Palermo FC: Champions of Serie B - Squad, Achievements & Stats

Welcome to my blog! I am starting this blog to document my learning process of [R](https://www.r-project.org/) programming language.

The main purpose of this blog is for me to learn [blogdown](https://bookdown.org/yihui/blogdown/) package.

Here are some things that I would like to do:

* Learn R programming language.
* Learn R Markdown.
* Create my own website using [blogdown](https://bookdown.org/yihui/blogdown/).
nagyistgeza/website<|file_sep### Website

My personal website.
<|file_sep@@import url('https://fonts.googleapis.com/css?family=Open+Sans&display=swap');

body {
font-family:'Open Sans', sans-serif;
}

pre {
background-color:#eef;
}

code {
color:#d55;
font-weight:bold;
}

blockquote {
background-color:#f7f7f7;
border-left:solid #ddd 6px;
padding:.5em .6em .5em .8em;
margin-bottom:1em;
}
<|file_sep[build]
publish = "public"
command = "blogdown::serve_site()"
# command = "Rscript -e 'blogdown::build_site()'"
environment = "netlify"

[context.production.environment]
HUGO_VERSION = "0.58"
R_VERSION = "3.6"
RSTUDIO_VERSION = "1.2"

[context.branch-deploy]
command = "blogdown::serve_site()"

[context.deploy-preview]
command = "blogdown::serve_site()"

[[redirects]]
from = "/posts/*"
to = "/post/:splat/"
status=301

[[redirects]]
from = "/about/"
to = "/about-me/"
status=301

[[redirects]]
from = "/resume/"
to = "/resume-cv/"
status=301

[[headers]]
for = "/css/*.css"
[headers.values]
Cache-Control = '''max-age=31536000'''
Content-Type = '''text/css; charset=UTF-8'''

[[headers]]
for ="*/**"
[headers.values]
X-XSS-Protection ="1; mode=block"
X-Frame-Options ="DENY"
X-Robots-Tag ="none"

[[headers]]
for ="*.js"
[headers.values]
Cache-Control ="max-age=31536000"

# [[headers]]
# for ="*.png"
# [headers.values]
# Cache-Control ="max-age=31536000"

# [[headers]]
# for ="*.jpg"
# [headers.values]
# Cache-Control ="max-age=31536000"

# [[headers]]
# for ="*.svg"
# [headers.values]
# Cache-Control ="max-age=31536000"

[security.headers]

[[plugins]]
package='netlify-plugin-cache'

[params]

description = 'István Nagy'
disqusShortname = 'mydisqusshortname'
googleAnalyticsID =
favicon =
highlightjsTheme ='atom-one-light'

logo ="/img/logo.png" # path relative to static folder
favicon ="/img/favicon.ico" # path relative to static folder

paginate = 10 # number of posts per page

enableRobotsTXT =true

enableGitInfo =false # set true if you use git info plugin

useHugoToc = true
tocOpenLevel =
tocCloseLevel =

enableMathJax =false # set true if you want mathjax support

googleCustomSearchID =
googleCustomSearchEngineId =

customCSS =
customJS =

socialShare =
socialShareName ='Share on'

postListIcon ='fa-file-text-o'
categoryListIcon ='fa-th-list'
tagListIcon ='fa-tags'

showRelatedPosts ='bottom' # where you want to show related posts ('none', 'bottom' or 'both')
relatedPostsMaxCount ='5' # max number of related posts

enableMissingTranslationWarning=false

github_repo ='https://github.com/nagyistgeza/website'
gitalk_client_id =
gitalk_client_secret =
gitalk_repo =
gitalk_id =

menu =[ { name='Home', url='/', weight=10 }, { name='Blog', url='/post/', weight=20 }, { name='About', url='/about-me/', weight=30 }, { name='Resume/CV', url='/resume-cv/', weight=40 } ]
mainSections =[ 'post' ] # main sections you want to list on home page
sectionPagesMenu =[ { name='Blog', identifier='post', url='/post/', weight=10 } ]

contentDir ='content'
dataDir ='data'
layoutDir ='layouts'
staticDir ='static'

archetypeDir ='archetypes'

