In a grid-based model, the next state of a grid point is often influenced by its neighbours. An array of functions is available in Cacatoo to get information on
neighbours in the Moore or Neumann neighboorhood (see table below). Each compass direction is also linked to a number (0 is self, 1 is North, etc) which can be used
to extract specific neighbours.
For example, to get the 'North-West' neighbour (5) directly within the nextState function, you can use:
To get multiple neighbours corresponding to a certain "type", store them in an array, you can use getNeighbours (plural!):
For example, to get the 'North' and 'South' neighbour (1 and 4), you can use:
If you want to use one of the default neighbourhoods, you can use one fo the following shortcuts:
Standardised functions are also available to count, sum, or sample from adjacent grid points, listed below.
The neighbour array can be any array which includes all neighbours ([0,1,2,3,4,5,6,7,8]) or only a few ([0,4,6]). A few frequently used neighbourhoods can be called like this:
The neighbour array can be any array which includes all neighbours ([0,1,2,3,4,5,6,7,8]) or only a few ([0,4,6]). A few frequently used neighbourhoods can be called like this:
The neighbour array can be any array which includes all neighbours ([0,1,2,3,4,5,6,7,8]) or only a few ([0,4,6]). A few frequently used neighbourhoods can be called like this:
The neighbour array can be any array which includes all neighbours ([0,1,2,3,4,5,6,7,8]) or only a few ([0,4,6]). A few frequently used neighbourhoods can be called like this:
Moore neighbourhood
| 5 | 1 | 6 |
| 2 | 0 | 3 |
| 7 | 4 | 8 |
Neumann neighbourhood
| 1 | ||
| 2 | 0 | 3 |
| 4 |
Directly retreiving neighbours
To get a single neighbour from one of these neighbourhood, you can use the following function:getNeighbour(model, col, row, direction)
For example, to get the 'North-West' neighbour (5) directly within the nextState function, you can use:
this.getNeighbour(this, i, j, 5)
To get multiple neighbours corresponding to a certain "type", store them in an array, you can use getNeighbours (plural!):
getNeighbours(model, col, row, property, value, neighbour_array)
For example, to get the 'North' and 'South' neighbour (1 and 4), you can use:
this.getNeighbours(this, i, j, [1,4])
If you want to use one of the default neighbourhoods, you can use one fo the following shortcuts:
getNeighbours8(model, col, row, property, value, neighbour_array) // Moore, self exclusive
getNeighbours9(model, col, row, property, value, neighbour_array) // Moore, self inclusive
getNeighbours4(model, col, row, property, value, neighbour_array) // Neumann, self exclusive
getNeighbours5(model, col, row, property, value, neighbour_array) // Neumann, self inclusive
Standardised functions are also available to count, sum, or sample from adjacent grid points, listed below.
Summing a property of all neighbours
sumNeighbours(model, col, row, property, neighbour_array)
The neighbour array can be any array which includes all neighbours ([0,1,2,3,4,5,6,7,8]) or only a few ([0,4,6]). A few frequently used neighbourhoods can be called like this:
sumMoore8(model, col, row, property, value)
sumNeighbours8(model, col, row, property, value) // same as above, different name
sumMoore9(model, col, row, property, value)
sumNeighbours9(model, col, row, property, value) // same as above, different name
sumNeumann4(model, col, row, property, value)
sumNeighbours4(model, col, row, property, value) // same as above, different name
sumNeumann5(model, col, row, property, value)
sumNeighbours5(model, col, row, property, value) // same as above, different name
Counting all neighbours with 'property' set to 'value'
countNeighbours(model, col, row, property, value, neighbour_array)
The neighbour array can be any array which includes all neighbours ([0,1,2,3,4,5,6,7,8]) or only a few ([0,4,6]). A few frequently used neighbourhoods can be called like this:
countMoore8(model, col, row, property)
countNeighbours8(model, col, row, property) // same as above, different name
countMoore9(model, col, row, property)
countNeighbours9(model, col, row, property) // same as above, different name
countNeumann4(model, col, row, property)
countNeighbours4(model, col, row, property) // same as above, different name
countNeumann5(model, col, row, property)
countNeighbours5(model, col, row, property) // same as above, different name
Getting a random neighbour
randomNeighbour(model, col, row, neighbour_array)
The neighbour array can be any array which includes all neighbours ([0,1,2,3,4,5,6,7,8]) or only a few ([0,4,6]). A few frequently used neighbourhoods can be called like this:
randomMoore8(model, col, row)
randomNeighbours8(model, col, row) // same as above, different name
randomMoore9(model, col, row)
randomNeighbours9(model, col, row) // same as above, different name
randomNeumann4(model, col, row)
randomNeighbours4(model, col, row) // same as above, different name
randomNeumann5(model, col, row)
randomNeighbours5(model, col, row) // same as above, different name
Roulette wheel selection
This is similar to random neighbour selection, but you can assign a grid point property to do weighted sampling:rouletteWheel(gridpoints, property)
For example, to sample an individual with 'fitness' as weight, you can do:this.getNeighbours(gridpoints, property)
rouletteWheel(gridpoints, property)
The neighbour array can be any array which includes all neighbours ([0,1,2,3,4,5,6,7,8]) or only a few ([0,4,6]). A few frequently used neighbourhoods can be called like this: