/// <summary>
///
/// </summary>
/// <param name="i"></param>
/// <param name="j"></param>
/// <param name="current"></param>
/// <returns></returns>
public int NextAt(Index2 index, int[,] current)
{
int sum = GetNeighborSum(index, current);
int state = current[index.I, index.J];
if (state == 0)
{
return((sum == 3) ? 1 : 0);
}
else
{
return((sum < 2 || sum > 3) ? 0 : 1);
}
}
/// <summary>
///
/// </summary>
private float GetNeighborDensity(Cell[,] cells, Index2 index, Index2[] neighborhood)
{
int nrows = cells.GetLength(0);
int ncols = cells.GetLength(1);
int sum = 0;
foreach (Index2 offset in neighborhood)
{
int i1 = Wrap(index.I + offset.I, nrows);
int j1 = Wrap(index.J + offset.J, ncols);
if (cells[i1, j1].State > 0)
{
sum++;
}
}
return((float)sum / neighborhood.Length);
}
/// <summary>
///
/// </summary>
/// <param name="i0"></param>
/// <param name="j0"></param>
/// <returns></returns>
private int GetNeighborSum(Index2 index, int[,] current)
{
int nrows = current.GetLength(0);
int ncols = current.GetLength(1);
int sum = 0;
foreach (Index2 offset in _offsets)
{
int i1 = Wrap(index.I + offset.I, nrows);
int j1 = Wrap(index.J + offset.J, ncols);
if (current[i1, j1] > 0)
{
sum++;
}
}
return(sum);
}
/// <summary>
///
/// </summary>
/// <param name="i"></param>
/// <param name="j"></param>
/// <param name="current"></param>
/// <returns></returns>
public int NextAt(Index2 index, int[,] current)
{
//get current state
int state = current[index.I, index.J];
//get local neighborhood data
int sumMO = GetNeighborSum(index, current, Neighborhoods.MooreR1);
int sumVNPair = GetNeighborSum(index, current, Neighborhoods.VonNeumannPair1);
//choose an instruction set
GOLInstructionSet instructionSet = _instSetMO1;
// collect relevant analysis results
CellLayer[] layers = _model.Stack.Layers;
int currentLayer = _model.CurrentLayer;
float prevLayerDensity;
int prevCellAge;
// get attributes of corresponding cell on the previous layer (if it exists)
if (currentLayer > 0)
{
var prevLayer = layers[currentLayer - 1];
prevLayerDensity = prevLayer.Density;
prevCellAge = prevLayer.Cells[index.I, index.J].Age;
}
else
{
prevLayerDensity = 1.0f;
prevCellAge = 0;
}
/*
* if (currentlayerdensity < .17)
* {
* instructionSet = _instSetMO3;
* }
*
* if (currentlayerdensity >= .17 && currentlayerdensity<.2)
* {
* instructionSet = _instSetMO1;
* }
*
* if (currentlayerdensity >.2)
* {
* instructionSet = _instSetMO2;
* }
*/
/*
* if(state==0 && sumVNPair == 2)
* {
* return 1;
* }
*
* if (state == 1 && sumVNPair == 2)
* {
* return 0;
* }
*/
/*
* if(currentlevel <= 40)
* {
* instructionSet = _instSetMO1;
* }
*
* if (currentlevel > 40 && currentlevel<65)
* {
* instructionSet = _instSetMO2;
* }
*
* if (currentlevel >= 65)
* {
* instructionSet = _instSetMO3;
* }
*/
int output = 0;
//if current state is "alive"
if (state == 1)
{
if (sumMO < instructionSet.getInstruction(0))
{
output = 0;
}
if (sumMO >= instructionSet.getInstruction(0) && sumMO <= instructionSet.getInstruction(1))
{
output = 1;
}
if (sumMO > instructionSet.getInstruction(1))
{
output = 0;
}
}
//if current state is "dead"
if (state == 0)
{
if (sumMO >= instructionSet.getInstruction(2) && sumMO <= instructionSet.getInstruction(3))
{
output = 1;
}
else
{
output = 0;
}
}
return(output);
}
/// <summary>
///
/// </summary>
/// <param name="i"></param>
/// <param name="j"></param>
/// <param name="current"></param>
/// <returns></returns>
public int NextAt(Index2 index, int[,] current)
{
//get current state
int state = current[index.I, index.J];
//get local neighborhood data
int sumMO = GetNeighborSum(index, current, Neighborhoods.MooreR1);
int sumVNPair = GetNeighborSum(index, current, Neighborhoods.VonNeumannPair1);
//choose an instruction set
GOLInstructionSet instructionSet = _instSetIncrease1;
// collect relevant analysis results
CellLayer[] layers = _model.Stack.Layers;
int currentLayer = _model.CurrentLayer;
float prevLayerDensity;
int prevCellAge;
// get attributes of corresponding cell on the previous layer (if it exists)
