Golang Vector.Copy方法代码示例

func (m *LBFGSMinimizer) Minimize(costfun DiffFunction, init *core.Vector) *core.Vector {
	m.costFun = costfun
	var cost float64 = costfun.Value(init)
	var grad *core.Vector = costfun.Gradient(init).Copy()
	var pos *core.Vector = init.Copy()
	var terminalCriterion *relativeMeanImprCriterion = NewRelativeMeanImprCriterion(m.tolerance)
	terminalCriterion.addCost(cost)

	var helper *QuasiNewtonHelper = NewQuasiNewtonHelper(m.numHist, m, pos, grad)
	if lbfgs_output_switch {
		fmt.Println("Iter\tcost\timprovement")
		fmt.Printf("%d\t%e\tUndefined", 0, cost)
	}
	for iter := 1; iter <= m.maxIteration; iter++ {
		dir := grad.Copy()
		dir.ApplyScale(-1.0)
		helper.ApplyQuasiInverseHession(dir)
		newCost, newPos := helper.BackTrackingLineSearch(cost, pos, grad, dir, iter == 1)
		if lbfgs_output_switch {
			fmt.Println("")
		}
		if cost == newCost {
			break
		}
		cost = newCost
		pos = newPos
		grad = costfun.Gradient(pos).Copy()
		terminalCriterion.addCost(cost)
		if lbfgs_output_switch {
			fmt.Printf("%d\t%e\t%e", iter, newCost, terminalCriterion.improvement)
		}
		if terminalCriterion.isTerminable() || helper.UpdateState(pos, grad) {
			if lbfgs_output_switch {
				fmt.Println("")
			}
			break
		}
	}
	return pos
}

func (lr *LROWLQN) updateValueGrad(pos *core.Vector, dataset *core.DataSet) {
	var totalLoss float64 = 0.0
	var grad *core.Vector = core.NewVector()
	for _, sample := range dataset.Samples {
		var score float64 = lr.getScore(pos, sample)
		var signScore float64 = score
		if sample.Label == 0 {
			signScore = -score
		}
		var prob float64
		var lnProb float64
		if signScore < -30 {
			prob = 0
			lnProb = signScore
		} else if signScore > 30 {
			prob = 1
			lnProb = 0
		} else {
			prob = 1.0 / (1.0 + math.Exp(-signScore))
			lnProb = math.Log(prob)
		}
		var scale float64
		if sample.Label == 0 {
			scale = (1 - prob)
		} else {
			scale = -(1 - prob)
		}
		totalLoss += -lnProb
		for _, fea := range sample.Features {
			grad.AddValue(fea.Id, scale*fea.Value)
		}
	}
	lr.lastPos = pos.Copy()
	lr.lastCost = totalLoss
	lr.lastGrad = grad
}

func (m *OWLQNMinimizer) Minimize(costfun DiffFunction, init *core.Vector) *core.Vector {
	m.costFun = costfun
	var cost float64 = m.Evaluate(init)
	var grad *core.Vector = costfun.Gradient(init).Copy()
	var pos *core.Vector = init.Copy()
	var terminalCriterion *relativeMeanImprCriterion = NewRelativeMeanImprCriterion(m.tolerance)
	terminalCriterion.addCost(cost)

	var helper *QuasiNewtonHelper = NewQuasiNewtonHelper(m.numHist, m, pos, grad)
	if owlqn_output_switch {
		fmt.Println("Iter\tcost\timprovement")
		fmt.Printf("%d\t%e\tUndefined", 0, cost)
	}
	for iter := 1; iter <= m.maxIteration; iter++ {
		// customed steepest descending dir
		steepestDescDir := grad.Copy()
		m.updateGrad(pos, steepestDescDir)
		steepestDescDir.ApplyScale(-1.0)
		dir := steepestDescDir.Copy()
		// quasi-newton dir
		helper.ApplyQuasiInverseHession(dir)
		m.fixDirSign(dir, steepestDescDir)
		// customed grad for the new position
		potentialGrad := grad.Copy()
		m.updateGradForNewPos(pos, potentialGrad, dir)
		newCost, newPos := helper.BackTrackingLineSearch(cost, pos, potentialGrad, dir, iter == 1)
		if owlqn_output_switch {
			fmt.Println("")
		}
		if cost == newCost {
			break
		}
		cost = newCost
		pos = newPos
		grad = costfun.Gradient(pos).Copy()
		terminalCriterion.addCost(cost)
		if owlqn_output_switch {
			fmt.Printf("%d\t%e\t%e", iter, newCost, terminalCriterion.improvement)
		}
		if terminalCriterion.isTerminable() || helper.UpdateState(pos, grad) {
			if owlqn_output_switch {
				fmt.Println("")
			}
			break
		}
	}
	return pos
}

func Distance(x, y *core.Vector) float64 {
	z := x.Copy()
	z.AddVector(y, -1)
	d := z.NormL2()
	return d
}

func (c *KNN) Kernel(x, y *core.Vector) float64 {
	z := x.Copy()
	z.AddVector(y, -1.0)
	ret := math.Exp(-1.0 * z.NormL2() / 20.0)
	return ret
}

