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annotate libinterp/corefcn/kron.cc @ 33623:4517f929c59d bytecode-interpreter tip

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maint: Merge default to bytecode-interpreter
author Arun Giridhar <arungiridhar@gmail.com>
date Fri, 24 May 2024 16:22:41 -0400
parents a258493e726a
children
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rev   line source
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1 ////////////////////////////////////////////////////////////////////////
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2 //
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3 // Copyright (C) 2002-2024 The Octave Project Developers
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4 //
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5 // See the file COPYRIGHT.md in the top-level directory of this
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6 // distribution or <https://octave.org/copyright/>.
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7 //
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8 // This file is part of Octave.
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9 //
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10 // Octave is free software: you can redistribute it and/or modify it
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11 // under the terms of the GNU General Public License as published by
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12 // the Free Software Foundation, either version 3 of the License, or
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13 // (at your option) any later version.
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14 //
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15 // Octave is distributed in the hope that it will be useful, but
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16 // WITHOUT ANY WARRANTY; without even the implied warranty of
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17 // MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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18 // GNU General Public License for more details.
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19 //
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20 // You should have received a copy of the GNU General Public License
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21 // along with Octave; see the file COPYING. If not, see
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22 // <https://www.gnu.org/licenses/>.
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23 //
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24 ////////////////////////////////////////////////////////////////////////
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25
21724
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26 #if defined (HAVE_CONFIG_H)
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27 # include "config.h"
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28 #endif
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29
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30 #include "dMatrix.h"
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31 #include "fMatrix.h"
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32 #include "CMatrix.h"
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33 #include "fCMatrix.h"
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34
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35 #include "dSparse.h"
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36 #include "CSparse.h"
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37
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38 #include "dDiagMatrix.h"
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39 #include "fDiagMatrix.h"
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40 #include "CDiagMatrix.h"
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41 #include "fCDiagMatrix.h"
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42
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43 #include "PermMatrix.h"
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44
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45 #include "mx-inlines.cc"
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46 #include "quit.h"
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47
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48 #include "defun.h"
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49 #include "error.h"
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50 #include "ovl.h"
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51
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52 OCTAVE_BEGIN_NAMESPACE(octave)
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53
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54 template <typename R, typename T>
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55 static MArray<T>
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56 kron (const MArray<R>& a, const MArray<T>& b)
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57 {
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58 panic_unless (a.ndims () == 2);
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59 panic_unless (b.ndims () == 2);
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60
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61 octave_idx_type nra = a.rows ();
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62 octave_idx_type nrb = b.rows ();
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63 octave_idx_type nca = a.cols ();
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64 octave_idx_type ncb = b.cols ();
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65
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66 MArray<T> c (dim_vector (nra*nrb, nca*ncb));
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67 T *cv = c.rwdata ();
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68
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69 for (octave_idx_type ja = 0; ja < nca; ja++)
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70 {
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71 octave_quit ();
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72 for (octave_idx_type jb = 0; jb < ncb; jb++)
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73 {
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74 for (octave_idx_type ia = 0; ia < nra; ia++)
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75 {
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76 mx_inline_mul (nrb, cv, a(ia, ja), b.data () + nrb*jb);
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77 cv += nrb;
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78 }
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79 }
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80 }
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81
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82 return c;
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83 }
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84
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85 template <typename R, typename T>
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86 static MArray<T>
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87 kron (const MDiagArray2<R>& a, const MArray<T>& b)
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88 {
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89 panic_unless (b.ndims () == 2);
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90
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91 octave_idx_type nra = a.rows ();
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92 octave_idx_type nrb = b.rows ();
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93 octave_idx_type dla = a.diag_length ();
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94 octave_idx_type nca = a.cols ();
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95 octave_idx_type ncb = b.cols ();
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96
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97 MArray<T> c (dim_vector (nra*nrb, nca*ncb), T ());
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98
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99 for (octave_idx_type ja = 0; ja < dla; ja++)
