func TestAppIterMu(t *testing.T) {
ExpectedMatrix := mat.NewDense(4, 3, []float64{
6, 4, 1,
2, 3, 4,
9, 1, 6,
6, 2, 2,
})
iter := &AppIter{mu: ExpectedMatrix}
ResultMat := iter.Mu()
if !mat.Equal(ExpectedMatrix, ResultMat) {
t.Errorf("Expected \n%v, got \n%v",
printMatrix(ExpectedMatrix), printMatrix(ResultMat))
}
}
func TestAppIterIdx(t *testing.T) {
expectedVec := mat.NewVector(4, []float64{
3,
4,
8,
1,
})
iter := &AppIter{idx: expectedVec}
resultVec := iter.Idx()
if !mat.Equal(expectedVec, resultVec) {
t.Errorf("Expected \n%v got,\n%v",
printMatrix(expectedVec), printMatrix(resultVec))
}
}
func TestGetRowVector(t *testing.T) {
A := mat.NewDense(3, 3, []float64{
6, 4, 2,
10, 3, 9,
4, 7, 1,
})
rowIndex := 1
expectedVector := mat.NewVector(3, []float64{
10,
3,
9,
})
if !mat.Equal(expectedVector, getRowVector(rowIndex, A)) {
t.Errorf("Expected true, got false")
}
}
func TestColumnMean(t *testing.T) {
M := mat.NewDense(4, 3, []float64{
6, 4, 1,
2, 3, 4,
9, 1, 6,
6, 2, 2,
})
_, cols := M.Dims()
ExpectedRes := mat.NewDense(1, cols, []float64{
5.75, 2.5, 3.25,
})
Result := columnMean(M)
if !mat.Equal(ExpectedRes, Result) {
t.Errorf("Expected \n%v, got\n%v", printMatrix(ExpectedRes), printMatrix(Result))
}
}
func TestFindIn(t *testing.T) {
v := mat.NewVector(4, []float64{
1,
0,
1,
0,
})
x := 1
expectedVec := mat.NewVector(2, []float64{
0,
2,
})
result := findIn(float64(x), v)
if !mat.Equal(result, expectedVec) {
t.Errorf("Expected \n%v, found \n%v",
printMatrix(expectedVec), printMatrix(result))
}
}
func TestMultiHypothesis(t *testing.T) {
for _, test := range []struct {
theta *mat64.Vector
x *mat64.Dense
y *mat64.Vector
}{
{
mat64.NewVector(2, []float64{0, 2}),
mat64.NewDense(2, 3, []float64{0, 0, 0, 1, 2, 10}),
mat64.NewVector(3, []float64{2, 4, 20}),
},
} {
h := MultiHypothesis(test.x, test.theta)
if !mat64.Equal(h, test.y) {
t.Errorf("MultiHypothesis(%v,%v) is expected to be equal to %v, found %v", test.x, test.theta, test.y, h)
}
}
}
func TestRowSum(t *testing.T) {
M := mat.NewDense(4, 3, []float64{
6, 4, 1,
2, 3, 4,
9, 1, 6,
6, 2, 2,
})
r, _ := M.Dims()
ExpectedM := mat.NewDense(r, 1, []float64{
11,
9,
16,
10,
})
ResultM := rowSum(M)
if !mat.Equal(ExpectedM, ResultM) {
t.Errorf("Expected \n%v, got\n%v", printMatrix(ExpectedM), printMatrix(ResultM))
}
}
func TestGetColumnVector(t *testing.T) {
M := mat.NewDense(4, 3, []float64{
6, 4, 1,
2, 3, 4,
9, 1, 6,
6, 2, 2,
})
columnIndex := 1
vectorLen, _ := M.Dims()
expVec := mat.NewVector(vectorLen, []float64{
4,
3,
1,
2,
})
resultVec := getColumnVector(columnIndex, M)
if !mat.Equal(expVec, resultVec) {
t.Errorf("Expected \n%v, got\n %v", printMatrix(expVec), printMatrix(resultVec))
}
}
func TestRowIndexIn(t *testing.T) {
Matrix := mat.NewDense(4, 3, []float64{
6, 4, 1,
2, 3, 4,
9, 1, 6,
6, 2, 2,
})
vecIndex := mat.NewVector(2, []float64{
1,
3,
})
ExpectedMatrix := mat.NewDense(2, 3, []float64{
2, 3, 4,
6, 2, 2,
})
Result := rowIndexIn(vecIndex, Matrix)
if !mat.Equal(ExpectedMatrix, Result) {
t.Errorf("Expected \n%v, got \n%v",
printMatrix(ExpectedMatrix),
printMatrix(Result))
