Esempio n. 1
0
func (c *FactorizeMachine) Predict(sample *core.Sample) float64 {
	for _, f := range sample.Features {
		c.w.RandomInit(f.Id, 0.1)
		for k, _ := range c.v {
			c.v[k].RandomInit(f.Id, 0.1)
		}
	}
	ret := c.w.DotFeatures(sample.Features)
	for k, _ := range c.v {
		a := c.v[k].DotFeatures(sample.Features)
		b := 0.0
		for _, f := range sample.Features {
			vkf := c.v[k].GetValue(f.Id)
			b += f.Value * f.Value * vkf * vkf
		}
		ret += 0.5 * (a*a - b)
	}
	return util.Sigmoid(ret)
}
Esempio n. 2
0
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
}
Esempio n. 3
0
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()
}