func ExampleCholesky() {
// Construct a symmetric positive definite matrix.
tmp := mat64.NewDense(4, 4, []float64{
2, 6, 8, -4,
1, 8, 7, -2,
2, 2, 1, 7,
8, -2, -2, 1,
})
var a mat64.SymDense
a.SymOuterK(1, tmp)
fmt.Printf("a = %0.4v\n", mat64.Formatted(&a, mat64.Prefix(" ")))
// Compute the cholesky factorization.
var chol mat64.Cholesky
if ok := chol.Factorize(&a); !ok {
fmt.Println("a matrix is not positive semi-definite.")
}
// Find the determinant.
fmt.Printf("\nThe determinant of a is %0.4g\n\n", chol.Det())
// Use the factorization to solve the system of equations a * x = b.
b := mat64.NewVector(4, []float64{1, 2, 3, 4})
var x mat64.Vector
if err := x.SolveCholeskyVec(&chol, b); err != nil {
fmt.Println("Matrix is near singular: ", err)
}
fmt.Println("Solve a * x = b")
fmt.Printf("x = %0.4v\n", mat64.Formatted(&x, mat64.Prefix(" ")))
// Extract the factorization and check that it equals the original matrix.
var t mat64.TriDense
t.LFromCholesky(&chol)
var test mat64.Dense
test.Mul(&t, t.T())
fmt.Println()
fmt.Printf("L * L^T = %0.4v\n", mat64.Formatted(&a, mat64.Prefix(" ")))
// Output:
// a = ⎡120 114 -4 -16⎤
// ⎢114 118 11 -24⎥
// ⎢ -4 11 58 17⎥
// ⎣-16 -24 17 73⎦
//
// The determinant of a is 1.543e+06
//
// Solve a * x = b
// x = ⎡ -0.239⎤
// ⎢ 0.2732⎥
// ⎢-0.04681⎥
// ⎣ 0.1031⎦
//
// L * L^T = ⎡120 114 -4 -16⎤
// ⎢114 118 11 -24⎥
// ⎢ -4 11 58 17⎥
// ⎣-16 -24 17 73⎦
}
// randomNormal constructs a random Normal distribution.
func randomNormal(dim int) (*distmv.Normal, bool) {
data := make([]float64, dim*dim)
for i := range data {
data[i] = rand.Float64()
}
a := mat64.NewDense(dim, dim, data)
var sigma mat64.SymDense
sigma.SymOuterK(1, a)
mu := make([]float64, dim)
for i := range mu {
mu[i] = rand.NormFloat64()
}
return distmv.NewNormal(mu, &sigma, nil)
}
// CovarianceMatrix calculates a covariance matrix (also known as a
// variance-covariance matrix) from a matrix of data, using a two-pass
// algorithm.
//
// The weights must have length equal to the number of rows in
// input data matrix x. If cov is nil, then a new matrix with appropriate size will
// be constructed. If cov is not nil, it should have the same number of columns as the
// input data matrix x, and it will be used as the destination for the covariance
// data. Weights must not be negative.
func CovarianceMatrix(cov *mat64.SymDense, x mat64.Matrix, weights []float64) *mat64.SymDense {
// This is the matrix version of the two-pass algorithm. It doesn't use the
// additional floating point error correction that the Covariance function uses
// to reduce the impact of rounding during centering.
r, c := x.Dims()
if cov == nil {
cov = mat64.NewSymDense(c, nil)
} else if n := cov.Symmetric(); n != c {
panic(matrix.ErrShape)
}
var xt mat64.Dense
xt.Clone(x.T())
// Subtract the mean of each of the columns.
for i := 0; i < c; i++ {
v := xt.RawRowView(i)
// This will panic with ErrShape if len(weights) != len(v), so
// we don't have to check the size later.
mean := Mean(v, weights)
floats.AddConst(-mean, v)
}
if weights == nil {
// Calculate the normalization factor
// scaled by the sample size.
cov.SymOuterK(1/(float64(r)-1), &xt)
return cov
}
// Multiply by the sqrt of the weights, so that multiplication is symmetric.
