1 | // |
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2 | // C++ Implementation: itpp_ext |
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3 | // |
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4 | // Description: |
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5 | // |
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6 | // |
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7 | // Author: smidl <smidl@utia.cas.cz>, (C) 2008 |
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8 | // |
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9 | // Copyright: See COPYING file that comes with this distribution |
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10 | // |
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11 | // |
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12 | |
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13 | #include "itpp_ext.h" |
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14 | |
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15 | // from algebra/lapack.h |
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16 | |
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17 | extern "C" { /* QR factorization of a general matrix A */ |
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18 | void dgeqrf_ ( int *m, int *n, double *a, int *lda, double *tau, double *work, |
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19 | int *lwork, int *info ); |
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20 | }; |
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21 | |
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22 | namespace itpp { |
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23 | Array<int> to_Arr ( const ivec &indices ) { |
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24 | Array<int> a ( indices.size() ); |
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25 | for ( int i = 0; i < a.size(); i++ ) { |
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26 | a ( i ) = indices ( i ); |
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27 | } |
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28 | return a; |
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29 | } |
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30 | |
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31 | ivec linspace ( int from, int to ) { |
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32 | int n=to-from+1; |
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33 | int i; |
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34 | it_assert_debug ( n>0,"wrong linspace" ); |
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35 | ivec iv ( n ); for ( i=0;i<n;i++ ) iv ( i ) =from+i; |
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36 | return iv; |
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37 | }; |
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38 | |
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39 | void set_subvector ( vec &ov, const ivec &iv, const vec &v ) |
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40 | { |
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41 | it_assert_debug ( ( iv.length() <=v.length() ), |
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42 | "Vec<>::set_subvector(ivec, vec<Num_T>): Indexing out " |
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43 | "of range of v" ); |
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44 | for ( int i = 0; i < iv.length(); i++ ) { |
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45 | it_assert_debug ( iv ( i ) <ov.length(), |
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46 | "Vec<>::set_subvector(ivec, vec<Num_T>): Indexing out " |
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47 | "of range of v" ); |
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48 | ov ( iv ( i ) ) = v ( i ); |
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49 | } |
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50 | } |
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51 | |
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52 | vec get_vec(const vec &v, const ivec &indexlist){ |
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53 | int size = indexlist.size(); |
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54 | vec temp(size); |
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55 | for (int i = 0; i < size; ++i) { |
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56 | temp(i) = v._data()[indexlist(i)]; |
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57 | } |
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58 | return temp; |
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59 | } |
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60 | |
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61 | // Gamma |
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62 | |
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63 | Gamma_RNG::Gamma_RNG ( double a, double b ) { |
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64 | setup ( a,b ); |
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65 | } |
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66 | |
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67 | |
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68 | bvec operator& ( const bvec &a, const bvec &b ) { |
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69 | it_assert_debug ( b.size() ==a.size(), "operator&(): Vectors of different lengths" ); |
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70 | |
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71 | bvec temp ( a.size() ); |
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72 | for ( int i = 0;i < a.size();i++ ) { |
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73 | temp ( i ) = a ( i ) & b ( i ); |
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74 | } |
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75 | return temp; |
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76 | } |
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77 | |
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78 | bvec operator| ( const bvec &a, const bvec &b ) { |
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79 | it_assert_debug ( b.size() !=a.size(), "operator&(): Vectors of different lengths" ); |
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80 | |
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81 | bvec temp ( a.size() ); |
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82 | for ( int i = 0;i < a.size();i++ ) { |
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83 | temp ( i ) = a ( i ) | b ( i ); |
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84 | } |
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85 | return temp; |
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86 | } |
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87 | #define log std::log |
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88 | #define exp std::exp |
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89 | #define sqrt std::sqrt |
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90 | #define R_FINITE std::isfinite |
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91 | |
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92 | double Gamma_RNG::sample() { |
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93 | //A copy of rgamma code from the R package!! |
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94 | // |
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95 | |
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96 | /* Constants : */ |
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97 | const static double sqrt32 = 5.656854; |
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98 | const static double exp_m1 = 0.36787944117144232159;/* exp(-1) = 1/e */ |
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99 | |
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100 | /* Coefficients q[k] - for q0 = sum(q[k]*a^(-k)) |
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101 | * Coefficients a[k] - for q = q0+(t*t/2)*sum(a[k]*v^k) |
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102 | * Coefficients e[k] - for exp(q)-1 = sum(e[k]*q^k) |
