Golang Dense.Dims方法代码示例

// Batch gradient descent finds the local minimum of a function.
// See http://en.wikipedia.org/wiki/Gradient_descent for more details.
func BatchGradientDescent(x, y, theta *mat64.Dense, alpha float64, epoch int) *mat64.Dense {
	m, _ := y.Dims()
	for i := 0; i < epoch; i++ {
		xFlat := mat64.DenseCopyOf(x)
		xFlat.TCopy(xFlat)
		temp := mat64.DenseCopyOf(x)

		// Calculate our best prediction, given theta
		temp.Mul(temp, theta)

		// Calculate our error from the real values
		temp.Sub(temp, y)
		xFlat.Mul(xFlat, temp)

		// Temporary hack to get around the fact there is no scalar division in mat64
		xFlatRow, _ := xFlat.Dims()
		gradient := make([]float64, 0)
		for k := 0; k < xFlatRow; k++ {
			row := xFlat.RowView(k)
			for v := range row {
				divd := row[v] / float64(m) * alpha
				gradient = append(gradient, divd)
			}
		}
		grows := len(gradient)
		grad := mat64.NewDense(grows, 1, gradient)
		theta.Sub(theta, grad)
	}
	return theta
}

func (nb *NaiveBayes) Predict(X *mat64.Dense) []Prediction {
	nSamples, _ := X.Dims()

	prediction := []Prediction{}

	for i := 0; i < nSamples; i++ {
		scores := map[int]float64{}
		for langIdx, _ := range nb.params.LangsCount {
			scores[langIdx] = nb.tokensProba(X.Row(nil, i), langIdx) + nb.langProba(langIdx)
		}

		bestScore := scores[0]
		bestLangIdx := 0

		for langIdx, score := range scores {
			if score > bestScore {
				bestScore = score
				bestLangIdx = langIdx
			}
		}

		prediction = append(prediction, Prediction{
			Label:    bestLangIdx,
			Language: "TODO: PENDING",
			Score:    bestScore,
		})
	}

	return prediction
}

func GradientDescent(X *mat64.Dense, y *mat64.Vector, alpha, tolerance float64, maxIters int) *mat64.Vector {
	// m = Number of Training Examples
	// n = Number of Features
	m, n := X.Dims()
	h := mat64.NewVector(m, nil)
	partials := mat64.NewVector(n, nil)
	new_theta := mat64.NewVector(n, nil)

Regression:
	for i := 0; i < maxIters; i++ {
		// Calculate partial derivatives
		h.MulVec(X, new_theta)
		for el := 0; el < m; el++ {
			val := (h.At(el, 0) - y.At(el, 0)) / float64(m)
			h.SetVec(el, val)
		}
		partials.MulVec(X.T(), h)

		// Update theta values
		for el := 0; el < n; el++ {
			new_val := new_theta.At(el, 0) - (alpha * partials.At(el, 0))
			new_theta.SetVec(el, new_val)
		}

		// Check the "distance" to the local minumum
		dist := math.Sqrt(mat64.Dot(partials, partials))

		if dist <= tolerance {
			break Regression
		}
	}
	return new_theta
}

func (n *InnerNormal) SetScale(data *mat64.Dense) error {
	rows, dim := data.Dims()
	if rows < 2 {
		return errors.New("scale: less than two inputs")
	}
	means := make([]float64, dim)
	stds := make([]float64, dim)
	for i := 0; i < dim; i++ {
		// Filter out the extremes
		r := data.Col(nil, i)
		if len(r) != rows {
			panic("bad lengths")
		}
		sort.Float64s(r)

		lowerIdx := int(math.Floor(float64(rows) * n.LowerQuantile))
		upperIdx := int(math.Ceil(float64(rows) * n.UpperQuantile))

		trimmed := r[lowerIdx:upperIdx]

		mean, std := stat.MeanStdDev(trimmed, nil)
		//std := stat.StdDev(trimmed, mean, nil)
		means[i] = mean
		stds[i] = std
	}
	n.Mu = means
	n.Sigma = stds
	fmt.Println(n.Mu, n.Sigma)
	n.Dim = dim
	n.Scaled = true
	return nil
}

func StackConstr(low, A, up *mat64.Dense) (stackA, b *mat64.Dense, ranges []float64) {
	neglow := &mat64.Dense{}
	neglow.Scale(-1, low)
	b = &mat64.Dense{}
	b.Stack(up, neglow)

	negA := &mat64.Dense{}
	negA.Scale(-1, A)
	stackA = &mat64.Dense{}
	stackA.Stack(A, negA)

