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<div class="title">Using Mixture of Gaussians (MOG) module to model data </div> </div>
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<div class="textblock"><p>This example demonstrates how to find the parameters of a MOG model via using the kmeans and EM based optimisers. Synthetic data is utilised.</p>
<div class="fragment"><pre class="fragment"><span class="preprocessor">#include <<a class="code" href="itstat_8h.html" title="Include file for the IT++ statistics module.">itpp/itstat.h</a>></span>
<span class="preprocessor">#include <fstream></span>
<span class="preprocessor">#include <iostream></span>
<span class="preprocessor">#include <iomanip></span>
<span class="preprocessor">#include <ios></span>
<span class="keyword">using</span> std::cout;
<span class="keyword">using</span> std::endl;
<span class="keyword">using</span> std::fixed;
<span class="keyword">using</span> std::setprecision;
<span class="keyword">using namespace </span>itpp;
<span class="keywordtype">int</span> main()
{
<span class="keywordtype">bool</span> print_progress = <span class="keyword">false</span>;
<span class="comment">//</span>
<span class="comment">// first, let's generate some synthetic data</span>
<span class="keywordtype">int</span> N = 100000; <span class="comment">// number of vectors</span>
<span class="keywordtype">int</span> D = 3; <span class="comment">// number of dimensions</span>
<span class="keywordtype">int</span> K = 5; <span class="comment">// number of Gaussians</span>
<a class="code" href="classitpp_1_1Array.html">Array<vec></a> X(N);
<span class="keywordflow">for</span> (<span class="keywordtype">int</span> n = 0;n < N;n++) { X(n).set_size(D); X(n) = 0.0; }
<span class="comment">// the means</span>
<a class="code" href="classitpp_1_1Array.html">Array<vec></a> mu(K);
mu(0) = <span class="stringliteral">"-6, -4, -2"</span>;
mu(1) = <span class="stringliteral">"-4, -2, 0"</span>;
mu(2) = <span class="stringliteral">"-2, 0, 2"</span>;
mu(3) = <span class="stringliteral">" 0, +2, +4"</span>;
mu(4) = <span class="stringliteral">"+2, +4, +6"</span>;
<span class="comment">// the diagonal variances</span>
<a class="code" href="classitpp_1_1Array.html">Array<vec></a> var(K);
var(0) = <span class="stringliteral">"0.1, 0.2, 0.3"</span>;
var(1) = <span class="stringliteral">"0.2, 0.3, 0.1"</span>;
var(2) = <span class="stringliteral">"0.3, 0.1, 0.2"</span>;
var(3) = <span class="stringliteral">"0.1, 0.2, 0.3"</span>;
var(4) = <span class="stringliteral">"0.2, 0.3, 0.1"</span>;
cout << fixed << setprecision(3);
cout << <span class="stringliteral">"user configured means and variances:"</span> << endl;
cout << <span class="stringliteral">"mu = "</span> << mu << endl;
cout << <span class="stringliteral">"var = "</span> << var << endl;
<span class="comment">// randomise the order of Gaussians "generating" the vectors</span>
<a class="code" href="classitpp_1_1I__Uniform__RNG.html" title="Integer uniform distributionExample: Generation of random uniformly distributed integers in the inter...">I_Uniform_RNG</a> rnd_uniform(0, K - 1);
ivec gaus_id = rnd_uniform(N);
ivec gaus_count(K);
gaus_count = 0;
<a class="code" href="classitpp_1_1Array.html">Array<vec></a> mu_test(K);
<span class="keywordflow">for</span> (<span class="keywordtype">int</span> k = 0;k < K;k++) { mu_test(k).set_size(D); mu_test(k) = 0.0; }
<a class="code" href="classitpp_1_1Array.html">Array<vec></a> var_test(K);
<span class="keywordflow">for</span> (<span class="keywordtype">int</span> k = 0;k < K;k++) { var_test(k).set_size(D); var_test(k) = 0.0; }
<a class="code" href="classitpp_1_1Normal__RNG.html" title="Normal distributionNormal (Gaussian) random variables, using a simplified Ziggurat method...">Normal_RNG</a> rnd_normal;
