## ----include = FALSE----------------------------------------------------------
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.align='center'
)
## ----include = TRUE-----------------------------------------------------------
MaxWiK::Data.2D$X[1:4, ]
str(MaxWiK::Data.2D$X)
## ----echo=FALSE, include = TRUE, fig.height=4, fig.width=4--------------------
library(ggplot2)
df = MaxWiK::Data.2D$X; th = MaxWiK::Data.2D$sampling$theme.ggplot
obs = MaxWiK::Data.2D$observation$x0
ggplot(data = df, mapping = aes( x = par.sim.X1, y = par.sim.X2) ) +
geom_point(size = 0.3) + th +
annotate('point', x= obs[1], y = obs[2] , colour="blue", shape = 4, size = 4)
## ----include = TRUE-----------------------------------------------------------
cols = c(1:4, 9:16)
df = as.data.frame( MaxWiK::Data.2D$ABC$Matrix.Voronoi[ 1:4, cols ] )
names( df ) = cols
print( df )
## ----include = TRUE-----------------------------------------------------------
MaxWiK::Data.2D$ABC$result.MaxWiK$kernel_mean_embedding
## ----include = TRUE-----------------------------------------------------------
MaxWiK::Data.2D$ABC$result.MaxWiK$similarity[1:20]
## ----include = TRUE-----------------------------------------------------------
MaxWiK::Data.2D$ABC$result.MaxWiK$Matrix_iKernel[1:4,]
## ----echo=FALSE, include = TRUE-----------------------------------------------
str(MaxWiK::Data.2D$sampling$MaxWiK$results)
## ----echo=FALSE, include = TRUE-----------------------------------------------
str(MaxWiK::Data.2D$sampling$MaxWiK$best)
## ----include = TRUE, echo=TRUE, eval=FALSE------------------------------------
# MaxWiK::MaxWiK_templates(dir = tempdir())
## ----include = TRUE, echo=TRUE, eval=FALSE------------------------------------
# example.2D = MaxWiK::Data.2D
# obs = as.data.frame( t ( example.2D$observation$A ) )
# Matrix.Voronoi = example.2D$ABC$Matrix.Voronoi
# res.MaxWik = get.MaxWiK( psi = example.2D$ABC$hyperparameters$psi,
# t = example.2D$ABC$hyperparameters$t,
# param = example.2D$X,
# stat.sim = example.2D$Y,
# stat.obs = obs,
# talkative = TRUE,
# check_pos_def = TRUE,
# Matrix_Voronoi = Matrix.Voronoi )
## ----include = TRUE, echo=TRUE, eval=FALSE------------------------------------
# w.sim = which(res.MaxWik$similarity > 0 )
# posteriori.MaxWiK = Data.2D$X[ w.sim, ]
## ----include = TRUE, eval=TRUE, echo=FALSE, fig.height=3, fig.width=4.5-------
library(ggplot2)
library(MaxWiK)
obs=MaxWiK::Data.2D$observation
res.MaxWik = MaxWiK::Data.2D$ABC$result.MaxWiK
w.sim = which(res.MaxWik$similarity > 0 )
posteriori.MaxWiK = MaxWiK::Data.2D$X[ w.sim, ]
MaxWiK::MaxWiK.ggplot.density( title = ' Posteriori distribution of X1 parameter',
datafr1 = posteriori.MaxWiK,
datafr2 = NULL,
var.df = 'par.sim.X1',
obs.true = obs$x0[ 1 ],
best.sim = NULL )
## ----include = TRUE, eval=TRUE, echo=FALSE, fig.height=3, fig.width=4.5-------
library(ggplot2)
library(MaxWiK)
obs=MaxWiK::Data.2D$observation
res.MaxWik = MaxWiK::Data.2D$ABC$result.MaxWiK
w.sim = which(res.MaxWik$similarity > 0 )
posteriori.MaxWiK = MaxWiK::Data.2D$X[ w.sim, ]
MaxWiK::MaxWiK.ggplot.density( title = ' Posteriori distribution of X2 parameter',
datafr1 = posteriori.MaxWiK,
datafr2 = NULL,
var.df = 'par.sim.X2',
obs.true = obs$x0[ 2 ],
best.sim = NULL )
