# !/usr/bin/python
# -*- coding:utf-8 -*-

import numpy as np
import matplotlib.pyplot as plt
import sklearn.datasets as ds
import matplotlib.colors
from sklearn.cluster import DBSCAN
from sklearn.preprocessing import StandardScaler
from sklearn.datasets import load_iris
iris=load_iris()
y=iris.target
data=iris.data[:,2:]

def expand(a, b):
    d = (b - a) * 0.1
    return a-d, b+d


if __name__ == "__main__":
    N = 500
    centers = [[1, 2], [-1, -1], [1, -1], [-1, 1]]

    data, y = ds.make_blobs(N, n_features=2, centers=centers, cluster_std=[0.5, 0.25, 0.7, 0.5], random_state=0)
    print(data.shape)
    data = StandardScaler().fit_transform(data)
    # 資料1的引數：(epsilon, min_sample)
    params = ((0.2, 5), (0.2, 10), (0.2, 15), (0.3, 5), (0.3, 10), (0.3, 15))

    # 資料2
    # t = np.arange(0, 2*np.pi, 0.1)
    # data1 = np.vstack((np.cos(t), np.sin(t))).T
    # data2 = np.vstack((2*np.cos(t), 2*np.sin(t))).T
    # data3 = np.vstack((3*np.cos(t), 3*np.sin(t))).T
    # data = np.vstack((data1, data2, data3))
    # # # 資料2的引數：(epsilon, min_sample)
    # params = ((0.5, 3), (0.5, 5), (0.5, 10), (1., 3), (1., 10), (1., 20))

    matplotlib.rcParams['font.sans-serif'] = ['SimHei']
    matplotlib.rcParams['axes.unicode_minus'] = False

    plt.figure(figsize=(10,8), facecolor='w')
    plt.suptitle('DBSCAN聚類', fontsize=12)

    for i in range(6):
        eps, min_samples = params[i]
        model = DBSCAN(eps=eps, min_samples=min_samples)
        model.fit(data)
        y_hat = model.labels_

        core_indices = np.zeros_like(y_hat, dtype=bool)
        core_indices[model.core_sample_indices_] = True

        y_unique = np.unique(y_hat)
        print(np.zeros_like)
        print(y_unique)
        n_clusters = y_unique.size - (1 if -1 in y_hat else 0)
        print(y_unique, '聚類簇的個數為：', n_clusters)

        # clrs = []
        # for c in np.linspace(16711680, 255, y_unique.size):
        #     clrs.append('#%06x' % c)
        plt.subplot(2, 3, i+1)
        clrs = plt.cm.Spectral(np.linspace(0, 0.8, y_unique.size))
        print(clrs)

        for k, clr in zip(y_unique, clrs):
        # for k in zip(y_unique):
            cur = (y_hat == k)
            if k == -1:
                plt.scatter(data[cur, 0], data[cur, 1], s=10, c='k')
                continue
            plt.scatter(data[cur, 0], data[cur, 1], s=15, c=clr, edgecolors='k')
            plt.scatter(data[cur & core_indices][:, 0], data[cur & core_indices][:, 1], s=30, c=clr, marker='o', edgecolors='k')
            # plt.scatter(data[cur, 0], data[cur, 1], s=15,  edgecolors='k')
            # plt.scatter(data[cur & core_indices][:, 0], data[cur & core_indices][:, 1], s=30, marker='o',
            #             edgecolors='k')
        x1_min, x2_min = np.min(data, axis=0)
        x1_max, x2_max = np.max(data, axis=0)
        x1_min, x1_max = expand(x1_min, x1_max)
        x2_min, x2_max = expand(x2_min, x2_max)
        plt.xlim((x1_min, x1_max))
        plt.ylim((x2_min, x2_max))
        plt.plot()
        plt.grid(b=True, ls=':', color='#606060')
        plt.title(r'$\epsilon$ = %.1f  m = %d，聚類數目：%d' % (eps, min_samples, n_clusters), fontsize=12)
    plt.tight_layout()
    plt.subplots_adjust(top=0.9)
    plt.show()