fuchsia / third_party / github.com / gonum / gonum / ffd939f8ca292b53f56531f190a8ae1e7cdea92c / . / mat / doc.go

// Copyright ©2015 The gonum Authors. All rights reserved. | |

// Use of this source code is governed by a BSD-style | |

// license that can be found in the LICENSE file. | |

// Package mat provides implementations of float64 and complex128 matrix | |

// structures and linear algebra operations on them. | |

// | |

// Overview | |

// | |

// This section provides a quick overview of the mat package. The following | |

// sections provide more in depth commentary. | |

// | |

// mat provides: | |

// - Interfaces for Matrix classes (Matrix, Symmetric, Triangular) | |

// - Concrete implementations (Dense, SymDense, TriDense) | |

// - Methods and functions for using matrix data (Add, Trace, SymRankOne) | |

// - Types for constructing and using matrix factorizations (QR, LU) | |

// - The complementary types for complex matrices, CMatrix, CSymDense, etc. | |

// | |

// A matrix may be constructed through the corresponding New function. If no | |

// backing array is provided the matrix will be initialized to all zeros. | |

// // Allocate a zeroed real matrix of size 3×5 | |

// zero := mat.NewDense(3, 5, nil) | |

// If a backing data slice is provided, the matrix will have those elements. | |

// Matrices are all stored in row-major format. | |

// // Generate a 6×6 matrix of random values. | |

// data := make([]float64, 36) | |

// for i := range data { | |

// data[i] = rand.NormFloat64() | |

// } | |

// a := mat.NewDense(6, 6, data) | |

// Operations involving matrix data are implemented as functions when the values | |

// of the matrix remain unchanged | |

// tr := mat.Trace(a) | |

// and are implemented as methods when the operation modifies the receiver. | |

// zero.Copy(a) | |

// | |

// Receivers must be the correct size for the matrix operations, otherwise the | |

// operation will panic. As a special case for convenience, a zero-value matrix | |

// will be modified to have the correct size, allocating data if necessary. | |

// var c mat.Dense // construct a new zero-sized matrix | |

// c.Mul(a, a) // c is automatically adjusted to be 6×6 | |

// | |

// Zero-value of a matrix | |

// | |

// A zero-value matrix is either the Go language definition of a zero-value or | |

// is a zero-sized matrix with zero-length stride. Matrix implementations may have | |

// a Reset method to revert the receiver into a zero-valued matrix and an IsZero | |

// method that returns whether the matrix is zero-valued. | |

// So the following will all result in a zero-value matrix. | |

// - var a mat.Dense | |

// - a := NewDense(0, 0, make([]float64, 0, 100)) | |

// - a.Reset() | |

// A zero-value matrix can not be sliced even if it does have an adequately sized | |

// backing data slice, but can be expanded using its Grow method if it exists. | |

// | |

// The Matrix Interfaces | |

// | |

// The Matrix interface is the common link between the concrete types of real | |

// matrices, The Matrix interface is defined by three functions: Dims, which | |

// returns the dimensions of the Matrix, At, which returns the element in the | |

// specified location, and T for returning a Transpose (discussed later). All of | |

// the concrete types can perform these behaviors and so implement the interface. | |

// Methods and functions are designed to use this interface, so in particular the method | |

// func (m *Dense) Mul(a, b Matrix) | |

// constructs a *Dense from the result of a multiplication with any Matrix types, | |

// not just *Dense. Where more restrictive requirements must be met, there are also the | |

// Symmetric and Triangular interfaces. For example, in | |

// func (s *SymDense) AddSym(a, b Symmetric) | |

// the Symmetric interface guarantees a symmetric result. | |

// | |

// The CMatrix interface plays the same role for complex matrices. The difference | |

// is that the CMatrix type has the H method instead T, for returning the conjugate | |

// transpose. | |

// | |

// (Conjugate) Transposes | |

// | |

// The T method is used for transposition on real matrices, and H is used for | |

// conjugate transposition on complex matrices. For example, c.Mul(a.T(), b) computes | |

// c = a^T * b. The mat types implement this method implicitly — | |

// see the Transpose and Conjugate types for more details. Note that some | |

// operations have a transpose as part of their definition, as in *SymDense.SymOuterK. | |

// | |

// Matrix Factorization | |

// | |

// Matrix factorizations, such as the LU decomposition, typically have their own | |