	// capture the range of each constraint from A because this information is
	// lost when converting from "low <= Ax <= up" via stacking to "Ax <= up".
	m, _ := A.Dims()
	ranges = make([]float64, m, 2*m)
	for i := 0; i < m; i++ {
		ranges[i] = up.At(i, 0) - low.At(i, 0)
		if ranges[i] == 0 {
			if up.At(i, 0) == 0 {
				ranges[i] = 1
			} else {
				ranges[i] = up.At(i, 0)
			}
		}
	}
	ranges = append(ranges, ranges...)

	return stackA, b, ranges
}

func (lr *LogisticRegression) Predict(X *mat64.Dense) []Prediction {
	nSamples, _ := X.Dims()

	prediction := []Prediction{}

	for i := 0; i < nSamples; i++ {
		scores := liblinear.PredictProba(lr.model, X)
		_, nClasses := scores.Dims()

		bestScore := scores.At(i, 0)
		bestLangIdx := 0

		for langIdx := 0; langIdx < nClasses; langIdx++ {
			score := scores.At(i, langIdx)
			if score > bestScore {
				bestScore = score
				bestLangIdx = langIdx
			}
		}

		prediction = append(prediction, Prediction{
			Label:    bestLangIdx,
			Language: "TODO: PENDING",
			Score:    bestScore,
		})
	}

	return prediction
}

// MetropolisHastings generates rows(batch) samples using the Metropolis Hastings
// algorithm (http://en.wikipedia.org/wiki/Metropolis%E2%80%93Hastings_algorithm),
// with the given target and proposal distributions, starting at the intial location
// and storing the results in-place into samples. If src != nil, it will be used to generate random
// numbers, otherwise rand.Float64 will be used.
//
// Metropolis-Hastings is a Markov-chain Monte Carlo algorithm that generates
// samples according to the distribution specified by target by using the Markov
// chain implicitly defined by the proposal distribution. At each
// iteration, a proposal point is generated randomly from the current location.
// This proposal point is accepted with probability
//  p = min(1, (target(new) * proposal(current|new)) / (target(current) * proposal(new|current)))
// If the new location is accepted, it is stored into batch and becomes the
// new current location. If it is rejected, the current location remains and
// is stored into samples. Thus, a location is stored into batch at every iteration.
//
// The samples in Metropolis Hastings are correlated with one another through the
// Markov chain. As a result, the initial value can have a significant influence
// on the early samples, and so, typically, the first samples generated by the chain
// are ignored. This is known as "burn-in", and can be accomplished with slicing.
// The best choice for burn-in length will depend on the sampling and target
// distributions.
//
// Many choose to have a sampling "rate" where a number of samples
// are ignored in between each kept sample. This helps decorrelate
// the samples from one another, but also reduces the number of available samples.
// A sampling rate can be implemented with successive calls to MetropolisHastings.
func MetropolisHastings(batch *mat64.Dense, initial []float64, target distmv.LogProber, proposal MHProposal, src *rand.Rand) {
	f64 := rand.Float64
	if src != nil {
		f64 = src.Float64
	}
	if len(initial) == 0 {
		panic("metropolishastings: zero length initial")
	}
	r, _ := batch.Dims()
	current := make([]float64, len(initial))
	copy(current, initial)
	proposed := make([]float64, len(initial))
	currentLogProb := target.LogProb(initial)
	for i := 0; i < r; i++ {
		proposal.ConditionalRand(proposed, current)
		proposedLogProb := target.LogProb(proposed)
		probTo := proposal.ConditionalLogProb(proposed, current)
		probBack := proposal.ConditionalLogProb(current, proposed)

		accept := math.Exp(proposedLogProb + probBack - probTo - currentLogProb)
		if accept > f64() {
			copy(current, proposed)
			currentLogProb = proposedLogProb
		}
		batch.SetRow(i, current)
	}
}

func (nb *NaiveBayes) Fit(X, y *mat64.Dense) {
	nSamples, nFeatures := X.Dims()

	tokensTotal := 0
	langsTotal, _ := y.Dims()

	langsCount := histogram(y.Col(nil, 0))

	tokensTotalPerLang := map[int]int{}
	tokenCountPerLang := map[int](map[int]int){}

	for i := 0; i < nSamples; i++ {
		langIdx := int(y.At(i, 0))

		for j := 0; j < nFeatures; j++ {
			tokensTotal += int(X.At(i, j))
			tokensTotalPerLang[langIdx] += int(X.At(i, j))

			if _, ok := tokenCountPerLang[langIdx]; !ok {
				tokenCountPerLang[langIdx] = map[int]int{}
			}
			tokenCountPerLang[langIdx][j] += int(X.At(i, j))
		}
	}

