def training_lda_TD4_intra(my_clfs, trains, classes, **kw):
start_time = time.time()
if (kw.has_key('log_fold')):
log_fold = root_path + '/result/' + kw['log_fold']
new_fold(log_fold)
channel_pos = kw['log_file_pos']
print "----training TD4 intra, channel_pos: ", channel_pos
cv = 5
results = []
results.append(
['Feat', 'Algorithm', 'Channel_Pos', 'Proportion', 'Accuracy', 'std'])
log_file = 'feat_TD4_intra'
clf = sklearn.lda.LDA(solver='svd',
shrinkage=None,
priors=None,
n_components=None,
store_covariance=False,
tol=0.0001)
# X_train, X_test, y_train, y_test = sklearn.cross_validation.train_test_split(trains, classes, test_size=0.8, random_state=random.randrange(1,51))
# # proportion is 1.0
# X_train, X_test, y_train, y_test = sklearn.cross_validation.train_test_split(trains, classes, test_size=0.8, random_state=random.randrange(1,51))
# scores = clf.fit(X_train, y_train).score(X_test, y_test)
# results.append(['feat_TD4', "LDA(solver='svd', shrinkage=None, priors=None, n_components=None, store_covariance=False, tol=0.0001)", 1, scores.mean(), scores.std()])
# test_rate_list = [i for i in range(1,9)]
# test_rate_list = [0.2, 0.4, 0.6, 0.8]
test_rate_list = [0.2]
# proportion is 1.0 cv=5
scores = sklearn.cross_validation.cross_val_score(clf,
trains,
classes,
cv=10)
results.append([
'feat_TD4_cv_5', 'lda(svd;tol=0.0001)', channel_pos, 1.0,
scores.mean(),
scores.std()
])
for i in test_rate_list:
iteration = 5
scores_1_0 = np.zeros((iteration + 1, ))
scores_1_1 = np.zeros((iteration + 1, ))
scores_1_2 = np.zeros((iteration + 1, ))
scores_1_12 = np.zeros((iteration + 1, ))
scores_0_9 = np.zeros((iteration + 1, ))
scores_0_8 = np.zeros((iteration + 1, ))
scores_0_89 = np.zeros((iteration + 1, ))
scores_19 = np.zeros((iteration + 1, ))
scores_1289 = np.zeros((iteration + 1, ))
while (iteration >= 0):
X_train, X_test, y_train, y_test = sklearn.cross_validation.train_test_split(
trains,
classes,
test_size=i,
random_state=random.randrange(1, 51))
print 'iteration: ', str(6 - iteration), ', test_size:', i
# print X_train.shape, X_test.shape, y_train.shape, y_test.shape
# proportion is 1.0
scores = clf.fit(X_train, y_train).score(X_test, y_test)
scores_1_0[iteration] = scores.mean()
# results.append(['feat_TD4', 'lda(svd,tol=0.0001)', 1.0, scores.mean(), scores.std()])
# proportion is 1.1
# X_train, X_test, y_train, y_test = sklearn.cross_validation.train_test_split(trains, classes, test_size=0.2, random_state=random.randrange(1,51))
trains_noise_1, classes_noise_1 = proportion_simu(
X_train, y_train, 0.9)
scores = clf.fit(trains_noise_1,
classes_noise_1).score(X_test, y_test)
scores_1_1[iteration] = scores.mean()
# proportion is 1.2
# X_train, X_test, y_train, y_test = sklearn.cross_validation.train_test_split(trains, classes, test_size=0.2, random_state=random.randrange(1,51))
trains_noise_2, classes_noise_2 = proportion_simu(
X_train, y_train, 0.8)
scores = clf.fit(trains_noise_2,
classes_noise_2).score(X_test, y_test)
scores_1_2[iteration] = scores.mean()
# # proportion is 1.1 + 1.2
trains_noise_12 = np.concatenate((trains_noise_1, trains_noise_2),
axis=0)
classes_noise_12 = np.concatenate(
(classes_noise_1, classes_noise_2), axis=0)
scores = clf.fit(trains_noise_12,
classes_noise_12).score(X_test, y_test)
scores_1_12[iteration] = scores.mean()
# proportion is 0.9
# X_train, X_test, y_train, y_test = sklearn.cross_validation.train_test_split(trains, classes, test_size=0.2, random_state=random.randrange(1,51))
trains_noise_9, classes_noise_9 = proportion_simu(
X_train, y_train, 0.9)
scores = clf.fit(trains_noise_9,
classes_noise_9).score(X_test, y_test)
scores_0_9[iteration] = scores.mean()
# results.append(['feat_TD4', 'lda(svd,tol=0.0001)', '0.9', scores.mean(), scores.std()])
# proportion is 0.8
# X_train, X_test, y_train, y_test = sklearn.cross_validation.train_test_split(trains, classes, test_size=0.2, random_state=random.randrange(1,51))
trains_noise_8, classes_noise_8 = proportion_simu(
X_train, y_train, 0.8)
scores = clf.fit(trains_noise_8,
classes_noise_8).score(X_test, y_test)
scores_0_8[iteration] = scores.mean()
# results.append(['feat_TD4', 'lda(svd,tol=0.0001)', '0.8', scores.mean(), scores.std()])
# # proportion is 0.8 + 0.9
trains_noise_89 = np.concatenate((trains_noise_8, trains_noise_9),
axis=0)
classes_noise_89 = np.concatenate(
(classes_noise_8, classes_noise_9), axis=0)
scores = clf.fit(trains_noise_89,
classes_noise_89).score(X_test, y_test)
scores_0_89[iteration] = scores.mean()
# results.append(['feat_TD4', 'lda(svd,tol=0.0001)', '0.8+0.9', scores.mean(), scores.std()])
# # proportion is 1.1 + 0.9
trains_noise_19 = np.concatenate((trains_noise_1, trains_noise_9),
axis=0)
classes_noise_19 = np.concatenate(
(classes_noise_1, classes_noise_9), axis=0)
scores = clf.fit(trains_noise_19,
classes_noise_19).score(X_test, y_test)
scores_19[iteration] = scores.mean()
# # proportion is 1.1 + 0.9 + 1.2 + 0.8
trains_noise_1289 = np.concatenate(
(trains_noise_12, trains_noise_89), axis=0)
classes_noise_1289 = np.concatenate(
(classes_noise_12, classes_noise_89), axis=0)
scores = clf.fit(trains_noise_1289,
classes_noise_1289).score(X_test, y_test)
scores_1289[iteration] = scores.mean()
# # proportion is 0.7
# # X_train, X_test, y_train, y_test = sklearn.cross_validation.train_test_split(trains, classes, test_size=0.2, random_state=random.randrange(1,51))
# trains_noise, classes_noise = proportion_simu(X_train, y_train, 0.7)
# scores = clf.fit(trains_noise, classes_noise).score(X_test, y_test)
# results.append(['feat_TD4', 'lda(svd,tol=0.0001)', '0.7', scores.mean(), scores.std()])
# # proportion is 0.6
# # X_train, X_test, y_train, y_test = sklearn.cross_validation.train_test_split(trains, classes, test_size=0.2, random_state=random.randrange(1,51))
# trains_noise, classes_noise = proportion_simu(X_train, y_train, 0.6)
# scores = clf.fit(trains_noise, classes_noise).score(X_test, y_test)
# results.append(['feat_TD4', 'lda(svd,tol=0.0001)', '0.6', scores.mean(), scores.std()])
iteration -= 1
results.append([
'feat_TD4', 'lda(svd;tol=0.0001;test_rate=' + str(i) + ')',
channel_pos, '1.0',
np.mean(scores_1_0),
np.std(scores_1_0)
])
results.append([
'feat_TD4', 'lda(svd;tol=0.0001;test_rate=' + str(i) + ')',
channel_pos, '0.9',
np.mean(scores_0_9),
np.std(scores_0_9)
])
results.append([
'feat_TD4', 'lda(svd;tol=0.0001;test_rate=' + str(i) + ')',
channel_pos, '0.8',
np.mean(scores_0_8),
np.std(scores_0_8)
])
results.append([
'feat_TD4', 'lda(svd;tol=0.0001;test_rate=' + str(i) + ')',
channel_pos, '0.8+0.9',
np.mean(scores_0_89),
np.std(scores_0_89)
])
results.append([
'feat_TD4', 'lda(svd;tol=0.0001;test_rate=' + str(i) + ')',
channel_pos, '1.1',
np.mean(scores_1_1),
np.std(scores_1_1)
])
results.append([
'feat_TD4', 'lda(svd;tol=0.0001;test_rate=' + str(i) + ')',
channel_pos, '1.2',
np.mean(scores_1_2),
np.std(scores_1_2)
])
results.append([
'feat_TD4', 'lda(svd;tol=0.0001;test_rate=' + str(i) + ')',
channel_pos, '1.1+0.9',
np.mean(scores_19),
np.std(scores_19)
])
results.append([
'feat_TD4', 'lda(svd;tol=0.0001;test_rate=' + str(i) + ')',
channel_pos, '1.1+0.9+1.2+0.8',
np.mean(scores_1289),
np.std(scores_1289)
])
log_result(
results,
log_fold + '/' + log_file + '_' + channel_pos + '_' + str(kw['num']),
2)
print '----Log Fold:', log_fold, ', log_file: ', log_file + '_' + channel_pos + '_' + str(
kw['num'])
print '----training TD4 time elapsed:', time.time() - start_time
def training_lda_TD4_cross(my_clfs, X_train, y_train, X_test, y_test, **kw):
start_time = time.time()
print "training TD4 cross............."
