def sscAdj_inNeighbour(file): ## use neighbour adj as prior, limiting ssc's adj choice to be within neighbours
"""fixe me..... :/"""
xtrj = file['x_re']
ytrj = file['y_re']
xspd = file['xspd']
yspd = file['yspd']
trjID = file['trjID']
trjAdj = (file['adj'] > 0).astype('float') ## adj mtx
CClabel = file['c'] # labels from connected Component
# dataFeature = np.concatenate((xtrj,xspd,ytrj,yspd), axis = 1)
dataFeature = np.concatenate((xtrj, ytrj), axis=1)
labels = np.zeros(CClabel.size)
adj = np.zeros((CClabel.size, CClabel.size))
for i in np.unique(CClabel):
print i
sub_index = np.where(CClabel == i)[1]
if sub_index.size > 3:
if sub_index.size < 100: # if connected component too big, just use the binary adj
project_dimension = int(np.floor(sub_index.size / 100) + 1)
ssc = sparse_subspace_clustering(lambd=2000, dataset=dataFeature[sub_index, :],
n_dimension=project_dimension)
ssc.construct_adjacency()
else:
project_dimension = int(np.floor(sub_index.size / 100) + 1)
sub_adjMtx = trjAdj[sub_index][:, sub_index]
ssc = sparse_subspace_clustering(lambd=2000000, dataset=trjAdj, n_dimension=project_dimension)
ssc.get_adjacency(sub_adjMtx)
"""adj assignment not successful!!! whyyyyyyyy"""
# adjInNeighbour = ssc.adjacency
# adj[sub_index][:,sub_index]= adjInNeighbour[:][:]
ssc.manifold()
sub_labels, model = ssc.clustering(n_components=int(np.floor(sub_index.size / 2) + 1), alpha=0.1)
# sub_labels = ssc.clustering_kmeans(int(np.floor(sub_index.size/4)+1))
# visulize(ssc.embedding_,sub_labels,model)
labels[sub_index] = np.max(labels) + (sub_labels + 1)
print 'number of trajectory in this connected components%s' % sub_labels.size + ' unique labels %s' % np.unique(
sub_labels).size
else:
sub_labels = np.ones(sub_index.size)
labels[sub_index] = np.max(labels) + sub_labels
print 'number of trajectory %s' % sub_labels.size + ' unique labels %s' % np.unique(sub_labels).size
j = 0
labels_new = np.zeros(labels.shape)
unique_array = np.unique(labels)
for i in range(unique_array.size):
labels_new[np.where(labels == unique_array[i])] = j
j = j + 1
labels = labels_new
return trjID, labels
def sscConstructedAdj_CC(
DataPathobj
): # use ssc to construct adj, use concatenated raw data????
"""fix me....??? :/"""
# if len(sorted(glob.glob(DataPathobj.adjpath+'extremeValue'+self.DirName[directionInd]+str(matidx+1).zfill(3))))>0:
# print "mean_std_ForKernel(ignore) and extremeValue already stored, load..."
# self.extremeValue = pickle.load(open(DataPathobj.adjpath+'extremeValue'+self.DirName[directionInd]+str(matidx+1).zfill(3),'rb'))
if len(
sorted(
glob.glob(self.savePath + feaName +
self.DirName[directionInd] +
str(matidx + 1).zfill(3)))) > 0:
print "distance diff already stored, load..."
self.feature_diff_tensor = pickle.load(
open(
self.savePath + feaName + self.DirName[directionInd] +
str(matidx + 1).zfill(3), 'rb'))
xtrj = self.feature_diff_tensor[:, :, 0]
ytrj = file['y_re']
xspd = file['xspd']
yspd = file['yspd']
trjID = file['trjID']
dataFeature = np.concatenate((xtrj, xspd, ytrj, yspd), axis=1)
project_dimension = int(np.floor(dataFeature.shape[0] / 10) +
1) # assume per group has max 10 trjs????
ssc = sparse_subspace_clustering(lambd=2000,
dataset=dataFeature,
n_dimension=project_dimension)
ssc.construct_adjacency()
adj = ssc.adjacency
ssc.manifold()
labels = ssc.clustering_connected(threshold=0,
min_sample_cluster=50,
alpha=0.1)
return trjID, labels, adj
def sscConstructedAdj_CC(DataPathobj): # use ssc to construct adj, use concatenated raw data????