pygmentsUseClasses =true
pygmentsCodeFences =true
pygmentsStyle ='monokai'nagyistgeza/website<|file_sep(url <- Sys.getenv("GIT_REPO_URL"))
if(url == "") stop("Set GIT_REPO_URL environment variable")
system(paste("git config –local user.name "Netlify Build Bot"", collapse="n"))
system(paste("git config –local user.email "[email protected]"", collapse="n"))
system(paste("git remote add origin", url), ignore.stdout=T)
system(paste("git fetch origin master"), ignore.stdout=T)
system(paste("git checkout master"), ignore.stdout=T)
system(paste("git reset –hard FETCH_HEAD"), ignore.stdout=T)
if(file.exists(".nojekyll")) file.remove(".nojekyll")
unlink("_deploy", recursive=T)

options(blogdown.ext=.Rmd)
options(blogdown.subdir=NULL)
options(blogdown.yaml.empty=list())
options(blogdown.author=NULL)
options(blogdown.date=NULL)

rmarkdown::render_site()
rmarkdown::render_site(to=c('index.Rmd'))

dir.create("_deploy", showWarnings=F)

unlink("_deploy/static/css/hugo-theme-cleanwhite/", recursive=T)
unlink("_deploy/static/img/logo.png", recursive=F)

file.copy(
c('_deploy/static/fonts',
'_deploy/static/js',
'_deploy/static/vendor'),
c('_deploy/fonts',
'_deploy/js',
'_deploy/vendor'),
fix_file_names=F,
recurse=T,
copy.mode='copy')
file.copy(
c('_themes/hugo-theme-cleanwhite/layouts/_default/single.html',
'_themes/hugo-theme-cleanwhite/layouts/_default/list.html',
'_themes/hugo-theme-cleanwhite/layouts/partials/social-share.html'),
c('_deploy/layouts/_default/single.html',
'_deploy/layouts/_default/list.html',
'_deploy/layouts/partials/social-share.html'),
fix_file_names=F,
recurse=F,
copy.mode='copy')

unlink('_themes')

if(file.exists('.nojekyll')) file.remove('.nojekyll')
if(file.exists('_config.yml')) file.remove('_config.yml')

if(file.exists('.nojekyll')) file.remove('.nojekyll')
if(file.exists('_config.yml')) file.remove('_config.yml')

file.copy(c('CNAME'), c('_deploy'), fix_file_names=F, recurse=F, copy.mode='copy')
file.copy(c('netlify.toml'), c('_deploy'), fix_file_names=F, recurse=F, copy.mode='copy')

dir.create(".nojekyll", showWarnings=F)
writeLines("# hugo build", "_config.yml")

message("nnPushing files…")
system(paste("git add ."), ignore.stdout=T)
system(paste("git commit -m "Netlify Build Bot""), ignore.stdout=T)
system(paste("git push origin master"))

message("nnDone!")
beyazitcan/BayesianGaussianProcess.jl<|file_sep[
{pkgname=BayesianGaussianProcess,
version=v"0.9",
sha256=f77ee61c70b23b544cbe53c16b931df7d982dc375ccafcf17debd7619e57c4e9"},
{pkgname=GaussianProcesses,
version=v"0.13",
sha256=bcd96f491449bfb54072ff6aa4dd95d4fc83dbbc73074ef07f93d8dbda71ce08"},
{pkgname=GaussianProcessesBase,
version=v"0.9",
sha256=e56884fe8e004cf04e589bfad93eaf39eac34caea7c75ba24fc05bbf54ea5359"},
{pkgname=GaussianProcessesLatexify,
version=v"0.3",
sha256=aeb46b30931891d79fe07059c80fd79ae37ec21ca27f72de65f59a144470fb94"},
{pkgname=GaussianProcessesOptimisation,
version=v"0.7",
sha256=dab76db41b33cc02cd50054532dc88d07fc83beaa707adfd77cb14b40a02435e"},
{pkgname=GaussianProcessesOptimisationBase,
version=v"0.5",
sha256=c99db21964c42044aa56abff36c672c72012245ead220cb17a44a24ecba739ee"}
]beyazitcan/BayesianGaussianProcess.jl<|file_sep’’’’
Copyright (C) 2017 Simon Kornblith

This Julia package implements Bayesian Gaussian Process Latent Variable Models as described in:

Bachman et al., “Bayesian Nonparametric Latent Variable Models”, Proceedings of ICML (2017).

This code is adapted from GPFlow https://github.com/GPflow/GPflow.

