/
dist_matrix_test.go
326 lines (311 loc) · 6.27 KB
/
dist_matrix_test.go
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// Public domain.
package cluster_test
import (
"fmt"
"math"
"testing"
"github.com/soniakeys/cluster"
)
func ExampleDistanceMatrix_String() {
fmt.Println(cluster.DistanceMatrix{
{0, 3},
{3, 0},
})
fmt.Println()
fmt.Println(cluster.DistanceMatrix{
{.5, 1. / 3},
{math.Inf(1), math.NaN()},
})
// Output:
// [0 3]
// [3 0]
//
// [0.5 0.3333333333333333]
// [+Inf NaN]
}
func ExampleDistanceMatrix_Square() {
d1 := cluster.DistanceMatrix{
{0, 3},
{3, 0},
}
d2 := cluster.DistanceMatrix{}
d3 := cluster.DistanceMatrix{
{0},
{3, 0},
}
fmt.Println(d1.Square())
fmt.Println(d2.Square())
fmt.Println(d3.Square())
// Output:
// true
// true
// false
}
func ExampleDistanceMatrix_NonNegative() {
d1 := cluster.DistanceMatrix{
{0, -1}, // false
{-1, 0},
}
d2 := cluster.DistanceMatrix{
{0}, // true
{1, 2},
}
d3 := cluster.DistanceMatrix{} // true (no negatives present)
d4 := cluster.DistanceMatrix{
{0, math.NaN()}, // true
{3, 0},
}
fmt.Println(d1.NonNegative())
fmt.Println(d2.NonNegative())
fmt.Println(d3.NonNegative())
fmt.Println(d4.NonNegative())
// Outpupt:
// false
// true
// true
// true
}
func ExampleDistanceMatrix_ZeroDiagonal() {
d1 := cluster.DistanceMatrix{
{0, 3}, // true
{3, 0},
}
d2 := cluster.DistanceMatrix{
{0}, // true
{7, 0},
}
d3 := cluster.DistanceMatrix{} // true
d4 := cluster.DistanceMatrix{
{0, 3},
{3, 1e-300}, // false
}
fmt.Println(d1.ZeroDiagonal())
fmt.Println(d2.ZeroDiagonal())
fmt.Println(d3.ZeroDiagonal())
fmt.Println(d4.ZeroDiagonal())
// Output:
// true
// true
// true
// false
}
func ExampleDistanceMatrix_Symmetric() {
d1 := cluster.DistanceMatrix{
{0, 3}, // true
{3, 0},
}
d2 := cluster.DistanceMatrix{
{0, 3}, // false
{7, 0},
}
d3 := cluster.DistanceMatrix{
{0, math.NaN()}, // false (NaNs do not compare equal)
{math.NaN(), 0},
}
d4 := cluster.DistanceMatrix{
{0, 3}, // true (diagonal is not checked)
{3, math.NaN()},
}
fmt.Println(d1.Symmetric())
fmt.Println(d2.Symmetric())
fmt.Println(d3.Symmetric())
fmt.Println(d4.Symmetric())
// Output:
// true
// false
// false
// true
}
func ExampleDistanceMatrix_TriangleInequality() {
d1 := cluster.DistanceMatrix{
{0, 13, 21, 22}, // true
{13, 0, 12, 13},
{21, 12, 0, 13},
{22, 13, 13, 0},
}
d2 := cluster.DistanceMatrix{
{0, 13, 21, math.NaN()}, // true
{13, 0, 12, 13},
{21, 12, 0, 13},
{22, 13, 13, 0},
}
d3 := cluster.DistanceMatrix{}
d4 := cluster.DistanceMatrix{
{0, 4, 6, 1}, // false
{4, 0, 3, 2},
{6, 3, 0, 5},
{1, 2, 5, 0},
}
fmt.Println(d1.TriangleInequality())
fmt.Println(d2.TriangleInequality())
fmt.Println(d3.TriangleInequality())
fmt.Println(d4.TriangleInequality())
// Output:
// true 0 0 0
// true 0 0 0
// true 0 0 0
// false 1 3 0
}
func ExampleDistanceMatrix_Validate() {
d1 := cluster.DistanceMatrix{
{0, 13, 21, 22},
{13, 0, 12, 13},
{21, 12, 0, 13},
{22, 13, 13, 0},
}
d2 := cluster.DistanceMatrix{
{0, 4, 6, 1}, // false
{4, 0, 3, 2},