"""fix me....??? :/"""
# if len(sorted(glob.glob(DataPathobj.adjpath+'extremeValue'+self.DirName[directionInd]+str(matidx+1).zfill(3))))>0:
# print "mean_std_ForKernel(ignore) and extremeValue already stored, load..."
# self.extremeValue = pickle.load(open(DataPathobj.adjpath+'extremeValue'+self.DirName[directionInd]+str(matidx+1).zfill(3),'rb'))
if len(sorted(glob.glob(self.savePath+feaName+self.DirName[directionInd]+str(matidx+1).zfill(3))))>0:
print "distance diff already stored, load..."
self.feature_diff_tensor = pickle.load(open(self.savePath+feaName+self.DirName[directionInd]+str(matidx+1).zfill(3),'rb'))
xtrj = self.feature_diff_tensor[:,:,0]
ytrj = file['y_re']
xspd = file['xspd']
yspd = file['yspd']
trjID = file['trjID']
dataFeature = np.concatenate((xtrj, xspd, ytrj, yspd), axis=1)
project_dimension = int(np.floor(dataFeature.shape[0] / 10) + 1) # assume per group has max 10 trjs????
ssc = sparse_subspace_clustering(lambd=2000, dataset=dataFeature, n_dimension=project_dimension)
ssc.construct_adjacency()
adj = ssc.adjacency
ssc.manifold()
labels = ssc.clustering_connected(threshold=0, min_sample_cluster=50, alpha=0.1)
return trjID, labels, adj
def ssc_with_Adj_CC(trjAdj, CClabel, trjID, Parameterobj):
small_CC_oneCls = []
labels = np.ones(CClabel.size) * (-222)
print 'connected component number:', len(np.unique(CClabel))
"""data is featureMtx or adj????"""
# FeatureMtx = pickle.load(open('./NGSIM_FeatureMtx', 'rb'))
# FeatureMtx = pickle.load(open('./Johnson00115_FeatureMtx', 'rb'))
# FeatureMtx[np.isnan(FeatureMtx)] = 0
# color_choice = np.array([np.random.randint(0, 255) for _ in range(3 * int(np.unique(CClabel).size))]).reshape(int(np.unique(CClabel).size), 3)
# for i in np.unique(CClabel):
for ind in range(len(np.unique(CClabel))):
i = np.unique(CClabel)[ind]
# color = ((color_choice[ind].T) / 255.)
sub_index = np.where(
CClabel == i
)[1] # noted, after saving to Mat, got appened zeros, should use [1] instead of [0]
sub_adjMtx = trjAdj[sub_index][:, sub_index]
# sub_FeatureMtx = FeatureMtx[sub_index,:]
sub_FeatureMtx = []
if sub_index.size > Parameterobj.smallclssize and sub_index.size < 50:
# project_dimension = int(np.floor(sub_index.size/Parameterobj.embedding_projection_factor) + 1)
project_dimension = 10
print "CC size:", sub_index.size
"""restrict the prj dim <=200, otherwise too slow"""
print "project dimension is: ", min(
int(np.sqrt(sub_index.size)),
project_dimension) ## embeded lower dimension
ssc = sparse_subspace_clustering(2000000,
sub_FeatureMtx,
n_dimension=min(
int(np.sqrt(sub_index.size)),
project_dimension))
sub_adjMtx = csr_matrix(sub_adjMtx,
shape=sub_adjMtx.shape).toarray()
ssc.get_adjacency(sub_adjMtx)
if Parameterobj.clustering_choice == 'labels_DPGMM_':
ssc.manifold()
"""DPGMM"""
# n_components_DPGMM = int(np.floor(sub_index.size/Parameterobj.DPGMM_num_component_shirink_factor))
# n_components_DPGMM = max(1,n_components_DPGMM)
n_components_DPGMM = sub_index.size
sub_labels_DPGMM = ssc.clustering_DPGMM(
n_components=n_components_DPGMM,
alpha=Parameterobj.DPGMM_alpha)
# visulize(ssc.embedding_,sub_labels,model,color)
"""vis rearrangement of the adj"""
# visAdj_rearrange(sub_adjMtx,sub_labels_DPGMM)
labels[sub_index] = max(np.max(labels),
0) + (sub_labels_DPGMM + 1)
elif Parameterobj.clustering_choice == 'labels_spectral_':
"""k-means"""
