如何在numpy数组中找到最近的值?例子:
np.find_nearest(array, value)
如何在numpy数组中找到最近的值?例子:
np.find_nearest(array, value)
当前回答
对于那些搜索多个最接近的,修改接受的答案:
import numpy as np
def find_nearest(array, value, k):
array = np.asarray(array)
idx = np.argsort(abs(array - value))[:k]
return array[idx]
看到的: https://stackoverflow.com/a/66937734/11671779
其他回答
下面是一个使用2D数组的版本,如果用户拥有scipy的cdist函数,则使用它,如果用户没有,则使用更简单的距离计算。
默认情况下,输出是最接近输入值的索引,但您可以使用output关键字将其更改为'index', 'value'或'both'之一,其中'value'输出数组[index], 'both'输出索引,数组[index]。
对于非常大的数组,您可能需要使用kind='euclidean',因为默认的scipy cdist函数可能会耗尽内存。
这可能不是绝对最快的解决方案,但已经很接近了。
def find_nearest_2d(array, value, kind='cdist', output='index'):
# 'array' must be a 2D array
# 'value' must be a 1D array with 2 elements
# 'kind' defines what method to use to calculate the distances. Can choose one
# of 'cdist' (default) or 'euclidean'. Choose 'euclidean' for very large
# arrays. Otherwise, cdist is much faster.
# 'output' defines what the output should be. Can be 'index' (default) to return
# the index of the array that is closest to the value, 'value' to return the
# value that is closest, or 'both' to return index,value
import numpy as np
if kind == 'cdist':
try: from scipy.spatial.distance import cdist
except ImportError:
print("Warning (find_nearest_2d): Could not import cdist. Reverting to simpler distance calculation")
kind = 'euclidean'
index = np.where(array == value)[0] # Make sure the value isn't in the array
if index.size == 0:
if kind == 'cdist': index = np.argmin(cdist([value],array)[0])
elif kind == 'euclidean': index = np.argmin(np.sum((np.array(array)-np.array(value))**2.,axis=1))
else: raise ValueError("Keyword 'kind' must be one of 'cdist' or 'euclidean'")
if output == 'index': return index
elif output == 'value': return array[index]
elif output == 'both': return index,array[index]
else: raise ValueError("Keyword 'output' must be one of 'index', 'value', or 'both'")
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])
对于那些搜索多个最接近的,修改接受的答案:
import numpy as np
def find_nearest(array, value, k):
array = np.asarray(array)
idx = np.argsort(abs(array - value))[:k]
return array[idx]
看到的: https://stackoverflow.com/a/66937734/11671779
import numpy as np
def find_nearest(array, value):
array = np.asarray(array)
idx = (np.abs(array - value)).argmin()
return array[idx]
使用示例:
array = np.random.random(10)
print(array)
# [ 0.21069679 0.61290182 0.63425412 0.84635244 0.91599191 0.00213826
# 0.17104965 0.56874386 0.57319379 0.28719469]
print(find_nearest(array, value=0.5))
# 0.568743859261
也许对ndarray有帮助:
def find_nearest(X, value):
return X[np.unravel_index(np.argmin(np.abs(X - value)), X.shape)]