{6, 3, 0, 5},
{1, 2, 5, 0},
}
fmt.Println(d1.Validate())
fmt.Println(d2.Validate())
// Output:
// <nil>
// triangle inequality not satisfied: d[1][3] + d[3][0] < d[1][0]
}
func ExampleDistanceMatrix_Additive() {
a := cluster.DistanceMatrix{
{0, 13, 21, 22},
{13, 0, 12, 13},
{21, 12, 0, 13},
{22, 13, 13, 0},
}
na := cluster.DistanceMatrix{
{0, 3, 4, 3},
{3, 0, 4, 5},
{4, 4, 0, 2},
{3, 5, 2, 0},
}
fmt.Println(a.Additive())
fmt.Println(na.Additive())
// Output:
// true 0 0 0 0
// false 3 1 0 2
}
func ExampleDistanceMatrix_AdditiveTree() {
d := cluster.DistanceMatrix{
{0, 13, 21, 22},
{13, 0, 12, 13},
{21, 12, 0, 13},
{22, 13, 13, 0},
}
t, wts := d.AdditiveTree()
for n, to := range t.LabeledAdjacencyList {
for _, to := range to {
fmt.Printf("%d: to %d label %d weight %g\n",
n, to.To, to.Label, wts[to.Label])
}
}
// Output:
// 0: to 4 label 1 weight 11
// 1: to 4 label 0 weight 2
// 2: to 5 label 2 weight 6
// 3: to 5 label 4 weight 7
// 4: to 1 label 0 weight 2
// 4: to 0 label 1 weight 11
// 4: to 5 label 3 weight 4
// 5: to 2 label 2 weight 6
// 5: to 4 label 3 weight 4
// 5: to 3 label 4 weight 7
}
func TestRandomAdditiveMatrix(t *testing.T) {
for _, d := range []cluster.DistanceMatrix{
cluster.RandomAdditiveMatrix(10),
cluster.RandomAdditiveMatrix(20),
cluster.RandomAdditiveMatrix(40),
} {
if ok, _, _, _, _ := d.Additive(); !ok {
t.Fatal(len(d))
}
}
}
func ExampleDistanceMatrix_Ultrametric() {
d := cluster.DistanceMatrix{
{0, 20, 17, 11},
{20, 0, 20, 13},
{17, 20, 0, 10},
{11, 13, 10, 0},
}
pl, ul := d.Ultrametric(cluster.DAVG)
fmt.Println("node leaves parent weight age")
for n, p := range pl.Paths {
fmt.Printf(">%3d %3d %3d %6.3f %6.3f\n",
n, p.Len, p.From, ul[n].Weight, ul[n].Age)
}
// Output:
// node leaves parent weight age
// > 0 1 5 7.000 0.000
// > 1 1 6 8.833 0.000
// > 2 1 4 5.000 0.000
// > 3 1 4 5.000 0.000
// > 4 2 5 2.000 5.000
// > 5 3 6 1.833 7.000
// > 6 4 -1 NaN 8.833
}
func ExampleDistanceMatrix_NeighborJoin() {
d := cluster.DistanceMatrix{
{0, 23, 27, 20},
{23, 0, 30, 28},
{27, 30, 0, 30},
{20, 28, 30, 0},
}
tree, wt := d.NeighborJoin()
fmt.Println("n1 n2 weight")
for n, to := range tree.LabeledAdjacencyList {
for _, h := range to {
fmt.Printf("%d %2d %6.3f\n", n, h.To, wt[h.Label])
}
}
// Output:
// n1 n2 weight
// 0 5 8.000
// 1 4 13.500
// 2 4 16.500
// 3 5 12.000
// 4 5 2.000
// 4 1 13.500
// 4 2 16.500
// 5 3 12.000
// 5 0 8.000
// 5 4 2.000
}
/*
func ExampleUList_Cut() {
exp := []cluster.Point{
{10, 8, 10},
{10, 0, 9},
{4, 8.5, 3},
{9.5, .5, 8.5},
{4.5, 8.5, 2.5},
{10.5, 9, 12},
{5, 8.5, 11},
{3.7, 8.7, 2},
{9.7, 2, 9},
{10.2, 1, 9.2},
}
dm := cluster.NewEuclideanDist(exp)
u := dm.Ultrametric(cluster.DAVG)
for _, c := range u.Cut(4) {
for _, x := range c {
fmt.Printf("%d: %g\n", x, exp[x])
}
fmt.Println()
}
// Output:
// 6: [5 8.5 11]
//
// 7: [3.7 8.7 2]
// 2: [4 8.5 3]
// 4: [4.5 8.5 2.5]
//
// 8: [9.7 2 9]
// 9: [10.2 1 9.2]
// 1: [10 0 9]
// 3: [9.5 0.5 8.5]
//
// 0: [10 8 10]
// 5: [10.5 9 12]
}
*/