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

In HVT: Constructing Hierarchical Voronoi Tessellations and Overlay Heatmaps for Data Analysis

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)

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.

Value

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

Author(s)

Shubhra Prakash <[e-mail]>

See Also

plotHVT

Examples

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) 

HVT documentation built on April 3, 2025, 8:45 p.m.