| 95 | | <a name="l00134"></a>00134 <span class="comment">// mlnorm<sq_T>* condition ( const RV &rvn ) const ;</span> |
| 96 | | <a name="l00135"></a>00135 <a class="code" href="classmpdf.html" title="Conditional probability density, e.g. modeling some dependencies.">mpdf</a>* <a class="code" href="classenorm.html#921024bd6d5a0e65f2af2e39bf38dfca" title="Return conditional density on the given RV, the remaining rvs will be in conditioning...">condition</a> ( <span class="keyword">const</span> <a class="code" href="classRV.html" title="Class representing variables, most often random variables.">RV</a> &rvn ) <span class="keyword">const</span> ; |
| 97 | | <a name="l00136"></a>00136 <span class="comment">// enorm<sq_T>* marginal ( const RV &rv ) const;</span> |
| 98 | | <a name="l00137"></a>00137 <a class="code" href="classepdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>* <a class="code" href="classenorm.html#af50a6102846060bcb23a670bf38117b" title="Return marginal density on the given RV, the remainig rvs are intergrated out.">marginal</a> ( <span class="keyword">const</span> <a class="code" href="classRV.html" title="Class representing variables, most often random variables.">RV</a> &<a class="code" href="classepdf.html#74da992e3f5d598da8850b646b79b9d9" title="Identified of the random variable.">rv</a> ) <span class="keyword">const</span>; |
| 99 | | <a name="l00138"></a>00138 <span class="comment">//Access methods</span> |
| 100 | | <a name="l00140"></a><a class="code" href="classenorm.html#0b8cb284e5af920a1b64a21d057ec5ac">00140</a> <span class="comment"></span> vec& <a class="code" href="classenorm.html#0b8cb284e5af920a1b64a21d057ec5ac" title="returns a pointer to the internal mean value. Use with Care!">_mu</a>() {<span class="keywordflow">return</span> <a class="code" href="classenorm.html#71fde0d54bba147e00f612577f95ad20" title="mean value">mu</a>;} |
| 101 | | <a name="l00141"></a>00141 |
| 102 | | <a name="l00143"></a><a class="code" href="classenorm.html#d892a38f03be12e572ea57d9689cef6b">00143</a> <span class="keywordtype">void</span> <a class="code" href="classenorm.html#d892a38f03be12e572ea57d9689cef6b" title="access function">set_mu</a> ( <span class="keyword">const</span> vec mu0 ) { <a class="code" href="classenorm.html#71fde0d54bba147e00f612577f95ad20" title="mean value">mu</a>=mu0;} |
| 103 | | <a name="l00144"></a>00144 |
| 104 | | <a name="l00146"></a><a class="code" href="classenorm.html#7a5034b25771a84450a990d10fc40ac9">00146</a> sq_T& <a class="code" href="classenorm.html#7a5034b25771a84450a990d10fc40ac9" title="returns pointers to the internal variance and its inverse. Use with Care!">_R</a>() {<span class="keywordflow">return</span> <a class="code" href="classenorm.html#4ccc8d8514d644ef1c98d8ab023748a1" title="Covariance matrix in decomposed form.">R</a>;} |
| 105 | | <a name="l00147"></a>00147 <span class="keyword">const</span> sq_T& <a class="code" href="classenorm.html#7a5034b25771a84450a990d10fc40ac9" title="returns pointers to the internal variance and its inverse. Use with Care!">_R</a>()<span class="keyword"> const </span>{<span class="keywordflow">return</span> <a class="code" href="classenorm.html#4ccc8d8514d644ef1c98d8ab023748a1" title="Covariance matrix in decomposed form.">R</a>;} |
| 106 | | <a name="l00148"></a>00148 |
| 107 | | <a name="l00150"></a>00150 <span class="comment">// mat getR () {return R.to_mat();}</span> |
| 108 | | <a name="l00151"></a>00151 }; |
| 109 | | <a name="l00152"></a>00152 |
| 110 | | <a name="l00159"></a><a class="code" href="classegiw.html">00159</a> <span class="keyword">class </span><a class="code" href="classegiw.html" title="Gauss-inverse-Wishart density stored in LD form.">egiw</a> : <span class="keyword">public</span> <a class="code" href="classeEF.html" title="General conjugate exponential family posterior density.">eEF</a> { |
| 111 | | <a name="l00160"></a>00160 <span class="keyword">protected</span>: |
| 112 | | <a name="l00162"></a><a class="code" href="classegiw.html#f343d03ede89db820edf44a6297fa442">00162</a> <a class="code" href="classldmat.html" title="Matrix stored in LD form, (commonly known as UD).">ldmat</a> <a class="code" href="classegiw.html#f343d03ede89db820edf44a6297fa442" title="Extended information matrix of sufficient statistics.">V</a>; |
| 113 | | <a name="l00164"></a><a class="code" href="classegiw.html#4a2f130b91afe84f6d62fed289d5d453">00164</a> <span class="keywordtype">double</span> <a class="code" href="classegiw.html#4a2f130b91afe84f6d62fed289d5d453" title="Number of data records (degrees of freedom) of sufficient statistics.">nu</a>; |
| 114 | | <a name="l00166"></a><a class="code" href="classegiw.html#3d5c719f15a5527a6c62c2a53160148e">00166</a> <span class="keywordtype">int</span> <a class="code" href="classegiw.html#3d5c719f15a5527a6c62c2a53160148e" title="Dimension of the output.">xdim</a>; |
| 115 | | <a name="l00168"></a><a class="code" href="classegiw.html#c70d13d86e0d9f0acede3e1dc0368812">00168</a> <span class="keywordtype">int</span> <a class="code" href="classegiw.html#c70d13d86e0d9f0acede3e1dc0368812" title="Dimension of the regressor.">nPsi</a>; |
| 116 | | <a name="l00169"></a>00169 <span class="keyword">public</span>: |
| 117 | | <a name="l00171"></a><a class="code" href="classegiw.html#056c094f01ca1cc308d72162f47617c9">00171</a> <a class="code" href="classegiw.html#056c094f01ca1cc308d72162f47617c9" title="Default constructor, if nu0&lt;0 a minimal nu0 will be computed.">egiw</a> ( <a class="code" href="classRV.html" title="Class representing variables, most often random variables.">RV</a> <a class="code" href="classepdf.html#74da992e3f5d598da8850b646b79b9d9" title="Identified of the random variable.">rv</a>, mat V0, <span class="keywordtype">double</span> nu0=-1.0 ) : <a class="code" href="classeEF.html" title="General conjugate exponential family posterior density.">eEF</a> ( rv ), <a class="code" href="classegiw.html#f343d03ede89db820edf44a6297fa442" title="Extended information matrix of sufficient statistics.">V</a> ( V0 ), <a class="code" href="classegiw.html#4a2f130b91afe84f6d62fed289d5d453" title="Number of data records (degrees of freedom) of sufficient statistics.">nu</a> ( nu0 ) { |
| 118 | | <a name="l00172"></a>00172 <a class="code" href="classegiw.html#3d5c719f15a5527a6c62c2a53160148e" title="Dimension of the output.">xdim</a> = rv.<a class="code" href="classRV.html#f5c7b8bd589eef09ccdf3329a0addea0" title="Return number of scalars in the RV.">count</a>() /<a class="code" href="classegiw.html#f343d03ede89db820edf44a6297fa442" title="Extended information matrix of sufficient statistics.">V</a>.<a class="code" href="group__math.html#g96dfb21865db4f5bd36fa70f9b0b1163" title="access function">rows</a>(); |
| 119 | | <a name="l00173"></a>00173 it_assert_debug ( rv.<a class="code" href="classRV.html#f5c7b8bd589eef09ccdf3329a0addea0" title="Return number of scalars in the RV.">count</a>() ==<a class="code" href="classegiw.html#3d5c719f15a5527a6c62c2a53160148e" title="Dimension of the output.">xdim</a>*<a class="code" href="classegiw.html#f343d03ede89db820edf44a6297fa442" title="Extended information matrix of sufficient statistics.">V</a>.<a class="code" href="group__math.html#g96dfb21865db4f5bd36fa70f9b0b1163" title="access function">rows</a>(),<span class="stringliteral">"Incompatible V0."</span> ); |
| 120 | | <a name="l00174"></a>00174 <a class="code" href="classegiw.html#c70d13d86e0d9f0acede3e1dc0368812" title="Dimension of the regressor.">nPsi</a> = <a class="code" href="classegiw.html#f343d03ede89db820edf44a6297fa442" title="Extended information matrix of sufficient statistics.">V</a>.<a class="code" href="group__math.html#g96dfb21865db4f5bd36fa70f9b0b1163" title="access function">rows</a>()-<a class="code" href="classegiw.html#3d5c719f15a5527a6c62c2a53160148e" title="Dimension of the output.">xdim</a>; |
| 121 | | <a name="l00175"></a>00175 <span class="comment">//set mu to have proper normalization and </span> |
| 122 | | <a name="l00176"></a>00176 <span class="keywordflow">if</span> (nu0<0){ |
| 123 | | <a name="l00177"></a>00177 <a class="code" href="classegiw.html#4a2f130b91afe84f6d62fed289d5d453" title="Number of data records (degrees of freedom) of sufficient statistics.">nu</a> = 0.1 +nPsi +2*<a class="code" href="classegiw.html#3d5c719f15a5527a6c62c2a53160148e" title="Dimension of the output.">xdim</a> +2; <span class="comment">// +2 assures finite expected value of R</span> |
| 124 | | <a name="l00178"></a>00178 <span class="comment">// terms before that are sufficient for finite normalization</span> |
| 125 | | <a name="l00179"></a>00179 } |
| 126 | | <a name="l00180"></a>00180 } |
| 127 | | <a name="l00182"></a><a class="code" href="classegiw.html#18c1bf6125652a6dcbca68dd02dddd8d">00182</a> <a class="code" href="classegiw.html#056c094f01ca1cc308d72162f47617c9" title="Default constructor, if nu0&lt;0 a minimal nu0 will be computed.">egiw</a> ( <a class="code" href="classRV.html" title="Class representing variables, most often random variables.">RV</a> <a class="code" href="classepdf.html#74da992e3f5d598da8850b646b79b9d9" title="Identified of the random variable.">rv</a>, <a class="code" href="classldmat.html" title="Matrix stored in LD form, (commonly known as UD).">ldmat</a> V0, <span class="keywordtype">double</span> nu0=-1.0 ) : <a class="code" href="classeEF.html" title="General conjugate exponential family posterior density.">eEF</a> ( rv ), <a class="code" href="classegiw.html#f343d03ede89db820edf44a6297fa442" title="Extended information matrix of sufficient statistics.">V</a> ( V0 ), <a class="code" href="classegiw.html#4a2f130b91afe84f6d62fed289d5d453" title="Number of data records (degrees of freedom) of sufficient statistics.">nu</a> ( nu0 ) { |
| 128 | | <a name="l00183"></a>00183 <a class="code" href="classegiw.html#3d5c719f15a5527a6c62c2a53160148e" title="Dimension of the output.">xdim</a> = rv.<a class="code" href="classRV.html#f5c7b8bd589eef09ccdf3329a0addea0" title="Return number of scalars in the RV.">count</a>() /<a class="code" href="classegiw.html#f343d03ede89db820edf44a6297fa442" title="Extended information matrix of sufficient statistics.">V</a>.<a class="code" href="group__math.html#g96dfb21865db4f5bd36fa70f9b0b1163" title="access function">rows</a>(); |
| 129 | | <a name="l00184"></a>00184 it_assert_debug ( rv.<a class="code" href="classRV.html#f5c7b8bd589eef09ccdf3329a0addea0" title="Return number of scalars in the RV.">count</a>() ==<a class="code" href="classegiw.html#3d5c719f15a5527a6c62c2a53160148e" title="Dimension of the output.">xdim</a>*<a class="code" href="classegiw.html#f343d03ede89db820edf44a6297fa442" title="Extended information matrix of sufficient statistics.">V</a>.<a class="code" href="group__math.html#g96dfb21865db4f5bd36fa70f9b0b1163" title="access function">rows</a>(),<span class="stringliteral">"Incompatible V0."</span> ); |
| 130 | | <a name="l00185"></a>00185 <a class="code" href="classegiw.html#c70d13d86e0d9f0acede3e1dc0368812" title="Dimension of the regressor.">nPsi</a> = <a class="code" href="classegiw.html#f343d03ede89db820edf44a6297fa442" title="Extended information matrix of sufficient statistics.">V</a>.<a class="code" href="group__math.html#g96dfb21865db4f5bd36fa70f9b0b1163" title="access function">rows</a>()-<a class="code" href="classegiw.html#3d5c719f15a5527a6c62c2a53160148e" title="Dimension of the output.">xdim</a>; |
| 131 | | <a name="l00186"></a>00186 <span class="keywordflow">if</span> (nu0<0){ |
| 132 | | <a name="l00187"></a>00187 <a class="code" href="classegiw.html#4a2f130b91afe84f6d62fed289d5d453" title="Number of data records (degrees of freedom) of sufficient statistics.">nu</a> = 0.1 +nPsi +2*<a class="code" href="classegiw.html#3d5c719f15a5527a6c62c2a53160148e" title="Dimension of the output.">xdim</a> +2; <span class="comment">// +2 assures finite expected value of R</span> |
| 133 | | <a name="l00188"></a>00188 <span class="comment">// terms before that are sufficient for finite normalization</span> |
| 134 | | <a name="l00189"></a>00189 } |
| 135 | | <a name="l00190"></a>00190 } |
| 136 | | <a name="l00191"></a>00191 |
| 137 | | <a name="l00192"></a>00192 vec <a class="code" href="classegiw.html#3d2c1f2ba0f9966781f1e0ae695e8a6f" title="Returns a sample, from density .">sample</a>() <span class="keyword">const</span>; |
| 138 | | <a name="l00193"></a>00193 vec <a class="code" href="classegiw.html#6deb0ff2859f41ef7cbdf6a842cabb29" title="return expected value">mean</a>() <span class="keyword">const</span>; |
| 139 | | <a name="l00194"></a>00194 <span class="keywordtype">void</span> mean_mat ( mat &M, mat&R ) <span class="keyword">const</span>; |
| 140 | | <a name="l00196"></a>00196 <span class="keywordtype">double</span> <a class="code" href="classegiw.html#2d94daac10d66bb743e4ddc8c1ba7268" title="In this instance, val= [theta, r]. For multivariate instances, it is stored columnwise...">evallog_nn</a> ( <span class="keyword">const</span> vec &val ) <span class="keyword">const</span>; |
| 141 | | <a name="l00197"></a>00197 <span class="keywordtype">double</span> <a class="code" href="classegiw.html#70eb1a0b88459b227f919b425b0d3359" title="logarithm of the normalizing constant, ">lognc</a> () <span class="keyword">const</span>; |
| 142 | | <a name="l00198"></a>00198 |
| 143 | | <a name="l00199"></a>00199 <span class="comment">//Access</span> |
| 144 | | <a name="l00201"></a><a class="code" href="classegiw.html#533e792e1175bfa06d5d595dc5d080d5">00201</a> <span class="comment"></span> <a class="code" href="classldmat.html" title="Matrix stored in LD form, (commonly known as UD).">ldmat</a>& <a class="code" href="classegiw.html#533e792e1175bfa06d5d595dc5d080d5" title="returns a pointer to the internal statistics. Use with Care!">_V</a>() {<span class="keywordflow">return</span> <a class="code" href="classegiw.html#f343d03ede89db820edf44a6297fa442" title="Extended information matrix of sufficient statistics.">V</a>;} |
| 145 | | <a name="l00203"></a><a class="code" href="classegiw.html#a46c8a206edf80b357a138d7491780c1">00203</a> <span class="keyword">const</span> <a class="code" href="classldmat.html" title="Matrix stored in LD form, (commonly known as UD).">ldmat</a>& <a class="code" href="classegiw.html#533e792e1175bfa06d5d595dc5d080d5" title="returns a pointer to the internal statistics. Use with Care!">_V</a>()<span class="keyword"> const </span>{<span class="keywordflow">return</span> <a class="code" href="classegiw.html#f343d03ede89db820edf44a6297fa442" title="Extended information matrix of sufficient statistics.">V</a>;} |
| 146 | | <a name="l00205"></a><a class="code" href="classegiw.html#08029c481ff95d24f093df0573879afe">00205</a> <span class="keywordtype">double</span>& <a class="code" href="classegiw.html#08029c481ff95d24f093df0573879afe" title="returns a pointer to the internal statistics. Use with Care!">_nu</a>() {<span class="keywordflow">return</span> <a class="code" href="classegiw.html#4a2f130b91afe84f6d62fed289d5d453" title="Number of data records (degrees of freedom) of sufficient statistics.">nu</a>;} |
| 147 | | <a name="l00206"></a>00206 <span class="keyword">const</span> <span class="keywordtype">double</span>& <a class="code" href="classegiw.html#08029c481ff95d24f093df0573879afe" title="returns a pointer to the internal statistics. Use with Care!">_nu</a>()<span class="keyword"> const </span>{<span class="keywordflow">return</span> <a class="code" href="classegiw.html#4a2f130b91afe84f6d62fed289d5d453" title="Number of data records (degrees of freedom) of sufficient statistics.">nu</a>;} |
| 148 | | <a name="l00207"></a><a class="code" href="classegiw.html#036306322a90a9977834baac07460816">00207</a> <span class="keywordtype">void</span> <a class="code" href="classegiw.html#036306322a90a9977834baac07460816" title="Power of the density, used e.g. to flatten the density.">pow</a> ( <span class="keywordtype">double</span> p ) {<a class="code" href="classegiw.html#f343d03ede89db820edf44a6297fa442" title="Extended information matrix of sufficient statistics.">V</a>*=p;<a class="code" href="classegiw.html#4a2f130b91afe84f6d62fed289d5d453" title="Number of data records (degrees of freedom) of sufficient statistics.">nu</a>*=p;}; |
| 149 | | <a name="l00208"></a>00208 }; |
| 150 | | <a name="l00209"></a>00209 |
| 151 | | <a name="l00218"></a><a class="code" href="classeDirich.html">00218</a> <span class="keyword">class </span><a class="code" href="classeDirich.html" title="Dirichlet posterior density.">eDirich</a>: <span class="keyword">public</span> <a class="code" href="classeEF.html" title="General conjugate exponential family posterior density.">eEF</a> { |
| 152 | | <a name="l00219"></a>00219 <span class="keyword">protected</span>: |
| 153 | | <a name="l00221"></a><a class="code" href="classeDirich.html#15e6b65e9595eedc8a1286c6cecd36d7">00221</a> vec <a class="code" href="classeDirich.html#15e6b65e9595eedc8a1286c6cecd36d7" title="sufficient statistics">beta</a>; |
| 154 | | <a name="l00222"></a>00222 <span class="keyword">public</span>: |
| 155 | | <a name="l00224"></a><a class="code" href="classeDirich.html#ac7e6116f3575c3860d07355e96cd4af">00224</a> <a class="code" href="classeDirich.html#ac7e6116f3575c3860d07355e96cd4af" title="Default constructor.">eDirich</a> ( <span class="keyword">const</span> <a class="code" href="classRV.html" title="Class representing variables, most often random variables.">RV</a> &<a class="code" href="classepdf.html#74da992e3f5d598da8850b646b79b9d9" title="Identified of the random variable.">rv</a>, <span class="keyword">const</span> vec &beta0 ) : <a class="code" href="classeEF.html" title="General conjugate exponential family posterior density.">eEF</a> ( rv ),<a class="code" href="classeDirich.html#15e6b65e9595eedc8a1286c6cecd36d7" title="sufficient statistics">beta</a> ( beta0 ) {it_assert_debug ( rv.<a class="code" href="classRV.html#f5c7b8bd589eef09ccdf3329a0addea0" title="Return number of scalars in the RV.">count</a>() ==<a class="code" href="classeDirich.html#15e6b65e9595eedc8a1286c6cecd36d7" title="sufficient statistics">beta</a>.length(),<span class="stringliteral">"Incompatible statistics"</span> ); }; |
| 156 | | <a name="l00226"></a><a class="code" href="classeDirich.html#55cccbc5eb44764dce722567acf5fd58">00226</a> <a class="code" href="classeDirich.html#ac7e6116f3575c3860d07355e96cd4af" title="Default constructor.">eDirich</a> ( <span class="keyword">const</span> <a class="code" href="classeDirich.html" title="Dirichlet posterior density.">eDirich</a> &D0 ) : <a class="code" href="classeEF.html" title="General conjugate exponential family posterior density.">eEF</a> ( D0.<a class="code" href="classepdf.html#74da992e3f5d598da8850b646b79b9d9" title="Identified of the random variable.">rv</a> ),<a class="code" href="classeDirich.html#15e6b65e9595eedc8a1286c6cecd36d7" title="sufficient statistics">beta</a> ( D0.<a class="code" href="classeDirich.html#15e6b65e9595eedc8a1286c6cecd36d7" title="sufficient statistics">beta</a> ) {}; |
| 157 | | <a name="l00227"></a><a class="code" href="classeDirich.html#23dff79110822e9639343fe8e177fd80">00227</a> vec <a class="code" href="classeDirich.html#23dff79110822e9639343fe8e177fd80" title="Returns a sample, from density .">sample</a>()<span class="keyword"> const </span>{it_error ( <span class="stringliteral">"Not implemented"</span> );<span class="keywordflow">return</span> vec_1 ( 0.0 );}; |
| 158 | | <a name="l00228"></a><a class="code" href="classeDirich.html#4206e1da149d51ff3b663c9241096b73">00228</a> vec <a class="code" href="classeDirich.html#4206e1da149d51ff3b663c9241096b73" title="return expected value">mean</a>()<span class="keyword"> const </span>{<span class="keywordflow">return</span> <a class="code" href="classeDirich.html#15e6b65e9595eedc8a1286c6cecd36d7" title="sufficient statistics">beta</a>/sum ( <a class="code" href="classeDirich.html#15e6b65e9595eedc8a1286c6cecd36d7" title="sufficient statistics">beta</a> );}; |
| 159 | | <a name="l00230"></a><a class="code" href="classeDirich.html#bb4b14ed7794777386de10608a83d142">00230</a> <span class="keywordtype">double</span> <a class="code" href="classeDirich.html#bb4b14ed7794777386de10608a83d142" title="In this instance, val is ...">evallog_nn</a> ( <span class="keyword">const</span> vec &val )<span class="keyword"> const </span>{<span class="keywordtype">double</span> tmp; tmp=( <a class="code" href="classeDirich.html#15e6b65e9595eedc8a1286c6cecd36d7" title="sufficient statistics">beta</a>-1 ) *log ( val ); it_assert_debug(std::isfinite(tmp),<span class="stringliteral">"Infinite value"</span>); |
| 160 | | <a name="l00231"></a>00231 <span class="keywordflow">return</span> tmp;}; |
| 161 | | <a name="l00232"></a><a class="code" href="classeDirich.html#7ce60be7119ffc639ede4e583c1f6e77">00232</a> <span class="keywordtype">double</span> <a class="code" href="classeDirich.html#7ce60be7119ffc639ede4e583c1f6e77" title="logarithm of the normalizing constant, ">lognc</a> ()<span class="keyword"> const </span>{ |
| 162 | | <a name="l00233"></a>00233 <span class="keywordtype">double</span> tmp; |
| 163 | | <a name="l00234"></a>00234 <span class="keywordtype">double</span> gam=sum ( <a class="code" href="classeDirich.html#15e6b65e9595eedc8a1286c6cecd36d7" title="sufficient statistics">beta</a> ); |
| 164 | | <a name="l00235"></a>00235 <span class="keywordtype">double</span> lgb=0.0; |
| 165 | | <a name="l00236"></a>00236 <span class="keywordflow">for</span> ( <span class="keywordtype">int</span> i=0;i<<a class="code" href="classeDirich.html#15e6b65e9595eedc8a1286c6cecd36d7" title="sufficient statistics">beta</a>.length();i++ ) {lgb+=lgamma ( <a class="code" href="classeDirich.html#15e6b65e9595eedc8a1286c6cecd36d7" title="sufficient statistics">beta</a> ( i ) );} |
| 166 | | <a name="l00237"></a>00237 tmp= lgb-lgamma ( gam ); |
| 167 | | <a name="l00238"></a>00238 it_assert_debug(std::isfinite(tmp),<span class="stringliteral">"Infinite value"</span>); |
| 168 | | <a name="l00239"></a>00239 <span class="keywordflow">return</span> tmp; |
| 169 | | <a name="l00240"></a>00240 }; |
| 170 | | <a name="l00242"></a><a class="code" href="classeDirich.html#6409d0362143a23976b43641ff19e53a">00242</a> vec& <a class="code" href="classeDirich.html#6409d0362143a23976b43641ff19e53a" title="access function">_beta</a>() {<span class="keywordflow">return</span> <a class="code" href="classeDirich.html#15e6b65e9595eedc8a1286c6cecd36d7" title="sufficient statistics">beta</a>;} |
| 171 | | <a name="l00244"></a><a class="code" href="classeDirich.html#c842acb2e1cce5cc9000769ff06c086d">00244</a> <span class="keywordtype">void</span> <a class="code" href="classeDirich.html#c842acb2e1cce5cc9000769ff06c086d" title="Set internal parameters.">set_parameters</a> ( <span class="keyword">const</span> vec &beta0 ) { |
| 172 | | <a name="l00245"></a>00245 <span class="keywordflow">if</span> ( beta0.length() !=<a class="code" href="classeDirich.html#15e6b65e9595eedc8a1286c6cecd36d7" title="sufficient statistics">beta</a>.length() ) { |
| 173 | | <a name="l00246"></a>00246 it_assert_debug ( <a class="code" href="classepdf.html#74da992e3f5d598da8850b646b79b9d9" title="Identified of the random variable.">rv</a>.<a class="code" href="classRV.html#c114a6f3ff06796cc2f4dacba74291eb" title="Return length (number of entries) of the RV.">length</a>() ==1,<span class="stringliteral">"Undefined"</span> ); |
| 174 | | <a name="l00247"></a>00247 <a class="code" href="classepdf.html#74da992e3f5d598da8850b646b79b9d9" title="Identified of the random variable.">rv</a>.<a class="code" href="classRV.html#70b24c39c5130b1e4753fa2eef495433" title="access function">set_size</a> ( 0,beta0.length() ); |
| 175 | | <a name="l00248"></a>00248 } |
| 176 | | <a name="l00249"></a>00249 <a class="code" href="classeDirich.html#15e6b65e9595eedc8a1286c6cecd36d7" title="sufficient statistics">beta</a>= beta0; |
| 177 | | <a name="l00250"></a>00250 } |
| 178 | | <a name="l00251"></a>00251 }; |
| 179 | | <a name="l00252"></a>00252 |
| 180 | | <a name="l00254"></a><a class="code" href="classmultiBM.html">00254</a> <span class="keyword">class </span><a class="code" href="classmultiBM.html" title="Estimator for Multinomial density.">multiBM</a> : <span class="keyword">public</span> <a class="code" href="classBMEF.html" title="Estimator for Exponential family.">BMEF</a> { |
| 181 | | <a name="l00255"></a>00255 <span class="keyword">protected</span>: |
| 182 | | <a name="l00257"></a><a class="code" href="classmultiBM.html#eddee08a724170de63f36e40c57b27b5">00257</a> <a class="code" href="classeDirich.html" title="Dirichlet posterior density.">eDirich</a> <a class="code" href="classmultiBM.html#eddee08a724170de63f36e40c57b27b5" title="Conjugate prior and posterior.">est</a>; |
| 183 | | <a name="l00259"></a><a class="code" href="classmultiBM.html#7b606116aed7e8834a339cbb0424b1d6">00259</a> vec &<a class="code" href="classmultiBM.html#7b606116aed7e8834a339cbb0424b1d6" title="Pointer inside est to sufficient statistics.">beta</a>; |
