func GradientDescent(X *mat64.Dense, y *mat64.Vector, alpha, tolerance float64, maxIters int) *mat64.Vector {
// m = Number of Training Examples
// n = Number of Features
m, n := X.Dims()
h := mat64.NewVector(m, nil)
partials := mat64.NewVector(n, nil)
new_theta := mat64.NewVector(n, nil)
Regression:
for i := 0; i < maxIters; i++ {
// Calculate partial derivatives
h.MulVec(X, new_theta)
for el := 0; el < m; el++ {
val := (h.At(el, 0) - y.At(el, 0)) / float64(m)
h.SetVec(el, val)
}
partials.MulVec(X.T(), h)
// Update theta values
for el := 0; el < n; el++ {
new_val := new_theta.At(el, 0) - (alpha * partials.At(el, 0))
new_theta.SetVec(el, new_val)
}
// Check the "distance" to the local minumum
dist := math.Sqrt(mat64.Dot(partials, partials))
if dist <= tolerance {
break Regression
}
}
return new_theta
}
// onesDotUnitary performs the equivalent of a Ddot of v with
// a ones vector of equal length. v must have have a unitary
// vector increment.
func onesDotUnitary(alpha float64, v *mat64.Vector) float64 {
var sum float64
for _, f := range v.RawVector().Data {
sum += alpha * f
}
return sum
}
// dotUnitary performs a simplified scatter-based Ddot operations on
// v and the receiver. v must have have a unitary vector increment.
func (r compressedRow) dotUnitary(v *mat64.Vector) float64 {
var sum float64
vec := v.RawVector().Data
for _, e := range r {
sum += vec[e.index] * e.value
}
return sum
}
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⎦
}
func vectorDistance(vec1, vec2 *mat.Vector) (v float64) {
result := mat.NewVector(vec1.Len(), nil)
result.SubVec(vec1, vec2)
result.MulElemVec(result, result)
v = mat.Sum(result)
return
}
// Scatter copies the values of x into the corresponding locations in the dense
// vector y. Both vectors must have the same dimension.
func Scatter(y *mat64.Vector, x *Vector) {
if x.N != y.Len() {
panic("sparse: vector dimension mismatch")
}
raw := y.RawVector()
for i, index := range x.Indices {
raw.Data[index*raw.Inc] = x.Data[i]
}
}
// findIn returns the indexes of the values in vec that match scalar
func findIn(scalar float64, vec *mat.Vector) *mat.Vector {
var result []float64
for i := 0; i < vec.Len(); i++ {
if scalar == vec.At(i, 0) {
result = append(result, float64(i))
}
}
return mat.NewVector(len(result), result)
}
// Dot computes the dot product of the sparse vector x with the dense vector y.
// The vectors must have the same dimension.
func Dot(x *Vector, y *mat64.Vector) (dot float64) {
if x.N != y.Len() {
panic("sparse: vector dimension mismatch")
}
raw := y.RawVector()
for i, index := range x.Indices {
dot += x.Data[i] * raw.Data[index*raw.Inc]
}
return
}
// Gather gathers entries given by indices of the dense vector y into the sparse
// vector x. Indices must not be nil.
func Gather(x *Vector, y *mat64.Vector, indices []int) {
if indices == nil {
panic("sparse: slice is nil")
}
x.reuseAs(y.Len(), len(indices))
copy(x.Indices, indices)
raw := y.RawVector()
for i, index := range x.Indices {
x.Data[i] = raw.Data[index*raw.Inc]
}
}
// Axpy scales the sparse vector x by alpha and adds the result to the dense
// vector y. If alpha is zero, y is not modified.
