每次使用GMM分类器都有不同的结果

Question

我目前正在做一个语音识别和机器学习相关的项目。我现在有两个类，我为每个类创建了两个GMM分类器，分别用于标签"happy"one_answers"sad"

我想用MFCC向量训练GMM分类器。

我为每个标签使用两个GMM分类器。(以前是每个文件GMM):

但每次我运行脚本，我有不同的结果。测试和训练样本相同的原因是什么?

请注意，在下面的输出中，我有10个测试样本和每行对应有序测试样品的结果

代码:

classifiers = {'happy':[],'sad':[]}
probability = {'happy':0,'sad':0}
def createGMMClassifiers():
    for name, data in training.iteritems():
        #For every class: In our case it is two, happy and sad
        classifier = mixture.GMM(n_components = n_classes,n_iter=50)
        #two classifiers.
        for mfcc in data:
            classifier.fit(mfcc)
        addClassifier(name, classifier)
    for testData in testing['happy']:
        classify(testData)
def addClassifier(name,classifier):
    classifiers[name]=classifier
def classify(testMFCC):
    for name, classifier in classifiers.iteritems():
        prediction = classifier.predict_proba(testMFCC)
        for f, s in prediction:
            probability[name]+=f
    print 'happy ',probability['happy'],'sad ',probability['sad']

示例输出1:

happy  154.300420496 sad  152.808941585
happy
happy  321.17737915 sad  318.621788517
happy
happy  465.294473363 sad  461.609246112
happy
happy  647.771003768 sad  640.451097035
happy
happy  792.420461416 sad  778.709674995
happy
happy  976.09526992 sad  961.337361541
happy
happy  1137.83592093 sad  1121.34722203
happy
happy  1297.14692405 sad  1278.51011583
happy
happy  1447.26926553 sad  1425.74595666
happy
happy  1593.00403707 sad  1569.85670672
happy

示例输出2:

happy  51.699579504 sad  152.808941585
sad
happy  81.8226208497 sad  318.621788517
sad
happy  134.705526637 sad  461.609246112
sad
happy  167.228996232 sad  640.451097035
sad
happy  219.579538584 sad  778.709674995
sad
happy  248.90473008 sad  961.337361541
sad
happy  301.164079068 sad  1121.34722203
sad
happy  334.853075952 sad  1278.51011583
sad
happy  378.730734469 sad  1425.74595666
sad
happy  443.995962929 sad  1569.85670672
sad

Answer 1

但每次我运行脚本，我有不同的结果。测试和训练样本相同的原因是什么?

scikit-learn使用随机初始化器。如果您想要可再现的结果，您可以设置random_state参数

random_state: RandomState or an int seed (None by default)

for name, data in training.iteritems():

这是不正确的，因为你只在最后一个样本上训练。在运行fit之前，您需要将每个标签的特性连接到单个数组中。您可以使用np.concatenate