mentaltasks.py 文件源码

def trainTDENet(self, segs):
        trainData = [seg.data for seg in segs]

        convs = ((25,7),) # first session
        maxIter = 400

        nHidden = None
        poolSize = 1
        poolMethod = 'average'

        self.stand = ml.ClassSegStandardizer(trainData)
        trainDataStd = self.stand.apply(trainData)

        dialog = wx.ProgressDialog('Training Classifier',
                                   'Featurizing', maximum=maxIter+2,
                                   style=wx.PD_ELAPSED_TIME | wx.PD_SMOOTH)

        def progressCB(optable, iteration, *args, **kwargs):
            dialog.Update(iteration, 'Iteration: %d/%d' % (iteration, maxIter))

        self.classifier = ml.CNA(trainDataStd, convs=convs, nHidden=nHidden, maxIter=maxIter,
                                 optimFunc=ml.optim.scg, accuracy=0.0, precision=0.0,
                                 poolSize=poolSize, poolMethod=poolMethod,
                                 verbose=False, callback=progressCB)

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

        dialog.Destroy()

        resultText = (('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)