def predict(self):
# Let the user chose a file.
# only allows mat files to be read in
filename = QFileDialog.getOpenFileName(self,
'Open File',
filter="*.mat")[0]
if filename:
# Make sure the user wants to use train.
choice = QMessageBox.question(
self, 'Extract!',
"Are you sure you want to run prediction? If you click yes, the prediction might take a long time to run",
QMessageBox.Yes | QMessageBox.No)
if choice == QMessageBox.Yes:
# User wants to train.
raw_data = Importer.mat(filename)
# Make data_raw smaller. (Should be removed in future versions).
for row in raw_data:
raw_data[row] = raw_data[row][:1]
prediction = self.wrapper.predict(raw_data, verbose=True)
print(list(prediction))
else:
# User don't want to train.
pass
def development(filename):
# Import mat file.
data_raw = Importer.mat(filename)
# Make data_raw smaller.
for row in data_raw:
data_raw[row] = data_raw[row][:1]
# Train.
wrapper.development(data_raw)
def train(self):
# Let the user chose a file.
# only allows mat files to be read in
filename = QFileDialog.getOpenFileName(self,
'Open File',
filter="*.mat")[0]
if filename:
# Make sure the user wants to use train.
choice = QMessageBox.question(
self, 'Train',
"Are you sure you want to train? If you click yes, the training might take long periods to train.",
QMessageBox.Yes | QMessageBox.No)
if choice == QMessageBox.Yes:
# User wants to train.
raw_data = Importer.mat(filename)
# Make data_raw smaller. (Should be removed in future versions).
for row in raw_data:
raw_data[row] = raw_data[row][:1]
self.wrapper.train(data_raw=raw_data,
shrink_percent=0.0,
verbose=True)
# Statistics obtained from the data
global raw_signal
global filtered_signal
global chunked_X
global chunked_Y
global accuracy
raw_signal = self.wrapper.bciObject.preprocessor.preprocess_statistics.raw_signal
filtered_signal = self.wrapper.bciObject.preprocessor.preprocess_statistics.filtered_signal
chunked_X = self.wrapper.bciObject.preprocessor.preprocess_statistics.chunked_X
chunked_Y = self.wrapper.bciObject.preprocessor.preprocess_statistics.chunked_Y
accuracy = self.wrapper.bciObject.prediction_model.model_statistics.accuracy
#print(accuracy)
msg = QMessageBox(self)
msg.resize(500, 400)
msg.setText("The training accuracy is %.3f" % accuracy)
msg.show()
else:
# User don't want to train.
pass
