R備忘録 - 記事一覧
- 投稿者: みゅ
- カテゴリ: なし
- 優先度: 普通
- 状態: 完了
- 日時: 2011年04月20日 22時42分18秒
library(nnet)
library(RSNNS)
data(snnsData)
patterns <- snnsData$art1_letters.pat
par(mfrow=c(3,3))
for (i in 1:9) plotActMap(matrixToActMapList(patterns,7)[[i]])
res <- nnet(patterns, y=patterns, skip=T, size=35, MaxNWts=20000, maxit=1000, lineout=F)
par(mfrow=c(3,3))
for (i in 1:9) plotActMap(matrixToActMapList(res$fitted,7)[[i]])
for (i in 10:18) plotActMap(matrixToActMapList(res$fitted,7)[[i]])
for (i in 19:26) plotActMap(matrixToActMapList(res$fitted,7)[[i]])
testData <-
rbind(
as.vector(t(rbind(c(0,0,0,0,1), c(0,0,0,1,0), c(0,0,0,1,0), c(0,0,1,0,0), c(0,1,0,0,0), c(0,1,0,0,0), c(1,0,0,0,0)))),
as.vector(t(rbind(c(1,0,0,0,0), c(1,0,0,0,0), c(0,1,0,0,0), c(0,0,1,0,0), c(0,0,0,1,0), c(0,0,0,0,1), c(0,0,0,0,1))))
)
par(mfrow=c(2,2))
plotActMap(matrixToActMapList(testData,7)[[1]])
plotActMap(matrixToActMapList(testData,7)[[2]])
pred <- predict(res, testData)
plotActMap(matrixToActMapList(pred,7)[[1]])
plotActMap(matrixToActMapList(pred,7)[[2]])
par(mfrow=c(2,2))
plotActMap(matrixToActMapList(testData,7)[[1]])
plotActMap(matrixToActMapList(testData,7)[[2]])
pred <- predict(res, pred)
plotActMap(matrixToActMapList(pred,7)[[1]])
plotActMap(matrixToActMapList(pred,7)[[2]])
library(genalg)
func <- function(param){
res$wts <- param
#sum(abs(patterns - predict(res,patterns)) %*% rep(1,35))
sum((patterns - predict(res,patterns))^2 %*% rep(1,35))
}
func(runif(length(res$wts)))
monFunc <- function(obj){
res$wts <- obj$population[1,]
pred <- predict(res, patterns)
par(mfrow=c(3,3))
plotActMap(matrixToActMapList(patterns,7)[[1]])
plotActMap(matrixToActMapList(patterns,7)[[2]])
plotActMap(matrixToActMapList(patterns,7)[[3]])
plotActMap(matrixToActMapList(pred,7)[[1]])
plotActMap(matrixToActMapList(pred,7)[[2]])
plotActMap(matrixToActMapList(pred,7)[[3]])
plot(obj$best, ty="l")
lines(obj$mean, col="red")
#for (i in 1:9) plotActMap(matrixToActMapList(pred,7)[[i]])
}
monFunc(res_ga)
res_ga <- rbga(stringMin=rep(-1,length(res$wts)), stringMax=rep(1,length(res$wts)), evalFunc=func, monitorFunc=monFunc,
verbose=T, iters=200, suggestions=rbind(res$wts,res$wts))
res_ga$population[1,]
res <- nnet(patterns, y=patterns, skip=T, size=35, MaxNWts=20000, maxit=1000, lineout=F, Wts=res_ga$population[1,])
par(mfrow=c(3,3))
for (i in 1:9) plotActMap(matrixToActMapList(res$fitted,7)[[i]])
for (i in 10:18) plotActMap(matrixToActMapList(res$fitted,7)[[i]])
for (i in 19:26) plotActMap(matrixToActMapList(res$fitted,7)[[i]])
res_ga <- rbga(stringMin=rep(-1,length(res$wts)), stringMax=rep(1,length(res$wts)), evalFunc=func, monitorFunc=monFunc,
verbose=T, iters=200, suggestions=rbind(res$wts,res$wts))
# パターンをひっくり返す
patterns2 <- 1-patterns
par(mfrow=c(3,3))
for (i in 1:9) plotActMap(matrixToActMapList(patterns2,7)[[i]])
pred <- predict(res, patterns2)
for (i in 1:9) plotActMap(matrixToActMapList(pred,7)[[i]])
library(RSNNS)
data(snnsData)
patterns <- snnsData$art1_letters.pat
inputMaps <- matrixToActMapList(patterns, nrow=7)
par(mfrow=c(3,3))
for (i in 1:9) plotActMap(inputMaps[[i]])
