#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Thu Mar 25 09:12:03 2021

@author: doug
"""
import matplotlib.pyplot as plt
import numpy as np
from sklearn.cluster import DBSCAN
import scipy.io

mat_contents = scipy.io.loadmat('HWMar26data.mat')
A = mat_contents['B']

db = DBSCAN(eps=0.06, min_samples=4).fit(A)

#%%
#
#  If you leave the data set labeled as "A" and the DBSCAN output as db,
#  you don't need to touch anything from here down-  This is just to plot
#  the results.

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)

print('Estimated number of clusters: %d' % n_clusters_)

# Plot result


# 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 = A[class_member_mask & core_samples_mask]
    plt.plot(xy[:, 0], xy[:, 1], 'o', markerfacecolor=tuple(col),
             markeredgecolor='k', markersize=14)

    xy = A[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()