IterativeRecommender.py 文件源码

def isConverged(self,iter):
        from math import isnan
        if isnan(self.loss):
            print 'Loss = NaN or Infinity: current settings does not fit the recommender! Change the settings and try again!'
            exit(-1)
        measure = self.performance()
        value = [item.strip()for item in measure]
        #with open(self.algorName+' iteration.txt')
        deltaLoss = (self.lastLoss-self.loss)
        print '%s %s iteration %d: loss = %.4f, delta_loss = %.5f learning_Rate = %.5f %s %s' %(self.algorName,self.foldInfo,iter,self.loss,deltaLoss,self.lRate,measure[0][:11],measure[1][:12])
        #check if converged
        cond = abs(deltaLoss) < 1e-3
        converged = cond
        if not converged:
            self.updateLearningRate(iter)
        self.lastLoss = self.loss
        shuffle(self.dao.trainingData)
        return converged