def process(signal): #输入信号为行向量，每一行为一个通道数据
sig = scipy_signal.filtfilt(notchB, notchA, signal)
sig = scipy_signal.filtfilt(bpB, bpA, sig)
sig = sig[:,-SRATE*EPOCH:]
#update(sig)
rr = []
for i in range(4):
A,B,r = cca(sig[-4:,:],template[i])
rr.append(np.max(r))
return np.argsort(rr)[-1]
def _validate_fold(self, view_data, epochs, batch_size, ids, fold,
fold_path, model_path):
view_train, view_test = utils.prepare_simple_multiview_fold(
utils.load_fold_ids(fold_path), view_data, ids)
train_generator = batch_generator(view_train, batch_size[0], ids,
view_data, True)
test_generator = batch_generator(view_test, batch_size[1], ids,
view_data, False)
checkpointer = keras.callbacks.ModelCheckpoint(
model_path + '_fold-{}.hdf5'.format(fold),
monitor='val_loss',
verbose=1,
save_best_only=True,
save_weights_only=True,
mode='min')
# train_generator = batch_generator([sorted(list(view_data[0].keys())), sorted(list(view_data[1].keys()))], batch_size[0], True, view_data, True)
# checkpointer = keras.callbacks.ModelCheckpoint(model_path + '_fold-{}.hdf5'.format(fold), monitor='loss', verbose=1, save_best_only=True, save_weights_only=True, mode='min')
history = self._model.fit_generator(generator=train_generator,
steps_per_epoch=None,
epochs=epochs,
verbose=1,
callbacks=[checkpointer],
validation_data=test_generator,
validation_steps=None,
max_queue_size=1,
workers=1,
use_multiprocessing=False,
initial_epoch=0)
# history = self._model.fit_generator(generator=train_generator, steps_per_epoch=None, epochs=epochs, verbose=1, callbacks=[checkpointer], max_queue_size=1, workers=1, use_multiprocessing=False, initial_epoch=0)
self._model.load_weights(model_path + '_fold-{}.hdf5'.format(fold))
train_generator = batch_generator(view_train, batch_size[0], ids,
view_data, False)
# train_generator = batch_generator([sorted(list(view_data[0].keys())), sorted(list(view_data[1].keys()))], batch_size[0], ids, view_data, True)
predictions = self._model.predict_generator(train_generator,
steps=None,
max_queue_size=1,
workers=1,
use_multiprocessing=False,
verbose=0)
# train linear cca
self._cca_model = cca.cca(self._emb_dims)
self._cca_model.fit(predictions[:, :self._view_units[0][-1]],
predictions[:, -self._view_units[1][-1]:])
del view_train, view_test
return history
def predict(self, X, return_all=0, metric=None):
T = X.copy()
T /= np.linalg.norm(T, axis=1, keepdims=1)
resultTable = np.zeros([T.shape[0], len(self.classes)])
for i in xrange(len(self.classes)):
currentClass = self.classes[i]
for k in xrange(T.shape[0]):
if metric is None:
a, b = cca(T[k:k + 1].T,
self.FeatureSpaces[currentClass],
N=1)
resultTable[k, i] = corr(a, b)[0, 1]
else:
resultTable[k,
i] = metric(T[k],
self.FeatureSpaces[currentClass])
ind = np.argmax(resultTable, axis=1)
if return_all:
data = pd.DataFrame(resultTable, columns=self.classes)
return self.classes[ind], data
return self.classes[ind]
def predict(self, X, return_all=0, metric=None):
T = X.copy()
if T.ndim > 2:
T = unfold(T,0)
T /= np.linalg.norm(T, axis=1, keepdims=1)
n = [T.shape[0], self.Nmz, 2]
T = reshape(T, n)
resultTable = np.zeros([T.shape[0], len(self.classes)])
for i in xrange(len(self.classes)):
