| // 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 *VecDense 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" |
