def learn_metal_cdtw(parentLabels, parent, childLabels, child,
symmetric, squared, parameters=None):
"""learn optimal parameters for gem on metal data"""
if parameters == None:
parameters = np.array(range(21))*0.01
errors = []
for relative_window in parameters:
window = int(np.round(len(parent[0])*relative_window))
func = lambda query, subject: dist.cdtw(query, subject, window, squared)
e, l = c.obtain_1NN_error(parentLabels, parent,
childLabels, child, func)
errors.append((e, (relative_window, squared)))
print errors[-1]
errs = list(sorted(errors, key=lambda x: x[0][0]))
# pick only the best and take parameter in the "middle"
best = filter(lambda (x, y): x[0]==errs[0][0][0], errs)
best.sort(key=lambda (x, y): y[0])
best = best[len(best)/2]
return best, errs
def learn_metal_cdtw(parentLabels,
parent,
childLabels,
child,
symmetric,
squared,
parameters=None):
"""learn optimal parameters for gem on metal data"""
if parameters == None:
parameters = np.array(range(21)) * 0.01
errors = []
for relative_window in parameters:
window = int(np.round(len(parent[0]) * relative_window))
func = lambda query, subject: dist.cdtw(query, subject, window, squared
)
e, l = c.obtain_1NN_error(parentLabels, parent, childLabels, child,
func)
errors.append((e, (relative_window, squared)))
print errors[-1]
errs = list(sorted(errors, key=lambda x: x[0][0]))
# pick only the best and take parameter in the "middle"
best = filter(lambda (x, y): x[0] == errs[0][0][0], errs)
best.sort(key=lambda (x, y): y[0])
best = best[len(best) / 2]
return best, errs
def learn_metal_gem(parentLabels, parent, childLabels, child,
symmetric, squared, parameters=None):
"""learn optimal parameters for gem on metal data"""
if parameters == None:
ST0 = [1, 2]
ST1 = [1, 2]
TAU = [0,0.00390625,0.0078125,0.015625,0.03125,0.0625,0.125,0.25,0.5]
else:
ST0, ST1, TAU = parameters
errors = []
for St0 in ST0:
for St1 in ST1:
for E in TAU :
func = lambda query, subject: \
dist.gem(query, subject, St0, St1, E, symmetric, squared)
e, l = c.obtain_1NN_error(parentLabels, parent,
childLabels, child, func)
errors.append((e, (St0, St1, E, symmetric, squared)))
print errors[-1]
errs = list(sorted(errors, key=lambda x: x[0][0]))
# pick only the best and take parameter in the "middle"
best = filter(lambda (x, y): x[0]==errs[0][0][0], errs)
best.sort(key=lambda (x, y): y[2])
best = best[len(best)/2]
return best, errs
def learn_metal_gem(parentLabels,
parent,
childLabels,
child,
symmetric,
squared,
parameters=None):
"""learn optimal parameters for gem on metal data"""
if parameters == None:
ST0 = [1, 2]
ST1 = [1, 2]
TAU = [
0, 0.00390625, 0.0078125, 0.015625, 0.03125, 0.0625, 0.125, 0.25,
0.5
]
else:
ST0, ST1, TAU = parameters
errors = []
for St0 in ST0:
for St1 in ST1:
for E in TAU:
func = lambda query, subject: \
dist.gem(query, subject, St0, St1, E, symmetric, squared)
e, l = c.obtain_1NN_error(parentLabels, parent, childLabels,
child, func)
errors.append((e, (St0, St1, E, symmetric, squared)))
print errors[-1]
errs = list(sorted(errors, key=lambda x: x[0][0]))
# pick only the best and take parameter in the "middle"
best = filter(lambda (x, y): x[0] == errs[0][0][0], errs)
best.sort(key=lambda (x, y): y[2])
best = best[len(best) / 2]
return best, errs
f.write("# gem ((error, size, error/size), (St0, St1, E, sym, sqr))\n")
f.write("BESTLEARNCONSDTW=%s\n" % str(best_dtw))
f.write("LISTLEARNCONSDTW=%s\n\n" % str(l_dtw))
f.write("BESTLEARNGEM=%s\n" % str(best_gem))
f.write("LISTLEARNGEM=%s\n\n" % str(l_gem))
f.write("\n")
print "######################### Calculate Errors ########################"
# write error rates to logging file
f.write("# error rates for different distance measures\n")
f.write("# (error, size, error/size) and binary mask\n")