if (currentLayer > 0)
{
var prevLayer = layers[currentLayer - 1];
prevLayerDensity = prevLayer.Density;
prevCellAge = prevLayer.Cells[index.I, index.J].Age;
}
else
{
prevLayerDensity = 1.0f;
prevCellAge = 0;
}
//new rule
if (currentLayer < 10)
{
instructionSet = _instSetIncrease1;
}
else
{
if (prevLayerDensity < 0.1 && prevLayerDensity >= 0.05)
{
instructionSet = _instSetIncrease3;
}
else if (prevLayerDensity >= 0.2)
{
instructionSet = _instSetDecrease1;
}
else
{
instructionSet = _instSetStripeImage;
}
}
int output = 0;
//if current state is "alive"
if (state == 1)
{
if (sumMO < instructionSet.getInstruction(0))
{
output = 0;
}
if (sumMO >= instructionSet.getInstruction(0) && sumMO <= instructionSet.getInstruction(1))
{
output = 1;
}
if (sumMO > instructionSet.getInstruction(1))
{
output = 0;
}
}
//if current state is "dead"
if (state == 0)
{
if (sumMO >= instructionSet.getInstruction(2) && sumMO <= instructionSet.getInstruction(3))
{
output = 1;
}
else
{
output = 0;
}
}
return(output);
}
/// <summary>
///
/// </summary>
/// <param name="i"></param>
/// <param name="j"></param>
/// <param name="current"></param>
/// <returns></returns>
public int NextAt(Index2 index, int[,] current)
{
//get current state
int state = current[index.I, index.J];
//get local neighborhood data
int sumMO = GetNeighborSum(index, current, Neighborhoods.MooreR1);
int sumVNPair = GetNeighborSum(index, current, Neighborhoods.VonNeumannPair1);
//choose an instruction set
GOLInstructionSet instructionSet = _instSetStable1;
// collect relevant analysis results
CellLayer[] layers = _model.Stack.Layers;
int currentLayer = _model.CurrentLayer;
float prevLayerDensity;
int prevCellAge;
// get attributes of corresponding cell on the previous layer (if it exists)
if (currentLayer > 0)
{
var prevLayer = layers[currentLayer - 1];
prevLayerDensity = prevLayer.Density;
prevCellAge = prevLayer.Cells[index.I, index.J].Age;
}
else
{
prevLayerDensity = 1.0f;
prevCellAge = 0;
}
Dropdown myDropdown = GameObject.Find("Rule").GetComponent <Dropdown>();
if (myDropdown.value == 1)
{
instructionSet = _instSetStable2;
}
if (myDropdown.value == 2)
{
instructionSet = _instSetStable3;
}
if (myDropdown.value == 3)
{
instructionSet = _instSetStable4;
}
if (myDropdown.value == 4)
{
instructionSet = _instSetStable5;
}
//垂直生长&成柱子
if (myDropdown.value == 5)
{
instructionSet = _instSetVerticle;
}
if (myDropdown.value == 6)
{
instructionSet = _instSetColumn;
}
//增长生长
if (myDropdown.value == 7)
{
instructionSet = _instSetIncrease1;
}
if (myDropdown.value == 8)
{
instructionSet = _instSetIncrease2;
}
if (myDropdown.value == 9)
{
instructionSet = _instSetIncrease3;
}
if (myDropdown.value == 10)
{
instructionSet = _instSetIncrease4;
}
//减少生长
if (myDropdown.value == 11)
{
instructionSet = _instSetDecrease1;
}
if (myDropdown.value == 12)
{
instructionSet = _instSetDecrease2;
}
//死亡
if (myDropdown.value == 13)
{
instructionSet = _instSetDead;
}
//桌面
if (myDropdown.value == 14)
{
instructionSet = _instSetOriginalImage;
}
if (myDropdown.value == 15)
{
instructionSet = _instSetStripeImage;
}
//line_outside
if (myDropdown.value == 16)
{
instructionSet = _instSetIncrease4;//1234
if (currentLayer >= 20)
{
instructionSet = _instSetDecrease2;//2333
}
if (currentLayer >= 40)
{
instructionSet = _instSetStable3;//3322
}
if (prevLayerDensity < 0.08)
{
instructionSet = _instSetStable3;//3322
}
if (prevLayerDensity >= 0.08)
{
instructionSet = _instSetDecrease2;//2333
}
}
//自定义
if (myDropdown.value == 0)
{
instructionSet = _instSetDecrease2;
}
int output = 0;
//if current state is "alive"
if (state == 1)
{
if (sumMO < instructionSet.getInstruction(0))
{
output = 0;
}
if (sumMO >= instructionSet.getInstruction(0) && sumMO <= instructionSet.getInstruction(1))
{
output = 1;
}
if (sumMO > instructionSet.getInstruction(1))
{
output = 0;
}
}
//if current state is "dead"
if (state == 0)
{
if (sumMO >= instructionSet.getInstruction(2) && sumMO <= instructionSet.getInstruction(3))
{
output = 1;
}
else
{
output = 0;
}
}
return(output);
}
