如何在numpy数组中找到最近的值?例子:

np.find_nearest(array, value)

当前回答

如果你不想使用numpy,可以这样做:

def find_nearest(array, value):
    n = [abs(i-value) for i in array]
    idx = n.index(min(n))
    return array[idx]

其他回答

这是在向量数组中找到最近向量的扩展。

import numpy as np

def find_nearest_vector(array, value):
  idx = np.array([np.linalg.norm(x+y) for (x,y) in array-value]).argmin()
  return array[idx]

A = np.random.random((10,2))*100
""" A = array([[ 34.19762933,  43.14534123],
   [ 48.79558706,  47.79243283],
   [ 38.42774411,  84.87155478],
   [ 63.64371943,  50.7722317 ],
   [ 73.56362857,  27.87895698],
   [ 96.67790593,  77.76150486],
   [ 68.86202147,  21.38735169],
   [  5.21796467,  59.17051276],
   [ 82.92389467,  99.90387851],
   [  6.76626539,  30.50661753]])"""
pt = [6, 30]  
print find_nearest_vector(A,pt)
# array([  6.76626539,  30.50661753])

下面是一个处理非标量“values”数组的版本:

import numpy as np

def find_nearest(array, values):
    indices = np.abs(np.subtract.outer(array, values)).argmin(0)
    return array[indices]

如果输入是标量,则返回数字类型(例如int, float)的版本:

def find_nearest(array, values):
    values = np.atleast_1d(values)
    indices = np.abs(np.subtract.outer(array, values)).argmin(0)
    out = array[indices]
    return out if len(out) > 1 else out[0]

我认为最python的方式是:

 num = 65 # Input number
 array = np.random.random((10))*100 # Given array 
 nearest_idx = np.where(abs(array-num)==abs(array-num).min())[0] # If you want the index of the element of array (array) nearest to the the given number (num)
 nearest_val = array[abs(array-num)==abs(array-num).min()] # If you directly want the element of array (array) nearest to the given number (num)

这是基本代码。你可以把它作为一个函数来使用

这是unutbu答案的矢量化版本:

def find_nearest(array, values):
    array = np.asarray(array)

    # the last dim must be 1 to broadcast in (array - values) below.
    values = np.expand_dims(values, axis=-1) 

    indices = np.abs(array - values).argmin(axis=-1)

    return array[indices]


image = plt.imread('example_3_band_image.jpg')

print(image.shape) # should be (nrows, ncols, 3)

quantiles = np.linspace(0, 255, num=2 ** 2, dtype=np.uint8)

quantiled_image = find_nearest(quantiles, image)

print(quantiled_image.shape) # should be (nrows, ncols, 3)

对于2d数组,要确定最近元素的i, j位置:

import numpy as np
def find_nearest(a, a0):
    idx = (np.abs(a - a0)).argmin()
    w = a.shape[1]
    i = idx // w
    j = idx - i * w
    return a[i,j], i, j