python - 每次使用 GMM 分类器运行都会得到不同的结果

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，我为每个类创建两个 GMM 分类器，分别用于标签“快乐”和“悲伤”

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。 (以前是每个文件的 GMM):

但是每次运行脚本时我都会得到不同的结果。使用相同的测试和训练样本可能是什么原因？

在下面的输出中，请注意我有 10 个测试样本，并且每行对应有序测试样本的结果

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

But every time I run the script I am having different results. What might be the cause for that with same test and train samples?

scikit-learn 使用随机初始化器。如果您想要可重现的结果，可以设置 random_state argument

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

for name, data in training.iteritems():

这是不正确的，因为您仅在最后一个样本上进行训练。在运行 fit 之前，您需要将每个标签的特征连接到单个数组中。您可以使用 np.concatenate 来实现此目的。