GPmat
====
The GPmat toolbox is the 'one stop shop' on github for a number of dependent toolboxes, each of which used to be released independently. Since May 2015 each toolbox is a sub-directory within GPmat. They are included as subtrees from the relevant repositories.
The summary of demos from each toolbox is given below. It is advisable that you also include netlab (https://github.com/sods/netlab) as a dependency.
The first release of the full GPmat toolbox is version 1.0.0, released on 28th May 2015 to coincide with the reformatting of the toolbox as sub-trees.
GP
==
### Version 0.136
Changes to gpReadFromFID for compatibility with C++ code.
#### Version 0.135
Modifications by Carl Henrik Ek for compatability with the SGPLVM toolbox.
#### Version 0.134
Updates to allow deconstruction of model files when writing to disk (gpWriteResult, gpLoadResult, gpDeconstruct, gpReconstruct).
#### Version 0.133
Updates for running a GPLVM/GP using the data's inner product matrix for Interspeech synthesis demos.
#### Version 0.132
Examples transfered from oxford toolbox, variational approximation from Titsias added as an option with 'dtcvar'.
#### Version 0.131
Changes to allow compatibility with SGPLVM and NCCA toolboxes.
#### Version 0.13
Changes to allow more flexibility in optimisation of beta.
#### Version 0.12
Various minor changes for enabling back constraints in hierarchical
GP-LVM models.
#### Version 0.11
Changes include the use of the optimiDefaultConstraint('positive') to
obtain the function to constrain beta to be positive (which now
returns 'exp' rather than 'negLogLogit' which was previously the
default). Similarly default optimiser is now given by a command in
optimiDefaultOptimiser.
#### Version 0.1
The first version which is spun out of the FGPLVM toolbox. The
corresponding FGPLVM toolbox is 0.15.
Release 0.1 splits away the Gaussian process section of the FGPLVM
toolbox into this separate toolbox.
## Other GP related software
The GP-LVM C++ software is available from <a
href="/~neill/gplvmcpp/">here</a>.
The IVM C++ software is available from <a
href="/~neill/ivmcpp/">here</a>.
The MATLAB IVM toolbox is available here <a
href="/~neill/ivm/">here</a>.
The original MATLAB GP-LVM toolbox is available here <a
href="/~neill/gplvm/">here</a>.
## Examples
### Functions from Gaussians
This example shows how points which look like they come from a
function to be sampled from a Gaussian distribution. The sample is 25
dimensional and is from a Gaussian with a particular covariance.
```matlab
>> demGpSample
```
<center><img src="./gp/diagrams/gpSample.png" width ="50%"><img
src="./gp/diagrams/gpCovariance.png" width ="50%"><br> <i>Left</i> A single, 25
dimensional, sample from a Gaussian distribution. <i>Right</i> the
covariance matrix of the Gaussian distribution.. </center>
### Joint Distribution over two Variables
Gaussian processes are about conditioning a Gaussian distribution
on the training data to make the test predictions. To illustrate this
process, we can look at the joint distribution over two variables.
```matlab
>> demGpCov2D([1 2])
```
Gives the joint distribution for <i>f</i><sub>1</sub> and
<i>f</i><sub>2</sub>. The plots show the joint distributions as well
as the conditional for <i>f</i><sub>2</sub> given
<i>f</i><sub>1</sub>.
<center><img src="./gp/diagrams/demGpCov2D1_2_3.png" Width ="50%"><img
src="./gp/diagrams/demGpCov2D1_5_3.png" width ="50%"><br> <i>Left</i> Blue line is
contour of joint distribution over the variables <i>f</i><sub>1</sub>
and <i>f</i><sub>2</sub>. Green line indicates an observation of
<i>f</i><sub>1</sub>. Red line is conditional distribution of
<i>f</i><sub>2</sub> given <i>f</i><sub>1</sub>. <i>Right</i> Similar
for <i>f</i><sub>1</sub> and <i>f</i><sub>5</sub>. </center>
### Different Samples from Gaussian Processes
A script is provided which samples from a Gaussian process with the
provided covariance function.
```matlab
>> gpSample('rbf', 10, [1 1], [-3 3], 1e5)
```
will give 10 samples from an RBF covariance function with a
parameter vector given by [1 1] (inverse width 1, variance 1) across
the range -3 to 3 on the <i>x</i>-axis. The random seed will be set to
1e5.
```matlab
>> gpSample('rbf', 10, [16 1], [-3 3], 1e5)
```
is similar, but the inverse width is now set to 16 (length scale 0.25).
