dictances 1.5.0

我怎么得到它？

只需输入终端：

pip install dictances

基本示例

对于每个度量，在the folder examples中都有一个示例。这里有一个基本的例子，对于那些懒得点击链接的人（比如我）。

importrandomfromdictancesimportcosine,euclidean,canberrarandom.seed(42)# for reproducibility# Simple function to generate the example dictionariesdefgenerate_example_dict(n=1000):return{random.randint(0,1000):random.uniform(0,1000)foriinrange(n)}a,b=generate_example_dict(),generate_example_dict()print(cosine(a,b))# >>> 0.52336690346601print(euclidean(a,b))# >>> 15119.400349404095print(canberra(a,b))# >>> 624.9088876554047

度量表

Metric name	Usage example	Average time on sample	Complexity
Euclidean distance	euclidean	90.4 µs ± 2.5 µs
Squared variation	squared_variation	90.8 µs ± 1.43
Total variation	total_variation	92.3 µs ± 1.28 µs
Nth variation	nth_variation	91.1 µs ± 1.2 µs
Manhattan distance	manhattan	92.7 µs ± 1.43 µs
Mean absolute error	mae	92.3 µs ± 1.28 µs
Mean squared error	mse	91.1 µs ± 1.2 µs
Chebyshev distance	chebyshev	101 µs ± 2.14 µs
Minkowski distance	minkowsky	91.1 µs ± 2.05 µs
Canberra distance	canberra	71.8 µs ± 1.95 µs
Cosine distance	cosine	61.3 µs ± 835 ns
Pearson distance	pearson	46.9 µs ± 1.23 µs
Hamming distance	hamming	28.7 µs ± 784 ns
Normalized Total Variation	normal_total_variation	34.6 µs ± 543 ns
Kullback Leibler divergence	kullback_leibler	24 µs ± 587 ns
Jensen Shannon divergence	jensen_shannon	38.2 µs ± 1.18 µs
Bhattacharyya distance	bhattacharyya	32.7 µs ± 655 ns
Hellinger distance	hellinger	42 µs ± 467 ns

测试计算机规格

在其上计时的度量值具有以下规格的计算机：