
def train_dbscan(X_train, eps=0.3, min_samples=10):
    from sklearn.cluster import DBSCAN
    from sklearn import metrics
    import numpy as np
    db = DBSCAN(eps, min_samples).fit(X_train)
    core_samples_mask = np.zeros_like(db.labels_, dtype=bool)
    core_samples_mask[db.core_sample_indices_] = True
    labels = db.labels_
    # Number of clusters in labels, ignoring noise if present.
    n_clusters_ = len(set(labels)) - (1 if -1 in labels else 0)
    n_noise_ = list(labels).count(-1)
    print('Estimated number of clusters: %d' % n_clusters_)
    print('Estimated number of noise points: %d' % n_noise_)
    # print("Homogeneity: %0.3f" % metrics.homogeneity_score(labels_true, labels))
    # print("Completeness: %0.3f" % metrics.completeness_score(labels_true, labels))
    # print("V-measure: %0.3f" % metrics.v_measure_score(labels_true, labels))
    # print("Adjusted Rand Index: %0.3f" % metrics.adjusted_rand_score(labels_true, labels))
    # print("Adjusted Mutual Information: %0.3f" % metrics.adjusted_mutual_info_score(labels_true, labels, average_method='arithmetic'))
    print("Silhouette Coefficient: %0.3f" % metrics.silhouette_score(X_train, labels))
    # #############################################################################
    # Plot result
    import matplotlib.pyplot as plt
    # Black removed and is used for noise instead.
    unique_labels = set(labels)
    colors = [plt.cm.Spectral(each) for each in np.linspace(0, 1, len(unique_labels))]
    for k, col in zip(unique_labels, colors):
        if k == -1:
            # Black used for noise.
            col = [0, 0, 0, 1]
        class_member_mask = (labels == k)
        xy = X_train[class_member_mask & core_samples_mask]
        plt.plot(xy[:, 0], xy[:, 1], 'o', markerfacecolor=tuple(col), markeredgecolor='k', markersize=14)
        xy = X_train[class_member_mask & ~core_samples_mask]
        plt.plot(xy[:, 0], xy[:, 1], 'o', markerfacecolor=tuple(col), markeredgecolor='k', markersize=6)
    plt.title('Estimated number of clusters: %d' % n_clusters_)
    plt.show()