}
}
func TestConstructXCentroidMatrix(t *testing.T) {
Mu := mat.NewDense(2, 3, []float64{
4, 9, 2,
3, 1, 4,
})
idx := mat.NewVector(4, []float64{
0,
1,
0,
1,
})
ExpectedMu := mat.NewDense(4, 3, []float64{
4, 9, 2,
3, 1, 4,
4, 9, 2,
3, 1, 4,
})
ResultMu := ConstructXCentroidMatrix(idx, Mu)
if !mat.Equal(ResultMu, ExpectedMu) {
t.Errorf("Expected \n%v, got\n%v", printMatrix(ExpectedMu), printMatrix(ResultMu))
}
}
func TestUndirect(t *testing.T) {
for _, test := range directedGraphs {
g := test.g()
for _, e := range test.edges {
g.SetEdge(e)
}
src := graph.Undirect{G: g, Absent: test.absent, Merge: test.merge}
dst := simple.NewUndirectedMatrixFrom(src.Nodes(), 0, 0, 0)
for _, u := range src.Nodes() {
for _, v := range src.From(u) {
dst.SetEdge(src.Edge(u, v))
}
}
if !mat64.Equal(dst.Matrix(), test.want) {
t.Errorf("unexpected result:\ngot:\n%.4v\nwant:\n%.4v",
mat64.Formatted(dst.Matrix()),
mat64.Formatted(test.want),
)
}
}
}
func TestAssignCentroid(t *testing.T) {
X := mat.NewDense(4, 3, []float64{
2, 6, 1,
3, 4, 9,
9, 8, 2,
6, 4, 0,
})
Mu := mat.NewDense(2, 3, []float64{
4, 9, 2,
3, 1, 4,
})
m, _ := X.Dims()
expectedVec := mat.NewVector(m, []float64{
0,
1,
0,
0,
})
Idx := AssignCentroid(X, Mu)
if !mat.Equal(expectedVec, Idx) {
t.Errorf("Expected \n%v, found \n%v", printMatrix(expectedVec), printMatrix(Idx))
}
}
func nnlsSubproblem(V, W, Ho *mat64.Dense, tol float64, outer, inner int) (H, G *mat64.Dense, i int, ok bool) {
H = new(mat64.Dense)
H.Clone(Ho)
var WtV, WtW mat64.Dense
WtV.Mul(W.T(), V)
WtW.Mul(W.T(), W)
alpha, beta := 1., 0.1
decFilt := func(r, c int, v float64) float64 {
// decFilt is applied to G, so v = G.At(r, c).
if v < 0 || H.At(r, c) > 0 {
return v
}
return 0
}
G = new(mat64.Dense)
for i = 0; i < outer; i++ {
G.Mul(&WtW, H)
G.Sub(G, &WtV)
G.Apply(decFilt, G)
if mat64.Norm(G, 2) < tol {
break
}
var (
reduce bool
Hp *mat64.Dense
d, dQ mat64.Dense
)
for j := 0; j < inner; j++ {
var Hn mat64.Dense
Hn.Scale(alpha, G)
Hn.Sub(H, &Hn)
Hn.Apply(posFilt, &Hn)
d.Sub(&Hn, H)
dQ.Mul(&WtW, &d)
dQ.MulElem(&dQ, &d)
d.MulElem(G, &d)
sufficient := 0.99*mat64.Sum(&d)+0.5*mat64.Sum(&dQ) < 0
if j == 0 {
reduce = !sufficient
Hp = H
}
if reduce {
if sufficient {
H = &Hn
ok = true
break
} else {
alpha *= beta
}
} else {
if !sufficient || mat64.Equal(Hp, &Hn) {
H = Hp
break
} else {
alpha /= beta
Hp = &Hn
}
}
}
}
return H, G, i, ok
}
func TestCorrelationMatrix(t *testing.T) {
for i, test := range []struct {
data *mat64.Dense
weights []float64
ans *mat64.Dense
}{
{
data: mat64.NewDense(3, 3, []float64{
1, 2, 3,
3, 4, 5,
5, 6, 7,
}),
weights: nil,
ans: mat64.NewDense(3, 3, []float64{
1, 1, 1,
1, 1, 1,
1, 1, 1,
}),
},
{
data: mat64.NewDense(5, 2, []float64{
-2, -4,
-1, 2,
0, 0,
1, -2,
2, 4,
}),
weights: nil,
ans: mat64.NewDense(2, 2, []float64{
1, 0.6,
0.6, 1,
}),
}, {
data: mat64.NewDense(3, 2, []float64{
1, 1,
2, 4,
3, 9,
}),
weights: []float64{
1,
1.5,
1,
},
ans: mat64.NewDense(2, 2, []float64{
1, 0.9868703275903379,
0.9868703275903379, 1,
}),
},
} {
// Make a copy of the data to check that it isn't changing.