| 184 | | <a name="l00260"></a>00260 <span class="keyword">public</span>: |
| 185 | | <a name="l00262"></a><a class="code" href="classmultiBM.html#7d7d7e78c129602bcde96078359dc6e5">00262</a> <a class="code" href="classmultiBM.html#7d7d7e78c129602bcde96078359dc6e5" title="Default constructor.">multiBM</a> ( <span class="keyword">const</span> <a class="code" href="classRV.html" title="Class representing variables, most often random variables.">RV</a> &<a class="code" href="classBM.html#af00f0612fabe66241dd507188cdbf88" title="Random variable of the posterior.">rv</a>, <span class="keyword">const</span> vec beta0 ) : <a class="code" href="classBMEF.html" title="Estimator for Exponential family.">BMEF</a> ( rv ),<a class="code" href="classmultiBM.html#eddee08a724170de63f36e40c57b27b5" title="Conjugate prior and posterior.">est</a> ( rv,beta0 ),<a class="code" href="classmultiBM.html#7b606116aed7e8834a339cbb0424b1d6" title="Pointer inside est to sufficient statistics.">beta</a> ( <a class="code" href="classmultiBM.html#eddee08a724170de63f36e40c57b27b5" title="Conjugate prior and posterior.">est</a>._beta() ) {<span class="keywordflow">if</span>(<a class="code" href="classmultiBM.html#7b606116aed7e8834a339cbb0424b1d6" title="Pointer inside est to sufficient statistics.">beta</a>.length()>0){<a class="code" href="classBMEF.html#308cf5d4133cd471fdf1ecd5dfa09d02" title="cached value of lognc() in the previous step (used in evaluation of ll )">last_lognc</a>=<a class="code" href="classmultiBM.html#eddee08a724170de63f36e40c57b27b5" title="Conjugate prior and posterior.">est</a>.<a class="code" href="classeDirich.html#7ce60be7119ffc639ede4e583c1f6e77" title="logarithm of the normalizing constant, ">lognc</a>();}<span class="keywordflow">else</span>{<a class="code" href="classBMEF.html#308cf5d4133cd471fdf1ecd5dfa09d02" title="cached value of lognc() in the previous step (used in evaluation of ll )">last_lognc</a>=0.0;}} |
| 186 | | <a name="l00264"></a><a class="code" href="classmultiBM.html#b92751adbfb9f259ca8c95232cfd9c09">00264</a> <a class="code" href="classmultiBM.html#7d7d7e78c129602bcde96078359dc6e5" title="Default constructor.">multiBM</a> ( <span class="keyword">const</span> <a class="code" href="classmultiBM.html" title="Estimator for Multinomial density.">multiBM</a> &B ) : <a class="code" href="classBMEF.html" title="Estimator for Exponential family.">BMEF</a> ( B ),<a class="code" href="classmultiBM.html#eddee08a724170de63f36e40c57b27b5" title="Conjugate prior and posterior.">est</a> ( <a class="code" href="classBM.html#af00f0612fabe66241dd507188cdbf88" title="Random variable of the posterior.">rv</a>,B.<a class="code" href="classmultiBM.html#7b606116aed7e8834a339cbb0424b1d6" title="Pointer inside est to sufficient statistics.">beta</a> ),<a class="code" href="classmultiBM.html#7b606116aed7e8834a339cbb0424b1d6" title="Pointer inside est to sufficient statistics.">beta</a> ( <a class="code" href="classmultiBM.html#eddee08a724170de63f36e40c57b27b5" title="Conjugate prior and posterior.">est</a>._beta() ) {} |
| 187 | | <a name="l00266"></a><a class="code" href="classmultiBM.html#42e36804041e551d3ceea6c75abc0562">00266</a> <span class="keywordtype">void</span> <a class="code" href="classmultiBM.html#42e36804041e551d3ceea6c75abc0562" title="Sets sufficient statistics to match that of givefrom mB0.">set_statistics</a> ( <span class="keyword">const</span> <a class="code" href="classBM.html" title="Bayesian Model of the world, i.e. all uncertainty is modeled by probabilities.">BM</a>* mB0 ) {<span class="keyword">const</span> <a class="code" href="classmultiBM.html" title="Estimator for Multinomial density.">multiBM</a>* mB=<span class="keyword">dynamic_cast<</span><span class="keyword">const </span><a class="code" href="classmultiBM.html" title="Estimator for Multinomial density.">multiBM</a>*<span class="keyword">></span> ( mB0 ); <a class="code" href="classmultiBM.html#7b606116aed7e8834a339cbb0424b1d6" title="Pointer inside est to sufficient statistics.">beta</a>=mB-><a class="code" href="classmultiBM.html#7b606116aed7e8834a339cbb0424b1d6" title="Pointer inside est to sufficient statistics.">beta</a>;} |
| 188 | | <a name="l00267"></a><a class="code" href="classmultiBM.html#11eeba7e97954e316e959116f90d80e2">00267</a> <span class="keywordtype">void</span> <a class="code" href="classmultiBM.html#11eeba7e97954e316e959116f90d80e2" title="Incremental Bayes rule.">bayes</a> ( <span class="keyword">const</span> vec &dt ) { |
| 189 | | <a name="l00268"></a>00268 <span class="keywordflow">if</span> ( <a class="code" href="classBMEF.html#538d632e59f9afa8daa1de74da12ce71" title="forgetting factor">frg</a><1.0 ) {<a class="code" href="classmultiBM.html#7b606116aed7e8834a339cbb0424b1d6" title="Pointer inside est to sufficient statistics.">beta</a>*=<a class="code" href="classBMEF.html#538d632e59f9afa8daa1de74da12ce71" title="forgetting factor">frg</a>;<a class="code" href="classBMEF.html#308cf5d4133cd471fdf1ecd5dfa09d02" title="cached value of lognc() in the previous step (used in evaluation of ll )">last_lognc</a>=<a class="code" href="classmultiBM.html#eddee08a724170de63f36e40c57b27b5" title="Conjugate prior and posterior.">est</a>.<a class="code" href="classeDirich.html#7ce60be7119ffc639ede4e583c1f6e77" title="logarithm of the normalizing constant, ">lognc</a>();} |
| 190 | | <a name="l00269"></a>00269 <a class="code" href="classmultiBM.html#7b606116aed7e8834a339cbb0424b1d6" title="Pointer inside est to sufficient statistics.">beta</a>+=dt; |
| 191 | | <a name="l00270"></a>00270 <span class="keywordflow">if</span> ( <a class="code" href="classBM.html#bf6fb59b30141074f8ee1e2f43d03129" title="If true, the filter will compute likelihood of the data record and store it in ll...">evalll</a> ) {<a class="code" href="classBM.html#5623fef6572a08c2b53b8c87b82dc979" title="Logarithm of marginalized data likelihood.">ll</a>=<a class="code" href="classmultiBM.html#eddee08a724170de63f36e40c57b27b5" title="Conjugate prior and posterior.">est</a>.<a class="code" href="classeDirich.html#7ce60be7119ffc639ede4e583c1f6e77" title="logarithm of the normalizing constant, ">lognc</a>()-<a class="code" href="classBMEF.html#308cf5d4133cd471fdf1ecd5dfa09d02" title="cached value of lognc() in the previous step (used in evaluation of ll )">last_lognc</a>;} |
| 192 | | <a name="l00271"></a>00271 } |
| 193 | | <a name="l00272"></a><a class="code" href="classmultiBM.html#13e26a61757278981fd8cac9a7ef91eb">00272</a> <span class="keywordtype">double</span> <a class="code" href="classmultiBM.html#13e26a61757278981fd8cac9a7ef91eb">logpred</a> ( <span class="keyword">const</span> vec &dt )<span class="keyword"> const </span>{ |
| 194 | | <a name="l00273"></a>00273 <a class="code" href="classeDirich.html" title="Dirichlet posterior density.">eDirich</a> pred ( <a class="code" href="classmultiBM.html#eddee08a724170de63f36e40c57b27b5" title="Conjugate prior and posterior.">est</a> ); |
| 195 | | <a name="l00274"></a>00274 vec &<a class="code" href="classmultiBM.html#7b606116aed7e8834a339cbb0424b1d6" title="Pointer inside est to sufficient statistics.">beta</a> = pred.<a class="code" href="classeDirich.html#6409d0362143a23976b43641ff19e53a" title="access function">_beta</a>(); |
| 196 | | <a name="l00275"></a>00275 |
| 197 | | <a name="l00276"></a>00276 <span class="keywordtype">double</span> lll; |
| 198 | | <a name="l00277"></a>00277 <span class="keywordflow">if</span> ( <a class="code" href="classBMEF.html#538d632e59f9afa8daa1de74da12ce71" title="forgetting factor">frg</a><1.0 ) |
| 199 | | <a name="l00278"></a>00278 {beta*=<a class="code" href="classBMEF.html#538d632e59f9afa8daa1de74da12ce71" title="forgetting factor">frg</a>;lll=pred.<a class="code" href="classeDirich.html#7ce60be7119ffc639ede4e583c1f6e77" title="logarithm of the normalizing constant, ">lognc</a>();} |
| 200 | | <a name="l00279"></a>00279 <span class="keywordflow">else</span> |
| 201 | | <a name="l00280"></a>00280 <span class="keywordflow">if</span> ( <a class="code" href="classBM.html#bf6fb59b30141074f8ee1e2f43d03129" title="If true, the filter will compute likelihood of the data record and store it in ll...">evalll</a> ) {lll=<a class="code" href="classBMEF.html#308cf5d4133cd471fdf1ecd5dfa09d02" title="cached value of lognc() in the previous step (used in evaluation of ll )">last_lognc</a>;} |
| 202 | | <a name="l00281"></a>00281 <span class="keywordflow">else</span>{lll=pred.<a class="code" href="classeDirich.html#7ce60be7119ffc639ede4e583c1f6e77" title="logarithm of the normalizing constant, ">lognc</a>();} |
| 203 | | <a name="l00282"></a>00282 |
| 204 | | <a name="l00283"></a>00283 beta+=dt; |
| 205 | | <a name="l00284"></a>00284 <span class="keywordflow">return</span> pred.<a class="code" href="classeDirich.html#7ce60be7119ffc639ede4e583c1f6e77" title="logarithm of the normalizing constant, ">lognc</a>()-lll; |
| 206 | | <a name="l00285"></a>00285 } |
| 207 | | <a name="l00286"></a><a class="code" href="classmultiBM.html#3988322f8f51b153622036f461f62a67">00286</a> <span class="keywordtype">void</span> <a class="code" href="classmultiBM.html#3988322f8f51b153622036f461f62a67" title="Flatten the posterior according to the given BMEF (of the same type!).">flatten</a> ( <span class="keyword">const</span> <a class="code" href="classBMEF.html" title="Estimator for Exponential family.">BMEF</a>* B ) { |
| 208 | | <a name="l00287"></a>00287 <span class="keyword">const</span> <a class="code" href="classmultiBM.html" title="Estimator for Multinomial density.">multiBM</a>* E=<span class="keyword">dynamic_cast<</span><span class="keyword">const </span><a class="code" href="classmultiBM.html" title="Estimator for Multinomial density.">multiBM</a>*<span class="keyword">></span> ( B ); |
| 209 | | <a name="l00288"></a>00288 <span class="comment">// sum(beta) should be equal to sum(B.beta)</span> |
| 210 | | <a name="l00289"></a>00289 <span class="keyword">const</span> vec &Eb=E-><a class="code" href="classmultiBM.html#7b606116aed7e8834a339cbb0424b1d6" title="Pointer inside est to sufficient statistics.">beta</a>;<span class="comment">//const_cast<multiBM*> ( E )->_beta();</span> |
| 211 | | <a name="l00290"></a>00290 <a class="code" href="classmultiBM.html#7b606116aed7e8834a339cbb0424b1d6" title="Pointer inside est to sufficient statistics.">beta</a>*= ( sum ( Eb ) /sum ( <a class="code" href="classmultiBM.html#7b606116aed7e8834a339cbb0424b1d6" title="Pointer inside est to sufficient statistics.">beta</a> ) ); |
| 212 | | <a name="l00291"></a>00291 <span class="keywordflow">if</span> ( <a class="code" href="classBM.html#bf6fb59b30141074f8ee1e2f43d03129" title="If true, the filter will compute likelihood of the data record and store it in ll...">evalll</a> ) {<a class="code" href="classBMEF.html#308cf5d4133cd471fdf1ecd5dfa09d02" title="cached value of lognc() in the previous step (used in evaluation of ll )">last_lognc</a>=<a class="code" href="classmultiBM.html#eddee08a724170de63f36e40c57b27b5" title="Conjugate prior and posterior.">est</a>.<a class="code" href="classeDirich.html#7ce60be7119ffc639ede4e583c1f6e77" title="logarithm of the normalizing constant, ">lognc</a>();} |
| 213 | | <a name="l00292"></a>00292 } |
| 214 | | <a name="l00293"></a><a class="code" href="classmultiBM.html#66cdfd83a70bc281840ab0646b941684">00293</a> <span class="keyword">const</span> <a class="code" href="classepdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>& <a class="code" href="classmultiBM.html#66cdfd83a70bc281840ab0646b941684" title="Returns a reference to the epdf representing posterior density on parameters.">_epdf</a>()<span class="keyword"> const </span>{<span class="keywordflow">return</span> <a class="code" href="classmultiBM.html#eddee08a724170de63f36e40c57b27b5" title="Conjugate prior and posterior.">est</a>;}; |
| 215 | | <a name="l00294"></a><a class="code" href="classmultiBM.html#66a0fa6966e40bb6c3e7ba22d26e9d35">00294</a> <span class="keyword">const</span> <a class="code" href="classeDirich.html" title="Dirichlet posterior density.">eDirich</a>* <a class="code" href="classmultiBM.html#66a0fa6966e40bb6c3e7ba22d26e9d35" title="Returns a pointer to the epdf representing posterior density on parameters. Use with...">_e</a>()<span class="keyword"> const </span>{<span class="keywordflow">return</span> &<a class="code" href="classmultiBM.html#eddee08a724170de63f36e40c57b27b5" title="Conjugate prior and posterior.">est</a>;}; |
| 216 | | <a name="l00295"></a>00295 <span class="keywordtype">void</span> set_parameters ( <span class="keyword">const</span> vec &beta0 ) { |
| 217 | | <a name="l00296"></a>00296 <a class="code" href="classmultiBM.html#eddee08a724170de63f36e40c57b27b5" title="Conjugate prior and posterior.">est</a>.<a class="code" href="classeDirich.html#c842acb2e1cce5cc9000769ff06c086d" title="Set internal parameters.">set_parameters</a> ( beta0 ); |
| 218 | | <a name="l00297"></a>00297 <a class="code" href="classBM.html#af00f0612fabe66241dd507188cdbf88" title="Random variable of the posterior.">rv</a> = <a class="code" href="classmultiBM.html#eddee08a724170de63f36e40c57b27b5" title="Conjugate prior and posterior.">est</a>.<a class="code" href="classepdf.html#ca0d32aabb4cbba347e0c37fe8607562" title="access function, possibly dangerous!">_rv</a>(); |
| 219 | | <a name="l00298"></a>00298 <span class="keywordflow">if</span> ( <a class="code" href="classBM.html#bf6fb59b30141074f8ee1e2f43d03129" title="If true, the filter will compute likelihood of the data record and store it in ll...">evalll</a> ) {<a class="code" href="classBMEF.html#308cf5d4133cd471fdf1ecd5dfa09d02" title="cached value of lognc() in the previous step (used in evaluation of ll )">last_lognc</a>=<a class="code" href="classmultiBM.html#eddee08a724170de63f36e40c57b27b5" title="Conjugate prior and posterior.">est</a>.<a class="code" href="classeDirich.html#7ce60be7119ffc639ede4e583c1f6e77" title="logarithm of the normalizing constant, ">lognc</a>();} |
| 220 | | <a name="l00299"></a>00299 } |
| 221 | | <a name="l00300"></a>00300 }; |
| 222 | | <a name="l00301"></a>00301 |
| 223 | | <a name="l00311"></a><a class="code" href="classegamma.html">00311</a> <span class="keyword">class </span><a class="code" href="classegamma.html" title="Gamma posterior density.">egamma</a> : <span class="keyword">public</span> <a class="code" href="classeEF.html" title="General conjugate exponential family posterior density.">eEF</a> { |
| 224 | | <a name="l00312"></a>00312 <span class="keyword">protected</span>: |
| 225 | | <a name="l00314"></a><a class="code" href="classegamma.html#376cebd8932546c440f21b182910b01b">00314</a> vec <a class="code" href="classegamma.html#376cebd8932546c440f21b182910b01b" title="Vector .">alpha</a>; |
| 226 | | <a name="l00316"></a><a class="code" href="classegamma.html#cfc5f136467488a421ab22f886323790">00316</a> vec <a class="code" href="classegamma.html#cfc5f136467488a421ab22f886323790" title="Vector .">beta</a>; |
| 227 | | <a name="l00317"></a>00317 <span class="keyword">public</span> : |
| 228 | | <a name="l00319"></a><a class="code" href="classegamma.html#4b1d34f3b244ea51a58ec10c468788c1">00319</a> <a class="code" href="classegamma.html#4b1d34f3b244ea51a58ec10c468788c1" title="Default constructor.">egamma</a> ( <span class="keyword">const</span> <a class="code" href="classRV.html" title="Class representing variables, most often random variables.">RV</a> &<a class="code" href="classepdf.html#74da992e3f5d598da8850b646b79b9d9" title="Identified of the random variable.">rv</a> ) :<a class="code" href="classeEF.html" title="General conjugate exponential family posterior density.">eEF</a> ( rv ) {}; |
| 229 | | <a name="l00321"></a><a class="code" href="classegamma.html#8e348b89be82b70471fe8c5630f61339">00321</a> <span class="keywordtype">void</span> <a class="code" href="classegamma.html#8e348b89be82b70471fe8c5630f61339" title="Sets parameters.">set_parameters</a> ( <span class="keyword">const</span> vec &a, <span class="keyword">const</span> vec &b ) {<a class="code" href="classegamma.html#376cebd8932546c440f21b182910b01b" title="Vector .">alpha</a>=a,<a class="code" href="classegamma.html#cfc5f136467488a421ab22f886323790" title="Vector .">beta</a>=b;}; |
| 230 | | <a name="l00322"></a>00322 vec <a class="code" href="classegamma.html#8e10c0021b5dfdd9cb62c6959b5ef425" title="Returns a sample, from density .">sample</a>() <span class="keyword">const</span>; |
| 231 | | <a name="l00324"></a>00324 <span class="comment">// mat sample ( int N ) const;</span> |
| 232 | | <a name="l00325"></a>00325 <span class="keywordtype">double</span> <a class="code" href="classegamma.html#74a49a4c696f44e54bb6b0515e155a9b" title="TODO: is it used anywhere?">evallog</a> ( <span class="keyword">const</span> vec &val ) <span class="keyword">const</span>; |
| 233 | | <a name="l00326"></a>00326 <span class="keywordtype">double</span> <a class="code" href="classegamma.html#d6dbbdb72360f9e54d64501f80318bb6" title="logarithm of the normalizing constant, ">lognc</a> () <span class="keyword">const</span>; |
| 234 | | <a name="l00328"></a><a class="code" href="classegamma.html#44445c56e60b91b377f207f8d5089790">00328</a> <span class="keywordtype">void</span> <a class="code" href="classegamma.html#44445c56e60b91b377f207f8d5089790" title="Returns poiter to alpha and beta. Potentially dengerous: use with care!">_param</a> ( vec* &a, vec* &b ) {a=&<a class="code" href="classegamma.html#376cebd8932546c440f21b182910b01b" title="Vector .">alpha</a>;b=&<a class="code" href="classegamma.html#cfc5f136467488a421ab22f886323790" title="Vector .">beta</a>;}; |
| 235 | | <a name="l00329"></a><a class="code" href="classegamma.html#6ab5ba56f7cdb2e5921c3e77524fa50a">00329</a> vec <a class="code" href="classegamma.html#6ab5ba56f7cdb2e5921c3e77524fa50a" title="return expected value">mean</a>()<span class="keyword"> const </span>{vec pom ( <a class="code" href="classegamma.html#376cebd8932546c440f21b182910b01b" title="Vector .">alpha</a> ); pom/=<a class="code" href="classegamma.html#cfc5f136467488a421ab22f886323790" title="Vector .">beta</a>; <span class="keywordflow">return</span> pom;} |
| 236 | | <a name="l00330"></a>00330 }; |
| 237 | | <a name="l00331"></a>00331 <span class="comment">/*</span> |
| 238 | | <a name="l00333"></a>00333 <span class="comment">class emix : public epdf {</span> |
| 239 | | <a name="l00334"></a>00334 <span class="comment">protected:</span> |
| 240 | | <a name="l00335"></a>00335 <span class="comment"> int n;</span> |
| 241 | | <a name="l00336"></a>00336 <span class="comment"> vec &w;</span> |
| 242 | | <a name="l00337"></a>00337 <span class="comment"> Array<epdf*> Coms;</span> |
| 243 | | <a name="l00338"></a>00338 <span class="comment">public:</span> |
| 244 | | <a name="l00340"></a>00340 <span class="comment"> emix ( const RV &rv, vec &w0): epdf(rv), n(w0.length()), w(w0), Coms(n) {};</span> |
| 245 | | <a name="l00341"></a>00341 <span class="comment"> void set_parameters( int &i, double wi, epdf* ep){w(i)=wi;Coms(i)=ep;}</span> |
| 246 | | <a name="l00342"></a>00342 <span class="comment"> vec mean(){vec pom; for(int i=0;i<n;i++){pom+=Coms(i)->mean()*w(i);} return pom;};</span> |
| 247 | | <a name="l00343"></a>00343 <span class="comment"> vec sample() {it_error ( "Not implemented" );return 0;}</span> |
| 248 | | <a name="l00344"></a>00344 <span class="comment">};</span> |
| 249 | | <a name="l00345"></a>00345 <span class="comment">*/</span> |
| 250 | | <a name="l00346"></a>00346 |
| 251 | | <a name="l00348"></a>00348 |
| 252 | | <a name="l00349"></a><a class="code" href="classeuni.html">00349</a> <span class="keyword">class </span><a class="code" href="classeuni.html" title="Uniform distributed density on a rectangular support.">euni</a>: <span class="keyword">public</span> <a class="code" href="classepdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a> { |
| 253 | | <a name="l00350"></a>00350 <span class="keyword">protected</span>: |
| 254 | | <a name="l00352"></a><a class="code" href="classeuni.html#ef42cd8d7645422048d46c46ec5cdac1">00352</a> vec <a class="code" href="classeuni.html#ef42cd8d7645422048d46c46ec5cdac1" title="lower bound on support">low</a>; |
| 255 | | <a name="l00354"></a><a class="code" href="classeuni.html#71b6d6b41aeb61a7f76f682b72119231">00354</a> vec <a class="code" href="classeuni.html#71b6d6b41aeb61a7f76f682b72119231" title="upper bound on support">high</a>; |
| 256 | | <a name="l00356"></a><a class="code" href="classeuni.html#52a6ff4a54010f88a6a19fca605c64a4">00356</a> vec <a class="code" href="classeuni.html#52a6ff4a54010f88a6a19fca605c64a4" title="internal">distance</a>; |
| 257 | | <a name="l00358"></a><a class="code" href="classeuni.html#63105490e946e43372d6187ad1bafdda">00358</a> <span class="keywordtype">double</span> <a class="code" href="classeuni.html#63105490e946e43372d6187ad1bafdda" title="normalizing coefficients">nk</a>; |
| 258 | | <a name="l00360"></a><a class="code" href="classeuni.html#f445a0ce24f39d14c1a4eed53fc8e2c3">00360</a> <span class="keywordtype">double</span> <a class="code" href="classeuni.html#f445a0ce24f39d14c1a4eed53fc8e2c3" title="cache of log( nk )">lnk</a>; |
| 259 | | <a name="l00361"></a>00361 <span class="keyword">public</span>: |
| 260 | | <a name="l00363"></a><a class="code" href="classeuni.html#2537a6c239cff52e3ba814851a1116cd">00363</a> <a class="code" href="classeuni.html#2537a6c239cff52e3ba814851a1116cd" title="Defualt constructor.">euni</a> ( <span class="keyword">const</span> <a class="code" href="classRV.html" title="Class representing variables, most often random variables.">RV</a> <a class="code" href="classepdf.html#74da992e3f5d598da8850b646b79b9d9" title="Identified of the random variable.">rv</a> ) :<a class="code" href="classepdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a> ( rv ) {} |
| 261 | | <a name="l00364"></a>00364 <span class="keywordtype">double</span> eval ( <span class="keyword">const</span> vec &val )<span class="keyword"> const </span>{<span class="keywordflow">return</span> <a class="code" href="classeuni.html#63105490e946e43372d6187ad1bafdda" title="normalizing coefficients">nk</a>;} |
| 262 | | <a name="l00365"></a><a class="code" href="classeuni.html#357b36417ef4c9211d12e7a4a602fd6a">00365</a> <span class="keywordtype">double</span> <a class="code" href="classeuni.html#357b36417ef4c9211d12e7a4a602fd6a" title="Compute log-probability of argument val.">evallog</a> ( <span class="keyword">const</span> vec &val )<span class="keyword"> const </span>{<span class="keywordflow">return</span> <a class="code" href="classeuni.html#f445a0ce24f39d14c1a4eed53fc8e2c3" title="cache of log( nk )">lnk</a>;} |
| 263 | | <a name="l00366"></a><a class="code" href="classeuni.html#4a0e09392be17beaee120ba98fc038cd">00366</a> vec <a class="code" href="classeuni.html#4a0e09392be17beaee120ba98fc038cd" title="Returns a sample, from density .">sample</a>()<span class="keyword"> const </span>{ |
| 264 | | <a name="l00367"></a>00367 vec smp ( <a class="code" href="classepdf.html#74da992e3f5d598da8850b646b79b9d9" title="Identified of the random variable.">rv</a>.<a class="code" href="classRV.html#f5c7b8bd589eef09ccdf3329a0addea0" title="Return number of scalars in the RV.">count</a>() ); |
| 265 | | <a name="l00368"></a>00368 <span class="preprocessor">#pragma omp critical</span> |
| 266 | | <a name="l00369"></a>00369 <span class="preprocessor"></span> UniRNG.sample_vector ( <a class="code" href="classepdf.html#74da992e3f5d598da8850b646b79b9d9" title="Identified of the random variable.">rv</a>.<a class="code" href="classRV.html#f5c7b8bd589eef09ccdf3329a0addea0" title="Return number of scalars in the RV.">count</a>(),smp ); |
| 267 | | <a name="l00370"></a>00370 <span class="keywordflow">return</span> <a class="code" href="classeuni.html#ef42cd8d7645422048d46c46ec5cdac1" title="lower bound on support">low</a>+elem_mult ( <a class="code" href="classeuni.html#52a6ff4a54010f88a6a19fca605c64a4" title="internal">distance</a>,smp ); |
| 268 | | <a name="l00371"></a>00371 } |
| 269 | | <a name="l00373"></a><a class="code" href="classeuni.html#4fd7c6a05100616ad16ece405cad7bf2">00373</a> <span class="keywordtype">void</span> <a class="code" href="classeuni.html#4fd7c6a05100616ad16ece405cad7bf2" title="set values of low and high ">set_parameters</a> ( <span class="keyword">const</span> vec &low0, <span class="keyword">const</span> vec &high0 ) { |
| 270 | | <a name="l00374"></a>00374 <a class="code" href="classeuni.html#52a6ff4a54010f88a6a19fca605c64a4" title="internal">distance</a> = high0-low0; |
| 271 | | <a name="l00375"></a>00375 it_assert_debug ( min ( <a class="code" href="classeuni.html#52a6ff4a54010f88a6a19fca605c64a4" title="internal">distance</a> ) >0.0,<span class="stringliteral">"bad support"</span> ); |
| 272 | | <a name="l00376"></a>00376 <a class="code" href="classeuni.html#ef42cd8d7645422048d46c46ec5cdac1" title="lower bound on support">low</a> = low0; |
| 273 | | <a name="l00377"></a>00377 <a class="code" href="classeuni.html#71b6d6b41aeb61a7f76f682b72119231" title="upper bound on support">high</a> = high0; |
