/// <summary>
/// Creates a prototype network, with the given number of inputs and
/// outputs. The network starts out maximily connected with axons between
/// each pair of input and output nurons. There are no hidden nurons.
/// The weight of each axon is initialy set to one.
/// </summary>
/// <param name="inputs">Number of input nurons</param>
/// <param name="outputs">Number of output nurons</param>
public NetworkAuto(int inputs, int outputs)
{
////initialises the tables for the nurons and axons
//nurons = new TableOpen<Int32, Nuron>(256);
//axons = new TableOpen<Int32, Axon>(1024);
//initialises the tables for the nurons and axons
nurons = new TableSystem <Int32, Nuron>(256);
axons = new TableSystem <Int32, Axon>(1024);
//creates arrays to remember the inputs and outputs
this.inputs = new int[inputs];
this.outputs = new int[outputs];
//creates all the input nurons
for (int i = 0; i < inputs; i++)
{
Nuron n = new Nuron(this, ActFunc.Input, i, 0);
nurons.Add(n.Index, n);
this.inputs[i] = n.Index; //i
}
//creates all the output nurons
for (int i = 0; i < outputs; i++)
{
Nuron n = new Nuron(this, ActFunc.Sigmoid, MAX_ID - i, MAX_LV);
nurons.Add(n.Index, n);
this.outputs[i] = n.Index; //MAX_ID - i
}
//adds conecitons between all the nodes
Initialise();
}
/// <summary>
/// Helper mehtod: Selects a pair of nurons at random from diffrent
/// levels of the network. The nurons are always listed in order. It
/// returns false if it was ubale to generate a valid pair.
/// </summary>
/// <param name="rng">A random number generator</param>
/// <param name="n1">The lower level nuron</param>
/// <param name="n2">The upper level nuron</param>
/// <returns>True if a vaild pair was generated</returns>
private bool GetRandomPair(VRandom rng, out Nuron n1, out Nuron n2)
{
//generates two random nurons
n1 = GetRandomNuron(rng);
n2 = GetRandomNuron(rng);
int count = 0;
Nuron n3 = null;
//keeps searching while the nurons are on the same level
while (n1.Level == n2.Level && count < MAX_TRY)
{
n3 = GetRandomNuron(rng);
n1 = n2;
n2 = n3;
count++;
}
//swaps the nurons if they are out of order
if (n1.Level > n2.Level)
{
n3 = n1;
n1 = n2;
n2 = n3;
}
//indicates if we found a valid pair
return(n1.Level != n2.Level);
}
public void Overwrite(NetworkAuto genome)
{
//NOTE: Need to assert that the inputs and outputs match
this.axons.Clear();
this.nurons.Clear();
//used in itterating all the axons and nurons
var g_axons = genome.axons.ListItems();
var g_nurons = genome.nurons.ListItems();
foreach (Nuron n1 in g_nurons)
{
//nurons.Overwrite(n1.Index, n1);
Nuron temp = new Nuron(this, n1);
nurons.Add(temp.Index, temp);
}
foreach (Axon a1 in g_axons)
{
//axons.Overwrite(a1.Index, a1);
Axon temp = new Axon(a1);
axons.Add(temp.Index, temp);
}
}
/// <summary>
/// Removes unused Nurons and Axons from the network, reducing the overall
/// size of the network without effecting it's functionality.
