Неверный тип входных параметров

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 numpy as np
import matplotlib.pyplot as plt
from matplotlib import patches
 
from sklearn import datasets
from sklearn.mixture import GaussianMixture
from sklearn.model_selection import StratifiedKFold
 
# Load the iris dataset
iris = datasets.load_iris()
 
# Split dataset into training and testing (80/20 split)
indices = StratifiedKFold(iris.target, 5)
 
# Take the first fold
train_index, test_index = next(iter(indices))
 
# Extract training data and labels
X_train = iris.data[train_index]
y_train = iris.target[train_index]
 
# Extract testing data and labels
X_test = iris.data[test_index]
y_test = iris.target[test_index]
 
# Extract the number of classes
num_classes = len(np.unique(y_train))
 
# Build GMM
classifier = GMM(n_components=num_classes, covariance_type='full', 
        init_params='wc', n_iter=20)
 
# Initialize the GMM means 
classifier.means_ = np.array([X_train[y_train == i].mean(axis=0)
                              for i in range(num_classes)])
 
# Train the GMM classifier 
classifier.fit(X_train)
 
# Draw boundaries
plt.figure()
colors = 'bgr'
for i, color in enumerate(colors):
    # Extract eigenvalues and eigenvectors
    eigenvalues, eigenvectors = np.linalg.eigh(
            classifier._get_covars()[i][:2, :2])
 
    # Normalize the first eigenvector
    norm_vec = eigenvectors[0] / np.linalg.norm(eigenvectors[0])
 
    # Extract the angle of tilt
    angle = np.arctan2(norm_vec[1], norm_vec[0])
    angle = 180 * angle / np.pi 
 
    # Scaling factor to magnify the ellipses
    # (random value chosen to suit our needs)
    scaling_factor = 8
    eigenvalues *= scaling_factor 
 
    # Draw the ellipse
    ellipse = patches.Ellipse(classifier.means_[i, :2], 
            eigenvalues[0], eigenvalues[1], 180 + angle, 
            color=color)
    axis_handle = plt.subplot(1, 1, 1)
    ellipse.set_clip_box(axis_handle.bbox)
    ellipse.set_alpha(0.6)
    axis_handle.add_artist(ellipse)
 
# Plot the data 
colors = 'bgr'
for i, color in enumerate(colors):
    cur_data = iris.data[iris.target == i]
    plt.scatter(cur_data[:,0], cur_data[:,1], marker='o', 
            facecolors='none', edgecolors='black', s=40, 
            label=iris.target_names[i])
 
    test_data = X_test[y_test == i]
    plt.scatter(test_data[:,0], test_data[:,1], marker='s', 
            facecolors='black', edgecolors='black', s=40, 
            label=iris.target_names[i])
 
# Compute predictions for training and testing data
y_train_pred = classifier.predict(X_train)
accuracy_training = np.mean(y_train_pred.ravel() == y_train.ravel()) * 100
print('Accuracy on training data =', accuracy_training)
         
y_test_pred = classifier.predict(X_test)
accuracy_testing = np.mean(y_test_pred.ravel() == y_test.ravel()) * 100
print('Accuracy on testing data =', accuracy_testing)
 
plt.title('GMM classifier')
plt.xticks(())
plt.yticks(())
 
plt.show()