plotQuantErrorHistogram: Make the quantization error plots for training and scoring.

Description

This is the function that produces histograms displaying the distribution of Quantization Error (QE) values for both train and test datasets, highlighting mean values with dashed lines for quick evaluation.

Usage

plotQuantErrorHistogram(hvt.results, hvt.scoring)

Value

Returns the ggplot object containing the quantization error distribution plots for the given HVT results of training and scoring

Arguments

hvt.results: List. A list of hvt.results obtained from the trainHVT function.
hvt.scoring: List. A list of hvt.scoring obtained from the scoreHVT function.

Author

Shubhra Prakash <shubhra.prakash@mu-sigma.com>

Examples

Run this code

data("EuStockMarkets")
dataset <- data.frame(date = as.numeric(time(EuStockMarkets)),
                     DAX = EuStockMarkets[, "DAX"],
                     SMI = EuStockMarkets[, "SMI"],
                     CAC = EuStockMarkets[, "CAC"],
                     FTSE = EuStockMarkets[, "FTSE"])
rownames(EuStockMarkets) <- dataset$date
#Split in train and test
train <- EuStockMarkets[1:1302, ]
test <- EuStockMarkets[1303:1860, ]

hvt.results<- trainHVT(train,n_cells = 60, depth = 1, quant.err = 0.1,
                      distance_metric = "L1_Norm", error_metric = "max",
                      normalize = TRUE, quant_method = "kmeans")
scoring <- scoreHVT(test, hvt.results)
plotQuantErrorHistogram(hvt.results, scoring)