def test_kdTrees(queryShape):
global methodList, exp_name
exp_name = 'kdTrees'
methodList = ['pure', 'true', 'standard', 'hybrid', 'cell', 'noisymean']
# Params.maxHeight = 8
epsList = [0.1, 0.5, 1.0]
data = data_readin()
res_cube_abs = np.zeros((len(epsList), len(seedList), len(methodList)))
res_cube_rel = np.zeros((len(epsList), len(seedList), len(methodList)))
for j in range(len(seedList)):
queryList = queryGen(queryShape, seedList[j])
kexp = KExp(data, queryList)
for i in range(len(epsList)):
for k in range(len(methodList)):
p = Params(seedList[j])
p.Eps = epsList[i]
if methodList[k] == 'pure':
res_cube_abs[i, j,
k], res_cube_rel[i, j,
k] = kexp.run_Kd_pure(p)
elif methodList[k] == 'true':
res_cube_abs[i, j,
k], res_cube_rel[i, j,
k] = kexp.run_Kd_true(p)
elif methodList[k] == 'standard':
res_cube_abs[i, j,
k], res_cube_rel[i, j,
k] = kexp.run_Kd_standard(p)
elif methodList[k] == 'hybrid':
res_cube_abs[i, j,
k], res_cube_rel[i, j,
k] = kexp.run_Kd_hybrid(p)
elif methodList[k] == 'noisymean':
res_cube_abs[i, j,
k], res_cube_rel[i, j,
k] = kexp.run_Kd_noisymean(p)
elif methodList[k] == 'cell':
res_cube_abs[i, j,
k], res_cube_rel[i, j,
k] = kexp.run_Kd_cell(p)
else:
logging.error('No such index structure!')
sys.exit(1)
res_abs_summary = np.average(res_cube_abs, axis=1)
res_rel_summary = np.average(res_cube_rel, axis=1)
np.savetxt(Params.resdir + exp_name + '_abs_' +
str(int(queryShape[0] * 10)) + '_' +
str(int(queryShape[1] * 10)),
res_abs_summary,
fmt='%.4f')
np.savetxt(Params.resdir + exp_name + '_rel_' +
str(int(queryShape[0] * 10)) + '_' +
str(int(queryShape[1] * 10)),
res_rel_summary,
fmt='%.4f')
def test_height(queryShape):
heightList = [6, 7, 8, 9, 10]
methodList = ['quad-opt', 'hybrid', 'cell', 'hilbert']
data = data_readin()
res_cube_abs = np.zeros((len(heightList), len(seedList), len(methodList)))
res_cube_rel = np.zeros((len(heightList), len(seedList), len(methodList)))
for j in range(len(seedList)):
queryList = queryGen(queryShape, seedList[j])
kexp = KExp(data, queryList)
p = Params(seedList[j])
for i in range(len(heightList)):
Params.maxHeight = heightList[i]
for k in range(len(methodList)):
if methodList[k] == 'quad-opt':
res_cube_abs[i, j, k], res_cube_rel[i, j, k] = kexp.run_Quad_opt(p)
elif methodList[k] == 'hybrid':
res_cube_abs[i, j, k], res_cube_rel[i, j, k] = kexp.run_Kd_hybrid(p)
elif methodList[k] == 'cell':
res_cube_abs[i, j, k], res_cube_rel[i, j, k] = kexp.run_Kd_cell(p)
elif methodList[k] == 'hilbert':
res_cube_abs[i, j, k], res_cube_rel[i, j, k] = kexp.run_Hilbert(p)
else:
logging.error('No such index structure!')
