// 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(objPts.Col(nil, j)...))
}
for j := 0; j < nObjPts; j++ {
imgPtsVec.Set(j, NewGcvPoint2f32(imgPts.Col(nil, j)...))
}
_imgSize := NewGcvSize2i(dims[0], dims[1])
camMat = GcvMatToMat64(GcvInitCameraMatrix2D_(
objPtsVec, imgPtsVec, _imgSize, aspectRatio))
return camMat
}
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
}
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 Accuracy(y_true, y_pred *mat64.Dense) float64 {
y1 := y_true.Col(nil, 0)
y2 := y_pred.Col(nil, 0)
total := 0.0
correct := 0.0
for i := 0; i < len(y1); i++ {
if y1[i] == y2[i] {
correct++
}
total++
}
return correct / total
}
// Wrapper for the `train` function in liblinear.
//
// `model* train(const struct problem *prob, const struct parameter *param);`
//
// The explanation of parameters are:
//
// solverType:
//
// for multi-class classification
// 0 -- L2-regularized logistic regression (primal)
// 1 -- L2-regularized L2-loss support vector classification (dual)
// 2 -- L2-regularized L2-loss support vector classification (primal)
// 3 -- L2-regularized L1-loss support vector classification (dual)
// 4 -- support vector classification by Crammer and Singer
// 5 -- L1-regularized L2-loss support vector classification
// 6 -- L1-regularized logistic regression
// 7 -- L2-regularized logistic regression (dual)
// for regression
// 11 -- L2-regularized L2-loss support vector regression (primal)
// 12 -- L2-regularized L2-loss support vector regression (dual)
// 13 -- L2-regularized L1-loss support vector regression (dual)
//
// eps is the stopping criterion.
//
// C_ is the cost of constraints violation.
//
// p is the sensitiveness of loss of support vector regression.
//
// classWeights is a map from int to float64, with the key be the class and the
// value be the weight. For example, {1: 10, -1: 0.5} means giving weight=10 for
// class=1 while weight=0.5 for class=-1
//
// If you do not want to change penalty for any of the classes, just set
// classWeights to nil.
func Train(X, y *mat64.Dense, bias float64, pm *Parameter) *Model {
var problem C.struct_problem
nRows, nCols := X.Dims()
cY := mapCDouble(y.Col(nil, 0))
cX := toFeatureNodes(X)
problem.x = &cX[0]
problem.y = &cY[0]
problem.n = C.int(nCols)
problem.l = C.int(nRows)
problem.bias = C.double(bias)
model := C.train(&problem, pm.GetPtr())
return &Model{
cModel: model,
}
}
func removeCol(df *mat64.Dense, col int) *mat64.Dense {
r, c := df.Dims()
if col > c || col < 0 {
panic("Column Index not supported")
}
cop := mat64.NewDense(r, c-1, nil)
m := 0
for i := 0; i < c; i++ {
if i != col {
cop.SetCol(m, df.Col(nil, i))
m++
}
}
return cop
}
func GcvCalibrateCamera(objPts, imgPts, camMat, distCoeffs *mat64.Dense,
dims [2]int, flags int) (calCamMat, rvec, tvec *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(objPts.Col(nil, j)...))
}
for j := 0; j < nObjPts; j++ {
imgPtsVec.Set(j, NewGcvPoint2f32(imgPts.Col(nil, j)...))
}
_camMat := Mat64ToGcvMat(camMat)
_distCoeffs := Mat64ToGcvMat(distCoeffs)
_rvec := NewGcvMat()
_tvec := NewGcvMat()
_imgSize := NewGcvSize2i(dims[0], dims[1])
GcvCalibrateCamera_(
objPtsVec, imgPtsVec,
_imgSize, _camMat, _distCoeffs,
_rvec, _tvec, flags)
calCamMat = GcvMatToMat64(_camMat)
rvec = GcvMatToMat64(_rvec)
tvec = GcvMatToMat64(_tvec)
return calCamMat, rvec, tvec
}