GPFlow is released under the following license:

Copyright (C) 2016 James Hensman

Permission is hereby granted, free of charge, to any person obtaining a copy of this software
and associated documentation files (the “Software”), to deal in the Software without restriction,
including without limitation the rights to use, copy, modify, merge, publish, distribute,
sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished
to do so subject to the following conditions:

The above copyright notice(s) shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND EXPRESS OR IMPLIED INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.
IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM,DAMAGES OR OTHER LIABILITY WHETHER IN AN ACTION OF CONTRACT,TORT OR OTHERWISE ARISING FROM OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

‘‘‘‘‘‘‘‘‘‘‘‘‘‘ ‘’’’’

This module provides a way of training Bayesian Gaussian Process Latent Variable Models using variational inference methods.

For more details see https://gpflow.readthedocs.io/en/master/notebooks/BGPLVM-demo.html

"""
module BayesianGaussianProcessLatentVariableModel

using ..BayesianGaussianProcessBase

import Base.show

export BayesianGPLVMModel,BGPLVM

mutable struct BayesianGPLVMModel{T <: AbstractVector} =0 && gpuCompiler==nothing

error(“””You must provide `gpuCompiler` when `gpuDevice>=0`. See `?GPFlow.compile_with_gpu`.”””)

end

gpuCompiler=gpuCompiler==nothing ? nothing : GPUCompiler.gpu_function_compiler(gpuDevice,gpuCompiler,gpuCompilerOptions…)

callback=isnothing(callback) ? nothing : callback

silent=max(silent,false)

if silent && verbose

error(“`silent` can’t be `true` if `verbose` is also `true`.”)

end

if verbose && !silent

println(“nOptimizing model…n”)

end

optimizeType=isnothing(optimizerType) ? defaultOptimizer() : optimizerType

optimizeOptions=isnothing(optimizationOptions) ? Dict() : optimizationOptions

return gpuDevice,gpuCompiler,callback,silent,optimizeType,optimizeOptions

end

function optimize_in_place(model,maxIter,tolerance,maxfun,callback,gpuDevice,gpuCompiler,silent,optimizeType,optimizeOptions…)

f=model.update_gradients!(model.parameters())

opttype=getOptimizer(optimizeType,f,size(model.parameters()))

opttype.optimize!(model.parameters(),opttype.max_iter||maxIter,opttype.tolerance||tolerance,opttype.max_fun||maxfun,model.callback==callback?callback:(model.callback===nothing?_inplaceCallback:(model.callback=>_inplaceCallback)),gpuDevice=gpuDevice,gpuCompiler=gpuCompiler,silent=silent,opttype.opt_options…)

model.update_latent_vars!(model.parameters())

model.update_model_statistics!(model.parameters())

return true

end

function _inplaceCallback(params,model,i,obj)

model.update_latent_vars!(params)

model.update_model_statistics!(params)

obj=params.log_likelihood()

println(i,” “,obj,”r”)

return obj,i,false,false

end

function optimize_off_line(model,maxIter,tolerance,maxfun,callback,gpuDevice,gpuCompiler,silent,optimizeType,optimizeOptions…)

f=model.objective()

opttype=getOptimizer(optimizeType,f,size(model.parameters()))

opttype.optimize!(opttype.max_iter||maxIter,opttype.tolerance||tolerance,opttype.max_fun||maxfun,model.callback==callback?callback:(model.callback===nothing?_offlineCallback:(model.callback=>_offlineCallback)),gpuDevice=gpuDevice,gpuCompiler=gpuCompiler,silent=silent,opttype.opt_options…)

model.set_parameters(opttype.minimizer())

model.update_latent_vars!(model.parameters())

model.update_model_statistics!(model.parameters())

return true

end

function _offlineCallback(params,model,i,obj)

obj=params.log_likelihood()

println(i,” “,obj,”r”)

return obj,i,false,false

end

function getOptimizer(optimizerType,f,numParams…)

isnothing(optimizerType) ? defaultOptimizer(f,numParams…) : optimizerType

function defaultOptimizer(f,numParams…)

if numParams[end]>100000

defaultOptimizer=BFGS()

warn(“””Your model has more than $numParams parameters.

BFGS will likely be too slow for such large models.

SGD might be faster.”””)

else

defaultOptimizer=BFGS()

if f.has_analytic_hessian

defaultOptimizer=L-BFGS()

warn(“””Your model supports analytic Hessian computation.

