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fix simatgrid sizing for labels and replace Col -> Column for all pca… #1193

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merged 3 commits into from
Aug 30, 2024
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fix simatgrid sizing for labels and replace Col -> Column for all pca… #1193

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ff7a5b5
fix simatgrid sizing for labels and replace Col -> Column for all pca…
rcoreilly Aug 30, 2024
ae4fee5
missed svd..
rcoreilly Aug 30, 2024
b6ff6f0
right, only did TableCol -- now good.
rcoreilly Aug 30, 2024
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12 changes: 6 additions & 6 deletions core/layout.go
View file
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Original file line number Diff line number Diff line change
Expand Up @@ -227,8 +227,8 @@ func (ls *geomSize) setInitContentMin(styMin math32.Vector2) {
styles.SetClampMaxVector(csz, ls.Max)
}

// fitSizeMax increases given size to fit given fm value, subject to Max constraints
func (ls *geomSize) fitSizeMax(to *math32.Vector2, fm math32.Vector2) {
// FitSizeMax increases given size to fit given fm value, subject to Max constraints
func (ls *geomSize) FitSizeMax(to *math32.Vector2, fm math32.Vector2) {
styles.SetClampMinVector(to, fm)
styles.SetClampMaxVector(to, ls.Max)
}
Expand Down Expand Up @@ -735,8 +735,8 @@ func (wb *WidgetBase) updateParentRelSizes() bool {
}
}
if got {
sz.fitSizeMax(&sz.Actual.Total, sz.Min)
sz.fitSizeMax(&sz.Alloc.Total, sz.Min)
sz.FitSizeMax(&sz.Actual.Total, sz.Min)
sz.FitSizeMax(&sz.Alloc.Total, sz.Min)
sz.setContentFromTotal(&sz.Actual)
sz.setContentFromTotal(&sz.Alloc)
}
Expand All @@ -750,7 +750,7 @@ func (wb *WidgetBase) sizeUpParts() {
}
wb.Parts.SizeUp()
sz := &wb.Geom.Size
sz.fitSizeMax(&sz.Actual.Content, wb.Parts.Geom.Size.Actual.Total)
sz.FitSizeMax(&sz.Actual.Content, wb.Parts.Geom.Size.Actual.Total)
}

func (fr *Frame) SizeUp() {
Expand Down Expand Up @@ -1532,7 +1532,7 @@ func (wb *WidgetBase) sizeFinalParts() {
}
wb.Parts.SizeFinal()
sz := &wb.Geom.Size
sz.fitSizeMax(&sz.Actual.Content, wb.Parts.Geom.Size.Actual.Total)
sz.FitSizeMax(&sz.Actual.Content, wb.Parts.Geom.Size.Actual.Total)
}