r := test.data.RawMatrix()
d := make([]float64, len(r.Data))
copy(d, r.Data)
w := make([]float64, len(test.weights))
if test.weights != nil {
copy(w, test.weights)
}
c := CorrelationMatrix(nil, test.data, test.weights)
if !mat64.Equal(c, test.ans) {
t.Errorf("%d: expected corr %v, found %v", i, test.ans, c)
}
if !floats.Equal(d, r.Data) {
t.Errorf("%d: data was modified during execution", i)
}
if !floats.Equal(w, test.weights) {
t.Errorf("%d: weights was modified during execution", i)
}
// compare with call to Covariance
_, cols := c.Dims()
for ci := 0; ci < cols; ci++ {
for cj := 0; cj < cols; cj++ {
x := mat64.Col(nil, ci, test.data)
y := mat64.Col(nil, cj, test.data)
corr := Correlation(x, y, test.weights)
if math.Abs(corr-c.At(ci, cj)) > 1e-14 {
t.Errorf("CorrMat does not match at (%v, %v). Want %v, got %v.", ci, cj, corr, c.At(ci, cj))
}
}
}
}
if !Panics(func() { CorrelationMatrix(nil, mat64.NewDense(5, 2, nil), []float64{}) }) {
t.Errorf("CorrelationMatrix did not panic with weight size mismatch")
}
if !Panics(func() { CorrelationMatrix(mat64.NewDense(1, 1, nil), mat64.NewDense(5, 2, nil), nil) }) {
t.Errorf("CorrelationMatrix did not panic with preallocation size mismatch")
}
if !Panics(func() { CorrelationMatrix(nil, mat64.NewDense(2, 2, []float64{1, 2, 3, 4}), []float64{1, -1}) }) {
t.Errorf("CorrelationMatrix did not panic with negative weights")
}
}
func TestCovarianceMatrix(t *testing.T) {
// An alternative way to test this is to call the Variance and
// Covariance functions and ensure that the results are identical.
for i, test := range []struct {
data *mat64.Dense
weights []float64
ans *mat64.Dense
}{
{
data: mat64.NewDense(5, 2, []float64{
-2, -4,
-1, 2,
0, 0,
1, -2,
2, 4,
}),
weights: nil,
ans: mat64.NewDense(2, 2, []float64{
2.5, 3,
3, 10,
}),
}, {
data: mat64.NewDense(3, 2, []float64{
1, 1,
2, 4,
3, 9,
}),
weights: []float64{
1,
1.5,
1,
},
ans: mat64.NewDense(2, 2, []float64{
.8, 3.2,
3.2, 13.142857142857146,
}),
},
} {
// Make a copy of the data to check that it isn't changing.
r := test.data.RawMatrix()
d := make([]float64, len(r.Data))
copy(d, r.Data)
w := make([]float64, len(test.weights))
if test.weights != nil {
copy(w, test.weights)
}
c := CovarianceMatrix(nil, test.data, test.weights)
if !mat64.Equal(c, test.ans) {
t.Errorf("%d: expected cov %v, found %v", i, test.ans, c)
}
if !floats.Equal(d, r.Data) {
t.Errorf("%d: data was modified during execution", i)
}
if !floats.Equal(w, test.weights) {
t.Errorf("%d: weights was modified during execution", i)
}
// compare with call to Covariance
_, cols := c.Dims()
for ci := 0; ci < cols; ci++ {
for cj := 0; cj < cols; cj++ {
x := mat64.Col(nil, ci, test.data)
y := mat64.Col(nil, cj, test.data)
cov := Covariance(x, y, test.weights)
if math.Abs(cov-c.At(ci, cj)) > 1e-14 {
t.Errorf("CovMat does not match at (%v, %v). Want %v, got %v.", ci, cj, cov, c.At(ci, cj))
}
}
}
}
if !Panics(func() { CovarianceMatrix(nil, mat64.NewDense(5, 2, nil), []float64{}) }) {
t.Errorf("CovarianceMatrix did not panic with weight size mismatch")
}
if !Panics(func() { CovarianceMatrix(mat64.NewDense(1, 1, nil), mat64.NewDense(5, 2, nil), nil) }) {
t.Errorf("CovarianceMatrix did not panic with preallocation size mismatch")
}
if !Panics(func() { CovarianceMatrix(nil, mat64.NewDense(2, 2, []float64{1, 2, 3, 4}), []float64{1, -1}) }) {
t.Errorf("CovarianceMatrix did not panic with negative weights")
}
}