| 274 | | <a name="l00378"></a>00378 <a class="code" href="classeuni.html#63105490e946e43372d6187ad1bafdda" title="normalizing coefficients">nk</a> = prod ( 1.0/<a class="code" href="classeuni.html#52a6ff4a54010f88a6a19fca605c64a4" title="internal">distance</a> ); |
| 275 | | <a name="l00379"></a>00379 <a class="code" href="classeuni.html#f445a0ce24f39d14c1a4eed53fc8e2c3" title="cache of log( nk )">lnk</a> = log ( <a class="code" href="classeuni.html#63105490e946e43372d6187ad1bafdda" title="normalizing coefficients">nk</a> ); |
| 276 | | <a name="l00380"></a>00380 } |
| 277 | | <a name="l00381"></a><a class="code" href="classeuni.html#8050087e421a9cfd1b4b1f8bd33b1cc1">00381</a> vec <a class="code" href="classeuni.html#8050087e421a9cfd1b4b1f8bd33b1cc1" title="return expected value">mean</a>()<span class="keyword"> const </span>{vec pom=<a class="code" href="classeuni.html#71b6d6b41aeb61a7f76f682b72119231" title="upper bound on support">high</a>; pom-=<a class="code" href="classeuni.html#ef42cd8d7645422048d46c46ec5cdac1" title="lower bound on support">low</a>; pom/=2.0; <span class="keywordflow">return</span> pom;} |
| 278 | | <a name="l00382"></a>00382 }; |
| 279 | | <a name="l00383"></a>00383 |
| 280 | | <a name="l00384"></a>00384 |
| 281 | | <a name="l00390"></a>00390 <span class="keyword">template</span><<span class="keyword">class</span> sq_T> |
| 282 | | <a name="l00391"></a><a class="code" href="classmlnorm.html">00391</a> <span class="keyword">class </span><a class="code" href="classmlnorm.html" title="Normal distributed linear function with linear function of mean value;.">mlnorm</a> : <span class="keyword">public</span> <a class="code" href="classmEF.html" title="Exponential family model.">mEF</a> { |
| 283 | | <a name="l00392"></a>00392 <span class="keyword">protected</span>: |
| 284 | | <a name="l00394"></a><a class="code" href="classmlnorm.html#b76ee2171ace4fb3ff95a131ae8fc421">00394</a> <a class="code" href="classenorm.html" title="Gaussian density with positive definite (decomposed) covariance matrix.">enorm<sq_T></a> <a class="code" href="classepdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>; |
| 285 | | <a name="l00395"></a>00395 mat A; |
| 286 | | <a name="l00396"></a>00396 vec mu_const; |
| 287 | | <a name="l00397"></a>00397 vec& _mu; <span class="comment">//cached epdf.mu;</span> |
| 288 | | <a name="l00398"></a>00398 <span class="keyword">public</span>: |
| 289 | | <a name="l00400"></a>00400 <a class="code" href="classmlnorm.html#3a5ad4798d8a3878c5e93b8e796c8837" title="Constructor.">mlnorm</a> ( <span class="keyword">const</span> <a class="code" href="classRV.html" title="Class representing variables, most often random variables.">RV</a> &<a class="code" href="classmpdf.html#f6687c07ff07d47812dd565368ca59eb" title="modeled random variable">rv</a>, <span class="keyword">const</span> <a class="code" href="classRV.html" title="Class representing variables, most often random variables.">RV</a> &<a class="code" href="classmpdf.html#acb7dda792b3cd5576f39fa3129abbab" title="random variable in condition">rvc</a> ); |
| 290 | | <a name="l00402"></a>00402 <span class="keywordtype">void</span> <a class="code" href="classmlnorm.html#f95dfce0b500636a44ecd7e5210de999" title="Set A and R.">set_parameters</a> ( <span class="keyword">const</span> mat &A, <span class="keyword">const</span> vec &mu0, <span class="keyword">const</span> sq_T &R ); |
| 291 | | <a name="l00403"></a>00403 <span class="comment">// //!Generate one sample of the posterior</span> |
| 292 | | <a name="l00404"></a>00404 <span class="comment">// vec samplecond (const vec &cond, double &lik );</span> |
| 293 | | <a name="l00405"></a>00405 <span class="comment">// //!Generate matrix of samples of the posterior</span> |
| 294 | | <a name="l00406"></a>00406 <span class="comment">// mat samplecond (const vec &cond, vec &lik, int n );</span> |
| 295 | | <a name="l00408"></a>00408 <span class="comment"></span> <span class="keywordtype">void</span> <a class="code" href="classmlnorm.html#d41126455ac64b888a38f677886e1b40" title="Set value of rvc . Result of this operation is stored in epdf use function _ep to...">condition</a> ( <span class="keyword">const</span> vec &cond ); |
| 296 | | <a name="l00409"></a>00409 |
| 297 | | <a name="l00411"></a><a class="code" href="classmlnorm.html#2732ae47835dd25d5784bf08fde0a546">00411</a> vec& <a class="code" href="classmlnorm.html#2732ae47835dd25d5784bf08fde0a546" title="access function">_mu_const</a>() {<span class="keywordflow">return</span> mu_const;} |
| 298 | | <a name="l00413"></a><a class="code" href="classmlnorm.html#65ec3840c21b21102896bfd2282b47b3">00413</a> mat& <a class="code" href="classmlnorm.html#65ec3840c21b21102896bfd2282b47b3" title="access function">_A</a>() {<span class="keywordflow">return</span> A;} |
| 299 | | <a name="l00415"></a><a class="code" href="classmlnorm.html#3ff2b03fbb5e1133a5fe1bf831939f63">00415</a> mat <a class="code" href="classmlnorm.html#3ff2b03fbb5e1133a5fe1bf831939f63" title="access function">_R</a>() {<span class="keywordflow">return</span> <a class="code" href="classepdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>._R().to_mat();} |
| 300 | | <a name="l00416"></a>00416 |
| 301 | | <a name="l00417"></a>00417 <span class="keyword">template</span><<span class="keyword">class</span> sq_M> |
| 302 | | <a name="l00418"></a>00418 <span class="keyword">friend</span> std::ostream &operator<< ( std::ostream &os, mlnorm<sq_M> &ml ); |
| 303 | | <a name="l00419"></a>00419 }; |
| 304 | | <a name="l00420"></a>00420 |
| 305 | | <a name="l00423"></a><a class="code" href="classmlstudent.html">00423</a> <span class="keyword">class </span><a class="code" href="classmlstudent.html">mlstudent</a> : <span class="keyword">public</span> <a class="code" href="classmlnorm.html" title="Normal distributed linear function with linear function of mean value;.">mlnorm</a><ldmat> { |
| 306 | | <a name="l00424"></a>00424 <span class="keyword">protected</span>: |
| 307 | | <a name="l00425"></a>00425 <a class="code" href="classldmat.html" title="Matrix stored in LD form, (commonly known as UD).">ldmat</a> Lambda; |
| 308 | | <a name="l00426"></a>00426 <a class="code" href="classldmat.html" title="Matrix stored in LD form, (commonly known as UD).">ldmat</a> &<a class="code" href="classmlnorm.html#3ff2b03fbb5e1133a5fe1bf831939f63" title="access function">_R</a>; |
| 309 | | <a name="l00427"></a>00427 <a class="code" href="classldmat.html" title="Matrix stored in LD form, (commonly known as UD).">ldmat</a> Re; |
| 310 | | <a name="l00428"></a>00428 <span class="keyword">public</span>: |
| 311 | | <a name="l00429"></a>00429 <a class="code" href="classmlstudent.html">mlstudent</a> ( <span class="keyword">const</span> <a class="code" href="classRV.html" title="Class representing variables, most often random variables.">RV</a> &rv0, <span class="keyword">const</span> <a class="code" href="classRV.html" title="Class representing variables, most often random variables.">RV</a> &rvc0 ) :<a class="code" href="classmlnorm.html" title="Normal distributed linear function with linear function of mean value;.">mlnorm<ldmat></a> ( rv0,rvc0 ), |
| 312 | | <a name="l00430"></a>00430 Lambda ( rv0.<a class="code" href="classRV.html#f5c7b8bd589eef09ccdf3329a0addea0" title="Return number of scalars in the RV.">count</a>() ), |
| 313 | | <a name="l00431"></a>00431 <a class="code" href="classmlnorm.html#3ff2b03fbb5e1133a5fe1bf831939f63" title="access function">_R</a> ( <a class="code" href="classepdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>._R() ) {} |
| 314 | | <a name="l00432"></a>00432 <span class="keywordtype">void</span> set_parameters ( <span class="keyword">const</span> mat &A0, <span class="keyword">const</span> vec &mu0, <span class="keyword">const</span> <a class="code" href="classldmat.html" title="Matrix stored in LD form, (commonly known as UD).">ldmat</a> &R0, <span class="keyword">const</span> <a class="code" href="classldmat.html" title="Matrix stored in LD form, (commonly known as UD).">ldmat</a>& Lambda0) { |
| 315 | | <a name="l00433"></a>00433 <a class="code" href="classepdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>.set_parameters ( zeros ( <a class="code" href="classmpdf.html#f6687c07ff07d47812dd565368ca59eb" title="modeled random variable">rv</a>.<a class="code" href="classRV.html#f5c7b8bd589eef09ccdf3329a0addea0" title="Return number of scalars in the RV.">count</a>() ),Lambda ); |
| 316 | | <a name="l00434"></a>00434 A = A0; |
| 317 | | <a name="l00435"></a>00435 mu_const = mu0; |
| 318 | | <a name="l00436"></a>00436 Re=R0; |
| 319 | | <a name="l00437"></a>00437 Lambda = Lambda0; |
| 320 | | <a name="l00438"></a>00438 } |
| 321 | | <a name="l00439"></a><a class="code" href="classmlstudent.html#d153460ae0180f4bc7f28301f5cde876">00439</a> <span class="keywordtype">void</span> <a class="code" href="classmlstudent.html#d153460ae0180f4bc7f28301f5cde876" title="Set value of rvc . Result of this operation is stored in epdf use function _ep to...">condition</a> ( <span class="keyword">const</span> vec &cond ) { |
| 322 | | <a name="l00440"></a>00440 _mu = A*cond + mu_const; |
| 323 | | <a name="l00441"></a>00441 <span class="keywordtype">double</span> zeta; |
| 324 | | <a name="l00442"></a>00442 <span class="comment">//ugly hack!</span> |
| 325 | | <a name="l00443"></a>00443 <span class="keywordflow">if</span> ((cond.length()+1)==Lambda.<a class="code" href="group__math.html#g96dfb21865db4f5bd36fa70f9b0b1163" title="access function">rows</a>()){ |
| 326 | | <a name="l00444"></a>00444 zeta = Lambda.<a class="code" href="classldmat.html#d876c5f83e02b3e809b35c9de5068f14" title="Evaluates quadratic form ;.">invqform</a> ( <a class="code" href="group__core.html#g33c114e83980d883c5b211c47d5322a4" title="Concat two random variables.">concat</a>(cond, vec_1(1.0)) ); |
| 327 | | <a name="l00445"></a>00445 } <span class="keywordflow">else</span> { |
| 328 | | <a name="l00446"></a>00446 zeta = Lambda.<a class="code" href="classldmat.html#d876c5f83e02b3e809b35c9de5068f14" title="Evaluates quadratic form ;.">invqform</a> ( cond ); |
| 329 | | <a name="l00447"></a>00447 } |
| 330 | | <a name="l00448"></a>00448 <a class="code" href="classmlnorm.html#3ff2b03fbb5e1133a5fe1bf831939f63" title="access function">_R</a> = Re; |
| 331 | | <a name="l00449"></a>00449 <a class="code" href="classmlnorm.html#3ff2b03fbb5e1133a5fe1bf831939f63" title="access function">_R</a>*=( 1+zeta );<span class="comment">// / ( nu ); << nu is in Re!!!!!!</span> |
| 332 | | <a name="l00450"></a>00450 }; |
| 333 | | <a name="l00451"></a>00451 |
| 334 | | <a name="l00452"></a>00452 }; |
| 335 | | <a name="l00462"></a><a class="code" href="classmgamma.html">00462</a> <span class="keyword">class </span><a class="code" href="classmgamma.html" title="Gamma random walk.">mgamma</a> : <span class="keyword">public</span> <a class="code" href="classmEF.html" title="Exponential family model.">mEF</a> { |
| 336 | | <a name="l00463"></a>00463 <span class="keyword">protected</span>: |
| 337 | | <a name="l00465"></a><a class="code" href="classmgamma.html#612dbf35c770a780027619aaac2c443e">00465</a> <a class="code" href="classegamma.html" title="Gamma posterior density.">egamma</a> <a class="code" href="classepdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>; |
| 338 | | <a name="l00467"></a><a class="code" href="classmgamma.html#43f733cce0245a52363d566099add687">00467</a> <span class="keywordtype">double</span> <a class="code" href="classmgamma.html#43f733cce0245a52363d566099add687" title="Constant .">k</a>; |
| 339 | | <a name="l00469"></a><a class="code" href="classmgamma.html#5e90652837448bcc29707e7412f99691">00469</a> vec* <a class="code" href="classmgamma.html#5e90652837448bcc29707e7412f99691" title="cache of epdf.beta">_beta</a>; |
| 340 | | <a name="l00470"></a>00470 |
| 341 | | <a name="l00471"></a>00471 <span class="keyword">public</span>: |
| 342 | | <a name="l00473"></a>00473 <a class="code" href="classmgamma.html#af43e61b86900c0398d5c0ffc83b94e6" title="Constructor.">mgamma</a> ( <span class="keyword">const</span> <a class="code" href="classRV.html" title="Class representing variables, most often random variables.">RV</a> &<a class="code" href="classmpdf.html#f6687c07ff07d47812dd565368ca59eb" title="modeled random variable">rv</a>,<span class="keyword">const</span> <a class="code" href="classRV.html" title="Class representing variables, most often random variables.">RV</a> &<a class="code" href="classmpdf.html#acb7dda792b3cd5576f39fa3129abbab" title="random variable in condition">rvc</a> ); |
| 343 | | <a name="l00475"></a>00475 <span class="keywordtype">void</span> <a class="code" href="classmgamma.html#a9d646cf758a70126dde7c48790b6e94" title="Set value of k.">set_parameters</a> ( <span class="keywordtype">double</span> <a class="code" href="classmgamma.html#43f733cce0245a52363d566099add687" title="Constant .">k</a> ); |
| 344 | | <a name="l00476"></a><a class="code" href="classmgamma.html#a61094c9f7a2d64ea77b130cbc031f97">00476</a> <span class="keywordtype">void</span> <a class="code" href="classmgamma.html#a61094c9f7a2d64ea77b130cbc031f97" title="Update ep so that it represents this mpdf conditioned on rvc = cond.">condition</a> ( <span class="keyword">const</span> vec &val ) {*<a class="code" href="classmgamma.html#5e90652837448bcc29707e7412f99691" title="cache of epdf.beta">_beta</a>=<a class="code" href="classmgamma.html#43f733cce0245a52363d566099add687" title="Constant .">k</a>/val;}; |
| 345 | | <a name="l00477"></a>00477 }; |
| 346 | | <a name="l00478"></a>00478 |
| 347 | | <a name="l00490"></a><a class="code" href="classmgamma__fix.html">00490</a> <span class="keyword">class </span><a class="code" href="classmgamma__fix.html" title="Gamma random walk around a fixed point.">mgamma_fix</a> : <span class="keyword">public</span> <a class="code" href="classmgamma.html" title="Gamma random walk.">mgamma</a> { |
| 348 | | <a name="l00491"></a>00491 <span class="keyword">protected</span>: |
| 349 | | <a name="l00493"></a><a class="code" href="classmgamma__fix.html#3f48c09caddc298901ad75fe7c0529f6">00493</a> <span class="keywordtype">double</span> <a class="code" href="classmgamma__fix.html#3f48c09caddc298901ad75fe7c0529f6" title="parameter l">l</a>; |
| 350 | | <a name="l00495"></a><a class="code" href="classmgamma__fix.html#81ce49029ecc385418619b200dcafeb0">00495</a> vec <a class="code" href="classmgamma__fix.html#81ce49029ecc385418619b200dcafeb0" title="reference vector">refl</a>; |
| 351 | | <a name="l00496"></a>00496 <span class="keyword">public</span>: |
| 352 | | <a name="l00498"></a><a class="code" href="classmgamma__fix.html#b92c3d2e5fd0381033a072e5ef3bcf80">00498</a> <a class="code" href="classmgamma__fix.html#b92c3d2e5fd0381033a072e5ef3bcf80" title="Constructor.">mgamma_fix</a> ( <span class="keyword">const</span> <a class="code" href="classRV.html" title="Class representing variables, most often random variables.">RV</a> &<a class="code" href="classmpdf.html#f6687c07ff07d47812dd565368ca59eb" title="modeled random variable">rv</a>,<span class="keyword">const</span> <a class="code" href="classRV.html" title="Class representing variables, most often random variables.">RV</a> &<a class="code" href="classmpdf.html#acb7dda792b3cd5576f39fa3129abbab" title="random variable in condition">rvc</a> ) : <a class="code" href="classmgamma.html" title="Gamma random walk.">mgamma</a> ( rv,rvc ),<a class="code" href="classmgamma__fix.html#81ce49029ecc385418619b200dcafeb0" title="reference vector">refl</a> ( rv.count() ) {}; |
| 353 | | <a name="l00500"></a><a class="code" href="classmgamma__fix.html#ec6f846896749e27cb7be9fa48dd1cb1">00500</a> <span class="keywordtype">void</span> <a class="code" href="classmgamma__fix.html#ec6f846896749e27cb7be9fa48dd1cb1" title="Set value of k.">set_parameters</a> ( <span class="keywordtype">double</span> k0 , vec ref0, <span class="keywordtype">double</span> l0 ) { |
| 354 | | <a name="l00501"></a>00501 <a class="code" href="classmgamma.html#a9d646cf758a70126dde7c48790b6e94" title="Set value of k.">mgamma::set_parameters</a> ( k0 ); |
| 355 | | <a name="l00502"></a>00502 <a class="code" href="classmgamma__fix.html#81ce49029ecc385418619b200dcafeb0" title="reference vector">refl</a>=pow ( ref0,1.0-l0 );<a class="code" href="classmgamma__fix.html#3f48c09caddc298901ad75fe7c0529f6" title="parameter l">l</a>=l0; |
| 356 | | <a name="l00503"></a>00503 }; |
| 357 | | <a name="l00504"></a>00504 |
| 358 | | <a name="l00505"></a><a class="code" href="classmgamma__fix.html#6ea3931eec7b7da7b693e45981052460">00505</a> <span class="keywordtype">void</span> <a class="code" href="classmgamma__fix.html#6ea3931eec7b7da7b693e45981052460" title="Update ep so that it represents this mpdf conditioned on rvc = cond.">condition</a> ( <span class="keyword">const</span> vec &val ) {vec mean=elem_mult ( <a class="code" href="classmgamma__fix.html#81ce49029ecc385418619b200dcafeb0" title="reference vector">refl</a>,pow ( val,<a class="code" href="classmgamma__fix.html#3f48c09caddc298901ad75fe7c0529f6" title="parameter l">l</a> ) ); *<a class="code" href="classmgamma.html#5e90652837448bcc29707e7412f99691" title="cache of epdf.beta">_beta</a>=<a class="code" href="classmgamma.html#43f733cce0245a52363d566099add687" title="Constant .">k</a>/mean;}; |
| 359 | | <a name="l00506"></a>00506 }; |
| 360 | | <a name="l00507"></a>00507 |
| 361 | | <a name="l00509"></a><a class="code" href="libEF_8h.html#99497a3ff630f761cf6bff7babd23212">00509</a> <span class="keyword">enum</span> <a class="code" href="libEF_8h.html#99497a3ff630f761cf6bff7babd23212" title="Switch between various resampling methods.">RESAMPLING_METHOD</a> { MULTINOMIAL = 0, STRATIFIED = 1, SYSTEMATIC = 3 }; |
| 362 | | <a name="l00515"></a><a class="code" href="classeEmp.html">00515</a> <span class="keyword">class </span><a class="code" href="classeEmp.html" title="Weighted empirical density.">eEmp</a>: <span class="keyword">public</span> <a class="code" href="classepdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a> { |
| 363 | | <a name="l00516"></a>00516 <span class="keyword">protected</span> : |
| 364 | | <a name="l00518"></a><a class="code" href="classeEmp.html#8c33034de0e35f03f8bb85d3d67438fd">00518</a> <span class="keywordtype">int</span> <a class="code" href="classeEmp.html#8c33034de0e35f03f8bb85d3d67438fd" title="Number of particles.">n</a>; |
| 365 | | <a name="l00520"></a><a class="code" href="classeEmp.html#ae78d144404ddba843c93b171b215de8">00520</a> vec <a class="code" href="classeEmp.html#ae78d144404ddba843c93b171b215de8" title="Sample weights .">w</a>; |
| 366 | | <a name="l00522"></a><a class="code" href="classeEmp.html#a4d6f4bbd6a6824fc39f14676701279a">00522</a> Array<vec> <a class="code" href="classeEmp.html#a4d6f4bbd6a6824fc39f14676701279a" title="Samples .">samples</a>; |
| 367 | | <a name="l00523"></a>00523 <span class="keyword">public</span>: |
| 368 | | <a name="l00525"></a><a class="code" href="classeEmp.html#0c04b073ecd0dae3d498e680ae27e9e4">00525</a> <a class="code" href="classeEmp.html#0c04b073ecd0dae3d498e680ae27e9e4" title="Default constructor.">eEmp</a> ( <span class="keyword">const</span> <a class="code" href="classRV.html" title="Class representing variables, most often random variables.">RV</a> &rv0 ,<span class="keywordtype">int</span> n0 ) :<a class="code" href="classepdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a> ( rv0 ),<a class="code" href="classeEmp.html#8c33034de0e35f03f8bb85d3d67438fd" title="Number of particles.">n</a> ( n0 ),<a class="code" href="classeEmp.html#ae78d144404ddba843c93b171b215de8" title="Sample weights .">w</a> ( <a class="code" href="classeEmp.html#8c33034de0e35f03f8bb85d3d67438fd" title="Number of particles.">n</a> ),<a class="code" href="classeEmp.html#a4d6f4bbd6a6824fc39f14676701279a" title="Samples .">samples</a> ( <a class="code" href="classeEmp.html#8c33034de0e35f03f8bb85d3d67438fd" title="Number of particles.">n</a> ) {}; |
| 369 | | <a name="l00527"></a>00527 <span class="keywordtype">void</span> <a class="code" href="classeEmp.html#eab03bd3381aaea11ce34d5a26556353" title="Set samples and weights.">set_parameters</a> ( <span class="keyword">const</span> vec &w0, <span class="keyword">const</span> <a class="code" href="classepdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>* pdf0 ); |
| 370 | | <a name="l00529"></a>00529 <span class="keywordtype">void</span> <a class="code" href="classeEmp.html#e31bc9e6196173c3480b06a761a3e716" title="Set sample.">set_samples</a> ( <span class="keyword">const</span> <a class="code" href="classepdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>* pdf0 ); |
| 371 | | <a name="l00531"></a><a class="code" href="classeEmp.html#a4215f6a5a04d07b43f7ebaa942e15f1">00531</a> <span class="keywordtype">void</span> <a class="code" href="classeEmp.html#a4215f6a5a04d07b43f7ebaa942e15f1" title="Set sample.">set_n</a> ( <span class="keywordtype">int</span> n0, <span class="keywordtype">bool</span> copy=<span class="keyword">true</span> ){<a class="code" href="classeEmp.html#ae78d144404ddba843c93b171b215de8" title="Sample weights .">w</a>.set_size(n0,copy);<a class="code" href="classeEmp.html#a4d6f4bbd6a6824fc39f14676701279a" title="Samples .">samples</a>.set_size(n0,copy);}; |
| 372 | | <a name="l00533"></a><a class="code" href="classeEmp.html#31b2bfb73b72486a5c89f2ab850c7a9b">00533</a> vec& <a class="code" href="classeEmp.html#31b2bfb73b72486a5c89f2ab850c7a9b" title="Potentially dangerous, use with care.">_w</a>() {<span class="keywordflow">return</span> <a class="code" href="classeEmp.html#ae78d144404ddba843c93b171b215de8" title="Sample weights .">w</a>;}; |
| 373 | | <a name="l00535"></a><a class="code" href="classeEmp.html#d6f4ae1a67ecd2bff8b9f176ee261afc">00535</a> <span class="keyword">const</span> vec& <a class="code" href="classeEmp.html#31b2bfb73b72486a5c89f2ab850c7a9b" title="Potentially dangerous, use with care.">_w</a>()<span class="keyword"> const </span>{<span class="keywordflow">return</span> <a class="code" href="classeEmp.html#ae78d144404ddba843c93b171b215de8" title="Sample weights .">w</a>;}; |
| 374 | | <a name="l00537"></a><a class="code" href="classeEmp.html#31b747eca73b16f30370827ba4cc3575">00537</a> Array<vec>& <a class="code" href="classeEmp.html#31b747eca73b16f30370827ba4cc3575" title="access function">_samples</a>() {<span class="keywordflow">return</span> <a class="code" href="classeEmp.html#a4d6f4bbd6a6824fc39f14676701279a" title="Samples .">samples</a>;}; |
| 375 | | <a name="l00539"></a><a class="code" href="classeEmp.html#bd48c1c36e2e9e78dbcea7df66dcbf25">00539</a> <span class="keyword">const</span> Array<vec>& <a class="code" href="classeEmp.html#31b747eca73b16f30370827ba4cc3575" title="access function">_samples</a>()<span class="keyword"> const </span>{<span class="keywordflow">return</span> <a class="code" href="classeEmp.html#a4d6f4bbd6a6824fc39f14676701279a" title="Samples .">samples</a>;}; |
| 376 | | <a name="l00541"></a>00541 ivec <a class="code" href="classeEmp.html#77268292fc4465cb73ddbfb1f2932a59" title="Function performs resampling, i.e. removal of low-weight samples and duplication...">resample</a> ( <a class="code" href="libEF_8h.html#99497a3ff630f761cf6bff7babd23212" title="Switch between various resampling methods.">RESAMPLING_METHOD</a> method = SYSTEMATIC ); |
| 377 | | <a name="l00543"></a><a class="code" href="classeEmp.html#83f9283f92b805508d896479dc1ccf12">00543</a> vec <a class="code" href="classeEmp.html#83f9283f92b805508d896479dc1ccf12" title="inherited operation : NOT implemneted">sample</a>()<span class="keyword"> const </span>{it_error ( <span class="stringliteral">"Not implemented"</span> );<span class="keywordflow">return</span> 0;} |
| 378 | | <a name="l00545"></a><a class="code" href="classeEmp.html#884f16c9fc1f888408686a660a95dacd">00545</a> <span class="keywordtype">double</span> <a class="code" href="classeEmp.html#884f16c9fc1f888408686a660a95dacd" title="inherited operation : NOT implemneted">evallog</a> ( <span class="keyword">const</span> vec &val )<span class="keyword"> const </span>{it_error ( <span class="stringliteral">"Not implemented"</span> );<span class="keywordflow">return</span> 0.0;} |
| 379 | | <a name="l00546"></a><a class="code" href="classeEmp.html#ba055c19038cc72628d98e25197e982d">00546</a> vec <a class="code" href="classeEmp.html#ba055c19038cc72628d98e25197e982d" title="return expected value">mean</a>()<span class="keyword"> const </span>{ |
| 380 | | <a name="l00547"></a>00547 vec pom=zeros ( <a class="code" href="classepdf.html#74da992e3f5d598da8850b646b79b9d9" title="Identified of the random variable.">rv</a>.<a class="code" href="classRV.html#f5c7b8bd589eef09ccdf3329a0addea0" title="Return number of scalars in the RV.">count</a>() ); |