func (fr *Frame) SizeFinal() {
Expand Down
4 changes: 2 additions & 2 deletions core/text.go
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Original file line number Diff line number Diff line change
Expand Up @@ -328,7 +328,7 @@ func (tx *Text) SizeUp() {
tx.configTextSize(sz.Actual.Content)
}
rsz := tx.paintText.BBox.Size().Ceil()
sz.fitSizeMax(&sz.Actual.Content, rsz)
sz.FitSizeMax(&sz.Actual.Content, rsz)
sz.setTotalFromContent(&sz.Actual)
if DebugSettings.LayoutTrace {
fmt.Println(tx, "Text SizeUp:", rsz, "Actual:", sz.Actual.Content)
Expand All @@ -344,7 +344,7 @@ func (tx *Text) SizeDown(iter int) bool {
prevContent := sz.Actual.Content
// start over so we don't reflect hysteresis of prior guess
sz.setInitContentMin(tx.Styles.Min.Dots().Ceil())
sz.fitSizeMax(&sz.Actual.Content, rsz)
sz.FitSizeMax(&sz.Actual.Content, rsz)
sz.setTotalFromContent(&sz.Actual)
chg := prevContent != sz.Actual.Content
if chg {
Expand Down
4 changes: 2 additions & 2 deletions core/textfield.go
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Original file line number Diff line number Diff line change
Expand Up @@ -1859,7 +1859,7 @@ func (tf *TextField) SizeUp() {
rsz = tf.configTextSize(availSz)
}
rsz.SetAdd(icsz)
sz.fitSizeMax(&sz.Actual.Content, rsz)
sz.FitSizeMax(&sz.Actual.Content, rsz)
sz.setTotalFromContent(&sz.Actual)
tf.fontHeight = tf.Styles.Font.Face.Metrics.Height
tf.editText = tmptxt
Expand All @@ -1881,7 +1881,7 @@ func (tf *TextField) SizeDown(iter int) bool {
rsz.SetAdd(icsz)
// start over so we don't reflect hysteresis of prior guess
sz.setInitContentMin(tf.Styles.Min.Dots().Ceil())
sz.fitSizeMax(&sz.Actual.Content, rsz)
sz.FitSizeMax(&sz.Actual.Content, rsz)
sz.setTotalFromContent(&sz.Actual)
chg := prevContent != sz.Actual.Content
if chg {
Expand Down
6 changes: 0 additions & 6 deletions tensor/projection2d.go
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Original file line number Diff line number Diff line change
Expand Up @@ -19,7 +19,6 @@ const (
// For any odd number of dimensions, the remaining outer-most dimension
// can either be multipliexed across the row or column, given the oddRow arg.
// Even multiples of inner-most dimensions are assumed to be row, then column.
// RowMajor and ColMajor layouts are handled appropriately.
// rowEx returns the number of "extra" (higher dimensional) rows
// and colEx returns the number of extra cols
func Projection2DShape(shp *Shape, oddRow bool) (rows, cols, rowEx, colEx int) {
Expand Down Expand Up @@ -59,7 +58,6 @@ func Projection2DShape(shp *Shape, oddRow bool) (rows, cols, rowEx, colEx int) {
// For any odd number of dimensions, the remaining outer-most dimension
// can either be multipliexed across the row or column, given the oddRow arg.
// Even multiples of inner-most dimensions are assumed to be row, then column.
// RowMajor and ColMajor layouts are handled appropriately.
func Projection2DIndex(shp *Shape, oddRow bool, row, col int) int {
nd := shp.NumDims()
switch nd {
Expand Down Expand Up @@ -146,7 +144,6 @@ func Projection2DCoords(shp *Shape, oddRow bool, row, col int) (rowCoords, colCo
// For any odd number of dimensions, the remaining outer-most dimension
// can either be multipliexed across the row or column, given the oddRow arg.
// Even multiples of inner-most dimensions are assumed to be row, then column.
// RowMajor and ColMajor layouts are handled appropriately.
func Projection2DValue(tsr Tensor, oddRow bool, row, col int) float64 {
idx := Projection2DIndex(tsr.Shape(), oddRow, row, col)
return tsr.Float1D(idx)
Expand All @@ -157,7 +154,6 @@ func Projection2DValue(tsr Tensor, oddRow bool, row, col int) float64 {
// For any odd number of dimensions, the remaining outer-most dimension
// can either be multipliexed across the row or column, given the oddRow arg.
// Even multiples of inner-most dimensions are assumed to be row, then column.
// RowMajor and ColMajor layouts are handled appropriately.
func Projection2DString(tsr Tensor, oddRow bool, row, col int) string {
idx := Projection2DIndex(tsr.Shape(), oddRow, row, col)
return tsr.String1D(idx)
Expand All @@ -168,7 +164,6 @@ func Projection2DString(tsr Tensor, oddRow bool, row, col int) string {
// For any odd number of dimensions, the remaining outer-most dimension
// can either be multipliexed across the row or column, given the oddRow arg.
// Even multiples of inner-most dimensions are assumed to be row, then column.
// RowMajor and ColMajor layouts are handled appropriately.
func Projection2DSet(tsr Tensor, oddRow bool, row, col int, val float64) {
idx := Projection2DIndex(tsr.Shape(), oddRow, row, col)
tsr.SetFloat1D(idx, val)
Expand All @@ -179,7 +174,6 @@ func Projection2DSet(tsr Tensor, oddRow bool, row, col int, val float64) {
// For any odd number of dimensions, the remaining outer-most dimension
// can either be multipliexed across the row or column, given the oddRow arg.
// Even multiples of inner-most dimensions are assumed to be row, then column.
// RowMajor and ColMajor layouts are handled appropriately.
func Projection2DSetString(tsr Tensor, oddRow bool, row, col int, val string) {
idx := Projection2DIndex(tsr.Shape(), oddRow, row, col)
tsr.SetString1D(idx, val)
Expand Down
2 changes: 1 addition & 1 deletion tensor/stats/clust/clust_test.go
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Original file line number Diff line number Diff line change
Expand Up @@ -35,7 +35,7 @@ func TestClust(t *testing.T) {
}
ix := table.NewIndexView(dt)
smat := &simat.SimMat{}
smat.TableCol(ix, "Input", "Name", false, metric.Euclidean64)
smat.TableColumn(ix, "Input", "Name", false, metric.Euclidean64)