func Axpy(y *mat64.Vector, alpha float64, x *Vector) {
if x.N != y.Len() {
panic("sparse: vector dimension mismatch")
}
if alpha == 0 {
return
}
raw := y.RawVector()
for i, index := range x.Indices {
raw.Data[index*raw.Inc] += alpha * x.Data[i]
}
}
// rowIndexIn returns a matrix contains the rows in indexes vector
func rowIndexIn(indexes *mat.Vector, M mat.Matrix) mat.Matrix {
m := indexes.Len()
_, n := M.Dims()
Res := mat.NewDense(m, n, nil)
for i := 0; i < m; i++ {
Res.SetRow(i, mat.Row(
nil,
int(indexes.At(i, 0)),
M))
}
return Res
}
func Cost(x *mat64.Dense, y, theta *mat64.Vector) float64 {
//initialize receivers
m, _ := x.Dims()
h := mat64.NewDense(m, 1, make([]float64, m))
squaredErrors := mat64.NewDense(m, 1, make([]float64, m))
//actual calculus
h.Mul(x, theta)
squaredErrors.Apply(func(r, c int, v float64) float64 {
return math.Pow(h.At(r, c)-y.At(r, c), 2)
}, h)
j := mat64.Sum(squaredErrors) * 1.0 / (2.0 * float64(m))
return j
}
func Solve(a sparse.Matrix, b, xInit *mat64.Vector, settings *Settings, method Method) (result Result, err error) {
stats := Stats{
StartTime: time.Now(),
}
dim, c := a.Dims()
if dim != c {
panic("iterative: matrix is not square")
}
if xInit != nil && dim != xInit.Len() {
panic("iterative: mismatched size of the initial guess")
}
if b.Len() != dim {
panic("iterative: mismatched size of the right-hand side vector")
}
if xInit == nil {
xInit = mat64.NewVector(dim, nil)
}
if settings == nil {
settings = DefaultSettings(dim)
}
ctx := Context{
X: mat64.NewVector(dim, nil),
Residual: mat64.NewVector(dim, nil),
}
// X = xInit
ctx.X.CopyVec(xInit)
if mat64.Norm(ctx.X, math.Inf(1)) > 0 {
// Residual = Ax
sparse.MulMatVec(ctx.Residual, 1, false, a, ctx.X)
stats.MatVecMultiplies++
}
// Residual = Ax - b
ctx.Residual.SubVec(ctx.Residual, b)
if mat64.Norm(ctx.Residual, 2) >= settings.Tolerance {
err = iterate(method, a, b, settings, &ctx, &stats)
}
result = Result{
X: ctx.X,
Stats: stats,
Runtime: time.Since(stats.StartTime),
}
return result, err
}
// Map produces a vector that is within the bounds of the
// rectangular manifold of toroidal space, given a vector
// that is on the torus but may be outside these bounds.
func (t Torus) Map(v *mat64.Vector) {
x := v.At(0, 0)
y := v.At(1, 0)
remx := x
right := t.W / 2
if math.Abs(x) > right {
remx = math.Mod(t.W, -x)
}
remy := y
top := t.H / 2
if math.Abs(y) > top {
remy = math.Mod(t.H, -y)
}
v.SetVec(0, remx)
v.SetVec(1, remy)
}
// StdDev predicts the standard deviation of the function at x.
func (g *GP) StdDev(x []float64) float64 {
if len(x) != g.inputDim {
panic(badInputLength)
}
// nu_* = k(x_*, k_*) - k_*^T * K^-1 * k_*
n := len(g.outputs)
kstar := mat64.NewVector(n, nil)
for i := 0; i < n; i++ {
v := g.kernel.Distance(g.inputs.RawRowView(i), x)
kstar.SetVec(i, v)
}
self := g.kernel.Distance(x, x)
var tmp mat64.Vector
tmp.SolveCholeskyVec(g.cholK, kstar)
var tmp2 mat64.Vector
tmp2.MulVec(kstar.T(), &tmp)
rt, ct := tmp2.Dims()
if rt != 1 || ct != 1 {
panic("bad size")
}
return math.Sqrt(self-tmp2.At(0, 0)) * g.std
}
// StdDevBatch predicts the standard deviation at a set of locations of x.
