func (c *FactorizeMachine) Init(params map[string]string) {
	c.w = core.NewVector()
	factor_number, _ := strconv.ParseInt(params["factors"], 10, 64)
	c.params.FactorNumber = int(factor_number)
	c.params.LearningRate, _ = strconv.ParseFloat(params["learning-rate"], 64)
	c.params.Regularization, _ = strconv.ParseFloat(params["regularization"], 64)

	for i := 0; i < c.params.FactorNumber; i++ {
		c.v = append(c.v, core.NewVector())
	}
}
func (algo *GaussianProcess) ExtractTargetValuesAsVector(samples []*core.RealSample) *core.Vector {
	targets := core.NewVector()
	for i := 0; i < len(samples); i++ {
		targets.SetValue(int64(i), samples[i].Value)
	}
	return targets
}
Exemple #3
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func RandomInitVector(dim int64) *core.Vector {
	v := core.NewVector()
	var i int64
	for i = 0; i < dim; i++ {
		v.Data[i] = (rand.Float64() - 0.5) / math.Sqrt(float64(dim))
	}
	return v
}
func CovVector(X []*core.RealSample, y *core.RealSample, cov_func CovFunc) *core.Vector {
	l := int64(len(X))
	ret := core.NewVector()
	for i := int64(0); i < l; i++ {
		ret.SetValue(i, cov_func(X[i].GetFeatureVector(), y.GetFeatureVector()))
	}
	return ret
}
Exemple #5
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func (dt *RegressionTree) FindBestSplit(samples []*core.MapBasedSample, node *TreeNode, select_features map[int64]bool){
	feature_weight_labels := make(map[int64]*core.FeatureGoalDistribution)
	sum_total := 0.0
	sum_total2 := 0.0
	count_total := 0.0
	for _, k := range node.samples{
		sum_total += samples[k].Prediction
		sum_total2 += samples[k].Prediction * samples[k].Prediction
		count_total += 1.0
	}

	feature_sum_right := core.NewVector()
	feature_sum_right2 := core.NewVector()
	feature_count_right := core.NewVector()

	for _, k := range node.samples{
		for fid, fvalue := range samples[k].Features {
			feature_count_right.AddValue(fid, 1.0)
			feature_sum_right.AddValue(fid, samples[k].Prediction)
			feature_sum_right2.AddValue(fid, samples[k].Prediction * samples[k].Prediction)
			_, ok := feature_weight_labels[fid]
			if !ok {
				feature_weight_labels[fid] = core.NewFeatureGoalDistribution()
			}
			feature_weight_labels[fid].AddWeightGoal(fvalue, samples[k].Prediction)
		}
	}
	
	min_vari := 1e20
	node.feature_split = core.Feature{Id:-1, Value: 0}
	for fid, distribution := range feature_weight_labels{
		sort.Sort(distribution)
		split, vari := distribution.BestSplitByVariance(sum_total - feature_sum_right.GetValue(fid),
			sum_total2 - feature_sum_right2.GetValue(fid),
			count_total - feature_count_right.GetValue(fid),
			feature_sum_right.GetValue(fid),
			feature_sum_right2.GetValue(fid),
			feature_count_right.GetValue(fid))
		if min_vari > vari {
			min_vari = vari
			node.feature_split.Id = fid
			node.feature_split.Value = split
		}
	}
}
func (algo *GaussianProcess) Init(params map[string]string) {

	dim, _ := strconv.ParseInt(params["dim"], 10, 64)

	algo.Params = GaussianProcessParameters{}
	algo.Params.Dim = dim    // Pass in dim as a param.. and require feature space to be continous.
	algo.Params.Theta = 1e-7 // Used by approximate inversion as the diagonal noise

	radius := 0.1
	camp := 40.0
	cf := CovSEARD{}
	radiuses := core.NewVector()
	for i := int64(1); i <= dim; i++ {
		radiuses.SetValue(i, radius)
	}
	cf.Init(radiuses, camp)

	algo.CovarianceFunc = cf.Cov
}
Exemple #7
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func (algo *NeuralNetwork) PredictMultiClass(sample *core.Sample) *core.ArrayVector {
	y := core.NewVector()
	z := core.NewArrayVector()
	for i := int64(0); i < algo.Params.Hidden; i++ {
		sum := float64(0)
		for _, f := range sample.Features {
			sum += f.Value * algo.Model.L1.Data[i].GetValue(f.Id)
		}
		y.Data[i] = util.Sigmoid(sum)
	}
	y.Data[algo.Params.Hidden] = 1
	for i := 0; i <= int(algo.MaxLabel); i++ {
		sum := float64(0)
		for j := int64(0); j <= algo.Params.Hidden; j++ {
			sum += y.GetValue(j) * algo.Model.L2.GetValue(j, int64(i))
		}
		z.SetValue(i, sum)
	}
	z = z.SoftMaxNorm()
	return z
}
Exemple #8
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func (algo *NeuralNetwork) Train(dataset *core.DataSet) {
	algo.Model = TwoLayerWeights{}
	algo.Model.L1 = core.NewMatrix()
	algo.Model.L2 = core.NewMatrix()

