all: update packages from mat64 to mat.
This mostly changes package name and code, but also fixes a couple of name clashes with the new package names
diff --git a/stat/cca_example_test.go b/stat/cca_example_test.go
index 3d311f0..eafc951 100644
--- a/stat/cca_example_test.go
+++ b/stat/cca_example_test.go
@@ -9,13 +9,13 @@
"log"
"gonum.org/v1/gonum/floats"
- "gonum.org/v1/gonum/matrix/mat64"
+ "gonum.org/v1/gonum/mat"
"gonum.org/v1/gonum/stat"
)
// symView is a helper for getting a View of a SymDense.
type symView struct {
- sym *mat64.SymDense
+ sym *mat.SymDense
i, j, r, c int
}
@@ -32,7 +32,7 @@
return s.sym.At(s.i+i, s.j+j)
}
-func (s symView) T() mat64.Matrix { return mat64.Transpose{s} }
+func (s symView) T() mat.Matrix { return mat.Transpose{s} }
func ExampleCC() {
// This example is directly analogous to Example 3.5 on page 87 of
@@ -65,7 +65,7 @@
ydata := bostonData.Slice(0, n, xd, xd+yd)
// For comparison, calculate the correlation matrix for the original data.
- var cor mat64.SymDense
+ var cor mat.SymDense
stat.CorrelationMatrix(&cor, bostonData, nil)
// Extract just those correlations that are between xdata and ydata.
@@ -75,7 +75,7 @@
// between the 5th variable of xdata (index of accessibility to radial
// highways) and the 3rd variable of ydata (full-value property-tax rate per
// $10000).
- fmt.Printf("corRaw = %.4f", mat64.Formatted(corRaw, mat64.Prefix(" ")))
+ fmt.Printf("corRaw = %.4f", mat.Formatted(corRaw, mat.Prefix(" ")))
// Calculate the canonical correlations.
var cc stat.CC
@@ -93,16 +93,16 @@
// Canonical Correlation Matrix, or the correlations between the sphered
// data.
- var corSph mat64.Dense
+ var corSph mat.Dense
corSph.Clone(pVecs)
col := make([]float64, xd)
for j := 0; j < yd; j++ {
- mat64.Col(col, j, &corSph)
+ mat.Col(col, j, &corSph)
floats.Scale(ccors[j], col)
corSph.SetCol(j, col)
}
corSph.Product(&corSph, qVecs.T())
- fmt.Printf("\n\ncorSph = %.4f", mat64.Formatted(&corSph, mat64.Prefix(" ")))
+ fmt.Printf("\n\ncorSph = %.4f", mat.Formatted(&corSph, mat.Prefix(" ")))
// Canonical Correlations. Note that the first canonical correlation is
// 0.95, stronger than the greatest correlation in the original data, and
@@ -110,13 +110,13 @@
fmt.Printf("\n\nccors = %.4f", ccors)
// Left and right eigenvectors of the canonical correlation matrix.
- fmt.Printf("\n\npVecs = %.4f", mat64.Formatted(pVecs, mat64.Prefix(" ")))
- fmt.Printf("\n\nqVecs = %.4f", mat64.Formatted(qVecs, mat64.Prefix(" ")))
+ fmt.Printf("\n\npVecs = %.4f", mat.Formatted(pVecs, mat.Prefix(" ")))
+ fmt.Printf("\n\nqVecs = %.4f", mat.Formatted(qVecs, mat.Prefix(" ")))
// Canonical Correlation Transforms. These can be useful as they represent
// the canonical variables as linear combinations of the original variables.
- fmt.Printf("\n\nphiVs = %.4f", mat64.Formatted(phiVs, mat64.Prefix(" ")))
- fmt.Printf("\n\npsiVs = %.4f", mat64.Formatted(psiVs, mat64.Prefix(" ")))
+ fmt.Printf("\n\nphiVs = %.4f", mat.Formatted(phiVs, mat.Prefix(" ")))
+ fmt.Printf("\n\npsiVs = %.4f", mat.Formatted(psiVs, mat.Prefix(" ")))
// Output:
// corRaw = ⎡-0.2192 0.3527 0.5828 -0.3883⎤