# obtain error for lp-norm
dist = ds.euc if squared else ds.man
E, L = cl.obtain_1NN_error(testLabels, testSet, trainLabels, trainSet, dist)
print "BESTLP=%s\n" % str(E)
f.write("BESTLP=%s\n" % str(E))
f.write("LISTLP=%s\n\n" % str(L))
# obtain error for unconstrained dtw
dist = lambda query, subject: ds.dtw(query, subject, squared)
E, L = cl.obtain_1NN_error(testLabels, testSet, trainLabels, trainSet, dist)
print "BESTFULLDTW=%s\n" % str(E)
f.write("BESTFULLDTW=%s\n" % str(E))
f.write("LISTFULLDTW=%s\n\n" % str(L))
# obtain error for constrained dtw
window = int(np.round(best_dtw[1][0]*len(trainSet[0])))
f.write("# gem ((error, size, error/size), (St0, St1, E, sym, sqr))\n")
f.write("BESTLEARNCONSDTW=%s\n" % str(best_dtw))
f.write("LISTLEARNCONSDTW=%s\n\n" % str(l_dtw))
f.write("BESTLEARNGEM=%s\n" % str(best_gem))
f.write("LISTLEARNGEM=%s\n\n" % str(l_gem))
f.write("\n")
print "######################### Calculate Errors ########################"
# write error rates to logging file
f.write("# error rates for different distance measures\n")
f.write("# (error, size, error/size) and binary mask\n")
# obtain error for lp-norm
dist = ds.euc if squared else ds.man
E, L = cl.obtain_1NN_error(testLabels, testSet, trainLabels, trainSet,
dist)
print "BESTLP=%s\n" % str(E)
f.write("BESTLP=%s\n" % str(E))
f.write("LISTLP=%s\n\n" % str(L))
# obtain error for unconstrained dtw
dist = lambda query, subject: ds.dtw(query, subject, squared)
E, L = cl.obtain_1NN_error(testLabels, testSet, trainLabels, trainSet,
dist)
print "BESTFULLDTW=%s\n" % str(E)
f.write("BESTFULLDTW=%s\n" % str(E))
f.write("LISTFULLDTW=%s\n\n" % str(L))
# obtain error for constrained dtw
f.write("BESTLEARNCONSDTWTWO=%s\n" % str(best_dtw_two))
f.write("LISTLEARNCONSDTWTWO=%s\n\n" % str(l_dtw_two))
f.write("BESTLEARNGEMTWO=%s\n" % str(best_gem_two))
f.write("LISTLEARNGEMTWO=%s\n\n" % str(l_gem_two))
f.write("\n")
print "######################### Calculate Errors ########################"
# write error rates to logging file
f.write("# error rates for different distance measures\n")
f.write("# (error, size, error/size) and binary mask\n")
# obtain error for lp-norm
dist = ds.euc if squared else ds.man
e, l = cl.obtain_1NN_error(parentLabels_one, zparent_one,
childLabels_one, zchild_one, dist)
print "BESTLPONE=%s\n" % str(e)
f.write("BESTLPONE=%s\n" % str(e))
f.write("LISTLPONE=%s\n\n" % str(l))
dist = ds.euc if squared else ds.man
e, l = cl.obtain_1NN_error(parentLabels_two, zparent_two,
childLabels_two, zchild_two, dist)
print "BESTLPTWO=%s\n" % str(e)
f.write("BESTLPTWO=%s\n" % str(e))
f.write("LISTLPTWO=%s\n\n" % str(l))
# obtain error for unconstrained dtw
f.write("LISTLEARNGEMONE=%s\n\n" % str(l_gem_one))
f.write("BESTLEARNCONSDTWTWO=%s\n" % str(best_dtw_two))
f.write("LISTLEARNCONSDTWTWO=%s\n\n" % str(l_dtw_two))
f.write("BESTLEARNGEMTWO=%s\n" % str(best_gem_two))
f.write("LISTLEARNGEMTWO=%s\n\n" % str(l_gem_two))
f.write("\n")
print "######################### Calculate Errors ########################"
# write error rates to logging file
f.write("# error rates for different distance measures\n")
f.write("# (error, size, error/size) and binary mask\n")
# obtain error for lp-norm
dist = ds.euc if squared else ds.man
e, l = cl.obtain_1NN_error(parentLabels_one, zparent_one, childLabels_one,
zchild_one, dist)
print "BESTLPONE=%s\n" % str(e)
f.write("BESTLPONE=%s\n" % str(e))
f.write("LISTLPONE=%s\n\n" % str(l))
dist = ds.euc if squared else ds.man
e, l = cl.obtain_1NN_error(parentLabels_two, zparent_two, childLabels_two,
zchild_two, dist)
print "BESTLPTWO=%s\n" % str(e)
f.write("BESTLPTWO=%s\n" % str(e))
f.write("LISTLPTWO=%s\n\n" % str(l))
# obtain error for unconstrained dtw
dist = lambda query, subject: ds.dtw(query, subject, squared)