/*
   Given matrix m and vector v, compute inv(m)*v.
   Based on Gibbs and MacKay 1997, and Mark N. Gibbs's PhD dissertation

   Details:
   A - positive seminidefinite matrix
   u - a vector
   theta - positive number
   C = A + I*theta
   Returns inv(C)*u - So you need the diagonal noise term for covariance matrix in a sense.
   However, this algorithm is numerically stable, the noise term can be very small and the inversion can still be calculated...
*/
func (algo *GaussianProcess) ApproximateInversion(A *core.Matrix, u *core.Vector, theta float64, dim int64) *core.Vector {
	max_itr := 500
	tol := 0.01

	C := core.NewMatrix()
	for key, val := range A.Data {
		C.Data[key] = val.Copy()
	}

	// Add theta to diagonal elements
	for i := int64(0); i < dim; i++ {
		_, ok := C.Data[i]
		if !ok {
			C.Data[i] = core.NewVector()
		}
		C.Data[i].Data[i] = C.Data[i].Data[i] + theta
	}

	var Q_l float64
	var Q_u float64
	var dQ float64
	u_norm := u.Dot(u) / 2

	// Lower bound
	y_l := core.NewVector()
	g_l := u.Copy()
	h_l := u.Copy()
	lambda_l := float64(0)
	gamma_l := float64(0)
	var tmp_f1 float64
	var tmp_f2 float64
	var tmp_v1 *core.Vector
	tmp_f1 = g_l.Dot(g_l)
	tmp_v1 = C.MultiplyVector(h_l)

	// Upper bound
	y_u := core.NewVector()
	g_u := u.Copy()
	h_u := u.Copy()
	lambda_u := float64(0)
	gamma_u := float64(0)
	var tmp_f3 float64
	var tmp_f4 float64
	var tmp_v3 *core.Vector
	var tmp_v4 *core.Vector
	tmp_v3 = g_u.MultiplyMatrix(A)
	tmp_v4 = C.MultiplyVector(h_u)
	tmp_f3 = tmp_v1.Dot(g_u)

	for i := 0; i < max_itr; i++ {
		// Lower bound
		lambda_l = tmp_f1 / h_l.Dot(tmp_v1)
		y_l.AddVector(h_l, lambda_l) //y_l next
		Q_l = y_l.Dot(u) - 0.5*(y_l.MultiplyMatrix(C)).Dot(y_l)

		// Upper bound
		lambda_u = tmp_f3 / tmp_v3.Dot(tmp_v4)
		y_u.AddVector(h_u, lambda_u) //y_u next
		Q_u = (y_u.MultiplyMatrix(A)).Dot(u) - 0.5*((y_u.MultiplyMatrix(C)).MultiplyMatrix(A)).Dot(y_u)

		dQ = (u_norm-Q_u)/theta - Q_l
		if dQ < tol {
			break
		}

		// Lower bound var updates
		g_l.AddVector(tmp_v1, -lambda_l) //g_l next
		tmp_f2 = g_l.Dot(g_l)
		gamma_l = tmp_f2 / tmp_f1
		for key, val := range h_l.Data {
			h_l.SetValue(key, val*gamma_l)
		}
		h_l.AddVector(g_l, 1)          //h_l next
		tmp_f1 = tmp_f2                //tmp_f1 next
		tmp_v1 = C.MultiplyVector(h_l) //tmp_v1 next

		// Upper bound var updates
		g_u.AddVector(tmp_v4, -lambda_u) //g_u next
		tmp_v3 = g_u.MultiplyMatrix(A)   //tmp_v3 next
		tmp_f4 = tmp_v3.Dot(g_u)
		gamma_u = tmp_f4 / tmp_f3
		for key, val := range h_u.Data {
			h_u.SetValue(key, val*gamma_u)
		}
		h_u.AddVector(g_u, 1)          //h_u next
		tmp_v4 = C.MultiplyVector(h_u) //tmp_v4 next
		tmp_f3 = tmp_f4                // tmp_f3 next
	}
//.........这里部分代码省略.........