def train(filename):
cnt = io.load_bcicomp3_ds2(filename)
fs_n = cnt.fs / 2
b, a = proc.signal.butter(5, [38 / fs_n], btype='low')
cnt = proc.lfilter(cnt, b, a)
b, a = proc.signal.butter(5, [.1 / fs_n], btype='high')
cnt = proc.lfilter(cnt, b, a)
cnt = proc.subsample(cnt, 60)
epo = proc.segment_dat(cnt, MARKER_DEF_TRAIN, SEG_IVAL)
# from wyrm import plot
# logger.debug('Ploting channels...')
# plot.plot_spatio_temporal_r2_values(proc.sort_channels(epo))
# print JUMPING_MEANS_IVALS
# plot.plt.show()
fv = proc.jumping_means(epo, JUMPING_MEANS_IVALS)
fv = proc.create_feature_vectors(fv)
cfy = proc.lda_train(fv)
return cfy
def train(filename):
dat = io.load_bcicomp3_ds2(filename)
fs_n = dat.fs / 2
b, a = proc.signal.butter(16, [30 / fs_n], btype='low')
dat = proc.lfilter(dat, b, a)
b, a = proc.signal.butter(5, [.4 / fs_n], btype='high')
dat = proc.lfilter(dat, b, a)
dat = proc.subsample(dat, 60)
epo = proc.segment_dat(dat, MARKER_DEF_TRAIN, SEG_IVAL)
#from wyrm import plot
#plot.plot_spatio_temporal_r2_values(proc.sort_channels(epo))
#print JUMPING_MEANS_IVALS
#plot.plt.show()
fv = proc.jumping_means(epo, JUMPING_MEANS_IVALS)
fv = proc.create_feature_vectors(fv)
clf = proc.lda_train(fv)
return clf
def train(filename):
cnt = io.load_bcicomp3_ds2(filename)
fs_n = cnt.fs / 2
b, a = proc.signal.butter(5, [30 / fs_n], btype='low')
cnt = proc.lfilter(cnt, b, a)
b, a = proc.signal.butter(5, [.4 / fs_n], btype='high')
cnt = proc.lfilter(cnt, b, a)
cnt = proc.subsample(cnt, 60)
epo = proc.segment_dat(cnt, MARKER_DEF_TRAIN, SEG_IVAL)
#from wyrm import plot
#plot.plot_spatio_temporal_r2_values(proc.sort_channels(epo))
#print JUMPING_MEANS_IVALS
#plot.plt.show()
fv = proc.jumping_means(epo, JUMPING_MEANS_IVALS)
fv = proc.create_feature_vectors(fv)
cfy = proc.lda_train(fv)
return cfy
def train(filename_):
cnt = io.load_bcicomp3_ds2(filename_)
fs_n = cnt.fs / 2
b, a = proc.signal.butter(5, [HIGH_CUT / fs_n], btype='low')
cnt = proc.lfilter(cnt, b, a)
b, a = proc.signal.butter(5, [LOWER_CUT / fs_n], btype='high')
cnt = proc.lfilter(cnt, b, a)
print("Filtragem aplicada em [{} Hz ~ {} Hz]".format(LOWER_CUT, HIGH_CUT))
cnt = proc.subsample(cnt, SUBSAMPLING)
print("Sub-amostragem em {} Hz".format(SUBSAMPLING))
epo = proc.segment_dat(cnt, MARKER_DEF_TRAIN, SEG_IVAL)
print("Dados segmentados em intervalos de [{} ~ {}]".format(
SEG_IVAL[0], SEG_IVAL[1]))
fv = proc.jumping_means(epo, JUMPING_MEANS_INTERVALS)
fv = proc.create_feature_vectors(fv)
print("Iniciando treinamento da LDA...")
cfy = proc.lda_train(fv)
print("Treinamento concluido!")
