class ImageClassifier:
    """Class to create an ImageClassifier from a regular classifier with 5
    methods that are common amongst classifiers.
    """

    def __init__(self, clf, *args, **kwargs):
        self.clf = clf(*args, **kwargs)

    def fit(self, X, *args, **kwargs):
        X = X.reshape((len(X), -1))
        return self.clf.fit(X, *args, **kwargs)

    def predict(self, X, *args, **kwargs):
        X = X.reshape((len(X), -1))
        return self.clf.predict(X, *args, **kwargs)

    def score(self, X, *args, **kwargs):
        X = X.reshape((len(X), -1))
        return self.clf.score(X, *args, **kwargs)

    def get_params(self, *args, **kwargs):
        return self.clf.get_params(*args, **kwargs)

    def set_params(self, **params):
        return self.set_params(**params)

if __name__ == '__main__':
    
    # Import datasets, classifiers and performance metrics
    from sklearn import datasets, svm, metrics

    # The digits dataset
    digits = datasets.load_digits()

    n_samples = len(digits.images)
    data = digits.images

    # Create a classifier: a support vector classifier
    classifier = ImageClassifier(svm.SVC, gamma=0.001)

    # We learn the digits on the first half of the digits
    classifier.fit(data[:n_samples // 2], digits.target[:n_samples // 2])

    # Now predict the value of the digit on the second half:
    expected = digits.target[n_samples // 2:]
    predicted = classifier.predict(data[n_samples // 2:])

    print("Classification report for classifier %s:\n%s\n"
          % (classifier, metrics.classification_report(expected, predicted)))
    print("Confusion matrix:\n%s" % metrics.confusion_matrix(expected, predicted))