Question

我正在从包含平面中点的数据集中读取数据。每个点都有x和y坐标。

with open('SJC324.txt') as f:
    data=[]
    for line in f:
        x,y=(line.strip('\n').split())
        data.append([int(x),int(y)])
    initialisation(data)

然后，我在这些点上进行了K-medoid聚类。我已将medoid点存储在列表中。然后，我将检查距这些类固醇特定半径内的哪些点。通过这种方式，我可以计算覆盖率。

def initialisation(data):
    data=np.array(data)
    D=pairwise_distances(data,metric='euclidean')
    coverage=[]
    for i in range(20):
        covered_point=set()
        M, C = kmedoids.kMedoids(D, len(data)//15)
        medoid=data[M]
        for temp in medoid:
            for x in data:
                if check_within_radius(temp,x):
                    covered_point.add(x)
        coverage.append((len(covered_point)/len(data))*100)
    print(coverage)

在这里，我正在检查哪些点位于这些类固醇的特定半径内。

def check_within_radius(temp,x):
    #temp is medoid point
    #x is any random point
    radius=10
    if (((temp[0]-x[0])**2) + ((temp[1]-x[1])**2))< radius*radius:
        return True
    else:
        return False

现在我遇到以下错误。

<ipython-input-23-d04cdfb631a8> in initialisation(data)
     16             for x in data:
     17                 if check_within_radius(temp,x):
---> 18                     covered_point.add(x)
     19         coverage.append((len(covered_point)/len(data))*100)
     20     print(coverage)

TypeError: unhashable type: 'numpy.ndarray'

Answer 1

您正在尝试在2D列表中查找唯一元素。您可以稍微修改一下代码。

from collections import Counter

temp = Counter([tuple(x) for x in covered_point])
#Counter will count the frequency of each element(1D list) in a 2D list

z = [list(k) for k, v in temp.items() if v >= 1]
'''
 When one element(1D list) appears more than once in that 2D list you are
 taking that only  one time.
'''

coverage.append((len(z)/len(data))*100)

TypeError：不可散列的类型：Python中的“ numpy.ndarray”

1 个答案: