func main() {
	// task 1: show qr decomp of wp example
	a := mat64.NewDense(3, 3, []float64{
		12, -51, 4,
		6, 167, -68,
		-4, 24, -41,
	})
	var qr mat64.QR
	qr.Factorize(a)
	var q, r mat64.Dense
	q.QFromQR(&qr)
	r.RFromQR(&qr)
	fmt.Printf("q: %.3f\n\n", mat64.Formatted(&q, mat64.Prefix("   ")))
	fmt.Printf("r: %.3f\n\n", mat64.Formatted(&r, mat64.Prefix("   ")))

	// task 2: use qr decomp for polynomial regression example
	x := []float64{0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10}
	y := []float64{1, 6, 17, 34, 57, 86, 121, 162, 209, 262, 321}
	a = Vandermonde(x, 2)
	b := mat64.NewDense(11, 1, y)
	qr.Factorize(a)
	var f mat64.Dense
	f.SolveQR(&qr, false, b)
	fmt.Printf("polyfit: %.3f\n",
		mat64.Formatted(&f, mat64.Prefix("         ")))
}
func main() {
	a := Vandermonde(x, 2)
	b := mat64.NewDense(11, 1, y)
	c := mat64.NewDense(3, 1, nil)

	qr := new(mat64.QR)
	qr.Factorize(a)

	err := c.SolveQR(qr, false, b)
	if err != nil {
		fmt.Println(err)
	} else {
		fmt.Printf("%.3f\n", mat64.Formatted(c))
	}
}
Esempio n. 3
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func (lr *LinearRegression) Fit(inst base.FixedDataGrid) error {

	// Retrieve row size
	_, rows := inst.Size()

	// Validate class Attribute count
	classAttrs := inst.AllClassAttributes()
	if len(classAttrs) != 1 {
		return fmt.Errorf("Only 1 class variable is permitted")
	}
	classAttrSpecs := base.ResolveAttributes(inst, classAttrs)

	// Retrieve relevant Attributes
	allAttrs := base.NonClassAttributes(inst)
	attrs := make([]base.Attribute, 0)
	for _, a := range allAttrs {
		if _, ok := a.(*base.FloatAttribute); ok {
			attrs = append(attrs, a)
		}
	}

	cols := len(attrs) + 1

	if rows < cols {
		return NotEnoughDataError
	}

	// Retrieve relevant Attribute specifications
	attrSpecs := base.ResolveAttributes(inst, attrs)

	// Split into two matrices, observed results (dependent variable y)
	// and the explanatory variables (X) - see http://en.wikipedia.org/wiki/Linear_regression
	observed := mat64.NewDense(rows, 1, nil)
	explVariables := mat64.NewDense(rows, cols, nil)

	// Build the observed matrix
	inst.MapOverRows(classAttrSpecs, func(row [][]byte, i int) (bool, error) {
		val := base.UnpackBytesToFloat(row[0])
		observed.Set(i, 0, val)
		return true, nil
	})

	// Build the explainatory variables
	inst.MapOverRows(attrSpecs, func(row [][]byte, i int) (bool, error) {
		// Set intercepts to 1.0
		explVariables.Set(i, 0, 1.0)
		for j, r := range row {
			explVariables.Set(i, j+1, base.UnpackBytesToFloat(r))
		}
		return true, nil
	})

	n := cols
	qr := new(mat64.QR)
	qr.Factorize(explVariables)
	var q, reg mat64.Dense
	q.QFromQR(qr)
	reg.RFromQR(qr)

	var transposed, qty mat64.Dense
	transposed.Clone(q.T())
	qty.Mul(&transposed, observed)

	regressionCoefficients := make([]float64, n)
	for i := n - 1; i >= 0; i-- {
		regressionCoefficients[i] = qty.At(i, 0)
		for j := i + 1; j < n; j++ {
			regressionCoefficients[i] -= regressionCoefficients[j] * reg.At(i, j)
		}
		regressionCoefficients[i] /= reg.At(i, i)
	}

	lr.disturbance = regressionCoefficients[0]
	lr.regressionCoefficients = regressionCoefficients[1:]
	lr.fitted = true
	lr.attrs = attrs
	lr.cls = classAttrs[0]
	return nil
}