model <- art1(patterns, dimX=7, dimY=5)
encodeClassLabels(model$fitted.values)
> ls(envir=model$snnsObject@variables)
[1] "serialization" "snnsCLibPointer"
predict(model, newdata=patterns)
encodeClassLabels(predict(model, newdata=patterns))
summary(model)
library(RSNNS)
data(snnsData)
patterns <- snnsData$art2_tetra_med.pat
model <- art2(patterns, f2Units=5, learnFuncParams=c(0.99, 20, 20, 0.1, 0), updateFuncParams=c(0.99, 20, 20, 0.1, 0))
model
testPatterns <- snnsData$art2_tetra_med.pat
predictions <- predict(model, testPatterns)
library(scatterplot3d)
par(mfrow=c(1,2))
scatterplot3d(model$fitted.values[,1:3])
scatterplot3d(predictions[,1:3])
model
library(RSNNS)
demo(dlvq_ziff)
data(snnsData)
dataset <- snnsData$dlvq_ziff_100.pat
inputs <- dataset[,inputColumns(dataset)]
outputs <- dataset[,outputColumns(dataset)]
model <- dlvq(inputs, outputs)
mean(fitted(model) - outputs)
confusionMatrix(outputs, fitted(model))
par(mfrow=c(2,2))
plotActMap(matrixToActMapList(inputs, 16)[[1]])
plotActMap(matrixToActMapList(inputs, 16)[[2]])
plotActMap(matrixToActMapList(inputs, 16)[[3]])
plotActMap(matrixToActMapList(inputs, 16)[[4]])
plotActMap(matrixToActMapList(inputs, 16)[[5]])
plotActMap(matrixToActMapList(inputs, 16)[[50]])
plotActMap(matrixToActMapList(inputs, 16)[[15]])
plotActMap(matrixToActMapList(inputs, 16)[[2]])
plotActMap(matrixToActMapList(inputs, 16)[[7]])
plotActMap(matrixToActMapList(inputs, 16)[[13]])
patterns <- dataset[,inputColumns(dataset)][c(1,2,3,4,5,6,8,10,11,12),]
plotActMap(matrixToActMapList(patterns,16)[[1]])
> library(RSNNS)
> seed <- 2
> set.seed(seed)
> setSnnsRSeedValue(seed)
NULL
> data(iris)
> #shuffle the vector
> iris <- iris[sample(1:nrow(iris),length(1:nrow(iris))),1:ncol(iris)]
> irisValues <- iris[,1:4]
> irisTargets <- decodeClassLabels(iris[,5])
> #irisTargets <- decodeClassLabels(iris[,5], valTrue=0.9, valFalse=0.1)
>
> iris <- splitForTrainingAndTest(irisValues, irisTargets, ratio=0.15)
> #normalize data
> iris <- normTrainingAndTestSet(iris)
> #model <- mlp(iris$inputsTrain, iris$targetsTrain, size=5, learnFunc="Quickprop", learnFuncParams=c(0.1, 2.0, 0.0001, 0.1),
> # maxit=50, inputsTest=iris$inputsTest, targetsTest=iris$targetsTest)
>
> model <- mlp(iris$inputsTrain, iris$targetsTrain, size=5, learnFunc="BackpropBatch", learnFuncParams=c(10, 0.1),
+ maxit=100, inputsTest=iris$inputsTest, targetsTest=iris$targetsTest)
> #model <- mlp(iris$inputsTrain, iris$targetsTrain, size=5, learnFunc="SCG", learnFuncParams=c(0, 0, 0, 0),
> # maxit=30, inputsTest=iris$inputsTest, targetsTest=iris$targetsTest)
>
>
> #model <- rbfDDA(iris$inputsTrain, iris$targetsTrain)
>
> #model <- elman(iris$inputsTrain, iris$targetsTrain, size=5, learnFuncParams=c(0.1), maxit=100, inputsTest=iris$inputsTest, targetsTest=iris$targetsTest)
>
> #model <- rbf(iris$inputsTrain, iris$targetsTrain, size=40, maxit=200, initFuncParams=c(-4, 4, 0.0, 0.2, 0.04),
> # learnFuncParams=c(1e-3, 0, 1e-3, 0.1, 0.8), linOut=FALSE)
>
> #model <- rbf(iris$inputsTrain, iris$targetsTrain, size=40, maxit=600, initFuncParams=c(0, 1, 0.0, 0.2, 0.04),