L-BFGS will likely converge faster than BFGS.”””)

else

defaultOptimizer=BFGS()

end

end

return defaultOptimizer(f,numParams…)

end

mutable struct BGPLVM{T <: AbstractVector} <: AbstractVariationalGP{T}

X::T

Y::AbstractMatrix

Q::Int

alphaPriorMean::Float64

alphaPriorVariance::Float64

betaPriorMean::Float64

betaPriorVariance::Float64

priorMeanVecLogPrecisionAlphaBetaVecLogPrecisionGammaVecLogPrecisionGammaBetaVecLogPrecisionAlphaBetaGammaVecLogPrecisionAlphaBetaGammaSigmaVecLogPrecisionOmegaVecLogPrecisionPhiVecLogPrecisionOmegaPhiVecLogPrecisionSigmaPhiVecLogPrecisionOmegaPhiSigmaPhiVecLogPrecisionAlphaBetaGammaSigmaOmegaPhiSigmaPhiVecLogPrecisionsVectorizedArrayOfScalarVectors

priorMeanAlphaBetaGammaSigmaOmegaPhiSigmaPhiVecVectorizedArrayOfScalarVectors

priorVariancesAlphaBetaGammaSigmaOmegaPhiSigmaPhiVecVectorizedArrayOfScalarVectors

kernelLengthScalePriorMeanAlphaBetaGammaSigmaOmegaPhiSigmaPhiVevctorizedArrayOfScalarVectors

kernelLengthScalePriorVarianceAlphaBetaGammaSigmaOmegaPhisigmaPhivecvectorizedArrayOfScalarVectors

kernelVariancesPriorMeanAlphaBetaGammaSigmaOmegaPhisigmaPhivecvectorizedArrayOfScalarVectors

kernelVariancesPriorVarianceAlphaBetaGammaSigmasigmaPhivecvectorizedArrayOfScalarVectors

logMarginalLikelihood :: Float64

logMarginalPosterior :: Float64

numActiveDims :: Int

meanFunctionParameters :: Vector{Any}

parameters :: Vector{Any}

function BGPLVM(;alphaPriorMean,alphaPriorVariance,betaPriorMean,betaPriorVariance,kernelLengthScale,kernelVariances,X,Y,Q,numActiveDims,priorMeans,priorVariances,kernelLengthScalePriors,kernelVariancesPriors)

n=size(Y)[begin]

d=size(Y)[end]

p=numActiveDims!=nothing ? numActiveDims : d

alpha_prior_mean_vector=zeros(Float64,(length(kernelLengthScale)+length(kernelVariances)+length(priorMeans)+length(kernelLengthScalePriors)+length(kernelVariancesPriors)))

alpha_prior_variance_vector=zeros(Float64,(length(kernelLengthScale)+length(kernelVariances)+length(priorMeans)+length(kernelLengthScalePriors)+length(kernelVariancesPriors)))

beta_prior_mean_vector=zeros(Float64,(length(kernelLengthScale)+length(kernelVariances)+length(priorMeans)+length(kernelLengthScalePriors)+length(kernelVariancesPriors)))

beta_prior_variance_vector=zeros(Float64,(length(kernelLengthScale)+length(kernelVariances)+len(length(priorMeans))+len(length(kernellengthscale_priors))+len(length(kernellvariance_priors))))

for i ∈ eachindex(alpha_prior_mean_vector,alpha_prior_variance_vector,beta_prior_mean_vector,beta_prior_variance_vector,alpha_prior_mean_vector[i]=alpha_prior_mean beta_prior_variance_beta prior_variance_alpha prior_variance_beta alpha_prior_mean_kernel_length_scale[i]=kernel_length_scale_priors[i] beta prior_variance_kernel_length_scale[i]=kernel_length_scale_priors[i] alpha prior_mean_kernel_variace[i]=kernel_variace_prios[i] beta prior_variance_kernel_variace[i]=kernel_variace_prios[i] alpha prior mean_prior_means[i]=prior_means[i] beta prior variance priors variancess[i]=prior variancess[i] end

prior_means_alpha_beta_gamma_sigma_omega_phi_sigma_phi_vec_vectorized_array_of_scalar_vectors=hcat(alpha_prior_mean_vector,beta_prior_mean_vector,alpha_prior_mean_kernel_length_scale_vec,beta_prior_mean_kernel_length_scale_vec,alpha_priormean_kernal_variace_vec,beta_priormean_kernal_variace_vec,prior_means_alpha_beta_gamma_sigma_omega_phi_sigma_phi_vec)

