[英]Minimum Euclidean Distance
I have two dataframes (attached image).我有两个数据框(附图)。 For each of the given row in Table-1 -
对于表 1 中给定的每一行 -
Part1 - I need to find the row in Table-2 which gives the minimum Euclidian distance.第 1 部分- 我需要在表 2 中找到给出最小欧几里得距离的行。 Output-1 is the expected answer.
输出 1 是预期的答案。
Part2 - I need to find the row in Table-2 which gives the minimum Euclidian distance.第 2 部分- 我需要在表 2 中找到给出最小欧几里得距离的行。 Output-2 is the expected answer.
输出 2 是预期的答案。 Here the only difference is that a row from Table-2 cannot be selected two times.
这里唯一的区别是表 2 中的一行不能被选择两次。
I tried this code to get the distance but not sure on how to add other fields -我尝试使用此代码来获取距离,但不确定如何添加其他字段 -
import numpy as np
from scipy.spatial import distance
s1 = np.array([(2,2), (3,0), (4,1)])
s2 = np.array([(1,3), (2,2),(3,0),(0,1)])
print(distance.cdist(s1,s2).min(axis=1))
Two dataframes and the expected output:两个数据帧和预期输出:
The code now gives the desired output, and there's a commented out print statement for extra output.代码现在给出了所需的输出,并且有一个注释掉的打印语句用于额外的输出。
It's also flexible to different list lengths.它对不同的列表长度也很灵活。
Credit also to: How can the Euclidean distance be calculated with NumPy?也归功于: 如何使用 NumPy 计算欧几里得距离?
Hope it helps:希望能帮助到你:
from numpy import linalg as LA
list1 = [(2,2), (3,0), (4,1)]
list2 = [(1,3), (2,2),(3,0),(0,1)]
names = range(0, len(list1) + len(list2))
names = [chr(ord('`') + number + 1) for number in names]
i = -1
j = len(list1) #Start Table2 names
for tup1 in list1:
collector = {} #Let's collect values for each minimum check
j = len(list1)
i += 1
name1 = names[i]
for tup2 in list2:
name2 = names[j]
a = numpy.array(tup1)
b = numpy.array(tup2)
# print ("{} | {} -->".format(name1, name2), tup1, tup2, " ", numpy.around(LA.norm(a - b), 2))
j += 1
collector["{} | {}".format(name1, name2)] = numpy.around(LA.norm(a - b), 2)
if j == len(names):
min_key = min(collector, key=collector.get)
print (min_key, "-->" , collector[min_key])
Output:输出:
a | e --> 0.0
b | f --> 0.0
c | f --> 1.41
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