	params := nbParams{
		TokensTotal:        tokensTotal,
		LangsTotal:         langsTotal,
		LangsCount:         langsCount,
		TokensTotalPerLang: tokensTotalPerLang,
		TokenCountPerLang:  tokenCountPerLang,
	}

	nb.params = params
}

// GcvInitCameraMatrix2D takes one 3-by-N matrix and one 2-by-N Matrix as input.
// Each column in the input matrix represents a point in real world (objPts) or
// in image (imgPts).
// Return: the camera matrix.
func GcvInitCameraMatrix2D(objPts, imgPts *mat64.Dense, dims [2]int,
	aspectRatio float64) (camMat *mat64.Dense) {

	objDim, nObjPts := objPts.Dims()
	imgDim, nImgPts := imgPts.Dims()

	if objDim != 3 || imgDim != 2 || nObjPts != nImgPts {
		panic("Invalid dimensions for objPts and imgPts")
	}

	objPtsVec := NewGcvPoint3f32Vector(int64(nObjPts))
	imgPtsVec := NewGcvPoint2f32Vector(int64(nObjPts))

	for j := 0; j < nObjPts; j++ {
		objPtsVec.Set(j, NewGcvPoint3f32(mat64.Col(nil, j, objPts)...))
	}

	for j := 0; j < nObjPts; j++ {
		imgPtsVec.Set(j, NewGcvPoint2f32(mat64.Col(nil, j, imgPts)...))
	}

	_imgSize := NewGcvSize2i(dims[0], dims[1])

	camMat = GcvMatToMat64(GcvInitCameraMatrix2D_(
		objPtsVec, imgPtsVec, _imgSize, aspectRatio))
	return camMat
}

// LatinHypercube generates rows(batch) samples using Latin hypercube sampling
// from the given distribution. If src is not nil, it will be used to generate
// random numbers, otherwise rand.Float64 will be used.
//
// Latin hypercube sampling divides the cumulative distribution function into equally
// spaced bins and guarantees that one sample is generated per bin. Within each bin,
// the location is randomly sampled. The distmv.UnitNormal variable can be used
// for easy generation from the unit interval.
func LatinHypercube(batch *mat64.Dense, q distmv.Quantiler, src *rand.Rand) {
	r, c := batch.Dims()
	var f64 func() float64
	var perm func(int) []int
	if src != nil {
		f64 = src.Float64
		perm = src.Perm
	} else {
		f64 = rand.Float64
		perm = rand.Perm
	}
	r64 := float64(r)
	for i := 0; i < c; i++ {
		p := perm(r)
		for j := 0; j < r; j++ {
			var v float64
			v = f64()
			v = v/r64 + float64(j)/r64
			batch.Set(p[j], i, v)
		}
	}
	p := make([]float64, c)
	for i := 0; i < r; i++ {
		copy(p, batch.RawRowView(i))
		q.Quantile(batch.RawRowView(i), p)
	}
}

func toFeatureNodes(X *mat64.Dense) []*C.struct_feature_node {
	featureNodes := []*C.struct_feature_node{}

	nRows, nCols := X.Dims()

	for i := 0; i < nRows; i++ {
		row := []C.struct_feature_node{}
		for j := 0; j < nCols; j++ {
			val := X.At(i, j)
			if val != 0 {
				row = append(row, C.struct_feature_node{
					index: C.int(j + 1),
					value: C.double(val),
				})
			}
		}

		row = append(row, C.struct_feature_node{
			index: C.int(-1),
			value: C.double(0),
		})
		featureNodes = append(featureNodes, &row[0])
	}

	return featureNodes
}

func DfFromMat(mat *mat64.Dense) *DataFrame {
	rows, cols := mat.Dims()
	return &DataFrame{
		data: mat,
		rows: rows,
		cols: cols,
	}
}

func rowSum(matrix *mat64.Dense, rowId int) float64 {
	_, col := matrix.Dims()
	sum := float64(0)
	for c := 0; c < col; c++ {
		sum += matrix.At(rowId, c)
	}
	return sum
}

func colSum(matrix *mat64.Dense, colId int) float64 {
	row, _ := matrix.Dims()
	sum := float64(0)
	for r := 0; r < row; r++ {
		sum += matrix.At(r, colId)
	}
	return sum
}

// double predict(const struct model *model_, const struct feature_node *x);
func Predict(model *Model, X *mat64.Dense) *mat64.Dense {
	nRows, nCols := X.Dims()
	cX := mapCDouble(X.RawMatrix().Data)
	y := mat64.NewDense(nRows, 1, nil)
	result := doubleToFloats(C.call_predict(
		model.cModel, &cX[0], C.int(nRows), C.int(nCols)), nRows)
	y.SetCol(0, result)
	return y
}