func (g *GP) StdDevBatch(std []float64, x mat64.Matrix) []float64 {
r, c := x.Dims()
if c != g.inputDim {
panic(badInputLength)
}
if std == nil {
std = make([]float64, r)
}
if len(std) != r {
panic(badStorage)
}
// For a single point, the stddev is
// sigma = k(x,x) - k_*^T * K^-1 * k_*
// where k is the vector of kernels between the input points and the output points
// For many points, the formula is:
// nu_* = k(x_*, k_*) - k_*^T * K^-1 * k_*
// This creates the full covariance matrix which is an rxr matrix. However,
// the standard deviations are just the diagonal of this matrix. Instead, be
// smart about it and compute the diagonal terms one at a time.
kStar := g.formKStar(x)
var tmp mat64.Dense
tmp.SolveCholesky(g.cholK, kStar)
// set k(x_*, x_*) into std then subtract k_*^T K^-1 k_* , computed one row at a time
var tmp2 mat64.Vector
row := make([]float64, c)
for i := range std {
for k := 0; k < c; k++ {
row[k] = x.At(i, k)
}
std[i] = g.kernel.Distance(row, row)
tmp2.MulVec(kStar.ColView(i).T(), tmp.ColView(i))
rt, ct := tmp2.Dims()
if rt != 1 && ct != 1 {
panic("bad size")
}
std[i] -= tmp2.At(0, 0)
std[i] = math.Sqrt(std[i])
}
// Need to scale the standard deviation to be in the same units as y.
floats.Scale(g.std, std)
return std
}
func BlasVec2UserVec(v *mat64.Vector) UserVec {
u := UserVec{}
u.X = v.At(0, 0)
u.Y = v.At(1, 0)
return u
}
// mulVecUnitary multiplies the receiver by the src vector, storing
// the result in dst. It assumes src and dst are the same length as m
// and that both have unitary vector increments.
func (m rowCompressedMatrix) mulVecUnitary(dst, src *mat64.Vector) {
dMat := dst.RawVector().Data
for i, r := range m {
dMat[i] = r.dotUnitary(src)
}
}
func VectorToMatrix(vector *mat64.Vector) *mat64.Dense {
vec := vector.RawVector()
return mat64.NewDense(1, len(vec.Data), vec.Data)
}
// ConditionNormal returns the Normal distribution that is the receiver conditioned
// on the input evidence. The returned multivariate normal has dimension
// n - len(observed), where n is the dimension of the original receiver. The updated
// mean and covariance are
// mu = mu_un + sigma_{ob,un}^T * sigma_{ob,ob}^-1 (v - mu_ob)
// sigma = sigma_{un,un} - sigma_{ob,un}^T * sigma_{ob,ob}^-1 * sigma_{ob,un}
// where mu_un and mu_ob are the original means of the unobserved and observed
// variables respectively, sigma_{un,un} is the unobserved subset of the covariance
// matrix, sigma_{ob,ob} is the observed subset of the covariance matrix, and
// sigma_{un,ob} are the cross terms. The elements of x_2 have been observed with
// values v. The dimension order is preserved during conditioning, so if the value
// of dimension 1 is observed, the returned normal represents dimensions {0, 2, ...}
// of the original Normal distribution.
//
// ConditionNormal returns {nil, false} if there is a failure during the update.
// Mathematically this is impossible, but can occur with finite precision arithmetic.
