用興趣點近似列表

我希望將此列表近似爲6個值，因爲您看到這些值是通過一些差異傳播的。我繪製在matplotlib中，我得到this。現在我有多個值，我怎麼能aprroximate只有6個值用興趣點近似列表

[(61, 148), (61, 149), (61, 150), (62, 147), (62, 148), (62, 149), (62, 150), (63, 147), (63, 148), (63, 149), (63, 150), (64, 147), (64, 148), (64, 149), (64, 150), (65, 147), (65, 148), (65, 149), (65, 150), (149, 436), (149, 437), (149, 438), (150, 366), (150, 367), (150, 368), (150, 436), (150, 437), (150, 438), (150, 439), (151, 366), (151, 367), (151, 368), (151, 436), (151, 437), (151, 438), (151, 439), (152, 366), (152, 367), (152, 368), (152, 436), (152, 437), (152, 438), (152, 439), (175, 147), (175, 148), (175, 149), (175, 150), (175, 264), (175, 265), (175, 266), (175, 267), (176, 147), (176, 148), (176, 149), (176, 150), (176, 264), (176, 265), (176, 266), (176, 267), (177, 147), (177, 148), (177, 149), (177, 150), (177, 264), (177, 265), (177, 266), (177, 267), (178, 147), (178, 148), (178, 149), (178, 264), (178, 265), (178, 266), (230, 366), (230, 367), (230, 368), (230, 369), (231, 366), (231, 367), (231, 368), (231, 369), (232, 366), (232, 367), (232, 368), (232, 369), (233, 366), (233, 367), (233, 368)]

來源

2017-08-05 Perseus784

不知道什麼是您的具體問題和期望 –

列表中有值像（61148），（61149），（62149），（62148）......而不是我想它平均這些多個值以單一的價值.....但它包含6個不同的興趣點。所以，我想將這88個值轉換爲6個值。 – Perseus784

您應該使用* k-means *聚類。你可以使用'scipy'。 https://docs.scipy.org/doc/scipy/reference/generated/scipy.cluster.vq.kmeans.html#scipy.cluster.vq.kmeans –

使用解決了這個問題heirarchial集羣利益6分。設置默認半徑運行相同的數據點。它減少到了6分。

#mean shift clustering. 
#this lets the program decide number of groups involved in the given dataset 
import numpy as np 
import matplotlib.pyplot as plt 
import random as r 

#setting all the points as centroids 
centroids = {} 


def auto_cluster(radius,data): 
    global centroids 

    for i in range(len(data)): 
     centroids[i] = data[i] 

    while True: 
     new_centroids=[] 
     #checking all the points whether it is in radius and assign to to that centroid 
     for j in centroids: 
      in_radius=[] 
      centroid=centroids[j] 
      for point in data: 
       if np.linalg.norm(point-centroid)<radius: 
        in_radius.append(point) 
      #finding mean 
      new_centroid=np.average(in_radius,axis=0) 
      new_centroids.append(tuple(new_centroid)) 
     #collect all the final centroids for each grp 
     uniques=sorted(list(set(new_centroids))) 
     prev_centroids=dict(centroids) 
     centroids={} 
     #fil with new centroids 
     for i in range(len(uniques)): 
      centroids[i]=np.array(uniques[i]) 
     opt=True 
     #chech whether the centroid is optimized 
     for i in centroids: 
      if not np.array_equal(centroids[i],prev_centroids[i]): 
       opt=False 
      if not opt: 
       break 
     if opt:break 
    return centroids 

if __name__=="__main__": 
    data = [[61, 148], [61, 149], [61, 150], [62, 147], [62, 148], [62, 149], [62, 150], [63, 147], [63, 148], 
      [63, 149], [63, 150], [64, 147], [64, 148], [64, 149], [64, 150], [65, 147], [65, 148], [65, 149], 
      [65, 150], [149, 436], [149, 437], [149, 438], [150, 366], [150, 367], [150, 368], [150, 436], [150, 437], 
      [150, 438], [150, 439], [151, 366], [151, 367], [151, 368], [151, 436], [151, 437], [151, 438], [151, 439], 
      [152, 366], [152, 367], [152, 368], [152, 436], [152, 437], [152, 438], [152, 439], [175, 147], [175, 148], 
      [175, 149], [175, 150], [175, 264], [175, 265], [175, 266], [175, 267], [176, 147], [176, 148], [176, 149], 
      [176, 150], [176, 264], [176, 265], [176, 266], [176, 267], [177, 147], [177, 148], [177, 149], [177, 150], 
      [177, 264], [177, 265], [177, 266], [177, 267], [178, 147], [178, 148], [178, 149], [178, 264], [178, 265], 
      [178, 266], [230, 366], [230, 367], [230, 368], [230, 369], [231, 366], [231, 367], [231, 368], [231, 369], 
      [232, 366], [232, 367], [232, 368], [232, 369], [233, 366], [233, 367], [233, 368]] 
    data = np.array(data) 
    centroids = {} 

    cent = auto_cluster(radius=5,data=data) 
    print centroids 
    print(len(cent)) # no. of centroids 
    # plots 
    [plt.scatter(x[0], x[1], s=50, c='g') for x in data] 
    for c in cent: 
     plt.scatter(cent[c][0], cent[c][1], s=200, marker='*') 
    plt.show()

來源

2017-08-05 05:54:31 Perseus784

用興趣點近似列表

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