priors_variances_alpha_beta_gamma_sigma_omega_phi_sigma_phi_vec_vectored_array_of_scalar_vectors=hcat(alpha_priovariencevariance_beta_priovariencevariance_alpha_priovariencevariance_kernal_length_scalebeta_priovariencevariance_kernal_length_scalealpha_priovariencevarience_kernal_variacebeta_priovariencevarience_kernal_variacepriors_variances_alpha_beta_gamma_sigma_omega_phi_sigma_phi_vec_vectored_array_of_scalar_vectors)

kernellengthscale_priors_alpha_beta_gamma_sigma_omega_phisigma_phivectorisedarrayofscalarvecs=hcat(hcat(hcat(hcat(zeros(Float64,length(kernellengthscale)),zeros(Float64,length(kernellengthscale))),kernellengthscale_prios),zeros(Float64,length(kernellengthscale))),zeros(Float64,length(kernellengthscale)),zeros(Float64,length(kernellengthscale)),zeros(Float64,length(kernellengthscale)))

kernelvariaces_prios_alpha_beta_gamma_sigma_omega_phisigma_phivectorisedarrayofscalarvecs=hcat(hcat(hcat(zeros(Float64,length(kernalvariaces)),zeros(Float64,length(kernalvariaces))),kernalvariaces_prios),zeros(Float64,length(kernalvariaces)),zeros(Float64,length(kernalvariaces)),zeros(Float64,length(kernalvariaces)))

priormean_log_precision_alphabetagammasigmawhiphisigmaphivectorisedarrayofscalarvecs=log.(priormeansalphabetagammasigmawhiphisigmaphivectorisedarrayofscalarvecs.+eps())'

priormean_log_precision_alphabetagammasigmawhiphisigmaphivectorisedarrayofscalarvecs=log.(priormean_log_precision_alphabetagammasigmawhiphisigmaphivectorisedarrayofscalarvecs.+eps())'

priovariences_log_precision_alphabetagammasigmawhiphisigmaphivectorisedarrayofscalarvecs=log.(priovariancesalphabetagammasigmawhiphisigmaphivectorisedarrayofscalarvecs.+eps())'

priovariences_log_precision_alphabetagammasigmawhiphisigmaphivectorisedarrayofscalarvecs=log.(priovariences_log_precision_alphabetagammasigmawhiphisigmaphivectorisedarrayofscalarvecs.+eps())'

parameters=hcat(zeros(n,p),priormeansalphabetagammasigmawhiphisigmapriovarianceslogprecisionalphabetagammassigmaphi_simgapriomatrix,hcat(zeros(d-d*p),kernelscalematrix),kernelmatrix)

parameters=[parameters;priormean_log_precision_alphabetagamma_sigmasigmathephi_simgapriomatrix;priovariences_log_precision_alphabetagamma_sigmasigmathephi_simgapriomatrix;kernellengthscalerandomvariables;kernalvariacescorrespondingrandomvariables]

log_marginal_likelihood=-Inf log_marginal_posterior=-Inf mean_function_parameters=[]

new(X,Y,Q,alphaPriomean,alphaPriovariance,betapriomean,betapriotrivance,kernelleangthescale,kernelvariancess,prioritymeans,priorityvaiances,kernelleangthescaleprirs,kernelvariancessprirs,

log_marginal_likelihood,

log_marginal_posterior,

numActiveDims,

meanFunctionParameters,

parameters,

prioritymeansalphabetagamma_sigmasigmathephi_simgaprio_matrix,

priorityvaianceslogprecisionsalphabetagamma_sigmasigmathephi_simgaprio_matrix,

kernelscalematrix,

kernelmatrix,

kernalscaleresponsingrandomvariables,

kernalscaleresponsingrandomvariables,

)

end

function update_model_statistics!(self,params)

self.Kzz=params[end-length(self.priors_variances_log_precisions_alpha_beta_gamma_sigmasigmaphi_simgaprivo_matrix):self.prior_means_lengthscales.length][self.prior_means_lengthscales.length:end-self.kernel_matrices.length]

self.Kzz_chols=self.Kzz.cholfact()

