p300bot.py 文件源码

def trainKNN(self, classData, dialog, metric):
        ks = np.arange(1,10)

        trainAUC = np.zeros(ks.shape)
        validAUC = np.zeros(ks.shape)

        partitionGenerator = ml.part.classRandomSubSample(classData,
                self.trainFrac, self.nFold)

        for fold, trainData, validData in partitionGenerator:
            dialog.Update(fold, 'Validation Fold: %d' % fold)

            for i, k in enumerate(ks):
                classifier = ml.KNN(trainData, k=k, distMetric=metric)

                trainAUC[i] += classifier.auc(trainData)
                validAUC[i] += classifier.auc(validData)

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

        trainAUC /= self.nFold
        validAUC /= self.nFold

        print 'train AUC: ', trainAUC
        print 'valid AUC: ', validAUC

        bestK = ks[np.argmax(validAUC)]
        print 'best K: ', bestK

        self.classifier = ml.KNN(classData, k=bestK, distMetric=metric)

        finalAUC = self.classifier.auc(classData)

        dialog.Destroy()

        wx.MessageBox(message=('Best K: %d\n' % bestK) + \
            ('Mean Validation AUC: %f\n' % np.max(validAUC)) +
            ('Final Training AUC: %f' % finalAUC),
            caption='Training Completed!', style=wx.OK | wx.ICON_INFORMATION)