# num_cluster_prior = len(np.unique(sub_labels_DPGMM))
# num_cluster_prior = n_components_DPGMM
# sub_labels_k_means = ssc.clustering_kmeans(num_cluster_prior)
###################################################################################
"""N cut spectral"""
# n_components_spectral = int(np.floor(sub_index.size/Parameterobj.spectral_num_component_shirink_factor))
# n_components_spectral = max(2,n_components_spectral)
# n_components_spectral = min(sub_index.size,n_components_spectral)
n_components_spectral = 2
print 'spectral clustering num_cluster is', n_components_spectral
sub_labels_spectral = ssc.clustering_spectral(
n_components_spectral)
# visAdj_rearrange(sub_adjMtx,sub_labels_spectral)
labels[sub_index] = max(np.max(labels),
0) + (sub_labels_spectral + 1)
elif Parameterobj.clustering_choice == 'labels_affinity_':
"""affinity propogation"""
sub_labels_affinity = ssc.clustering_Affini_prpoga()
labels[sub_index] = max(np.max(labels),
0) + (sub_labels_affinity + 1)
else: ## if size small, treat as one group
sub_labels = np.ones(sub_index.size)
labels[sub_index] = max(np.max(labels), 0) + sub_labels
small_CC_oneCls.append(sub_index)
labels = uniqulizeLabel(labels)
return labels, small_CC_oneCls
def sscAdj_inNeighbour(
file
): ## use neighbour adj as prior, limiting ssc's adj choice to be within neighbours
"""fixe me..... :/"""
xtrj = file['x_re']
ytrj = file['y_re']
xspd = file['xspd']
yspd = file['yspd']
trjID = file['trjID']
trjAdj = (file['adj'] > 0).astype('float') ## adj mtx
CClabel = file['c'] # labels from connected Component
# dataFeature = np.concatenate((xtrj,xspd,ytrj,yspd), axis = 1)
dataFeature = np.concatenate((xtrj, ytrj), axis=1)
labels = np.zeros(CClabel.size)
adj = np.zeros((CClabel.size, CClabel.size))
for i in np.unique(CClabel):
print i
sub_index = np.where(CClabel == i)[1]
if sub_index.size > 3:
if sub_index.size < 100: # if connected component too big, just use the binary adj
project_dimension = int(np.floor(sub_index.size / 100) + 1)
ssc = sparse_subspace_clustering(
lambd=2000,
dataset=dataFeature[sub_index, :],
n_dimension=project_dimension)
ssc.construct_adjacency()
else:
project_dimension = int(np.floor(sub_index.size / 100) + 1)
sub_adjMtx = trjAdj[sub_index][:, sub_index]
ssc = sparse_subspace_clustering(lambd=2000000,
dataset=trjAdj,
n_dimension=project_dimension)
ssc.get_adjacency(sub_adjMtx)
"""adj assignment not successful!!! whyyyyyyyy"""
# adjInNeighbour = ssc.adjacency
# adj[sub_index][:,sub_index]= adjInNeighbour[:][:]
ssc.manifold()
sub_labels, model = ssc.clustering(
n_components=int(np.floor(sub_index.size / 2) + 1), alpha=0.1)
# sub_labels = ssc.clustering_kmeans(int(np.floor(sub_index.size/4)+1))
# visulize(ssc.embedding_,sub_labels,model)
labels[sub_index] = np.max(labels) + (sub_labels + 1)
print 'number of trajectory in this connected components%s' % sub_labels.size + ' unique labels %s' % np.unique(
sub_labels).size
else:
sub_labels = np.ones(sub_index.size)
labels[sub_index] = np.max(labels) + sub_labels
print 'number of trajectory %s' % sub_labels.size + ' unique labels %s' % np.unique(
sub_labels).size
j = 0
labels_new = np.zeros(labels.shape)
unique_array = np.unique(labels)
for i in range(unique_array.size):
labels_new[np.where(labels == unique_array[i])] = j
j = j + 1
labels = labels_new
return trjID, labels
def ssc_with_Adj_CC(trjAdj,CClabel,trjID,Parameterobj):
small_CC_oneCls = []
labels = np.ones(CClabel.size)*(-222)
print 'connected component number:',len(np.unique(CClabel))
"""data is featureMtx or adj????"""