func (n *Normal) ConditionNormal(observed []int, values []float64, src *rand.Rand) (*Normal, bool) {
if len(observed) == 0 {
panic("normal: no observed value")
}
if len(observed) != len(values) {
panic("normal: input slice length mismatch")
}
for _, v := range observed {
if v < 0 || v >= n.Dim() {
panic("normal: observed value out of bounds")
}
}
ob := len(observed)
unob := n.Dim() - ob
obMap := make(map[int]struct{})
for _, v := range observed {
if _, ok := obMap[v]; ok {
panic("normal: observed dimension occurs twice")
}
obMap[v] = struct{}{}
}
if len(observed) == n.Dim() {
panic("normal: all dimensions observed")
}
unobserved := make([]int, 0, unob)
for i := 0; i < n.Dim(); i++ {
if _, ok := obMap[i]; !ok {
unobserved = append(unobserved, i)
}
}
mu1 := make([]float64, unob)
for i, v := range unobserved {
mu1[i] = n.mu[v]
}
mu2 := make([]float64, ob) // really v - mu2
for i, v := range observed {
mu2[i] = values[i] - n.mu[v]
}
n.setSigma()
var sigma11, sigma22 mat64.SymDense
sigma11.SubsetSym(n.sigma, unobserved)
sigma22.SubsetSym(n.sigma, observed)
sigma21 := mat64.NewDense(ob, unob, nil)
for i, r := range observed {
for j, c := range unobserved {
v := n.sigma.At(r, c)
sigma21.Set(i, j, v)
}
}
var chol mat64.Cholesky
ok := chol.Factorize(&sigma22)
if !ok {
return nil, ok
}
// Compute sigma_{2,1}^T * sigma_{2,2}^-1 (v - mu_2).
v := mat64.NewVector(ob, mu2)
var tmp, tmp2 mat64.Vector
err := tmp.SolveCholeskyVec(&chol, v)
if err != nil {
return nil, false
}
tmp2.MulVec(sigma21.T(), &tmp)
// Compute sigma_{2,1}^T * sigma_{2,2}^-1 * sigma_{2,1}.
// TODO(btracey): Should this be a method of SymDense?
var tmp3, tmp4 mat64.Dense
err = tmp3.SolveCholesky(&chol, sigma21)
if err != nil {
return nil, false
}
tmp4.Mul(sigma21.T(), &tmp3)
for i := range mu1 {
mu1[i] += tmp2.At(i, 0)
}
// TODO(btracey): If tmp2 can constructed with a method, then this can be
// replaced with SubSym.
for i := 0; i < len(unobserved); i++ {
for j := i; j < len(unobserved); j++ {
v := sigma11.At(i, j)
sigma11.SetSym(i, j, v-tmp4.At(i, j))
}
}
return NewNormal(mu1, &sigma11, src)
}
func dokMulMatVec(y *mat64.Vector, alpha float64, transA bool, a *DOK, x *mat64.Vector) {
r, c := a.Dims()
if transA {
if r != x.Len() || c != y.Len() {
panic("sparse: dimension mismatch")
}
} else {
if r != y.Len() || c != x.Len() {
panic("sparse: dimension mismatch")
}
}
if alpha == 0 {
return
}
xRaw := x.RawVector()
yRaw := y.RawVector()
if transA {
for ij, aij := range a.data {
yRaw.Data[ij[1]*yRaw.Inc] += alpha * aij * xRaw.Data[ij[0]*xRaw.Inc]
}
} else {
for ij, aij := range a.data {
yRaw.Data[ij[0]*yRaw.Inc] += alpha * aij * xRaw.Data[ij[1]*xRaw.Inc]
}
}
}
func vec3(vec *mat64.Vector) Vec3 {
return Vec3{vec.At(0, 0), vec.At(1, 0), vec.At(2, 0)}
}
func simplex(initialBasic []int, c []float64, A mat64.Matrix, b []float64, tol float64) (float64, []float64, []int, error) {
err := verifyInputs(initialBasic, c, A, b)
if err != nil {
if err == ErrUnbounded {
return math.Inf(-1), nil, nil, ErrUnbounded
}
return math.NaN(), nil, nil, err
}
m, n := A.Dims()
// There is at least one optimal solution to the LP which is at the intersection
// to a set of constraint boundaries. For a standard form LP with m variables
// and n equality constraints, at least m-n elements of x must equal zero
// at optimality. The Simplex algorithm solves the standard-form LP by starting
// at an initial constraint vertex and successively moving to adjacent constraint
// vertices. At every vertex, the set of non-zero x values is the "basic
// feasible solution". The list of non-zero x's are maintained in basicIdxs,
// the respective columns of A are in ab, and the actual non-zero values of
// x are in xb.
