stat: append To suffix to extraction methods

stat/cca_example_test.go

@@ -85,11 +85,11 @@ func ExampleCC() {
 	}
 
 	// Unpack cc.
-	ccors := cc.Corrs(nil)
+	ccors := cc.CorrsTo(nil)
-	pVecs := cc.Left(nil, true)
+	pVecs := cc.LeftTo(nil, true)
-	qVecs := cc.Right(nil, true)
+	qVecs := cc.RightTo(nil, true)
-	phiVs := cc.Left(nil, false)
+	phiVs := cc.LeftTo(nil, false)
-	psiVs := cc.Right(nil, false)
+	psiVs := cc.RightTo(nil, false)
 
 	// Canonical Correlation Matrix, or the correlations between the sphered
 	// data.

stat/cca_test.go

@@ -160,11 +160,11 @@ tests:
 				continue tests
 			}
 
-			corrs = cc.Corrs(corrs)
+			corrs = cc.CorrsTo(corrs)
-			pVecs = cc.Left(pVecs, true)
+			pVecs = cc.LeftTo(pVecs, true)
-			qVecs = cc.Right(qVecs, true)
+			qVecs = cc.RightTo(qVecs, true)
-			phiVs = cc.Left(phiVs, false)
+			phiVs = cc.LeftTo(phiVs, false)
-			psiVs = cc.Right(psiVs, false)
+			psiVs = cc.RightTo(psiVs, false)
 
 			if !floats.EqualApprox(corrs, test.wantCorrs, test.epsilon) {
 				t.Errorf("%d use %d: unexpected variance result got:%v, want:%v",

stat/pca_cca.go

@@ -46,12 +46,12 @@ func (c *PC) PrincipalComponents(a mat.Matrix, weights []float64) (ok bool) {
 	return c.ok
 }
 
-// Vectors returns the component direction vectors of a principal components
+// VectorsTo returns the component direction vectors of a principal components
 // analysis. The vectors are returned in the columns of a d×min(n, d) matrix.
 // If dst is not nil it must either be zero-sized or be a d×min(n, d) matrix.
 // dst will  be used as the destination for the direction vector data. If dst
 // is nil, a new mat.Dense is allocated for the destination.
-func (c *PC) Vectors(dst *mat.Dense) *mat.Dense {
+func (c *PC) VectorsTo(dst *mat.Dense) *mat.Dense {
 	if !c.ok {
 		panic("stat: use of unsuccessful principal components analysis")
 	}
@@ -64,13 +64,13 @@ func (c *PC) Vectors(dst *mat.Dense) *mat.Dense {
 	return c.svd.VTo(dst)
 }
 
-// Vars returns the column variances of the principal component scores,
+// VarsTo returns the column variances of the principal component scores,
 // b * vecs, where b is a matrix with centered columns. Variances are returned
 // in descending order.
 // If dst is not nil it is used to store the variances and returned.
 // Vars will panic if the receiver has not successfully performed a principal
 // components analysis or dst is not nil and the length of dst is not min(n, d).
-func (c *PC) Vars(dst []float64) []float64 {
+func (c *PC) VarsTo(dst []float64) []float64 {
 	if !c.ok {
 		panic("stat: use of unsuccessful principal components analysis")
 	}
@@ -199,10 +199,10 @@ func (c *CC) CanonicalCorrelations(x, y mat.Matrix, weights []float64) error {
 	return nil
 }
 
-// Corrs returns the canonical correlations, using dst if it is not nil.
+// CorrsTo returns the canonical correlations, using dst if it is not nil.
 // If dst is not nil and len(dst) does not match the number of columns in
 // the y input matrix, Corrs will panic.
-func (c *CC) Corrs(dst []float64) []float64 {
+func (c *CC) CorrsTo(dst []float64) []float64 {
 	if !c.ok {
 		panic("stat: canonical correlations missing or invalid")
 	}
@@ -213,14 +213,14 @@ func (c *CC) Corrs(dst []float64) []float64 {
 	return c.c.Values(dst)
 }
 
-// Left returns the left eigenvectors of the canonical correlation matrix if
+// LeftTo returns the left eigenvectors of the canonical correlation matrix if
 // spheredSpace is true. If spheredSpace is false it returns these eigenvectors
 // back-transformed to the original data space.
 // If dst is not nil it must either be zero-sized or be an xd×yd matrix where xd
 // and yd are the number of variables in the input x and y matrices. dst will
 // be used as the destination for the vector data. If dst is nil, a new
 // mat.Dense is allocated for the destination.
-func (c *CC) Left(dst *mat.Dense, spheredSpace bool) *mat.Dense {
+func (c *CC) LeftTo(dst *mat.Dense, spheredSpace bool) *mat.Dense {
 	if !c.ok || c.n < 2 {
 		panic("stat: canonical correlations missing or invalid")
 	}
@@ -245,14 +245,14 @@ func (c *CC) Left(dst *mat.Dense, spheredSpace bool) *mat.Dense {
 	return dst
 }
 
-// Right returns the right eigenvectors of the canonical correlation matrix if
+// RightTo returns the right eigenvectors of the canonical correlation matrix if
 // spheredSpace is true. If spheredSpace is false it returns these eigenvectors
 // back-transformed to the original data space.
 // If dst is not nil it must either be zero-sized or be an yd×yd matrix where yd
 // is the number of variables in the input y matrix. dst will
 // be used as the destination for the vector data. If dst is nil, a new
 // mat.Dense is allocated for the destination.
-func (c *CC) Right(dst *mat.Dense, spheredSpace bool) *mat.Dense {
+func (c *CC) RightTo(dst *mat.Dense, spheredSpace bool) *mat.Dense {
 	if !c.ok || c.n < 2 {
 		panic("stat: canonical correlations missing or invalid")
 	}

stat/pca_example_test.go

@@ -35,12 +35,12 @@ func ExamplePC() {
 	if !ok {
 		return
 	}
-	fmt.Printf("variances = %.4f\n\n", pc.Vars(nil))
+	fmt.Printf("variances = %.4f\n\n", pc.VarsTo(nil))
 
 	// Project the data onto the first 2 principal components.
 	k := 2
 	var proj mat.Dense
-	proj.Mul(iris, pc.Vectors(nil).Slice(0, d, 0, k))
+	proj.Mul(iris, pc.VectorsTo(nil).Slice(0, d, 0, k))
 
 	fmt.Printf("proj = %.4f", mat.Formatted(&proj, mat.Prefix("       ")))
 

stat/pca_test.go

@@ -157,8 +157,8 @@ tests:
 		var vars []float64
 		for j := 0; j < 2; j++ {
 			ok := pc.PrincipalComponents(test.data, test.weights)
-			vecs = pc.Vectors(vecs)
+			vecs = pc.VectorsTo(vecs)
-			vars = pc.Vars(vars)
+			vars = pc.VarsTo(vars)
 			if !ok {
 				t.Errorf("unexpected SVD failure for test %d use %d", i, j)
 				continue tests