> # learnFuncParams=c(1e-5, 0, 1e-5, 0.1, 0.8), linOut=TRUE)
>
> ##experimental..:
> ##model <- rbf(iris$inputsTrain, iris$targetsTrain, size=20, maxit=50, initFunc="RBF_Weights_Kohonen",
> ## initFuncParams=c(50, 0.4, 0), learnFuncParams=c(0.01, 0, 0.01, 0.1, 0.8))
>
> #summary(model)
>
> par(mfrow=c(2,2))
> plotIterativeError(model)
次の図を見るためには<Return>キーを押して下さい:
> predictions <- predict(model,iris$inputsTest)
> plotRegressionError(predictions[,2], iris$targetsTest[,2])
> confusionMatrix(iris$targetsTrain,fitted.values(model))
predictions
targets 1 2 3
1 41 0 0
2 0 45 1
3 0 0 40
> confusionMatrix(iris$targetsTest,predictions)
predictions
targets 1 2 3
1 9 0 0
2 0 3 1
3 0 1 9
> plotROC(fitted.values(model)[,2], iris$targetsTrain[,2])
> plotROC(predictions[,2], iris$targetsTest[,2])
> #confusion matrix with 402040-method
> confusionMatrix(iris$targetsTrain, encodeClassLabels(fitted.values(model),method="402040", l=0.4, h=0.6))
predictions
targets 0 1 2 3
1 0 41 0 0
2 2 0 43 1
3 2 0 0 38
> model
Class: mlp->rsnns
Number of inputs: 4
Number of outputs: 3
Maximal iterations: 100
Initialization function: Randomize_Weights
Initialization function parameters: -0.3 0.3
Learning function: BackpropBatch
Learning function parameters: 10 0.1
Update function:Topological_Order
Update function parameters: 0
Patterns are shuffled internally: TRUE
Compute error in every iteration: TRUE
Architecture Parameters:
$size
[1] 5
All members of model:
[1] "nInputs" "maxit" "initFunc" "initFuncParams" "learnFunc" "learnFuncParams"
[7] "updateFunc" "updateFuncParams" "shufflePatterns" "computeIterativeError" "snnsObject" "archParams"
[13] "IterativeFitError" "IterativeTestError" "fitted.values" "fittedTestValues" "nOutputs"
> weightMatrix(model)
Input_1 Input_2 Input_3 Input_4 Hidden_2_1 Hidden_2_2 Hidden_2_3 Hidden_2_4 Hidden_2_5 Output_1 Output_2 Output_3
Input_1 0 0 0 0 -0.3508658 0.2684588 -0.5913861 -0.5612198 0.3195967 0.00000000 0.000000 0.000000
Input_2 0 0 0 0 -0.5082296 -0.9437067 1.2823328 0.9810203 0.2950290 0.00000000 0.000000 0.000000
Input_3 0 0 0 0 2.2801065 1.2012331 -1.8615422 -1.2803886 -1.9130263 0.00000000 0.000000 0.000000
Input_4 0 0 0 0 2.7568083 1.5704848 -1.6876845 -1.3023156 -2.6831374 0.00000000 0.000000 0.000000
Hidden_2_1 0 0 0 0 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 -1.40362704 -3.796962 3.113594
Hidden_2_2 0 0 0 0 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 -2.07435131 1.398071 1.325584
Hidden_2_3 0 0 0 0 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 2.11041474 -3.148410 -2.083688
Hidden_2_4 0 0 0 0 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 1.70966542 -1.791131 -2.028595
Hidden_2_5 0 0 0 0 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 -0.04861032 3.213499 -3.448702
Output_1 0 0 0 0 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.00000000 0.000000 0.000000
Output_2 0 0 0 0 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.00000000 0.000000 0.000000
Output_3 0 0 0 0 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.00000000 0.000000 0.000000