| 381 | | <a name="l00548"></a>00548 <span class="keywordflow">for</span> ( <span class="keywordtype">int</span> i=0;i<<a class="code" href="classeEmp.html#8c33034de0e35f03f8bb85d3d67438fd" title="Number of particles.">n</a>;i++ ) {pom+=<a class="code" href="classeEmp.html#a4d6f4bbd6a6824fc39f14676701279a" title="Samples .">samples</a> ( i ) *<a class="code" href="classeEmp.html#ae78d144404ddba843c93b171b215de8" title="Sample weights .">w</a> ( i );} |
| 382 | | <a name="l00549"></a>00549 <span class="keywordflow">return</span> pom; |
| 383 | | <a name="l00550"></a>00550 } |
| 384 | | <a name="l00551"></a>00551 }; |
| 385 | | <a name="l00552"></a>00552 |
| 386 | | <a name="l00553"></a>00553 |
| 387 | | <a name="l00555"></a>00555 |
| 388 | | <a name="l00556"></a>00556 <span class="keyword">template</span><<span class="keyword">class</span> sq_T> |
| 389 | | <a name="l00557"></a><a class="code" href="classenorm.html#0caf54fed9e48f9fe28b534b2027df2f">00557</a> <a class="code" href="classenorm.html#0caf54fed9e48f9fe28b534b2027df2f" title="Default constructor.">enorm<sq_T>::enorm</a> ( <span class="keyword">const</span> <a class="code" href="classRV.html" title="Class representing variables, most often random variables.">RV</a> &rv ) :<a class="code" href="classeEF.html" title="General conjugate exponential family posterior density.">eEF</a> ( rv ), mu ( rv.count() ),R ( rv.count() ),dim ( rv.count() ) {}; |
| 390 | | <a name="l00558"></a>00558 |
| 391 | | <a name="l00559"></a>00559 <span class="keyword">template</span><<span class="keyword">class</span> sq_T> |
| 392 | | <a name="l00560"></a><a class="code" href="classenorm.html#1394a65caa6e00d42e00cc99b12227af">00560</a> <span class="keywordtype">void</span> <a class="code" href="classenorm.html#1394a65caa6e00d42e00cc99b12227af" title="Set mean value mu and covariance R.">enorm<sq_T>::set_parameters</a> ( <span class="keyword">const</span> vec &mu0, <span class="keyword">const</span> sq_T &R0 ) { |
| 393 | | <a name="l00561"></a>00561 <span class="comment">//Fixme test dimensions of mu0 and R0;</span> |
| 394 | | <a name="l00562"></a>00562 <a class="code" href="classenorm.html#71fde0d54bba147e00f612577f95ad20" title="mean value">mu</a> = mu0; |
| 395 | | <a name="l00563"></a>00563 <a class="code" href="classenorm.html#4ccc8d8514d644ef1c98d8ab023748a1" title="Covariance matrix in decomposed form.">R</a> = R0; |
| 396 | | <a name="l00564"></a>00564 }; |
| 397 | | <a name="l00565"></a>00565 |
| 398 | | <a name="l00566"></a>00566 <span class="keyword">template</span><<span class="keyword">class</span> sq_T> |
| 399 | | <a name="l00567"></a><a class="code" href="classenorm.html#5bf185e31e5954fceb90ada3debd2ff2">00567</a> <span class="keywordtype">void</span> <a class="code" href="classenorm.html#5bf185e31e5954fceb90ada3debd2ff2" title="dupdate in exponential form (not really handy)">enorm<sq_T>::dupdate</a> ( mat &v, <span class="keywordtype">double</span> nu ) { |
| 400 | | <a name="l00568"></a>00568 <span class="comment">//</span> |
| 401 | | <a name="l00569"></a>00569 }; |
| 402 | | <a name="l00570"></a>00570 |
| 403 | | <a name="l00571"></a>00571 <span class="comment">// template<class sq_T></span> |
| 404 | | <a name="l00572"></a>00572 <span class="comment">// void enorm<sq_T>::tupdate ( double phi, mat &vbar, double nubar ) {</span> |
| 405 | | <a name="l00573"></a>00573 <span class="comment">// //</span> |
| 406 | | <a name="l00574"></a>00574 <span class="comment">// };</span> |
| 407 | | <a name="l00575"></a>00575 |
| 408 | | <a name="l00576"></a>00576 <span class="keyword">template</span><<span class="keyword">class</span> sq_T> |
| 409 | | <a name="l00577"></a><a class="code" href="classenorm.html#60b47544f6181ffd4530d3e415ce12c5">00577</a> vec <a class="code" href="classenorm.html#60b47544f6181ffd4530d3e415ce12c5" title="Returns a sample, from density .">enorm<sq_T>::sample</a>()<span class="keyword"> const </span>{ |
| 410 | | <a name="l00578"></a>00578 vec x ( <a class="code" href="classenorm.html#6938fc390a19cdaf6ad4503fcbaada4e" title="dimension (redundant from rv.count() for easier coding )">dim</a> ); |
| 411 | | <a name="l00579"></a>00579 NorRNG.sample_vector ( <a class="code" href="classenorm.html#6938fc390a19cdaf6ad4503fcbaada4e" title="dimension (redundant from rv.count() for easier coding )">dim</a>,x ); |
| 412 | | <a name="l00580"></a>00580 vec smp = <a class="code" href="classenorm.html#4ccc8d8514d644ef1c98d8ab023748a1" title="Covariance matrix in decomposed form.">R</a>.sqrt_mult ( x ); |
| 413 | | <a name="l00581"></a>00581 |
| 414 | | <a name="l00582"></a>00582 smp += <a class="code" href="classenorm.html#71fde0d54bba147e00f612577f95ad20" title="mean value">mu</a>; |
| 415 | | <a name="l00583"></a>00583 <span class="keywordflow">return</span> smp; |
| 416 | | <a name="l00584"></a>00584 }; |
| 417 | | <a name="l00585"></a>00585 |
| 418 | | <a name="l00586"></a>00586 <span class="keyword">template</span><<span class="keyword">class</span> sq_T> |
| 419 | | <a name="l00587"></a><a class="code" href="classenorm.html#60f0f3bfa53d6e65843eea9532b16d36">00587</a> mat <a class="code" href="classenorm.html#60b47544f6181ffd4530d3e415ce12c5" title="Returns a sample, from density .">enorm<sq_T>::sample</a> ( <span class="keywordtype">int</span> N )<span class="keyword"> const </span>{ |
| 420 | | <a name="l00588"></a>00588 mat X ( <a class="code" href="classenorm.html#6938fc390a19cdaf6ad4503fcbaada4e" title="dimension (redundant from rv.count() for easier coding )">dim</a>,N ); |
| 421 | | <a name="l00589"></a>00589 vec x ( <a class="code" href="classenorm.html#6938fc390a19cdaf6ad4503fcbaada4e" title="dimension (redundant from rv.count() for easier coding )">dim</a> ); |
| 422 | | <a name="l00590"></a>00590 vec pom; |
| 423 | | <a name="l00591"></a>00591 <span class="keywordtype">int</span> i; |
| 424 | | <a name="l00592"></a>00592 |
| 425 | | <a name="l00593"></a>00593 <span class="keywordflow">for</span> ( i=0;i<N;i++ ) { |
| 426 | | <a name="l00594"></a>00594 NorRNG.sample_vector ( <a class="code" href="classenorm.html#6938fc390a19cdaf6ad4503fcbaada4e" title="dimension (redundant from rv.count() for easier coding )">dim</a>,x ); |
| 427 | | <a name="l00595"></a>00595 pom = <a class="code" href="classenorm.html#4ccc8d8514d644ef1c98d8ab023748a1" title="Covariance matrix in decomposed form.">R</a>.sqrt_mult ( x ); |
| 428 | | <a name="l00596"></a>00596 pom +=<a class="code" href="classenorm.html#71fde0d54bba147e00f612577f95ad20" title="mean value">mu</a>; |
| 429 | | <a name="l00597"></a>00597 X.set_col ( i, pom ); |
| 430 | | <a name="l00598"></a>00598 } |
| 431 | | <a name="l00599"></a>00599 |
| 432 | | <a name="l00600"></a>00600 <span class="keywordflow">return</span> X; |
| 433 | | <a name="l00601"></a>00601 }; |
| 434 | | <a name="l00602"></a>00602 |
| 435 | | <a name="l00603"></a>00603 <span class="comment">// template<class sq_T></span> |
| 436 | | <a name="l00604"></a>00604 <span class="comment">// double enorm<sq_T>::eval ( const vec &val ) const {</span> |
| 437 | | <a name="l00605"></a>00605 <span class="comment">// double pdfl,e;</span> |
| 438 | | <a name="l00606"></a>00606 <span class="comment">// pdfl = evallog ( val );</span> |
| 439 | | <a name="l00607"></a>00607 <span class="comment">// e = exp ( pdfl );</span> |
| 440 | | <a name="l00608"></a>00608 <span class="comment">// return e;</span> |
| 441 | | <a name="l00609"></a>00609 <span class="comment">// };</span> |
| 442 | | <a name="l00610"></a>00610 |
| 443 | | <a name="l00611"></a>00611 <span class="keyword">template</span><<span class="keyword">class</span> sq_T> |
| 444 | | <a name="l00612"></a><a class="code" href="classenorm.html#50cb0a083d97a7adbbd97c92e712c46c">00612</a> <span class="keywordtype">double</span> <a class="code" href="classenorm.html#50cb0a083d97a7adbbd97c92e712c46c" title="Evaluate normalized log-probability.">enorm<sq_T>::evallog_nn</a> ( <span class="keyword">const</span> vec &val )<span class="keyword"> const </span>{ |
| 445 | | <a name="l00613"></a>00613 <span class="comment">// 1.83787706640935 = log(2pi)</span> |
| 446 | | <a name="l00614"></a>00614 <span class="keywordtype">double</span> tmp=-0.5* ( <a class="code" href="classenorm.html#4ccc8d8514d644ef1c98d8ab023748a1" title="Covariance matrix in decomposed form.">R</a>.invqform ( <a class="code" href="classenorm.html#71fde0d54bba147e00f612577f95ad20" title="mean value">mu</a>-val ) );<span class="comment">// - lognc();</span> |
| 447 | | <a name="l00615"></a>00615 <span class="keywordflow">return</span> tmp; |
| 448 | | <a name="l00616"></a>00616 }; |
| 449 | | <a name="l00617"></a>00617 |
| 450 | | <a name="l00618"></a>00618 <span class="keyword">template</span><<span class="keyword">class</span> sq_T> |
| 451 | | <a name="l00619"></a><a class="code" href="classenorm.html#b289a36a69db59d182bb6eba9c05d4a8">00619</a> <span class="keyword">inline</span> <span class="keywordtype">double</span> <a class="code" href="classenorm.html#b289a36a69db59d182bb6eba9c05d4a8" title="logarithm of the normalizing constant, ">enorm<sq_T>::lognc</a> ()<span class="keyword"> const </span>{ |
| 452 | | <a name="l00620"></a>00620 <span class="comment">// 1.83787706640935 = log(2pi)</span> |
| 453 | | <a name="l00621"></a>00621 <span class="keywordtype">double</span> tmp=0.5* ( <a class="code" href="classenorm.html#4ccc8d8514d644ef1c98d8ab023748a1" title="Covariance matrix in decomposed form.">R</a>.cols() * 1.83787706640935 +<a class="code" href="classenorm.html#4ccc8d8514d644ef1c98d8ab023748a1" title="Covariance matrix in decomposed form.">R</a>.logdet() ); |
| 454 | | <a name="l00622"></a>00622 <span class="keywordflow">return</span> tmp; |
| 455 | | <a name="l00623"></a>00623 }; |
| 456 | | <a name="l00624"></a>00624 |
| 457 | | <a name="l00625"></a>00625 <span class="keyword">template</span><<span class="keyword">class</span> sq_T> |
| 458 | | <a name="l00626"></a><a class="code" href="classmlnorm.html#3a5ad4798d8a3878c5e93b8e796c8837">00626</a> <a class="code" href="classmlnorm.html#3a5ad4798d8a3878c5e93b8e796c8837" title="Constructor.">mlnorm<sq_T>::mlnorm</a> ( <span class="keyword">const</span> <a class="code" href="classRV.html" title="Class representing variables, most often random variables.">RV</a> &rv0, <span class="keyword">const</span> <a class="code" href="classRV.html" title="Class representing variables, most often random variables.">RV</a> &rvc0 ) :<a class="code" href="classmEF.html" title="Exponential family model.">mEF</a> ( rv0,rvc0 ),<a class="code" href="classepdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a> ( rv0 ),A ( rv0.count(),rv0.count() ),<a class="code" href="classenorm.html#0b8cb284e5af920a1b64a21d057ec5ac" title="returns a pointer to the internal mean value. Use with Care!">_mu</a> ( <a class="code" href="classepdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>.<a class="code" href="classenorm.html#0b8cb284e5af920a1b64a21d057ec5ac" title="returns a pointer to the internal mean value. Use with Care!">_mu</a>() ) { |
| 459 | | <a name="l00627"></a>00627 <a class="code" href="classmpdf.html#7aa894208a32f3487827df6d5054424c" title="pointer to internal epdf">ep</a> =&<a class="code" href="classepdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>; |
| 460 | | <a name="l00628"></a>00628 } |
| 461 | | <a name="l00629"></a>00629 |
| 462 | | <a name="l00630"></a>00630 <span class="keyword">template</span><<span class="keyword">class</span> sq_T> |
| 463 | | <a name="l00631"></a><a class="code" href="classmlnorm.html#f95dfce0b500636a44ecd7e5210de999">00631</a> <span class="keywordtype">void</span> <a class="code" href="classmlnorm.html#f95dfce0b500636a44ecd7e5210de999" title="Set A and R.">mlnorm<sq_T>::set_parameters</a> ( <span class="keyword">const</span> mat &A0, <span class="keyword">const</span> vec &mu0, <span class="keyword">const</span> sq_T &R0 ) { |
| 464 | | <a name="l00632"></a>00632 <a class="code" href="classepdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>.set_parameters ( zeros ( <a class="code" href="classmpdf.html#f6687c07ff07d47812dd565368ca59eb" title="modeled random variable">rv</a>.<a class="code" href="classRV.html#f5c7b8bd589eef09ccdf3329a0addea0" title="Return number of scalars in the RV.">count</a>() ),R0 ); |
| 465 | | <a name="l00633"></a>00633 A = A0; |
| 466 | | <a name="l00634"></a>00634 mu_const = mu0; |
| 467 | | <a name="l00635"></a>00635 } |
| 468 | | <a name="l00636"></a>00636 |
| 469 | | <a name="l00637"></a>00637 <span class="comment">// template<class sq_T></span> |
| 470 | | <a name="l00638"></a>00638 <span class="comment">// vec mlnorm<sq_T>::samplecond (const vec &cond, double &lik ) {</span> |
| 471 | | <a name="l00639"></a>00639 <span class="comment">// this->condition ( cond );</span> |
| 472 | | <a name="l00640"></a>00640 <span class="comment">// vec smp = epdf.sample();</span> |
| 473 | | <a name="l00641"></a>00641 <span class="comment">// lik = epdf.eval ( smp );</span> |
| 474 | | <a name="l00642"></a>00642 <span class="comment">// return smp;</span> |
| 475 | | <a name="l00643"></a>00643 <span class="comment">// }</span> |
| 476 | | <a name="l00644"></a>00644 |
| 477 | | <a name="l00645"></a>00645 <span class="comment">// template<class sq_T></span> |
| 478 | | <a name="l00646"></a>00646 <span class="comment">// mat mlnorm<sq_T>::samplecond (const vec &cond, vec &lik, int n ) {</span> |
| 479 | | <a name="l00647"></a>00647 <span class="comment">// int i;</span> |
| 480 | | <a name="l00648"></a>00648 <span class="comment">// int dim = rv.count();</span> |
| 481 | | <a name="l00649"></a>00649 <span class="comment">// mat Smp ( dim,n );</span> |
| 482 | | <a name="l00650"></a>00650 <span class="comment">// vec smp ( dim );</span> |
| 483 | | <a name="l00651"></a>00651 <span class="comment">// this->condition ( cond );</span> |
| 484 | | <a name="l00652"></a>00652 <span class="comment">//</span> |
| 485 | | <a name="l00653"></a>00653 <span class="comment">// for ( i=0; i<n; i++ ) {</span> |
| 486 | | <a name="l00654"></a>00654 <span class="comment">// smp = epdf.sample();</span> |
| 487 | | <a name="l00655"></a>00655 <span class="comment">// lik ( i ) = epdf.eval ( smp );</span> |
| 488 | | <a name="l00656"></a>00656 <span class="comment">// Smp.set_col ( i ,smp );</span> |
| 489 | | <a name="l00657"></a>00657 <span class="comment">// }</span> |
| 490 | | <a name="l00658"></a>00658 <span class="comment">//</span> |
| 491 | | <a name="l00659"></a>00659 <span class="comment">// return Smp;</span> |
| 492 | | <a name="l00660"></a>00660 <span class="comment">// }</span> |
| 493 | | <a name="l00661"></a>00661 |
| 494 | | <a name="l00662"></a>00662 <span class="keyword">template</span><<span class="keyword">class</span> sq_T> |
| 495 | | <a name="l00663"></a><a class="code" href="classmlnorm.html#d41126455ac64b888a38f677886e1b40">00663</a> <span class="keywordtype">void</span> <a class="code" href="classmlnorm.html#d41126455ac64b888a38f677886e1b40" title="Set value of rvc . Result of this operation is stored in epdf use function _ep to...">mlnorm<sq_T>::condition</a> ( <span class="keyword">const</span> vec &cond ) { |
| 496 | | <a name="l00664"></a>00664 _mu = A*cond + mu_const; |
| 497 | | <a name="l00665"></a>00665 <span class="comment">//R is already assigned;</span> |
| 498 | | <a name="l00666"></a>00666 } |
| 499 | | <a name="l00667"></a>00667 |
| 500 | | <a name="l00668"></a>00668 <span class="keyword">template</span><<span class="keyword">class</span> sq_T> |
| 501 | | <a name="l00669"></a><a class="code" href="classenorm.html#af50a6102846060bcb23a670bf38117b">00669</a> <a class="code" href="classepdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>* <a class="code" href="classenorm.html#af50a6102846060bcb23a670bf38117b" title="Return marginal density on the given RV, the remainig rvs are intergrated out.">enorm<sq_T>::marginal</a> ( <span class="keyword">const</span> <a class="code" href="classRV.html" title="Class representing variables, most often random variables.">RV</a> &rvn )<span class="keyword"> const </span>{ |
| 502 | | <a name="l00670"></a>00670 ivec irvn = rvn.<a class="code" href="classRV.html#bb724fa4e2d9ed7bfd0993b5975018a4">dataind</a> ( <a class="code" href="classepdf.html#74da992e3f5d598da8850b646b79b9d9" title="Identified of the random variable.">rv</a> ); |
| 503 | | <a name="l00671"></a>00671 |
| 504 | | <a name="l00672"></a>00672 sq_T Rn ( <a class="code" href="classenorm.html#4ccc8d8514d644ef1c98d8ab023748a1" title="Covariance matrix in decomposed form.">R</a>,irvn ); |
| 505 | | <a name="l00673"></a>00673 <a class="code" href="classenorm.html" title="Gaussian density with positive definite (decomposed) covariance matrix.">enorm<sq_T></a>* tmp = <span class="keyword">new</span> <a class="code" href="classenorm.html" title="Gaussian density with positive definite (decomposed) covariance matrix.">enorm<sq_T></a> ( rvn ); |
| 506 | | <a name="l00674"></a>00674 tmp-><a class="code" href="classenorm.html#1394a65caa6e00d42e00cc99b12227af" title="Set mean value mu and covariance R.">set_parameters</a> ( <a class="code" href="classenorm.html#71fde0d54bba147e00f612577f95ad20" title="mean value">mu</a> ( irvn ), Rn ); |
| 507 | | <a name="l00675"></a>00675 <span class="keywordflow">return</span> tmp; |
| 508 | | <a name="l00676"></a>00676 } |
| 509 | | <a name="l00677"></a>00677 |
| 510 | | <a name="l00678"></a>00678 <span class="keyword">template</span><<span class="keyword">class</span> sq_T> |
| 511 | | <a name="l00679"></a><a class="code" href="classenorm.html#921024bd6d5a0e65f2af2e39bf38dfca">00679</a> <a class="code" href="classmpdf.html" title="Conditional probability density, e.g. modeling some dependencies.">mpdf</a>* <a class="code" href="classenorm.html#921024bd6d5a0e65f2af2e39bf38dfca" title="Return conditional density on the given RV, the remaining rvs will be in conditioning...">enorm<sq_T>::condition</a> ( <span class="keyword">const</span> <a class="code" href="classRV.html" title="Class representing variables, most often random variables.">RV</a> &rvn )<span class="keyword"> const </span>{ |
| 512 | | <a name="l00680"></a>00680 |
| 513 | | <a name="l00681"></a>00681 <a class="code" href="classRV.html" title="Class representing variables, most often random variables.">RV</a> rvc = <a class="code" href="classepdf.html#74da992e3f5d598da8850b646b79b9d9" title="Identified of the random variable.">rv</a>.<a class="code" href="classRV.html#b9d175c327c21488b1e2fb756a84e149" title="Subtract another variable from the current one.">subt</a> ( rvn ); |
| 514 | | <a name="l00682"></a>00682 it_assert_debug ( ( rvc.<a class="code" href="classRV.html#f5c7b8bd589eef09ccdf3329a0addea0" title="Return number of scalars in the RV.">count</a>() +rvn.<a class="code" href="classRV.html#f5c7b8bd589eef09ccdf3329a0addea0" title="Return number of scalars in the RV.">count</a>() ==<a class="code" href="classepdf.html#74da992e3f5d598da8850b646b79b9d9" title="Identified of the random variable.">rv</a>.<a class="code" href="classRV.html#f5c7b8bd589eef09ccdf3329a0addea0" title="Return number of scalars in the RV.">count</a>() ),<span class="stringliteral">"wrong rvn"</span> ); |
| 515 | | <a name="l00683"></a>00683 <span class="comment">//Permutation vector of the new R</span> |
| 516 | | <a name="l00684"></a>00684 ivec irvn = rvn.<a class="code" href="classRV.html#bb724fa4e2d9ed7bfd0993b5975018a4">dataind</a> ( <a class="code" href="classepdf.html#74da992e3f5d598da8850b646b79b9d9" title="Identified of the random variable.">rv</a> ); |
| 517 | | <a name="l00685"></a>00685 ivec irvc = rvc.<a class="code" href="classRV.html#bb724fa4e2d9ed7bfd0993b5975018a4">dataind</a> ( <a class="code" href="classepdf.html#74da992e3f5d598da8850b646b79b9d9" title="Identified of the random variable.">rv</a> ); |
| 518 | | <a name="l00686"></a>00686 ivec perm=<a class="code" href="group__core.html#g33c114e83980d883c5b211c47d5322a4" title="Concat two random variables.">concat</a> ( irvn , irvc ); |
| 519 | | <a name="l00687"></a>00687 sq_T Rn ( <a class="code" href="classenorm.html#4ccc8d8514d644ef1c98d8ab023748a1" title="Covariance matrix in decomposed form.">R</a>,perm ); |
| 520 | | <a name="l00688"></a>00688 |
| 521 | | <a name="l00689"></a>00689 <span class="comment">//fixme - could this be done in general for all sq_T?</span> |
| 522 | | <a name="l00690"></a>00690 mat S=Rn.to_mat(); |
| 523 | | <a name="l00691"></a>00691 <span class="comment">//fixme</span> |
| 524 | | <a name="l00692"></a>00692 <span class="keywordtype">int</span> n=rvn.<a class="code" href="classRV.html#f5c7b8bd589eef09ccdf3329a0addea0" title="Return number of scalars in the RV.">count</a>()-1; |
| 525 | | <a name="l00693"></a>00693 <span class="keywordtype">int</span> end=<a class="code" href="classenorm.html#4ccc8d8514d644ef1c98d8ab023748a1" title="Covariance matrix in decomposed form.">R</a>.rows()-1; |
| 526 | | <a name="l00694"></a>00694 mat S11 = S.get ( 0,n, 0, n ); |
| 527 | | <a name="l00695"></a>00695 mat S12 = S.get ( 0, n , rvn.<a class="code" href="classRV.html#f5c7b8bd589eef09ccdf3329a0addea0" title="Return number of scalars in the RV.">count</a>(), end ); |
| 528 | | <a name="l00696"></a>00696 mat S22 = S.get ( rvn.<a class="code" href="classRV.html#f5c7b8bd589eef09ccdf3329a0addea0" title="Return number of scalars in the RV.">count</a>(), end, rvn.<a class="code" href="classRV.html#f5c7b8bd589eef09ccdf3329a0addea0" title="Return number of scalars in the RV.">count</a>(), end ); |
| 529 | | <a name="l00697"></a>00697 |
| 530 | | <a name="l00698"></a>00698 vec mu1 = <a class="code" href="classenorm.html#71fde0d54bba147e00f612577f95ad20" title="mean value">mu</a> ( irvn ); |
| 531 | | <a name="l00699"></a>00699 vec mu2 = <a class="code" href="classenorm.html#71fde0d54bba147e00f612577f95ad20" title="mean value">mu</a> ( irvc ); |
| 532 | | <a name="l00700"></a>00700 mat A=S12*inv ( S22 ); |
| 533 | | <a name="l00701"></a>00701 sq_T R_n ( S11 - A *S12.T() ); |
| 534 | | <a name="l00702"></a>00702 |
| 535 | | <a name="l00703"></a>00703 <a class="code" href="classmlnorm.html" title="Normal distributed linear function with linear function of mean value;.">mlnorm<sq_T></a>* tmp=<span class="keyword">new</span> <a class="code" href="classmlnorm.html" title="Normal distributed linear function with linear function of mean value;.">mlnorm<sq_T></a> ( rvn,rvc ); |
| 536 | | <a name="l00704"></a>00704 |
| 537 | | <a name="l00705"></a>00705 tmp->set_parameters ( A,mu1-A*mu2,R_n ); |
| 538 | | <a name="l00706"></a>00706 <span class="keywordflow">return</span> tmp; |
| 539 | | <a name="l00707"></a>00707 } |
| 540 | | <a name="l00708"></a>00708 |
| | 95 | <a name="l00134"></a><a class="code" href="classenorm.html#d4b4bfec464fe971bf57e69d71a1cf2e">00134</a> vec <a class="code" href="classenorm.html#d4b4bfec464fe971bf57e69d71a1cf2e" title="return expected variance (not covariance!)">variance</a>()<span class="keyword"> const </span>{<span class="keywordflow">return</span> diag(<a class="code" href="classenorm.html#4ccc8d8514d644ef1c98d8ab023748a1" title="Covariance matrix in decomposed form.">R</a>.to_mat());} |
| | 96 | <a name="l00135"></a>00135 <span class="comment">// mlnorm<sq_T>* condition ( const RV &rvn ) const ;</span> |
| | 97 | <a name="l00136"></a>00136 <a class="code" href="classmpdf.html" title="Conditional probability density, e.g. modeling some dependencies.">mpdf</a>* <a class="code" href="classenorm.html#921024bd6d5a0e65f2af2e39bf38dfca" title="Return conditional density on the given RV, the remaining rvs will be in conditioning...">condition</a> ( <span class="keyword">const</span> <a class="code" href="classRV.html" title="Class representing variables, most often random variables.">RV</a> &rvn ) <span class="keyword">const</span> ; |
| | 98 | <a name="l00137"></a>00137 <span class="comment">// enorm<sq_T>* marginal ( const RV &rv ) const;</span> |
| | 99 | <a name="l00138"></a>00138 <a class="code" href="classepdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>* <a class="code" href="classenorm.html#af50a6102846060bcb23a670bf38117b" title="Return marginal density on the given RV, the remainig rvs are intergrated out.">marginal</a> ( <span class="keyword">const</span> <a class="code" href="classRV.html" title="Class representing variables, most often random variables.">RV</a> &<a class="code" href="classepdf.html#74da992e3f5d598da8850b646b79b9d9" title="Identified of the random variable.">rv</a> ) <span class="keyword">const</span>; |
| | 100 | <a name="l00139"></a>00139 <span class="comment">//Access methods</span> |
| | 101 | <a name="l00141"></a><a class="code" href="classenorm.html#0b8cb284e5af920a1b64a21d057ec5ac">00141</a> <span class="comment"></span> vec& <a class="code" href="classenorm.html#0b8cb284e5af920a1b64a21d057ec5ac" title="returns a pointer to the internal mean value. Use with Care!">_mu</a>() {<span class="keywordflow">return</span> <a class="code" href="classenorm.html#71fde0d54bba147e00f612577f95ad20" title="mean value">mu</a>;} |
| | 102 | <a name="l00142"></a>00142 |
