import numpy as np
from sklearn.preprocessing import StandardScaler, MinMaxScaler
import matplotlib.pyplot as plt
import sklearn.datasets as datasets
import DBSCAN
import KMeansRelevant
def show_cluster_result(cluster, normal_data, cluster_method):
# 第一幅图展示原始图
plt.figure(figsize=(24, 10), dpi=80)
plt.subplot(1, 2, 1)
plt.scatter(normal_data[:, 0], normal_data[:, 1], color='black')
plt.title('raw graph')
# 第二幅图聚类结果
plt.subplot(1, 2, 2)
plt.scatter(normal_data[:, 0], normal_data[:, 1], c=cluster, marker='o')
plt.title(cluster_method)
plt.legend(labels=['x', 'y'])
plt.show()
def show_K_dis(data, K):
distance = DBSCAN.k_nearest_neighbour_distance(dataSet, K)
x = [i for i in range(distance.shape[0])]
plt.scatter(x, distance)
plt.title("Points Sorted by Distance to %ith Nearest Neighbor" % K)
plt.legend()
plt.show()
scutJPG = np.load("scutVec.npy") # 窝工校徽
n_samples = 2500
noisy_circles, _ = datasets.make_circles(n_samples=n_samples, factor=0.5, noise=0.05,random_state=8)
noisy_moons, _ = datasets.make_moons(n_samples=n_samples, noise=0.05,random_state=8)
blobs, _ = datasets.make_blobs(n_samples=n_samples, random_state=8)
# 1,选择不同的数据集
dataSet = scutJPG # ['noisy_circles', 'noisy_moons', 'blobs', 'scutJPG']
# 2, 对数据进行处理
scaler = StandardScaler()
normal_data = scaler.fit_transform(dataSet)
# 3, 选择并运行聚类算法
cluster_method = "kMeans" # ['kMeans', 'biKMeans', 'kMeans++', 'DBSCAN']
if cluster_method == "DBSCAN":
# DBSCAN的参数设置与运行, 请根据K-dis图像来获得大致的eps值
min_pts = 8
# 查看k-dist
show_K_dis(normal_data, min_pts)
eps = 0.08 # 0.09
cluster = DBSCAN.dbscan(normal_data, eps, min_pts)
elif cluster_method == "biKMeans": # 运行二分K-means算法，请指定k值
cluster = KMeansRelevant.biKMeans(normal_data, 10)
elif cluster_method == "kMeans++": # 运行K-means++算法，请指定k值
cluster = KMeansRelevant.kMeansPP(normal_data, 10)
else: # 默认其他跑KMeans
cluster = KMeansRelevant.origin_kMeans(normal_data, 10)
# 4, 输出聚类结果
num = cluster.max(0)
print("cluster num is:", num + 1)
show_cluster_result(cluster, normal_data, cluster_method)