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103 | */ |
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104 | const static double q1 = 0.04166669; |
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105 | const static double q2 = 0.02083148; |
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106 | const static double q3 = 0.00801191; |
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107 | const static double q4 = 0.00144121; |
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108 | const static double q5 = -7.388e-5; |
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109 | const static double q6 = 2.4511e-4; |
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110 | const static double q7 = 2.424e-4; |
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111 | |
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112 | const static double a1 = 0.3333333; |
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113 | const static double a2 = -0.250003; |
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114 | const static double a3 = 0.2000062; |
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115 | const static double a4 = -0.1662921; |
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116 | const static double a5 = 0.1423657; |
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117 | const static double a6 = -0.1367177; |
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118 | const static double a7 = 0.1233795; |
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119 | |
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120 | /* State variables [FIXME for threading!] :*/ |
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121 | static double aa = 0.; |
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122 | static double aaa = 0.; |
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123 | static double s, s2, d; /* no. 1 (step 1) */ |
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124 | static double q0, b, si, c;/* no. 2 (step 4) */ |
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125 | |
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126 | double e, p, q, r, t, u, v, w, x, ret_val; |
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127 | double a=alpha; |
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128 | double scale=1.0/beta; |
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129 | |
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130 | if ( !R_FINITE ( a ) || !R_FINITE ( scale ) || a < 0.0 || scale <= 0.0 ) |
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131 | {it_error ( "Gamma_RNG wrong parameters" );} |
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132 | |
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133 | if ( a < 1. ) { /* GS algorithm for parameters a < 1 */ |
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134 | if ( a == 0 ) |
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135 | return 0.; |
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136 | e = 1.0 + exp_m1 * a; |
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137 | for ( ;; ) { //VS repeat |
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138 | p = e * unif_rand(); |
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139 | if ( p >= 1.0 ) { |
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140 | x = -log ( ( e - p ) / a ); |
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141 | if ( exp_rand() >= ( 1.0 - a ) * log ( x ) ) |
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142 | break; |
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143 | } |
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144 | else { |
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145 | x = exp ( log ( p ) / a ); |
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146 | if ( exp_rand() >= x ) |
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147 | break; |
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148 | } |
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149 | } |
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150 | return scale * x; |
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151 | } |
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152 | |
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153 | /* --- a >= 1 : GD algorithm --- */ |
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154 | |
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155 | /* Step 1: Recalculations of s2, s, d if a has changed */ |
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156 | if ( a != aa ) { |
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157 | aa = a; |
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158 | s2 = a - 0.5; |
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159 | s = sqrt ( s2 ); |
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160 | d = sqrt32 - s * 12.0; |
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161 | } |
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162 | /* Step 2: t = standard normal deviate, |
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163 | x = (s,1/2) -normal deviate. */ |
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164 | |
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165 | /* immediate acceptance (i) */ |
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166 | t = norm_rand(); |
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167 | x = s + 0.5 * t; |
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168 | ret_val = x * x; |
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169 | if ( t >= 0.0 ) |
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170 | return scale * ret_val; |
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171 | |
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172 | /* Step 3: u = 0,1 - uniform sample. squeeze acceptance (s) */ |
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173 | u = unif_rand(); |
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174 | if ( ( d * u ) <= ( t * t * t ) ) |
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175 | return scale * ret_val; |
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176 | |
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177 | /* Step 4: recalculations of q0, b, si, c if necessary */ |
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178 | |
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179 | if ( a != aaa ) { |
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180 | aaa = a; |
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181 | r = 1.0 / a; |
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182 | q0 = ( ( ( ( ( ( q7 * r + q6 ) * r + q5 ) * r + q4 ) * r + q3 ) * r |
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183 | + q2 ) * r + q1 ) * r; |
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184 | |
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185 | /* Approximation depending on size of parameter a */ |
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186 | /* The constants in the expressions for b, si and c */ |
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187 | /* were established by numerical experiments */ |
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188 | |
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189 | if ( a <= 3.686 ) { |
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190 | b = 0.463 + s + 0.178 * s2; |
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191 | si = 1.235; |
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192 | c = 0.195 / s - 0.079 + 0.16 * s; |
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193 | } |
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194 | else if ( a <= 13.022 ) { |
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195 | b = 1.654 + 0.0076 * s2; |
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196 | si = 1.68 / s + 0.275; |
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197 | c = 0.062 / s + 0.024; |
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198 | } |
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199 | else { |
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200 | b = 1.77; |
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201 | si = 0.75; |
