def __init__(self):
self.g = build_factor_synonyms_graph()
self.component_map = self.g.add_vertex_property(
name='connected_components', type='integer')
self.num_components = bgl.connected_components(self.g,
self.component_map)
self._build_partition_synonyms()
def graph_component_map( g ):
"""Returns (component_map, num_components)
"""
import boost.graph as bgl
try: g.component_map
except:
g.component_map = g.add_vertex_property( 'components', 'integer' )
g.num_components = bgl.connected_components( g, g.component_map )
return ( g.component_map, g.num_components )
def graph_component_map(g):
"""Returns (component_map, num_components)
"""
import boost.graph as bgl
try:
g.component_map
except:
g.component_map = g.add_vertex_property('components', 'integer')
g.num_components = bgl.connected_components(g, g.component_map)
return (g.component_map, g.num_components)
def write_as_connected_components( g, file_prefix ):
import boost.graph as bgl
components = g.add_vertex_property( 'integer' )
num_components = bgl.connected_components( g, components )
for i in range( num_components ):
filename = file_prefix + '_' + str( i ) + '.mm'
f = open( filename, 'w' )
# print f
( num_vertices, num_non_zeros, vertex_indices ) = \
write_matrix_market_format(
g,
f,
lambda v: components[ v ] == i )
# print i, num_vertices, num_non_zeros
return ( components, num_components )
def write_as_connected_components(g, file_prefix):
import boost.graph as bgl
components = g.add_vertex_property('integer')
num_components = bgl.connected_components(g, components)
for i in range(num_components):
filename = file_prefix + '_' + str(i) + '.mm'
f = open(filename, 'w')
# print f
( num_vertices, num_non_zeros, vertex_indices ) = \
write_matrix_market_format(
g,
f,
lambda v: components[ v ] == i )
# print i, num_vertices, num_non_zeros
return (components, num_components)
def graph_2_distance_map( g, components_dir, name, beta, eps ):
"""Calculates the distances between all nodes in the graph
Returns a dict where tuples of node_ids in the graph are the keys and
the distances are the values. Distances below eps are not returned.
beta is the parameter for the diffusion kernel on the graph
components_dir is a directory to place working files in
name is a string to use when generating file names
"""
import boost.graph as bgl
#
# Calculate the connected components
#
# print 'Calculating connected components'
components = g.add_vertex_property( 'integer' )
num_components = bgl.connected_components( g, components )
# print num_components, 'connected component(s)'
result = { }
#
# For each component
#
if not os.path.exists( components_dir ):
os.mkdir( components_dir )
components_prefix = os.path.join( components_dir, name )
for i in range( num_components ): # for each component
# for testing just do one component
# if 2 != i: continue
# create an adjacency matrix for this component
adj_filename = components_prefix + '_' + str( i ) + '_adj.mm'
predicate = lambda v: components[ v ] == i
( num_vertices, num_non_zeros, vertex_indices, index_vertices ) = \
write_matrix_market_format(
g,
open( adj_filename, 'w' ),
predicate )
# we need a map from adjacency matrix row/col indices to nodes
adjacency_nodes = { }
idx = 1
for v in filter( predicate, g.vertices ):
adjacency_nodes[ idx ] = v
idx += 1
# create and run matlab script to calculate the kernel
kernel_filename = components_prefix + '_' + str( i ) + '_ker.mm'
if not os.path.exists( kernel_filename ):
tmp_script = "\
A = mmread( '%ADJACENCY_FILE%' );\n\
size( A )\n\
L = calc_laplacian( A );\n\
K = calc_kernel( L, %BETA% );\n\
K_sp = sparsify( K, %EPSILON% );\n\
mmwrite( '%KERNEL_FILE%', K_sp );\n\
exit\n"
script_text = tmp_script. \
replace( '%ADJACENCY_FILE%', adj_filename ). \
replace( '%EPSILON%', str( eps ) ). \
replace( '%BETA%', str( beta ) ). \
replace( '%KERNEL_FILE%', kernel_filename )
script_filename = 'tmp_script.m'
open( script_filename, 'w' ).write( script_text )
matlab_command = 'C:\\apps\\MATLAB7\\bin\\win32\\MATLAB.exe /nosplash ' \
+ '/r ' + script_filename.strip( '.m' )
# print matlab_command
os.system( matlab_command )
# once we have the kernel we can populate the edge weights
# are the kernel's values
f = open( kernel_filename, 'r' )
n = m = nnz = 0
node_ids = g.vertex_properties[ 'node_id' ]
for line in f: # this for loop reads a matrix market format file
if line.startswith( '%' ): continue
line = line.strip( '\n' )
split = line.split()
