def fit_randomWalk_mod(graphSize, graphID, dkPath, original2k, resultPath,
interval):
"""
Runs synthetic graph tests for various 'qe' and 'qv' values
If an interval was specified for fine grained sampling, please
note that the interal is closed bounds, that is [x, y] so qe_end and qv_end
will terminate after sampling the end values specified, not before.
"""
if not interval:
outfile = open(resultPath + graphID + '_rwCoarse_dkDistances.txt', 'w')
qe = 0.1
qe_end = 0.9
step = 0.1
else:
outfile = open(resultPath + graphID + '_rwFine_dkDistances.txt', 'w')
qe = interval[0] * 100
qe_end = interval[1] * 100
step = 1
outfile.write('dk-2 Distance\tqe\tqv\n')
while qe <= qe_end:
if not interval:
qv = 0.1
qv_end = 0.9
else:
qv = interval[2] * 100
qv_end = interval[3] * 100
while qv <= qv_end:
qeFloat = float(qe) / 100 if not interval else qe
qvFloat = float(qv) / 100 if not interval else qv
print 'Running modified Random Walk (coarse) with parameters: n = ', graphSize, ' qe = ', qeFloat, ' qv = ', qvFloat
newFile = graphID + '_rwCoarse_' + str(qeFloat) + '_' + str(
qvFloat)
# Create synthetic graph
syntheticGraph = sm.randomWalk_mod(graphSize, qeFloat, qvFloat)
# Write pickle, edge list, and 2k distro to file
print 'Calculating dK-2...\n'
getdk2(syntheticGraph, newFile, dkPath, resultPath)
# Find distance between the dK-2 distributions
dkDistance = tk.get_2k_distance(
original2k, resultPath + newFile + '_target.2k')
outfile.write(
str(dkDistance) + '\tqe = ' + str(qeFloat) + '\tqv = ' +
str(qvFloat) + '\n')
outfile.flush()
qv += step
qe += step
outfile.write('\n')
outfile.close()
def fit_forestFire_mod(graphSize, graphID, dkPath, original2k, resultPath):
"""
Runs synthetic graph tests for various 'p' values (burn rate).
"""
outfile = open(resultPath + graphID + '_ff_dkDistances.txt', 'w')
p = 0.01
while p < 1.0:
print 'Running modified Forest Fire with parameters: n = ', graphSize, ' p = ', p
newFile = graphID + '_ff_' + str(p)
# Create synthetic graph
syntheticGraph = sm.forestFire_mod(graphSize, p)
# Write pickle, edge list, and 2k distro to file
print 'Calculating dK-2...\n'
getdk2(syntheticGraph, newFile, dkPath, resultPath)
# Find distance between the dK-2 distributions
dkDistance = tk.get_2k_distance(original2k,
resultPath + newFile + '_target.2k')
outfile.write(str(dkDistance) + '\tp = ' + str(p) + '\n')
outfile.flush()
p += 0.01
outfile.close()
def fit_forestFire_mod(graphSize, graphID, dkPath, original2k, resultPath):
"""
Runs synthetic graph tests for various 'p' values (burn rate).
"""
outfile = open(resultPath + graphID + '_ff_dkDistances.txt', 'w')
p = 0.01
while p < 1.0:
print 'Running modified Forest Fire with parameters: n = ', graphSize, ' p = ', p
newFile = graphID + '_ff_' + str(p)
# Create synthetic graph
syntheticGraph = sm.forestFire_mod(graphSize, p)
# Write pickle, edge list, and 2k distro to file
print 'Writing pickle and calculating dK-2...\n'
nx.write_gpickle(syntheticGraph, resultPath + newFile + '.pickle')
getdk2(syntheticGraph, newFile, dkPath, resultPath)
# Find distance between the dK-2 distributions
dkDistance = tk.get_2k_distance(original2k, resultPath + newFile + '_target.2k')
outfile.write(str(dkDistance) + '\tp = ' + str(p) + '\n')
outfile.flush()
p += 0.01
outfile.close()
def fit_randomWalk_mod(graphSize, graphID, dkPath, original2k, resultPath, interval):
"""
Runs synthetic graph tests for various 'qe' and 'qv' values
If an interval was specified for fine grained sampling, please
note that the interal is closed bounds, that is [x, y] so qe_end and qv_end
will terminate after sampling the end values specified, not before.