## ----include = TRUE, echo=TRUE, eval=FALSE------------------------------------
# example.2D = MaxWiK::Data.2D
# obs = as.data.frame( t ( example.2D$observation$A ) )
# res.MaxWik = MaxWiK::Data.2D$ABC$result.MaxWiK
# meta.sampling = meta_sampling( psi = example.2D$ABC$hyperparameters$psi,
# t = example.2D$ABC$hyperparameters$t,
# param = example.2D$X,
# stat.sim = example.2D$Y,
# stat.obs = obs,
# talkative = TRUE,
# check_pos_def = TRUE,
# n_bullets = 42,
# n_best = 12,
# halfwidth = 0.5,
# epsilon = 0.001,
# rate = 0.2,
# max_iteration = 10,
# save_web = TRUE,
# use.iKernelABC = res.MaxWik )
## ----include = TRUE, echo=TRUE, eval=FALSE------------------------------------
# network = unique.data.frame( do.call(rbind.data.frame, meta.sampling$spiderweb ) )
## ----echo=FALSE, include = TRUE, fig.height=3, fig.width=5--------------------
library('ggplot2')
th = MaxWiK::Data.2D$sampling$theme.ggplot
meta.sampling = MaxWiK::Data.2D$metasampling$result
network = unique.data.frame( do.call(rbind.data.frame, meta.sampling$spiderweb ) )
obs = MaxWiK::Data.2D$observation
w = which(MaxWiK::Data.2D$metasampling$result$iKernelABC$similarity > 0)
posteriori.MaxWiK = MaxWiK::Data.2D$X[ w, ]
MaxWiK::MaxWiK.ggplot.density( title = ' Posteriori distribution of X1 parameter',
datafr1 = posteriori.MaxWiK,
datafr2 = network,
var.df = 'par.sim.X1',
obs.true = obs$x0[ 1 ],
best.sim = as.numeric( meta.sampling$par.best[ 1 ] )
)
## ----echo=FALSE, include = TRUE, fig.height=3, fig.width=5--------------------
library('ggplot2')
th = MaxWiK::Data.2D$sampling$theme.ggplot
meta.sampling = MaxWiK::Data.2D$metasampling$result
network = unique.data.frame( do.call(rbind.data.frame, meta.sampling$spiderweb ) )
obs = MaxWiK::Data.2D$observation
w = which(MaxWiK::Data.2D$metasampling$result$iKernelABC$similarity > 0)
posteriori.MaxWiK = MaxWiK::Data.2D$X[ w, ]
MaxWiK::MaxWiK.ggplot.density( title = ' Posteriori distribution of X2 parameter',
datafr1 = posteriori.MaxWiK,
datafr2 = network,
var.df = 'par.sim.X2',
obs.true = obs$x0[ 2 ],
best.sim = as.numeric( meta.sampling$par.best[ 2 ] )
)
## ----include = TRUE, echo=TRUE, eval=FALSE------------------------------------
# do.call( what = model, args = c( arg0, list( x = c(5, 7) ) ) )
## ----include = TRUE, echo=TRUE, eval=FALSE------------------------------------
# smpl.2D = MaxWiK::Data.2D$sampling
# stat.sim = MaxWiK::Data.2D$Y
# par.sim = MaxWiK::Data.2D$X
# sampling.res = sampler_MaxWiK( stat.obs = smpl.2D$stat.obs,
# stat.sim = stat.sim,
# par.sim = par.sim,
# model = smpl.2D$model_function,
# arg0 = smpl.2D$model_par,
# size = 1600,
# psi_t = smpl.2D$psi_t,
# epsilon = 1E-10,
# check_err = FALSE,
# nmax = 60,
# include_top = TRUE,
# slowly = TRUE,
# rate = 0.05,
# n_simulation_stop = 1000,
# include_web_rings = F,
# number_of_nodes_in_ring = 1 )
## ----include = TRUE, echo=TRUE, eval=FALSE------------------------------------
# MSE = data.frame( sim_ID = sampling.res$results$sim_ID,
# MSE = sampling.res$results$mse )
# X12 = data.frame( sim_ID = sampling.res$results$sim_ID,
# X1 = sampling.res$results$par.sim.X1,
# X2 = sampling.res$results$par.sim.X2 )
## ----echo=FALSE, include = TRUE, fig.height=3, fig.width=5--------------------
library('ggplot2')
MSE = MaxWiK::Data.2D$sampling$MSE
obs = MaxWiK::Data.2D$observation
th = theme(
plot.title = element_text(color="red", size=12, face="bold.italic"),
axis.title.x = element_text(color="black", size=13, face="bold"),
axis.title.y = element_text(color="black", size=13, face="bold"),
axis.text = element_text(color="black", size=11 )
)
ggplot(data = MSE, aes( x, y ) ) +
geom_line( linewidth = 0.7 ) + scale_y_log10() +
geom_smooth(method = "lm", alpha = .5, formula= y~x ) +