sys.exit(1)
res_abs_summary = np.average(res_cube_abs, axis=1)
res_rel_summary = np.average(res_cube_rel, axis=1)
for s in ['abs', 'rel']:
summary = eval('res_' + s + '_summary')
outName = Params.resdir + 'height_' + str(int(queryShape[0] * 10)) + '_' + str(int(queryShape[1] * 10)) + str
outFile = open(outName, 'w')
outFile.write('#; ' + '; '.join(methodList) + '\n')
for i in range(len(heightList)):
outFile.write(heightList[i])
for j in range(len(methodList)):
outFile.write(' ' + summary[i, j])
outFile.write('\n')
outFile.close()
def test_quadtreeOpt(queryShape):
global methodList, exp_name
exp_name = 'quadtreeOpt'
methodList = ['Quad-baseline', 'Quad-geo', 'Quad-post', 'Quad-opt']
# Params.maxHeight = 10
epsList = [0.1, 0.5, 1.0]
data = data_readin()
res_cube_abs = np.zeros((len(epsList), len(seedList), len(methodList)))
res_cube_rel = np.zeros((len(epsList), len(seedList), len(methodList)))
for j in range(len(seedList)):
queryList = queryGen(queryShape, seedList[j])
kexp = KExp(data, queryList)
for i in range(len(epsList)):
for k in range(len(methodList)):
p = Params(seedList[j])
p.Eps = epsList[i]
if methodList[k] == 'Quad-baseline':
res_cube_abs[i, j, k], res_cube_rel[i, j, k] = kexp.run_Quad_baseline(p)
elif methodList[k] == 'Quad-geo':
res_cube_abs[i, j, k], res_cube_rel[i, j, k] = kexp.run_Quad_geo(p)
elif methodList[k] == 'Quad-post':
res_cube_abs[i, j, k], res_cube_rel[i, j, k] = kexp.run_Quad_post(p)
elif methodList[k] == 'Quad-opt':
res_cube_abs[i, j, k], res_cube_rel[i, j, k] = kexp.run_Quad_opt(p)
else:
logging.error('No such index structure!')
sys.exit(1)
res_abs_summary = np.average(res_cube_abs, axis=1)
res_rel_summary = np.average(res_cube_rel, axis=1)
np.savetxt(Params.resdir + exp_name + '_abs_' + str(int(queryShape[0] * 10)) + '_' + str(int(queryShape[1] * 10)),
res_abs_summary, fmt='%.4f')
np.savetxt(Params.resdir + exp_name + '_rel_' + str(int(queryShape[0] * 10)) + '_' + str(int(queryShape[1] * 10)),
res_rel_summary, fmt='%.4f')
def test_quadtreeOpt(queryShape):
global methodList, exp_name
exp_name = 'quadtreeOpt'
methodList = ['Quad-baseline', 'Quad-geo', 'Quad-post', 'Quad-opt']
# Params.maxHeight = 10
epsList = [0.1, 0.5, 1.0]
data = data_readin()
res_cube_abs = np.zeros((len(epsList), len(seedList), len(methodList)))
res_cube_rel = np.zeros((len(epsList), len(seedList), len(methodList)))
for j in range(len(seedList)):
queryList = queryGen(queryShape, seedList[j])
kexp = KExp(data, queryList)
for i in range(len(epsList)):
for k in range(len(methodList)):
p = Params(seedList[j])
p.Eps = epsList[i]
if methodList[k] == 'Quad-baseline':
res_cube_abs[i, j, k], res_cube_rel[
i, j, k] = kexp.run_Quad_baseline(p)
elif methodList[k] == 'Quad-geo':
res_cube_abs[i, j,
k], res_cube_rel[i, j,
k] = kexp.run_Quad_geo(p)
elif methodList[k] == 'Quad-post':
res_cube_abs[i, j,
k], res_cube_rel[i, j,
k] = kexp.run_Quad_post(p)
elif methodList[k] == 'Quad-opt':
res_cube_abs[i, j,
k], res_cube_rel[i, j,
k] = kexp.run_Quad_opt(p)
else:
logging.error('No such index structure!')