if (kw.has_key('log_fold')):
log_fold = root_path + '/result/' + kw['log_fold']
if os.path.isdir(log_fold) == False:
try:
os.makedirs(log_fold)
except:
print "Can not create log fold! "
return
os.chdir(log_fold)
results = []
results.append(['Feat', 'Algorithm', 'Proportion', 'Accuracy', 'std'])
log_file = 'feat_TD4'
clf = sklearn.lda.LDA(solver='svd',
shrinkage=None,
priors=None,
n_components=None,
store_covariance=False,
tol=0.0001)
test_rate_list = [0.2, 0.4, 0.6, 0.8]
for i in test_rate_list:
iteration = 4
scores_1_0 = np.zeros((iteration + 1, ))
scores_0_9 = np.zeros((iteration + 1, ))
scores_0_8 = np.zeros((iteration + 1, ))
scores_0_89 = np.zeros((iteration + 1, ))
while (iteration >= 0):
print 'test_rate:', i, 'iteration:', iteration
# X_train, X_test, y_train, y_test = sklearn.cross_validation.train_test_split(trains, classes, test_size=i, random_state=random.randrange(1,51))
# print 'test_size:', i
print X_train.shape, X_test.shape, y_train.shape, y_test.shape
# proportion is 1.0
scores = clf.fit(X_train, y_train).score(X_test, y_test)
scores_1_0[iteration] = scores.mean()
# results.append(['feat_TD4', 'lda(svd,tol=0.0001)', 1.0, scores.mean(), scores.std()])
# proportion is 0.9
# X_train, X_test, y_train, y_test = sklearn.cross_validation.train_test_split(trains, classes, test_size=0.2, random_state=random.randrange(1,51))
trains_noise_1, classes_noise_1 = proportion_simu(
X_train, y_train, 0.9)
scores = clf.fit(trains_noise_1,
classes_noise_1).score(X_test, y_test)
scores_0_9[iteration] = scores.mean()
# results.append(['feat_TD4', 'lda(svd,tol=0.0001)', '0.9', scores.mean(), scores.std()])
# proportion is 0.8
# X_train, X_test, y_train, y_test = sklearn.cross_validation.train_test_split(trains, classes, test_size=0.2, random_state=random.randrange(1,51))
trains_noise_2, classes_noise_2 = proportion_simu(
X_train, y_train, 0.8)
scores = clf.fit(trains_noise_2,
classes_noise_2).score(X_test, y_test)
scores_0_8[iteration] = scores.mean()
# results.append(['feat_TD4', 'lda(svd,tol=0.0001)', '0.8', scores.mean(), scores.std()])
# # proportion is 0.8 + 0.9
trains_noise_3 = np.concatenate((trains_noise_1, trains_noise_2),
axis=0)
classes_noise_3 = np.concatenate(
(classes_noise_1, classes_noise_2), axis=0)
scores = clf.fit(trains_noise_3,
classes_noise_3).score(X_test, y_test)
scores_0_89[iteration] = scores.mean()
# results.append(['feat_TD4', 'lda(svd,tol=0.0001)', '0.8+0.9', scores.mean(), scores.std()])
iteration -= 1
results.append([
'feat_TD4', 'lda(svd;tol=0.0001;test_rate=' + str(i) + ')', '1.0',
np.mean(scores_1_0),
np.std(scores_1_0)
])
results.append([
'feat_TD4', 'lda(svd;tol=0.0001;test_rate=' + str(i) + ')', '0.9',
np.mean(scores_0_9),
np.std(scores_0_9)
])
results.append([
'feat_TD4', 'lda(svd;tol=0.0001;test_rate=' + str(i) + ')', '0.8',
np.mean(scores_0_8),
np.std(scores_0_8)
])
results.append([
'feat_TD4', 'lda(svd;tol=0.0001;test_rate=' + str(i) + ')',
'0.8+0.9',
np.mean(scores_0_89),
np.std(scores_0_89)
])
log_result(results, log_file + '_' + str(kw['num']), 2)
print 'training TD4 time elapsed:', time.time() - start_time
def training_lda_TD4(my_clfs, trains, classes, **kw):
start_time = time.time()
print "training TD4............."
