求距离2——distance.cdist()方法，返回距离值

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最新推荐文章于 2025-06-19 14:32:32 发布

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本文详细介绍如何使用Python的scipy库计算不同类型的点间距离，包括欧几里得距离和曼哈顿距离等，适用于二维及三维空间，并提供代码实例。

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目的求具体的距离，距离类型有：

The distance metric to use. If a string, the distance function can be ‘braycurtis’, ‘canberra’, ‘chebyshev’, ‘cityblock’（曼哈顿距离）, ‘correlation’, ‘cosine’, ‘dice’, ‘euclidean’（欧几里得距离）, ‘hamming’, ‘jaccard’, ‘jensenshannon’, ‘kulsinski’, ‘mahalanobis’, ‘matching’, ‘minkowski’, ‘rogerstanimoto’, ‘russellrao’, ‘seuclidean’, ‘sokalmichener’, ‘sokalsneath’, ‘sqeuclidean’, ‘wminkowski’, ‘yule’.

使用：求欧几里得距离

from scipy.spatial import distance #导包
import numpy as np

#在二维数据点中求欧几里得距离
py.spatial.distance.cdist
coords = [(1, 1),
          (2, 2),
          (3, 3),
          (4, 4)]
print(distance.cdist(coords, coords, 'euclidean'))

array([[0.        , 1.41421356, 2.82842712, 4.24264069],
       [1.41421356, 0.        , 1.41421356, 2.82842712],
       [2.82842712, 1.41421356, 0.        , 1.41421356],
       [4.24264069, 2.82842712, 1.41421356, 0.        ]])

#若求coords里面样本到目标的距离为（0,0）的点
distance.cdist(coords,[(0,0)], 'euclidean')

array([[1.41421356],
       [2.82842712],
       [4.24264069],
       [5.65685425]])

#从3D数据点中找到曼哈顿距离，目标是X=a,Y=b  X是对象，Y是目标
a = np.array([[0, 0, 0],
              [0, 0, 1],
              [0, 1, 0],
              [0, 1, 1],
              [1, 0, 0],
              [1, 0, 1],
              [1, 1, 0],
              [1, 1, 1]])
b = np.array([[ 0.1,  0.2,  0.4]])
distance.cdist(a, b, 'cityblock')

array([[0.7],
       [0.9],
       [1.3],
       [1.5],
       [1.5],
       [1.7],
       [2.1],
       [2.3]])

API：scipy.spatial.distance.cdist(XA, XB, metric='euclidean', *args, **kwargs)

详情参考：https://docs.scipy.org/doc/scipy/reference/generated/scipy.spatial.distance.cdist.html

参数：

XAndarray

An mA by n array of mA original observations in an n-dimensional space. Inputs are converted to float type.

XBndarray

An mB by n array of mB original observations in an n-dimensional space. Inputs are converted to float type.

metricstr or callable, optional

The distance metric to use. If a string, the distance function can be ‘braycurtis’, ‘canberra’, ‘chebyshev’, ‘cityblock’, ‘correlation’, ‘cosine’, ‘dice’, ‘euclidean’, ‘hamming’, ‘jaccard’, ‘jensenshannon’, ‘kulsinski’, ‘mahalanobis’, ‘matching’, ‘minkowski’, ‘rogerstanimoto’, ‘russellrao’, ‘seuclidean’, ‘sokalmichener’, ‘sokalsneath’, ‘sqeuclidean’, ‘wminkowski’, ‘yule’.

*argstuple. Deprecated.

Additional arguments should be passed as keyword arguments

**kwargsdict, optional

Extra arguments to metric: refer to each metric documentation for a list of all possible arguments.

Some possible arguments:

p : scalar The p-norm to apply for Minkowski, weighted and unweighted. Default: 2.

w : ndarray The weight vector for metrics that support weights (e.g., Minkowski).

V : ndarray The variance vector for standardized Euclidean. Default: var(vstack([XA, XB]), axis=0, ddof=1)

VI : ndarray The inverse of the covariance matrix for Mahalanobis. Default: inv(cov(vstack([XA, XB].T))).T

out : ndarray The output array If not None, the distance matrix Y is stored in this array. Note: metric independent, it will become a regular keyword arg in a future scipy version

Returns

Yndarray

A mA by mB distance matrix is returned. For each i and j, the metricdist(u=XA[i], v=XB[j]) is computed and stored in the ij th entry.