高斯混合模型，折叠次数必须是Integral类型

如何解决高斯混合模型，折叠次数必须是Integral类型

我刚刚在学习高斯混合模型。我在网上找到了一个例子，我正在审查它。但是代码报错了。我无法理解此错误的原因。

错误：

ValueError: The number of folds must be of Integral type. [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2
 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
 2 2] of type <class 'numpy.ndarray'> was passed.

import matplotlib.pyplot as plt
import matplotlib as mpl
import numpy as np

from sklearn import datasets
from sklearn.model_selection import StratifiedKFold
from sklearn.mixture import GaussianMixture


def make_ellipses(gmm,ax):
    for n,color in enumerate('rgb'):
        v,w = np.linalg.eigh(gmm._get_covars()[n][:2,:2])
        u = w[0] / np.linalg.norm(w[0])
        angle = np.arctan2(u[1],u[0])
        angle = 180 * angle / np.pi  # convert to degrees
        v *= 9
        ell = mpl.patches.Ellipse(gmm.means_[n,:2],v[0],v[1],180 + angle,color=color)
        ell.set_clip_box(ax.bbox)
        ell.set_alpha(0.5)
        ax.add_artist(ell)

iris = datasets.load_iris()

# Break up the dataset into non-overlapping training (75%) and testing
# (25%) sets.
skf = StratifiedKFold(iris.target,n_splits=4)
# Only take the first fold.
train_index,test_index = next(iter(skf))


X_train = iris.data[train_index]
y_train = iris.target[train_index]
X_test = iris.data[test_index]
y_test = iris.target[test_index]

n_classes = len(np.unique(y_train))

# Try GMMs using different types of covariances.
classifiers = dict((covar_type,GaussianMixture(n_components=n_classes,covariance_type=covar_type,init_params='wc',n_iter=20))
                   for covar_type in ['spherical','diag','tied','full'])

n_classifiers = len(classifiers)

plt.figure(figsize=(3 * n_classifiers / 2,6))
plt.subplots_adjust(bottom=.01,top=0.95,hspace=.15,wspace=.05,left=.01,right=.99)


for index,(name,classifier) in enumerate(classifiers.items()):
    # Since we have class labels for the training data,we can
    # initialize the GMM parameters in a supervised manner.
    classifier.means_ = np.array([X_train[y_train == i].mean(axis=0)
                                  for i in xrange(n_classes)])

    # Train the other parameters using the EM algorithm.
    classifier.fit(X_train)

    h = plt.subplot(2,n_classifiers / 2,index + 1)
    make_ellipses(classifier,h)

    for n,color in enumerate('rgb'):
        data = iris.data[iris.target == n]
        plt.scatter(data[:,0],data[:,1],0.8,color=color,label=iris.target_names[n])
    # Plot the test data with crosses
    for n,color in enumerate('rgb'):
        data = X_test[y_test == n]
        plt.plot(data[:,'x',color=color)

    y_train_pred = classifier.predict(X_train)
    train_accuracy = np.mean(y_train_pred.ravel() == y_train.ravel()) * 100
    plt.text(0.05,0.9,'Train accuracy: %.1f' % train_accuracy,transform=h.transAxes)

    y_test_pred = classifier.predict(X_test)
    test_accuracy = np.mean(y_test_pred.ravel() == y_test.ravel()) * 100
    plt.text(0.05,'Test accuracy: %.1f' % test_accuracy,transform=h.transAxes)

    plt.xticks(())
    plt.yticks(())
    plt.title(name)

plt.legend(loc='lower right',prop=dict(size=12))


plt.show()