cv = 5
if (kw.has_key('log_fold')):
log_fold = root_path + '/result/' + kw['log_fold']
if os.path.isdir(log_fold) == False:
try:
os.makedirs(log_fold)
except:
print "Can not create log fold! "
return
os.chdir(log_fold)
results = []
results.append(['Feat', 'Algorithm', 'Proportion', 'Accuracy', 'std'])
log_file = 'feat_TD4'
clf = sklearn.lda.LDA(solver='svd',
shrinkage=None,
priors=None,
n_components=None,
store_covariance=False,
tol=0.0001)
# X_train, X_test, y_train, y_test = sklearn.cross_validation.train_test_split(trains, classes, test_size=0.8, random_state=random.randrange(1,51))
# # proportion is 1.0
# X_train, X_test, y_train, y_test = sklearn.cross_validation.train_test_split(trains, classes, test_size=0.8, random_state=random.randrange(1,51))
# scores = clf.fit(X_train, y_train).score(X_test, y_test)
# results.append(['feat_TD4', "LDA(solver='svd', shrinkage=None, priors=None, n_components=None, store_covariance=False, tol=0.0001)", 1, scores.mean(), scores.std()])
iteration = 10
while (iteration > 0):
# proportion is 1.0 cv=5
X_train, X_test, y_train, y_test = sklearn.cross_validation.train_test_split(
trains,
classes,
test_size=0.8,
random_state=random.randrange(1, 51))
scores = sklearn.cross_validation.cross_val_score(clf,
trains,
classes,
cv=5)
results.append([
'feat_TD4',
"LDA(solver='svd', shrinkage=None, priors=None, n_components=None, store_covariance=False, tol=0.0001)",
1,
scores.mean(),
scores.std()
])
# proportion is 0.9
X_train, X_test, y_train, y_test = sklearn.cross_validation.train_test_split(
trains,
classes,
test_size=0.2,
random_state=random.randrange(1, 51))
trains_noise_1, classes_noise_1 = proportion_simu(
X_train, y_train, 0.9)
scores = clf.fit(trains_noise_1, classes_noise_1).score(X_test, y_test)
results.append(['feat_TD4', clf, '0.9', scores.mean(), scores.std()])
# proportion is 0.8
X_train, X_test, y_train, y_test = sklearn.cross_validation.train_test_split(
trains,
classes,
test_size=0.2,
random_state=random.randrange(1, 51))
trains_noise_2, classes_noise_2 = proportion_simu(
X_train, y_train, 0.8)
scores = clf.fit(trains_noise_2, classes_noise_2).score(X_test, y_test)
results.append(['feat_TD4', clf, '0.8', scores.mean(), scores.std()])
# proportion is 0.8 + 0.9
trains_noise_3 = np.concatenate((trains_noise_1, trains_noise_2),
axis=0)
classes_noise_3 = np.concatenate((classes_noise_1, classes_noise_2),
axis=0)
scores = clf.fit(trains_noise_3, classes_noise_3).score(X_test, y_test)
results.append(
['feat_TD4', clf, '0.8+0.9',
scores.mean(),
scores.std()])
# proportion is 0.7
X_train, X_test, y_train, y_test = sklearn.cross_validation.train_test_split(
trains,
classes,
test_size=0.2,
random_state=random.randrange(1, 51))
trains_noise, classes_noise = proportion_simu(X_train, y_train, 0.7)
scores = clf.fit(trains_noise, classes_noise).score(X_test, y_test)
results.append(['feat_TD4', clf, '0.7', scores.mean(), scores.std()])
# proportion is 0.6
X_train, X_test, y_train, y_test = sklearn.cross_validation.train_test_split(
trains,
classes,
test_size=0.2,
random_state=random.randrange(1, 51))
trains_noise, classes_noise = proportion_simu(X_train, y_train, 0.6)
scores = clf.fit(trains_noise, classes_noise).score(X_test, y_test)
results.append(['feat_TD4', clf, '0.6', scores.mean(), scores.std()])
iteration -= 1
log_result(results, log_file + '_without_noise', 2)
print 'training TD4 time elapsed:', time.time() - start_time
def training_lda_signal(my_clfs, trains, classes, **kw):
start_time = time.time()
print "training lda signal............."
cv = 5
if(kw.has_key('log_fold')):
log_fold = root_path + '/result/' + kw['log_fold']
if os.path.isdir(log_fold) == False:
try:
os.makedirs(log_fold)
except:
print "Can not create log fold! "
return
os.chdir(log_fold)
results = []
results.append(['Feat', 'Algorithm', 'Proportion', 'Accuracy', 'std'])
log_file = 'signal'
clf = sklearn.lda.LDA(solver='svd', shrinkage=None, priors=None,
n_components=None, store_covariance=False, tol=0.0001)
# X_train, X_test, y_train, y_test = sklearn.cross_validation.train_test_split(trains, classes, test_size=0.8, random_state=random.randrange(1,51))
# test_rate_list = [i for i in range(1,9)]
test_rate_list = [0.2, 0.4, 0.6, 0.8]
# proportion is 1.0 cv=5
# scores = sklearn.cross_validation.cross_val_score(clf, trains, classes, cv=10)
# results.append(['signal_cv_10', 'lda(svd,tol=0.0001)', 1.0, scores.mean(), scores.std()])
for i in test_rate_list:
iteration = 9
scores_1_0 = np.zeros((iteration + 1,))
scores_0_9 = np.zeros((iteration + 1,))
scores_0_8 = np.zeros((iteration + 1,))
scores_0_89 = np.zeros((iteration + 1,))
while(iteration >= 0):
X_train, X_test, y_train, y_test = sklearn.cross_validation.train_test_split(
trains, classes, test_size=i, random_state=random.randrange(1, 51))
print 'test_size:', i
print X_train.shape, X_test.shape, y_train.shape, y_test.shape
# proportion is 1.0
scores = clf.fit(X_train, y_train).score(X_test, y_test)
scores_1_0[iteration] = scores.mean()
# results.append(['feat_TD4', 'lda(svd,tol=0.0001)', 1.0, scores.mean(), scores.std()])
# proportion is 0.9
# X_train, X_test, y_train, y_test = sklearn.cross_validation.train_test_split(trains, classes, test_size=0.2, random_state=random.randrange(1,51))
trains_noise_1, classes_noise_1 = proportion_simu(
X_train, y_train, 0.9)
scores = clf.fit(trains_noise_1, classes_noise_1).score(
X_test, y_test)
scores_0_9[iteration] = scores.mean()
# results.append(['feat_TD4', 'lda(svd,tol=0.0001)', '0.9', scores.mean(), scores.std()])
# proportion is 0.8
# X_train, X_test, y_train, y_test = sklearn.cross_validation.train_test_split(trains, classes, test_size=0.2, random_state=random.randrange(1,51))
trains_noise_2, classes_noise_2 = proportion_simu(
X_train, y_train, 0.8)
scores = clf.fit(trains_noise_2, classes_noise_2).score(
X_test, y_test)
scores_0_8[iteration] = scores.mean()
# results.append(['feat_TD4', 'lda(svd,tol=0.0001)', '0.8', scores.mean(), scores.std()])
# # proportion is 0.8 + 0.9
trains_noise_3 = np.concatenate(
(trains_noise_1, trains_noise_2), axis=0)
classes_noise_3 = np.concatenate(
(classes_noise_1, classes_noise_2), axis=0)
scores = clf.fit(trains_noise_3, classes_noise_3).score(
X_test, y_test)
scores_0_89[iteration] = scores.mean()
# results.append(['feat_TD4', 'lda(svd,tol=0.0001)', '0.8+0.9', scores.mean(), scores.std()])
iteration -= 1