# FeatureMtx = pickle.load(open('./NGSIM_FeatureMtx', 'rb'))
# FeatureMtx = pickle.load(open('./Johnson00115_FeatureMtx', 'rb'))
# FeatureMtx[np.isnan(FeatureMtx)] = 0
# color_choice = np.array([np.random.randint(0, 255) for _ in range(3 * int(np.unique(CClabel).size))]).reshape(int(np.unique(CClabel).size), 3)
# for i in np.unique(CClabel):
for ind in range(len(np.unique(CClabel))):
i = np.unique(CClabel)[ind]
# color = ((color_choice[ind].T) / 255.)
sub_index = np.where(CClabel == i)[1] # noted, after saving to Mat, got appened zeros, should use [1] instead of [0]
sub_adjMtx = trjAdj[sub_index][:, sub_index]
# sub_FeatureMtx = FeatureMtx[sub_index,:]
sub_FeatureMtx = []
if sub_index.size > Parameterobj.smallclssize and sub_index.size < 50:
# project_dimension = int(np.floor(sub_index.size/Parameterobj.embedding_projection_factor) + 1)
project_dimension = 10
print "CC size:", sub_index.size
"""restrict the prj dim <=200, otherwise too slow"""
print "project dimension is: ", min(int(np.sqrt(sub_index.size)), project_dimension) ## embeded lower dimension
ssc = sparse_subspace_clustering(2000000, sub_FeatureMtx, n_dimension=min(int(np.sqrt(sub_index.size)), project_dimension))
sub_adjMtx = csr_matrix(sub_adjMtx, shape=sub_adjMtx.shape).toarray()
ssc.get_adjacency(sub_adjMtx)
if Parameterobj.clustering_choice == 'labels_DPGMM_':
ssc.manifold()
"""DPGMM"""
# n_components_DPGMM = int(np.floor(sub_index.size/Parameterobj.DPGMM_num_component_shirink_factor))
# n_components_DPGMM = max(1,n_components_DPGMM)
n_components_DPGMM = sub_index.size
sub_labels_DPGMM = ssc.clustering_DPGMM(n_components=n_components_DPGMM, alpha=Parameterobj.DPGMM_alpha)
# visulize(ssc.embedding_,sub_labels,model,color)
"""vis rearrangement of the adj"""
# visAdj_rearrange(sub_adjMtx,sub_labels_DPGMM)
labels[sub_index] = max(np.max(labels),0) + (sub_labels_DPGMM + 1)
elif Parameterobj.clustering_choice == 'labels_spectral_':
"""k-means"""
# num_cluster_prior = len(np.unique(sub_labels_DPGMM))
# num_cluster_prior = n_components_DPGMM
# sub_labels_k_means = ssc.clustering_kmeans(num_cluster_prior)
###################################################################################
"""N cut spectral"""
# n_components_spectral = int(np.floor(sub_index.size/Parameterobj.spectral_num_component_shirink_factor))
# n_components_spectral = max(2,n_components_spectral)
# n_components_spectral = min(sub_index.size,n_components_spectral)
n_components_spectral = 2
print 'spectral clustering num_cluster is', n_components_spectral
sub_labels_spectral = ssc.clustering_spectral(n_components_spectral)
# visAdj_rearrange(sub_adjMtx,sub_labels_spectral)
labels[sub_index] = max(np.max(labels),0) + (sub_labels_spectral + 1)
elif Parameterobj.clustering_choice == 'labels_affinity_':
"""affinity propogation"""
sub_labels_affinity = ssc.clustering_Affini_prpoga()
labels[sub_index] = max(np.max(labels),0) + (sub_labels_affinity + 1)
else: ## if size small, treat as one group
sub_labels = np.ones(sub_index.size)
labels[sub_index] = max(np.max(labels),0) + sub_labels
small_CC_oneCls.append(sub_index)
labels = uniqulizeLabel(labels)
return labels, small_CC_oneCls