currentClass = self.classes[i]
Z = prodTenMat(T, self.Polspaces_inv[currentClass], 2)
for k in xrange(T.shape[0]):
if metric is None:
a, b = cca(Z[k], self.MZspaces[currentClass], N=1)
resultTable[k, i] = corr(a, b)[0, 1]
else:
resultTable[k, i] = metric(Z[k], self.MZspaces[currentClass])
ind = np.argmax(resultTable, axis=1)
if return_all:
data = pd.DataFrame(resultTable, columns=self.classes)
return self.classes[ind], data
return self.classes[ind]
def tca_with_cca(k=1):
from sklearn.cross_validation import train_test_split
from sklearn.neural_network import MLPClassifier
from sklearn import tree
from sklearn import metrics
import pandas as pd
from cca import cca
import numpy as np
import tca
import ascr
file_path1 = 'sample.csv'
file_path2 = 'out_of_sample.csv'
#train_sample = pd.read_csv(file_path1)
train_sample = pd.read_csv(file_path1).sample(frac=0.05,
replace=True,
random_state=k,
axis=0)
#train_sample=ascr.ascr(ascr.ascr(ascr.ascr(ascr.ascr(ascr.ascr(ascr.ascr(pd.read_csv(file_path1).sample(frac=0.02,replace=False, random_state=11, axis=0),'S1','T1'),'S2','T2'),'S3','T3'),'S4','T4'),'S5','T5'),'S6','T6')
cca_s, cca_t = cca(H1=train_sample.loc[:, 'S1':'S_S7'],
H2=train_sample.loc[:, 'T1':'T_S7'])
##################################################################
x_train_st, x_test_st, y_train_st, y_test_st = train_test_split(
np.append(cca_s, cca_t, axis=1),
pd.concat([train_sample[['S_Y']], train_sample[['T_Y']]],
axis=1,
join='inner'),
test_size=0.25,
random_state=k)
##################################################################
#x_test_st=x_test_st.values
y_test_st = y_test_st.values
y_train_st = y_train_st.values
#x_train_st=x_train_st.values
x_src = x_train_st[:, 0:6]
x_tar = x_train_st[:, 6:12]
y_src = y_train_st[:, 0:1]
y_tar = y_train_st[:, 1:2]
x_tar_o = x_test_st[:, 6:12]
y_tar_o = y_test_st[:, 1:2]
my_tca = tca.TCA(dim=6)
V, x_src_tca, x_tar_tca, x_tar_o_tca = my_tca.fit_transform(
x_src, x_tar, x_tar_o)
acc_tar_o, y_tar_o_pre, kappa_tar = my_tca.classify_svm(
x_src_tca, y_src, x_tar_o_tca, y_tar_o)
#print('SVM',acc_tar_o,kappa_tar)
#
dr = tree.DecisionTreeClassifier()
dr = dr.fit(x_src_tca, y_src)
print('DT', metrics.accuracy_score(y_tar_o, dr.predict(x_tar_o_tca)),
metrics.cohen_kappa_score(y_tar_o, dr.predict(x_tar_o_tca)))
temp = pd.DataFrame(
data={
'SVM': [acc_tar_o],
'DT': [metrics.accuracy_score(y_tar_o, dr.predict(x_tar_o_tca))]
})
print(temp)
for i in ('sgd', 'adam', 'lbfgs'):
nn = MLPClassifier(solver=i,
alpha=1e-5,
hidden_layer_sizes=(84, 6),
random_state=k + 31)
nn = nn.fit(x_src_tca, y_src)
temp = pd.concat([
temp,
pd.DataFrame(data={
i: [metrics.accuracy_score(y_tar_o, nn.predict(x_tar_o_tca))]
})
],
axis=1,
join='inner')
print(temp)
print(i, 'NN', metrics.accuracy_score(y_tar_o,
nn.predict(x_tar_o_tca)),
metrics.cohen_kappa_score(y_tar_o, nn.predict(x_tar_o_tca)))
record = pd.read_csv('record.csv')
print(record)
record = pd.concat([record, temp], axis=0, join='inner')
record.to_csv('record.csv', index=False)
print(record)
#!/bin/python3
from dec import Oracle
from cca import cca
if __name__ == "__main__":
init = open('./init')
oracle = Oracle(init)
print("plaintext:", cca(oracle))