Golang Dense.Apply方法代码示例

func forward(x *mat64.Dense) (yHat mat64.Dense) {
	z2 := mat64.Dense{} // 3x3
	a2 := mat64.Dense{} // 3x3
	z3 := mat64.Dense{} // 3x1
	z2.Mul(x, w1)
	a2.Apply(sigmoidApplyable, &z2)
	z3.Mul(&a2, w2)
	// yHat is 3x1
	yHat.Apply(sigmoidApplyable, &z3)
	return
}

// Activate propagates the given input matrix (with) across the network
// a certain number of times (up to maxIterations).
//
// The with matrix should be size * size elements, with only the values
// of input neurons set (everything else should be zero).
//
// If the network is conceptually organised into layers, maxIterations
// should be set to the number of layers.
//
// This function overwrites whatever's stored in its first argument.
func (n *Network) Activate(with *mat64.Dense, maxIterations int) {

	// Add bias and feed to activation
	biasFunc := func(r, c int, v float64) float64 {
		return v + n.biases[r]
	}
	activFunc := func(r, c int, v float64) float64 {
		return n.funcs[r].Forward(v)
	}

	tmp := new(mat64.Dense)
	tmp.Clone(with)

	// Main loop
	for i := 0; i < maxIterations; i++ {
		with.Mul(n.weights, with)
		with.Apply(biasFunc, with)
		with.Apply(activFunc, with)
	}
}

func nnlsSubproblem(V, W, Ho *mat64.Dense, tol float64, outer, inner int) (H, G *mat64.Dense, i int, ok bool) {
	H = new(mat64.Dense)
	H.Clone(Ho)

	var WtV, WtW mat64.Dense
	WtV.Mul(W.T(), V)
	WtW.Mul(W.T(), W)

	alpha, beta := 1., 0.1

	decFilt := func(r, c int, v float64) float64 {
		// decFilt is applied to G, so v = G.At(r, c).
		if v < 0 || H.At(r, c) > 0 {
			return v
		}
		return 0
	}

	G = new(mat64.Dense)
	for i = 0; i < outer; i++ {
		G.Mul(&WtW, H)
		G.Sub(G, &WtV)
		G.Apply(decFilt, G)

		if mat64.Norm(G, 2) < tol {
			break
		}

		var (
			reduce bool
			Hp     *mat64.Dense
			d, dQ  mat64.Dense
		)
		for j := 0; j < inner; j++ {
			var Hn mat64.Dense
			Hn.Scale(alpha, G)
			Hn.Sub(H, &Hn)
			Hn.Apply(posFilt, &Hn)

			d.Sub(&Hn, H)
			dQ.Mul(&WtW, &d)
			dQ.MulElem(&dQ, &d)
			d.MulElem(G, &d)

			sufficient := 0.99*mat64.Sum(&d)+0.5*mat64.Sum(&dQ) < 0

			if j == 0 {
				reduce = !sufficient
				Hp = H
			}
			if reduce {
				if sufficient {
					H = &Hn
					ok = true
					break
				} else {
					alpha *= beta
				}
			} else {
				if !sufficient || mat64.Equal(Hp, &Hn) {
					H = Hp
					break
				} else {
					alpha /= beta
					Hp = &Hn
				}
			}
		}
	}

	return H, G, i, ok
}

func denormalize(matrix *mat64.Dense, max float64) {
	matrix.Apply(func(i, j int, elem float64) float64 {
		return elem * max
	}, matrix)
}