//
// The LP is equality constrained such that A * x = b. This can be expanded
// to
// ab * xb + an * xn = b
// where ab are the columns of a in the basic set, and an are all of the
// other columns. Since each element of xn is zero by definition, this means
// that for all feasible solutions xb = ab^-1 * b.
//
// Before the simplex algorithm can start, an initial feasible solution must
// be found. If initialBasic is non-nil a feasible solution has been supplied.
// Otherwise the "Phase I" problem must be solved to find an initial feasible
// solution.
var basicIdxs []int // The indices of the non-zero x values.
var ab *mat64.Dense // The subset of columns of A listed in basicIdxs.
var xb []float64 // The non-zero elements of x. xb = ab^-1 b
if initialBasic != nil {
// InitialBasic supplied. Panic if incorrect length or infeasible.
if len(initialBasic) != m {
panic("lp: incorrect number of initial vectors")
}
ab = extractColumns(A, initialBasic)
xb, err = initializeFromBasic(ab, b)
if err != nil {
panic(err)
}
basicIdxs = make([]int, len(initialBasic))
copy(basicIdxs, initialBasic)
} else {
// No inital basis supplied. Solve the PhaseI problem.
basicIdxs, ab, xb, err = findInitialBasic(A, b)
if err != nil {
return math.NaN(), nil, nil, err
}
}
// basicIdxs contains the indexes for an initial feasible solution,
// ab contains the extracted columns of A, and xb contains the feasible
// solution. All x not in the basic set are 0 by construction.
// nonBasicIdx is the set of nonbasic variables.
nonBasicIdx := make([]int, 0, n-m)
inBasic := make(map[int]struct{})
for _, v := range basicIdxs {
inBasic[v] = struct{}{}
}
for i := 0; i < n; i++ {
_, ok := inBasic[i]
if !ok {
nonBasicIdx = append(nonBasicIdx, i)
}
}
// cb is the subset of c for the basic variables. an and cn
// are the equivalents to ab and cb but for the nonbasic variables.
cb := make([]float64, len(basicIdxs))
for i, idx := range basicIdxs {
cb[i] = c[idx]
}
cn := make([]float64, len(nonBasicIdx))
for i, idx := range nonBasicIdx {
cn[i] = c[idx]
}
an := extractColumns(A, nonBasicIdx)
bVec := mat64.NewVector(len(b), b)
cbVec := mat64.NewVector(len(cb), cb)
// Temporary data needed each iteration. (Described later)
r := make([]float64, n-m)
move := make([]float64, m)
// Solve the linear program starting from the initial feasible set. This is
// the "Phase 2" problem.
//
// Algorithm:
// 1) Compute the "reduced costs" for the non-basic variables. The reduced
// costs are the lagrange multipliers of the constraints.
// r = cn - an^T * ab^-T * cb
// 2) If all of the reduced costs are positive, no improvement is possible,
// and the solution is optimal (xn can only increase because of
// non-negativity constraints). Otherwise, the solution can be improved and
// one element will be exchanged in the basic set.
// 3) Choose the x_n with the most negative value of r. Call this value xe.
// This variable will be swapped into the basic set.
// 4) Increase xe until the next constraint boundary is met. This will happen
// when the first element in xb becomes 0. The distance xe can increase before
// a given element in xb becomes negative can be found from
// xb = Ab^-1 b - Ab^-1 An xn
// = Ab^-1 b - Ab^-1 Ae xe
// = bhat + d x_e
// xe = bhat_i / - d_i
// where Ae is the column of A corresponding to xe.
// The constraining basic index is the first index for which this is true,
// so remove the element which is min_i (bhat_i / -d_i), assuming d_i is negative.
