def compress_rowcols(x, axis=None):
"""Suppresses the rows and/or columns of a 2D array that contains masked values.
The suppression behavior is selected with the `axis`parameter.
- If axis is None, rows and columns are suppressed.
- If axis is 0, only rows are suppressed.
- If axis is 1 or -1, only columns are suppressed.
Returns a *pure* ndarray.
"""
x = asarray(x)
if x.ndim <> 2:
raise NotImplementedError, "compress2d works for 2D arrays only."
m = getmask(x)
# Nothing is masked: return x
if m is nomask or not m.any():
return x._data
# All is masked: return empty
if m.all():
return nxarray([])
# Builds a list of rows/columns indices
(idxr, idxc) = (range(len(x)), range(x.shape[1]))
masked = m.nonzero()
if not axis:
for i in function_base.unique(masked[0]):
idxr.remove(i)
if axis in [None, 1, -1]:
for j in function_base.unique(masked[1]):
idxc.remove(j)
return x._data[idxr][:,idxc]
def _removeNonTracklikeClusterCenters(self):
'''NOTE : Much of this code is copied from LPCMImpl.followXSingleDirection (factor out?)
'''
labels = self._meanShift.labels_
labels_unique = unique(labels)
cluster_centers = self._meanShift.cluster_centers_
rsp = lpcRandomStartPoints()
cluster_representatives = []
for k in range(len(labels_unique)):
cluster_members = labels == k
cluster_center = cluster_centers[k]
cluster = self._Xi[cluster_members,:]
mean_sub = cluster - cluster_center
cov_x = dot(transpose(mean_sub), mean_sub)
eigen_cov = eigh(cov_x)
sorted_eigen_cov = zip(eigen_cov[0],map(ravel,vsplit(eigen_cov[1].transpose(),len(eigen_cov[1]))))
sorted_eigen_cov.sort(key = lambda elt: elt[0], reverse = True)
rho = sorted_eigen_cov[1][0] / sorted_eigen_cov[0][0] #Ratio of two largest eigenvalues
if rho < self._lpcParameters['rho_threshold']:
cluster_representatives.append(cluster_center)
else: #append a random element of the cluster
random_cluster_element = rsp(cluster, 1)[0]
cluster_representatives.append(random_cluster_element)
return array(cluster_representatives)
def twoDisjointLinesWithMSClustering():
t = arange(-1,1,0.002)
x = map(lambda x: x + gauss(0,0.02)*(1-x*x), t)
y = map(lambda x: x + gauss(0,0.02)*(1-x*x), t)
z = map(lambda x: x + gauss(0,0.02)*(1-x*x), t)
line1 = array(zip(x,y,z))
line = vstack((line1, line1 + 3))
lpc = LPCImpl(start_points_generator = lpcMeanShift(ms_h = 1), h = 0.05, mult = None, it = 200, cross = False, scaled = False, convergence_at = 0.001)
lpc_curve = lpc.lpc(X=line)
#Plot results
fig = plt.figure()
ax = Axes3D(fig)
labels = lpc._startPointsGenerator._meanShift.labels_
labels_unique = unique(labels)
cluster_centers = lpc._startPointsGenerator._meanShift.cluster_centers_
n_clusters = len(labels_unique)
colors = cycle('bgrcmyk')
for k, col in zip(range(n_clusters), colors):
cluster_members = labels == k
cluster_center = cluster_centers[k]
ax.scatter(line[cluster_members, 0], line[cluster_members, 1], line[cluster_members, 2], c = col, alpha = 0.1)
ax.scatter([cluster_center[0]], [cluster_center[1]], [cluster_center[2]], c = 'b', marker= '^')
curve = lpc_curve[k]['save_xd']
ax.plot(curve[:,0],curve[:,1],curve[:,2], c = col, linewidth = 3)
plt.show()
def mask_rowcols(a, axis=None):
"""Masks whole rows and/or columns of a 2D array that contain masked values.
The masking behavior is selected with the `axis`parameter.
- If axis is None, rows and columns are suppressed.
- If axis is 0, only rows are suppressed.
- If axis is 1 or -1, only columns are suppressed.
Returns a *pure* ndarray.
"""
a = asarray(a)
if a.ndim != 2:
raise NotImplementedError, "compress2d works for 2D arrays only."
m = getmask(a)
# Nothing is masked: return a
if m is nomask or not m.any():
return a
maskedval = m.nonzero()
a._mask = a._mask.copy()
if not axis:
a[function_base.unique(maskedval[0])] = masked
if axis in [None, 1, -1]:
a[:,function_base.unique(maskedval[1])] = masked
return a
