func (this *TrainingSet) AddX0() {
m := this.Xs.GetMRows()
x0 := Matrix.NullMatrix(m, 1)
for i := 1; i <= m; i++ {
x0.SetValue(i, 1, 1.0)
}
this.Xs = x0.AddColumn(this.Xs)
}
func CreateANN(Inputs int, NeuronsByLayer []int, Act func(*Matrix.Matrix) *Matrix.Matrix, Derivate func(*Matrix.Matrix) *Matrix.Matrix, Cost func(*Matrix.Matrix, *Matrix.Matrix) *Matrix.Matrix, DCost func(*Matrix.Matrix, *Matrix.Matrix) *Matrix.Matrix, path string) ANN {
var out ANN
out.Weights = make([]*Matrix.Matrix, len(NeuronsByLayer), len(NeuronsByLayer))
out.BestWeightsFound = make([]*Matrix.Matrix, len(NeuronsByLayer), len(NeuronsByLayer))
out.LearningRates = make([]*Matrix.Matrix, len(NeuronsByLayer), len(NeuronsByLayer))
out.Δ = make([]*Matrix.Matrix, len(NeuronsByLayer), len(NeuronsByLayer))
out.Δ1 = make([]*Matrix.Matrix, len(NeuronsByLayer), len(NeuronsByLayer))
out.ð = make([]*Matrix.Matrix, len(NeuronsByLayer)+1, len(NeuronsByLayer)+1)
out.Inputs = Inputs
out.Outputs = NeuronsByLayer[len(NeuronsByLayer)-1]
out.ActivationLayer = Act
out.DarivateActivationLayer = Derivate
out.CostFunction = Cost
out.DerviateCostFunction = DCost
out.PathWeightsInCSV = path
m := Inputs
for i := 0; i < (len(NeuronsByLayer)); i++ {
n := NeuronsByLayer[i]
// one row extra for Bias weights, we need to change to random values for this matrixes
//temp := Matrix.RandomRealMatrix(m+1, n)
out.Weights[i] = Matrix.RandomRealMatrix(m+1, n, 1.2)
out.BestWeightsFound[i] = Matrix.NullMatrixP(m+1, n)
out.LearningRates[i] = Matrix.FixValueMatrix(m+1, n, 0.0001)
//tempdelta := Matrix.NullMatrix(m+1, n)
out.ð[i] = Matrix.NullMatrix(m+1, n)
out.Δ[i] = Matrix.NullMatrixP(m+1, n)
out.Δ1[i] = Matrix.NullMatrixP(m+1, n)
m = n
}
out.AcumatedError = Matrix.NullMatrixP(m, 1)
out.MinimumErrorFound = Matrix.NullMatrixP(m, 1)
out.AcumatedError1 = Matrix.NullMatrixP(m, 1)
return out
}
func main() {
l := make([]int, 3)
l[0] = 2
l[1] = 2
l[2] = 2
ann := ANN.CreateANN(2, l, ANN.SigmoidLayer, ANN.DSigmoidLayer, ANN.HalfDistance, ANN.DerivateHalfDistance, "plot")
p1 := Matrix.NullMatrix(2, 1)
p1.SetValue(1, 1, 1.0)
p1.SetValue(1, 2, 1.0)
ro1 := Matrix.NullMatrix(2, 1)
ro1.SetValue(1, 1, 0.0)
ro1.SetValue(2, 1, 1.0)
p2 := Matrix.NullMatrix(2, 1)
p2.SetValue(1, 1, 1.0)
p2.SetValue(1, 2, 0.0)
ro2 := Matrix.NullMatrix(2, 1)
ro2.SetValue(1, 1, 1.0)
ro2.SetValue(2, 1, 0.0)
p3 := Matrix.NullMatrix(2, 1)
p3.SetValue(1, 1, 0.0)
p3.SetValue(1, 2, 1.0)
ro3 := Matrix.NullMatrix(2, 1)
ro3.SetValue(1, 1, 1.0)
ro3.SetValue(2, 1, 0.0)
p4 := Matrix.NullMatrix(2, 1)
p4.SetValue(1, 1, 0.0)
p4.SetValue(1, 2, 0.0)
ro4 := Matrix.NullMatrix(2, 1)
ro4.SetValue(1, 1, 0.0)
ro4.SetValue(2, 1, 1.0)
Inputs := make([]*Matrix.Matrix, 4)
ROutputs := make([]*Matrix.Matrix, 4)
Inputs[0] = p1
Inputs[1] = p2
Inputs[2] = p3
Inputs[3] = p4
ROutputs[0] = ro1
ROutputs[1] = ro2
ROutputs[2] = ro3
ROutputs[3] = ro4
ann.Train(Inputs, ROutputs, 0.01, 0.65, 0.0001, 1000)
_, _, Output := ann.ForwardPropagation(Inputs[0])
fmt.Println(Output.ToString())
_, _, Output = ann.ForwardPropagation(Inputs[1])
fmt.Println(Output.ToString())
_, _, Output = ann.ForwardPropagation(Inputs[2])
fmt.Println(Output.ToString())
_, _, Output = ann.ForwardPropagation(Inputs[3])
fmt.Println(Output.ToString())
}