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100 {
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101 octave_quit ();
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102 for (octave_idx_type jb = 0; jb < ncb; jb++)
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103 {
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104 mx_inline_mul (nrb, &c.xelem (ja*nrb, ja*ncb + jb), a.dgelem (ja),
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105 b.data () + nrb*jb);
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106 }
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107 }
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108
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109 return c;
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110 }
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111
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112 template <typename T>
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113 static MSparse<T>
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114 kron (const MSparse<T>& A, const MSparse<T>& B)
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115 {
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116 octave_idx_type idx = 0;
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117 MSparse<T> C (A.rows () * B.rows (), A.columns () * B.columns (),
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118 A.nnz () * B.nnz ());
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119
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120 C.cidx (0) = 0;
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121
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122 for (octave_idx_type Aj = 0; Aj < A.columns (); Aj++)
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123 {
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124 octave_quit ();
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125 for (octave_idx_type Bj = 0; Bj < B.columns (); Bj++)
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126 {
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127 for (octave_idx_type Ai = A.cidx (Aj); Ai < A.cidx (Aj+1); Ai++)
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128 {
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129 octave_idx_type Ci = A.ridx (Ai) * B.rows ();
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130 const T v = A.data (Ai);
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131
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132 for (octave_idx_type Bi = B.cidx (Bj); Bi < B.cidx (Bj+1); Bi++)
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133 {
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134 C.data (idx) = v * B.data (Bi);
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135 C.ridx (idx++) = Ci + B.ridx (Bi);
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136 }
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137 }
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138 C.cidx (Aj * B.columns () + Bj + 1) = idx;
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139 }
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140 }
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141
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142 return C;
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143 }
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144
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145 static PermMatrix
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146 kron (const PermMatrix& a, const PermMatrix& b)
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147 {
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148 octave_idx_type na = a.rows ();
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149 octave_idx_type nb = b.rows ();
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150 const Array<octave_idx_type>& pa = a.col_perm_vec ();
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151 const Array<octave_idx_type>& pb = b.col_perm_vec ();
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152 Array<octave_idx_type> res_perm (dim_vector (na * nb, 1));
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153 octave_idx_type rescol = 0;
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154 for (octave_idx_type i = 0; i < na; i++)
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155 {
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156 octave_idx_type a_add = pa(i) * nb;
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157 for (octave_idx_type j = 0; j < nb; j++)
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158 res_perm.xelem (rescol++) = a_add + pb(j);
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159 }
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160
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161 return PermMatrix (res_perm, true);
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162 }
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163
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164 template <typename MTA, typename MTB>
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165 octave_value
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166 do_kron (const octave_value& a, const octave_value& b)
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167 {
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168 MTA am = octave_value_extract<MTA> (a);
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169 MTB bm = octave_value_extract<MTB> (b);
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170
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171 return octave_value (kron (am, bm));
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172 }
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173
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174 octave_value
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175 dispatch_kron (const octave_value& a, const octave_value& b)
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176 {
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177 octave_value retval;
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178 if (a.is_perm_matrix () && b.is_perm_matrix ())
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179 retval = do_kron<PermMatrix, PermMatrix> (a, b);
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180 else if (a.issparse () || b.issparse ())
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181 {
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182 if (a.iscomplex () || b.iscomplex ())
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183 retval = do_kron<SparseComplexMatrix, SparseComplexMatrix> (a, b);
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184 else
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185 retval = do_kron<SparseMatrix, SparseMatrix> (a, b);
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186 }
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187 else if (a.is_diag_matrix ())
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188 {
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189 if (b.is_diag_matrix () && a.rows () == a.columns ()
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190 && b.rows () == b.columns ())
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191 {
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192 // We have two diagonal matrices, the product of those will be
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193 // another diagonal matrix. To do that efficiently, extract
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194 // the diagonals as vectors and compute the product. That
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195 // will be another vector, which we then use to construct a
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196 // diagonal matrix object. Note that this will fail if our
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197 // digaonal matrix object is modified to allow the nonzero
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198 // values to be stored off of the principal diagonal (i.e., if
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199 // diag ([1,2], 3) is modified to return a diagonal matrix
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200 // object instead of a full matrix object).
