距离向量算法 java_高效精确地计算欧氏距离

作为一般的经验法则，尽可能坚持 scipy 和 numpy 实现，因为它们被矢量化并且比本机Python代码快得多 . (主要原因是：在C中实现，向量化消除了循环所做的类型检查开销 . )

(旁白：我的答案不包括这里的精度，但我认为同样的原则适用于效率的精确度 . )

作为一个奖励，我重新使用IPython解释器，秘诀是使用 %prun 线魔术 .

In [1]: import numpy

In [2]: from scipy.spatial import distance

In [3]: c1 = numpy.array((52, 106, 35, 12))

In [4]: c2 = numpy.array((33, 153, 75, 10))

In [5]: %prun distance.euclidean(c1, c2)

35 function calls in 0.000 seconds

Ordered by: internal time

ncalls tottime percall cumtime percall filename:lineno(function)

1 0.000 0.000 0.000 0.000 {built-in method builtins.exec}

1 0.000 0.000 0.000 0.000 linalg.py:1976(norm)

1 0.000 0.000 0.000 0.000 {built-in method numpy.core.multiarray.dot}

6 0.000 0.000 0.000 0.000 {built-in method numpy.core.multiarray.array}

4 0.000 0.000 0.000 0.000 numeric.py:406(asarray)

1 0.000 0.000 0.000 0.000 distance.py:232(euclidean)

2 0.000 0.000 0.000 0.000 distance.py:152(_validate_vector)

2 0.000 0.000 0.000 0.000 shape_base.py:9(atleast_1d)

1 0.000 0.000 0.000 0.000 misc.py:11(norm)

1 0.000 0.000 0.000 0.000 function_base.py:605(asarray_chkfinite)

2 0.000 0.000 0.000 0.000 numeric.py:476(asanyarray)

1 0.000 0.000 0.000 0.000 {method 'ravel' of 'numpy.ndarray' objects}

1 0.000 0.000 0.000 0.000 linalg.py:111(isComplexType)

1 0.000 0.000 0.000 0.000 :1()

2 0.000 0.000 0.000 0.000 {method 'append' of 'list' objects}

1 0.000 0.000 0.000 0.000 {built-in method builtins.issubclass}

4 0.000 0.000 0.000 0.000 {built-in method builtins.len}

1 0.000 0.000 0.000 0.000 {method 'disable' of '_lsprof.Profiler' objects}

2 0.000 0.000 0.000 0.000 {method 'squeeze' of 'numpy.ndarray' objects}

In [6]: %prun numpy.linalg.norm(c1 - c2)

10 function calls in 0.000 seconds