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ckmeans-1d-dp-4.3.4.post0
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A python interface for the Ckmeans.1d.dp R package
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | ckmeans-1d-dp-4.3.4.post0 |
| نام | ckmeans-1d-dp |
| نسخه کتابخانه | 4.3.4.post0 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | Alden Bradford |
| ایمیل نویسنده | - |
| آدرس صفحه اصلی | - |
| آدرس اینترنتی | https://pypi.org/project/ckmeans-1d-dp/ |
| مجوز | GNU LESSER GENERAL PUBLIC LICENSE Version 3, 29 June 2007 Copyright (C) 2007 Free Software Foundation, Inc. <https://fsf.org/> Everyone is permitted to copy and distribute verbatim copies of this license document, but changing it is not allowed. This version of the GNU Lesser General Public License incorporates the terms and conditions of version 3 of the GNU General Public License, supplemented by the additional permissions listed below. 0. Additional Definitions. As used herein, "this License" refers to version 3 of the GNU Lesser General Public License, and the "GNU GPL" refers to version 3 of the GNU General Public License. "The Library" refers to a covered work governed by this License, other than an Application or a Combined Work as defined below. An "Application" is any work that makes use of an interface provided by the Library, but which is not otherwise based on the Library. 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You may convey a covered work under sections 3 and 4 of this License without being bound by section 3 of the GNU GPL. 2. Conveying Modified Versions. If you modify a copy of the Library, and, in your modifications, a facility refers to a function or data to be supplied by an Application that uses the facility (other than as an argument passed when the facility is invoked), then you may convey a copy of the modified version: a) under this License, provided that you make a good faith effort to ensure that, in the event an Application does not supply the function or data, the facility still operates, and performs whatever part of its purpose remains meaningful, or b) under the GNU GPL, with none of the additional permissions of this License applicable to that copy. 3. Object Code Incorporating Material from Library Header Files. The object code form of an Application may incorporate material from a header file that is part of the Library. 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# Python interface for the Ckmeans.1d.dp package
The R package [Ckmeans.1d.dp](https://cran.r-project.org/web/packages/Ckmeans.1d.dp/index.html) by Song, Zhong, and Wang provides a C++ implementation of a few dynamic programming algorithms related to optimal kmeans in one dimension. Here we provide a Python interface to that library.
## Installation
You can install this by
```
pip install ckmeans-1d-dp
```
or by
```
conda install ckmeans-1d-dp
```
## Usage
There is only one function available:
```
from ckmeans_1d_dp import ckmeans
```
The docstring describes all the options in detail.
```
help(ckmeans)
```
A major advantage of this implementation is that it can broadcast over x, saving memory, and potentially saving a lot of time. This broadcasts along the last axis, treating each row independently.
```
>>> x = np.sqrt(np.linspace(0, 2, 80)).reshape(2, 2, 20)
>>> ckmeans(z, k=2).cluster
array([[[0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]],
[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]],
dtype=int32)
```
## Related
- [llimllib/ckmeans](https://github.com/llimllib/ckmeans) implements the main ckmeans algorithm directly in Python. This may be more appropriate if speed is not an issue and you wish to limit dependencies.
- [rocketrip/ckmeans](https://github.com/rocketrip/ckmeans) also wraps the original C++ implementation. It is based on an older release of the package so it is missing the latest improvements. The interface it provides is not vectorized, which I expect will make it slow when doing many repeated clusterings. Also, it uses Cython, which I would prefer to avoid.
- [AldenMB/NTarp](https://github.com/AldenMB/NTarp) includes a function to solve the same problem in the specific case of `k=2`, using purely vectorized Numpy.
The purpose of this repository is to make it easy to use the latest version of ckmeans directly, using vectorized numpy code.
# Original Readme
Below is the Readme for the original R package.
[](https://www.repostatus.org/#active)
[](https://cran.r-project.org/package=Ckmeans.1d.dp)
[](https://cran.r-project.org/package=Ckmeans.1d.dp)
[](https://cran.r-project.org/package=Ckmeans.1d.dp)
[](https://cran.r-project.org/package=Ckmeans.1d.dp)
### Overview
The package provides a powerful set of tools for fast, optimal, and reproducible univariate clustering by dynamic programming. It is practical to cluster millions of sample points into a few clusters in seconds using a single core on a typical desktop computer. It solves four types of problem, including univariate $k$-means, $k$-median, $k$-segments, and multi-channel weighted $k$-means. Dynamic programming is used to minimize the (weighted) sum of within-cluster distances using respective metrics. Its advantage over heuristic clustering in efficiency and accuracy is increasingly pronounced as the number of clusters $k$ increases. Weighted $k$-means can also optimally segment time series to perform peak calling. An auxiliary function generates histograms that are adaptive to patterns in data. The package was recently used to map dysregulated zones in genomes of 17 human cancer types (Song and Zhong, 2020).
### The main method
The Ckmeans.1d.dp algorithms cluster (weighted) univariate data given by a numeric vector $x$ into $k$ groups by dynamic programming (Wang and Song, 2011; Zhong, 2019; Song and Zhong, 2020). They guarantee the optimality of clustering---the total of within-cluster sums of squared distances is always minimized given the number of clusters $k$. In contrast, heuristic univariate clustering algorithms may be sub-optimal at large $k$ or inconsistent from run to run. As non-negative weights are supported for each point, the algorithm can also partition a time course using the time points as input and the values at each time point as weight. Utilizing the optimal clusters, a function can generate histograms adaptive to patterns in data.
Excluding the time for sorting $x$, the default weighted univariate clustering algorithm takes a runtime of $O(kn)$ (Song and Zhong, 2020), linear in both sample size $n$ and the number of clusters $k$, using a divide-and-conquer strategy based on a theoretical result on matrix search [(Aggarwal et al., 1987) <doi:10.1007/BF01840359>](https://doi.org/10.1007/BF01840359) implemented via a novel in-place search space reduction method (Song and Zhong, 2020). The space complexity is $O(kn)$. This method is numerically stable.
### When to use the package
As an alternative to popular heuristic clustering methods, this package provides functionality for (weighted) univariate clustering, segmentation, and peak calling with guaranteed optimality and efficiency.
An adaptive histogram based on optimal clusters is also recommended if an equal-bin-width histogram is inadequate to characterize clusters that vary in width.
### To download and install the package
```
install.packages("Ckmeans.1d.dp")
```
### Citing the package
Song M, Zhong H (2020). "Efficient weighted univariate clustering maps outstanding dysregulated genomic zones in human cancers." _Bioinformatics_, 36(20), 5027–5036.
https://doi.org/10.1093/bioinformatics/btaa613
Wang H, Song M (2011). "Ckmeans.1d.dp: Optimal k-means clustering in one dimension by dynamic programming." _The R Journal_, 3(2), 29–33. https://doi.org/10.32614/RJ-2011-015
زبان مورد نیاز
| مقدار | نام |
|---|---|
| >=3.7 | Python |
نحوه نصب
نصب پکیج whl ckmeans-1d-dp-4.3.4.post0:
pip install ckmeans-1d-dp-4.3.4.post0.whl
نصب پکیج tar.gz ckmeans-1d-dp-4.3.4.post0:
pip install ckmeans-1d-dp-4.3.4.post0.tar.gz