def accept(self):
hdf = guessHdfType(self._file)
dmodel = ClfDataModel(self.classifier_name.currentText())
try:
name = hdf[dmodel.path].attrs[dmodel.NAME]
except KeyError:
return
lib = hdf[dmodel.path].attrs[dmodel.LIB]
# switch classifier before loading
idx = self.parent().classifiers.findText(name)
if idx < 0:
QMessageBox.critical(self, "Error", "Classifier %s not supported" % name)
return
self.parent().classifiers.setCurrentIndex(idx)
clf = self.parent().currentClassifier()
model = self.parent().model
self.parent().removeAll()
self.parent().clearItems()
if lib in (dmodel.OneClassSvm,):
self._loadSingle(model, hdf, dmodel)
elif lib in (dmodel.SupportVectorClassifier,):
self._loadMultiClass(model, hdf, dmodel)
elif lib in (dmodel.ManualClassifier,):
self._loadMultiClass(model, hdf, dmodel)
self.parent().setClassifierState(True, True)
self.parent().onPredict()
else:
msg = "Classifier %s not supported" % lib
QMessageBox.critical(self, "Error", msg)
params = dict(hdf[dmodel.classdef].attrs)
for k, v in params.items():
if v == "None":
params[k] = None
self.parent().setClassifierParameters(params)
hdf.close()
super(LoadClassifierDialog, self).accept()
def accept(self):
hdf = guessHdfType(self._file)
dmodel = ClfDataModel(self.classifier_name.currentText())
try:
name = hdf[dmodel.path].attrs[dmodel.NAME]
except KeyError:
return
lib = hdf[dmodel.path].attrs[dmodel.LIB]
# switch classifier before loading
idx = self.parent().classifiers.findText(name)
if idx < 0:
QMessageBox.critical(self, "Error",
"Classifier %s not supported" % name)
return
self.parent().classifiers.setCurrentIndex(idx)
clf = self.parent().currentClassifier()
model = self.parent().model
self.parent().removeAll()
self.parent().clearItems()
if lib in (dmodel.OneClassSvm, ):
self._loadSingle(model, hdf, dmodel)
elif lib in (dmodel.SupportVectorClassifier, ):
self._loadMultiClass(model, hdf, dmodel)
else:
msg = "Classifier %s not supported" % lib
QMessageBox.critical(self, "Error", msg)
params = dict(hdf[dmodel.classdef].attrs)
for k, v in params.items():
if v == 'None':
params[k] = None
self.parent().setClassifierParameters(params)
hdf.close()
super(LoadClassifierDialog, self).accept()
def openFile(self, file_):
self.close()
self._h5f = guessHdfType(file_)
self.fileOpened.emit(self._h5f.fileinfo)
def openFile(self, file_):
self.close()
self._h5f = guessHdfType(file_)
self.fileOpened.emit(self._h5f.fileinfo)