// If no d_i is less than 0, then the problem is unbounded.
// 5) If the new xe is 0 (that is, bhat_i == 0), then this location is at
// the intersection of several constraints. Use the Bland rule instead
// of the rule in step 4 to avoid cycling.
for {
// Compute reduced costs -- r = cn - an^T ab^-T cb
var tmp mat64.Vector
err = tmp.SolveVec(ab.T(), cbVec)
if err != nil {
break
}
data := make([]float64, n-m)
tmp2 := mat64.NewVector(n-m, data)
tmp2.MulVec(an.T(), &tmp)
floats.SubTo(r, cn, data)
// Replace the most negative element in the simplex. If there are no
// negative entries then the optimal solution has been found.
minIdx := floats.MinIdx(r)
if r[minIdx] >= -tol {
break
}
for i, v := range r {
if math.Abs(v) < rRoundTol {
r[i] = 0
}
}
// Compute the moving distance.
err = computeMove(move, minIdx, A, ab, xb, nonBasicIdx)
if err != nil {
if err == ErrUnbounded {
return math.Inf(-1), nil, nil, ErrUnbounded
}
break
}
// Replace the basic index along the tightest constraint.
replace := floats.MinIdx(move)
if move[replace] <= 0 {
replace, minIdx, err = replaceBland(A, ab, xb, basicIdxs, nonBasicIdx, r, move)
if err != nil {
if err == ErrUnbounded {
return math.Inf(-1), nil, nil, ErrUnbounded
}
break
}
}
// Replace the constrained basicIdx with the newIdx.
basicIdxs[replace], nonBasicIdx[minIdx] = nonBasicIdx[minIdx], basicIdxs[replace]
cb[replace], cn[minIdx] = cn[minIdx], cb[replace]
tmpCol1 := mat64.Col(nil, replace, ab)
tmpCol2 := mat64.Col(nil, minIdx, an)
ab.SetCol(replace, tmpCol2)
an.SetCol(minIdx, tmpCol1)
// Compute the new xb.
xbVec := mat64.NewVector(len(xb), xb)
err = xbVec.SolveVec(ab, bVec)
if err != nil {
break
}
}
// Found the optimum successfully or died trying. The basic variables get
// their values, and the non-basic variables are all zero.
opt := floats.Dot(cb, xb)
xopt := make([]float64, n)
for i, v := range basicIdxs {
xopt[v] = xb[i]
}
return opt, xopt, basicIdxs, err
}
func testSimplex(t *testing.T, initialBasic []int, c []float64, a mat64.Matrix, b []float64, convergenceTol float64) error {
primalOpt, primalX, _, errPrimal := simplex(initialBasic, c, a, b, convergenceTol)
if errPrimal == nil {
// No error solving the simplex, check that the solution is feasible.
var bCheck mat64.Vector
bCheck.MulVec(a, mat64.NewVector(len(primalX), primalX))
if !mat64.EqualApprox(&bCheck, mat64.NewVector(len(b), b), 1e-10) {
t.Errorf("No error in primal but solution infeasible")
}
}
primalInfeasible := errPrimal == ErrInfeasible
primalUnbounded := errPrimal == ErrUnbounded
primalBounded := errPrimal == nil
primalASingular := errPrimal == ErrSingular
primalZeroRow := errPrimal == ErrZeroRow
primalZeroCol := errPrimal == ErrZeroColumn
primalBad := !primalInfeasible && !primalUnbounded && !primalBounded && !primalASingular && !primalZeroRow && !primalZeroCol
// It's an error if it's not one of the known returned errors. If it's
// singular the problem is undefined and so the result cannot be compared
// to the dual.
if errPrimal == ErrSingular || primalBad {
if primalBad {
t.Errorf("non-known error returned: %s", errPrimal)
}
return errPrimal
}
// Compare the result to the answer found from solving the dual LP.
// Construct and solve the dual LP.