// fmt.Printf("%v\n", smat.Mat)
// cl := Glom(smat, MinDist)
Expand Down
20 changes: 10 additions & 10 deletions tensor/stats/pca/covar.go
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Original file line number Diff line number Diff line change
Expand Up @@ -12,7 +12,7 @@ import (
"cogentcore.org/core/tensor/table"
)

// CovarTableCol generates a covariance matrix from given column name
// CovarTableColumn generates a covariance matrix from given column name
// in given IndexView of an table.Table, and given metric function
// (typically Covariance or Correlation -- use Covar if vars have similar
// overall scaling, which is typical in neural network models, and use
Expand All @@ -22,15 +22,15 @@ import (
// cell co-varies in its value with each other cell across the rows of the table.
// This is the input to the PCA eigenvalue decomposition of the resulting
// covariance matrix.
func CovarTableCol(cmat tensor.Tensor, ix *table.IndexView, column string, mfun metric.Func64) error {
func CovarTableColumn(cmat tensor.Tensor, ix *table.IndexView, column string, mfun metric.Func64) error {
col, err := ix.Table.ColumnByName(column)
if err != nil {
return err
}
rows := ix.Len()
nd := col.NumDims()
if nd < 2 || rows == 0 {
return fmt.Errorf("pca.CovarTableCol: must have 2 or more dims and rows != 0")
return fmt.Errorf("pca.CovarTableColumn: must have 2 or more dims and rows != 0")
}
ln := col.Len()
sz := ln / col.DimSize(0) // size of cell
Expand All @@ -43,10 +43,10 @@ func CovarTableCol(cmat tensor.Tensor, ix *table.IndexView, column string, mfun
sdim := []int{0, 0}
for ai := 0; ai < sz; ai++ {
sdim[0] = ai
TableColRowsVec(av, ix, col, ai)
TableColumnRowsVec(av, ix, col, ai)
for bi := 0; bi <= ai; bi++ { // lower diag
sdim[1] = bi
TableColRowsVec(bv, ix, col, bi)
TableColumnRowsVec(bv, ix, col, bi)
cv := mfun(av, bv)
cmat.SetFloat(sdim, cv)
}
Expand Down Expand Up @@ -137,9 +137,9 @@ func CovarTensor(cmat tensor.Tensor, tsr tensor.Tensor, mfun metric.Func64) erro
return nil
}

// TableColRowsVec extracts row-wise vector from given cell index into vec.
// TableColumnRowsVec extracts row-wise vector from given cell index into vec.
// vec must be of size ix.Len() -- number of rows
func TableColRowsVec(vec []float64, ix *table.IndexView, col tensor.Tensor, cidx int) {
func TableColumnRowsVec(vec []float64, ix *table.IndexView, col tensor.Tensor, cidx int) {
rows := ix.Len()
ln := col.Len()
sz := ln / col.DimSize(0) // size of cell
Expand All @@ -161,7 +161,7 @@ func TensorRowsVec(vec []float64, tsr tensor.Tensor, cidx int) {
}
}

