diff --git a/metrics/pairwise/chebyshev.go b/metrics/pairwise/chebyshev.go
index 0068433..86f5663 100644
--- a/metrics/pairwise/chebyshev.go
+++ b/metrics/pairwise/chebyshev.go
@@ -12,7 +12,7 @@ func NewChebyshev() *Chebyshev {
 	return &Chebyshev{}
 }
 
-func (self *Chebyshev) Distance(vectorX *mat64.Dense, vectorY *mat64.Dense) float64 {
+func (c *Chebyshev) Distance(vectorX *mat64.Dense, vectorY *mat64.Dense) float64 {
 	r1, c1 := vectorX.Dims()
 	r2, c2 := vectorY.Dims()
 	if r1 != r2 || c1 != c2 {
diff --git a/metrics/pairwise/cranberra.go b/metrics/pairwise/cranberra.go
index 5f673bb..09551de 100644
--- a/metrics/pairwise/cranberra.go
+++ b/metrics/pairwise/cranberra.go
@@ -19,7 +19,7 @@ func cranberraDistanceStep(num float64, denom float64) float64 {
 	return num / denom
 }
 
-func (self *Cranberra) Distance(vectorX *mat64.Dense, vectorY *mat64.Dense) float64 {
+func (c *Cranberra) Distance(vectorX *mat64.Dense, vectorY *mat64.Dense) float64 {
 	r1, c1 := vectorX.Dims()
 	r2, c2 := vectorY.Dims()
 	if r1 != r2 || c1 != c2 {
diff --git a/metrics/pairwise/euclidean.go b/metrics/pairwise/euclidean.go
index 17302e0..8be0931 100644
--- a/metrics/pairwise/euclidean.go
+++ b/metrics/pairwise/euclidean.go
@@ -13,18 +13,18 @@ func NewEuclidean() *Euclidean {
 }
 
 // InnerProduct computes a Eucledian inner product.
-func (self *Euclidean) InnerProduct(vectorX *mat64.Dense, vectorY *mat64.Dense) float64 {
+func (e *Euclidean) InnerProduct(vectorX *mat64.Dense, vectorY *mat64.Dense) float64 {
 	result := vectorX.Dot(vectorY)
 
 	return result
 }
 
 // Distance computes Euclidean distance (also known as L2 distance).
-func (self *Euclidean) Distance(vectorX *mat64.Dense, vectorY *mat64.Dense) float64 {
+func (e *Euclidean) Distance(vectorX *mat64.Dense, vectorY *mat64.Dense) float64 {
 	subVector := mat64.NewDense(0, 0, nil)
 	subVector.Sub(vectorX, vectorY)
 
-	result := self.InnerProduct(subVector, subVector)
+	result := e.InnerProduct(subVector, subVector)
 
 	return math.Sqrt(result)
 }
diff --git a/metrics/pairwise/manhattan.go b/metrics/pairwise/manhattan.go
index 14f0597..5664a9d 100644
--- a/metrics/pairwise/manhattan.go
+++ b/metrics/pairwise/manhattan.go
@@ -14,7 +14,7 @@ func NewManhattan() *Manhattan {
 
 // Distance computes the Manhattan distance, also known as L1 distance.
 // == the sum of the absolute values of elements.
-func (self *Manhattan) Distance(vectorX *mat64.Dense, vectorY *mat64.Dense) float64 {
+func (m *Manhattan) Distance(vectorX *mat64.Dense, vectorY *mat64.Dense) float64 {
 	r1, c1 := vectorX.Dims()
 	r2, c2 := vectorY.Dims()
 	if r1 != r2 || c1 != c2 {
diff --git a/metrics/pairwise/poly_kernel.go b/metrics/pairwise/poly_kernel.go
index b5628fc..a4844bb 100644
--- a/metrics/pairwise/poly_kernel.go
+++ b/metrics/pairwise/poly_kernel.go
@@ -17,18 +17,18 @@ func NewPolyKernel(degree int) *PolyKernel {
 
 // InnerProduct computes the inner product through a kernel trick
 // K(x, y) = (x^T y + 1)^d
-func (self *PolyKernel) InnerProduct(vectorX *mat64.Dense, vectorY *mat64.Dense) float64 {
+func (p *PolyKernel) InnerProduct(vectorX *mat64.Dense, vectorY *mat64.Dense) float64 {
 	result := vectorX.Dot(vectorY)
-	result = math.Pow(result+1, float64(self.degree))
+	result = math.Pow(result+1, float64(p.degree))
 
 	return result
 }
 
 // Distance computes distance under the polynomial kernel (maybe not needed?)
-func (self *PolyKernel) Distance(vectorX *mat64.Dense, vectorY *mat64.Dense) float64 {
+func (p *PolyKernel) Distance(vectorX *mat64.Dense, vectorY *mat64.Dense) float64 {
 	subVector := mat64.NewDense(0, 0, nil)
 	subVector.Sub(vectorX, vectorY)
-	result := self.InnerProduct(subVector, subVector)
+	result := p.InnerProduct(subVector, subVector)
 
 	return math.Sqrt(result)
 }
diff --git a/metrics/pairwise/rbf_kernel.go b/metrics/pairwise/rbf_kernel.go
index 1c46710..1f3fc14 100644
--- a/metrics/pairwise/rbf_kernel.go
+++ b/metrics/pairwise/rbf_kernel.go
@@ -17,11 +17,11 @@ func NewRBFKernel(gamma float64) *RBFKernel {
 
 // InnerProduct computes the inner product through a kernel trick
 // K(x, y) = exp(-gamma * ||x - y||^2)
-func (self *RBFKernel) InnerProduct(vectorX *mat64.Dense, vectorY *mat64.Dense) float64 {
+func (r *RBFKernel) InnerProduct(vectorX *mat64.Dense, vectorY *mat64.Dense) float64 {
 	euclidean := NewEuclidean()
 	distance := euclidean.Distance(vectorX, vectorY)
 
-	result := math.Exp(-self.gamma * math.Pow(distance, 2))
+	result := math.Exp(-r.gamma * math.Pow(distance, 2))
 
 	return result
 }