// specific data storage, and so are each implemented as a specific type. The | |

// factorization can be computed through a call to Factorize | |

// var lu mat.LU | |

// lu.Factorize(a) | |

// The elements of the factorization can be extracted through methods on the | |

// factorized type, i.e. *LU.UTo. The factorization types can also be used directly, | |

// as in *Dense.SolveCholesky. Some factorizations can be updated directly, | |

// without needing to update the original matrix and refactorize, | |

// as in *LU.RankOne. | |

// | |

// BLAS and LAPACK | |

// | |

// BLAS and LAPACK are the standard APIs for linear algebra routines. Many | |

// operations in mat are implemented using calls to the wrapper functions | |

// in gonum/blas/blas64 and gonum/lapack/lapack64 and their complex equivalents. | |

// By default, blas64 and lapack64 call the native Go implementations of the | |

// routines. Alternatively, it is possible to use C-based implementations of the | |

// APIs through the respective cgo packages and "Use" functions. The Go | |

// implementation of LAPACK (used by default) makes calls | |

// through blas64, so if a cgo BLAS implementation is registered, the lapack64 | |

// calls will be partially executed in Go and partially executed in C. | |

// | |

// Type Switching | |

// | |

// The Matrix abstraction enables efficiency as well as interoperability. Go's | |

// type reflection capabilities are used to choose the most efficient routine | |

// given the specific concrete types. For example, in | |

// c.Mul(a, b) | |

// if a and b both implement RawMatrixer, that is, they can be represented as a | |

// blas64.General, blas64.Gemm (general matrix multiplication) is called, while | |

// instead if b is a RawSymmetricer blas64.Symm is used (general-symmetric | |

// multiplication), and if b is a *Vector blas64.Gemv is used. | |

// | |

// There are many possible type combinations and special cases. No specific guarantees | |

// are made about the performance of any method, and in particular, note that an | |

// abstract matrix type may be copied into a concrete type of the corresponding | |

// value. If there are specific special cases that are needed, please submit a | |

// pull-request or file an issue. | |

// | |

// Invariants | |

// | |

// Matrix input arguments to functions are never directly modified. If an operation | |

// changes Matrix data, the mutated matrix will be the receiver of a function. | |

// | |

// For convenience, a matrix may be used as both a receiver and as an input, e.g. | |

// a.Pow(a, 6) | |

// v.SolveVec(a.T(), v) | |

// though in many cases this will cause an allocation (see Element Aliasing). | |

// An exception to this rule is Copy, which does not allow a.Copy(a.T()). | |

// | |

// Element Aliasing | |

// | |

// Most methods in mat modify receiver data. It is forbidden for the modified | |

// data region of the receiver to overlap the used data area of the input | |

// arguments. The exception to this rule is when the method receiver is equal to one | |

// of the input arguments, as in the a.Pow(a, 6) call above, or its implicit transpose. | |

// | |

// This prohibition is to help avoid subtle mistakes when the method needs to read | |

// from and write to the same data region. There are ways to make mistakes using the | |

// mat API, and mat functions will detect and complain about those. | |

// There are many ways to make mistakes by excursion from the mat API via | |

// interaction with raw matrix values. | |

// | |

// If you need to read the rest of this section to understand the behavior of | |

// your program, you are being clever. Don't be clever. If you must be clever, | |

// blas64 and lapack64 may be used to call the behavior directly. | |

// | |

// mat will use the following rules to detect overlap between the receiver and one | |

// of the inputs: | |

// - the input implements one of the Raw methods, and | |

// - the Raw type matches that of the receiver or | |

// one is a RawMatrixer and the other is a RawVectorer, and | |

// - the address ranges of the backing data slices overlap, and | |

// - the strides differ or there is an overlap in the used data elements. | |

// If such an overlap is detected, the method will panic. | |

// | |

// The following cases will not panic: | |

// - the data slices do not overlap, | |

// - there is pointer identity between the receiver and input values after | |

// the value has been untransposed if necessary. | |

// | |

// mat will not attempt to detect element overlap if the input does not implement a | |

// Raw method, or if the Raw method differs from that of the receiver except when a | |

// conversion has occurred through a mat API function. Method behavior is undefined | |

// if there is undetected overlap. | |

// | |

package mat // import "gonum.org/v1/gonum/mat" |