Plot 47

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### Inquiries: email 2to32minus1@gmail.com

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### Globals

fontScale = 1.2 # Global font size variable

lf = 1.2 # Enlarge font of plot labels

verbose = FALSE # Verbose printing

lw = 2 # Line width

xMargin = 1 # X-axis margin for plots

yMargin = 1 # Y-axis margin for plots

de = 0.01 # Small constant for estimating derivatives numerically

smlPts = 1 # Small data point/circle

medPts = 1.5 # Medium data point/circle

lrgPts = 2.0 # Large data point/circle

### Run this function to generate this plot

runFunc = function() {

dw = 1000

dh = 750

path = "D:/Code/R/LeastSquaresPlay/PlotsAndAssocCode/Plot47/"

savePngVerbose( paste(path, "Plot47.png", sep=""), plotGaussErrorSurfaceStereoPair, w=1280, h=768, un="px" )

}

### Cross-eyes pair of Gaussian cost function surface

plotGaussErrorSurfaceStereoPair = function() {

set.seed( 3 )

gd = makeGaussianTestData( trueAmpl=5, trueSigma=2 )

a = gd$trueAmpl

s = gd$trueSigma

x = gd$x

y = gd$y

xSpan = 44

ySpan = 24

ctrAmpl = 0

ctrSigma = 0

minAmpl = ctrAmpl - xSpan/2

maxAmpl = ctrAmpl + xSpan/2

minSigma = ctrSigma - ySpan/2

maxSigma = ctrSigma + ySpan/2

aspect = xSpan/ySpan

nr = 20

nc = as.integer( nr *xSpan / ySpan )

ampls = seq( minAmpl, maxAmpl, length.out=nc )

sigmas = seq( minSigma, maxSigma, length.out=nr )

z = getGaussErrorGrid( meanGaussSsdErr, ampls, sigmas, x, y )

m = matrix( c( 1, 2 ), byrow=TRUE, nrow=1, ncol=2 )

layout( m, widths = c( 1, 1 ), heights = c(1 ) )

parSave = par( mar=c( 1, 4, 5, 4 ) )

xyAngle=-1.5

zAngle=20

plotTitle = sprintf( "1D Gaussian Least-Squares Cost Function\nSSD Fit Error vs. Amplitude & Sigma\nRight Eye", s, a )

persp( ampls, sigmas, t(z), xlab="Amplitude", ylab="", zlab="", main=plotTitle, cex.lab=1.3*fontScale,

theta=xyAngle+3, phi=zAngle, expand=0.5, shade=0.3, col="lightblue", ticktype="detailed", cex.main=1.3*fontScale, cex.axis=fontScale )

plotTitle = sprintf( "1D Gaussian Least-Squares Cost Function\nSSD Fit Error vs. Amplitude & Sigma\nLeft Eye", s, a )

persp( ampls, sigmas, t(z), xlab="Amplitude", ylab="", zlab="", main=plotTitle, cex.lab=1.3*fontScale,

theta=xyAngle, phi=zAngle, expand=0.5, shade=0.3, col="lightblue", ticktype="detailed", cex.main=1.3*fontScale, cex.axis=fontScale )

par( parSave )

}

### Shared use case Gaussian data set generator used by several cooperating functions

makeGaussianTestData = function( trueAmpl=5, trueSigma=2, aGuess=sqrt(trueAmpl), sGuess=sqrt(trueSigma), nPts=40 ) {

set.seed( 3 )

x = seq( -5*trueSigma, 5*trueSigma, length=nPts )

y = gauss1dFunc( x, trueAmpl, trueSigma ) + rnorm( length(x), sd=0.2 )

guesses = list( a=aGuess, s=sGuess )

tuple = list( trueAmpl=trueAmpl, trueSigma=trueSigma, x=x, y=y, nPts=nPts, guesses=guesses )

return( tuple )

}

### 1D Gaussian

gauss1dFunc = function( x, a, sigma ) {

return( a * exp( -0.5 * (x/sigma)^2 ) )

}

### Gaussian error/cost function - SSD

meanGaussSsdErr = function( x, y, a, sigma ) {

return( (1/length(x)) * sum( (gauss1dFunc( x, a, sigma ) - y)^2 ) )

}

### Compute mean SSD Gaussian cost function over a grid of Amplitude and Sigma values

### Note: returned matrix may need to be transposed for some functions such as contour()

### TODO: Try to use outer()

getGaussErrorGrid = function( errorFunc, ampls, sigmas, x, y ) {

nr = length( sigmas )

nc = length( ampls )

z = matrix( nrow=nr, ncol=nc )

for ( row in 1:nr ) { # loop over y-coordinates (sigma)

sigma = sigmas[row]

for ( col in 1:nc ) { # loop over x-coordinates (amplitude)

ampl = ampls[col]

z[row, col] = errorFunc( x, y, a=ampl, sigma=sigma )

}

return( z )

}

### Save to PNG file, specify width and height

savePngVerbose = function( path, plotFunc, w=512, h=512, un="px", doCopyright=TRUE, ... ) {

png( filename = path, type="cairo", units=un, width=w, height=h, pointsize=12, res=96 )

plotFunc( ... )

if ( doCopyright )

addCopyright()

dev.off()

}

### Add copyright notice to plot via text()

addCopyright = function() {

mtext( "Email: 2to32minus1@gmail.com", side=4, line=1, adj=0, cex=1.1 )

}

runFunc()