xval-noise-models: Test the models

xval-noise-models-p3.r

# Get the weighted errors `w.mean` and `w.var`
test.error.weighted.var <- function(sel.channel, coeffs) {
  tests.pics <- read.csv('test-pics.csv', stringsAsFactors = FALSE, row.names=NULL)
  tests.pics <- as.vector(tests.pics[,1])
  data.df <- subset(data.frame(vvm.all$var.df), 
                 channel == sel.channel & 
                 pict %in% tests.pics)

  predicted.var <- (data.df$mean*coeffs[3] + coeffs[2])*data.df$mean + coeffs[1]
  error.var <- mean((data.df$var - predicted.var)^2/predicted.var)
  error.mean <- mean((data.df$var - predicted.var)^2/data.df$mean^2)
  list('w.var' = sqrt(error.var), 'w.mean'=sqrt(error.mean))
}
# Get the `SNR` error
test.error.snr <- function(sel.channel, coeffs) {
  tests.pics <- read.csv('test-pics.csv', stringsAsFactors = FALSE, row.names=NULL)
  tests.pics <- as.vector(tests.pics[,1])
  data.df <- subset(data.frame(vvm.all$var.df), 
                 channel == sel.channel & 
                 pict %in% tests.pics)

  predicted.var <- (data.df$mean*coeffs[3] + coeffs[2])*data.df$mean + coeffs[1]
  pred.snr <- snr(data.df$mean, predicted.var)
  real.snr <- snr(data.df$mean, data.df$var)
  error <- mean((real.snr - pred.snr)^2)
  # Result
  sqrt(error);
}