sys.exit(1)
res_abs_summary = np.average(res_cube_abs, axis=1)
res_rel_summary = np.average(res_cube_rel, axis=1)
np.savetxt(Params.resdir + exp_name + '_abs_' +
str(int(queryShape[0] * 10)) + '_' +
str(int(queryShape[1] * 10)),
res_abs_summary,
fmt='%.4f')
np.savetxt(Params.resdir + exp_name + '_rel_' +
str(int(queryShape[0] * 10)) + '_' +
str(int(queryShape[1] * 10)),
res_rel_summary,
fmt='%.4f')
def test_height(queryShape):
heightList = [6, 7, 8, 9, 10]
methodList = ['quad-opt', 'hybrid', 'cell', 'hilbert']
data = data_readin()
res_cube_abs = np.zeros((len(heightList), len(seedList), len(methodList)))
res_cube_rel = np.zeros((len(heightList), len(seedList), len(methodList)))
for j in range(len(seedList)):
queryList = queryGen(queryShape, seedList[j])
kexp = KExp(data, queryList)
p = Params(seedList[j])
for i in range(len(heightList)):
Params.maxHeight = heightList[i]
for k in range(len(methodList)):
if methodList[k] == 'quad-opt':
res_cube_abs[i, j,
k], res_cube_rel[i, j,
k] = kexp.run_Quad_opt(p)
elif methodList[k] == 'hybrid':
res_cube_abs[i, j,
k], res_cube_rel[i, j,
k] = kexp.run_Kd_hybrid(p)
elif methodList[k] == 'cell':
res_cube_abs[i, j,
k], res_cube_rel[i, j,
k] = kexp.run_Kd_cell(p)
elif methodList[k] == 'hilbert':
res_cube_abs[i, j,
k], res_cube_rel[i, j,
k] = kexp.run_Hilbert(p)
else:
logging.error('No such index structure!')
sys.exit(1)
res_abs_summary = np.average(res_cube_abs, axis=1)
res_rel_summary = np.average(res_cube_rel, axis=1)
for s in ['abs', 'rel']:
summary = eval('res_' + s + '_summary')
outName = Params.resdir + 'height_' + str(int(
queryShape[0] * 10)) + '_' + str(int(queryShape[1] * 10)) + str
outFile = open(outName, 'w')
outFile.write('#; ' + '; '.join(methodList) + '\n')
for i in range(len(heightList)):
outFile.write(heightList[i])
for j in range(len(methodList)):
outFile.write(' ' + summary[i, j])
outFile.write('\n')
outFile.close()
def test_kdTrees(queryShape):
global methodList, exp_name
exp_name = 'kdTrees'
methodList = ['pure', 'true', 'standard', 'hybrid', 'cell', 'noisymean']
# Params.maxHeight = 8
epsList = [0.1, 0.5, 1.0]
data = data_readin()
res_cube_abs = np.zeros((len(epsList), len(seedList), len(methodList)))
res_cube_rel = np.zeros((len(epsList), len(seedList), len(methodList)))
for j in range(len(seedList)):
queryList = queryGen(queryShape, seedList[j])
kexp = KExp(data, queryList)
for i in range(len(epsList)):
for k in range(len(methodList)):
p = Params(seedList[j])
p.Eps = epsList[i]
if methodList[k] == 'pure':
res_cube_abs[i, j, k], res_cube_rel[i, j, k] = kexp.run_Kd_pure(p)
elif methodList[k] == 'true':
res_cube_abs[i, j, k], res_cube_rel[i, j, k] = kexp.run_Kd_true(p)
elif methodList[k] == 'standard':
res_cube_abs[i, j, k], res_cube_rel[i, j, k] = kexp.run_Kd_standard(p)
elif methodList[k] == 'hybrid':
res_cube_abs[i, j, k], res_cube_rel[i, j, k] = kexp.run_Kd_hybrid(p)
elif methodList[k] == 'noisymean':
res_cube_abs[i, j, k], res_cube_rel[i, j, k] = kexp.run_Kd_noisymean(p)
elif methodList[k] == 'cell':
res_cube_abs[i, j, k], res_cube_rel[i, j, k] = kexp.run_Kd_cell(p)
else:
logging.error('No such index structure!')
sys.exit(1)
res_abs_summary = np.average(res_cube_abs, axis=1)
res_rel_summary = np.average(res_cube_rel, axis=1)
np.savetxt(Params.resdir + exp_name + '_abs_' + str(int(queryShape[0] * 10)) + '_' + str(int(queryShape[1] * 10)),
res_abs_summary, fmt='%.4f')
np.savetxt(Params.resdir + exp_name + '_rel_' + str(int(queryShape[0] * 10)) + '_' + str(int(queryShape[1] * 10)),
res_rel_summary, fmt='%.4f')