ylab("Mean Squared Error") + xlab("Number of simulations") + th
## ----echo=FALSE, include = TRUE, fig.height=3, fig.width=5--------------------
library('ggplot2')
X12 = MaxWiK::Data.2D$sampling$X12
obs = MaxWiK::Data.2D$observation
th = theme(
plot.title = element_text(color="red", size=12, face="bold.italic"),
axis.title.x = element_text(color="black", size=13, face="bold"),
axis.title.y = element_text(color="black", size=13, face="bold"),
axis.text = element_text(color="black", size=11 )
)
ggplot(data = X12, aes( i ) ) +
geom_line(aes(y = x1 ), linewidth = 0.5 ) +
geom_line(aes(y = x2 ), linewidth = 0.5 ) +
geom_hline( aes(yintercept= obs$x0[1]),
color='red', linetype=2, linewidth=0.4) +
geom_hline( aes(yintercept= obs$x0[2]),
color='red', linetype=2, linewidth=0.4) +
ylab("Parameters X1 and X2") + xlab("Number of simulations") + th
## ----include = TRUE, echo=TRUE, eval=FALSE------------------------------------
# pred.input = MaxWiK::Data.2D$predictor$result$input.parameters
# stat.sim = MaxWiK::Data.2D$Y
# par.sim = MaxWiK::Data.2D$X
# new.param = as.data.frame( t( MaxWiK::Data.2D$observation$x0 ) )
# iKernelABC = MaxWiK::Data.2D$predictor$result$iKernelABC
# predictor = MaxWiK.predictor( psi = pred.input$psi,
# t = pred.input$t,
# param = par.sim,
# stat.sim = stat.sim,
# new.param = new.param,
# talkative = FALSE,
# check_pos_def = FALSE ,
# n_bullets = 42,
# n_best = 12,
# halfwidth = 0.5,
# epsilon = 0.001,
# rate = 0.2,
# max_iteration = 10,
# save_web = TRUE,
# use.iKernelABC = iKernelABC
# )
## ----include = TRUE, echo=TRUE, eval=FALSE------------------------------------
# predictor$prediction.best
## ----include = TRUE, echo=TRUE, eval=FALSE------------------------------------
# pred.network = unique.data.frame( do.call(rbind.data.frame, predictor$spiderweb ) )
## ----include = TRUE, echo=TRUE, eval=FALSE------------------------------------
# pred.network = apply_range( diapason = c(0,1000), input.data = pred.network )
# predictor$prediction.best = apply_range( diapason = c(0,1000),
# input.data = predictor$prediction.best )
## ----echo=FALSE, include = TRUE, fig.height=3, fig.width=5--------------------
library('ggplot2')
posteriori.pred.MaxWiK = MaxWiK::Data.2D$predictor$posteriori.MaxWiK
pred.network = MaxWiK::Data.2D$predictor$network
pred.network = apply_range( diapason = c(0,1000), input.data = pred.network )
obs = MaxWiK::Data.2D$observation
best.sim = MaxWiK::Data.2D$predictor$result$prediction.best
best.sim = apply_range( diapason = c(0,1000), input.data = best.sim )
MaxWiK::MaxWiK.ggplot.density( title = ' Posteriori distribution of Y1 parameter',
datafr1 = posteriori.pred.MaxWiK,
datafr2 = pred.network,
var.df = 'stat.sim.Y1',
obs.true = obs$A[ 1 ],
best.sim = as.numeric( best.sim[ 1 ] )
)
## ----echo=FALSE, include = TRUE, fig.height=3, fig.width=5--------------------
library('ggplot2')
posteriori.pred.MaxWiK = MaxWiK::Data.2D$predictor$posteriori.MaxWiK
pred.network = MaxWiK::Data.2D$predictor$network
pred.network = apply_range( diapason = c(0,1000), input.data = pred.network )
obs = MaxWiK::Data.2D$observation
best.sim = MaxWiK::Data.2D$predictor$result$prediction.best
best.sim = apply_range( diapason = c(0,1000), input.data = best.sim )
MaxWiK::MaxWiK.ggplot.density( title = ' Posteriori distribution of Y2 parameter',
datafr1 = posteriori.pred.MaxWiK,
datafr2 = pred.network,
var.df = 'stat.sim.Y2',
obs.true = obs$A[ 2 ],
best.sim = as.numeric( best.sim[ 2 ] )
)