import numpy as npimport matplotlib.pyplot as pltfrom sklearn.naive_bayes import GaussianNBfrom sklearn.svm import SVCfrom sklearn.datasets import load_digitsfrom sklearn.model_selection import learning_curvefrom sklearn.model_selection import ShuffleSplitdef plot_learning_curve(estimator, title, X, y, ylim=None, cv=None,                        n_jobs=1, train_sizes=np.linspace(.1, 1.0, 5)):    """    Generate a simple plot of the test and training learning curve.    Parameters    ----------    estimator : object type that implements the "fit" and "predict" methods        An object of that type which is cloned for each validation.    title : string        Title for the chart.    X : array-like, shape (n_samples, n_features)        Training vector, where n_samples is the number of samples and        n_features is the number of features.    y : array-like, shape (n_samples) or (n_samples, n_features), optional        Target relative to X for classification or regression;        None for unsupervised learning.    ylim : tuple, shape (ymin, ymax), optional        Defines minimum and maximum yvalues plotted.    cv : int, cross-validation generator or an iterable, optional        Determines the cross-validation splitting strategy.        Possible inputs for cv are:          - None, to use the default 3-fold cross-validation,          - integer, to specify the number of folds.          - An object to be used as a cross-validation generator.          - An iterable yielding train/test splits.        For integer/None inputs, if ``y`` is binary or multiclass,        :class:`StratifiedKFold` used. If the estimator is not a classifier        or if ``y`` is neither binary nor multiclass, :class:`KFold` is used.        Refer :ref:`User Guide 
     
      ` for the various        cross-validators that can be used here.    n_jobs : integer, optional        Number of jobs to run in parallel (default 1).    """    plt.figure()    plt.title(title)    if ylim is not None:        plt.ylim(*ylim)    plt.xlabel("Training examples")    plt.ylabel("Score")    train_sizes, train_scores, test_scores = learning_curve(        estimator, X, y, cv=cv, n_jobs=n_jobs, train_sizes=train_sizes)    train_scores_mean = np.mean(train_scores, axis=1)    train_scores_std = np.std(train_scores, axis=1)    test_scores_mean = np.mean(test_scores, axis=1)    test_scores_std = np.std(test_scores, axis=1)    plt.grid()    plt.fill_between(train_sizes, train_scores_mean - train_scores_std,                     train_scores_mean + train_scores_std, alpha=0.1,                     color="r")    plt.fill_between(train_sizes, test_scores_mean - test_scores_std,                     test_scores_mean + test_scores_std, alpha=0.1, color="g")    plt.plot(train_sizes, train_scores_mean, 'o-', color="r",             label="Training score")    plt.plot(train_sizes, test_scores_mean, 'o-', color="g",             label="Cross-validation score")    plt.legend(loc="best")    return pltdigits = load_digits()X, y = digits.data, digits.targettitle = "Learning Curves (Naive Bayes)"# Cross validation with 100 iterations to get smoother mean test and train# score curves, each time with 20% data randomly selected as a validation set.cv = ShuffleSplit(n_splits=100, test_size=0.2, random_state=0)estimator = GaussianNB()plot_learning_curve(estimator, title, X, y, ylim=(0.7, 1.01), cv=cv, n_jobs=4)title = "Learning Curves (SVM, RBF kernel, $\gamma=0.001$)"# SVC is more expensive so we do a lower number of CV iterations:cv = ShuffleSplit(n_splits=10, test_size=0.2, random_state=0)estimator = SVC(gamma=0.001)plot_learning_curve(estimator, title, X, y, (0.7, 1.01), cv=cv, n_jobs=4)plt.show()