newaxis],alpha = True)
ax.imshow(activity,aspect = 'auto',interpolation = 'nearest')
def draw_hclustered(clustered):
c = clustered['HCluster']
clusters = c.cut(0)
f = plt.figure(0)
f.clear()
ax = f.add_axes([0,0,1,1],aspect = 'auto')
ax.set_aspect('auto')
c0 = c.cut(0)
n = max(array(c0)) +1
nlvl = 100
h = zeros((nlvl,n))
cuts = linspace(0,.9,nlvl)
appearances = zeros(n)-1
hinds = zeros((nlvl,n),int)
for i in range(nlvl):
cut = cuts[i]
clusters = c.cut(cut)
for j in range(n):
cval = clusters[j]
if appearances[cval] == -1: appearances[cval] = j
h[i,j] = appearances[cval]
hinds[i,:] = argsort(h[i,:])
h[i,:]=h[i,hinds[i,:]]
for i in range(shape(h)[0]):
h[i,:] /= max(h[i,:])
ax.imshow(mycolors.imgcolor(h, BW = True), aspect = 'auto', interpolation = 'nearest')
saff,ks = nu.net_square_affinity()
raff,kt,ktf = nu.net_affinity()
tfidxs = []
for tf in ktf.keys():
tfidxs.append(ks.index(tf))
tfidxs= array(tfidxs)
is_clustered = tfidxs[nonzero(less(tfidxs,n))[0]]
ntf = len(is_clustered)
tf_alpha = zeros((nlvl,n))
for i in range(ntf):
tf_alpha +=equal(hinds,is_clustered[i])
tf_rgba = mycolors.imgcolor(tf_alpha,alpha = True,color = [1,0,0])
ax.imshow(tf_rgba, aspect = 'auto', interpolation = 'nearest')
def specview(means, membership, fig = 12):
f = plt.figure(fig)
f.clear()
ax = f.add_axes([.05,.05,.9,.9])
#ax.plot(membership[np.argsort(membership)])
img = means
activity = mycolors.imgcolor(img,BW = True)
ax.imshow(img,aspect = 'auto')
