R/feature_misc_dispersion.R In flacco: Feature-Based Landscape Analysis of Continuous and Constrained Optimization Problems

Defines functions calculateDispersionFeatures

```calculateDispersionFeatures = function(feat.object, control) {
assertClass(feat.object, "FeatureObject")
if (missing(control))
control = list()
assertList(control)
measureTime(expression({
X = extractFeatures(feat.object)
y = extractObjective(feat.object)
if (!feat.object\$minimize)
y = -1 * y
quantiles = control_parameter(control, "disp.quantiles",
c(0.02, 0.05, 0.1, 0.25))
meth = control_parameter(control, "disp.dist_method", "euclidean")
index = lapply(quantile(y, quantiles),
function(quant) which(y <= quant))
if (meth != "minkowski") {
dists = lapply(seq_along(index),
function(i) as.numeric(dist(X[index[[i]], ], method = meth)))
dists.full_sample = as.numeric(dist(X, method = meth))
} else {
mink = control_parameter(control, "disp.minkowski_p", 2)
dists = lapply(seq_along(index),
function(i) as.numeric(dist(X[index[[i]], ], method = meth, p = mink)))
dists.full_sample = as.numeric(dist(X, method = meth, p = mink))
}
means = vapply(dists, mean, double(1))
medians = vapply(dists, median, double(1))
res = c(means / mean(dists.full_sample), medians / median(dists.full_sample),
means - mean(dists.full_sample), medians - median(dists.full_sample))
res = as.vector(res, mode = "list")
names(res) = c(sprintf("disp.ratio_mean_%02i", quantiles * 100),
sprintf("disp.ratio_median_%02i", quantiles * 100),
sprintf("disp.diff_mean_%02i", quantiles * 100),
sprintf("disp.diff_median_%02i", quantiles * 100))
return(res)
}), "disp")
}
```

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flacco documentation built on June 20, 2017, 9:06 a.m.