/// </summary>
public void ReduceNetwork()
{
var ittr1 = axons.ListItems();
var trash1 = new Stack <Axon>();
foreach (Axon ax in ittr1)
{
if (ax.Enabled)
{
continue;
}
else
{
trash1.Push(ax);
}
}
while (trash1.Count > 0)
{
Axon ax = trash1.Pop();
axons.Remove(ax.Index);
}
var ittr2 = nurons.ListItems();
var trash2 = new Stack <Nuron>();
foreach (Nuron n in ittr2)
{
var ittr3 = axons.ListItems();
bool clear = false;
foreach (Axon ax in ittr3)
{
if (ax.Source == n.Index || ax.Target == n.Index)
{
clear = true;
break;
}
}
if (clear)
{
continue;
}
else
{
trash2.Push(n);
}
}
while (trash2.Count > 0)
{
Nuron n = trash2.Pop();
nurons.Remove(n.Index);
}
}
public void SetInput(Vector data)
{
//fills each of the input nurons with the vector data
for (int i = 0; i < inputs.Length; i++)
{
Nuron n = nurons.GetValue(inputs[i]);
n.Value = data.GetExtended(i);
n.Prev = n.Value;
}
}
public Vector ReadOutput()
{
//creates a new vector to hold the result
Vector result = new Vector(outputs.Length);
//copies the output nurons into the result vector
for (int i = 0; i < outputs.Length; i++)
{
Nuron n = nurons.GetValue(outputs[i]);
result[i] = n.Value;
}
//returns the result
return(result);
}
public NetworkAuto(NetworkAuto other)
{
//copies the global settings
this.recurent = other.recurent;
//obtains the raw statistics of the opposing network
int ninputs = other.inputs.Length;
int noutputs = other.outputs.Length;
int nnurons = other.nurons.Count;
int naxons = other.axons.Count;
//makes tables large enough to hold the nurons and axons
nurons = new TableSystem <Int32, Nuron>(nnurons);
axons = new TableSystem <Int32, Axon>(naxons);
//dose the same for the inputs and outputs
inputs = new int[ninputs];
outputs = new int[noutputs];
//makes copies of all the nurons in the original network
var ittr1 = other.nurons.ListItems();
foreach (Nuron n in ittr1)
{
Nuron temp = new Nuron(this, n);
nurons.Add(temp.Index, temp);
}
//makes copies of all the axons in the original network
var ittr2 = other.axons.ListItems();
foreach (Axon ax in ittr2)
{
Axon temp = new Axon(ax);
axons.Add(temp.Index, temp);
}
//copies the input and output indicies
for (int i = 0; i < inputs.Length; i++)
{
this.inputs[i] = other.inputs[i];
}
for (int i = 0; i < outputs.Length; i++)
{
this.outputs[i] = other.outputs[i];
}
}
public void WriteOutputTo(Vector result)
{
//enshures that the result vector can hold the output
if (result.Length != outputs.Length)
{
throw new ArgumentException
("Reslut vector dose not match network topology.", "result");
}
//copies the output nurons into the result vector
for (int i = 0; i < outputs.Length; i++)
{
Nuron n = nurons.GetValue(outputs[i]);
result[i] = n.Value;
}
}
public void Propergate()
{
var ittr0 = nurons.ListItems();
foreach (Nuron nx in ittr0)
{
//Skips input nurons
if (nx.IsInput)
{
continue;
}
//clears the value and updates the previous value
nx.Prev = nx.Value;
nx.Value = 0.0;
}
var ittr1 = axons.ListItems();
foreach (Axon ax in ittr1)
{
//skips disabled nurons
if (!ax.Enabled)
{
continue;
}
//obtains both nurons from the axon
Nuron n1 = nurons.GetValue(ax.Source);
Nuron n2 = nurons.GetValue(ax.Target);
//updates the target based on the input
n2.Value += (n1.Prev * ax.Weight);
}
var ittr2 = nurons.ListItems();
foreach (Nuron nx in ittr2)
{
//activates each of the nurons
nx.Activate();
}
}
private void Expand(VRandom rng)
{
Nuron n1, n2;
//generates a pair of random nurons on diffrent levels
bool pass = GetRandomPair(rng, out n1, out n2);
if (!pass)
{
return;
}
//determins if this should be a recurent conneciton
bool testrec = recurent;
testrec = testrec && !n1.IsInput;
testrec = testrec && rng.RandBool(P_Recur);
if (testrec)
{
Nuron nx = n1;
n1 = n2;
n2 = nx;
}
//creates a temporary axon between the two nurons
double w = rng.RandGauss() * SD_NEW;
Axon temp = new Axon(n1.Index, n2.Index, w);
//atempts to gain the actual axon if it exists
Axon actual = axons.GetValue(temp.Index);
if (actual == null)
{
//adds the new axon into the network
axons.Add(temp.Index, temp);
}
else if (actual.Enabled == false)
{
//reneables the axon and updates it weight