results.append(['signal', 'lda(svd,tol=0.0001,test_rate=' +
str(i) + ')', '1.0', np.mean(scores_1_0), np.std(scores_1_0)])
results.append(['signal', 'lda(svd,tol=0.0001,test_rate=' +
str(i) + ')', '0.9', np.mean(scores_0_9), np.std(scores_0_9)])
results.append(['signal', 'lda(svd,tol=0.0001,test_rate=' +
str(i) + ')', '0.8', np.mean(scores_0_8), np.std(scores_0_8)])
results.append(['signal', 'lda(svd,tol=0.0001,test_rate=' + str(i) + ')',
'0.8+0.9', np.mean(scores_0_89), np.std(scores_0_89)])
log_result(results, log_file + '_' + str(kw['num']), 2)
print 'training TD4 time elapsed:', time.time() - start_time
def training_lda_TD4_inter(my_clfs, trains, tests, classes, **kw):
start_time = time.time()
if(kw.has_key('log_fold')):
log_fold = root_path + '/result/' + kw['log_fold']
new_fold(log_fold)
chan_len = kw['chan_len']
action_num = kw['action_num']
print "----training TD4 inter, training by position O, testing by electrode shift ",
cv = 5
results = []
results.append(
['Feat', 'Algorithm','Channel_Pos', 'Proportion', 'Accuracy', 'std'])
log_file = 'feat_TD4_inter'
clf = sklearn.lda.LDA(solver='svd', shrinkage=None, priors=None,
n_components=None, store_covariance=False,
tol=0.0001)
test_rate_list = [0.2]
scores = sklearn.cross_validation.cross_val_score(
clf, trains, classes, cv=10)
results.append(['feat_TD4_cv_5', 'lda', 'O',
1.0, scores.mean(), scores.std()])
for i in test_rate_list:
for idx, channel_pos in enumerate(kw['pos_list']):
X_train = trains
y_train = classes
X_test = tests[:,idx*chan_len:idx*chan_len+chan_len]
y_test = classes
# print tests.shape, chan_len+chan_len
# print X_train.shape, y_train.shape, X_test.shape
# sys.exit(0)
iteration = 1
scores_1_0 = np.zeros((iteration + 1,))
scores_1_1 = np.zeros((iteration + 1,))
scores_1_2 = np.zeros((iteration + 1,))
scores_1_12 = np.zeros((iteration + 1,))
scores_0_9 = np.zeros((iteration + 1,))
scores_0_8 = np.zeros((iteration + 1,))
scores_0_89 = np.zeros((iteration + 1,))
scores_19 = np.zeros((iteration + 1,))
scores_1289 = np.zeros((iteration + 1,))
while(iteration >= 0):
# print 'iteration: ', str(1-iteration), ', test_size:', i
# print X_train.shape, X_test.shape, y_train.shape, y_test.shape
# proportion is 1.0
scores = clf.fit(X_train, y_train).score(X_test, y_test)
scores_1_0[iteration] = scores.mean()
# results.append(['feat_TD4', 'lda(svd,tol=0.0001)', 1.0, scores.mean(), scores.std()])
# proportion is 1.1
# X_train, X_test, y_train, y_test = sklearn.cross_validation.train_test_split(trains, classes, test_size=0.2, random_state=random.randrange(1,51))
trains_noise_1, classes_noise_1 = proportion_simu(
X_train, y_train, 1.1)
scores = clf.fit(trains_noise_1, classes_noise_1).score(
X_test, y_test)
scores_1_1[iteration] = scores.mean()
# proportion is 1.2
# X_train, X_test, y_train, y_test = sklearn.cross_validation.train_test_split(trains, classes, test_size=0.2, random_state=random.randrange(1,51))
trains_noise_2, classes_noise_2 = proportion_simu(
X_train, y_train, 1.2)
scores = clf.fit(trains_noise_2, classes_noise_2).score(
X_test, y_test)
scores_1_2[iteration] = scores.mean()
# # proportion is 1.1 + 1.2
trains_noise_12 = np.concatenate(
(trains_noise_1, trains_noise_2), axis=0)
classes_noise_12 = np.concatenate(
(classes_noise_1, classes_noise_2), axis=0)
scores = clf.fit(trains_noise_12, classes_noise_12).score(
X_test, y_test)
scores_1_12[iteration] = scores.mean()
# proportion is 0.9
# X_train, X_test, y_train, y_test = sklearn.cross_validation.train_test_split(trains, classes, test_size=0.2, random_state=random.randrange(1,51))
trains_noise_9, classes_noise_9 = proportion_simu(
X_train, y_train, 0.9)
scores = clf.fit(trains_noise_9, classes_noise_9).score(
X_test, y_test)
scores_0_9[iteration] = scores.mean()
# results.append(['feat_TD4', 'lda(svd,tol=0.0001)', '0.9', scores.mean(), scores.std()])
# proportion is 0.8
# X_train, X_test, y_train, y_test = sklearn.cross_validation.train_test_split(trains, classes, test_size=0.2, random_state=random.randrange(1,51))
trains_noise_8, classes_noise_8 = proportion_simu(
X_train, y_train, 0.8)
scores = clf.fit(trains_noise_8, classes_noise_8).score(
X_test, y_test)
scores_0_8[iteration] = scores.mean()
# results.append(['feat_TD4', 'lda(svd,tol=0.0001)', '0.8', scores.mean(), scores.std()])
# # proportion is 0.8 + 0.9
trains_noise_89 = np.concatenate(
(trains_noise_8, trains_noise_9), axis=0)
classes_noise_89 = np.concatenate(
(classes_noise_8, classes_noise_9), axis=0)
scores = clf.fit(trains_noise_89, classes_noise_89).score(
X_test, y_test)
scores_0_89[iteration] = scores.mean()
# results.append(['feat_TD4', 'lda(svd,tol=0.0001)', '0.8+0.9', scores.mean(), scores.std()])
# # proportion is 1.1 + 0.9
trains_noise_19 = np.concatenate(
(trains_noise_1, trains_noise_9), axis=0)
classes_noise_19 = np.concatenate(
(classes_noise_1, classes_noise_9), axis=0)
scores = clf.fit(trains_noise_19, classes_noise_19).score(
X_test, y_test)
scores_19[iteration] = scores.mean()
# # proportion is 1.1 + 0.9 + 1.2 + 0.8
trains_noise_1289 = np.concatenate(
(trains_noise_12, trains_noise_89), axis=0)
classes_noise_1289 = np.concatenate(
(classes_noise_12, classes_noise_89), axis=0)
scores = clf.fit(trains_noise_1289,
classes_noise_1289).score(X_test, y_test)
scores_1289[iteration] = scores.mean()
iteration -= 1
results.append(['feat_TD4', 'lda', channel_pos, '1.0', np.mean(scores_1_0), np.std(scores_1_0)])
results.append(['feat_TD4', 'lda', channel_pos, '0.9', np.mean(scores_0_9), np.std(scores_0_9)])
results.append(['feat_TD4', 'lda', channel_pos, '0.8', np.mean(scores_0_8), np.std(scores_0_8)])
results.append(['feat_TD4', 'lda', channel_pos, '0.8+0.9', np.mean(scores_0_89), np.std(scores_0_89)])
results.append(['feat_TD4', 'lda', channel_pos, '1.1', np.mean(scores_1_1), np.std(scores_1_1)])
results.append(['feat_TD4', 'lda', channel_pos, '1.2', np.mean(scores_1_2), np.std(scores_1_2)])
results.append(['feat_TD4', 'lda', channel_pos, '1.1+1.2', np.mean(scores_1_12), np.std(scores_1_12)])
results.append(['feat_TD4', 'lda', channel_pos, '1.1+0.9', np.mean(scores_19), np.std(scores_19)])
results.append(['feat_TD4', 'lda', channel_pos, '1.1+0.9+1.2+0.8', np.mean(scores_1289), np.std(scores_1289)])
log_result(results, log_fold + '/' + log_file + '_action_1-' + str(action_num), 2)
print '----Log Fold:', log_fold, ', log_file: ', log_file + '_action_1-' + str(action_num)
