SHOGUN  4.0.0
Core.h
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2  * Copyright (c) The Shogun Machine Learning Toolbox
3  * Written (w) 2014 Soumyajit De
4  * Written (w) 2014 Khaled Nasr
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31 
32 #ifndef CORE_H_
33 #define CORE_H_
34 
41 
42 namespace shogun
43 {
44 
45 namespace linalg
46 {
47 
58 template <Backend backend=linalg_traits<Core>::backend,class Matrix>
59 void matrix_product(Matrix A, Matrix B, Matrix C,
60  bool transpose_A=false, bool transpose_B=false, bool overwrite=true)
61 {
62  implementation::matrix_product<backend, Matrix>::compute(A, B, C, transpose_A, transpose_B, overwrite);
63 }
64 
66 template <Backend backend=linalg_traits<Core>::backend,class Matrix>
67 void add(Matrix A, Matrix B, Matrix C,
68  typename Matrix::Scalar alpha=1.0, typename Matrix::Scalar beta=1.0)
69 {
71 }
72 
74 template <Backend backend=linalg_traits<Core>::backend,class Matrix>
75 void subtract(Matrix A, Matrix B, Matrix C,
76  typename Matrix::Scalar alpha=1.0, typename Matrix::Scalar beta=1.0)
77 {
78  implementation::add<backend, Matrix>::compute(A, B, C, alpha, -1*beta);
79 }
80 
82 template <Backend backend=linalg_traits<Core>::backend,class Matrix>
83 void scale(Matrix A, Matrix B, typename Matrix::Scalar alpha)
84 {
86 }
87 
89 template <Backend backend=linalg_traits<Core>::backend,class Matrix>
90 void elementwise_product(Matrix A, Matrix B, Matrix C)
91 {
93 }
94 
103 template <Backend backend=linalg_traits<Core>::backend,class Matrix>
104 typename implementation::elementwise_square<backend,Matrix>::ReturnType elementwise_square(Matrix m)
105 {
107 }
108 
116 template <Backend backend=linalg_traits<Core>::backend,class Matrix, class ResultMatrix>
117 void elementwise_square(Matrix m, ResultMatrix result)
118 {
120 }
121 
138 template <Backend backend=linalg_traits<Core>::backend,class Matrix>
139 void convolve(Matrix X, Matrix W, Matrix Y, bool flip = false,
140  bool overwrite=true, int32_t stride_x=1, int32_t stride_y=1)
141 {
142  implementation::convolve<backend, Matrix>::compute(X, W, Y, flip, overwrite, stride_x, stride_y);
143 }
144 
145 }
146 
147 }
148 #endif // CORE_H_
static void compute(Matrix A, Matrix B, Matrix C)
implementation::elementwise_square< backend, Matrix >::ReturnType elementwise_square(Matrix m)
Definition: Core.h:104
static void compute(Matrix A, Matrix B, Matrix C, bool transpose_A, bool transpose_B, bool overwrite)
void add(Matrix A, Matrix B, Matrix C, typename Matrix::Scalar alpha=1.0, typename Matrix::Scalar beta=1.0)
Definition: Core.h:67
static void compute(Matrix A, Matrix B, Matrix C, T alpha, T beta)
void elementwise_product(Matrix A, Matrix B, Matrix C)
Definition: Core.h:90
static void compute(Matrix X, Matrix W, Matrix Y, bool flip, bool overwrite, int32_t stride_x, int32_t stride_y)
void matrix_product(Matrix A, Matrix B, Matrix C, bool transpose_A=false, bool transpose_B=false, bool overwrite=true)
Definition: Core.h:59
void subtract(Matrix A, Matrix B, Matrix C, typename Matrix::Scalar alpha=1.0, typename Matrix::Scalar beta=1.0)
Definition: Core.h:75
void convolve(Matrix X, Matrix W, Matrix Y, bool flip=false, bool overwrite=true, int32_t stride_x=1, int32_t stride_y=1)
Definition: Core.h:139
all of classes and functions are contained in the shogun namespace
Definition: class_list.h:18
void scale(Matrix A, Matrix B, typename Matrix::Scalar alpha)
Definition: Core.h:83
static void compute(Matrix A, Matrix B, Matrix C, T alpha, T beta)

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