actual.Enabled = true;
actual.Weight = temp.Weight;
}
else
{
//determins if we are able to insert a new node
int nid1 = GetRandomIndex(rng);
if (nid1 < 0)
{
return;
}
//sets the level of the new node in the middle
if (Math.Abs(n1.Level - n2.Level) < 3)
{
return;
}
int level = (n1.Level + n2.Level) / 2;
//adds a new node to the network
ActFunc act = GetRandomActivation(rng);
Nuron n3 = new Nuron(this, act, nid1, level);
nurons.Add(n3.Index, n3);
//inserts two new axons where the old one used to be
int nid0 = actual.Source;
int nid2 = actual.Target;
//Axon ax1 = new Axon(nid0, nid1, actual.Weight);
//Axon ax2 = new Axon(nid1, nid2, temp.Weight);
Axon ax1 = new Axon(nid0, nid1, actual.Weight);
Axon ax2 = new Axon(nid1, nid2, 1.0);
//we use the overwitre comand because there may be hash collisions
axons.Overwrite(ax1.Index, ax1);
axons.Overwrite(ax2.Index, ax2);
//deactivates the old axon
actual.Enabled = false;
}
}
public void Crossover(VRandom rng, NetworkAuto genome)
{
//throw new NotImplementedException();
//determins weather or not to do liniar crossover
bool liniar = rng.RandBool(P_Linear);
double a = rng.NextDouble();
//lists all the axons and nurons in the mate
var ittr1 = genome.nurons.ListItems();
var ittr2 = genome.axons.ListItems();
foreach (Nuron n in ittr1)
{
//obtains the matching child nuron
Nuron nc = nurons.GetValue(n.Index);
if (nc == null)
{
//adds the missing nuron
nc = new Nuron(this, n);
nurons.Add(nc.Index, nc);
}
//else
//{
// //determins the activation based on crossover
// bool cross = rng.RandBool();
// if (cross) nc.Func = n.Func;
//}
}
foreach (Axon ax in ittr2)
{
//obtains the matching child axon
Axon axc = axons.GetValue(ax.Index);
if (axc == null)
{
//adds the missing axon, but disabled
axc = new Axon(ax);
axc.Enabled = false;
axons.Add(axc.Index, axc);
}
else
{
if (liniar)
{
//chooses a value between the two weights
double weight = axc.Weight * (1.0 - a);
axc.Weight = weight + (ax.Weight * a);
}
else
{
//determins the new weight based on crossover
bool cross = rng.RandBool();
if (cross)
{
axc.Weight = ax.Weight;
}
}
//if the axon is present in both networks, it has a
//strong chance of becoming enabled
bool en = ax.Enabled && axc.Enabled;
axc.Enabled = en || rng.RandBool(0.25);
}
}
}
public void Overwrite2(NetworkAuto genome)
{
//used in itterating all the axons and nurons
var g_axons = genome.axons.ListItems();
var g_nurons = genome.nurons.ListItems();
foreach (Nuron n1 in g_nurons)
{
//tries to find the matching nuron in the current network
Nuron n0 = nurons.GetValue(n1.Index);
if (n0 != null)
{
////copies the genome data if it finds a match
//n0.Func = n1.Func;
}
else
{
//creates a copy of the first nurons and adds it
n0 = new Nuron(this, n1);
nurons.Add(n0.Index, n0);
}
}
foreach (Axon a1 in g_axons)
{
//tries to find the matching axon in the current network
Axon a0 = axons.GetValue(a1.Index);
if (a0 != null)
{
//copies the genome data if it finds a match
a0.Weight = a1.Weight;
a0.Enabled = a1.Enabled;
}
else
{
//creates a copy of the first axon and adds it
a0 = new Axon(a1);
axons.Add(a0.Index, a0);
}
}
////////////////////////////////////////////////////////////////////
//used to clean up the remaining Axons
int count1 = genome.axons.Count - axons.Count;
count1 = Math.Min(count1, 0) + 8;
var trash1 = new DequeArray <Axon>(count1);
var ittr1 = axons.ListItems();
//marks missing Axons for deletion
foreach (Axon a in ittr1)
{
if (genome.axons.HasKey(a.Index))
{
continue;
}
else
{
trash1.PushFront(a);
}
}
//deletes the missing Axons
while (!trash1.Empty)
{
Axon del = trash1.PopFront();
axons.Remove(del.Index);
}
//used to clean up the remaining Nurons
int count2 = genome.nurons.Count - nurons.Count;
count2 = Math.Min(count2, 0) + 8;
var trash2 = new DequeArray <Nuron>(count2);
var ittr2 = nurons.ListItems();
//marks missing Nurons for deletion
foreach (Nuron n in ittr2)
{
if (genome.nurons.HasKey(n.Index))
{
continue;
}
else
{
trash2.PushFront(n);
}
}
//deletes the missing Nurons
while (!trash2.Empty)
{
Nuron del = trash2.PopFront();
nurons.Remove(del.Index);
del.ClearData();
}
trash1.Dispose();
trash2.Dispose();
}