# Is this the first line of data with matrix sizes?
if n == 0:
( n, m, nnz ) = [ int( x ) for x in split ]
# print n, m, nnz
else:
idx1 = int( split[ 0 ] )
idx2 = int( split[ 1 ] )
prot1 = node_ids[ adjacency_nodes[ idx1 ] ]
prot2 = node_ids[ adjacency_nodes[ idx2 ] ]
# we are only interested in 'A' -> 'B' weights
if prot1.startswith( 'A_' ) and prot2.startswith( 'B_' ):
prot1 = prot1[ 2: ]
prot2 = prot2[ 2: ]
distance = float( split[ 2 ] )
# only put value in if prot1 < prot2
if prot1 <= prot2:
result[ ( prot1, prot2 ) ] = distance
return result
def graph_2_distance_map(g, components_dir, name, beta, eps):
"""Calculates the distances between all nodes in the graph
Returns a dict where tuples of node_ids in the graph are the keys and
the distances are the values. Distances below eps are not returned.
beta is the parameter for the diffusion kernel on the graph
components_dir is a directory to place working files in
name is a string to use when generating file names
"""
import boost.graph as bgl
#
# Calculate the connected components
#
# print 'Calculating connected components'
components = g.add_vertex_property('integer')
num_components = bgl.connected_components(g, components)
# print num_components, 'connected component(s)'
result = {}
#
# For each component
#
if not os.path.exists(components_dir):
os.mkdir(components_dir)
components_prefix = os.path.join(components_dir, name)
for i in range(num_components): # for each component
# for testing just do one component
# if 2 != i: continue
# create an adjacency matrix for this component
adj_filename = components_prefix + '_' + str(i) + '_adj.mm'
predicate = lambda v: components[v] == i
( num_vertices, num_non_zeros, vertex_indices, index_vertices ) = \
write_matrix_market_format(
g,
open( adj_filename, 'w' ),
predicate )
# we need a map from adjacency matrix row/col indices to nodes
adjacency_nodes = {}
idx = 1
for v in filter(predicate, g.vertices):
adjacency_nodes[idx] = v
idx += 1
# create and run matlab script to calculate the kernel
kernel_filename = components_prefix + '_' + str(i) + '_ker.mm'
if not os.path.exists(kernel_filename):
tmp_script = "\
A = mmread( '%ADJACENCY_FILE%' );\n\
size( A )\n\
L = calc_laplacian( A );\n\
K = calc_kernel( L, %BETA% );\n\
K_sp = sparsify( K, %EPSILON% );\n\
mmwrite( '%KERNEL_FILE%', K_sp );\n\
exit\n"
script_text = tmp_script. \
replace( '%ADJACENCY_FILE%', adj_filename ). \
replace( '%EPSILON%', str( eps ) ). \
replace( '%BETA%', str( beta ) ). \
replace( '%KERNEL_FILE%', kernel_filename )
script_filename = 'tmp_script.m'
open(script_filename, 'w').write(script_text)
matlab_command = 'C:\\apps\\MATLAB7\\bin\\win32\\MATLAB.exe /nosplash ' \
+ '/r ' + script_filename.strip( '.m' )
# print matlab_command
os.system(matlab_command)
# once we have the kernel we can populate the edge weights
# are the kernel's values
f = open(kernel_filename, 'r')
n = m = nnz = 0
node_ids = g.vertex_properties['node_id']
for line in f: # this for loop reads a matrix market format file
if line.startswith('%'): continue
line = line.strip('\n')
split = line.split()
# Is this the first line of data with matrix sizes?
if n == 0:
(n, m, nnz) = [int(x) for x in split]
# print n, m, nnz
else:
idx1 = int(split[0])
idx2 = int(split[1])
prot1 = node_ids[adjacency_nodes[idx1]]
prot2 = node_ids[adjacency_nodes[idx2]]
# we are only interested in 'A' -> 'B' weights
if prot1.startswith('A_') and prot2.startswith('B_'):
prot1 = prot1[2:]
prot2 = prot2[2:]
distance = float(split[2])
# only put value in if prot1 < prot2
if prot1 <= prot2:
result[(prot1, prot2)] = distance
return result
def __init__(self):
self.g = build_factor_synonyms_graph()
self.component_map = self.g.add_vertex_property(name='connected_components', type='integer')
self.num_components = bgl.connected_components(self.g, self.component_map)
self._build_partition_synonyms()
# Copyright (C) 2006 The Trustees of Indiana University.
# Use, modification and distribution is subject to the Boost Software
# License, Version 1.0. (See accompanying file LICENSE_1_0.txt or copy at
# http:#www.boost.org/LICENSE_1_0.txt)
# Authors: Douglas Gregor
# Andrew Lumsdaine
# Test property conversions
import boost.graph as bgl
g = bgl.Graph([(1, 2), (1, 3), (4, 6), (5, 6)])
color = g.add_vertex_property('color')
bgl.connected_components(g, color)
for v in g.vertices:
print color[v]
