code: def runExperiment():
    
    trainSeqN = [5, 10, 20, 50, 100, 200]
    rptPerCondition = 5
    correctRateAll = np.zeros((len(trainSeqN), rptPerCondition))
    missRateAll = np.zeros((len(trainSeqN), rptPerCondition))
    fpRateAll = np.zeros((len(trainSeqN), rptPerCondition))
    for i in xrange(len(trainSeqN)):
      for rpt in xrange(rptPerCondition):
        train_seed = 1
        numTrainSequence = trainSeqN[i]
        net = initializeLSTMnet()
        net = trainLSTMnet(net, numTrainSequence, seedSeq=train_seed)
        (correctRate, missRate, fpRate) = testLSTMnet(net, numTestSequence, seedSeq=train_seed+rpt)
        correctRateAll[i, rpt] = correctRate
        missRateAll[i, rpt] = missRate
        fpRateAll[i, rpt] = fpRate
  
    np.savez('result/reberSequenceLSTM.npz',
             correctRateAll=correctRateAll, missRateAll=missRateAll,
             fpRateAll=fpRateAll, trainSeqN=trainSeqN)
  
    plt.figure()
    plt.subplot(2,2,1)
    plt.semilogx(trainSeqN, 100*np.mean(correctRateAll,1),'-*')
    plt.xlabel(' Training Sequence Number')
    plt.ylabel(' Hit Rate - Best Match (%)')
    plt.subplot(2,2,2)
    plt.semilogx(trainSeqN, 100*np.mean(missRateAll,1),'-*')
    plt.xlabel(' Training Sequence Number')
    plt.ylabel(' Miss Rate (%)')
    plt.subplot(2,2,3)
    plt.semilogx(trainSeqN, 100*np.mean(fpRateAll,1),'-*')
    plt.xlabel(' Training Sequence Number')
    plt.ylabel(' False Positive Rate (%)')
    plt.savefig('result/ReberSequence_LSTMperformance.pdf')
docs: Experiment 1: Calculate error rate as a function of training sequence numbers
    :return: