/// <summary>Defaults.</summary>
/// <param name="network">The network.</param>
/// <param name="d">The Descriptor to process.</param>
/// <param name="x">The Vector to process.</param>
/// <param name="y">The Vector to process.</param>
/// <param name="activationFunction">The activation.</param>
/// <param name="outputFunction">The ouput function for hidden nodes (Optional).</param>
/// <param name="epsilon">epsilon</param>
/// <returns>A Network.</returns>
public static Network Create(
this Network network,
Descriptor d,
Matrix x,
Vector y,
IFunction activationFunction,
IFunction outputFunction = null,
double epsilon = double.NaN)
{
// set output to number of choices of available
// 1 if only two choices
var distinct = y.Distinct().Count();
var output = distinct > 2 ? distinct : 1;
// identity funciton for bias nodes
IFunction ident = new Ident();
// set number of hidden units to (Input + Hidden) * 2/3 as basic best guess.
var hidden = (int)System.Math.Ceiling((x.Cols + output) * 2.0 / 3.0);
return(network.Create(
x.Cols,
output,
activationFunction,
outputFunction,
fnNodeInitializer: (l, i) =>
{
if (l == 0)
{
return new Neuron(false)
{
Label = d.ColumnAt(i - 1),
ActivationFunction = activationFunction,
NodeId = i,
LayerId = l
}
}
;
if (l == 2)
{
return new Neuron(false)
{
Label = Network.GetLabel(i, d),
ActivationFunction = activationFunction,
NodeId = i,
LayerId = l
}
}
;
return new Neuron(false)
{
ActivationFunction = activationFunction, NodeId = i, LayerId = l
};
},
hiddenLayers: hidden));
}
/// <summary>Defaults.</summary>
/// <param name="d">The Descriptor to process.</param>
/// <param name="x">The Vector to process.</param>
/// <param name="y">The Vector to process.</param>
/// <param name="activationFunction">The activation.</param>
/// <param name="outputFunction">The ouput function for hidden nodes (Optional).</param>
/// <param name="epsilon">epsilon</param>
/// <returns>A Network.</returns>
public static Network Create(this Network network, Descriptor d, Matrix x, Vector y, IFunction activationFunction, IFunction outputFunction = null,
double epsilon = double.NaN, ILossFunction lossFunction = null)
{
// set output to number of choices of available
// 1 if only two choices
int distinct = y.Distinct().Count();
int output = distinct > 2 ? distinct : 1;
// identity funciton for bias nodes
IFunction ident = new Ident();
// set number of hidden units to (Input + Hidden) * 2/3 as basic best guess.
int hidden = (int)System.Math.Ceiling((double)(x.Cols + output) * 2.0 / 3.0);
return(network.Create(x.Cols, output, activationFunction, outputFunction,
fnNodeInitializer: new Func <int, int, NodeType, Neuron>((l, i, type) =>
{
if (type == NodeType.Input)
{
return new Neuron(false)
{
Label = d.ColumnAt(i - 1), ActivationFunction = activationFunction, NodeId = i, LayerId = l
}
}
;
else if (type == NodeType.Output)
{
return new Neuron(false)
{
Label = Network.GetLabel(i, d), ActivationFunction = activationFunction, NodeId = i, LayerId = l
}
}
;
else
{
return new Neuron(false)
{
ActivationFunction = activationFunction, NodeId = i, LayerId = l
}
};
}), lossFunction: lossFunction, hiddenLayers: hidden));
}