# findNearestPrototype: Find Nearest Prototype In flacco: Feature-Based Landscape Analysis of Continuous and Constrained Optimization Problems

## Description

For each cell of the initial design, select the closest observation to its center and use it as a representative for that cell.

## Usage

 `1` ```findNearestPrototype(feat.object, dist_meth, mink_p, fast_k, ...) ```

## Arguments

 `feat.object` [`FeatureObject`] A feature object as created by `createFeatureObject`. `dist_meth` [`character(1)`] Which distance method should be used for computing the distance between two observations? All methods of `dist` are possible options with `"euclidean"` being the default. `mink_p` [`integer(1)`] Value of `p` in case `dist_meth` is `"minkowski"`. The default is `2`, i.e. the euclidean distance. `fast_k` [`numeric(1)`] Percentage of elements that should be considered within the nearest neighbour computation. The default is `0.05`. `...` [any] Further arguments, which might be used within the distance computation (`dist`).

## Value

[`data.frame`].
A `data.frame` containing one prototype (i.e. a representative observation) per cell. Each prototype consists of its values from the decision space, the corresponding objective value, its own cell ID and the cell ID of the cell, which it represents.

## Examples

 ```1 2 3 4 5 6 7 8``` ```# (1) create the initial sample and feature object: X = createInitialSample(n.obs = 1000, dim = 2, control = list(init_sample.lower = -10, init_sample.upper = 10)) feat.object = createFeatureObject(X = X, fun = function(x) sum(x^2), blocks = 10) # (2) find the nearest prototypes of all cells: findNearestPrototype(feat.object) ```

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