print '----training TD4 time elapsed:', time.time() - start_time
# def training_lda_TD4_cross(my_clfs, X_train, y_train, X_test, y_test, **kw):
# start_time = time.time()
# print "training TD4 cross............."
# if(kw.has_key('log_fold')):
# log_fold = root_path + '/result/' + kw['log_fold']
# if os.path.isdir(log_fold) == False:
# try:
# os.makedirs(log_fold)
# except:
# print "Can not create log fold! "
# return
# os.chdir(log_fold)
# results = []
# results.append(['Feat', 'Algorithm', 'Proportion', 'Accuracy', 'std'])
# log_file = 'feat_TD4'
# clf = sklearn.lda.LDA(solver='svd', shrinkage=None, priors=None,
# n_components=None, store_covariance=False, tol=0.0001)
# test_rate_list = [0.2, 0.4, 0.6, 0.8]
# for i in test_rate_list:
# iteration = 4
# scores_1_0 = np.zeros((iteration + 1,))
# scores_0_9 = np.zeros((iteration + 1,))
# scores_0_8 = np.zeros((iteration + 1,))
# scores_0_89 = np.zeros((iteration + 1,))
# while(iteration >= 0):
# print 'test_rate:', i, 'iteration:', iteration
# # X_train, X_test, y_train, y_test = sklearn.cross_validation.train_test_split(trains, classes, test_size=i, random_state=random.randrange(1,51))
# # print 'test_size:', i
# print X_train.shape, X_test.shape, y_train.shape, y_test.shape
# # proportion is 1.0
# scores = clf.fit(X_train, y_train).score(X_test, y_test)
# scores_1_0[iteration] = scores.mean()
# # results.append(['feat_TD4', 'lda(svd,tol=0.0001)', 1.0, scores.mean(), scores.std()])
# # proportion is 0.9
# # X_train, X_test, y_train, y_test = sklearn.cross_validation.train_test_split(trains, classes, test_size=0.2, random_state=random.randrange(1,51))
# trains_noise_1, classes_noise_1 = proportion_simu(
# X_train, y_train, 0.9)
# scores = clf.fit(trains_noise_1, classes_noise_1).score(
# X_test, y_test)
# scores_0_9[iteration] = scores.mean()
# # results.append(['feat_TD4', 'lda(svd,tol=0.0001)', '0.9', scores.mean(), scores.std()])
# # proportion is 0.8
# # X_train, X_test, y_train, y_test = sklearn.cross_validation.train_test_split(trains, classes, test_size=0.2, random_state=random.randrange(1,51))
# trains_noise_2, classes_noise_2 = proportion_simu(
# X_train, y_train, 0.8)
# scores = clf.fit(trains_noise_2, classes_noise_2).score(
# X_test, y_test)
# scores_0_8[iteration] = scores.mean()
# # results.append(['feat_TD4', 'lda(svd,tol=0.0001)', '0.8', scores.mean(), scores.std()])
# # # proportion is 0.8 + 0.9
# trains_noise_3 = np.concatenate(
# (trains_noise_1, trains_noise_2), axis=0)
# classes_noise_3 = np.concatenate(
# (classes_noise_1, classes_noise_2), axis=0)
# scores = clf.fit(trains_noise_3, classes_noise_3).score(
# X_test, y_test)
# scores_0_89[iteration] = scores.mean()
# # results.append(['feat_TD4', 'lda(svd,tol=0.0001)', '0.8+0.9', scores.mean(), scores.std()])
# iteration -= 1
# results.append(['feat_TD4', 'lda(svd;tol=0.0001;test_rate=' +
# str(i) + ')', '1.0', np.mean(scores_1_0), np.std(scores_1_0)])
# results.append(['feat_TD4', 'lda(svd;tol=0.0001;test_rate=' +
# str(i) + ')', '0.9', np.mean(scores_0_9), np.std(scores_0_9)])
# results.append(['feat_TD4', 'lda(svd;tol=0.0001;test_rate=' +
# str(i) + ')', '0.8', np.mean(scores_0_8), np.std(scores_0_8)])
# results.append(['feat_TD4', 'lda(svd;tol=0.0001;test_rate=' + str(i) + ')',
# '0.8+0.9', np.mean(scores_0_89), np.std(scores_0_89)])
# log_result(results, log_file + '_' + str(kw['num']), 2)
# print 'training TD4 time elapsed:', time.time() - start_time
def training_lda_TD4(my_clfs, trains, classes, **kw):
start_time = time.time()
print "training TD4............."
cv = 5
if(kw.has_key('log_fold')):
log_fold = root_path + '/result/' + kw['log_fold']
if os.path.isdir(log_fold) == False:
try:
os.makedirs(log_fold)
except:
print "Can not create log fold! "
return
os.chdir(log_fold)
results = []
results.append(['Feat','Algorithm','Proportion', 'Accuracy', 'std'])
log_file = 'feat_TD4'
clf = sklearn.lda.LDA(solver='svd', shrinkage=None, priors=None, n_components=None, store_covariance=False, tol=0.0001)
# X_train, X_test, y_train, y_test = sklearn.cross_validation.train_test_split(trains, classes, test_size=0.8, random_state=random.randrange(1,51))
# # proportion is 1.0
# X_train, X_test, y_train, y_test = sklearn.cross_validation.train_test_split(trains, classes, test_size=0.8, random_state=random.randrange(1,51))
# scores = clf.fit(X_train, y_train).score(X_test, y_test)
# results.append(['feat_TD4', "LDA(solver='svd', shrinkage=None, priors=None, n_components=None, store_covariance=False, tol=0.0001)", 1, scores.mean(), scores.std()])
iteration = 10
while(iteration>0):
# proportion is 1.0 cv=5
X_train, X_test, y_train, y_test = sklearn.cross_validation.train_test_split(trains, classes, test_size=0.8, random_state=random.randrange(1,51))
scores = sklearn.cross_validation.cross_val_score(clf, trains, classes, cv=5)
results.append(['feat_TD4', "LDA(solver='svd', shrinkage=None, priors=None, n_components=None, store_covariance=False, tol=0.0001)", 1, scores.mean(), scores.std()])
# proportion is 0.9
X_train, X_test, y_train, y_test = sklearn.cross_validation.train_test_split(trains, classes, test_size=0.2, random_state=random.randrange(1,51))
trains_noise_1, classes_noise_1 = proportion_simu(X_train, y_train, 0.9)
scores = clf.fit(trains_noise_1, classes_noise_1).score(X_test, y_test)
results.append(['feat_TD4', clf, '0.9', scores.mean(), scores.std()])
# proportion is 0.8
X_train, X_test, y_train, y_test = sklearn.cross_validation.train_test_split(trains, classes, test_size=0.2, random_state=random.randrange(1,51))
trains_noise_2, classes_noise_2 = proportion_simu(X_train, y_train, 0.8)
scores = clf.fit(trains_noise_2, classes_noise_2).score(X_test, y_test)
results.append(['feat_TD4', clf, '0.8', scores.mean(), scores.std()])
# proportion is 0.8 + 0.9
trains_noise_3 = np.concatenate((trains_noise_1, trains_noise_2), axis=0)
classes_noise_3 = np.concatenate((classes_noise_1, classes_noise_2), axis=0)
scores = clf.fit(trains_noise_3, classes_noise_3).score(X_test, y_test)
