相关疑难解决方法(0)

目前我正在做一个可能需要使用kNN算法来找到给定点的前k个最近邻居的项目,比如P. im使用python,sklearn包来完成这项工作,但是我们的预定义度量不是那些默认值指标.所以我必须使用用户定义的度量标准,来自sklearn的文档,可以在这里和这里找到.

似乎最新版本的sklearn kNN支持用户定义的度量标准,但我无法找到如何使用它:

import sklearn
from sklearn.neighbors import NearestNeighbors
import numpy as np
from sklearn.neighbors import DistanceMetric
from sklearn.neighbors.ball_tree import BallTree
BallTree.valid_metrics

我已经定义了一个名为mydist = max(xy)的度量,然后使用DistanceMetric.get_metric使其成为DistanceMetric对象:

dt=DistanceMetric.get_metric('pyfunc',func=mydist)

从文档中,该行应该如下所示

nbrs = NearestNeighbors(n_neighbors=4, algorithm='auto',metric='pyfunc').fit(A)
distances, indices = nbrs.kneighbors(A)

但我dt在哪里可以放入？谢谢

我正在尝试为聚类实现自定义距离度量。代码片段如下所示：

import numpy as np
from sklearn.cluster import KMeans, DBSCAN, MeanShift

def distance(x, y):
    # print(x, y) -> This x and y aren't one-hot vectors and is the source of this question
    match_count = 0.
    for xi, yi in zip(x, y):
        if float(xi) == 1. and xi == yi:
            match_count += 1
    return match_count

def custom_metric(x, y):
    # x, y are two vectors
    # distance(.,.) calculates count of elements when both xi and yi are True
    return distance(x, y)


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