Create an empty (square) grid equipped with topology.

initGrid(
dimension = c(5, 5),
topo = c("square", "hexagonal"),
dist.type = c("euclidean", "maximum", "manhattan", "canberra", "minkowski",
"letremy")
)

## Arguments

dimension |
a 2-dimensional vector giving the dimensions (width, length)
of the grid |

topo |
topology of the grid. Accept values `"square"` (Default) or
`"hexagonal"` . |

dist.type |
distance type that defines the topology of the grid (see
'Details'). Default to `"euclidean"` |

## Value

an object of class `myGrid`

with the following entries:

`coord`

2-column matrix with x and y coordinates of the grid
units

`topo`

topology of the grid;

`dim`

dimensions of the grid (width corresponds to x
coordinates)

`dist.type`

distance type that defines the topology of the
grid.

## Details

The units (neurons) of the grid are positionned at coordinates
(1,1), (1,2), (1,3), ..., (2,1), (2,2), ..., for the `square`

topology.
The topology of the map is defined by a distance based on those coordinates,
that can be one of `"euclidean"`

, `"maximum"`

, `"manhattan"`

,
`"canberra"`

, `"minkowski"`

, `"letremy"`

, where the first 5
ones correspond to distance methods implemented in `dist`

and
`"letremy"`

is the distance of the original implementation by Patrick
Letremy that switches between `"maximum"`

and `"euclidean"`

during
the training.

## References

Letrémy P. (2005) Programmes basés sur l'algorithme de Kohonen
et dédiés à l'analyse des données. SAS/IML programs for 'korresp'.
http://samm.univ-paris1.fr/Programmes-SAS-de-cartes-auto

## See also

## Examples

initGrid()

#>
#> Self-Organizing Map structure
#>
#> Features :
#> topology : square
#> x dimension : 5
#> y dimension : 5
#> distance type: euclidean
#>

#>
#> Self-Organizing Map structure
#>
#> Features :
#> topology : square
#> x dimension : 5
#> y dimension : 7
#> distance type: maximum
#>