# SVDMethod¶

class SVDMethod(*args)

Least squares solver using SVD decomposition.

Available constructors:

SVDMethod(proxy, weight, indices)

SVDMethod(proxy, indices)

Parameters: proxy : DesignProxy Input sample weight : sequence of float Output weights indices : sequence of int Indices allowed in the basis

Methods

 computeWeightedDesign([whole]) Build the design matrix. getBasis() Accessor to the basis. getClassName() Accessor to the object’s name. getCurrentIndices() Current indices accessor. getGramInverse() Get the inverse Gram matrix of input sample. getGramInverseDiag() Get the diagonal of the inverse Gram matrix. getGramInverseTrace() Get the trace of the inverse Gram matrix. getH() Get the projection matrix H. getHDiag() Get the diagonal of the projection matrix H. getId() Accessor to the object’s id. getInitialIndices() Initial indices accessor. getInputSample() Input sample accessor. getName() Accessor to the object’s name. getShadowedId() Accessor to the object’s shadowed id. getVisibility() Accessor to the object’s visibility state. getWeight() Accessor to the weights. hasName() Test if the object is named. hasVisibleName() Test if the object has a distinguishable name. setName(name) Accessor to the object’s name. setShadowedId(id) Accessor to the object’s shadowed id. setVisibility(visible) Accessor to the object’s visibility state. solve(rhs) Solve the least-squares problem. solveNormal(rhs) Solve the least-squares problem using normal equation. trashDecomposition() Drop the current decomposition. update(addedIndices, conservedIndices, …) Update the current decomposition.
__init__(*args)

Initialize self. See help(type(self)) for accurate signature.

computeWeightedDesign(whole=False)

Build the design matrix.

Parameters: whole : bool, defaults to False Whether to use the initial indices instead of the current indices psiAk : Matrix The design matrix
getBasis()

Accessor to the basis.

Returns: basis : collection of Function Basis.
getClassName()

Accessor to the object’s name.

Returns: class_name : str The object class name (object.__class__.__name__).
getCurrentIndices()

Current indices accessor.

Returns: indices : Indices Indices of the current decomposition in the global basis.
getGramInverse()

Get the inverse Gram matrix of input sample.

Returns: c : CovarianceMatrix The inverse Gram matrix.
getGramInverseDiag()

Get the diagonal of the inverse Gram matrix.

Returns: d : Point The diagonal of the inverse Gram matrix.
getGramInverseTrace()

Get the trace of the inverse Gram matrix.

Returns: x : Scalar The trace of inverse Gram matrix.
getH()

Get the projection matrix H.

Returns: h : SymmetricMatrix The projection matrix H.
getHDiag()

Get the diagonal of the projection matrix H.

Returns: d : Point The diagonal of H.
getId()

Accessor to the object’s id.

Returns: id : int Internal unique identifier.
getInitialIndices()

Initial indices accessor.

Returns: indices : Indices Initial indices of the terms in the global basis.
getInputSample()

Input sample accessor.

Returns: inputSample : Sample Input sample.
getName()

Accessor to the object’s name.

Returns: name : str The name of the object.
getShadowedId()

Accessor to the object’s shadowed id.

Returns: id : int Internal unique identifier.
getVisibility()

Accessor to the object’s visibility state.

Returns: visible : bool Visibility flag.
getWeight()

Accessor to the weights.

Returns: weight : Point Weights.
hasName()

Test if the object is named.

Returns: hasName : bool True if the name is not empty.
hasVisibleName()

Test if the object has a distinguishable name.

Returns: hasVisibleName : bool True if the name is not empty and not the default one.
setName(name)

Accessor to the object’s name.

Parameters: name : str The name of the object.
setShadowedId(id)

Accessor to the object’s shadowed id.

Parameters: id : int Internal unique identifier.
setVisibility(visible)

Accessor to the object’s visibility state.

Parameters: visible : bool Visibility flag.
solve(rhs)

Solve the least-squares problem.

Parameters: b : sequence of float Second term of the equation a : Point The solution.
solveNormal(rhs)

Solve the least-squares problem using normal equation.

Parameters: b : sequence of float Second term of the equation x : Point The solution.
trashDecomposition()

Drop the current decomposition.

update(addedIndices, conservedIndices, removedIndices, row=False)

Update the current decomposition.

Parameters: addedIndices : sequence of int Indices of added basis terms. conservedIndices : sequence of int Indices of conserved basis terms. removedIndices : sequence of int Indices of removed basis terms.