Python DBSCAN.get_max_distance方法代码示例

# 需要导入模块: from sklearn.cluster import DBSCAN [as 别名]
# 或者: from sklearn.cluster.DBSCAN import get_max_distance [as 别名]

#.........这里部分代码省略.........
        if method == "areas":
            # Plot the decision boundary. For that, we will assign a color to each
            x_min, x_max = reduced_data[:, 0].min() - 1, reduced_data[:, 0].max() + 1
            y_min, y_max = reduced_data[:, 1].min() - 1, reduced_data[:, 1].max() + 1

            # Step size of the mesh. Decrease to increase the quality of the VQ.
            parts_n = kwargs.pop("parts_n", 10)
            h_x = (x_max - x_min) / parts_n  # point in the mesh [x_min, m_max]x[y_min, y_max].
            h_y = (y_max - y_min) / parts_n  # point in the mesh [x_min, m_max]x[y_min, y_max].

            xx, yy = np.meshgrid(np.arange(x_min, x_max, h_x), np.arange(y_min, y_max, h_y))

            neighbors_classifier = KNeighborsClassifier().fit(reduced_data, self.labels)
            # Obtain labels for each point in mesh. Use last trained model.
            Z = neighbors_classifier.predict(np.c_[xx.ravel(), yy.ravel()])

            # Put the result into a color plot
            Z = Z.reshape(xx.shape)
            draw_obj.imshow(Z, interpolation='nearest',
                            extent=(xx.min(), xx.max(), yy.min(), yy.max()),
                            cmap=plt.cm.Paired,
                            aspect='auto', origin='lower')
            draw_obj.plot(reduced_data[:, 0], reduced_data[:, 1], 'o', markersize=9)

            if "cluster_centers_" in self.base_model.__dict__:
                # Plot the centroids as a white X
                centroids = self.base_model.cluster_centers_
                plt.scatter(centroids[:, 0], centroids[:, 1],
                            marker='x', s=169, linewidths=9,
                            color='w', zorder=10)
                # plt.xlim(x_min, x_max)
                # plt.ylim(y_min, y_max)
                # plt.xticks(())
                # plt.yticks(())
        elif method == "dendrogram":
            def fancy_dendrogram(*args, **kwargs):
                max_d = kwargs.pop('max_d', None)
                if max_d and 'color_threshold' not in kwargs:
                    kwargs['color_threshold'] = max_d
                annotate_above = kwargs.pop('annotate_above', 0)

                ddata = dendrogram(*args, **kwargs)

                if not kwargs.get('no_plot', False):
                    plt.xlabel('sample index or (cluster size)')
                    plt.ylabel('distance')
                    for i, d, c in zip(ddata['icoord'], ddata['dcoord'], ddata['color_list']):
                        x = 0.5 * sum(i[1:3])
                        y = d[1]
                        if y > annotate_above:
                            plt.plot(x, y, 'o', c=c)
                            plt.annotate("%.3g" % y, (x, y), xytext=(0, -5),
                                         textcoords='offset points',
                                         va='top', ha='center')
                    if max_d:
                        plt.axhline(y=max_d, c='k')
                return ddata

            plt.xlabel('sample index or (cluster size)')
            plt.ylabel('distance')
            fancy_dendrogram(
                self.base_model.Z,
                truncate_mode='lastp',  # show only the last p merged clusters
                p=400,  # show only the last p merged clusters
                leaf_rotation=90.,  # rotates the x axis labels
                leaf_font_size=8.,  # font size for the x axis labels
                max_d=self.base_model.get_max_distance(),
                annotate_above=3,  # useful in small plots so annotations don't overlap
                show_contracted=True,  # to get a distribution impression in truncated branches
            )
        else:
            core_samples_mask = np.zeros_like(self.labels, dtype=bool)
            if 'core_sample_indices_' in self.base_model.__dict__:
                core_samples_mask[self.base_model.core_sample_indices_] = True

            # Black removed and is used for noise instead.
            unique_labels = set(self.labels)
            colors = plt.cm.Spectral(np.linspace(0, 1, len(unique_labels)))
            for k, col in zip(unique_labels, colors):
                if k == -1:
                    # Black used for noise.
                    col = 'k'

                class_member_mask = (self.labels == k)

                xy = reduced_data[class_member_mask & core_samples_mask]
                xy2 = reduced_data[class_member_mask & ~core_samples_mask]
                draw_obj.plot(xy[:, 0], xy[:, 1], 'o', markerfacecolor=col,
                              markeredgecolor='k', markersize=14)
                draw_obj.plot(xy2[:, 0], xy2[:, 1], 'o', markerfacecolor=col,
                              markeredgecolor='k', markersize=9)

        patch = mpatches.Rectangle([0, 0], 0, 0, color="black", label="score = " + str(self.get_silhouette_score()))
        draw_obj.legend(handles=[patch])
        if axis is None:
            plt.title(title)
        else:
            axis.set_title(title)
        if show:
            plt.show()