| | 103 | <a name="l00144"></a><a class="code" href="classenorm.html#d892a38f03be12e572ea57d9689cef6b">00144</a> <span class="keywordtype">void</span> <a class="code" href="classenorm.html#d892a38f03be12e572ea57d9689cef6b" title="access function">set_mu</a> ( <span class="keyword">const</span> vec mu0 ) { <a class="code" href="classenorm.html#71fde0d54bba147e00f612577f95ad20" title="mean value">mu</a>=mu0;} |
| | 104 | <a name="l00145"></a>00145 |
| | 105 | <a name="l00147"></a><a class="code" href="classenorm.html#7a5034b25771a84450a990d10fc40ac9">00147</a> sq_T& <a class="code" href="classenorm.html#7a5034b25771a84450a990d10fc40ac9" title="returns pointers to the internal variance and its inverse. Use with Care!">_R</a>() {<span class="keywordflow">return</span> <a class="code" href="classenorm.html#4ccc8d8514d644ef1c98d8ab023748a1" title="Covariance matrix in decomposed form.">R</a>;} |
| | 106 | <a name="l00148"></a>00148 <span class="keyword">const</span> sq_T& <a class="code" href="classenorm.html#7a5034b25771a84450a990d10fc40ac9" title="returns pointers to the internal variance and its inverse. Use with Care!">_R</a>()<span class="keyword"> const </span>{<span class="keywordflow">return</span> <a class="code" href="classenorm.html#4ccc8d8514d644ef1c98d8ab023748a1" title="Covariance matrix in decomposed form.">R</a>;} |
| | 107 | <a name="l00149"></a>00149 |
| | 108 | <a name="l00151"></a>00151 <span class="comment">// mat getR () {return R.to_mat();}</span> |
| | 109 | <a name="l00152"></a>00152 }; |
| | 110 | <a name="l00153"></a>00153 |
| | 111 | <a name="l00160"></a><a class="code" href="classegiw.html">00160</a> <span class="keyword">class </span><a class="code" href="classegiw.html" title="Gauss-inverse-Wishart density stored in LD form.">egiw</a> : <span class="keyword">public</span> <a class="code" href="classeEF.html" title="General conjugate exponential family posterior density.">eEF</a> { |
| | 112 | <a name="l00161"></a>00161 <span class="keyword">protected</span>: |
| | 113 | <a name="l00163"></a><a class="code" href="classegiw.html#f343d03ede89db820edf44a6297fa442">00163</a> <a class="code" href="classldmat.html" title="Matrix stored in LD form, (commonly known as UD).">ldmat</a> <a class="code" href="classegiw.html#f343d03ede89db820edf44a6297fa442" title="Extended information matrix of sufficient statistics.">V</a>; |
| | 114 | <a name="l00165"></a><a class="code" href="classegiw.html#4a2f130b91afe84f6d62fed289d5d453">00165</a> <span class="keywordtype">double</span> <a class="code" href="classegiw.html#4a2f130b91afe84f6d62fed289d5d453" title="Number of data records (degrees of freedom) of sufficient statistics.">nu</a>; |
| | 115 | <a name="l00167"></a><a class="code" href="classegiw.html#3d5c719f15a5527a6c62c2a53160148e">00167</a> <span class="keywordtype">int</span> <a class="code" href="classegiw.html#3d5c719f15a5527a6c62c2a53160148e" title="Dimension of the output.">xdim</a>; |
| | 116 | <a name="l00169"></a><a class="code" href="classegiw.html#c70d13d86e0d9f0acede3e1dc0368812">00169</a> <span class="keywordtype">int</span> <a class="code" href="classegiw.html#c70d13d86e0d9f0acede3e1dc0368812" title="Dimension of the regressor.">nPsi</a>; |
| | 117 | <a name="l00170"></a>00170 <span class="keyword">public</span>: |
| | 118 | <a name="l00172"></a><a class="code" href="classegiw.html#056c094f01ca1cc308d72162f47617c9">00172</a> <a class="code" href="classegiw.html#056c094f01ca1cc308d72162f47617c9" title="Default constructor, if nu0&lt;0 a minimal nu0 will be computed.">egiw</a> ( <a class="code" href="classRV.html" title="Class representing variables, most often random variables.">RV</a> <a class="code" href="classepdf.html#74da992e3f5d598da8850b646b79b9d9" title="Identified of the random variable.">rv</a>, mat V0, <span class="keywordtype">double</span> nu0=-1.0 ) : <a class="code" href="classeEF.html" title="General conjugate exponential family posterior density.">eEF</a> ( rv ), <a class="code" href="classegiw.html#f343d03ede89db820edf44a6297fa442" title="Extended information matrix of sufficient statistics.">V</a> ( V0 ), <a class="code" href="classegiw.html#4a2f130b91afe84f6d62fed289d5d453" title="Number of data records (degrees of freedom) of sufficient statistics.">nu</a> ( nu0 ) { |
| | 119 | <a name="l00173"></a>00173 <a class="code" href="classegiw.html#3d5c719f15a5527a6c62c2a53160148e" title="Dimension of the output.">xdim</a> = rv.<a class="code" href="classRV.html#f5c7b8bd589eef09ccdf3329a0addea0" title="Return number of scalars in the RV.">count</a>() /<a class="code" href="classegiw.html#f343d03ede89db820edf44a6297fa442" title="Extended information matrix of sufficient statistics.">V</a>.<a class="code" href="group__math.html#g96dfb21865db4f5bd36fa70f9b0b1163" title="access function">rows</a>(); |
| | 120 | <a name="l00174"></a>00174 it_assert_debug ( rv.<a class="code" href="classRV.html#f5c7b8bd589eef09ccdf3329a0addea0" title="Return number of scalars in the RV.">count</a>() ==<a class="code" href="classegiw.html#3d5c719f15a5527a6c62c2a53160148e" title="Dimension of the output.">xdim</a>*<a class="code" href="classegiw.html#f343d03ede89db820edf44a6297fa442" title="Extended information matrix of sufficient statistics.">V</a>.<a class="code" href="group__math.html#g96dfb21865db4f5bd36fa70f9b0b1163" title="access function">rows</a>(),<span class="stringliteral">"Incompatible V0."</span> ); |
| | 121 | <a name="l00175"></a>00175 <a class="code" href="classegiw.html#c70d13d86e0d9f0acede3e1dc0368812" title="Dimension of the regressor.">nPsi</a> = <a class="code" href="classegiw.html#f343d03ede89db820edf44a6297fa442" title="Extended information matrix of sufficient statistics.">V</a>.<a class="code" href="group__math.html#g96dfb21865db4f5bd36fa70f9b0b1163" title="access function">rows</a>()-<a class="code" href="classegiw.html#3d5c719f15a5527a6c62c2a53160148e" title="Dimension of the output.">xdim</a>; |
| | 122 | <a name="l00176"></a>00176 <span class="comment">//set mu to have proper normalization and </span> |
| | 123 | <a name="l00177"></a>00177 <span class="keywordflow">if</span> (nu0<0){ |
| | 124 | <a name="l00178"></a>00178 <a class="code" href="classegiw.html#4a2f130b91afe84f6d62fed289d5d453" title="Number of data records (degrees of freedom) of sufficient statistics.">nu</a> = 0.1 +nPsi +2*<a class="code" href="classegiw.html#3d5c719f15a5527a6c62c2a53160148e" title="Dimension of the output.">xdim</a> +2; <span class="comment">// +2 assures finite expected value of R</span> |
| | 125 | <a name="l00179"></a>00179 <span class="comment">// terms before that are sufficient for finite normalization</span> |
| | 126 | <a name="l00180"></a>00180 } |
| | 127 | <a name="l00181"></a>00181 } |
| | 128 | <a name="l00183"></a><a class="code" href="classegiw.html#18c1bf6125652a6dcbca68dd02dddd8d">00183</a> <a class="code" href="classegiw.html#056c094f01ca1cc308d72162f47617c9" title="Default constructor, if nu0&lt;0 a minimal nu0 will be computed.">egiw</a> ( <a class="code" href="classRV.html" title="Class representing variables, most often random variables.">RV</a> <a class="code" href="classepdf.html#74da992e3f5d598da8850b646b79b9d9" title="Identified of the random variable.">rv</a>, <a class="code" href="classldmat.html" title="Matrix stored in LD form, (commonly known as UD).">ldmat</a> V0, <span class="keywordtype">double</span> nu0=-1.0 ) : <a class="code" href="classeEF.html" title="General conjugate exponential family posterior density.">eEF</a> ( rv ), <a class="code" href="classegiw.html#f343d03ede89db820edf44a6297fa442" title="Extended information matrix of sufficient statistics.">V</a> ( V0 ), <a class="code" href="classegiw.html#4a2f130b91afe84f6d62fed289d5d453" title="Number of data records (degrees of freedom) of sufficient statistics.">nu</a> ( nu0 ) { |
| | 129 | <a name="l00184"></a>00184 <a class="code" href="classegiw.html#3d5c719f15a5527a6c62c2a53160148e" title="Dimension of the output.">xdim</a> = rv.<a class="code" href="classRV.html#f5c7b8bd589eef09ccdf3329a0addea0" title="Return number of scalars in the RV.">count</a>() /<a class="code" href="classegiw.html#f343d03ede89db820edf44a6297fa442" title="Extended information matrix of sufficient statistics.">V</a>.<a class="code" href="group__math.html#g96dfb21865db4f5bd36fa70f9b0b1163" title="access function">rows</a>(); |
| | 130 | <a name="l00185"></a>00185 it_assert_debug ( rv.<a class="code" href="classRV.html#f5c7b8bd589eef09ccdf3329a0addea0" title="Return number of scalars in the RV.">count</a>() ==<a class="code" href="classegiw.html#3d5c719f15a5527a6c62c2a53160148e" title="Dimension of the output.">xdim</a>*<a class="code" href="classegiw.html#f343d03ede89db820edf44a6297fa442" title="Extended information matrix of sufficient statistics.">V</a>.<a class="code" href="group__math.html#g96dfb21865db4f5bd36fa70f9b0b1163" title="access function">rows</a>(),<span class="stringliteral">"Incompatible V0."</span> ); |
| | 131 | <a name="l00186"></a>00186 <a class="code" href="classegiw.html#c70d13d86e0d9f0acede3e1dc0368812" title="Dimension of the regressor.">nPsi</a> = <a class="code" href="classegiw.html#f343d03ede89db820edf44a6297fa442" title="Extended information matrix of sufficient statistics.">V</a>.<a class="code" href="group__math.html#g96dfb21865db4f5bd36fa70f9b0b1163" title="access function">rows</a>()-<a class="code" href="classegiw.html#3d5c719f15a5527a6c62c2a53160148e" title="Dimension of the output.">xdim</a>; |
| | 132 | <a name="l00187"></a>00187 <span class="keywordflow">if</span> (nu0<0){ |
| | 133 | <a name="l00188"></a>00188 <a class="code" href="classegiw.html#4a2f130b91afe84f6d62fed289d5d453" title="Number of data records (degrees of freedom) of sufficient statistics.">nu</a> = 0.1 +nPsi +2*<a class="code" href="classegiw.html#3d5c719f15a5527a6c62c2a53160148e" title="Dimension of the output.">xdim</a> +2; <span class="comment">// +2 assures finite expected value of R</span> |
| | 134 | <a name="l00189"></a>00189 <span class="comment">// terms before that are sufficient for finite normalization</span> |
| | 135 | <a name="l00190"></a>00190 } |
| | 136 | <a name="l00191"></a>00191 } |
| | 137 | <a name="l00192"></a>00192 |
| | 138 | <a name="l00193"></a>00193 vec <a class="code" href="classegiw.html#3d2c1f2ba0f9966781f1e0ae695e8a6f" title="Returns a sample, from density .">sample</a>() <span class="keyword">const</span>; |
| | 139 | <a name="l00194"></a>00194 vec <a class="code" href="classegiw.html#6deb0ff2859f41ef7cbdf6a842cabb29" title="return expected value">mean</a>() <span class="keyword">const</span>; |
| | 140 | <a name="l00195"></a><a class="code" href="classegiw.html#458a89e32dfcc363daa4b6d5335ac791">00195</a> vec <a class="code" href="classegiw.html#458a89e32dfcc363daa4b6d5335ac791" title="return expected variance (not covariance!)">variance</a>()<span class="keyword"> const</span>{it_error(<span class="stringliteral">"Not implemented"</span>); <span class="keywordflow">return</span> vec(0);}; |
| | 141 | <a name="l00196"></a>00196 <span class="keywordtype">void</span> mean_mat ( mat &M, mat&R ) <span class="keyword">const</span>; |
| | 142 | <a name="l00198"></a>00198 <span class="keywordtype">double</span> <a class="code" href="classegiw.html#2d94daac10d66bb743e4ddc8c1ba7268" title="In this instance, val= [theta, r]. For multivariate instances, it is stored columnwise...">evallog_nn</a> ( <span class="keyword">const</span> vec &val ) <span class="keyword">const</span>; |
| | 143 | <a name="l00199"></a>00199 <span class="keywordtype">double</span> <a class="code" href="classegiw.html#70eb1a0b88459b227f919b425b0d3359" title="logarithm of the normalizing constant, ">lognc</a> () <span class="keyword">const</span>; |
| | 144 | <a name="l00200"></a>00200 |
| | 145 | <a name="l00201"></a>00201 <span class="comment">//Access</span> |
| | 146 | <a name="l00203"></a><a class="code" href="classegiw.html#533e792e1175bfa06d5d595dc5d080d5">00203</a> <span class="comment"></span> <a class="code" href="classldmat.html" title="Matrix stored in LD form, (commonly known as UD).">ldmat</a>& <a class="code" href="classegiw.html#533e792e1175bfa06d5d595dc5d080d5" title="returns a pointer to the internal statistics. Use with Care!">_V</a>() {<span class="keywordflow">return</span> <a class="code" href="classegiw.html#f343d03ede89db820edf44a6297fa442" title="Extended information matrix of sufficient statistics.">V</a>;} |
| | 147 | <a name="l00205"></a><a class="code" href="classegiw.html#a46c8a206edf80b357a138d7491780c1">00205</a> <span class="keyword">const</span> <a class="code" href="classldmat.html" title="Matrix stored in LD form, (commonly known as UD).">ldmat</a>& <a class="code" href="classegiw.html#533e792e1175bfa06d5d595dc5d080d5" title="returns a pointer to the internal statistics. Use with Care!">_V</a>()<span class="keyword"> const </span>{<span class="keywordflow">return</span> <a class="code" href="classegiw.html#f343d03ede89db820edf44a6297fa442" title="Extended information matrix of sufficient statistics.">V</a>;} |
| | 148 | <a name="l00207"></a><a class="code" href="classegiw.html#08029c481ff95d24f093df0573879afe">00207</a> <span class="keywordtype">double</span>& <a class="code" href="classegiw.html#08029c481ff95d24f093df0573879afe" title="returns a pointer to the internal statistics. Use with Care!">_nu</a>() {<span class="keywordflow">return</span> <a class="code" href="classegiw.html#4a2f130b91afe84f6d62fed289d5d453" title="Number of data records (degrees of freedom) of sufficient statistics.">nu</a>;} |
| | 149 | <a name="l00208"></a>00208 <span class="keyword">const</span> <span class="keywordtype">double</span>& <a class="code" href="classegiw.html#08029c481ff95d24f093df0573879afe" title="returns a pointer to the internal statistics. Use with Care!">_nu</a>()<span class="keyword"> const </span>{<span class="keywordflow">return</span> <a class="code" href="classegiw.html#4a2f130b91afe84f6d62fed289d5d453" title="Number of data records (degrees of freedom) of sufficient statistics.">nu</a>;} |
| | 150 | <a name="l00209"></a><a class="code" href="classegiw.html#036306322a90a9977834baac07460816">00209</a> <span class="keywordtype">void</span> <a class="code" href="classegiw.html#036306322a90a9977834baac07460816" title="Power of the density, used e.g. to flatten the density.">pow</a> ( <span class="keywordtype">double</span> p ) {<a class="code" href="classegiw.html#f343d03ede89db820edf44a6297fa442" title="Extended information matrix of sufficient statistics.">V</a>*=p;<a class="code" href="classegiw.html#4a2f130b91afe84f6d62fed289d5d453" title="Number of data records (degrees of freedom) of sufficient statistics.">nu</a>*=p;}; |
| | 151 | <a name="l00210"></a>00210 }; |
| | 152 | <a name="l00211"></a>00211 |
| | 153 | <a name="l00220"></a><a class="code" href="classeDirich.html">00220</a> <span class="keyword">class </span><a class="code" href="classeDirich.html" title="Dirichlet posterior density.">eDirich</a>: <span class="keyword">public</span> <a class="code" href="classeEF.html" title="General conjugate exponential family posterior density.">eEF</a> { |
| | 154 | <a name="l00221"></a>00221 <span class="keyword">protected</span>: |
| | 155 | <a name="l00223"></a><a class="code" href="classeDirich.html#15e6b65e9595eedc8a1286c6cecd36d7">00223</a> vec <a class="code" href="classeDirich.html#15e6b65e9595eedc8a1286c6cecd36d7" title="sufficient statistics">beta</a>; |
| | 156 | <a name="l00225"></a><a class="code" href="classeDirich.html#a4f34a1b98ee6d09688b8d0f043ac10d">00225</a> <span class="keywordtype">double</span> <a class="code" href="classeDirich.html#a4f34a1b98ee6d09688b8d0f043ac10d" title="speedup variable">gamma</a>; |
| | 157 | <a name="l00226"></a>00226 <span class="keyword">public</span>: |
| | 158 | <a name="l00228"></a><a class="code" href="classeDirich.html#ac7e6116f3575c3860d07355e96cd4af">00228</a> <a class="code" href="classeDirich.html#ac7e6116f3575c3860d07355e96cd4af" title="Default constructor.">eDirich</a> ( <span class="keyword">const</span> <a class="code" href="classRV.html" title="Class representing variables, most often random variables.">RV</a> &<a class="code" href="classepdf.html#74da992e3f5d598da8850b646b79b9d9" title="Identified of the random variable.">rv</a>, <span class="keyword">const</span> vec &beta0 ) : <a class="code" href="classeEF.html" title="General conjugate exponential family posterior density.">eEF</a> ( rv ),<a class="code" href="classeDirich.html#15e6b65e9595eedc8a1286c6cecd36d7" title="sufficient statistics">beta</a> ( beta0 ) {it_assert_debug ( rv.<a class="code" href="classRV.html#f5c7b8bd589eef09ccdf3329a0addea0" title="Return number of scalars in the RV.">count</a>() ==<a class="code" href="classeDirich.html#15e6b65e9595eedc8a1286c6cecd36d7" title="sufficient statistics">beta</a>.length(),<span class="stringliteral">"Incompatible statistics"</span> ); }; |
| | 159 | <a name="l00230"></a><a class="code" href="classeDirich.html#55cccbc5eb44764dce722567acf5fd58">00230</a> <a class="code" href="classeDirich.html#ac7e6116f3575c3860d07355e96cd4af" title="Default constructor.">eDirich</a> ( <span class="keyword">const</span> <a class="code" href="classeDirich.html" title="Dirichlet posterior density.">eDirich</a> &D0 ) : <a class="code" href="classeEF.html" title="General conjugate exponential family posterior density.">eEF</a> ( D0.<a class="code" href="classepdf.html#74da992e3f5d598da8850b646b79b9d9" title="Identified of the random variable.">rv</a> ),<a class="code" href="classeDirich.html#15e6b65e9595eedc8a1286c6cecd36d7" title="sufficient statistics">beta</a> ( D0.<a class="code" href="classeDirich.html#15e6b65e9595eedc8a1286c6cecd36d7" title="sufficient statistics">beta</a> ) {}; |
| | 160 | <a name="l00231"></a><a class="code" href="classeDirich.html#23dff79110822e9639343fe8e177fd80">00231</a> vec <a class="code" href="classeDirich.html#23dff79110822e9639343fe8e177fd80" title="Returns a sample, from density .">sample</a>()<span class="keyword"> const </span>{it_error ( <span class="stringliteral">"Not implemented"</span> );<span class="keywordflow">return</span> vec_1 ( 0.0 );}; |
| | 161 | <a name="l00232"></a><a class="code" href="classeDirich.html#4206e1da149d51ff3b663c9241096b73">00232</a> vec <a class="code" href="classeDirich.html#4206e1da149d51ff3b663c9241096b73" title="return expected value">mean</a>()<span class="keyword"> const </span>{<span class="keywordflow">return</span> <a class="code" href="classeDirich.html#15e6b65e9595eedc8a1286c6cecd36d7" title="sufficient statistics">beta</a>/<a class="code" href="classeDirich.html#a4f34a1b98ee6d09688b8d0f043ac10d" title="speedup variable">gamma</a>;}; |
| | 162 | <a name="l00233"></a><a class="code" href="classeDirich.html#eaf157ad4c0d191bc17061f29fb76da1">00233</a> vec <a class="code" href="classeDirich.html#eaf157ad4c0d191bc17061f29fb76da1" title="return expected variance (not covariance!)">variance</a>()<span class="keyword"> const </span>{<span class="keywordflow">return</span> elem_mult(<a class="code" href="classeDirich.html#15e6b65e9595eedc8a1286c6cecd36d7" title="sufficient statistics">beta</a>,(<a class="code" href="classeDirich.html#15e6b65e9595eedc8a1286c6cecd36d7" title="sufficient statistics">beta</a>+1))/ (<a class="code" href="classeDirich.html#a4f34a1b98ee6d09688b8d0f043ac10d" title="speedup variable">gamma</a>*(<a class="code" href="classeDirich.html#a4f34a1b98ee6d09688b8d0f043ac10d" title="speedup variable">gamma</a>+1));} |
| | 163 | <a name="l00235"></a><a class="code" href="classeDirich.html#bb4b14ed7794777386de10608a83d142">00235</a> <span class="keywordtype">double</span> <a class="code" href="classeDirich.html#bb4b14ed7794777386de10608a83d142" title="In this instance, val is ...">evallog_nn</a> ( <span class="keyword">const</span> vec &val )<span class="keyword"> const </span>{<span class="keywordtype">double</span> tmp; tmp=( <a class="code" href="classeDirich.html#15e6b65e9595eedc8a1286c6cecd36d7" title="sufficient statistics">beta</a>-1 ) *log ( val ); it_assert_debug(std::isfinite(tmp),<span class="stringliteral">"Infinite value"</span>); |
| | 164 | <a name="l00236"></a>00236 <span class="keywordflow">return</span> tmp;}; |
| | 165 | <a name="l00237"></a><a class="code" href="classeDirich.html#7ce60be7119ffc639ede4e583c1f6e77">00237</a> <span class="keywordtype">double</span> <a class="code" href="classeDirich.html#7ce60be7119ffc639ede4e583c1f6e77" title="logarithm of the normalizing constant, ">lognc</a> ()<span class="keyword"> const </span>{ |
| | 166 | <a name="l00238"></a>00238 <span class="keywordtype">double</span> tmp; |
| | 167 | <a name="l00239"></a>00239 <span class="keywordtype">double</span> gam=sum ( <a class="code" href="classeDirich.html#15e6b65e9595eedc8a1286c6cecd36d7" title="sufficient statistics">beta</a> ); |
| | 168 | <a name="l00240"></a>00240 <span class="keywordtype">double</span> lgb=0.0; |
| | 169 | <a name="l00241"></a>00241 <span class="keywordflow">for</span> ( <span class="keywordtype">int</span> i=0;i<<a class="code" href="classeDirich.html#15e6b65e9595eedc8a1286c6cecd36d7" title="sufficient statistics">beta</a>.length();i++ ) {lgb+=lgamma ( <a class="code" href="classeDirich.html#15e6b65e9595eedc8a1286c6cecd36d7" title="sufficient statistics">beta</a> ( i ) );} |
| | 170 | <a name="l00242"></a>00242 tmp= lgb-lgamma ( gam ); |
| | 171 | <a name="l00243"></a>00243 it_assert_debug(std::isfinite(tmp),<span class="stringliteral">"Infinite value"</span>); |
| | 172 | <a name="l00244"></a>00244 <span class="keywordflow">return</span> tmp; |
| | 173 | <a name="l00245"></a>00245 }; |
| | 174 | <a name="l00247"></a><a class="code" href="classeDirich.html#6409d0362143a23976b43641ff19e53a">00247</a> vec& <a class="code" href="classeDirich.html#6409d0362143a23976b43641ff19e53a" title="access function">_beta</a>() {<span class="keywordflow">return</span> <a class="code" href="classeDirich.html#15e6b65e9595eedc8a1286c6cecd36d7" title="sufficient statistics">beta</a>;} |
| | 175 | <a name="l00249"></a><a class="code" href="classeDirich.html#c842acb2e1cce5cc9000769ff06c086d">00249</a> <span class="keywordtype">void</span> <a class="code" href="classeDirich.html#c842acb2e1cce5cc9000769ff06c086d" title="Set internal parameters.">set_parameters</a> ( <span class="keyword">const</span> vec &beta0 ) { |
| | 176 | <a name="l00250"></a>00250 <span class="keywordflow">if</span> ( beta0.length() !=<a class="code" href="classeDirich.html#15e6b65e9595eedc8a1286c6cecd36d7" title="sufficient statistics">beta</a>.length() ) { |
| | 177 | <a name="l00251"></a>00251 it_assert_debug ( <a class="code" href="classepdf.html#74da992e3f5d598da8850b646b79b9d9" title="Identified of the random variable.">rv</a>.<a class="code" href="classRV.html#c114a6f3ff06796cc2f4dacba74291eb" title="Return length (number of entries) of the RV.">length</a>() ==1,<span class="stringliteral">"Undefined"</span> ); |
| | 178 | <a name="l00252"></a>00252 <a class="code" href="classepdf.html#74da992e3f5d598da8850b646b79b9d9" title="Identified of the random variable.">rv</a>.<a class="code" href="classRV.html#70b24c39c5130b1e4753fa2eef495433" title="access function">set_size</a> ( 0,beta0.length() ); |
| | 179 | <a name="l00253"></a>00253 } |
| | 180 | <a name="l00254"></a>00254 <a class="code" href="classeDirich.html#15e6b65e9595eedc8a1286c6cecd36d7" title="sufficient statistics">beta</a>= beta0; |
| | 181 | <a name="l00255"></a>00255 <a class="code" href="classeDirich.html#a4f34a1b98ee6d09688b8d0f043ac10d" title="speedup variable">gamma</a> = sum(<a class="code" href="classeDirich.html#15e6b65e9595eedc8a1286c6cecd36d7" title="sufficient statistics">beta</a>); |
| | 182 | <a name="l00256"></a>00256 } |
| | 183 | <a name="l00257"></a>00257 }; |
| | 184 | <a name="l00258"></a>00258 |
| | 185 | <a name="l00260"></a><a class="code" href="classmultiBM.html">00260</a> <span class="keyword">class </span><a class="code" href="classmultiBM.html" title="Estimator for Multinomial density.">multiBM</a> : <span class="keyword">public</span> <a class="code" href="classBMEF.html" title="Estimator for Exponential family.">BMEF</a> { |
| | 186 | <a name="l00261"></a>00261 <span class="keyword">protected</span>: |
| | 187 | <a name="l00263"></a><a class="code" href="classmultiBM.html#eddee08a724170de63f36e40c57b27b5">00263</a> <a class="code" href="classeDirich.html" title="Dirichlet posterior density.">eDirich</a> <a class="code" href="classmultiBM.html#eddee08a724170de63f36e40c57b27b5" title="Conjugate prior and posterior.">est</a>; |
| | 188 | <a name="l00265"></a><a class="code" href="classmultiBM.html#7b606116aed7e8834a339cbb0424b1d6">00265</a> vec &<a class="code" href="classmultiBM.html#7b606116aed7e8834a339cbb0424b1d6" title="Pointer inside est to sufficient statistics.">beta</a>; |
| | 189 | <a name="l00266"></a>00266 <span class="keyword">public</span>: |