"""
if not interval:
outfile = open(resultPath + graphID + '_rwCoarse_dkDistances.txt', 'w')
qe = 0.1
qe_end = 0.9
step = 0.1
else:
outfile = open(resultPath + graphID + '_rwFine_dkDistances.txt', 'w')
qe = interval[0] * 100
qe_end = interval[1] * 100
step = 1
outfile.write('dk-2 Distance\tqe\tqv\n')
while qe <= qe_end:
if not interval:
qv = 0.1
qv_end = 0.9
else:
qv = interval[2] * 100
qv_end = interval[3] * 100
while qv <= qv_end:
qeFloat = float(qe) / 100 if not interval else qe
qvFloat = float(qv) / 100 if not interval else qv
print 'Running modified Random Walk (coarse) with parameters: n = ', graphSize, ' qe = ', qeFloat, ' qv = ', qvFloat
newFile = graphID + '_rwCoarse_' + str(qeFloat) + '_' + str(qvFloat)
# Create synthetic graph
syntheticGraph = sm.randomWalk_mod(graphSize, qeFloat, qvFloat)
# Write pickle, edge list, and 2k distro to file
print 'Writing pickle and calculating dK-2...\n'
nx.write_gpickle(syntheticGraph, resultPath + newFile + '.pickle')
getdk2(syntheticGraph, newFile, dkPath, resultPath)
# Find distance between the dK-2 distributions
dkDistance = tk.get_2k_distance(original2k, resultPath + newFile + '_target.2k')
outfile.write(str(dkDistance) + '\tqe = ' + str(qeFloat) + '\tqv = ' + str(qvFloat) + '\n')
outfile.flush()
qv += step
qe += step
outfile.write('\n')
outfile.close()
def fit_nearestNeighbor_mod(graphSize, graphID, dkPath, original2k, resultPath,
k):
"""
Runs synthetic graph tests for various 'k' and 'u' values and stores them
"""
if k:
outfile = open(resultPath + graphID + '_nnFine_dkDistances.txt', 'w')
kList = [k]
uList = []
myU = 0.01
while myU < 1:
uList.append(myU)
myU += 0.01
else:
outfile = open(resultPath + graphID + '_nnCoarse_dkDistances.txt', 'w')
kList = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
uList = [
.01, .05, .10, .20, .25, .30, .35, .40, .45, .50, .55, .60, .65,
.70, .75, .80, .85, .95
]
outfile.write('dk-2 Distance\tk\tu\n')
for k in kList:
for u in uList:
print 'Running modified Nearest Neighbor with parameters: n = ', graphSize, ' k = ', k, ' u = ', u
newFile = graphID + '_nn_' + str(k) + '_' + str(u)
# Create synthetic graph
syntheticGraph = sm.nearestNeighbor_mod(graphSize, u, k)
# Write pickle, edge list, and 2k distro to file
print 'Writing pickle and calculating dK-2...\n'
nx.write_gpickle(syntheticGraph, resultPath + newFile + '.pickle')
getdk2(syntheticGraph, newFile, dkPath, resultPath)
# Find distance between the dK-2 distributions
dkDistance = tk.get_2k_distance(
original2k, resultPath + newFile + '_target.2k')
outfile.write(
str(dkDistance) + '\tk = ' + str(k) + '\tu = ' + str(u) + '\n')
outfile.flush()
outfile.write('\n')
outfile.close()
def fit_nearestNeighbor_mod(graphSize, graphID, dkPath, original2k, resultPath, k):
"""
Runs synthetic graph tests for various 'k' and 'u' values and stores them
"""
if k:
outfile = open(resultPath + graphID + '_nnFine_dkDistances.txt', 'w')
kList = [k]
uList = []
myU = 0.01
while myU < 1:
uList.append(myU)
myU += 0.01
else:
outfile = open(resultPath + graphID + '_nnCoarse_dkDistances.txt', 'w')
kList = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
uList = [.01, .05, .10, .20, .25,.30, .35, .40, .45, .50,
.55, .60, .65, .70, .75,.80, .85, .95]
outfile.write('dk-2 Distance\tk\tu\n')
for k in kList:
for u in uList:
print 'Running modified Nearest Neighbor with parameters: n = ', graphSize, ' k = ', k, ' u = ', u
newFile = graphID + '_nn_' + str(k) + '_' + str(u)
# Create synthetic graph
syntheticGraph = sm.nearestNeighbor_mod(graphSize, u, k)
# Write pickle, edge list, and 2k distro to file
print 'Writing pickle and calculating dK-2...\n'
nx.write_gpickle(syntheticGraph, resultPath + newFile + '.pickle')
getdk2(syntheticGraph, newFile, dkPath, resultPath)
# Find distance between the dK-2 distributions
dkDistance = tk.get_2k_distance(original2k, resultPath + newFile + '_target.2k')
outfile.write(str(dkDistance) + '\tk = ' + str(k) + '\tu = ' + str(u) + '\n')
outfile.flush()
outfile.write('\n')
outfile.close()