results.append(['feat_TD4', clf, '0.8+0.9', scores.mean(), scores.std()])
# proportion is 0.7
X_train, X_test, y_train, y_test = sklearn.cross_validation.train_test_split(trains, classes, test_size=0.2, random_state=random.randrange(1,51))
trains_noise, classes_noise = proportion_simu(X_train, y_train, 0.7)
scores = clf.fit(trains_noise, classes_noise).score(X_test, y_test)
results.append(['feat_TD4', clf, '0.7', scores.mean(), scores.std()])
# proportion is 0.6
X_train, X_test, y_train, y_test = sklearn.cross_validation.train_test_split(trains, classes, test_size=0.2, random_state=random.randrange(1,51))
trains_noise, classes_noise = proportion_simu(X_train, y_train, 0.6)
scores = clf.fit(trains_noise, classes_noise).score(X_test, y_test)
results.append(['feat_TD4', clf, '0.6', scores.mean(), scores.std()])
iteration -= 1
log_result(results, log_file+'_without_noise', 2)
print 'training TD4 time elapsed:', time.time() - start_time
def training_lda_TD4_intra(my_clfs, trains, classes, **kw):
start_time = time.time()
if(kw.has_key('log_fold')):
log_fold = root_path + '/result_test1/' + kw['log_fold']
new_fold(log_fold)
chan_len = kw['chan_len']
action_num = kw['action_num']
cv = 5
results = []
results.append(
['Feat', 'Algorithm','Channel_Pos', 'Proportion', 'Accuracy', 'std'])
log_file = 'feat_TD4_intra'
clf = sklearn.lda.LDA(solver='svd', shrinkage=None, priors=None,
n_components=None, store_covariance=False,
tol=0.0001)
# X_train, X_test, y_train, y_test = sklearn.cross_validation.train_test_split(trains, classes, test_size=0.8, random_state=random.randrange(1,51))
# # proportion is 1.0
# X_train, X_test, y_train, y_test = sklearn.cross_validation.train_test_split(trains, classes, test_size=0.8, random_state=random.randrange(1,51))
# scores = clf.fit(X_train, y_train).score(X_test, y_test)
# results.append(['feat_TD4', "LDA(solver='svd', shrinkage=None, priors=None, n_components=None, store_covariance=False, tol=0.0001)", 1, scores.mean(), scores.std()])
# test_rate_list = [i for i in range(1,9)]
# test_rate_list = [0.2, 0.4, 0.6, 0.8]
test_rate_list = [0.2]
data_num = trains.shape[0]/action_num
kf = KFold(data_num, n_folds=cv)
# proportion is 1.0 cv=5
scores = sklearn.cross_validation.cross_val_score(clf, trains, classes, cv=10)
results.append(['feat_TD4_cv_'+str(cv), 'lda', 'All', 1.0, scores.mean(), scores.std()])
for idx, channel_pos in enumerate(kw['pos_list']):
print '----training TD4 intra, channel_pos: ', channel_pos,'......'
trains_intra = trains[:,idx*chan_len: idx*chan_len+chan_len]
iteration = cv
scores_1_0 = np.zeros((iteration,))
scores_0_9 = np.zeros((iteration,))
scores_0_8 = np.zeros((iteration,))
scores_0_7 = np.zeros((iteration,))
scores_0_6 = np.zeros((iteration,))
scores_0_98 = np.zeros((iteration,))
scores_0_987 = np.zeros((iteration,))
scores_0_9876 = np.zeros((iteration,))
iteration -= 1
for train_idx, test_idx in kf:
# print '------iteration: ', str(5-iteration)
train_idx_all = np.array([], np.int)
test_idx_all = np.array([], np.int)
for action_idx in range(action_num):
train_idx_all = np.concatenate( (train_idx_all, train_idx*action_idx), axis=0)
test_idx_all = np.concatenate( (test_idx_all, test_idx*action_idx), axis=0)
X_train, y_train = trains_intra[train_idx_all], classes[train_idx_all]
X_test, y_test = trains_intra[test_idx_all], classes[test_idx_all]
# proportion is 1.0
scores = clf.fit(X_train, y_train).score(X_test, y_test)
scores_1_0[iteration] = scores.mean()
# proportion is 0.9
trains_noise_9, classes_noise_9 = proportion_simu(
X_train, y_train, 0.9)
scores = clf.fit(trains_noise_9, classes_noise_9).score(
X_test, y_test)
scores_0_9[iteration] = scores.mean()
# proportion is 0.8
trains_noise_8, classes_noise_8 = proportion_simu(
X_train, y_train, 0.8)
scores = clf.fit(trains_noise_8, classes_noise_8).score(
X_test, y_test)
scores_0_8[iteration] = scores.mean()
# proportion is 0.7
trains_noise_7, classes_noise_7 = proportion_simu(
X_train, y_train, 0.7)
scores = clf.fit(trains_noise_7, classes_noise_7).score(
X_test, y_test)
scores_0_7[iteration] = scores.mean()
# proportion is 0.6
trains_noise_6, classes_noise_6 = proportion_simu(
X_train, y_train, 0.6)
scores = clf.fit(trains_noise_6, classes_noise_6).score(
X_test, y_test)
scores_0_6[iteration] = scores.mean()
# proportion is 0.8 + 0.9
trains_noise_98 = np.concatenate(
(trains_noise_8, trains_noise_9), axis=0)
classes_noise_98 = np.concatenate(
(classes_noise_8, classes_noise_9), axis=0)
scores = clf.fit(trains_noise_98, classes_noise_98).score(
X_test, y_test)
scores_0_98[iteration] = scores.mean()
# proportion is 0.7 + 0.8 + 0.9
trains_noise_987 = np.concatenate(
(trains_noise_7, trains_noise_98), axis=0)
classes_noise_987 = np.concatenate(
(classes_noise_7, classes_noise_98), axis=0)
scores = clf.fit(trains_noise_987, classes_noise_987).score(
X_test, y_test)
scores_0_987[iteration] = scores.mean()
# proportion is 0.6 + 0.7 + 0.8 + 0.9
trains_noise_9876 = np.concatenate(
(trains_noise_6, trains_noise_987), axis=0)
classes_noise_9876 = np.concatenate(
(classes_noise_6, classes_noise_987), axis=0)
scores = clf.fit(trains_noise_9876, classes_noise_9876).score(
X_test, y_test)
scores_0_9876[iteration] = scores.mean()
iteration -= 1
results.append(['feat_TD4', 'lda', channel_pos, '1.0', np.mean(scores_1_0), np.std(scores_1_0)])
results.append(['feat_TD4', 'lda', channel_pos, '0.9', np.mean(scores_0_9), np.std(scores_0_9)])
results.append(['feat_TD4', 'lda', channel_pos, '0.8', np.mean(scores_0_8), np.std(scores_0_8)])
results.append(['feat_TD4', 'lda', channel_pos, '0.7', np.mean(scores_0_7), np.std(scores_0_7)])
results.append(['feat_TD4', 'lda', channel_pos, '0.6', np.mean(scores_0_6), np.std(scores_0_6)])
results.append(['feat_TD4', 'lda', channel_pos, '0.8-0.9', np.mean(scores_0_98), np.std(scores_0_98)])
results.append(['feat_TD4', 'lda', channel_pos, '0.7-0.9', np.mean(scores_0_987), np.std(scores_0_987)])
results.append(['feat_TD4', 'lda', channel_pos, '0.6-0.9', np.mean(scores_0_9876), np.std(scores_0_9876)])