| | 190 | <a name="l00268"></a><a class="code" href="classmultiBM.html#7d7d7e78c129602bcde96078359dc6e5">00268</a> <a class="code" href="classmultiBM.html#7d7d7e78c129602bcde96078359dc6e5" title="Default constructor.">multiBM</a> ( <span class="keyword">const</span> <a class="code" href="classRV.html" title="Class representing variables, most often random variables.">RV</a> &<a class="code" href="classBM.html#af00f0612fabe66241dd507188cdbf88" title="Random variable of the posterior.">rv</a>, <span class="keyword">const</span> vec beta0 ) : <a class="code" href="classBMEF.html" title="Estimator for Exponential family.">BMEF</a> ( rv ),<a class="code" href="classmultiBM.html#eddee08a724170de63f36e40c57b27b5" title="Conjugate prior and posterior.">est</a> ( rv,beta0 ),<a class="code" href="classmultiBM.html#7b606116aed7e8834a339cbb0424b1d6" title="Pointer inside est to sufficient statistics.">beta</a> ( <a class="code" href="classmultiBM.html#eddee08a724170de63f36e40c57b27b5" title="Conjugate prior and posterior.">est</a>._beta() ) {<span class="keywordflow">if</span>(<a class="code" href="classmultiBM.html#7b606116aed7e8834a339cbb0424b1d6" title="Pointer inside est to sufficient statistics.">beta</a>.length()>0){<a class="code" href="classBMEF.html#308cf5d4133cd471fdf1ecd5dfa09d02" title="cached value of lognc() in the previous step (used in evaluation of ll )">last_lognc</a>=<a class="code" href="classmultiBM.html#eddee08a724170de63f36e40c57b27b5" title="Conjugate prior and posterior.">est</a>.<a class="code" href="classeDirich.html#7ce60be7119ffc639ede4e583c1f6e77" title="logarithm of the normalizing constant, ">lognc</a>();}<span class="keywordflow">else</span>{<a class="code" href="classBMEF.html#308cf5d4133cd471fdf1ecd5dfa09d02" title="cached value of lognc() in the previous step (used in evaluation of ll )">last_lognc</a>=0.0;}} |
| | 191 | <a name="l00270"></a><a class="code" href="classmultiBM.html#b92751adbfb9f259ca8c95232cfd9c09">00270</a> <a class="code" href="classmultiBM.html#7d7d7e78c129602bcde96078359dc6e5" title="Default constructor.">multiBM</a> ( <span class="keyword">const</span> <a class="code" href="classmultiBM.html" title="Estimator for Multinomial density.">multiBM</a> &B ) : <a class="code" href="classBMEF.html" title="Estimator for Exponential family.">BMEF</a> ( B ),<a class="code" href="classmultiBM.html#eddee08a724170de63f36e40c57b27b5" title="Conjugate prior and posterior.">est</a> ( <a class="code" href="classBM.html#af00f0612fabe66241dd507188cdbf88" title="Random variable of the posterior.">rv</a>,B.<a class="code" href="classmultiBM.html#7b606116aed7e8834a339cbb0424b1d6" title="Pointer inside est to sufficient statistics.">beta</a> ),<a class="code" href="classmultiBM.html#7b606116aed7e8834a339cbb0424b1d6" title="Pointer inside est to sufficient statistics.">beta</a> ( <a class="code" href="classmultiBM.html#eddee08a724170de63f36e40c57b27b5" title="Conjugate prior and posterior.">est</a>._beta() ) {} |
| | 192 | <a name="l00272"></a><a class="code" href="classmultiBM.html#42e36804041e551d3ceea6c75abc0562">00272</a> <span class="keywordtype">void</span> <a class="code" href="classmultiBM.html#42e36804041e551d3ceea6c75abc0562" title="Sets sufficient statistics to match that of givefrom mB0.">set_statistics</a> ( <span class="keyword">const</span> <a class="code" href="classBM.html" title="Bayesian Model of the world, i.e. all uncertainty is modeled by probabilities.">BM</a>* mB0 ) {<span class="keyword">const</span> <a class="code" href="classmultiBM.html" title="Estimator for Multinomial density.">multiBM</a>* mB=<span class="keyword">dynamic_cast<</span><span class="keyword">const </span><a class="code" href="classmultiBM.html" title="Estimator for Multinomial density.">multiBM</a>*<span class="keyword">></span> ( mB0 ); <a class="code" href="classmultiBM.html#7b606116aed7e8834a339cbb0424b1d6" title="Pointer inside est to sufficient statistics.">beta</a>=mB-><a class="code" href="classmultiBM.html#7b606116aed7e8834a339cbb0424b1d6" title="Pointer inside est to sufficient statistics.">beta</a>;} |
| | 193 | <a name="l00273"></a><a class="code" href="classmultiBM.html#11eeba7e97954e316e959116f90d80e2">00273</a> <span class="keywordtype">void</span> <a class="code" href="classmultiBM.html#11eeba7e97954e316e959116f90d80e2" title="Incremental Bayes rule.">bayes</a> ( <span class="keyword">const</span> vec &dt ) { |
| | 194 | <a name="l00274"></a>00274 <span class="keywordflow">if</span> ( <a class="code" href="classBMEF.html#538d632e59f9afa8daa1de74da12ce71" title="forgetting factor">frg</a><1.0 ) {<a class="code" href="classmultiBM.html#7b606116aed7e8834a339cbb0424b1d6" title="Pointer inside est to sufficient statistics.">beta</a>*=<a class="code" href="classBMEF.html#538d632e59f9afa8daa1de74da12ce71" title="forgetting factor">frg</a>;<a class="code" href="classBMEF.html#308cf5d4133cd471fdf1ecd5dfa09d02" title="cached value of lognc() in the previous step (used in evaluation of ll )">last_lognc</a>=<a class="code" href="classmultiBM.html#eddee08a724170de63f36e40c57b27b5" title="Conjugate prior and posterior.">est</a>.<a class="code" href="classeDirich.html#7ce60be7119ffc639ede4e583c1f6e77" title="logarithm of the normalizing constant, ">lognc</a>();} |
| | 195 | <a name="l00275"></a>00275 <a class="code" href="classmultiBM.html#7b606116aed7e8834a339cbb0424b1d6" title="Pointer inside est to sufficient statistics.">beta</a>+=dt; |
| | 196 | <a name="l00276"></a>00276 <span class="keywordflow">if</span> ( <a class="code" href="classBM.html#bf6fb59b30141074f8ee1e2f43d03129" title="If true, the filter will compute likelihood of the data record and store it in ll...">evalll</a> ) {<a class="code" href="classBM.html#5623fef6572a08c2b53b8c87b82dc979" title="Logarithm of marginalized data likelihood.">ll</a>=<a class="code" href="classmultiBM.html#eddee08a724170de63f36e40c57b27b5" title="Conjugate prior and posterior.">est</a>.<a class="code" href="classeDirich.html#7ce60be7119ffc639ede4e583c1f6e77" title="logarithm of the normalizing constant, ">lognc</a>()-<a class="code" href="classBMEF.html#308cf5d4133cd471fdf1ecd5dfa09d02" title="cached value of lognc() in the previous step (used in evaluation of ll )">last_lognc</a>;} |
| | 197 | <a name="l00277"></a>00277 } |
| | 198 | <a name="l00278"></a><a class="code" href="classmultiBM.html#13e26a61757278981fd8cac9a7ef91eb">00278</a> <span class="keywordtype">double</span> <a class="code" href="classmultiBM.html#13e26a61757278981fd8cac9a7ef91eb">logpred</a> ( <span class="keyword">const</span> vec &dt )<span class="keyword"> const </span>{ |
| | 199 | <a name="l00279"></a>00279 <a class="code" href="classeDirich.html" title="Dirichlet posterior density.">eDirich</a> pred ( <a class="code" href="classmultiBM.html#eddee08a724170de63f36e40c57b27b5" title="Conjugate prior and posterior.">est</a> ); |
| | 200 | <a name="l00280"></a>00280 vec &<a class="code" href="classmultiBM.html#7b606116aed7e8834a339cbb0424b1d6" title="Pointer inside est to sufficient statistics.">beta</a> = pred.<a class="code" href="classeDirich.html#6409d0362143a23976b43641ff19e53a" title="access function">_beta</a>(); |
| | 201 | <a name="l00281"></a>00281 |
| | 202 | <a name="l00282"></a>00282 <span class="keywordtype">double</span> lll; |
| | 203 | <a name="l00283"></a>00283 <span class="keywordflow">if</span> ( <a class="code" href="classBMEF.html#538d632e59f9afa8daa1de74da12ce71" title="forgetting factor">frg</a><1.0 ) |
| | 204 | <a name="l00284"></a>00284 {beta*=<a class="code" href="classBMEF.html#538d632e59f9afa8daa1de74da12ce71" title="forgetting factor">frg</a>;lll=pred.<a class="code" href="classeDirich.html#7ce60be7119ffc639ede4e583c1f6e77" title="logarithm of the normalizing constant, ">lognc</a>();} |
| | 205 | <a name="l00285"></a>00285 <span class="keywordflow">else</span> |
| | 206 | <a name="l00286"></a>00286 <span class="keywordflow">if</span> ( <a class="code" href="classBM.html#bf6fb59b30141074f8ee1e2f43d03129" title="If true, the filter will compute likelihood of the data record and store it in ll...">evalll</a> ) {lll=<a class="code" href="classBMEF.html#308cf5d4133cd471fdf1ecd5dfa09d02" title="cached value of lognc() in the previous step (used in evaluation of ll )">last_lognc</a>;} |
| | 207 | <a name="l00287"></a>00287 <span class="keywordflow">else</span>{lll=pred.<a class="code" href="classeDirich.html#7ce60be7119ffc639ede4e583c1f6e77" title="logarithm of the normalizing constant, ">lognc</a>();} |
| | 208 | <a name="l00288"></a>00288 |
| | 209 | <a name="l00289"></a>00289 beta+=dt; |
| | 210 | <a name="l00290"></a>00290 <span class="keywordflow">return</span> pred.<a class="code" href="classeDirich.html#7ce60be7119ffc639ede4e583c1f6e77" title="logarithm of the normalizing constant, ">lognc</a>()-lll; |
| | 211 | <a name="l00291"></a>00291 } |
| | 212 | <a name="l00292"></a><a class="code" href="classmultiBM.html#3988322f8f51b153622036f461f62a67">00292</a> <span class="keywordtype">void</span> <a class="code" href="classmultiBM.html#3988322f8f51b153622036f461f62a67" title="Flatten the posterior according to the given BMEF (of the same type!).">flatten</a> ( <span class="keyword">const</span> <a class="code" href="classBMEF.html" title="Estimator for Exponential family.">BMEF</a>* B ) { |
| | 213 | <a name="l00293"></a>00293 <span class="keyword">const</span> <a class="code" href="classmultiBM.html" title="Estimator for Multinomial density.">multiBM</a>* E=<span class="keyword">dynamic_cast<</span><span class="keyword">const </span><a class="code" href="classmultiBM.html" title="Estimator for Multinomial density.">multiBM</a>*<span class="keyword">></span> ( B ); |
| | 214 | <a name="l00294"></a>00294 <span class="comment">// sum(beta) should be equal to sum(B.beta)</span> |
| | 215 | <a name="l00295"></a>00295 <span class="keyword">const</span> vec &Eb=E-><a class="code" href="classmultiBM.html#7b606116aed7e8834a339cbb0424b1d6" title="Pointer inside est to sufficient statistics.">beta</a>;<span class="comment">//const_cast<multiBM*> ( E )->_beta();</span> |
| | 216 | <a name="l00296"></a>00296 <a class="code" href="classmultiBM.html#7b606116aed7e8834a339cbb0424b1d6" title="Pointer inside est to sufficient statistics.">beta</a>*= ( sum ( Eb ) /sum ( <a class="code" href="classmultiBM.html#7b606116aed7e8834a339cbb0424b1d6" title="Pointer inside est to sufficient statistics.">beta</a> ) ); |
| | 217 | <a name="l00297"></a>00297 <span class="keywordflow">if</span> ( <a class="code" href="classBM.html#bf6fb59b30141074f8ee1e2f43d03129" title="If true, the filter will compute likelihood of the data record and store it in ll...">evalll</a> ) {<a class="code" href="classBMEF.html#308cf5d4133cd471fdf1ecd5dfa09d02" title="cached value of lognc() in the previous step (used in evaluation of ll )">last_lognc</a>=<a class="code" href="classmultiBM.html#eddee08a724170de63f36e40c57b27b5" title="Conjugate prior and posterior.">est</a>.<a class="code" href="classeDirich.html#7ce60be7119ffc639ede4e583c1f6e77" title="logarithm of the normalizing constant, ">lognc</a>();} |
| | 218 | <a name="l00298"></a>00298 } |
| | 219 | <a name="l00299"></a><a class="code" href="classmultiBM.html#66cdfd83a70bc281840ab0646b941684">00299</a> <span class="keyword">const</span> <a class="code" href="classepdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>& <a class="code" href="classmultiBM.html#66cdfd83a70bc281840ab0646b941684" title="Returns a reference to the epdf representing posterior density on parameters.">_epdf</a>()<span class="keyword"> const </span>{<span class="keywordflow">return</span> <a class="code" href="classmultiBM.html#eddee08a724170de63f36e40c57b27b5" title="Conjugate prior and posterior.">est</a>;}; |
| | 220 | <a name="l00300"></a><a class="code" href="classmultiBM.html#66a0fa6966e40bb6c3e7ba22d26e9d35">00300</a> <span class="keyword">const</span> <a class="code" href="classeDirich.html" title="Dirichlet posterior density.">eDirich</a>* <a class="code" href="classmultiBM.html#66a0fa6966e40bb6c3e7ba22d26e9d35" title="Returns a pointer to the epdf representing posterior density on parameters. Use with...">_e</a>()<span class="keyword"> const </span>{<span class="keywordflow">return</span> &<a class="code" href="classmultiBM.html#eddee08a724170de63f36e40c57b27b5" title="Conjugate prior and posterior.">est</a>;}; |
| | 221 | <a name="l00301"></a>00301 <span class="keywordtype">void</span> set_parameters ( <span class="keyword">const</span> vec &beta0 ) { |
| | 222 | <a name="l00302"></a>00302 <a class="code" href="classmultiBM.html#eddee08a724170de63f36e40c57b27b5" title="Conjugate prior and posterior.">est</a>.<a class="code" href="classeDirich.html#c842acb2e1cce5cc9000769ff06c086d" title="Set internal parameters.">set_parameters</a> ( beta0 ); |
| | 223 | <a name="l00303"></a>00303 <a class="code" href="classBM.html#af00f0612fabe66241dd507188cdbf88" title="Random variable of the posterior.">rv</a> = <a class="code" href="classmultiBM.html#eddee08a724170de63f36e40c57b27b5" title="Conjugate prior and posterior.">est</a>.<a class="code" href="classepdf.html#ca0d32aabb4cbba347e0c37fe8607562" title="access function, possibly dangerous!">_rv</a>(); |
| | 224 | <a name="l00304"></a>00304 <span class="keywordflow">if</span> ( <a class="code" href="classBM.html#bf6fb59b30141074f8ee1e2f43d03129" title="If true, the filter will compute likelihood of the data record and store it in ll...">evalll</a> ) {<a class="code" href="classBMEF.html#308cf5d4133cd471fdf1ecd5dfa09d02" title="cached value of lognc() in the previous step (used in evaluation of ll )">last_lognc</a>=<a class="code" href="classmultiBM.html#eddee08a724170de63f36e40c57b27b5" title="Conjugate prior and posterior.">est</a>.<a class="code" href="classeDirich.html#7ce60be7119ffc639ede4e583c1f6e77" title="logarithm of the normalizing constant, ">lognc</a>();} |
| | 225 | <a name="l00305"></a>00305 } |
| | 226 | <a name="l00306"></a>00306 }; |
| | 227 | <a name="l00307"></a>00307 |
| | 228 | <a name="l00317"></a><a class="code" href="classegamma.html">00317</a> <span class="keyword">class </span><a class="code" href="classegamma.html" title="Gamma posterior density.">egamma</a> : <span class="keyword">public</span> <a class="code" href="classeEF.html" title="General conjugate exponential family posterior density.">eEF</a> { |
| | 229 | <a name="l00318"></a>00318 <span class="keyword">protected</span>: |
| | 230 | <a name="l00320"></a><a class="code" href="classegamma.html#376cebd8932546c440f21b182910b01b">00320</a> vec <a class="code" href="classegamma.html#376cebd8932546c440f21b182910b01b" title="Vector .">alpha</a>; |
| | 231 | <a name="l00322"></a><a class="code" href="classegamma.html#cfc5f136467488a421ab22f886323790">00322</a> vec <a class="code" href="classegamma.html#cfc5f136467488a421ab22f886323790" title="Vector .">beta</a>; |
| | 232 | <a name="l00323"></a>00323 <span class="keyword">public</span> : |
| | 233 | <a name="l00325"></a><a class="code" href="classegamma.html#4b1d34f3b244ea51a58ec10c468788c1">00325</a> <a class="code" href="classegamma.html#4b1d34f3b244ea51a58ec10c468788c1" title="Default constructor.">egamma</a> ( <span class="keyword">const</span> <a class="code" href="classRV.html" title="Class representing variables, most often random variables.">RV</a> &<a class="code" href="classepdf.html#74da992e3f5d598da8850b646b79b9d9" title="Identified of the random variable.">rv</a> ) :<a class="code" href="classeEF.html" title="General conjugate exponential family posterior density.">eEF</a> ( rv ), <a class="code" href="classegamma.html#376cebd8932546c440f21b182910b01b" title="Vector .">alpha</a>(rv.count()), <a class="code" href="classegamma.html#cfc5f136467488a421ab22f886323790" title="Vector .">beta</a>(rv.count()) {}; |
| | 234 | <a name="l00327"></a><a class="code" href="classegamma.html#8e348b89be82b70471fe8c5630f61339">00327</a> <span class="keywordtype">void</span> <a class="code" href="classegamma.html#8e348b89be82b70471fe8c5630f61339" title="Sets parameters.">set_parameters</a> ( <span class="keyword">const</span> vec &a, <span class="keyword">const</span> vec &b ) {<a class="code" href="classegamma.html#376cebd8932546c440f21b182910b01b" title="Vector .">alpha</a>=a,<a class="code" href="classegamma.html#cfc5f136467488a421ab22f886323790" title="Vector .">beta</a>=b;}; |
| | 235 | <a name="l00328"></a>00328 vec <a class="code" href="classegamma.html#8e10c0021b5dfdd9cb62c6959b5ef425" title="Returns a sample, from density .">sample</a>() <span class="keyword">const</span>; |
| | 236 | <a name="l00330"></a>00330 <span class="comment">// mat sample ( int N ) const;</span> |
| | 237 | <a name="l00331"></a>00331 <span class="keywordtype">double</span> <a class="code" href="classegamma.html#74a49a4c696f44e54bb6b0515e155a9b" title="TODO: is it used anywhere?">evallog</a> ( <span class="keyword">const</span> vec &val ) <span class="keyword">const</span>; |
| | 238 | <a name="l00332"></a>00332 <span class="keywordtype">double</span> <a class="code" href="classegamma.html#d6dbbdb72360f9e54d64501f80318bb6" title="logarithm of the normalizing constant, ">lognc</a> () <span class="keyword">const</span>; |
| | 239 | <a name="l00334"></a><a class="code" href="classegamma.html#44445c56e60b91b377f207f8d5089790">00334</a> <span class="keywordtype">void</span> <a class="code" href="classegamma.html#44445c56e60b91b377f207f8d5089790" title="Returns poiter to alpha and beta. Potentially dengerous: use with care!">_param</a> ( vec* &a, vec* &b ) {a=&<a class="code" href="classegamma.html#376cebd8932546c440f21b182910b01b" title="Vector .">alpha</a>;b=&<a class="code" href="classegamma.html#cfc5f136467488a421ab22f886323790" title="Vector .">beta</a>;}; |
| | 240 | <a name="l00335"></a><a class="code" href="classegamma.html#6ab5ba56f7cdb2e5921c3e77524fa50a">00335</a> vec <a class="code" href="classegamma.html#6ab5ba56f7cdb2e5921c3e77524fa50a" title="return expected value">mean</a>()<span class="keyword"> const </span>{<span class="keywordflow">return</span> elem_div(<a class="code" href="classegamma.html#376cebd8932546c440f21b182910b01b" title="Vector .">alpha</a>,<a class="code" href="classegamma.html#cfc5f136467488a421ab22f886323790" title="Vector .">beta</a>);} |
| | 241 | <a name="l00336"></a><a class="code" href="classegamma.html#1dee6186a084565de4f9ceb3410148e4">00336</a> vec <a class="code" href="classegamma.html#1dee6186a084565de4f9ceb3410148e4" title="return expected variance (not covariance!)">variance</a>()<span class="keyword"> const </span>{<span class="keywordflow">return</span> elem_div(<a class="code" href="classegamma.html#376cebd8932546c440f21b182910b01b" title="Vector .">alpha</a>,elem_mult(<a class="code" href="classegamma.html#cfc5f136467488a421ab22f886323790" title="Vector .">beta</a>,<a class="code" href="classegamma.html#cfc5f136467488a421ab22f886323790" title="Vector .">beta</a>)); } |
| | 242 | <a name="l00337"></a>00337 }; |
| | 243 | <a name="l00338"></a>00338 |
| | 244 | <a name="l00353"></a><a class="code" href="classeigamma.html">00353</a> <span class="keyword">class </span><a class="code" href="classeigamma.html" title="Inverse-Gamma posterior density.">eigamma</a> : <span class="keyword">public</span> <a class="code" href="classeEF.html" title="General conjugate exponential family posterior density.">eEF</a> { |
| | 245 | <a name="l00354"></a>00354 <span class="keyword">protected</span>: |
| | 246 | <a name="l00356"></a><a class="code" href="classeigamma.html#ea00e33f405ebd918e06cede968a735b">00356</a> vec* <a class="code" href="classeigamma.html#ea00e33f405ebd918e06cede968a735b" title="Vector .">alpha</a>; |
| | 247 | <a name="l00358"></a><a class="code" href="classeigamma.html#ee446ec667a4df391e0db41decb2d558">00358</a> vec* <a class="code" href="classeigamma.html#ee446ec667a4df391e0db41decb2d558" title="Vector (in fact it is 1/beta as used in definition of iG).">beta</a>; |
| | 248 | <a name="l00360"></a><a class="code" href="classeigamma.html#906f2a3a8fbf08b2af49776f2f1be5d4">00360</a> <a class="code" href="classegamma.html" title="Gamma posterior density.">egamma</a> <a class="code" href="classeigamma.html#906f2a3a8fbf08b2af49776f2f1be5d4" title="internal egamma">eg</a>; |
| | 249 | <a name="l00361"></a>00361 <span class="keyword">public</span> : |
| | 250 | <a name="l00363"></a><a class="code" href="classeigamma.html#ea0edc0a1f32350219f55cf35d83a5f6">00363</a> <a class="code" href="classeigamma.html#ea0edc0a1f32350219f55cf35d83a5f6" title="Default constructor.">eigamma</a> ( <span class="keyword">const</span> <a class="code" href="classRV.html" title="Class representing variables, most often random variables.">RV</a> &<a class="code" href="classepdf.html#74da992e3f5d598da8850b646b79b9d9" title="Identified of the random variable.">rv</a> ) :<a class="code" href="classeEF.html" title="General conjugate exponential family posterior density.">eEF</a> ( rv ), <a class="code" href="classeigamma.html#906f2a3a8fbf08b2af49776f2f1be5d4" title="internal egamma">eg</a>(rv) {<a class="code" href="classeigamma.html#906f2a3a8fbf08b2af49776f2f1be5d4" title="internal egamma">eg</a>.<a class="code" href="classegamma.html#44445c56e60b91b377f207f8d5089790" title="Returns poiter to alpha and beta. Potentially dengerous: use with care!">_param</a>(<a class="code" href="classeigamma.html#ea00e33f405ebd918e06cede968a735b" title="Vector .">alpha</a>,<a class="code" href="classeigamma.html#ee446ec667a4df391e0db41decb2d558" title="Vector (in fact it is 1/beta as used in definition of iG).">beta</a>);}; |
| | 251 | <a name="l00365"></a><a class="code" href="classeigamma.html#a86b94a5f9189cae1b6651838dc153aa">00365</a> <span class="keywordtype">void</span> <a class="code" href="classeigamma.html#a86b94a5f9189cae1b6651838dc153aa" title="Sets parameters.">set_parameters</a> ( <span class="keyword">const</span> vec &a, <span class="keyword">const</span> vec &b ) {*<a class="code" href="classeigamma.html#ea00e33f405ebd918e06cede968a735b" title="Vector .">alpha</a>=a,*<a class="code" href="classeigamma.html#ee446ec667a4df391e0db41decb2d558" title="Vector (in fact it is 1/beta as used in definition of iG).">beta</a>=b;}; |
| | 252 | <a name="l00366"></a><a class="code" href="classeigamma.html#b70deffdf41b590377fd6743e4d306f1">00366</a> vec <a class="code" href="classeigamma.html#b70deffdf41b590377fd6743e4d306f1" title="Returns a sample, from density .">sample</a>()<span class="keyword"> const </span>{<span class="keywordflow">return</span> 1.0/<a class="code" href="classeigamma.html#906f2a3a8fbf08b2af49776f2f1be5d4" title="internal egamma">eg</a>.<a class="code" href="classegamma.html#8e10c0021b5dfdd9cb62c6959b5ef425" title="Returns a sample, from density .">sample</a>();}; |
| | 253 | <a name="l00368"></a>00368 <span class="comment">// mat sample ( int N ) const;</span> |
| | 254 | <a name="l00369"></a><a class="code" href="classeigamma.html#960cf366101389f58f11c5f748dd7e80">00369</a> <span class="keywordtype">double</span> <a class="code" href="classeigamma.html#960cf366101389f58f11c5f748dd7e80" title="TODO: is it used anywhere?">evallog</a> ( <span class="keyword">const</span> vec &val )<span class="keyword"> const </span>{<span class="keywordflow">return</span> <a class="code" href="classeigamma.html#906f2a3a8fbf08b2af49776f2f1be5d4" title="internal egamma">eg</a>.<a class="code" href="classegamma.html#74a49a4c696f44e54bb6b0515e155a9b" title="TODO: is it used anywhere?">evallog</a>(val);}; |
| | 255 | <a name="l00370"></a><a class="code" href="classeigamma.html#efcc280de487d8b81f9b31f286404c72">00370</a> <span class="keywordtype">double</span> <a class="code" href="classeigamma.html#efcc280de487d8b81f9b31f286404c72" title="logarithm of the normalizing constant, ">lognc</a> ()<span class="keyword"> const </span>{<span class="keywordflow">return</span> <a class="code" href="classeigamma.html#906f2a3a8fbf08b2af49776f2f1be5d4" title="internal egamma">eg</a>.<a class="code" href="classegamma.html#d6dbbdb72360f9e54d64501f80318bb6" title="logarithm of the normalizing constant, ">lognc</a>();}; |
| | 256 | <a name="l00372"></a><a class="code" href="classeigamma.html#86389685695f6948d2e52070cd89a9ed">00372</a> <span class="keywordtype">void</span> <a class="code" href="classeigamma.html#86389685695f6948d2e52070cd89a9ed" title="Returns poiter to alpha and beta. Potentially dangerous: use with care!">_param</a> ( vec* &a, vec* &b ) {a=<a class="code" href="classeigamma.html#ea00e33f405ebd918e06cede968a735b" title="Vector .">alpha</a>;b=<a class="code" href="classeigamma.html#ee446ec667a4df391e0db41decb2d558" title="Vector (in fact it is 1/beta as used in definition of iG).">beta</a>;}; |
| | 257 | <a name="l00373"></a><a class="code" href="classeigamma.html#0ff10e82b0f0d07c2dd4ff5f23b3c70f">00373</a> vec <a class="code" href="classeigamma.html#0ff10e82b0f0d07c2dd4ff5f23b3c70f" title="return expected value">mean</a>()<span class="keyword"> const </span>{<span class="keywordflow">return</span> elem_div(*<a class="code" href="classeigamma.html#ee446ec667a4df391e0db41decb2d558" title="Vector (in fact it is 1/beta as used in definition of iG).">beta</a>,*<a class="code" href="classeigamma.html#ea00e33f405ebd918e06cede968a735b" title="Vector .">alpha</a>-1);} |
