misc.py 文件源码

def plot_in_2d(self, X, y=None, title=None, accuracy=None, legend_labels=None):
        X_transformed = self._transform(X, dim=2)
        x1 = X_transformed[:, 0]
        x2 = X_transformed[:, 1]
        class_distr = []

        y = np.array(y).astype(int)

        colors = [self.cmap(i) for i in np.linspace(0, 1, len(np.unique(y)))]

        # Plot the different class distributions
        for i, l in enumerate(np.unique(y)):
            _x1 = x1[y == l]
            _x2 = x2[y == l]
            _y = y[y == l]
            class_distr.append(plt.scatter(_x1, _x2, color=colors[i]))

        # Plot legend
        if not legend_labels is None: 
            plt.legend(class_distr, legend_labels, loc=1)

        # Plot title
        if title:
            if accuracy:
                perc = 100 * accuracy
                plt.suptitle(title)
                plt.title("Accuracy: %.1f%%" % perc, fontsize=10)
            else:
                plt.title(title)

        # Axis labels
        plt.xlabel('Principal Component 1')
        plt.ylabel('Principal Component 2')

        plt.show()

    # Plot the dataset X and the corresponding labels y in 3D using PCA.