Distances and divergences between distributions implemented in python.
Project description
Distances and divergences between dictionaries implemented in python 3.6.
In the complexity notations, n is len(a) and m is len(b).
The samples are dictionaries generated by the test utilities here.
How do I get it?
Just type into your terminal:
pip install dictancesBasic example
For each metric, an example is present in the folder examples. Here’s a basic example for those too lazy to click links (like me).
import random
from dictances import cosine, euclidean, canberra
random.seed(42) # for reproducibility
# Simple function to generate the example dictionaries
def generate_example_dict(n=1000):
return {random.randint(0,1000):random.uniform(0,1000) for i in range(n)}
a, b = generate_example_dict(), generate_example_dict()
print(cosine(a,b))
# >>> 0.52336690346601
print(euclidean(a,b))
# >>> 15119.400349404095
print(canberra(a,b))
# >>> 624.9088876554047Metrics table
Metric name |
Usage example |
Average time on sample |
Complexity |
|---|---|---|---|
53.7 µs ± 981 ns |
|||
Squared variation |
50.6 µs ± 982 ns |
||
50.6 µs ± 2.75 µs |
|||
Nth variation |
50.6 µs ± 1.07 µs |
||
51 µs ± 1.2 µs |
|||
51.9 µs ± 2.4 µs |
|||
51.8 µs ± 1.67 µs |
|||
49.8 µs ± 628 ns |
|||
50.4 µs ± 2.94 µs |
|||
30.9 µs ± 340 ns |
|||
41.7 µs ± 1.42 µs |
|||
49.8 µs ± 2.51 µs |
|||
5.57 µs ± 191 ns |
|||
Normalized Total Variation |
15.2 µs ± 573 ns |
||
31.6 µs ± 591 ns |
|||
21.1 µs ± 442 ns |
|||
13.7 µs ± 71.9 ns |
|||
16.8 µs ± 372 ns |
Test computer specifications
The computer on which the metrics where timed had the following specifications:
Computer specifications |
|
|---|---|
Model Name |
MacBook Pro |
Processor Name |
Intel Core i7 |
Processor Speed |
2.3 GHz |
Number of Processors |
1 |
Total Number of Cores |
4 |
L2 Cache (per Core) |
256 KB |
L3 Cache |
6 MB |
Memory |
16 GB |
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