| | 258 | <a name="l00374"></a><a class="code" href="classeigamma.html#a9ad6cb7514ffc46605f28316eda54ff">00374</a> vec <a class="code" href="classeigamma.html#a9ad6cb7514ffc46605f28316eda54ff" title="return expected variance (not covariance!)">variance</a>()<span class="keyword"> const </span>{vec mea=<a class="code" href="classeigamma.html#0ff10e82b0f0d07c2dd4ff5f23b3c70f" title="return expected value">mean</a>(); <span class="keywordflow">return</span> elem_div(elem_mult(mea,mea),*<a class="code" href="classeigamma.html#ea00e33f405ebd918e06cede968a735b" title="Vector .">alpha</a>-2);} |
| | 259 | <a name="l00375"></a>00375 }; |
| | 260 | <a name="l00376"></a>00376 <span class="comment">/*</span> |
| | 261 | <a name="l00378"></a>00378 <span class="comment">class emix : public epdf {</span> |
| | 262 | <a name="l00379"></a>00379 <span class="comment">protected:</span> |
| | 263 | <a name="l00380"></a>00380 <span class="comment"> int n;</span> |
| | 264 | <a name="l00381"></a>00381 <span class="comment"> vec &w;</span> |
| | 265 | <a name="l00382"></a>00382 <span class="comment"> Array<epdf*> Coms;</span> |
| | 266 | <a name="l00383"></a>00383 <span class="comment">public:</span> |
| | 267 | <a name="l00385"></a>00385 <span class="comment"> emix ( const RV &rv, vec &w0): epdf(rv), n(w0.length()), w(w0), Coms(n) {};</span> |
| | 268 | <a name="l00386"></a>00386 <span class="comment"> void set_parameters( int &i, double wi, epdf* ep){w(i)=wi;Coms(i)=ep;}</span> |
| | 269 | <a name="l00387"></a>00387 <span class="comment"> vec mean(){vec pom; for(int i=0;i<n;i++){pom+=Coms(i)->mean()*w(i);} return pom;};</span> |
| | 270 | <a name="l00388"></a>00388 <span class="comment"> vec sample() {it_error ( "Not implemented" );return 0;}</span> |
| | 271 | <a name="l00389"></a>00389 <span class="comment">};</span> |
| | 272 | <a name="l00390"></a>00390 <span class="comment">*/</span> |
| | 273 | <a name="l00391"></a>00391 |
| | 274 | <a name="l00393"></a>00393 |
| | 275 | <a name="l00394"></a><a class="code" href="classeuni.html">00394</a> <span class="keyword">class </span><a class="code" href="classeuni.html" title="Uniform distributed density on a rectangular support.">euni</a>: <span class="keyword">public</span> <a class="code" href="classepdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a> { |
| | 276 | <a name="l00395"></a>00395 <span class="keyword">protected</span>: |
| | 277 | <a name="l00397"></a><a class="code" href="classeuni.html#ef42cd8d7645422048d46c46ec5cdac1">00397</a> vec <a class="code" href="classeuni.html#ef42cd8d7645422048d46c46ec5cdac1" title="lower bound on support">low</a>; |
| | 278 | <a name="l00399"></a><a class="code" href="classeuni.html#71b6d6b41aeb61a7f76f682b72119231">00399</a> vec <a class="code" href="classeuni.html#71b6d6b41aeb61a7f76f682b72119231" title="upper bound on support">high</a>; |
| | 279 | <a name="l00401"></a><a class="code" href="classeuni.html#52a6ff4a54010f88a6a19fca605c64a4">00401</a> vec <a class="code" href="classeuni.html#52a6ff4a54010f88a6a19fca605c64a4" title="internal">distance</a>; |
| | 280 | <a name="l00403"></a><a class="code" href="classeuni.html#63105490e946e43372d6187ad1bafdda">00403</a> <span class="keywordtype">double</span> <a class="code" href="classeuni.html#63105490e946e43372d6187ad1bafdda" title="normalizing coefficients">nk</a>; |
| | 281 | <a name="l00405"></a><a class="code" href="classeuni.html#f445a0ce24f39d14c1a4eed53fc8e2c3">00405</a> <span class="keywordtype">double</span> <a class="code" href="classeuni.html#f445a0ce24f39d14c1a4eed53fc8e2c3" title="cache of log( nk )">lnk</a>; |
| | 282 | <a name="l00406"></a>00406 <span class="keyword">public</span>: |
| | 283 | <a name="l00408"></a><a class="code" href="classeuni.html#2537a6c239cff52e3ba814851a1116cd">00408</a> <a class="code" href="classeuni.html#2537a6c239cff52e3ba814851a1116cd" title="Defualt constructor.">euni</a> ( <span class="keyword">const</span> <a class="code" href="classRV.html" title="Class representing variables, most often random variables.">RV</a> <a class="code" href="classepdf.html#74da992e3f5d598da8850b646b79b9d9" title="Identified of the random variable.">rv</a> ) :<a class="code" href="classepdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a> ( rv ) {} |
| | 284 | <a name="l00409"></a>00409 <span class="keywordtype">double</span> eval ( <span class="keyword">const</span> vec &val )<span class="keyword"> const </span>{<span class="keywordflow">return</span> <a class="code" href="classeuni.html#63105490e946e43372d6187ad1bafdda" title="normalizing coefficients">nk</a>;} |
| | 285 | <a name="l00410"></a><a class="code" href="classeuni.html#357b36417ef4c9211d12e7a4a602fd6a">00410</a> <span class="keywordtype">double</span> <a class="code" href="classeuni.html#357b36417ef4c9211d12e7a4a602fd6a" title="Compute log-probability of argument val.">evallog</a> ( <span class="keyword">const</span> vec &val )<span class="keyword"> const </span>{<span class="keywordflow">return</span> <a class="code" href="classeuni.html#f445a0ce24f39d14c1a4eed53fc8e2c3" title="cache of log( nk )">lnk</a>;} |
| | 286 | <a name="l00411"></a><a class="code" href="classeuni.html#4a0e09392be17beaee120ba98fc038cd">00411</a> vec <a class="code" href="classeuni.html#4a0e09392be17beaee120ba98fc038cd" title="Returns a sample, from density .">sample</a>()<span class="keyword"> const </span>{ |
| | 287 | <a name="l00412"></a>00412 vec smp ( <a class="code" href="classepdf.html#74da992e3f5d598da8850b646b79b9d9" title="Identified of the random variable.">rv</a>.<a class="code" href="classRV.html#f5c7b8bd589eef09ccdf3329a0addea0" title="Return number of scalars in the RV.">count</a>() ); |
| | 288 | <a name="l00413"></a>00413 <span class="preprocessor">#pragma omp critical</span> |
| | 289 | <a name="l00414"></a>00414 <span class="preprocessor"></span> UniRNG.sample_vector ( <a class="code" href="classepdf.html#74da992e3f5d598da8850b646b79b9d9" title="Identified of the random variable.">rv</a>.<a class="code" href="classRV.html#f5c7b8bd589eef09ccdf3329a0addea0" title="Return number of scalars in the RV.">count</a>(),smp ); |
| | 290 | <a name="l00415"></a>00415 <span class="keywordflow">return</span> <a class="code" href="classeuni.html#ef42cd8d7645422048d46c46ec5cdac1" title="lower bound on support">low</a>+elem_mult ( <a class="code" href="classeuni.html#52a6ff4a54010f88a6a19fca605c64a4" title="internal">distance</a>,smp ); |
| | 291 | <a name="l00416"></a>00416 } |
| | 292 | <a name="l00418"></a><a class="code" href="classeuni.html#4fd7c6a05100616ad16ece405cad7bf2">00418</a> <span class="keywordtype">void</span> <a class="code" href="classeuni.html#4fd7c6a05100616ad16ece405cad7bf2" title="set values of low and high ">set_parameters</a> ( <span class="keyword">const</span> vec &low0, <span class="keyword">const</span> vec &high0 ) { |
| | 293 | <a name="l00419"></a>00419 <a class="code" href="classeuni.html#52a6ff4a54010f88a6a19fca605c64a4" title="internal">distance</a> = high0-low0; |
| | 294 | <a name="l00420"></a>00420 it_assert_debug ( min ( <a class="code" href="classeuni.html#52a6ff4a54010f88a6a19fca605c64a4" title="internal">distance</a> ) >0.0,<span class="stringliteral">"bad support"</span> ); |
| | 295 | <a name="l00421"></a>00421 <a class="code" href="classeuni.html#ef42cd8d7645422048d46c46ec5cdac1" title="lower bound on support">low</a> = low0; |
| | 296 | <a name="l00422"></a>00422 <a class="code" href="classeuni.html#71b6d6b41aeb61a7f76f682b72119231" title="upper bound on support">high</a> = high0; |
| | 297 | <a name="l00423"></a>00423 <a class="code" href="classeuni.html#63105490e946e43372d6187ad1bafdda" title="normalizing coefficients">nk</a> = prod ( 1.0/<a class="code" href="classeuni.html#52a6ff4a54010f88a6a19fca605c64a4" title="internal">distance</a> ); |
| | 298 | <a name="l00424"></a>00424 <a class="code" href="classeuni.html#f445a0ce24f39d14c1a4eed53fc8e2c3" title="cache of log( nk )">lnk</a> = log ( <a class="code" href="classeuni.html#63105490e946e43372d6187ad1bafdda" title="normalizing coefficients">nk</a> ); |
| | 299 | <a name="l00425"></a>00425 } |
| | 300 | <a name="l00426"></a><a class="code" href="classeuni.html#8050087e421a9cfd1b4b1f8bd33b1cc1">00426</a> vec <a class="code" href="classeuni.html#8050087e421a9cfd1b4b1f8bd33b1cc1" title="return expected value">mean</a>()<span class="keyword"> const </span>{<span class="keywordflow">return</span> (<a class="code" href="classeuni.html#71b6d6b41aeb61a7f76f682b72119231" title="upper bound on support">high</a>-<a class="code" href="classeuni.html#ef42cd8d7645422048d46c46ec5cdac1" title="lower bound on support">low</a>)/2.0;} |
| | 301 | <a name="l00427"></a><a class="code" href="classeuni.html#15d7d8b2c8f13a0b1535ebc35551f01c">00427</a> vec <a class="code" href="classeuni.html#15d7d8b2c8f13a0b1535ebc35551f01c" title="return expected variance (not covariance!)">variance</a>()<span class="keyword"> const </span>{<span class="keywordflow">return</span> (pow(<a class="code" href="classeuni.html#71b6d6b41aeb61a7f76f682b72119231" title="upper bound on support">high</a>,2)+pow(<a class="code" href="classeuni.html#ef42cd8d7645422048d46c46ec5cdac1" title="lower bound on support">low</a>,2)+elem_mult(<a class="code" href="classeuni.html#71b6d6b41aeb61a7f76f682b72119231" title="upper bound on support">high</a>,<a class="code" href="classeuni.html#ef42cd8d7645422048d46c46ec5cdac1" title="lower bound on support">low</a>))/3.0;} |
| | 302 | <a name="l00428"></a>00428 }; |
| | 303 | <a name="l00429"></a>00429 |
| | 304 | <a name="l00430"></a>00430 |
| | 305 | <a name="l00436"></a>00436 <span class="keyword">template</span><<span class="keyword">class</span> sq_T> |
| | 306 | <a name="l00437"></a><a class="code" href="classmlnorm.html">00437</a> <span class="keyword">class </span><a class="code" href="classmlnorm.html" title="Normal distributed linear function with linear function of mean value;.">mlnorm</a> : <span class="keyword">public</span> <a class="code" href="classmEF.html" title="Exponential family model.">mEF</a> { |
| | 307 | <a name="l00438"></a>00438 <span class="keyword">protected</span>: |
| | 308 | <a name="l00440"></a><a class="code" href="classmlnorm.html#b76ee2171ace4fb3ff95a131ae8fc421">00440</a> <a class="code" href="classenorm.html" title="Gaussian density with positive definite (decomposed) covariance matrix.">enorm<sq_T></a> <a class="code" href="classepdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>; |
| | 309 | <a name="l00441"></a>00441 mat A; |
| | 310 | <a name="l00442"></a>00442 vec mu_const; |
| | 311 | <a name="l00443"></a>00443 vec& _mu; <span class="comment">//cached epdf.mu;</span> |
| | 312 | <a name="l00444"></a>00444 <span class="keyword">public</span>: |
| | 313 | <a name="l00446"></a>00446 <a class="code" href="classmlnorm.html#3a5ad4798d8a3878c5e93b8e796c8837" title="Constructor.">mlnorm</a> ( <span class="keyword">const</span> <a class="code" href="classRV.html" title="Class representing variables, most often random variables.">RV</a> &<a class="code" href="classmpdf.html#f6687c07ff07d47812dd565368ca59eb" title="modeled random variable">rv</a>, <span class="keyword">const</span> <a class="code" href="classRV.html" title="Class representing variables, most often random variables.">RV</a> &<a class="code" href="classmpdf.html#acb7dda792b3cd5576f39fa3129abbab" title="random variable in condition">rvc</a> ); |
| | 314 | <a name="l00448"></a>00448 <span class="keywordtype">void</span> <a class="code" href="classmlnorm.html#f95dfce0b500636a44ecd7e5210de999" title="Set A and R.">set_parameters</a> ( <span class="keyword">const</span> mat &A, <span class="keyword">const</span> vec &mu0, <span class="keyword">const</span> sq_T &R ); |
| | 315 | <a name="l00449"></a>00449 <span class="comment">// //!Generate one sample of the posterior</span> |
| | 316 | <a name="l00450"></a>00450 <span class="comment">// vec samplecond (const vec &cond, double &lik );</span> |
| | 317 | <a name="l00451"></a>00451 <span class="comment">// //!Generate matrix of samples of the posterior</span> |
| | 318 | <a name="l00452"></a>00452 <span class="comment">// mat samplecond (const vec &cond, vec &lik, int n );</span> |
| | 319 | <a name="l00454"></a>00454 <span class="comment"></span> <span class="keywordtype">void</span> <a class="code" href="classmlnorm.html#d41126455ac64b888a38f677886e1b40" title="Set value of rvc . Result of this operation is stored in epdf use function _ep to...">condition</a> ( <span class="keyword">const</span> vec &cond ); |
| | 320 | <a name="l00455"></a>00455 |
| | 321 | <a name="l00457"></a><a class="code" href="classmlnorm.html#2732ae47835dd25d5784bf08fde0a546">00457</a> vec& <a class="code" href="classmlnorm.html#2732ae47835dd25d5784bf08fde0a546" title="access function">_mu_const</a>() {<span class="keywordflow">return</span> mu_const;} |
| | 322 | <a name="l00459"></a><a class="code" href="classmlnorm.html#65ec3840c21b21102896bfd2282b47b3">00459</a> mat& <a class="code" href="classmlnorm.html#65ec3840c21b21102896bfd2282b47b3" title="access function">_A</a>() {<span class="keywordflow">return</span> A;} |
| | 323 | <a name="l00461"></a><a class="code" href="classmlnorm.html#3ff2b03fbb5e1133a5fe1bf831939f63">00461</a> mat <a class="code" href="classmlnorm.html#3ff2b03fbb5e1133a5fe1bf831939f63" title="access function">_R</a>() {<span class="keywordflow">return</span> <a class="code" href="classepdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>._R().to_mat();} |
| | 324 | <a name="l00462"></a>00462 |
| | 325 | <a name="l00463"></a>00463 <span class="keyword">template</span><<span class="keyword">class</span> sq_M> |
| | 326 | <a name="l00464"></a>00464 <span class="keyword">friend</span> std::ostream &operator<< ( std::ostream &os, mlnorm<sq_M> &ml ); |
| | 327 | <a name="l00465"></a>00465 }; |
| | 328 | <a name="l00466"></a>00466 |
| | 329 | <a name="l00469"></a><a class="code" href="classmlstudent.html">00469</a> <span class="keyword">class </span><a class="code" href="classmlstudent.html">mlstudent</a> : <span class="keyword">public</span> <a class="code" href="classmlnorm.html" title="Normal distributed linear function with linear function of mean value;.">mlnorm</a><ldmat> { |
| | 330 | <a name="l00470"></a>00470 <span class="keyword">protected</span>: |
| | 331 | <a name="l00471"></a>00471 <a class="code" href="classldmat.html" title="Matrix stored in LD form, (commonly known as UD).">ldmat</a> Lambda; |
| | 332 | <a name="l00472"></a>00472 <a class="code" href="classldmat.html" title="Matrix stored in LD form, (commonly known as UD).">ldmat</a> &<a class="code" href="classmlnorm.html#3ff2b03fbb5e1133a5fe1bf831939f63" title="access function">_R</a>; |
| | 333 | <a name="l00473"></a>00473 <a class="code" href="classldmat.html" title="Matrix stored in LD form, (commonly known as UD).">ldmat</a> Re; |
| | 334 | <a name="l00474"></a>00474 <span class="keyword">public</span>: |
| | 335 | <a name="l00475"></a>00475 <a class="code" href="classmlstudent.html">mlstudent</a> ( <span class="keyword">const</span> <a class="code" href="classRV.html" title="Class representing variables, most often random variables.">RV</a> &rv0, <span class="keyword">const</span> <a class="code" href="classRV.html" title="Class representing variables, most often random variables.">RV</a> &rvc0 ) :<a class="code" href="classmlnorm.html" title="Normal distributed linear function with linear function of mean value;.">mlnorm<ldmat></a> ( rv0,rvc0 ), |
| | 336 | <a name="l00476"></a>00476 Lambda ( rv0.<a class="code" href="classRV.html#f5c7b8bd589eef09ccdf3329a0addea0" title="Return number of scalars in the RV.">count</a>() ), |
| | 337 | <a name="l00477"></a>00477 <a class="code" href="classmlnorm.html#3ff2b03fbb5e1133a5fe1bf831939f63" title="access function">_R</a> ( <a class="code" href="classepdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>._R() ) {} |
| | 338 | <a name="l00478"></a>00478 <span class="keywordtype">void</span> set_parameters ( <span class="keyword">const</span> mat &A0, <span class="keyword">const</span> vec &mu0, <span class="keyword">const</span> <a class="code" href="classldmat.html" title="Matrix stored in LD form, (commonly known as UD).">ldmat</a> &R0, <span class="keyword">const</span> <a class="code" href="classldmat.html" title="Matrix stored in LD form, (commonly known as UD).">ldmat</a>& Lambda0) { |
| | 339 | <a name="l00479"></a>00479 <a class="code" href="classepdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>.set_parameters ( zeros ( <a class="code" href="classmpdf.html#f6687c07ff07d47812dd565368ca59eb" title="modeled random variable">rv</a>.<a class="code" href="classRV.html#f5c7b8bd589eef09ccdf3329a0addea0" title="Return number of scalars in the RV.">count</a>() ),Lambda ); |
| | 340 | <a name="l00480"></a>00480 A = A0; |
| | 341 | <a name="l00481"></a>00481 mu_const = mu0; |
| | 342 | <a name="l00482"></a>00482 Re=R0; |
| | 343 | <a name="l00483"></a>00483 Lambda = Lambda0; |
| | 344 | <a name="l00484"></a>00484 } |
| | 345 | <a name="l00485"></a><a class="code" href="classmlstudent.html#d153460ae0180f4bc7f28301f5cde876">00485</a> <span class="keywordtype">void</span> <a class="code" href="classmlstudent.html#d153460ae0180f4bc7f28301f5cde876" title="Set value of rvc . Result of this operation is stored in epdf use function _ep to...">condition</a> ( <span class="keyword">const</span> vec &cond ) { |
| | 346 | <a name="l00486"></a>00486 _mu = A*cond + mu_const; |
| | 347 | <a name="l00487"></a>00487 <span class="keywordtype">double</span> zeta; |
| | 348 | <a name="l00488"></a>00488 <span class="comment">//ugly hack!</span> |
| | 349 | <a name="l00489"></a>00489 <span class="keywordflow">if</span> ((cond.length()+1)==Lambda.<a class="code" href="group__math.html#g96dfb21865db4f5bd36fa70f9b0b1163" title="access function">rows</a>()){ |
| | 350 | <a name="l00490"></a>00490 zeta = Lambda.<a class="code" href="classldmat.html#d876c5f83e02b3e809b35c9de5068f14" title="Evaluates quadratic form ;.">invqform</a> ( <a class="code" href="group__core.html#g33c114e83980d883c5b211c47d5322a4" title="Concat two random variables.">concat</a>(cond, vec_1(1.0)) ); |
| | 351 | <a name="l00491"></a>00491 } <span class="keywordflow">else</span> { |
| | 352 | <a name="l00492"></a>00492 zeta = Lambda.<a class="code" href="classldmat.html#d876c5f83e02b3e809b35c9de5068f14" title="Evaluates quadratic form ;.">invqform</a> ( cond ); |
| | 353 | <a name="l00493"></a>00493 } |
| | 354 | <a name="l00494"></a>00494 <a class="code" href="classmlnorm.html#3ff2b03fbb5e1133a5fe1bf831939f63" title="access function">_R</a> = Re; |
| | 355 | <a name="l00495"></a>00495 <a class="code" href="classmlnorm.html#3ff2b03fbb5e1133a5fe1bf831939f63" title="access function">_R</a>*=( 1+zeta );<span class="comment">// / ( nu ); << nu is in Re!!!!!!</span> |
| | 356 | <a name="l00496"></a>00496 }; |
| | 357 | <a name="l00497"></a>00497 |
| | 358 | <a name="l00498"></a>00498 }; |
| | 359 | <a name="l00508"></a><a class="code" href="classmgamma.html">00508</a> <span class="keyword">class </span><a class="code" href="classmgamma.html" title="Gamma random walk.">mgamma</a> : <span class="keyword">public</span> <a class="code" href="classmEF.html" title="Exponential family model.">mEF</a> { |
| | 360 | <a name="l00509"></a>00509 <span class="keyword">protected</span>: |
| | 361 | <a name="l00511"></a><a class="code" href="classmgamma.html#612dbf35c770a780027619aaac2c443e">00511</a> <a class="code" href="classegamma.html" title="Gamma posterior density.">egamma</a> <a class="code" href="classepdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>; |
| | 362 | <a name="l00513"></a><a class="code" href="classmgamma.html#43f733cce0245a52363d566099add687">00513</a> <span class="keywordtype">double</span> <a class="code" href="classmgamma.html#43f733cce0245a52363d566099add687" title="Constant .">k</a>; |
| | 363 | <a name="l00515"></a><a class="code" href="classmgamma.html#5e90652837448bcc29707e7412f99691">00515</a> vec* <a class="code" href="classmgamma.html#5e90652837448bcc29707e7412f99691" title="cache of epdf.beta">_beta</a>; |
| | 364 | <a name="l00516"></a>00516 |
| | 365 | <a name="l00517"></a>00517 <span class="keyword">public</span>: |
| | 366 | <a name="l00519"></a><a class="code" href="classmgamma.html#af43e61b86900c0398d5c0ffc83b94e6">00519</a> <a class="code" href="classmgamma.html#af43e61b86900c0398d5c0ffc83b94e6" title="Constructor.">mgamma</a> ( <span class="keyword">const</span> <a class="code" href="classRV.html" title="Class representing variables, most often random variables.">RV</a> &<a class="code" href="classmpdf.html#f6687c07ff07d47812dd565368ca59eb" title="modeled random variable">rv</a>,<span class="keyword">const</span> <a class="code" href="classRV.html" title="Class representing variables, most often random variables.">RV</a> &<a class="code" href="classmpdf.html#acb7dda792b3cd5576f39fa3129abbab" title="random variable in condition">rvc</a> ): <a class="code" href="classmEF.html" title="Exponential family model.">mEF</a> ( rv,rvc ), <a class="code" href="classepdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a> ( rv ) {vec* tmp; <a class="code" href="classepdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>._param ( tmp,<a class="code" href="classmgamma.html#5e90652837448bcc29707e7412f99691" title="cache of epdf.beta">_beta</a> );<a class="code" href="classmpdf.html#7aa894208a32f3487827df6d5054424c" title="pointer to internal epdf">ep</a>=&<a class="code" href="classepdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>;}; |
| | 367 | <a name="l00521"></a>00521 <span class="keywordtype">void</span> <a class="code" href="classmgamma.html#a9d646cf758a70126dde7c48790b6e94" title="Set value of k.">set_parameters</a> ( <span class="keywordtype">double</span> <a class="code" href="classmgamma.html#43f733cce0245a52363d566099add687" title="Constant .">k</a> ); |
| | 368 | <a name="l00522"></a><a class="code" href="classmgamma.html#a61094c9f7a2d64ea77b130cbc031f97">00522</a> <span class="keywordtype">void</span> <a class="code" href="classmgamma.html#a61094c9f7a2d64ea77b130cbc031f97" title="Update ep so that it represents this mpdf conditioned on rvc = cond.">condition</a> ( <span class="keyword">const</span> vec &val ) {*<a class="code" href="classmgamma.html#5e90652837448bcc29707e7412f99691" title="cache of epdf.beta">_beta</a>=<a class="code" href="classmgamma.html#43f733cce0245a52363d566099add687" title="Constant .">k</a>/val;}; |
| | 369 | <a name="l00523"></a>00523 }; |
| | 370 | <a name="l00524"></a>00524 |
| | 371 | <a name="l00534"></a><a class="code" href="classmigamma.html">00534</a> <span class="keyword">class </span><a class="code" href="classmigamma.html" title="Inverse-Gamma random walk.">migamma</a> : <span class="keyword">public</span> <a class="code" href="classmEF.html" title="Exponential family model.">mEF</a> { |
| | 372 | <a name="l00535"></a>00535 <span class="keyword">protected</span>: |
| | 373 | <a name="l00537"></a><a class="code" href="classmigamma.html#74712a98f587efdf35da540f7f5b5d0d">00537</a> <a class="code" href="classeigamma.html" title="Inverse-Gamma posterior density.">eigamma</a> <a class="code" href="classepdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>; |
| | 374 | <a name="l00539"></a><a class="code" href="classmigamma.html#8425bc642c6f7876b578e666c841fa9c">00539</a> <span class="keywordtype">double</span> <a class="code" href="classmigamma.html#8425bc642c6f7876b578e666c841fa9c" title="Constant .">k</a>; |
| | 375 | <a name="l00541"></a><a class="code" href="classmigamma.html#92c2e81705d8edb58181b61af75574e0">00541</a> vec* <a class="code" href="classmigamma.html#92c2e81705d8edb58181b61af75574e0" title="cache of epdf.beta">_beta</a>; |
| | 376 | <a name="l00543"></a><a class="code" href="classmigamma.html#fb9bf89eb2c15fc267c97eef2218ebfa">00543</a> vec* <a class="code" href="classmigamma.html#fb9bf89eb2c15fc267c97eef2218ebfa" title="chaceh of epdf.alpha">_alpha</a>; |
| | 377 | <a name="l00544"></a>00544 |
| | 378 | <a name="l00545"></a>00545 <span class="keyword">public</span>: |
| | 379 | <a name="l00547"></a><a class="code" href="classmigamma.html#81d6f9fe46acec656ccde245220b7090">00547</a> <a class="code" href="classmigamma.html#81d6f9fe46acec656ccde245220b7090" title="Constructor.">migamma</a> ( <span class="keyword">const</span> <a class="code" href="classRV.html" title="Class representing variables, most often random variables.">RV</a> &<a class="code" href="classmpdf.html#f6687c07ff07d47812dd565368ca59eb" title="modeled random variable">rv</a>,<span class="keyword">const</span> <a class="code" href="classRV.html" title="Class representing variables, most often random variables.">RV</a> &<a class="code" href="classmpdf.html#acb7dda792b3cd5576f39fa3129abbab" title="random variable in condition">rvc</a> ): <a class="code" href="classmEF.html" title="Exponential family model.">mEF</a> ( rv,rvc ), <a class="code" href="classepdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a> ( rv ) {<a class="code" href="classepdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>._param ( <a class="code" href="classmigamma.html#fb9bf89eb2c15fc267c97eef2218ebfa" title="chaceh of epdf.alpha">_alpha</a>,<a class="code" href="classmigamma.html#92c2e81705d8edb58181b61af75574e0" title="cache of epdf.beta">_beta</a> );<a class="code" href="classmpdf.html#7aa894208a32f3487827df6d5054424c" title="pointer to internal epdf">ep</a>=&<a class="code" href="classepdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>;}; |