log_result(results, log_fold + '/' + log_file + '_action_1-' + str(action_num), 2)
print '----Log Fold:', log_fold, ', log_file: ', log_file + '_action_1-' + str(action_num)
print '----training TD4 time elapsed:', time.time() - start_time
def training_lda_TD4_inter(my_clfs, trains, tests, classes, **kw):
start_time = time.time()
if(kw.has_key('log_fold')):
log_fold = root_path + '/result_test1/' + kw['log_fold']
new_fold(log_fold)
chan_len = kw['chan_len']
action_num = kw['action_num']
print "----training TD4 inter, training by position O, testing by electrode shift ",
cv = 5
results = []
results.append(
['Feat', 'Algorithm','Channel_Pos', 'Proportion', 'Accuracy', 'std'])
log_file = 'feat_TD4_inter'
clf = sklearn.lda.LDA(solver='svd', shrinkage=None, priors=None,
n_components=None, store_covariance=False,
tol=0.0001)
test_rate_list = [0.2]
scores = sklearn.cross_validation.cross_val_score(
clf, trains, classes, cv=10)
results.append(['feat_TD4_cv_5', 'lda', 'O',
1.0, scores.mean(), scores.std()])
for i in test_rate_list:
for idx, channel_pos in enumerate(kw['pos_list']):
X_train = trains
y_train = classes
X_test = tests[:,idx*chan_len:idx*chan_len+chan_len]
y_test = classes
# print tests.shape, chan_len+chan_len
# print X_train.shape, y_train.shape, X_test.shape
# sys.exit(0)
iteration = 1
scores_1 = np.zeros((iteration + 1,))
scores_0_9 = np.zeros((iteration + 1,))
scores_0_8 = np.zeros((iteration + 1,))
scores_0_7 = np.zeros((iteration + 1,))
scores_0_6 = np.zeros((iteration + 1,))
scores_0_98 = np.zeros((iteration + 1,))
scores_0_987 = np.zeros((iteration + 1,))
scores_0_9876 = np.zeros((iteration + 1,))
while(iteration >= 0):
# proportion is 1.0
scores = clf.fit(X_train, y_train).score(X_test, y_test)
scores_1[iteration] = scores.mean()
# proportion is 0.9
trains_noise_9, classes_noise_9 = proportion_simu(
X_train, y_train, 0.9)
scores = clf.fit(trains_noise_9, classes_noise_9).score(
X_test, y_test)
scores_0_9[iteration] = scores.mean()
# proportion is 0.8
trains_noise_8, classes_noise_8 = proportion_simu(
X_train, y_train, 0.8)
scores = clf.fit(trains_noise_8, classes_noise_8).score(
X_test, y_test)
scores_0_8[iteration] = scores.mean()
# proportion is 0.7
trains_noise_7, classes_noise_7 = proportion_simu(
X_train, y_train, 0.7)
scores = clf.fit(trains_noise_7, classes_noise_7).score(
X_test, y_test)
scores_0_7[iteration] = scores.mean()
# proportion is 0.6
trains_noise_6, classes_noise_6 = proportion_simu(
X_train, y_train, 0.6)
scores = clf.fit(trains_noise_6, classes_noise_6).score(
X_test, y_test)
scores_0_6[iteration] = scores.mean()
# proportion is 0.8 + 0.9
trains_noise_98 = np.concatenate(
(trains_noise_8, trains_noise_9), axis=0)
classes_noise_98 = np.concatenate(
(classes_noise_8, classes_noise_9), axis=0)
scores = clf.fit(trains_noise_98, classes_noise_98).score(
X_test, y_test)
scores_0_98[iteration] = scores.mean()
# proportion is 0.7 + 0.8 + 0.9
trains_noise_987 = np.concatenate(
(trains_noise_8, trains_noise_9), axis=0)
classes_noise_987 = np.concatenate(
(classes_noise_8, classes_noise_9), axis=0)
scores = clf.fit(trains_noise_987, classes_noise_987).score(
X_test, y_test)
scores_0_987[iteration] = scores.mean()
# proportion is 0.6 + 0.7 + 0.8 + 0.9
trains_noise_9876 = np.concatenate(
(trains_noise_8, trains_noise_9), axis=0)
classes_noise_9876 = np.concatenate(
(classes_noise_8, classes_noise_9), axis=0)
scores = clf.fit(trains_noise_9876, classes_noise_9876).score(
X_test, y_test)
scores_0_9876[iteration] = scores.mean()
iteration -= 1
results.append(['feat_TD4', 'lda', channel_pos, '1.0', np.mean(scores_1), np.std(scores_1)])
results.append(['feat_TD4', 'lda', channel_pos, '0.9', np.mean(scores_0_9), np.std(scores_0_9)])
results.append(['feat_TD4', 'lda', channel_pos, '0.8', np.mean(scores_0_8), np.std(scores_0_8)])
results.append(['feat_TD4', 'lda', channel_pos, '0.7', np.mean(scores_0_7), np.std(scores_0_7)])
results.append(['feat_TD4', 'lda', channel_pos, '0.6', np.mean(scores_0_6), np.std(scores_0_6)])
results.append(['feat_TD4', 'lda', channel_pos, '0.8-0.9', np.mean(scores_0_98), np.std(scores_0_98)])
results.append(['feat_TD4', 'lda', channel_pos, '0.7-0.9', np.mean(scores_0_987), np.std(scores_0_987)])
results.append(['feat_TD4', 'lda', channel_pos, '0.6-0.9', np.mean(scores_0_9876), np.std(scores_0_9876)])
log_result(results, log_fold + '/' + log_file + '_action_1-' + str(action_num), 2)
print '----Log Fold:', log_fold, ', log_file: ', log_file + '_action_1-' + str(action_num)
print '----training TD4 time elapsed:', time.time() - start_time
def training_lda_TD4_intra(my_clfs, trains, classes, **kw):
start_time = time.time()
if(kw.has_key('log_fold')):
log_fold = root_path + '/result/' + kw['log_fold']
new_fold(log_fold)
channel_pos = kw['log_file_pos']
print "----training TD4 intra, channel_pos: ", channel_pos
cv = 5
results = []
results.append(
['Feat', 'Algorithm','Channel_Pos', 'Proportion', 'Accuracy', 'std'])
log_file = 'feat_TD4_intra'
clf = sklearn.lda.LDA(solver='svd', shrinkage=None, priors=None,
n_components=None, store_covariance=False,
tol=0.0001)
# X_train, X_test, y_train, y_test = sklearn.cross_validation.train_test_split(trains, classes, test_size=0.8, random_state=random.randrange(1,51))
# # proportion is 1.0
# X_train, X_test, y_train, y_test = sklearn.cross_validation.train_test_split(trains, classes, test_size=0.8, random_state=random.randrange(1,51))
# scores = clf.fit(X_train, y_train).score(X_test, y_test)
# results.append(['feat_TD4', "LDA(solver='svd', shrinkage=None, priors=None, n_components=None, store_covariance=False, tol=0.0001)", 1, scores.mean(), scores.std()])
# test_rate_list = [i for i in range(1,9)]
# test_rate_list = [0.2, 0.4, 0.6, 0.8]
test_rate_list = [0.2]
# proportion is 1.0 cv=5
scores = sklearn.cross_validation.cross_val_score(
clf, trains, classes, cv=10)
results.append(['feat_TD4_cv_5', 'lda(svd;tol=0.0001)', channel_pos,
1.0, scores.mean(), scores.std()])
