# createInitialSample: Create Initial Sample In flacco: Feature-Based Landscape Analysis of Continuous and Constrained Optimization Problems

## Description

Convenient helper function, which creates an initial sample - either based on random (uniform) sampling or using latin hypercube sampling.

## Usage

 `1` ```createInitialSample(n.obs, dim, control) ```

## Arguments

 `n.obs` [`integer(1)`] Number of observations. `dim` [`integer(1)`] Number of dimensions. `control` [`list`] Control argument. For further information refer to the details.

## Details

Per default, this function will produce `n.obs` observations of size `dim` in the range from 0 to 1. If you want to create a more specific initial sample, the following control arguments might be helpful:

• `init_sample.type`: Should the initial sample be created based on random uniform sampling (`"random"`) or on a latin hypercube sample (`"lhs"`)? The default is `"random"`.

• `init_sample.lower`: The lower bounds of the initial sample. Either a vector of size `dim` or a scalar (if all lower bounds are identical). The default is `0`.

• `init_sample.upper`: The upper bounds of the initial sample. Either a vector of size `dim` or a scalar (if all upper bounds are identical). The default is `1`.

## Value

[`matrix`].
A matrix, consisting of `n.obs` rows of `dim`-dimensional observations.

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10``` ```# (1) create a simple initial sample: X = createInitialSample(300, 5) summary(X) # (2) create a more specific initial sample: ctrl = list(init_sample.type = "lhs", init_sample.lower = c(-5, 2, 0), init_sample.upper = 10) X = createInitialSample(200, 3, control = ctrl) summary(X) ```

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