| | 380 | <a name="l00549"></a><a class="code" href="classmigamma.html#6cf801c0319ffcfc6317e9f2ecef4cf8">00549</a> <span class="keywordtype">void</span> <a class="code" href="classmigamma.html#6cf801c0319ffcfc6317e9f2ecef4cf8" title="Set value of k.">set_parameters</a> ( <span class="keywordtype">double</span> k0 ){<a class="code" href="classmigamma.html#8425bc642c6f7876b578e666c841fa9c" title="Constant .">k</a>=k0;*<a class="code" href="classmigamma.html#fb9bf89eb2c15fc267c97eef2218ebfa" title="chaceh of epdf.alpha">_alpha</a>=1.0/(<a class="code" href="classmigamma.html#8425bc642c6f7876b578e666c841fa9c" title="Constant .">k</a>*<a class="code" href="classmigamma.html#8425bc642c6f7876b578e666c841fa9c" title="Constant .">k</a>)+2;}; |
| | 381 | <a name="l00550"></a><a class="code" href="classmigamma.html#739c196dfcc586dec49043150da6ed0d">00550</a> <span class="keywordtype">void</span> <a class="code" href="classmigamma.html#739c196dfcc586dec49043150da6ed0d" title="Update ep so that it represents this mpdf conditioned on rvc = cond.">condition</a> ( <span class="keyword">const</span> vec &val ) { |
| | 382 | <a name="l00551"></a>00551 *<a class="code" href="classmigamma.html#92c2e81705d8edb58181b61af75574e0" title="cache of epdf.beta">_beta</a>=elem_mult(val,(*<a class="code" href="classmigamma.html#fb9bf89eb2c15fc267c97eef2218ebfa" title="chaceh of epdf.alpha">_alpha</a>-1)); |
| | 383 | <a name="l00552"></a>00552 }; |
| | 384 | <a name="l00553"></a>00553 }; |
| | 385 | <a name="l00554"></a>00554 |
| | 386 | <a name="l00566"></a><a class="code" href="classmgamma__fix.html">00566</a> <span class="keyword">class </span><a class="code" href="classmgamma__fix.html" title="Gamma random walk around a fixed point.">mgamma_fix</a> : <span class="keyword">public</span> <a class="code" href="classmgamma.html" title="Gamma random walk.">mgamma</a> { |
| | 387 | <a name="l00567"></a>00567 <span class="keyword">protected</span>: |
| | 388 | <a name="l00569"></a><a class="code" href="classmgamma__fix.html#3f48c09caddc298901ad75fe7c0529f6">00569</a> <span class="keywordtype">double</span> <a class="code" href="classmgamma__fix.html#3f48c09caddc298901ad75fe7c0529f6" title="parameter l">l</a>; |
| | 389 | <a name="l00571"></a><a class="code" href="classmgamma__fix.html#81ce49029ecc385418619b200dcafeb0">00571</a> vec <a class="code" href="classmgamma__fix.html#81ce49029ecc385418619b200dcafeb0" title="reference vector">refl</a>; |
| | 390 | <a name="l00572"></a>00572 <span class="keyword">public</span>: |
| | 391 | <a name="l00574"></a><a class="code" href="classmgamma__fix.html#b92c3d2e5fd0381033a072e5ef3bcf80">00574</a> <a class="code" href="classmgamma__fix.html#b92c3d2e5fd0381033a072e5ef3bcf80" title="Constructor.">mgamma_fix</a> ( <span class="keyword">const</span> <a class="code" href="classRV.html" title="Class representing variables, most often random variables.">RV</a> &<a class="code" href="classmpdf.html#f6687c07ff07d47812dd565368ca59eb" title="modeled random variable">rv</a>,<span class="keyword">const</span> <a class="code" href="classRV.html" title="Class representing variables, most often random variables.">RV</a> &<a class="code" href="classmpdf.html#acb7dda792b3cd5576f39fa3129abbab" title="random variable in condition">rvc</a> ) : <a class="code" href="classmgamma.html" title="Gamma random walk.">mgamma</a> ( rv,rvc ),<a class="code" href="classmgamma__fix.html#81ce49029ecc385418619b200dcafeb0" title="reference vector">refl</a> ( rv.count() ) {}; |
| | 392 | <a name="l00576"></a><a class="code" href="classmgamma__fix.html#ec6f846896749e27cb7be9fa48dd1cb1">00576</a> <span class="keywordtype">void</span> <a class="code" href="classmgamma__fix.html#ec6f846896749e27cb7be9fa48dd1cb1" title="Set value of k.">set_parameters</a> ( <span class="keywordtype">double</span> k0 , vec ref0, <span class="keywordtype">double</span> l0 ) { |
| | 393 | <a name="l00577"></a>00577 <a class="code" href="classmgamma.html#a9d646cf758a70126dde7c48790b6e94" title="Set value of k.">mgamma::set_parameters</a> ( k0 ); |
| | 394 | <a name="l00578"></a>00578 <a class="code" href="classmgamma__fix.html#81ce49029ecc385418619b200dcafeb0" title="reference vector">refl</a>=pow ( ref0,1.0-l0 );<a class="code" href="classmgamma__fix.html#3f48c09caddc298901ad75fe7c0529f6" title="parameter l">l</a>=l0; |
| | 395 | <a name="l00579"></a>00579 }; |
| | 396 | <a name="l00580"></a>00580 |
| | 397 | <a name="l00581"></a><a class="code" href="classmgamma__fix.html#6ea3931eec7b7da7b693e45981052460">00581</a> <span class="keywordtype">void</span> <a class="code" href="classmgamma__fix.html#6ea3931eec7b7da7b693e45981052460" title="Update ep so that it represents this mpdf conditioned on rvc = cond.">condition</a> ( <span class="keyword">const</span> vec &val ) {vec mean=elem_mult ( <a class="code" href="classmgamma__fix.html#81ce49029ecc385418619b200dcafeb0" title="reference vector">refl</a>,pow ( val,<a class="code" href="classmgamma__fix.html#3f48c09caddc298901ad75fe7c0529f6" title="parameter l">l</a> ) ); *<a class="code" href="classmgamma.html#5e90652837448bcc29707e7412f99691" title="cache of epdf.beta">_beta</a>=<a class="code" href="classmgamma.html#43f733cce0245a52363d566099add687" title="Constant .">k</a>/mean;}; |
| | 398 | <a name="l00582"></a>00582 }; |
| | 399 | <a name="l00583"></a>00583 |
| | 400 | <a name="l00584"></a>00584 |
| | 401 | <a name="l00597"></a><a class="code" href="classmigamma__fix.html">00597</a> <span class="keyword">class </span><a class="code" href="classmigamma__fix.html" title="Inverse-Gamma random walk around a fixed point.">migamma_fix</a> : <span class="keyword">public</span> <a class="code" href="classmigamma.html" title="Inverse-Gamma random walk.">migamma</a> { |
| | 402 | <a name="l00598"></a>00598 <span class="keyword">protected</span>: |
| | 403 | <a name="l00600"></a><a class="code" href="classmigamma__fix.html#13e0b9e3faf370a5ac24f2d8534047ec">00600</a> <span class="keywordtype">double</span> <a class="code" href="classmigamma__fix.html#13e0b9e3faf370a5ac24f2d8534047ec" title="parameter l">l</a>; |
| | 404 | <a name="l00602"></a><a class="code" href="classmigamma__fix.html#7d0576daba2a1de5dc040dbfbd7dd446">00602</a> vec <a class="code" href="classmigamma__fix.html#7d0576daba2a1de5dc040dbfbd7dd446" title="reference vector">refl</a>; |
| | 405 | <a name="l00603"></a>00603 <span class="keyword">public</span>: |
| | 406 | <a name="l00605"></a><a class="code" href="classmigamma__fix.html#85ff4fae4d3faefed060c515f255207e">00605</a> <a class="code" href="classmigamma__fix.html#85ff4fae4d3faefed060c515f255207e" title="Constructor.">migamma_fix</a> ( <span class="keyword">const</span> <a class="code" href="classRV.html" title="Class representing variables, most often random variables.">RV</a> &<a class="code" href="classmpdf.html#f6687c07ff07d47812dd565368ca59eb" title="modeled random variable">rv</a>,<span class="keyword">const</span> <a class="code" href="classRV.html" title="Class representing variables, most often random variables.">RV</a> &<a class="code" href="classmpdf.html#acb7dda792b3cd5576f39fa3129abbab" title="random variable in condition">rvc</a> ) : <a class="code" href="classmigamma.html" title="Inverse-Gamma random walk.">migamma</a> ( rv,rvc ),<a class="code" href="classmigamma__fix.html#7d0576daba2a1de5dc040dbfbd7dd446" title="reference vector">refl</a> ( rv.count() ) {}; |
| | 407 | <a name="l00607"></a><a class="code" href="classmigamma__fix.html#6266e14eb59fe36f494cfb5934a8e987">00607</a> <span class="keywordtype">void</span> <a class="code" href="classmigamma__fix.html#6266e14eb59fe36f494cfb5934a8e987" title="Set value of k.">set_parameters</a> ( <span class="keywordtype">double</span> k0 , vec ref0, <span class="keywordtype">double</span> l0 ) { |
| | 408 | <a name="l00608"></a>00608 <a class="code" href="classmigamma.html#6cf801c0319ffcfc6317e9f2ecef4cf8" title="Set value of k.">migamma::set_parameters</a> ( k0 ); |
| | 409 | <a name="l00609"></a>00609 <a class="code" href="classmigamma__fix.html#7d0576daba2a1de5dc040dbfbd7dd446" title="reference vector">refl</a>=pow ( ref0,1.0-l0 );<a class="code" href="classmigamma__fix.html#13e0b9e3faf370a5ac24f2d8534047ec" title="parameter l">l</a>=l0; |
| | 410 | <a name="l00610"></a>00610 }; |
| | 411 | <a name="l00611"></a>00611 |
| | 412 | <a name="l00612"></a><a class="code" href="classmigamma__fix.html#a69739eebfe05835db11bc3544cec6a1">00612</a> <span class="keywordtype">void</span> <a class="code" href="classmigamma__fix.html#a69739eebfe05835db11bc3544cec6a1" title="Update ep so that it represents this mpdf conditioned on rvc = cond.">condition</a> ( <span class="keyword">const</span> vec &val ) {vec mean=elem_mult ( <a class="code" href="classmigamma__fix.html#7d0576daba2a1de5dc040dbfbd7dd446" title="reference vector">refl</a>,pow ( val,<a class="code" href="classmigamma__fix.html#13e0b9e3faf370a5ac24f2d8534047ec" title="parameter l">l</a> ) ); <a class="code" href="classmigamma.html#739c196dfcc586dec49043150da6ed0d" title="Update ep so that it represents this mpdf conditioned on rvc = cond.">migamma::condition</a>(mean);}; |
| | 413 | <a name="l00613"></a>00613 }; |
| | 414 | <a name="l00615"></a><a class="code" href="libEF_8h.html#99497a3ff630f761cf6bff7babd23212">00615</a> <span class="keyword">enum</span> <a class="code" href="libEF_8h.html#99497a3ff630f761cf6bff7babd23212" title="Switch between various resampling methods.">RESAMPLING_METHOD</a> { MULTINOMIAL = 0, STRATIFIED = 1, SYSTEMATIC = 3 }; |
| | 415 | <a name="l00621"></a><a class="code" href="classeEmp.html">00621</a> <span class="keyword">class </span><a class="code" href="classeEmp.html" title="Weighted empirical density.">eEmp</a>: <span class="keyword">public</span> <a class="code" href="classepdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a> { |
| | 416 | <a name="l00622"></a>00622 <span class="keyword">protected</span> : |
| | 417 | <a name="l00624"></a><a class="code" href="classeEmp.html#8c33034de0e35f03f8bb85d3d67438fd">00624</a> <span class="keywordtype">int</span> <a class="code" href="classeEmp.html#8c33034de0e35f03f8bb85d3d67438fd" title="Number of particles.">n</a>; |
| | 418 | <a name="l00626"></a><a class="code" href="classeEmp.html#ae78d144404ddba843c93b171b215de8">00626</a> vec <a class="code" href="classeEmp.html#ae78d144404ddba843c93b171b215de8" title="Sample weights .">w</a>; |
| | 419 | <a name="l00628"></a><a class="code" href="classeEmp.html#a4d6f4bbd6a6824fc39f14676701279a">00628</a> Array<vec> <a class="code" href="classeEmp.html#a4d6f4bbd6a6824fc39f14676701279a" title="Samples .">samples</a>; |
| | 420 | <a name="l00629"></a>00629 <span class="keyword">public</span>: |
| | 421 | <a name="l00631"></a><a class="code" href="classeEmp.html#0c04b073ecd0dae3d498e680ae27e9e4">00631</a> <a class="code" href="classeEmp.html#0c04b073ecd0dae3d498e680ae27e9e4" title="Default constructor.">eEmp</a> ( <span class="keyword">const</span> <a class="code" href="classRV.html" title="Class representing variables, most often random variables.">RV</a> &rv0 ,<span class="keywordtype">int</span> n0 ) :<a class="code" href="classepdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a> ( rv0 ),<a class="code" href="classeEmp.html#8c33034de0e35f03f8bb85d3d67438fd" title="Number of particles.">n</a> ( n0 ),<a class="code" href="classeEmp.html#ae78d144404ddba843c93b171b215de8" title="Sample weights .">w</a> ( <a class="code" href="classeEmp.html#8c33034de0e35f03f8bb85d3d67438fd" title="Number of particles.">n</a> ),<a class="code" href="classeEmp.html#a4d6f4bbd6a6824fc39f14676701279a" title="Samples .">samples</a> ( <a class="code" href="classeEmp.html#8c33034de0e35f03f8bb85d3d67438fd" title="Number of particles.">n</a> ) {}; |
| | 422 | <a name="l00633"></a>00633 <span class="keywordtype">void</span> <a class="code" href="classeEmp.html#eab03bd3381aaea11ce34d5a26556353" title="Set samples and weights.">set_parameters</a> ( <span class="keyword">const</span> vec &w0, <span class="keyword">const</span> <a class="code" href="classepdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>* pdf0 ); |
| | 423 | <a name="l00635"></a>00635 <span class="keywordtype">void</span> <a class="code" href="classeEmp.html#e31bc9e6196173c3480b06a761a3e716" title="Set sample.">set_samples</a> ( <span class="keyword">const</span> <a class="code" href="classepdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>* pdf0 ); |
| | 424 | <a name="l00637"></a><a class="code" href="classeEmp.html#a4215f6a5a04d07b43f7ebaa942e15f1">00637</a> <span class="keywordtype">void</span> <a class="code" href="classeEmp.html#a4215f6a5a04d07b43f7ebaa942e15f1" title="Set sample.">set_n</a> ( <span class="keywordtype">int</span> n0, <span class="keywordtype">bool</span> copy=<span class="keyword">true</span> ){<a class="code" href="classeEmp.html#ae78d144404ddba843c93b171b215de8" title="Sample weights .">w</a>.set_size(n0,copy);<a class="code" href="classeEmp.html#a4d6f4bbd6a6824fc39f14676701279a" title="Samples .">samples</a>.set_size(n0,copy);}; |
| | 425 | <a name="l00639"></a><a class="code" href="classeEmp.html#31b2bfb73b72486a5c89f2ab850c7a9b">00639</a> vec& <a class="code" href="classeEmp.html#31b2bfb73b72486a5c89f2ab850c7a9b" title="Potentially dangerous, use with care.">_w</a>() {<span class="keywordflow">return</span> <a class="code" href="classeEmp.html#ae78d144404ddba843c93b171b215de8" title="Sample weights .">w</a>;}; |
| | 426 | <a name="l00641"></a><a class="code" href="classeEmp.html#d6f4ae1a67ecd2bff8b9f176ee261afc">00641</a> <span class="keyword">const</span> vec& <a class="code" href="classeEmp.html#31b2bfb73b72486a5c89f2ab850c7a9b" title="Potentially dangerous, use with care.">_w</a>()<span class="keyword"> const </span>{<span class="keywordflow">return</span> <a class="code" href="classeEmp.html#ae78d144404ddba843c93b171b215de8" title="Sample weights .">w</a>;}; |
| | 427 | <a name="l00643"></a><a class="code" href="classeEmp.html#31b747eca73b16f30370827ba4cc3575">00643</a> Array<vec>& <a class="code" href="classeEmp.html#31b747eca73b16f30370827ba4cc3575" title="access function">_samples</a>() {<span class="keywordflow">return</span> <a class="code" href="classeEmp.html#a4d6f4bbd6a6824fc39f14676701279a" title="Samples .">samples</a>;}; |
| | 428 | <a name="l00645"></a><a class="code" href="classeEmp.html#bd48c1c36e2e9e78dbcea7df66dcbf25">00645</a> <span class="keyword">const</span> Array<vec>& <a class="code" href="classeEmp.html#31b747eca73b16f30370827ba4cc3575" title="access function">_samples</a>()<span class="keyword"> const </span>{<span class="keywordflow">return</span> <a class="code" href="classeEmp.html#a4d6f4bbd6a6824fc39f14676701279a" title="Samples .">samples</a>;}; |
| | 429 | <a name="l00647"></a>00647 ivec <a class="code" href="classeEmp.html#77268292fc4465cb73ddbfb1f2932a59" title="Function performs resampling, i.e. removal of low-weight samples and duplication...">resample</a> ( <a class="code" href="libEF_8h.html#99497a3ff630f761cf6bff7babd23212" title="Switch between various resampling methods.">RESAMPLING_METHOD</a> method = SYSTEMATIC ); |
| | 430 | <a name="l00649"></a><a class="code" href="classeEmp.html#83f9283f92b805508d896479dc1ccf12">00649</a> vec <a class="code" href="classeEmp.html#83f9283f92b805508d896479dc1ccf12" title="inherited operation : NOT implemneted">sample</a>()<span class="keyword"> const </span>{it_error ( <span class="stringliteral">"Not implemented"</span> );<span class="keywordflow">return</span> 0;} |
| | 431 | <a name="l00651"></a><a class="code" href="classeEmp.html#884f16c9fc1f888408686a660a95dacd">00651</a> <span class="keywordtype">double</span> <a class="code" href="classeEmp.html#884f16c9fc1f888408686a660a95dacd" title="inherited operation : NOT implemneted">evallog</a> ( <span class="keyword">const</span> vec &val )<span class="keyword"> const </span>{it_error ( <span class="stringliteral">"Not implemented"</span> );<span class="keywordflow">return</span> 0.0;} |
| | 432 | <a name="l00652"></a><a class="code" href="classeEmp.html#ba055c19038cc72628d98e25197e982d">00652</a> vec <a class="code" href="classeEmp.html#ba055c19038cc72628d98e25197e982d" title="return expected value">mean</a>()<span class="keyword"> const </span>{ |
| | 433 | <a name="l00653"></a>00653 vec pom=zeros ( <a class="code" href="classepdf.html#74da992e3f5d598da8850b646b79b9d9" title="Identified of the random variable.">rv</a>.<a class="code" href="classRV.html#f5c7b8bd589eef09ccdf3329a0addea0" title="Return number of scalars in the RV.">count</a>() ); |
| | 434 | <a name="l00654"></a>00654 <span class="keywordflow">for</span> ( <span class="keywordtype">int</span> i=0;i<<a class="code" href="classeEmp.html#8c33034de0e35f03f8bb85d3d67438fd" title="Number of particles.">n</a>;i++ ) {pom+=<a class="code" href="classeEmp.html#a4d6f4bbd6a6824fc39f14676701279a" title="Samples .">samples</a> ( i ) *<a class="code" href="classeEmp.html#ae78d144404ddba843c93b171b215de8" title="Sample weights .">w</a> ( i );} |
| | 435 | <a name="l00655"></a>00655 <span class="keywordflow">return</span> pom; |
| | 436 | <a name="l00656"></a>00656 } |
| | 437 | <a name="l00657"></a><a class="code" href="classeEmp.html#738f6ec90cf1a8306ed8c259142a2f79">00657</a> vec <a class="code" href="classeEmp.html#738f6ec90cf1a8306ed8c259142a2f79" title="return expected variance (not covariance!)">variance</a>()<span class="keyword"> const </span>{ |
| | 438 | <a name="l00658"></a>00658 vec pom=zeros ( <a class="code" href="classepdf.html#74da992e3f5d598da8850b646b79b9d9" title="Identified of the random variable.">rv</a>.<a class="code" href="classRV.html#f5c7b8bd589eef09ccdf3329a0addea0" title="Return number of scalars in the RV.">count</a>() ); |
| | 439 | <a name="l00659"></a>00659 <span class="keywordflow">for</span> ( <span class="keywordtype">int</span> i=0;i<<a class="code" href="classeEmp.html#8c33034de0e35f03f8bb85d3d67438fd" title="Number of particles.">n</a>;i++ ) {pom+=pow(<a class="code" href="classeEmp.html#a4d6f4bbd6a6824fc39f14676701279a" title="Samples .">samples</a> ( i ),2) *<a class="code" href="classeEmp.html#ae78d144404ddba843c93b171b215de8" title="Sample weights .">w</a> ( i );} |
| | 440 | <a name="l00660"></a>00660 <span class="keywordflow">return</span> pom-pow(<a class="code" href="classeEmp.html#ba055c19038cc72628d98e25197e982d" title="return expected value">mean</a>(),2); |
| | 441 | <a name="l00661"></a>00661 } |
| | 442 | <a name="l00662"></a>00662 }; |
| | 443 | <a name="l00663"></a>00663 |
| | 444 | <a name="l00664"></a>00664 |
| | 445 | <a name="l00666"></a>00666 |
| | 446 | <a name="l00667"></a>00667 <span class="keyword">template</span><<span class="keyword">class</span> sq_T> |
| | 447 | <a name="l00668"></a><a class="code" href="classenorm.html#0caf54fed9e48f9fe28b534b2027df2f">00668</a> <a class="code" href="classenorm.html#0caf54fed9e48f9fe28b534b2027df2f" title="Default constructor.">enorm<sq_T>::enorm</a> ( <span class="keyword">const</span> <a class="code" href="classRV.html" title="Class representing variables, most often random variables.">RV</a> &rv ) :<a class="code" href="classeEF.html" title="General conjugate exponential family posterior density.">eEF</a> ( rv ), mu ( rv.count() ),R ( rv.count() ),dim ( rv.count() ) {}; |
| | 448 | <a name="l00669"></a>00669 |
| | 449 | <a name="l00670"></a>00670 <span class="keyword">template</span><<span class="keyword">class</span> sq_T> |
| | 450 | <a name="l00671"></a><a class="code" href="classenorm.html#1394a65caa6e00d42e00cc99b12227af">00671</a> <span class="keywordtype">void</span> <a class="code" href="classenorm.html#1394a65caa6e00d42e00cc99b12227af" title="Set mean value mu and covariance R.">enorm<sq_T>::set_parameters</a> ( <span class="keyword">const</span> vec &mu0, <span class="keyword">const</span> sq_T &R0 ) { |
| | 451 | <a name="l00672"></a>00672 <span class="comment">//Fixme test dimensions of mu0 and R0;</span> |
| | 452 | <a name="l00673"></a>00673 <a class="code" href="classenorm.html#71fde0d54bba147e00f612577f95ad20" title="mean value">mu</a> = mu0; |
| | 453 | <a name="l00674"></a>00674 <a class="code" href="classenorm.html#4ccc8d8514d644ef1c98d8ab023748a1" title="Covariance matrix in decomposed form.">R</a> = R0; |
| | 454 | <a name="l00675"></a>00675 }; |
| | 455 | <a name="l00676"></a>00676 |
| | 456 | <a name="l00677"></a>00677 <span class="keyword">template</span><<span class="keyword">class</span> sq_T> |
| | 457 | <a name="l00678"></a><a class="code" href="classenorm.html#5bf185e31e5954fceb90ada3debd2ff2">00678</a> <span class="keywordtype">void</span> <a class="code" href="classenorm.html#5bf185e31e5954fceb90ada3debd2ff2" title="dupdate in exponential form (not really handy)">enorm<sq_T>::dupdate</a> ( mat &v, <span class="keywordtype">double</span> nu ) { |
| | 458 | <a name="l00679"></a>00679 <span class="comment">//</span> |
| | 459 | <a name="l00680"></a>00680 }; |
| | 460 | <a name="l00681"></a>00681 |
| | 461 | <a name="l00682"></a>00682 <span class="comment">// template<class sq_T></span> |
| | 462 | <a name="l00683"></a>00683 <span class="comment">// void enorm<sq_T>::tupdate ( double phi, mat &vbar, double nubar ) {</span> |
| | 463 | <a name="l00684"></a>00684 <span class="comment">// //</span> |
| | 464 | <a name="l00685"></a>00685 <span class="comment">// };</span> |
| | 465 | <a name="l00686"></a>00686 |
| | 466 | <a name="l00687"></a>00687 <span class="keyword">template</span><<span class="keyword">class</span> sq_T> |
| | 467 | <a name="l00688"></a><a class="code" href="classenorm.html#60b47544f6181ffd4530d3e415ce12c5">00688</a> vec <a class="code" href="classenorm.html#60b47544f6181ffd4530d3e415ce12c5" title="Returns a sample, from density .">enorm<sq_T>::sample</a>()<span class="keyword"> const </span>{ |
| | 468 | <a name="l00689"></a>00689 vec x ( <a class="code" href="classenorm.html#6938fc390a19cdaf6ad4503fcbaada4e" title="dimension (redundant from rv.count() for easier coding )">dim</a> ); |
| | 469 | <a name="l00690"></a>00690 NorRNG.sample_vector ( <a class="code" href="classenorm.html#6938fc390a19cdaf6ad4503fcbaada4e" title="dimension (redundant from rv.count() for easier coding )">dim</a>,x ); |
| | 470 | <a name="l00691"></a>00691 vec smp = <a class="code" href="classenorm.html#4ccc8d8514d644ef1c98d8ab023748a1" title="Covariance matrix in decomposed form.">R</a>.sqrt_mult ( x ); |
| | 471 | <a name="l00692"></a>00692 |
| | 472 | <a name="l00693"></a>00693 smp += <a class="code" href="classenorm.html#71fde0d54bba147e00f612577f95ad20" title="mean value">mu</a>; |
| | 473 | <a name="l00694"></a>00694 <span class="keywordflow">return</span> smp; |
| | 474 | <a name="l00695"></a>00695 }; |
| | 475 | <a name="l00696"></a>00696 |
| | 476 | <a name="l00697"></a>00697 <span class="keyword">template</span><<span class="keyword">class</span> sq_T> |
| | 477 | <a name="l00698"></a><a class="code" href="classenorm.html#60f0f3bfa53d6e65843eea9532b16d36">00698</a> mat <a class="code" href="classenorm.html#60b47544f6181ffd4530d3e415ce12c5" title="Returns a sample, from density .">enorm<sq_T>::sample</a> ( <span class="keywordtype">int</span> N )<span class="keyword"> const </span>{ |
| | 478 | <a name="l00699"></a>00699 mat X ( <a class="code" href="classenorm.html#6938fc390a19cdaf6ad4503fcbaada4e" title="dimension (redundant from rv.count() for easier coding )">dim</a>,N ); |
| | 479 | <a name="l00700"></a>00700 vec x ( <a class="code" href="classenorm.html#6938fc390a19cdaf6ad4503fcbaada4e" title="dimension (redundant from rv.count() for easier coding )">dim</a> ); |
| | 480 | <a name="l00701"></a>00701 vec pom; |
| | 481 | <a name="l00702"></a>00702 <span class="keywordtype">int</span> i; |
| | 482 | <a name="l00703"></a>00703 |
| | 483 | <a name="l00704"></a>00704 <span class="keywordflow">for</span> ( i=0;i<N;i++ ) { |
| | 484 | <a name="l00705"></a>00705 NorRNG.sample_vector ( <a class="code" href="classenorm.html#6938fc390a19cdaf6ad4503fcbaada4e" title="dimension (redundant from rv.count() for easier coding )">dim</a>,x ); |
| | 485 | <a name="l00706"></a>00706 pom = <a class="code" href="classenorm.html#4ccc8d8514d644ef1c98d8ab023748a1" title="Covariance matrix in decomposed form.">R</a>.sqrt_mult ( x ); |
| | 486 | <a name="l00707"></a>00707 pom +=<a class="code" href="classenorm.html#71fde0d54bba147e00f612577f95ad20" title="mean value">mu</a>; |
| | 487 | <a name="l00708"></a>00708 X.set_col ( i, pom ); |
| | 488 | <a name="l00709"></a>00709 } |