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201
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202 octave_value tmp = dispatch_kron (a.diag (), b.diag ());
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203 retval = tmp.diag ();
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204 }
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205 else if (a.is_single_type () || b.is_single_type ())
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206 {
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207 if (a.iscomplex ())
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208 retval = do_kron<FloatComplexDiagMatrix, FloatComplexMatrix> (a, b);
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209 else if (b.iscomplex ())
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210 retval = do_kron<FloatDiagMatrix, FloatComplexMatrix> (a, b);
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211 else
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212 retval = do_kron<FloatDiagMatrix, FloatMatrix> (a, b);
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213 }
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214 else
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215 {
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216 if (a.iscomplex ())
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217 retval = do_kron<ComplexDiagMatrix, ComplexMatrix> (a, b);
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218 else if (b.iscomplex ())
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219 retval = do_kron<DiagMatrix, ComplexMatrix> (a, b);
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220 else
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221 retval = do_kron<DiagMatrix, Matrix> (a, b);
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222 }
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223 }
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224 else if (a.is_single_type () || b.is_single_type ())
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225 {
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226 if (a.iscomplex ())
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227 retval = do_kron<FloatComplexMatrix, FloatComplexMatrix> (a, b);
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228 else if (b.iscomplex ())
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229 retval = do_kron<FloatMatrix, FloatComplexMatrix> (a, b);
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230 else
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231 retval = do_kron<FloatMatrix, FloatMatrix> (a, b);
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232 }
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233 else
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234 {
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235 if (a.iscomplex ())
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236 retval = do_kron<ComplexMatrix, ComplexMatrix> (a, b);
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237 else if (b.iscomplex ())
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238 retval = do_kron<Matrix, ComplexMatrix> (a, b);
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239 else
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240 retval = do_kron<Matrix, Matrix> (a, b);
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241 }
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242 return retval;
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243 }
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244
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245
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246 DEFUN (kron, args, ,
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247 doc: /* -*- texinfo -*-
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248 @deftypefn {} {@var{C} =} kron (@var{A}, @var{B})
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249 @deftypefnx {} {@var{C} =} kron (@var{A1}, @var{A2}, @dots{})
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250 Form the Kronecker product of two or more matrices.
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251
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252 This is defined block by block as
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253
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254 @example
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255 c = [ a(i,j)*b ]
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256 @end example
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257
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258 For example:
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259
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260 @example
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261 @group
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262 kron (1:4, ones (3, 1))
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263 @result{} 1 2 3 4
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264 1 2 3 4
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265 1 2 3 4
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266 @end group
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267 @end example
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268
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269 If there are more than two input arguments @var{A1}, @var{A2}, @dots{},
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270 @var{An} the Kronecker product is computed as
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271
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272 @example
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273 kron (kron (@var{A1}, @var{A2}), @dots{}, @var{An})
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274 @end example
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275
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276 @noindent
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277 Since the Kronecker product is associative, this is well-defined.
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278 @seealso{tensorprod}
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279 @end deftypefn */)
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280 {
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281 int nargin = args.length ();
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282
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283 if (nargin < 2)
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284 print_usage ();
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285
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286 octave_value retval;
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287
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288 octave_value a = args(0);
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289 octave_value b = args(1);
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290
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291 retval = dispatch_kron (a, b);
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292
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293 for (octave_idx_type i = 2; i < nargin; i++)
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294 retval = dispatch_kron (retval, args(i));
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295
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296 return retval;
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297 }
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298
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299 /*
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300 %!test
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301 %! x = ones (2);
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302 %! assert (kron (x, x), ones (4));
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303
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304 %!shared x, y, z, p1, p2, d1, d2
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305 %! x = [1, 2];
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306 %! y = [-1, -2];
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307 %! z = [1, 2, 3, 4; 1, 2, 3, 4; 1, 2, 3, 4];
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308 %! p1 = eye (3)([2, 3, 1], :); ## Permutation matrix
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309 %! p2 = [0 1 0; 0 0 1; 1 0 0]; ## Non-permutation equivalent
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310 %! d1 = diag ([1 2 3]); ## Diag type matrix
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311 %! d2 = [1 0 0; 0 2 0; 0 0 3]; ## Non-diag equivalent
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312 %!assert (kron (1:4, ones (3, 1)), z)
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313 %!assert (kron (single (1:4), ones (3, 1)), single (z))
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314 %!assert (kron (sparse (1:4), ones (3, 1)), sparse (z))
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315 %!assert (kron (complex (1:4), ones (3, 1)), z)
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316 %!assert (kron (complex (single (1:4)), ones (3, 1)), single (z))
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317 %!assert (kron (x, y, z), kron (kron (x, y), z))
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318 %!assert (kron (x, y, z), kron (x, kron (y, z)))
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319 %!assert (kron (p1, p1), kron (p2, p2))
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320 %!assert (kron (p1, p2), kron (p2, p1))
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321 %!assert (kron (d1, d1), kron (d2, d2))
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322 %!assert (kron (d1, d2), kron (d2, d1))
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323
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324 %!assert (kron (diag ([1, 2]), diag ([3, 4])), diag ([3, 4, 6, 8]))
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325
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326 ## Test for two diag matrices.
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327 ## See the comments above in dispatch_kron for this case.
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328 %!test
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329 %! expected = zeros (16, 16);
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330 %! expected (1, 11) = 3;
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331 %! expected (2, 12) = 4;
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332 %! expected (5, 15) = 6;
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333 %! expected (6, 16) = 8;
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334 %! assert (kron (diag ([1, 2], 2), diag ([3, 4], 2)), expected);
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335 */
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336
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337 OCTAVE_END_NAMESPACE(octave)