mentaltasks.py 文件源码

def trainAutoregRR(self, trainData):
        self.stand = ml.ClassSegStandardizer(trainData)
        trainDataStd = self.stand.apply(trainData)

        orders = np.arange(2,30)

        nFold = self.nTrainTrial

        trnCA = np.zeros((nFold, orders.size))
        valCA = np.zeros((nFold, orders.size))

        dialog = wx.ProgressDialog('Training Classifier',
                                   'Featurizing', maximum=nFold+1,
                                   style=wx.PD_ELAPSED_TIME | wx.PD_SMOOTH)

        for fold, trnData, valData in ml.part.classStratified(trainDataStd, nFold=nFold):
            dialog.Update(fold, 'Validation Fold: %d' % fold)

            for i, order in enumerate(orders):
                classifier = ml.ARC(trnData, order=order)

                trnCA[fold,i] = classifier.ca(trnData)
                valCA[fold,i] = classifier.ca(valData)

        dialog.Update(nFold, 'Training Final Classifier')

        meanTrnCA = np.mean(trnCA, axis=0)
        meanValCA = np.mean(valCA, axis=0)

        bestOrderIndex = np.argmax(meanValCA)
        bestOrder = orders[bestOrderIndex]

        bestMeanTrnCA = meanTrnCA[bestOrderIndex]
        bestMeanValCA = meanValCA[bestOrderIndex]

        self.classifier = ml.ARC(trainDataStd, order=bestOrder)

        trainCA = self.classifier.ca(trainDataStd)
        trainConfusion = np.round(100*self.classifier.confusion(trainDataStd))

        dialog.Destroy()

        resultText = (('Best Order: %f\n' % bestOrder) +
                      ('Best Mean Training CA: %f\n' % bestMeanTrnCA) + 
                      ('Best Mean Validation CA: %f\n' % bestMeanValCA) +
                      ('Final Training CA: %f\n' % trainCA) +
                      ('Confusion Matrix:\n' + str(trainConfusion) + '\n') +
                      ('Choices: ' + str(self.choices)))

        wx.MessageBox(message=resultText,
                      caption='Training Completed!',
                      style=wx.OK | wx.ICON_INFORMATION)

        self.saveResultText(resultText)