JointMoments
所属分类：Julia编程
开发工具：Julia
文件大小：0KB
下载次数：0
上传日期：2017-08-04 06:54:10
上 传 者：
sh-1993
说明： 关节中心力矩的张量_和_统计。
(Tensors _and_ statistics for joint central moments.)
文件列表:
.travis.yml (398, 2017-08-03)
LICENSE (1059, 2017-08-03)
REQUIRE (21, 2017-08-03)
src/ (0, 2017-08-03)
src/JointMoments.jl (443, 2017-08-03)
src/center.jl (1387, 2017-08-03)
src/collapse.jl (1615, 2017-08-03)
src/replicate.jl (713, 2017-08-03)
src/statistics.jl (1902, 2017-08-03)
src/tensors.jl (5090, 2017-08-03)
test/ (0, 2017-08-03)
test/center.jl (257, 2017-08-03)
test/collapse.jl (1936, 2017-08-03)
test/data.jl (13772, 2017-08-03)
test/env.jl (132, 2017-08-03)
test/precomputed.jl (1828, 2017-08-03)
test/replicate.jl (330, 2017-08-03)
test/runtests.jl (349, 2017-08-03)
test/statistics.jl (1242, 2017-08-03)
test/tensors.jl (1033, 2017-08-03)
test/timing.jl (4731, 2017-08-03)
test/tolerance.jl (511, 2017-08-03)
# JointMoments
[](https://travis-ci.org/tinybike/JointMoments.jl) [](https://coveralls.io/r/tinybike/JointMoments.jl?branch=master) [](http://pkg.julialang.org/?pkg=JointMoments&ver=0.3)
Tensors and statistics for third and fourth joint central moments.
### Usage
Installation:
julia> Pkg.add("JointMoments")
Use:
julia> using JointMoments
julia> data = [ 0.837698 0.49452 2.54352
-0.294096 -0.39636 0.728619
-1.62089 -0.44919 1.20592
-1.06458 -0.68214 -1.12841 ];
Third and fourth joint central moment tensors:
julia> coskew(data)
3x3x3 Array{Float64,3}:
[:, :, 1] =
0.294091 0.26697 0.773618
0.26697 0.162051 0.350696
0.773618 0.350696 0.451934
[:, :, 2] =
0.26697 0.162051 0.350696
0.162051 0.0852269 0.156275
0.350696 0.156275 0.131448
[:, :, 3] =
0.773618 0.350696 0.451934
0.350696 0.156275 0.131448
0.451934 0.131448 -0.645484
julia> cokurt(data)
3x3x3x3 Array{Float64,4}:
[:, :, 1, 1] =
1.2563 0.563538 1.05898
0.563538 0.290737 0.643266
1.05898 0.643266 1.68278
[:, :, 2, 1] =
0.563538 0.290737 0.643266
0.290737 0.158262 0.374873
0.643266 0.374873 0.97585
[:, :, 3, 1] =
1.05898 0.643266 1.68278
0.643266 0.374873 0.97585
1.68278 0.97585 2.69607
[:, :, 1, 2] =
0.563538 0.290737 0.643266
0.290737 0.158262 0.374873
0.643266 0.374873 0.97585
[:, :, 2, 2] =
0.290737 0.158262 0.374873
0.158262 0.0887859 0.218824
0.374873 0.218824 0.58726
[:, :, 3, 2] =
0.643266 0.374873 0.97585
0.374873 0.218824 0.58726
0.97585 0.58726 1.73728
[:, :, 1, 3] =
1.05898 0.643266 1.68278
0.643266 0.374873 0.97585
1.68278 0.97585 2.69607
[:, :, 2, 3] =
0.643266 0.374873 0.97585
0.374873 0.218824 0.58726
0.97585 0.58726 1.73728
[:, :, 3, 3] =
1.68278 0.97585 2.69607
0.97585 0.58726 1.73728
2.69607 1.73728 5.85635
Statistics:
julia> coskewness(data)
0.2838850631006579
julia> cokurtosis(data)
0.8916763961210045
`coskewness` and `cokurtosis` can use an optional weight vector, which assigns a weight to each column of the data matrix:
julia> weights = [1.0, 0.1, 0.5];
julia> coskewness(data, weights)
0.46758589701357833
julia> cokurtosis(data, weights)
1.3203902349727108
The `coskew` and `cokurt` functions can also return flattened/unfolded tensors:
julia> coskew(data, flatten=true)
3x9 Array{Float64,2}:
0.294091 0.26697 0.773618 0.26697 0.162051 0.350696 0.773618 0.350696 0.451934
0.26697 0.162051 0.350696 0.162051 0.0852269 0.156275 0.350696 0.156275 0.131448
0.773618 0.350696 0.451934 0.350696 0.156275 0.131448 0.451934 0.131448 -0.645484
julia> cokurt(data,flatten=true)
3x27 Array{Float64,2}:
2.12678 1.11885 0.474782 1.11885 1.12294 0.187331 0.474782 0.187331 1.15524 1.11885 … 0.276558 0.474782 0.187331 1.15524 0.187331 -0.0266349 0.276558 1.15524 0.276558 0.178083
1.11885 1.12294 0.187331 1.12294 1.40462 -0.0266349 0.187331 -0.0266349 0.276558 1.12294 0.779221 0.187331 -0.0266349 0.276558 -0.0266349 -0.517198 0.779221 0.276558 0.779221 0.218732
0.474782 0.187331 1.15524 0.187331 -0.0266349 0.276558 1.15524 0.276558 0.178083 0.187331 0.218732 1.15524 0.276558 0.178083 0.276558 0.779221 0.218732 0.178083 0.218732 5.98947
The `coskew`, `cokurt`, `coskewness`, and `cokurtosis` functions have `standardize` and `bias` keyword arguments. Setting `standardize=true` standardizes the elements of the joint moment tensors (divides by the standard deviation). Setting `bias=1` uses Bessel's correction (divides by `N-1` instead of `N`).
### Tests
Unit tests can be run from the command line:
$ julia test/runtests.jl
Or from the Julia prompt:
julia> Pkg.test("JointMoments")
This package includes a rudimentary timing framework in `test/timing.jl`. To run the timed examples:
$ julia --color test/timing.jl
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