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])))
dist = lambda query, subject: ds.cdtw(query, subject, window, squared)
E, L = cl.obtain_1NN_error(testLabels, testSet, trainLabels, trainSet, dist)
print "BESTCONSDTW=%s\n" % str(E)
f.write("BESTCONSDTW=%s\n" % str(E))
f.write("LISTCONSDTW=%s\n\n" % str(L))
# obtain error for gem
St0, St1, E = best_gem[1][0], best_gem[1][1], best_gem[1][2]
dist = lambda query, subject: \
ds.gem(query, subject, St0, St1, E, symmetric, squared)
E, L = cl.obtain_1NN_error(testLabels, testSet, trainLabels, trainSet, dist)
print "BESTGEM=%s\n" % str(E)
f.write("BESTGEM=%s\n" % str(E))
f.write("LISTGEM=%s\n\n" % str(L))
# taken from dn_M-sn_0-lp_100-sq_True-sy_True LISTGEMONE
Mask = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0]
# get misclassified time series
for index in zip(*filter(lambda (i, v): v[0] == 1 and v[1] == 0,
enumerate(zip(mask, Mask))))[0][2:]:
pl.figure(1, figsize=(21, 14))
ax = pl.subplot("211")
# get query
query = zparent[100+index]
# taken from dn_M-sn_0-lp_100-sq_True-sy_True BESTLEARNCONSDTWONE
window = int(np.round(0.01*len(parent[0])))
dist_func= lambda query, subject: ds.cdtw(query, subject, window, True)
# search nearest neighbor for cdtw
dist = [dist_func(query, subject) for subject in zchild[100:500]]
best = np.array(dist).argmin()
# entries of distance matrix
AB = ds.cdtw(query, zchild[100+index], window, True)
AC = np.array(dist).min()
BC = ds.cdtw(zchild[100+best], zchild[100+index], window, True)
print index, best, AC, AB
# distance matrix
M = np.array([[0, AB, AC], [AB, 0, BC], [AC, BC, 0]])
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])))
dist = lambda query, subject: ds.cdtw(query, subject, window, squared)
E, L = cl.obtain_1NN_error(testLabels, testSet, trainLabels, trainSet,
dist)
print "BESTCONSDTW=%s\n" % str(E)
f.write("BESTCONSDTW=%s\n" % str(E))
f.write("LISTCONSDTW=%s\n\n" % str(L))
# obtain error for gem
St0, St1, E = best_gem[1][0], best_gem[1][1], best_gem[1][2]
dist = lambda query, subject: \
ds.gem(query, subject, St0, St1, E, symmetric, squared)
E, L = cl.obtain_1NN_error(testLabels, testSet, trainLabels, trainSet,
dist)
print "BESTGEM=%s\n" % str(E)
0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0
]
# get misclassified time series
for index in zip(*filter(lambda (i, v): v[0] == 1 and v[1] == 0,
enumerate(zip(mask, Mask))))[0][2:]:
pl.figure(1, figsize=(21, 14))
ax = pl.subplot("211")
# get query
query = zparent[100 + index]
# taken from dn_M-sn_0-lp_100-sq_True-sy_True BESTLEARNCONSDTWONE
window = int(np.round(0.01 * len(parent[0])))
dist_func = lambda query, subject: ds.cdtw(query, subject, window, True)
# search nearest neighbor for cdtw
dist = [dist_func(query, subject) for subject in zchild[100:500]]
best = np.array(dist).argmin()
# entries of distance matrix
AB = ds.cdtw(query, zchild[100 + index], window, True)
AC = np.array(dist).min()
BC = ds.cdtw(zchild[100 + best], zchild[100 + index], window, True)
print index, best, AC, AB
# distance matrix
M = np.array([[0, AB, AC], [AB, 0, BC], [AC, BC, 0]])