> summary(model)
SNNS network definition file V1.4-3D
generated at Mon Sep 3 14:54:38 2012
network name : RSNNS_untitled
source files :
no. of units : 12
no. of connections : 35
no. of unit types : 0
no. of site types : 0
learning function : BackpropBatch
update function : Topological_Order
unit default section :
act | bias | st | subnet | layer | act func | out func
---------|----------|----|--------|-------|--------------|-------------
0.00000 | 0.00000 | i | 0 | 1 | Act_Logistic | Out_Identity
---------|----------|----|--------|-------|--------------|-------------
unit definition section :
no. | typeName | unitName | act | bias | st | position | act func | out func | sites
----|----------|------------|----------|----------|----|----------|--------------|----------|-------
1 | | Input_1 | 0.81788 | 0.12059 | i | 1,0,0 | Act_Identity | |
2 | | Input_2 | -0.09058 | 0.18581 | i | 2,0,0 | Act_Identity | |
3 | | Input_3 | 0.82358 | -0.24672 | i | 3,0,0 | Act_Identity | |
4 | | Input_4 | 1.07735 | -0.22711 | i | 4,0,0 | Act_Identity | |
5 | | Hidden_2_1 | 0.82712 | -3.04162 | h | 1,2,0 |||
6 | | Hidden_2_2 | 0.97982 | 0.89637 | h | 2,2,0 |||
7 | | Hidden_2_3 | 0.00414 | -1.53274 | h | 3,2,0 |||
8 | | Hidden_2_4 | 0.01841 | -0.97075 | h | 4,2,0 |||
9 | | Hidden_2_5 | 0.17175 | 2.65829 | h | 5,2,0 |||
10 | | Output_1 | 0.01332 | -1.14370 | o | 1,4,0 |||
11 | | Output_2 | 0.12910 | -0.64419 | o | 2,4,0 |||
12 | | Output_3 | 0.88143 | -1.22978 | o | 3,4,0 |||
----|----------|------------|----------|----------|----|----------|--------------|----------|-------
connection definition section :
target | site | source:weight
-------|------|---------------------------------------------------------------------------------------------------------------------
5 | | 4: 2.75681, 3: 2.28011, 2:-0.50823, 1:-0.35087
6 | | 4: 1.57048, 3: 1.20123, 2:-0.94371, 1: 0.26846
7 | | 4:-1.68768, 3:-1.86154, 2: 1.28233, 1:-0.59139
8 | | 4:-1.30232, 3:-1.28039, 2: 0.98102, 1:-0.56122
9 | | 4:-2.68314, 3:-1.91303, 2: 0.29503, 1: 0.31960
10 | | 9:-0.04861, 8: 1.70967, 7: 2.11041, 6:-2.07435, 5:-1.40363
11 | | 9: 3.21350, 8:-1.79113, 7:-3.14841, 6: 1.39807, 5:-3.79696
12 | | 9:-3.44870, 8:-2.02859, 7:-2.08369, 6: 1.32558, 5: 3.11359
-------|------|---------------------------------------------------------------------------------------------------------------------
> extractNetInfo(model)
$infoHeader
name value
1 no. of units 12
2 no. of connections 35
3 no. of unit types 0
4 no. of site types 0
5 learning function BackpropBatch
6 update function Topological_Order
$unitDefinitions
unitNo unitName unitAct unitBias type posX posY posZ actFunc outFunc sites
1 1 Input_1 0.817876339 0.1205858 UNIT_INPUT 1 0 0 Act_Identity Out_Identity
2 2 Input_2 -0.090580426 0.1858058 UNIT_INPUT 2 0 0 Act_Identity Out_Identity
3 3 Input_3 0.823582947 -0.2467227 UNIT_INPUT 3 0 0 Act_Identity Out_Identity
4 4 Input_4 1.077348471 -0.2271125 UNIT_INPUT 4 0 0 Act_Identity Out_Identity
5 5 Hidden_2_1 0.827119827 -3.0416200 UNIT_HIDDEN 1 2 0 Act_Logistic Out_Identity
