from PIL import Image
import matplotlib.pyplot as plt
import numpy as np
from sklearn.cluster import KMeans, MiniBatchKMeans
import pandas as pd
?
if __name__ == "__main__":
? ? data = pd.read_csv(r"H:\CSDN_Test_Data\UseYourTestData.csv")
? ? x, y = data['Time (sec)'], data['Height (m HAE)']
? ? n = len(x)
? ? x = np.array(x)
? ? x = x.reshape(n, 1)#reshape 為一列
? ? y = np.array(y)
? ? y = y.reshape(n, 1)#reshape 為一列
? ? data = np.hstack((x, y)) #水平合并為兩列
? ? k = 8 ?# 設置顏色聚類的類別個數（我們分別設置8，16，32，64，128進行對比）
? ? cluster = KMeans(n_clusters=k) ?# 構造聚類器
? ? C = cluster.fit_predict(data)
? ? # C_Image = cluster.fit_predict(data)
? ? print("訓練總耗時為：%s(s)" % (Trainingtime).seconds)
? ? plt.figure()
? ? plt.scatter(data[:, 0], data[:, 1], marker='o', s=2, c=C)
? ? plt.show()

from PIL import Image
import matplotlib.pyplot as plt
import numpy as np
from sklearn.cluster import KMeans, MiniBatchKMeans
import pandas as pd
from mpl_toolkits.mplot3d import Axes3D
?
if __name__ == "__main__":
? ? data = pd.read_csv(r"H:\CSDN_Test_Data\UseYourTestData.csv")
? ? x, y,z = data['Longitude (deg)'],data['Latitude (deg)'], ?data['Height (m HAE)']
? ? n = len(x)
? ? x = np.array(x)
? ? x = x.reshape(n, 1)#reshape 為一列
? ? y = np.array(y)
? ? y = y.reshape(n, 1)#reshape 為一列
? ? z = np.array(z)
? ? z = z.reshape(n, 1) ?# reshape 為一列
? ? data = np.hstack((x, y, z)) #水平合并為兩列
? ? k = 8 ?# 設置顏色聚類的類別個數（我們分別設置8，16，32，64，128進行對比）
? ? cluster = KMeans(n_clusters=k) ?# 構造聚類器
? ? C = cluster.fit_predict(data)
?
? ? # C_Image = cluster.fit_predict(data)
? ? print("訓練總耗時為：%s(s)" % (Trainingtime).seconds)
? ? fig = plt.figure()
? ? ax = Axes3D(fig)
?
? ? ax.scatter(data[:, 0], data[:, 1],data[:, 2], s=1, c=C)
? ? # 繪制圖例
? ? ax.legend(loc='best')
? ? # 添加坐標軸
? ? ax.set_zlabel('Z Label', fontdict={'size': 15, 'color': 'red'})
? ? ax.set_ylabel('Y Label', fontdict={'size': 15, 'color': 'red'})
? ? ax.set_xlabel('X Label', fontdict={'size': 15, 'color': 'red'})
? ? plt.show()