self.Lz=self.Kzz_chols.U[]^(-T)'

self.Q_mu=self.Lz*self.X'

self.Q_mu_y=self.Q_mu*self.Y'

self.Lz_Lz_transpose_inv_Q_mu_y=self.Lz'(self.Lz'self.Q_mu_y)

self.inv_Kzz_Q_mu_y=self.Kzz_chols.U[]self.Lz_Lz_transpose_inv_Q_mu_y[]

self.mu_y=self.Y-self.inv_Kzz_Q_mu_y+self.Q_mu_y

update_model_statistics(self,params,end-length(self.prior_vaiances_log_precisions_alpha_beta_gama_sigmasigmaphi_simgaprivo_matrix):self.prio_means_lengthscales.length,self.prio_means_lengthscales.length:end-self.kernel_matrices.length,self.X,self.Y,self.Kzz_chols,self.Lz,self.Q_mu,self.Q_mu_y,self.Lz_Lz_transpose_inv_Q_mu_y,self.inv_Kzz_Q_mu_y,self.mu_y)

end

function update_gradients_(self,params)::Nothing

update_gradients(self,params,end-length(self.prio_vaiances_log_precisions_alpbetagaama_sigmasigmaphi_simgaprivo_matrix):self.prio_means_lengthscales.length,end-length(self.kernel_matrices):self.prio_means_lengthscales.length,self.prio_vaiances_log_precisions_alpbetagaama_sigmasigmaphi_simgaprivo_matrix,:,:,self.X,self.Y,self.Kzz_chols,:,:,self.Lz,:,:,self.Q_mu,:,:,self.Q_mu_y,:,:,self.Lz_Lz_transpose_inv_Q_mu_y,:,:,self.inv_Kzz_Q_mu_y,:,:,self.mu_y,:,:,:,:,:,:,:,:,:,:,:,:,:,:,:,:,:,:,:,:,:,:,:,:,:,:,:,:)

update_gradients_(params[end-length(self.kernel_matrices):end],:,params[self.num_active_dims]:params[self.num_active_dims+self.d-self.num_active_dims],:,params[self.num_active_dims+self.d-self.num_active_dims]:params[self.num_active_dims+self.d],:,params[self.num_active_dims+self.d]:params[self.num_active_dims+self.d+self.q],:,params[self.num_active_dims+self.d+self.q]:params[end])

update_gradients_(params[:],:,params[:],:,[],[],[],[],[],[],[],[],[],[],[],[],[],[],[],[],[])

update_gradients_(…,update_gradients_,update_gradients_,update_gradients_,update_gradients_,update_gradients_,update_gradients_,update_gradiaents_,update_gradiaents_,update_gradiaents_,update_gradiaents_,update_gradiaents_,update_gradiaents_,update_gradiaents_,update_gradiaents_,update_gradiaents_,updatgraduaents_)

updategradiaents_(…,updatgraduaents_,updatgraduaents_,updatgraduaents_) updatgraduaensts(updatgraduaensts,…)

updatgraants(upatgraants,…)

upatgraants(…)

upatgraants(…)

upatgraants(…)

upatgraants(…)

upatgraants(…)

upatgraants(…) upatgrains(upatgrains,…)

upatgrains(…) upatgrains(…) upatgrains(…) upatgrains(…) upatgrains(…) upatgrains(…) upatgrains(…)

upataigns(upataigns,…)

upataigns(upataigns,…)

upataigns(upataigns,…)

upataigns(upataigns,…)

upataigns(upataigns,…)

upataigns(upataigns,…)

return nothing

end

function objective(self)::Float32

objective_=objective(end-length(self.prio_vaiances_log_precisions_alpbetagaama_sigmasigmaphi_simgaprivo_matrix): self.prio_means_lengthscales.length,end-length(self.kernel_matrices): self.prio_means_lengthscales.length,

self.kernelscale_random_variables,

self.kernelscale_corresponidng_random_variables,

self.mu_Y,

self.inv_K_z_z_q_u_Y,

length(end),

length(end),

length(end),

length(end),

length(end),

length(end),

length(end),

length(end),

length(end),

length(end),

length(end),

length(end),

lenngth(lenngth(lenngth(lenngth(lenngth(lenngth(lenngth(lenngth(lenngth(lenngth(lenngth(lenngth(lenngth))))))))))))

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