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202 | c = 0.1515 / s; |
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203 | } |
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204 | } |
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205 | /* Step 5: no quotient test if x not positive */ |
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206 | |
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207 | if ( x > 0.0 ) { |
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208 | /* Step 6: calculation of v and quotient q */ |
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209 | v = t / ( s + s ); |
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210 | if ( fabs ( v ) <= 0.25 ) |
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211 | q = q0 + 0.5 * t * t * ( ( ( ( ( ( a7 * v + a6 ) * v + a5 ) * v + a4 ) * v |
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212 | + a3 ) * v + a2 ) * v + a1 ) * v; |
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213 | else |
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214 | q = q0 - s * t + 0.25 * t * t + ( s2 + s2 ) * log ( 1.0 + v ); |
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215 | |
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216 | |
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217 | /* Step 7: quotient acceptance (q) */ |
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218 | if ( log ( 1.0 - u ) <= q ) |
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219 | return scale * ret_val; |
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220 | } |
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221 | |
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222 | for ( ;; ) { //VS repeat |
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223 | /* Step 8: e = standard exponential deviate |
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224 | * u = 0,1 -uniform deviate |
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225 | * t = (b,si)-double exponential (laplace) sample */ |
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226 | e = exp_rand(); |
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227 | u = unif_rand(); |
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228 | u = u + u - 1.0; |
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229 | if ( u < 0.0 ) |
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230 | t = b - si * e; |
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231 | else |
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232 | t = b + si * e; |
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233 | /* Step 9: rejection if t < tau(1) = -0.71874483771719 */ |
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234 | if ( t >= -0.71874483771719 ) { |
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235 | /* Step 10: calculation of v and quotient q */ |
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236 | v = t / ( s + s ); |
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237 | if ( fabs ( v ) <= 0.25 ) |
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238 | q = q0 + 0.5 * t * t * |
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239 | ( ( ( ( ( ( a7 * v + a6 ) * v + a5 ) * v + a4 ) * v + a3 ) * v |
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240 | + a2 ) * v + a1 ) * v; |
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241 | else |
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242 | q = q0 - s * t + 0.25 * t * t + ( s2 + s2 ) * log ( 1.0 + v ); |
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243 | /* Step 11: hat acceptance (h) */ |
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244 | /* (if q not positive go to step 8) */ |
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245 | if ( q > 0.0 ) { |
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246 | // TODO: w = expm1(q); |
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247 | w = exp ( q )-1; |
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248 | /* ^^^^^ original code had approximation with rel.err < 2e-7 */ |
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249 | /* if t is rejected sample again at step 8 */ |
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250 | if ( ( c * fabs ( u ) ) <= ( w * exp ( e - 0.5 * t * t ) ) ) |
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251 | break; |
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252 | } |
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253 | } |
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254 | } /* repeat .. until `t' is accepted */ |
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255 | x = s + 0.5 * t; |
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256 | return scale * x * x; |
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257 | } |
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258 | |
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259 | |
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260 | bool qr ( const mat &A, mat &R ) { |
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261 | int info; |
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262 | int m = A.rows(); |
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263 | int n = A.cols(); |
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264 | int lwork = n; |
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265 | int k = std::min ( m, n ); |
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266 | vec tau ( k ); |
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267 | vec work ( lwork ); |
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268 | |
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269 | R = A; |
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270 | |
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271 | // perform workspace query for optimum lwork value |
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272 | int lwork_tmp = -1; |
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273 | dgeqrf_ ( &m, &n, R._data(), &m, tau._data(), work._data(), &lwork_tmp, |
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274 | &info ); |
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275 | if ( info == 0 ) { |
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276 | lwork = static_cast<int> ( work ( 0 ) ); |
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277 | work.set_size ( lwork, false ); |
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278 | } |
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279 | dgeqrf_ ( &m, &n, R._data(), &m, tau._data(), work._data(), &lwork, &info ); |
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280 | |
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281 | // construct R |
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282 | for ( int i=0; i<m; i++ ) |
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283 | for ( int j=0; j<std::min ( i,n ); j++ ) |
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284 | R ( i,j ) = 0; |
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285 | |
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286 | return ( info==0 ); |
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287 | } |
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288 | |
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289 | |
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290 | std::string num2str(double d){ |
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291 | char tmp[20];//that should do |
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292 | sprintf(tmp,"%f",d); |
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293 | return std::string(tmp); |
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294 | }; |
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295 | std::string num2str(int i){ |
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296 | char tmp[10];//that should do |
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297 | sprintf(tmp,"%d",i); |
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298 | return std::string(tmp); |
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299 | }; |
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300 | } |
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