sqrtwts := make([]float64, r)
for i, w := range weights {
if w < 0 {
panic("stat: negative covariance matrix weights")
}
sqrtwts[i] = math.Sqrt(w)
}
// Weight the rows.
for i := 0; i < c; i++ {
v := xt.RawRowView(i)
floats.Mul(v, sqrtwts)
}
// Calculate the normalization factor
// scaled by the weighted sample size.
cov.SymOuterK(1/(floats.Sum(weights)-1), &xt)
return cov
}
func randomNormal(sz int, rnd *rand.Rand) *Normal {
mu := make([]float64, sz)
for i := range mu {
mu[i] = rnd.Float64()
}
data := make([]float64, sz*sz)
for i := range data {
data[i] = rnd.Float64()
}
dM := mat64.NewDense(sz, sz, data)
var sigma mat64.SymDense
sigma.SymOuterK(1, dM)
normal, ok := NewNormal(mu, &sigma, nil)
if !ok {
log.Fatal("bad test, not pos def")
}
return normal
}
func TestMarginalSingle(t *testing.T) {
for _, test := range []struct {
mu []float64
sigma *mat64.SymDense
}{
{
mu: []float64{2, 3, 4},
sigma: mat64.NewSymDense(3, []float64{2, 0.5, 3, 0.5, 1, 0.6, 3, 0.6, 10}),
},
{
mu: []float64{2, 3, 4, 5},
sigma: mat64.NewSymDense(4, []float64{2, 0.5, 3, 0.1, 0.5, 1, 0.6, 0.2, 3, 0.6, 10, 0.3, 0.1, 0.2, 0.3, 3}),
},
} {
normal, ok := NewNormal(test.mu, test.sigma, nil)
if !ok {
t.Fatalf("Bad test, covariance matrix not positive definite")
}
// Verify with nil Sigma.
normal.sigma = nil
for i, mean := range test.mu {
norm := normal.MarginalNormalSingle(i, nil)
if norm.Mean() != mean {
t.Errorf("Mean mismatch nil Sigma, idx %v: want %v, got %v.", i, mean, norm.Mean())
}
std := math.Sqrt(test.sigma.At(i, i))
if math.Abs(norm.StdDev()-std) > 1e-14 {
t.Errorf("StdDev mismatch nil Sigma, idx %v: want %v, got %v.", i, std, norm.StdDev())
}
}
// Verify with non-nil Sigma.
normal.setSigma()
for i, mean := range test.mu {
norm := normal.MarginalNormalSingle(i, nil)
if norm.Mean() != mean {
t.Errorf("Mean mismatch non-nil Sigma, idx %v: want %v, got %v.", i, mean, norm.Mean())
}
std := math.Sqrt(test.sigma.At(i, i))
if math.Abs(norm.StdDev()-std) > 1e-14 {
t.Errorf("StdDev mismatch non-nil Sigma, idx %v: want %v, got %v.", i, std, norm.StdDev())
}
}
}
// Test matching with TestMarginal.
rnd := rand.New(rand.NewSource(1))
for cas := 0; cas < 10; cas++ {
dim := rnd.Intn(10) + 1
mu := make([]float64, dim)
for i := range mu {
mu[i] = rnd.Float64()
}
x := make([]float64, dim*dim)
for i := range x {
x[i] = rnd.Float64()
}
mat := mat64.NewDense(dim, dim, x)
var sigma mat64.SymDense
sigma.SymOuterK(1, mat)
normal, ok := NewNormal(mu, &sigma, nil)
if !ok {
t.Fatal("bad test")
}
for i := 0; i < dim; i++ {
single := normal.MarginalNormalSingle(i, nil)
mult, ok := normal.MarginalNormal([]int{i}, nil)
if !ok {
t.Fatal("bad test")
}
if math.Abs(single.Mean()-mult.Mean(nil)[0]) > 1e-14 {
t.Errorf("Mean mismatch")
}
if math.Abs(single.Variance()-mult.CovarianceMatrix(nil).At(0, 0)) > 1e-14 {
t.Errorf("Variance mismatch")
}
}
}
}