6 6 Hidden_2_2 0.979820251 0.8963667 UNIT_HIDDEN 2 2 0 Act_Logistic Out_Identity
7 7 Hidden_2_3 0.004135765 -1.5327449 UNIT_HIDDEN 3 2 0 Act_Logistic Out_Identity
8 8 Hidden_2_4 0.018411839 -0.9707544 UNIT_HIDDEN 4 2 0 Act_Logistic Out_Identity
9 9 Hidden_2_5 0.171753630 2.6582935 UNIT_HIDDEN 5 2 0 Act_Logistic Out_Identity
10 10 Output_1 0.013317095 -1.1436981 UNIT_OUTPUT 1 4 0 Act_Logistic Out_Identity
11 11 Output_2 0.129099220 -0.6441937 UNIT_OUTPUT 2 4 0 Act_Logistic Out_Identity
12 12 Output_3 0.881433606 -1.2297773 UNIT_OUTPUT 3 4 0 Act_Logistic Out_Identity
$fullWeightMatrix
Input_1 Input_2 Input_3 Input_4 Hidden_2_1 Hidden_2_2 Hidden_2_3 Hidden_2_4 Hidden_2_5 Output_1 Output_2 Output_3
Input_1 0 0 0 0 -0.3508658 0.2684588 -0.5913861 -0.5612198 0.3195967 0.00000000 0.000000 0.000000
Input_2 0 0 0 0 -0.5082296 -0.9437067 1.2823328 0.9810203 0.2950290 0.00000000 0.000000 0.000000
Input_3 0 0 0 0 2.2801065 1.2012331 -1.8615422 -1.2803886 -1.9130263 0.00000000 0.000000 0.000000
Input_4 0 0 0 0 2.7568083 1.5704848 -1.6876845 -1.3023156 -2.6831374 0.00000000 0.000000 0.000000
Hidden_2_1 0 0 0 0 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 -1.40362704 -3.796962 3.113594
Hidden_2_2 0 0 0 0 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 -2.07435131 1.398071 1.325584
Hidden_2_3 0 0 0 0 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 2.11041474 -3.148410 -2.083688
Hidden_2_4 0 0 0 0 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 1.70966542 -1.791131 -2.028595
Hidden_2_5 0 0 0 0 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 -0.04861032 3.213499 -3.448702
Output_1 0 0 0 0 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.00000000 0.000000 0.000000
Output_2 0 0 0 0 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.00000000 0.000000 0.000000
Output_3 0 0 0 0 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.00000000 0.000000 0.000000
library(RSNNS)
data(snnsData)
inputs <- snnsData$laser_1000.pat[,inputColumns(snnsData$laser_1000.pat)]
outputs <- snnsData$laser_1000.pat[,outputColumns(snnsData$laser_1000.pat)]
patterns <- splitForTrainingAndTest(inputs, outputs, ratio=0.15)
model <- elman(patterns$inputsTrain, patterns$targetsTrain, size=c(8,8), learnFuncParams=c(0.1), maxit=500,
inputsTest=patterns$inputsTest, targetsTest=patterns$targetsTest, linOut=FALSE)
modelJordan <- jordan(patterns$inputsTrain, patterns$targetsTrain, size=c(8), learnFuncParams=c(0.1), maxit=100,
inputsTest=patterns$inputsTest, targetsTest=patterns$targetsTest, linOut=FALSE)
modelMlp <- mlp(patterns$inputsTrain, patterns$targetsTrain, initFuncParams=c(-0.3,0.3),size=c(8),
learnFuncParams=c(0.05), maxit=500, inputsTest=patterns$inputsTest, targetsTest=patterns$targetsTest, linOut=TRUE)
names(model)
par(mfrow=c(3,3))
plotIterativeError(model)
plotIterativeError(modelJordan)
#plotIterativeError(modelMlp)
plotRegressionError(patterns$targetsTrain, model$fitted.values, main="Regression Plot Fit")
plotRegressionError(patterns$targetsTest, model$fittedTestValues, main="Regression Plot Test")
