#.........这里部分代码省略.........

    options, otherjunk = parser.parse_args(argv)
    if len(otherjunk) != 0: raise Exception('Command line input not understood: ' + str(otherjunk))
    args = {}

    # Set up variables according to the command line input.
    print "Doing classification"
    print "--------------------"
    print "data:\t\t" + options.data
    print "classifier:\t\t" + options.classifier
    if not options.classifier == 'minicontest':
        print "using enhanced features?:\t" + str(options.features)
    else:
        print "using minicontest feature extractor"
    print "training set size:\t" + str(options.training)
    if(options.data=="digits"):
        printImage = ImagePrinter(DIGIT_DATUM_WIDTH, DIGIT_DATUM_HEIGHT).printImage
        if (options.features):
            featureFunction = enhancedFeatureExtractorDigit
        else:
            featureFunction = basicFeatureExtractorDigit
        if (options.classifier == 'minicontest'):
            featureFunction = contestFeatureExtractorDigit
    elif(options.data=="faces"):
        printImage = ImagePrinter(FACE_DATUM_WIDTH, FACE_DATUM_HEIGHT).printImage
        if (options.features):
            featureFunction = enhancedFeatureExtractorFace
        else:
            featureFunction = basicFeatureExtractorFace
    elif(options.data=="pacman"):
        printImage = None
        if (options.features):
            featureFunction = enhancedFeatureExtractorPacman
        else:
            featureFunction = basicFeatureExtractorPacman
    else:
        print "Unknown dataset", options.data
        print USAGE_STRING
        sys.exit(2)

    if(options.data=="digits"):
        legalLabels = range(10)
    else:
        legalLabels = ['Stop', 'West', 'East', 'North', 'South']

    if options.training <= 0:
        print "Training set size should be a positive integer (you provided: %d)" % options.training
        print USAGE_STRING
        sys.exit(2)

    if options.smoothing <= 0:
        print "Please provide a positive number for smoothing (you provided: %f)" % options.smoothing
        print USAGE_STRING
        sys.exit(2)

    if options.odds:
        if options.label1 not in legalLabels or options.label2 not in legalLabels:
            print "Didn't provide a legal labels for the odds ratio: (%d,%d)" % (options.label1, options.label2)
            print USAGE_STRING
            sys.exit(2)

    if(options.classifier == "mostFrequent"):
        classifier = mostFrequent.MostFrequentClassifier(legalLabels)
    elif(options.classifier == "naiveBayes" or options.classifier == "nb"):
        classifier = naiveBayes.NaiveBayesClassifier(legalLabels)
        classifier.setSmoothing(options.smoothing)
        if (options.autotune):
            print "using automatic tuning for naivebayes"
            classifier.automaticTuning = True
        else:
            print "using smoothing parameter k=%f for naivebayes" %  options.smoothing
    elif(options.classifier == "perceptron"):
        if options.data != 'pacman':
            classifier = perceptron.PerceptronClassifier(legalLabels,options.iterations)
        else:
            classifier = perceptron_pacman.PerceptronClassifierPacman(legalLabels,options.iterations)
    elif(options.classifier == "mira"):
        if options.data != 'pacman':
            classifier = mira.MiraClassifier(legalLabels, options.iterations)
        if (options.autotune):
            print "using automatic tuning for MIRA"
            classifier.automaticTuning = True
        else:
            print "using default C=0.001 for MIRA"
    elif(options.classifier == 'minicontest'):
        import minicontest
        classifier = minicontest.contestClassifier(legalLabels)
    else:
        print "Unknown classifier:", options.classifier
        print USAGE_STRING

        sys.exit(2)

    args['agentToClone'] = options.agentToClone

    args['classifier'] = classifier
    args['featureFunction'] = featureFunction
    args['printImage'] = printImage

    return args, options

MINICONTEST_PATH = "minicontest_output.pickle"


if __name__ == '__main__':
    print "Loading training data"
    rawTrainingData = samples.loadDataFile("digitdata/trainingimages", 5000,DIGIT_DATUM_WIDTH,DIGIT_DATUM_HEIGHT)
    trainingLabels = samples.loadLabelsFile("digitdata/traininglabels", 5000)
    rawValidationData = samples.loadDataFile("digitdata/validationimages", 100,DIGIT_DATUM_WIDTH,DIGIT_DATUM_HEIGHT)
    validationLabels = samples.loadLabelsFile("digitdata/validationlabels", 100)
    rawTestData = samples.loadDataFile("digitdata/testimages", TEST_SIZE,DIGIT_DATUM_WIDTH,DIGIT_DATUM_HEIGHT)


    featureFunction = contestFeatureExtractorDigit
    legalLabels = range(10)
    classifier = minicontest.contestClassifier(legalLabels)

    print "Extracting features..."
    trainingData = map(featureFunction, rawTrainingData)
    validationData = map(featureFunction, rawValidationData)
    testData = map(featureFunction, rawTestData)

    print "Training..."
    classifier.train(trainingData, trainingLabels, validationData, validationLabels)
    print "Validating..."
    guesses = classifier.classify(validationData)
    correct = [guesses[i] == validationLabels[i] for i in range(len(validationLabels))].count(True)
    print str(correct), ("correct out of " + str(len(validationLabels)) + " (%.1f%%).") % (100.0 * correct / len(validationLabels))
    print "Testing..."
    guesses = classifier.classify(testData)