return cfy
def offline_experiment(filename_, cfy_, true_labels_):
print("\n")
cnt = io.load_bcicomp3_ds2(filename_)
fs_n = cnt.fs / 2
b, a = proc.signal.butter(5, [HIGH_CUT / fs_n], btype='low')
cnt = proc.filtfilt(cnt, b, a)
b, a = proc.signal.butter(5, [LOWER_CUT / fs_n], btype='high')
cnt = proc.filtfilt(cnt, b, a)
cnt = proc.subsample(cnt, SUBSAMPLING)
epo = proc.segment_dat(cnt, MARKER_DEF_TEST, SEG_IVAL)
fv = proc.jumping_means(epo, JUMPING_MEANS_INTERVALS)
fv = proc.create_feature_vectors(fv)
lda_out = proc.lda_apply(fv, cfy_)
markers = [fv.class_names[cls_idx] for cls_idx in fv.axes[0]]
result = zip(markers, lda_out)
endresult = []
markers_processed = 0
letter_prob = {i: 0 for i in 'abcdefghijklmnopqrstuvwxyz123456789_'}
for s, score in result:
if markers_processed == 180:
endresult.append(
sorted(letter_prob.items(), key=lambda x: x[1])[-1][0])
letter_prob = {
i: 0
for i in 'abcdefghijklmnopqrstuvwxyz123456789_'
}
markers_processed = 0
for letter in s:
letter_prob[letter] += score
markers_processed += 1
print('Letras Encontradas-: %s' % "".join(endresult))
print('Letras Corretas----: %s' % true_labels_)
acc = np.count_nonzero(
np.array(endresult) == np.array(
list(true_labels_.lower()[:len(endresult)]))) / len(endresult)
print("Acertividade Final : %d" % (acc * 100))
#from wyrm import plot
#plot.plot_spatio_temporal_r2_values(proc.sort_channels(epo))
#print JUMPING_MEANS_IVALS
#plot.plt.show()
fv = proc.jumping_means(epo, JUMPING_MEANS_IVALS)
fv = proc.create_feature_vectors(fv)
clf = proc.lda_train(fv)
return clf
if __name__ == '__main__':
logger.debug('Training...')
clf = train(TRAIN_DATA)
logger.debug('Starting Online experiment...')
cnt = io.load_bcicomp3_ds2(TEST_DATA)
amp = libmushu.get_amp('replayamp')
# fast (non-realtime)
amp.configure(data=cnt.data,
marker=cnt.markers,
channels=cnt.axes[-1],
fs=cnt.fs,
realtime=False,
samples=1000)
# slow (realtime)
#amp.configure(data=cnt.data, marker=cnt.markers, channels=cnt.axes[-1], fs=cnt.fs)
online_experiment(amp, clf)
acc = 0
for subject in range(2):
if subject == 0:
training_set = TRAIN_A
testing_set = TEST_A
labels = TRUE_LABELS_A
jumping_means_ivals = JUMPING_MEANS_IVALS_A
else:
training_set = TRAIN_B
testing_set = TEST_B
labels = TRUE_LABELS_B
jumping_means_ivals = JUMPING_MEANS_IVALS_B
# load the training set
print "before loading"
dat = load_bcicomp3_ds2(training_set)
print "after loading "
fv_train, epo[subject] = preprocessing(dat, MARKER_DEF_TRAIN, jumping_means_ivals)
# train the lda
print "before training"
cfy = proc.lda_train(fv_train)
print "after training"
# load the testing set
dat = load_bcicomp3_ds2(testing_set)
fv_test, _ = preprocessing(dat, MARKER_DEF_TEST, jumping_means_ivals)
# predict
lda_out_prob = proc.lda_apply(fv_test, cfy)
cnt = proc.subsample(cnt, 60)
epo = proc.segment_dat(cnt, MARKER_DEF_TRAIN, SEG_IVAL)
#from wyrm import plot
#plot.plot_spatio_temporal_r2_values(proc.sort_channels(epo))
#print JUMPING_MEANS_IVALS
#plot.plt.show()
fv = proc.jumping_means(epo, JUMPING_MEANS_IVALS)
fv = proc.create_feature_vectors(fv)
cfy = proc.lda_train(fv)
return cfy
if __name__ == '__main__':
logger.debug('Training...')
cfy = train(TRAIN_DATA)
logger.debug('Starting Online experiment...')
cnt = io.load_bcicomp3_ds2(TEST_DATA)
amp = libmushu.get_amp('replayamp')
if REALTIME:
amp.configure(data=cnt.data, marker=cnt.markers, channels=cnt.axes[-1], fs=cnt.fs, blocksize_samples=4)
else:
amp.configure(data=cnt.data, marker=cnt.markers, channels=cnt.axes[-1], fs=cnt.fs, realtime=False, blocksize_samples=40)
online_experiment(amp, cfy)
acc = 0
for subject in range(2):
if subject == 0:
training_set = TRAIN_A
testing_set = TEST_A
labels = TRUE_LABELS_A
jumping_means_ivals = JUMPING_MEANS_IVALS_A
else:
training_set = TRAIN_B
testing_set = TEST_B
labels = TRUE_LABELS_B
jumping_means_ivals = JUMPING_MEANS_IVALS_B
# load the training set
print "before loading"
dat = load_bcicomp3_ds2(training_set)
print "after loading "
fv_train, epo[subject] = preprocessing(dat, MARKER_DEF_TRAIN,
jumping_means_ivals)
labels = fv_train.axes[0]
y_as_categorical = to_categorical(labels)
lstm_model.fit(epo[subject].data,
y_as_categorical,
verbose=1,
show_accuracy=1,
validation_split=0.1,
nb_epoch=20,
class_weight={
0: 1,
1: 50
})