for i in test_rate_list:
iteration = 5
scores_1_0 = np.zeros((iteration + 1,))
scores_1_1 = np.zeros((iteration + 1,))
scores_1_2 = np.zeros((iteration + 1,))
scores_1_12 = np.zeros((iteration + 1,))
scores_0_9 = np.zeros((iteration + 1,))
scores_0_8 = np.zeros((iteration + 1,))
scores_0_89 = np.zeros((iteration + 1,))
scores_19 = np.zeros((iteration + 1,))
scores_1289 = np.zeros((iteration + 1,))
while(iteration >= 0):
X_train, X_test, y_train, y_test = sklearn.cross_validation.train_test_split(
trains, classes, test_size=i,
random_state=random.randrange(1, 51))
print 'iteration: ', str(6-iteration), ', test_size:', i
# print X_train.shape, X_test.shape, y_train.shape, y_test.shape
# proportion is 1.0
scores = clf.fit(X_train, y_train).score(X_test, y_test)
scores_1_0[iteration] = scores.mean()
# results.append(['feat_TD4', 'lda(svd,tol=0.0001)', 1.0, scores.mean(), scores.std()])
# proportion is 1.1
# X_train, X_test, y_train, y_test = sklearn.cross_validation.train_test_split(trains, classes, test_size=0.2, random_state=random.randrange(1,51))
trains_noise_1, classes_noise_1 = proportion_simu(
X_train, y_train, 0.9)
scores = clf.fit(trains_noise_1, classes_noise_1).score(
X_test, y_test)
scores_1_1[iteration] = scores.mean()
# proportion is 1.2
# X_train, X_test, y_train, y_test = sklearn.cross_validation.train_test_split(trains, classes, test_size=0.2, random_state=random.randrange(1,51))
trains_noise_2, classes_noise_2 = proportion_simu(
X_train, y_train, 0.8)
scores = clf.fit(trains_noise_2, classes_noise_2).score(
X_test, y_test)
scores_1_2[iteration] = scores.mean()
# # proportion is 1.1 + 1.2
trains_noise_12 = np.concatenate(
(trains_noise_1, trains_noise_2), axis=0)
classes_noise_12 = np.concatenate(
(classes_noise_1, classes_noise_2), axis=0)
scores = clf.fit(trains_noise_12, classes_noise_12).score(
X_test, y_test)
scores_1_12[iteration] = scores.mean()
# proportion is 0.9
# X_train, X_test, y_train, y_test = sklearn.cross_validation.train_test_split(trains, classes, test_size=0.2, random_state=random.randrange(1,51))
trains_noise_9, classes_noise_9 = proportion_simu(
X_train, y_train, 0.9)
scores = clf.fit(trains_noise_9, classes_noise_9).score(
X_test, y_test)
scores_0_9[iteration] = scores.mean()
# results.append(['feat_TD4', 'lda(svd,tol=0.0001)', '0.9', scores.mean(), scores.std()])
# proportion is 0.8
# X_train, X_test, y_train, y_test = sklearn.cross_validation.train_test_split(trains, classes, test_size=0.2, random_state=random.randrange(1,51))
trains_noise_8, classes_noise_8 = proportion_simu(
X_train, y_train, 0.8)
scores = clf.fit(trains_noise_8, classes_noise_8).score(
X_test, y_test)
scores_0_8[iteration] = scores.mean()
# results.append(['feat_TD4', 'lda(svd,tol=0.0001)', '0.8', scores.mean(), scores.std()])
# # proportion is 0.8 + 0.9
trains_noise_89 = np.concatenate(
(trains_noise_8, trains_noise_9), axis=0)
classes_noise_89 = np.concatenate(
(classes_noise_8, classes_noise_9), axis=0)
scores = clf.fit(trains_noise_89, classes_noise_89).score(
X_test, y_test)
scores_0_89[iteration] = scores.mean()
# results.append(['feat_TD4', 'lda(svd,tol=0.0001)', '0.8+0.9', scores.mean(), scores.std()])
# # proportion is 1.1 + 0.9
trains_noise_19 = np.concatenate(
(trains_noise_1, trains_noise_9), axis=0)
classes_noise_19 = np.concatenate(
(classes_noise_1, classes_noise_9), axis=0)
scores = clf.fit(trains_noise_19, classes_noise_19).score(
X_test, y_test)
scores_19[iteration] = scores.mean()
# # proportion is 1.1 + 0.9 + 1.2 + 0.8
trains_noise_1289 = np.concatenate(
(trains_noise_12, trains_noise_89), axis=0)
classes_noise_1289 = np.concatenate(
(classes_noise_12, classes_noise_89), axis=0)
scores = clf.fit(trains_noise_1289,
classes_noise_1289).score(X_test, y_test)
scores_1289[iteration] = scores.mean()
# # proportion is 0.7
# # X_train, X_test, y_train, y_test = sklearn.cross_validation.train_test_split(trains, classes, test_size=0.2, random_state=random.randrange(1,51))
# trains_noise, classes_noise = proportion_simu(X_train, y_train, 0.7)
# scores = clf.fit(trains_noise, classes_noise).score(X_test, y_test)
# results.append(['feat_TD4', 'lda(svd,tol=0.0001)', '0.7', scores.mean(), scores.std()])
# # proportion is 0.6
# # X_train, X_test, y_train, y_test = sklearn.cross_validation.train_test_split(trains, classes, test_size=0.2, random_state=random.randrange(1,51))
# trains_noise, classes_noise = proportion_simu(X_train, y_train, 0.6)
# scores = clf.fit(trains_noise, classes_noise).score(X_test, y_test)
# results.append(['feat_TD4', 'lda(svd,tol=0.0001)', '0.6', scores.mean(), scores.std()])
iteration -= 1
results.append(['feat_TD4', 'lda(svd;tol=0.0001;test_rate=' +
str(i) + ')', channel_pos, '1.0', np.mean(scores_1_0), np.std(scores_1_0)])
results.append(['feat_TD4', 'lda(svd;tol=0.0001;test_rate=' +
str(i) + ')', channel_pos, '0.9', np.mean(scores_0_9), np.std(scores_0_9)])
results.append(['feat_TD4', 'lda(svd;tol=0.0001;test_rate=' +
str(i) + ')', channel_pos, '0.8', np.mean(scores_0_8), np.std(scores_0_8)])
results.append(['feat_TD4', 'lda(svd;tol=0.0001;test_rate=' + str(i) + ')',
channel_pos, '0.8+0.9', np.mean(scores_0_89), np.std(scores_0_89)])
results.append(['feat_TD4', 'lda(svd;tol=0.0001;test_rate=' +
str(i) + ')', channel_pos, '1.1', np.mean(scores_1_1), np.std(scores_1_1)])
results.append(['feat_TD4', 'lda(svd;tol=0.0001;test_rate=' +
str(i) + ')', channel_pos, '1.2', np.mean(scores_1_2), np.std(scores_1_2)])
results.append(['feat_TD4', 'lda(svd;tol=0.0001;test_rate=' +
str(i) + ')', channel_pos, '1.1+0.9', np.mean(scores_19), np.std(scores_19)])
results.append(['feat_TD4', 'lda(svd;tol=0.0001;test_rate=' + str(i) + ')', channel_pos,
'1.1+0.9+1.2+0.8', np.mean(scores_1289), np.std(scores_1289)])
log_result(results, log_fold + '/' + log_file + '_' + channel_pos + '_' + str(kw['num']), 2)
print '----Log Fold:', log_fold, ', log_file: ', log_file + '_' + channel_pos + '_' + str(kw['num'])
print '----training TD4 time elapsed:', time.time() - start_time