<center><img
src="./gp/diagrams/gpSampleRbfSamples10Seed100000InverseWidth1Variance1.png" width
="50%"><img
src="./gp/diagrams/gpSampleRbfSamples10Seed100000InverseWidth16Variance1.png" width
="50%"><br> <i>Left</i> samples from an RBF style covariance function
with length scale 1. <i>Right</i> samples from an RBF style covariance
function with length scale 0.25. </center>
Other covariance functions can be sampled, an interesting one is
the MLP covariance which is non stationary and can produce point
symmetric functions,
```matlab
>> gpSample('mlp', 10, [100 100 1], [-1 1], 1e5)
```
gives 10 samples from the MLP covariance function where the "bias
variance" is 100 (basis functions are centered around the origin
with standard deviation of 10) and the "weight variance" is
100.
```matlab
>> gpSample('mlp', 10, [100 1e-16 1], [-1 1], 1e5)
```
gives 10 samples from the MLP covariance function where the "bias
variance" is approximately zero (basis functions are placed on
the origin) and the "weight variance" is 100.
<center><img
src="./gp/diagrams/gpSampleMlpSamples10Seed100000WeightVariance100BiasVariance100Variance1.png"
width ="50%"><img
src="./gp/diagrams/gpSampleMlpSamples10Seed100000WeightVariance100BiasVariance1e-16Variance1.png"
width ="50%"><br> <i>Left</i> samples from an MLP style covariance
function with bias and weight variances set to 100. <i>Right</i>
samples from an MLP style covariance function with weight variance 100
and bias variance approximately zero. </center>
### Posterior Samples
Gaussian processes are non-parametric models. They are specified by their covariance function and a mean function. When combined with data observations a posterior Gaussian process is induced. The demos below show samples from that posterior.
```matlab
>> gpPosteriorSample('rbf', 5, [1 1], [-3 3], 1e5)
```
and
```matlab
>> gpPosteriorSample('rbf', 5, [16 1], [-3 3], 1e5)
```
<center><img
src="./gp/diagrams/gpPosteriorSampleRbfSamples5Seed100000InverseWidth1Variance1bw.png" width
="50%"><img
src="./gp/diagrams/gpPosteriorSampleRbfSamples5Seed100000InverseWidth16Variance1bw.png" width
="50%"><br> <i>Left</i> samples from the posterior induced by an RBF style covariance function
with length scale 1 and 5 "training" data points taken from a sine wave. <i>Right</i> Similar but for a length scale of 0.25. </center>
### Simple Interpolation Demo
This simple demonstration plots, consecutively, an increasing
number of data points, followed by an interpolated fit through the
data points using a Gaussian process. This is a noiseless system, and
the data is sampled from a GP with a known covariance function. The
curve is then recovered with minimal uncertainty after only nine data
points are included. The code is run with
```matlab
>> demInterpolation
```
<center><img src="./gp/diagrams/demInterpolation3.png" width ="50%"><img
src="./gp/diagrams/demInterpolation4.png" width ="50%"><br>
Gaussian process prediction <i>left</i> after two points with a new
data point sampled <i>right</i> after the new data point is included
in the prediction.<br>
<img src="./gp/diagrams/demInterpolation7.png" width
="50%"><img src="./gp/diagrams/demInterpolation8.png" width ="50%"><br>
Gaussian process prediction <i>left</i> after five points with a four