// CovarTableColStd generates a covariance matrix from given column name
// CovarTableColumnStd generates a covariance matrix from given column name
// in given IndexView of an table.Table, and given metric function
// (typically Covariance or Correlation -- use Covar if vars have similar
// overall scaling, which is typical in neural network models, and use
Expand All @@ -172,8 +172,8 @@ func TensorRowsVec(vec []float64, tsr tensor.Tensor, cidx int) {
// This is the input to the PCA eigenvalue decomposition of the resulting
// covariance matrix.
// This Std version is usable e.g., in Python where the func cannot be passed.
func CovarTableColStd(cmat tensor.Tensor, ix *table.IndexView, column string, met metric.StdMetrics) error {
return CovarTableCol(cmat, ix, column, metric.StdFunc64(met))
func CovarTableColumnStd(cmat tensor.Tensor, ix *table.IndexView, column string, met metric.StdMetrics) error {
return CovarTableColumn(cmat, ix, column, metric.StdFunc64(met))
}

// CovarTensorStd generates a covariance matrix from given tensor.Tensor,
Expand Down
30 changes: 15 additions & 15 deletions tensor/stats/pca/pca.go
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Original file line number Diff line number Diff line change
Expand Up @@ -35,7 +35,7 @@ func (pa *PCA) Init() {
pa.Values = nil
}

// TableCol is a convenience method that computes a covariance matrix
// TableColumn is a convenience method that computes a covariance matrix
// on given column of table and then performs the PCA on the resulting matrix.
// If no error occurs, the results can be read out from Vectors and Values
// or used in Projection methods.
Expand All @@ -48,11 +48,11 @@ func (pa *PCA) Init() {
// This is the input to the PCA eigenvalue decomposition of the resulting
// covariance matrix, which extracts the eigenvectors as directions with maximal
// variance in this matrix.
func (pa *PCA) TableCol(ix *table.IndexView, column string, mfun metric.Func64) error {
func (pa *PCA) TableColumn(ix *table.IndexView, column string, mfun metric.Func64) error {
if pa.Covar == nil {
pa.Init()
}
err := CovarTableCol(pa.Covar, ix, column, mfun)
err := CovarTableColumn(pa.Covar, ix, column, mfun)
if err != nil {
return err
}
Expand Down Expand Up @@ -83,7 +83,7 @@ func (pa *PCA) Tensor(tsr tensor.Tensor, mfun metric.Func64) error {
return pa.PCA()
}

// TableColStd is a convenience method that computes a covariance matrix
// TableColumnStd is a convenience method that computes a covariance matrix
// on given column of table and then performs the PCA on the resulting matrix.
// If no error occurs, the results can be read out from Vectors and Values
// or used in Projection methods.
Expand All @@ -97,8 +97,8 @@ func (pa *PCA) Tensor(tsr tensor.Tensor, mfun metric.Func64) error {
// covariance matrix, which extracts the eigenvectors as directions with maximal
// variance in this matrix.
// This Std version is usable e.g., in Python where the func cannot be passed.
func (pa *PCA) TableColStd(ix *table.IndexView, column string, met metric.StdMetrics) error {
return pa.TableCol(ix, column, metric.StdFunc64(met))
func (pa *PCA) TableColumnStd(ix *table.IndexView, column string, met metric.StdMetrics) error {
return pa.TableColumn(ix, column, metric.StdFunc64(met))
}

// TensorStd is a convenience method that computes a covariance matrix
Expand Down Expand Up @@ -145,20 +145,20 @@ func (pa *PCA) PCA() error {
return nil
}

