// ANN constructor. Input and Hidden layers contain the same number of neurons.
public ANN(int inputnum, int layernum, int neuronnum, int outputnum,
double threshold, double lr, Random rand)
{
InputNum = inputnum;
LayerNum = layernum;
NeuronNum = neuronnum;
OutputNum = outputnum;
LR = lr;
Threshold = threshold;
rnd = rand;
// creating the network
Network = new ANNLayer[LayerNum];
// creating the input layer
Network[0] = new ANNLayer(NeuronNum, InputNum, Threshold, LR);
// assigning random weights
for (int j = 0; j < NeuronNum; j++)
{
for (int k = 0; k < InputNum; k++)
{
int sign = (rnd.Next(101) < 50) ? -1 : 1;
Network[0].Neurons[j].Weights[k] = sign * rnd.NextDouble();
}
}
// creating hidden layers
int i;
for (i = 1; i < LayerNum - 1; i++)
{
Network[i] = new ANNLayer(NeuronNum, NeuronNum, Threshold, LR);
// assigning random weights
for (int j = 0; j < NeuronNum; j++)
{
for (int k = 0; k < NeuronNum; k++)
{
int sign = (rnd.Next(101) < 50) ? -1 : 1;
Network[i].Neurons[j].Weights[k] = sign * rnd.NextDouble();
}
}
}
// creating the output layer
Network[LayerNum - 1] = new ANNLayer(OutputNum, NeuronNum, Threshold, LR);
// assigning random weights
for (int j = 0; j < OutputNum; j++)
{
for (int k = 0; k < NeuronNum; k++)
{
Network[LayerNum - 1].Neurons[j].Weights[k] = rnd.NextDouble();
}
}
}
// ANN constructor. Input and Hidden layers contain the same number of neurons.
public ANN(int inputnum, int layernum, int neuronnum, int outputnum,
double threshold, double lr, Random rand)
{
InputNum = inputnum;
LayerNum = layernum;
NeuronNum = neuronnum;
OutputNum = outputnum;
LR = lr;
Threshold = threshold;
rnd = rand;
// creating the network
Network = new ANNLayer[LayerNum];
// creating the input layer
Network[0] = new ANNLayer(NeuronNum, InputNum, Threshold, LR);
// assigning random weights
for (int j = 0; j < NeuronNum; j++)
for (int k = 0; k < InputNum; k++)
{
int sign = (rnd.Next(101) < 50) ? -1 : 1;
Network[0].Neurons[j].Weights[k] = sign*rnd.NextDouble();
}
// creating hidden layers
int i;
for (i = 1; i < LayerNum - 1; i++)
{
Network[i] = new ANNLayer(NeuronNum, NeuronNum, Threshold, LR);
// assigning random weights
for (int j = 0; j < NeuronNum; j++)
for (int k = 0; k < NeuronNum; k++)
{
int sign = (rnd.Next(101) < 50) ? -1 : 1;
Network[i].Neurons[j].Weights[k] = sign * rnd.NextDouble();
}
}
// creating the output layer
Network[LayerNum - 1] = new ANNLayer(OutputNum, NeuronNum, Threshold, LR);
// assigning random weights
for (int j = 0; j < OutputNum; j++)
for (int k = 0; k < NeuronNum; k++)
Network[LayerNum - 1].Neurons[j].Weights[k] = rnd.NextDouble();
}