new data point sampled <i>right</i> after all nine data points are
included.<br> </center>
### Simple Regression Demo
The regression demo very much follows the format of the
interpolation demo. Here the difference is that the data is sampled
with noise. Fitting a
没有合适的资源?快使用搜索试试~ 我知道了~
Matlab implementations of Gaussian processes and other
共2550个文件
m:2021个
png:313个
txt:76个
1.该资源内容由用户上传,如若侵权请联系客服进行举报
2.虚拟产品一经售出概不退款(资源遇到问题,请及时私信上传者)
2.虚拟产品一经售出概不退款(资源遇到问题,请及时私信上传者)
版权申诉
0 下载量 80 浏览量
2023-07-19
20:11:05
上传
评论
收藏 45.64MB ZIP 举报
温馨提示
Matlab implementations of Gaussian processes and other machine learning tools..zip
资源推荐
资源详情
资源评论
收起资源包目录
Matlab implementations of Gaussian processes and other (2550个子文件)
49_18.amc 862KB
49_19.amc 749KB
49_20.amc 703KB
35_01.amc 280KB
35.asf 7KB
49.asf 7KB
lvmVisualiseGeneral.asv 5KB
mexopts.bat 2KB
Swagger.bvh 242KB
lnDiffErfs.c 10KB
wofz.c 8KB
xgamrnd.c 3KB
pathWMatPos.c 2KB
chip2_HOWTO 419B
rbfKernGradX.cpp 2KB
combined.csv 2.38MB
ILEA567.DAT 270KB
diagnostics.dat 2KB
diagnostics.dat 131B
pydiagnostics.dat 131B
ionosphere.data 75KB
lfmGradientSigmaUpsilonMatrix.F 7KB
lfmGradientUpsilonMatrix.F 7KB
lfmGradientSigmaUpsilonVector.F 6KB
lfmComputeUpsilonMatrix.F 6KB
lfmGradientUpsilonVector.F 6KB
lfmComputeUpsilonVector.F 5KB
wofz.f 4KB
wofzPoppe.f 4KB
wofzHui.f 2KB
fourWalksLatent.fig 11KB
.gitignore 12B
.gitignore 2B
.gitmodules 85B
fintrf.h 137KB
mapLoadData.m 88KB
lvmLoadData.m 34KB
heatXheatKernGradient.m 21KB
gpnddisimCreate.m 21KB
sdlfmXsdlfmKernGradient.m 18KB
lfmXlfmKernGradient.m 17KB
gpnddisimLogLikeGradients.m 15KB
sdlfmKernGradientConstant.m 14KB
nddisimKernGradient.m 14KB
lfmvXlfmvKernGradient.m 14KB
lfmaXlfmaKernGradient.m 13KB
lfmaXlfmKernGradient.m 13KB
demBarenco1.m 13KB
lfmvXlfmKernGradient.m 13KB
lfmaXlfmvKernGradient.m 13KB
demBarenco2.m 12KB
nddisimXndsimKernGradient.m 11KB
gpCovGrads.m 10KB
gpLogLikeGradients.m 10KB
heatKernGradient.m 10KB
gpsimMapEcoliResults.m 10KB
Contents.m 10KB
disimXsimKernGradient.m 9KB
sdlfmXsdlfmKernGradientBlockILJ.m 9KB
demGpdisimMef2.m 9KB
sdlfmaXsdlfmKernGradientBlockILJ.m 9KB
multiKernTest.m 9KB
sdlfmXsdlfmKernGradientBlockIGJ.m 9KB
acclaimReadSkel.m 9KB
sdlfmaXsdlfmaKernGradientBlockIGJ.m 9KB
sdlfmXsdlfmvKernGradientBlockIGJ.m 9KB
kernTest.m 9KB
gpUpdateAD.m 9KB
heatXheatKernCompute.m 9KB
mappingLoadData.m 9KB
gpnddisimPredict.m 9KB
gpsimMapBarencoResults.m 8KB
scg2.m 8KB
sdlfmXsdlfmKernCompute.m 8KB
ivmLoadData.m 8KB
lfmXrbfKernGradient.m 8KB
lfmwhiteXlfmwhiteKernGradient.m 8KB
demToyProblem3.m 8KB
drosPlot.m 8KB
ggwhiteXgaussianwhiteKernCompute.m 7KB
lfmKernDiagGradient.m 7KB
heatKernDiagCompute.m 7KB
lfmwhiteXrbfwhiteKernGradient.m 7KB
gpsimLoadBarencoPUMAData.m 7KB
heatKernDiagGradient.m 7KB
multiKernGradient.m 7KB
disimXdisimKernGradient.m 7KB
gpLogLikelihood.m 7KB
sheatKernGradient.m 7KB
lfmvXrbfKernGradient.m 7KB
sdlfmXsdrbfKernGradient.m 7KB
demRegressionSimple.m 7KB
heatXrbfhKernGradient.m 7KB
lfmaXrbfKernGradient.m 7KB
sdlfmXsdrbfKernGradientBlockIGJ.m 6KB
lvmClassVisualise.m 6KB
demToyProblem7.m 6KB
gpsimMapCreate.m 6KB
drosPlotEvaluationMultitf.m 6KB
lvmClassVisualiseNoVar.m 6KB
共 2550 条
- 1
- 2
- 3
- 4
- 5
- 6
- 26
资源评论
AbelZ_01
- 粉丝: 896
- 资源: 5441
上传资源 快速赚钱
- 我的内容管理 展开
- 我的资源 快来上传第一个资源
- 我的收益 登录查看自己的收益
- 我的积分 登录查看自己的积分
- 我的C币 登录后查看C币余额
- 我的收藏
- 我的下载
- 下载帮助
安全验证
文档复制为VIP权益,开通VIP直接复制
信息提交成功