// ProjectCol projects values from the given column of given table (via IndexView)
// ProjectColumn projects values from the given column of given table (via IndexView)
// onto the idx'th eigenvector (0 = largest eigenvalue, 1 = next, etc).
// Must have already called PCA() method.
func (pa *PCA) ProjectCol(vals *[]float64, ix *table.IndexView, column string, idx int) error {
func (pa *PCA) ProjectColumn(vals *[]float64, ix *table.IndexView, column string, idx int) error {
col, err := ix.Table.ColumnByName(column)
if err != nil {
return err
}
if pa.Vectors == nil {
return fmt.Errorf("PCA.ProjectCol Vectors are nil -- must call PCA first")
return fmt.Errorf("PCA.ProjectColumn Vectors are nil -- must call PCA first")
}
nr := pa.Vectors.DimSize(0)
if idx >= nr {
return fmt.Errorf("PCA.ProjectCol eigenvector index > rank of matrix")
return fmt.Errorf("PCA.ProjectColumn eigenvector index > rank of matrix")
}
cvec := make([]float64, nr)
eidx := nr - 1 - idx // eigens in reverse order
Expand All @@ -173,7 +173,7 @@ func (pa *PCA) ProjectCol(vals *[]float64, ix *table.IndexView, column string, i
ln := col.Len()
sz := ln / col.DimSize(0) // size of cell
if sz != nr {
return fmt.Errorf("PCA.ProjectCol column cell size != pca eigenvectors")
return fmt.Errorf("PCA.ProjectColumn column cell size != pca eigenvectors")
}
rdim := []int{0}
for row := 0; row < rows; row++ {
Expand All @@ -188,17 +188,17 @@ func (pa *PCA) ProjectCol(vals *[]float64, ix *table.IndexView, column string, i
return nil
}

// ProjectColToTable projects values from the given column of given table (via IndexView)
// ProjectColumnToTable projects values from the given column of given table (via IndexView)
// onto the given set of eigenvectors (idxs, 0 = largest eigenvalue, 1 = next, etc),
// and stores results along with labels from column labNm into results table.
// Must have already called PCA() method.
func (pa *PCA) ProjectColToTable(projections *table.Table, ix *table.IndexView, column, labNm string, idxs []int) error {
func (pa *PCA) ProjectColumnToTable(projections *table.Table, ix *table.IndexView, column, labNm string, idxs []int) error {
_, err := ix.Table.ColumnByName(column)
if err != nil {
return err
}
if pa.Vectors == nil {
return fmt.Errorf("PCA.ProjectCol Vectors are nil -- must call PCA first")
return fmt.Errorf("PCA.ProjectColumn Vectors are nil -- must call PCA first")
}
rows := ix.Len()
projections.DeleteAll()
Expand All @@ -214,7 +214,7 @@ func (pa *PCA) ProjectColToTable(projections *table.Table, ix *table.IndexView,

for ii, idx := range idxs {
pcol := projections.Columns[pcolSt+ii].(*tensor.Float64)
pa.ProjectCol(&pcol.Values, ix, column, idx)
pa.ProjectColumn(&pcol.Values, ix, column, idx)
}

if labNm != "" {
Expand Down
6 changes: 3 additions & 3 deletions tensor/stats/pca/pca_test.go
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Original file line number Diff line number Diff line change
Expand Up @@ -26,9 +26,9 @@ func TestPCAIris(t *testing.T) {
}
ix := table.NewIndexView(dt)
pc := &PCA{}
// pc.TableCol(ix, "data", metric.Covariance64)
// pc.TableColumn(ix, "data", metric.Covariance64)
// fmt.Printf("covar: %v\n", pc.Covar)
err = pc.TableCol(ix, "data", metric.Correlation64)
err = pc.TableColumn(ix, "data", metric.Correlation64)
if err != nil {
t.Error(err)
}
Expand All @@ -47,7 +47,7 @@ func TestPCAIris(t *testing.T) {
}

prjt := &table.Table{}
err = pc.ProjectColToTable(prjt, ix, "data", "class", []int{0, 1})
err = pc.ProjectColumnToTable(prjt, ix, "data", "class", []int{0, 1})
if err != nil {
t.Error(err)
}
Expand Down
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