pyclustering.utils.sampling Namespace Reference

Module provides various random sampling algorithms. More...

Functions
def	reservoir_r (data, n)
	Performs data sampling using Reservoir Algorithm R. More...
def	reservoir_x (data, n)
	Performs data sampling using Reservoir Algorithm X. More...

Detailed Description

Module provides various random sampling algorithms.

Authors

Andrei Novikov (pyclu.nosp@m.ster.nosp@m.ing@y.nosp@m.ande.nosp@m.x.ru)

Date

2014-2020

Copyright

GNU Public License

Function Documentation

reservoir_r()

def pyclustering.utils.sampling.reservoir_r	(		data,
			n
	)

Performs data sampling using Reservoir Algorithm R.

Algorithm complexity O(n). Implementation is based on paper [40]. Average number of uniform random variates: .

Parameters

[in]	data	(list): Input data for sampling.
[in]	n	(uint): Size of sample that should be extracted from 'data'.

Returns

(list) Sample with size 'n' from 'data'.

Generate random samples with 5 elements and with 3 elements using Reservoir Algorithm R:

data = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20]
sample = reservoir_r(data, 5)   # generate sample with 5 elements for 'data'.
print(sample)

sample = reservoir_r(data, 3)   # generate sample with 3 elements for 'data'.
print(sample)

Output example for the code above:

[20, 7, 17, 12, 11]
[12, 2, 10]

Definition at line 30 of file sampling.py.

reservoir_x()

def pyclustering.utils.sampling.reservoir_x	(		data,
			n
	)

Performs data sampling using Reservoir Algorithm X.

Algorithm complexity O(n). Implementation is based on paper [40]. Average number of uniform random variates:

Parameters

[in]	data	(list): Input data for sampling.
[in]	n	(uint): Size of sample that should be extracted from 'data'.

Returns

(list) Sample with size 'n' from 'data'.

Generate random sample with 5 elements using Reservoir Algorithm X:

data = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20]
sample = reservoir_x(data, 10)   # generate sample with 5 elements for 'data'.
print(sample)

Output example for the code above:

[0, 20, 2, 16, 13, 15, 19, 18, 10, 9]

Definition at line 72 of file sampling.py.