	for i := int64(0); i < algo.Params.Hidden; i++ {
		algo.Model.L1.Data[i] = core.NewVector()
	}

	initalized := make(map[int64]int)
	max_label := 0
	for _, sample := range dataset.Samples {
		if max_label < sample.Label {
			max_label = sample.Label
		}
		for _, f := range sample.Features {
			_, ok := initalized[f.Id]
			if !ok {
				for i := int64(0); i < algo.Params.Hidden; i++ {
					algo.Model.L1.SetValue(i, f.Id, (rand.Float64()-0.5)/math.Sqrt(float64(algo.Params.Hidden)))
				}
				initalized[f.Id] = 1
			}
		}
	}
	algo.MaxLabel = int64(max_label)

	for i := int64(0); i <= algo.Params.Hidden; i++ {
		for j := int64(0); j <= algo.MaxLabel; j++ {
			algo.Model.L2.SetValue(i, j, (rand.NormFloat64() / math.Sqrt(float64(algo.MaxLabel)+1.0)))
		}
	}

	for step := 0; step < algo.Params.Steps; step++ {
		if algo.Params.Verbose <= 0 {
			fmt.Printf(".")
		}
		total := len(dataset.Samples)
		counter := 0
		for _, sample := range dataset.Samples {
			y := core.NewVector()
			z := core.NewVector()
			e := core.NewVector()
			delta_hidden := core.NewVector()

			for i := int64(0); i < algo.Params.Hidden; i++ {
				sum := float64(0)
				wi := algo.Model.L1.Data[i]
				for _, f := range sample.Features {
					sum += f.Value * wi.GetValue(f.Id)
				}
				y.Data[i] = util.Sigmoid(sum)
			}
			y.Data[algo.Params.Hidden] = 1.0
			for i := int64(0); i <= algo.MaxLabel; i++ {
				sum := float64(0)
				for j := int64(0); j <= algo.Params.Hidden; j++ {
					sum += y.GetValue(j) * algo.Model.L2.GetValue(j, i)
				}
				z.SetValue(i, sum)
			}
			z = z.SoftMaxNorm()
			e.SetValue(int64(sample.Label), 1.0)
			e.AddVector(z, -1.0)

			for i := int64(0); i <= algo.Params.Hidden; i++ {
				delta := float64(0)
				for j := int64(0); j <= algo.MaxLabel; j++ {
					wij := algo.Model.L2.GetValue(i, j)
					sig_ij := e.GetValue(j) * (1 - z.GetValue(j)) * z.GetValue(j)
					delta += sig_ij * wij
					wij += algo.Params.LearningRate * (y.GetValue(i)*sig_ij - algo.Params.Regularization*wij)
					algo.Model.L2.SetValue(i, j, wij)
				}
				delta_hidden.SetValue(i, delta)
			}

			for i := int64(0); i < algo.Params.Hidden; i++ {
				wi := algo.Model.L1.Data[i]
				for _, f := range sample.Features {
					wji := wi.GetValue(f.Id)
					wji += algo.Params.LearningRate * (delta_hidden.GetValue(i)*f.Value*y.GetValue(i)*(1-y.GetValue(i)) - algo.Params.Regularization*wji)
					wi.SetValue(f.Id, wji)
				}
			}
			counter++
			if algo.Params.Verbose > 0 && counter%2000 == 0 {
				fmt.Printf("Epoch %d %f%%\n", step+1, float64(counter)/float64(total)*100)
			}
		}

		if algo.Params.Verbose > 0 {
			algo.Evaluate(dataset)
		}
		algo.Params.LearningRate *= algo.Params.LearningRateDiscount
	}
	fmt.Println()
}
Exemple #9
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func (c *SVM) Init(params map[string]string) {
	c.C, _ = strconv.ParseFloat(params["c"], 64)
	c.e, _ = strconv.ParseFloat(params["e"], 64)

	c.w = core.NewVector()
}
Exemple #10
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/*
   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
	}

	return y_l
}