// Standard Form:
// minimize c^T * x
// subject to A * x = b, x >= 0
// The dual of this problem is
// maximize -b^T * nu
// subject to A^T * nu + c >= 0
// Which is
// minimize b^T * nu
// subject to -A^T * nu <= c
negAT := &mat64.Dense{}
negAT.Clone(a.T())
negAT.Scale(-1, negAT)
cNew, aNew, bNew := Convert(b, negAT, c, nil, nil)
dualOpt, dualX, _, errDual := simplex(nil, cNew, aNew, bNew, convergenceTol)
if errDual == nil {
// Check that the dual is feasible
var bCheck mat64.Vector
bCheck.MulVec(aNew, mat64.NewVector(len(dualX), dualX))
if !mat64.EqualApprox(&bCheck, mat64.NewVector(len(bNew), bNew), 1e-10) {
t.Errorf("No error in dual but solution infeasible")
}
}
// Check about the zero status.
if errPrimal == ErrZeroRow || errPrimal == ErrZeroColumn {
return errPrimal
}
// If the primal problem is feasible, then the primal and the dual should
// be the same answer. We have flopped the sign in the dual (minimizing
// b^T *nu instead of maximizing -b^T*nu), so flip it back.
if errPrimal == nil {
if errDual != nil {
fmt.Println("errDual", errDual)
panic("here")
t.Errorf("Primal feasible but dual errored: %s", errDual)
}
dualOpt *= -1
if !floats.EqualWithinAbsOrRel(dualOpt, primalOpt, convergenceTol, convergenceTol) {
t.Errorf("Primal and dual value mismatch. Primal %v, dual %v.", primalOpt, dualOpt)
}
}
// If the primal problem is unbounded, then the dual should be infeasible.
if errPrimal == ErrUnbounded && errDual != ErrInfeasible {
t.Errorf("Primal unbounded but dual not infeasible. ErrDual = %s", errDual)
}
// If the dual is unbounded, then the primal should be infeasible.
if errDual == ErrUnbounded && errPrimal != ErrInfeasible {
t.Errorf("Dual unbounded but primal not infeasible. ErrDual = %s", errPrimal)
}
// If the primal is infeasible, then the dual should be either infeasible
// or unbounded.
if errPrimal == ErrInfeasible {
if errDual != ErrUnbounded && errDual != ErrInfeasible && errDual != ErrZeroColumn {
t.Errorf("Primal infeasible but dual not infeasible or unbounded: %s", errDual)
}
}
return errPrimal
}
func MultiHypothesis(x *mat64.Dense, theta *mat64.Vector) *mat64.Vector {
var res mat64.Dense
res.Mul(theta.T(), x)
return res.RowView(0)
}
// Throttle behaves as Move but scales the speed
func (r Rudder) Jolt(pos *mat64.Vector, scale float64) {
pos.AddScaledVec(pos, scale*r.S, r.D)
}
func csrMulMatVec(y *mat64.Vector, alpha float64, transA bool, a *CSR, x *mat64.Vector) {
r, c := a.Dims()
if transA {
if r != x.Len() || c != y.Len() {
panic("sparse: dimension mismatch")
}
} else {
if r != y.Len() || c != x.Len() {
panic("sparse: dimension mismatch")
}
}
if alpha == 0 {
return
}
yRaw := y.RawVector()
if transA {
row := Vector{N: y.Len()}
for i := 0; i < r; i++ {
start := a.rowIndex[i]
end := a.rowIndex[i+1]
row.Data = a.values[start:end]
row.Indices = a.columns[start:end]
Axpy(y, alpha*x.At(i, 0), &row)
}
} else {
row := Vector{N: x.Len()}
for i := 0; i < r; i++ {
start := a.rowIndex[i]
end := a.rowIndex[i+1]
row.Data = a.values[start:end]
row.Indices = a.columns[start:end]
yRaw.Data[i*yRaw.Inc] += alpha * Dot(&row, x)
}
}
}
