def load_mat(self):
filename = QFileDialog.getOpenFileName(self, 'Open file',
'~', "mat files (*.mat)")
if filename[0] != "":
try:
self.holder.parse_mat(filename[0])
self.partener.histogram()
except Error as e:
print_error_message(e.what())
def save(self):
if not self.model.set():
print_error_message("Please train the model")
return
path = QFileDialog.getSaveFileName(
self, "Save file", "", ".mat"
)[0]
savemat(path, self.model.getWeightDic())
def load_imgs(self):
files = QFileDialog.getOpenFileNames(self, 'Open file',
'~', "Image files (*.jpg *.jpeg *.png)")
filenames = files[0]
if len(filenames) != 0:
try:
self.holder.parse_images(filenames)
self.partener.imshow(num=min(10, len(filenames)))
except Error as e:
print_error_message(e.what())
def plotEigenValues(self):
if not self.model.isFitted():
print_error_message("Please fit the model first")
return
idx = self.getSelectedEigenValues()
self.model.getEigenvectors()
vecs = self.model.getEigenvectors()[idx]
self.plotWidget.renderHolder().give(vecs)
self.plotWidget.imshow()
def chooseFirstKEigenValues(self):
k = self.firstKField.text()
if k == "":
print_error_message("K is required")
return
k = int(k)
if not self.model.isFitted():
print_error_message("Please fit the model first")
return
for i in range(k):
self.checkboxes[i].setChecked(True)
def fit(self):
self.checkboxes = []
self.clearPlot()
if not self.holder.loaded():
print_error_message("Please load data first")
return
X = self.holder.fetchAll()
self.model.setup(X)
self.model.fit()
evs = self.model.getEigenvalues()
self.showEigenValues(evs)
def fit(self):
self.historyField.setText("")
if not self.holder.loaded():
print_error_message("Please load data first")
return
try:
self.reg = float(self.regField.text())
print("reg: ", self.reg)
except:
print_error_message("Invalid regularization, must be a real number")
return
try:
self.order = int(self.orderField.text())
print("order: ", self.order)
except:
print_error_message("Invalid order, must be a integer")
return
try:
self.learning_rate = float(self.learningrateField.text())
print("learning rate: ", self.learning_rate)
except:
print_error_message("Invalid learning rate, must be a real number")
try:
self.maxiter = int(self.maxiterField.text())
print("max iter: ", self.maxiter)
except:
print_error_message("Invalid max iter, must be a real number")
self.optimizer = self.optimizerField.currentText()
print("optimizer: ", self.optimizer)
X, y, title = self.holder.fetch_ordered(self.order)
self.model.setup(X, y, regularization=self.reg)
def callback(iter, loss):
self.historyField.append("iter: %d, loss: %.2f"%(iter, loss))
self.model.fit_gradient_descent(self.learning_rate, self.maxiter, self.optimizer, callback=callback)
if len(self.holder.shape()) == 1:
self.plot()
def createNewWindow(self, classname:str):
try:
self.newwindow = render_window(classname)
self.newwindow.show()
except Error as e:
print_error_message(e.what())