hist(model$fitted.values - patterns$targetsTrain, col="lightblue", main="Error Histogram Fit")
#model$IterativeFitError[length(model$IterativeFitError)]
plot(inputs, type="l")
plot(inputs[1:100], type="l")
lines(outputs[1:100], col="red")
lines(model$fitted.values[1:100], col="green")
library(RSNNS)
#data(snnsData)
#inputs <- snnsData$som_cube.pat
data(iris)
inputs <- normalizeData(iris[,1:4], "norm")
model <- som(inputs, mapX=16, mapY=16, maxit=500, calculateActMaps=TRUE, targets=iris[,5])
par(mfrow=c(3,3))
for(i in 1:ncol(inputs)) plotActMap(model$componentMaps[[i]], col=rev(topo.colors(12)))
plotActMap(model$map, col=rev(heat.colors(12)))
plotActMap(log(model$map+1), col=rev(heat.colors(12)))
persp(1:model$archParams$mapX, 1:model$archParams$mapY, log(model$map+1), theta = 30, phi = 30, expand = 0.5, col = "lightblue")
plotActMap(model$labeledMap) # 各セルがどのラベルを持つか
model$componentMaps # 各ファクター(irisなら4ファクター)がマップ全体(各セル)にどのように分布しているかを示す
model$labeledUnits # 各セルにどのクラスが存在しているか.model$mapの内訳
model$map # 各セルに存在するデータの個数
names(model)
dataset <- snnsData$dlvq_ziff_100.pat
patterns <- dataset[,inputColumns(dataset)][c(1,2,3,4,5,6,8,10,11,12),]
model <- assoz(patterns, dimX=16, dimY=16)
#model$fitted.values
par(mfrow=c(3,3))
for (i in 1:9) plotActMap(matrixToActMapList(patterns,16)[[i]])
actMaps <- matrixToActMapList(model$fitted.values, nrow=16)
for (i in 1:9) plotActMap(actMaps[[i]])
pred <- predict(model, dataset[,inputColumns(dataset)])
for (i in 1:length(pred)) plotActMap(pred[[i]])
## Not run: demo(assoz_letters)
## Not run: demo(assoz_lettersSnnsR)
data(snnsData)
patterns <- snnsData$art1_letters.pat
model <- assoz(patterns, dimX=7, dimY=5)
actMaps <- matrixToActMapList(model$fitted.values, nrow=7)
par(mfrow=c(3,3))
for (i in 1:9) plotActMap(actMaps[[i]])
demo(assoz_letters)
library(RSNNS)
data(snnsData)
patterns <- snnsData$art1_letters.pat
model <- assoz(patterns, dimX=7, dimY=5)
#model$fitted.values
par(mfrow=c(3,3))
for (i in 1:9) plotActMap(model$fitted.values[[i]])
cbind(patterns[1,], as.vector(t(model$fitted[[1]])), predict(model, patterns)[1,])
testData <-
rbind(
as.vector(t(rbind(c(0,0,0,0,1), c(0,0,0,1,0), c(0,0,0,1,0), c(0,0,1,0,0), c(0,1,0,0,0), c(0,1,0,0,0), c(1,0,0,0,0)))),
as.vector(t(rbind(c(1,0,0,0,0), c(1,0,0,0,0), c(0,1,0,0,0), c(0,0,1,0,0), c(0,0,0,1,0), c(0,0,0,0,1), c(0,0,0,0,1))))
)
model <- assoz(patterns[1:5,], dimX=7, dimY=5, maxit=1000)
testData <- rbind(patterns[1,], patterns[1,])
testData[2,1] <- 1
testData[2,2] <- 0
cbind(patterns[1,], as.vector(t(model$fitted[[1]])), testData[2,], predict(model, testData)[2,])
par(mfrow=c(2,2))
pred <- predict(model, testData)
plotActMap(matrixToActMapList(patterns,7)[[1]])
plotActMap(model$fitted[[1]])
plotActMap(matrixToActMapList(testData,7)[[2]])
plotActMap(matrixToActMapList(pred,7)[[2]])
pred <- predict(model, pred)
plotActMap(matrixToActMapList(testData,7)[[1]])
plotActMap(matrixToActMapList(pred,7)[[1]])
plotActMap(matrixToActMapList(testData,7)[[2]])
plotActMap(matrixToActMapList(pred,7)[[2]])
R備忘録 /状態空間モデリング/donlp2/その他のメモ