def manage_addZGadflyConnectionForm(self, REQUEST, *args, **kw): " " DA=getDA() return DA.addConnectionForm( self, self, REQUEST, database_type=database_type, data_sources=DA.data_sources)
def manage_addFirebirdConnectionForm(self, REQUEST, *args, **kw): " " DA = getDA() return DA.addConnectionForm(self, self, REQUEST, database_type=database_type)
svclassifier.fit(x_train_t, y_train_t) y_test_tp = y_test_t.to_numpy() y_pred = svclassifier.predict(x_test_t) acc = calc_acc(y_test_tp, y_pred) if m < acc: m = acc x_train, x_test, y_train, y_test = x_train_t, x_test_t, y_train_t, y_test_t if n > acc: n = acc print(m) print(n) # orig=np.arange(len(x_train.iloc[0])) selected_features, fitness, precision, sensitivity, F1, AUC = da.DA( x_train, y_train, x_test, y_test, 100, m, orig) #selected_features = np.random.randint(288, size=) x_train_selected_features = pd.DataFrame() x_test_selected_features = pd.DataFrame() reduced_dataset = pd.DataFrame() for i in range(len(selected_features)): x_train_selected_features[str( selected_features[i])] = x_train.iloc[:, int( selected_features[i])] # X_Train from selected features x_test_selected_features[str( selected_features[i])] = x_test.iloc[:, int( selected_features[i])] # # X_Test from selected features reduced_dataset['attr ' + str(int(