def search_graph(query, nodes, DS, K):
dt = numpy.dtype('f8')
random.seed(100)
k = nodes.shape[1]
depth = 0
flag = 0
parent_id = randd(1, nodes.shape[0], 1); # Check this
visited = 1;
while 1:
parent_vec = MA(DS[parent_id,0:],dt) # parent node
child_ids = nodes[parent_id,0:];
Val = numpy.zeros((len(child_ids),DS.shape[1]),dt)
I = 0
while I < len(child_ids):
Val[I,0:] = DS[child_ids[I],0:]
I += 1
(nn1_ind, nn1_dist) = knnsearch(query,Val, K)
visited = visited + k
if (parent_dist <= nn1_dist):
flag=1
break
parent_id = child_ids(nn1_ind)
depth = depth+1
if flag == 1:
nn_id = parent_id
nn_dist = parent_dist
else:
nn_id = - 1
nn_dist = -1
return nn_id, nn_dist, visited
def search_graph(query, nodes, DS, K):
dt = numpy.dtype('f8')
random.seed(100)
k = nodes.shape[1]
depth = 0
flag = 0
parent_id = randd(1, nodes.shape[0], 1)
# Check this
visited = 1
while 1:
parent_vec = MA(DS[parent_id, 0:], dt) # parent node
child_ids = nodes[parent_id, 0:]
Val = numpy.zeros((len(child_ids), DS.shape[1]), dt)
I = 0
while I < len(child_ids):
Val[I, 0:] = DS[child_ids[I], 0:]
I += 1
(nn1_ind, nn1_dist) = knnsearch(query, Val, K)
visited = visited + k
if (parent_dist <= nn1_dist):
flag = 1
break
parent_id = child_ids(nn1_ind)
depth = depth + 1
if flag == 1:
nn_id = parent_id
nn_dist = parent_dist
else:
nn_id = -1
nn_dist = -1
return nn_id, nn_dist, visited
def search_graph(query, nodes, DS, K):
''' Authors: Ankush Roy ([email protected])
Kiana Hajebi ([email protected])
'''
dt = numpy.dtype('f8')
nodes = MA(nodes,dt)
query = MA(query,dt)
DS = MA(DS, dt)
random.seed(100)
k = nodes.shape[1]
depth = 0
flag = 0
parent_id = randd(1, nodes.shape[0], 1); # Check this
visited = 1;
while 1:
parent_vec = MA(DS[int(parent_id),0:],dt) # parent node
parent_dist = numpy.sqrt((query - parent_vec) * numpy.transpose(query - parent_vec))
child_ids = nodes[int(parent_id),0:];
Val = numpy.zeros((child_ids.shape[1],DS.shape[1]),dt)
I = 0
while I < child_ids.shape[1]:
Val[I,0:] = DS[int(child_ids[0,I]),0:]
I += 1
Val = MA(Val)
(nn1_ind, nn1_dist) = knnsearch(query,Val, K)
visited = visited + k
if (parent_dist <= nn1_dist):
flag=1
break
parent_id = child_ids[0,nn1_ind]
depth = depth+1
if flag == 1:
nn_id = parent_id
nn_dist = parent_dist
else:
nn_id = - 1
nn_dist = -1
return nn_id, nn_dist, visited
def build_graph(X, k):
''' Build a Connected graph using k neighbours
Author: Ankush Roy ([email protected])
Kiana Hajebi ([email protected])
'''
dt = numpy.dtype('f8')
f = []
nodes = numpy.zeros((X.shape[0], k), dt)
for i in range(X.shape[0]):
query = MA(X[(i - 1), 0:], dt)
(nns_inds, nns_dists) = knnsearch(query, X, k + 1)
I = 0
f = []
while I < len(nns_inds):
if nns_inds[I] == i - 1:
nns_inds.remove(i - 1)
nodes[i - 1, 0:] = nns_inds
break
else:
I += 1
return nodes
def search_graph(query, nodes, DS, K):
''' Authors: Ankush Roy ([email protected])
Kiana Hajebi ([email protected])
'''
dt = numpy.dtype('f8')
nodes = MA(nodes,dt)
query = MA(query,dt)
DS = MA(DS, dt)
random.seed(100)
k = nodes.shape[1]
depth = 0
flag = 0
parent_id = randd(1, nodes.shape[0], 1); # Check this
visited = 1;
while 1:
parent_vec = MA(DS[int(parent_id),0:],dt) # parent node
parent_dist = numpy.sqrt((query - parent_vec) * numpy.transpose(query - parent_vec))
child_ids = nodes[int(parent_id),0:];
Val = numpy.zeros((child_ids.shape[1],DS.shape[1]),dt)
I = 0
while I < child_ids.shape[1]:
Val[I,0:] = DS[int(child_ids[0,I]),0:]
I += 1
Val = MA(Val)
(nn1_ind, nn1_dist) = knnsearch(query,Val, K)
visited = visited + k
if (parent_dist <= nn1_dist):
flag=1
break
parent_id = child_ids[0,nn1_ind]
depth = depth+1
if flag == 1:
nn_id = parent_id
nn_dist = parent_dist
else:
nn_id = - 1
nn_dist = -1
return nn_id, nn_dist, visited
def build_graph(X, k):
dt = numpy.dtype('f8')
f = []
nodes = numpy.zeros((X.shape[1], k), dt)
i = 0
while i <= X.shape[0]:
# print i
query = MA(X[(i - 1), 0:], dt)
(nns_inds, nns_dists) = knnsearch(query, X, k + 1)
I = 0
f = []
# import pdb;pdb.set_trace()
while I < len(nns_inds):
if nns_inds[I] == i - 1:
nns_inds.remove(i - 1)
print i
nodes[i - 1, 0:] = nns_inds
break
else:
I += 1
i += 1
print nodes
return nodes
def build_graph(X, k):
''' Build a Connected graph using k neighbours
Author: Ankush Roy ([email protected])
Kiana Hajebi ([email protected])
'''
dt = numpy.dtype('f8')
f=[]
nodes = numpy.zeros((X.shape[0],k),dt)
for i in range(X.shape[0]):
query = MA(X[(i-1),0:],dt)
(nns_inds, nns_dists) = knnsearch(query,X,k+1)
I = 0
f = []
while I < len(nns_inds):
if nns_inds[I] == i-1:
nns_inds.remove(i-1)
nodes[i-1,0:] = nns_inds
break
else:
I += 1
return nodes
def build_graph(X, k):
dt = numpy.dtype('f8')
f=[]
nodes = numpy.zeros((X.shape[1],k),dt);
i = 0
while i <= X.shape[0]:
# print i
query = MA(X[(i-1),0:],dt)
(nns_inds, nns_dists) = knnsearch(query,X,k+1)
I = 0
f = []
# import pdb;pdb.set_trace()
while I < len(nns_inds):
if nns_inds[I] == i-1:
nns_inds.remove(i-1)
print i
nodes[i-1,0:] = nns_inds
break
else:
I += 1
i += 1
print nodes
return nodes