static double[][] MakeAllData(int numInput, int numHidden,
int numOutput, int numRows, int seed)
{
Random rnd = new Random(seed);
int numWeights = (numInput * numHidden) + numHidden +
(numHidden * numOutput) + numOutput;
double[] weights = new double[numWeights]; // actually weights & biases
for (int i = 0; i < numWeights; ++i)
{
weights[i] = 20.0 * rnd.NextDouble() - 10.0; // [-10.0 to 10.0]
}
Console.WriteLine("Generating weights and biases:");
ShowVector(weights, 2, 10, true);
double[][] result = new double[numRows][]; // allocate return-result
for (int i = 0; i < numRows; ++i)
{
result[i] = new double[numInput + numOutput]; // 1-of-N in last column
}
NeuralNetwork gnn =
new NeuralNetwork(numInput, numHidden, numOutput); // generating NN
gnn.SetWeights(weights);
for (int r = 0; r < numRows; ++r) // for each row
{
// generate random inputs
double[] inputs = new double[numInput];
for (int i = 0; i < numInput; ++i)
{
inputs[i] = 20.0 * rnd.NextDouble() - 10.0; // [-10.0 to -10.0]
}
// compute outputs
double[] outputs = gnn.ComputeOutputs(inputs);
// translate outputs to 1-of-N
double[] oneOfN = new double[numOutput]; // all 0.0
int maxIndex = 0;
double maxValue = outputs[0];
for (int i = 0; i < numOutput; ++i)
{
if (outputs[i] > maxValue)
{
maxIndex = i;
maxValue = outputs[i];
}
}
oneOfN[maxIndex] = 1.0;
// place inputs and 1-of-N output values into curr row
int c = 0; // column into result[][]
for (int i = 0; i < numInput; ++i) // inputs
{
result[r][c++] = inputs[i];
}
for (int i = 0; i < numOutput; ++i) // outputs
{
result[r][c++] = oneOfN[i];
}
} // each row
return(result);
} // MakeAllData
