//  [python]
def gaussianMixture(self, clusterID, trainPercent=75.0, covType=['spherical'], plot=True):
        """ Use a Gaussian Mixing Model to classify the data.
        clusterID is the initial assignment of the rows to their clusters """

        assert clusterID.size == self.shape[0], "size of clusterID "+str(
            clusterID.size)+" must match first dimension size "+str(self.shape[0])

        # Get the number of splits
        nSplits = int(100.0/(100.0-trainPercent))

        # Break up the dataset into non-overlapping sets, e.g. training (75%) and testing (25%)
        skf = StratifiedKFold(n_splits=nSplits)

        # Only take the first fold.
        trainIndex, testIndex = next(iter(skf.split(self, clusterID)))

        xTrain = self[trainIndex]
        xTest = self[testIndex]
        yTrain = clusterID[trainIndex]
        yTest = clusterID[testIndex]

        nClusters = unique(clusterID).size

        # Try GMMs using different types of covariances.
        models = dict((cov_type, mixture.GaussianMixture(n_components=nClusters,
                                                 covariance_type=cov_type, max_iter=20, random_state=None))
                      for cov_type in covType)

        nModels = len(models)

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

        for index, (name, estimator) in enumerate(models.items()):
            # Since we have class labels for the training data, we can
            # initialize the GMM parameters in a supervised manner.
            estimator.means_init = array(
                [xTrain[yTrain == i].mean(axis=0) for i in range(nClusters)])

            # Train the other parameters using the EM algorithm.
            estimator.fit(xTrain)

            if (plot):
                h = plt.subplot(2, nModels // 2, index + 1)
            #make_ellipses(estimator, h)

            #    tmp = x[z == n]
            #    c=zeros(tmp.shape[0])+n
                #cP.myscatter2D(tmp[:, 0], tmp[:, 1], s=0.8, c=c)
            #    plt.scatter(data[:, 0], data[:, 1], s=0.8, color=color)#,label=ris.target_names[n])

            # Plot the test data with crosses
            # for n, color in enumerate(colors):
            #    data = X_test[y_test == n]
            #    plt.scatter(data[:, 0], data[:, 1], marker='x', color=color)

            y_train_pred = estimator.predict(xTrain)
            z = estimator.predict(self)
            if (plot):
                cP.myscatter2D(self[:, 0], self[:, 1], c=z)

            train_accuracy = mean(
                y_train_pred.ravel() == yTrain.ravel()) * 100
            if (plot):
                plt.text(0.05, 0.9, 'Train accuracy: %.1f' %
                         train_accuracy, transform=h.transAxes)

            y_test_pred = estimator.predict(xTest)
            test_accuracy = mean(y_test_pred.ravel() == yTest.ravel()) * 100
            if (plot):
                plt.text(0.05, 0.8, 'Test accuracy: %.1f' %
                         test_accuracy, transform=h.transAxes)

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