public static void Run()
{
var test = new double[2, 3];
double temp = 1;
for (int i = 0; i < test.GetLength(0); i++)
{
for (int j = 0; j < test.GetLength(1); j++)
{
test[i, j] = temp++;
}
}
Console.WriteLine("Test matrix");
Console.WriteLine(test.Printable());
Console.WriteLine("Test matrix transpose");
Console.WriteLine(test.Transpose().Printable());
Console.WriteLine("Scalar matrix multiplication");
Console.WriteLine(MatrixUtils.Multiply(test.Transpose(), 3).Printable());
Console.WriteLine("Matrix multiplication");
Console.WriteLine(MatrixUtils.Multiply(test, test.Transpose()).Printable());
var singleDim = new double[1, 1];
for (int i = 0; i < singleDim.GetLength(0); i++)
{
for (int j = 0; j < singleDim.GetLength(1); j++)
{
Console.WriteLine("singleDim: " + singleDim[i, j]);
}
}
}
// REVIEW: input matrix has 1 column
public bool Train(double[,] input, double desiredValue)
{
double output;
double sum = MatrixUtils.ElementsSum(MatrixUtils.Multiply(weights, input));
output = Utils.StepF(sum, threshold);
bool correct = output == desiredValue;
if (!correct)
{
for (int i = 0; i < input.Length; i++)
{
if (input[i, 0] > 0)
{
weights[i, 1] += (desiredValue - output) * learningRate;
}
}
}
return(correct);
}