<span class="keywordflow">for</span> (<span class="keywordtype">int</span> n = 0;n < N;n++) {
<span class="keywordtype">int</span> k = gaus_id(n);
gaus_count(k)++;
<span class="keywordflow">for</span> (<span class="keywordtype">int</span> d = 0;d < D;d++) {
rnd_normal.<a class="code" href="classitpp_1_1Normal__RNG.html#a9c50b4c9b0bb07c69c9fbf16b9f01521" title="Set mean, and variance.">setup</a>(mu(k)(d), var(k)(d));
<span class="keywordtype">double</span> tmp = rnd_normal();
X(n)(d) = tmp;
mu_test(k)(d) += tmp;
}
}
<span class="comment">//</span>
<span class="comment">// find the stats for the generated data</span>
<span class="keywordflow">for</span> (<span class="keywordtype">int</span> k = 0;k < K;k++) mu_test(k) /= gaus_count(k);
<span class="keywordflow">for</span> (<span class="keywordtype">int</span> n = 0;n < N;n++) {
<span class="keywordtype">int</span> k = gaus_id(n);
<span class="keywordflow">for</span> (<span class="keywordtype">int</span> d = 0;d < D;d++) {
<span class="keywordtype">double</span> tmp = X(n)(d) - mu_test(k)(d);
var_test(k)(d) += tmp * tmp;
}
}
<span class="keywordflow">for</span> (<span class="keywordtype">int</span> k = 0;k < K;k++) var_test(k) /= (gaus_count(k) - 1.0);
cout << endl << endl;
cout << fixed << setprecision(3);
cout << <span class="stringliteral">"stats for X:"</span> << endl;
<span class="keywordflow">for</span> (<span class="keywordtype">int</span> k = 0;k < K;k++) {
cout << <span class="stringliteral">"k = "</span> << k << <span class="stringliteral">" count = "</span> << gaus_count(k) << <span class="stringliteral">" weight = "</span> << gaus_count(k) / double(N) << endl;
<span class="keywordflow">for</span> (<span class="keywordtype">int</span> d = 0;d < D;d++) cout << <span class="stringliteral">" d = "</span> << d << <span class="stringliteral">" mu_test = "</span> << mu_test(k)(d) << <span class="stringliteral">" var_test = "</span> << var_test(k)(d) << endl;
cout << endl;
}
<span class="comment">// make a model with initial values (zero mean and unit variance)</span>
<span class="comment">// the number of gaussians and dimensions of the model is specified here</span>
<a class="code" href="classitpp_1_1MOG__diag.html" title="Diagonal Mixture of Gaussians (MOG) class.">MOG_diag</a> mog(K, D);
cout << endl;
cout << fixed << setprecision(3);
cout << <span class="stringliteral">"mog.avg_log_lhood(X) = "</span> << mog.avg_log_lhood(X) << endl;
<span class="comment">//</span>
<span class="comment">// find initial parameters via k-means (which are then used as seeds for EM based optimisation)</span>
cout << endl << endl;
cout << <span class="stringliteral">"running kmeans optimiser"</span> << endl << endl;
<a class="code" href="group__MOG.html#gadeb91bf337a38234135d6d402e3143b3">MOG_diag_kmeans</a>(mog, X, 10, 0.5, <span class="keyword">true</span>, print_progress);
cout << fixed << setprecision(3);
cout << <span class="stringliteral">"mog.get_means() = "</span> << endl << mog.get_means() << endl;
cout << <span class="stringliteral">"mog.get_diag_covs() = "</span> << endl << mog.get_diag_covs() << endl;
cout << <span class="stringliteral">"mog.get_weights() = "</span> << endl << mog.get_weights() << endl;
cout << endl;
cout << <span class="stringliteral">"mog.avg_log_lhood(X) = "</span> << mog.avg_log_lhood(X) << endl;
<span class="comment">//</span>
<span class="comment">// EM ML based optimisation</span>
cout << endl << endl;
cout << <span class="stringliteral">"running ML optimiser"</span> << endl << endl;
<a class="code" href="group__MOG.html#ga7b86d84e61a3056418f08b7ac0686805">MOG_diag_ML</a>(mog, X, 10, 0.0, 0.0, print_progress);
cout << fixed << setprecision(3);
cout << <span class="stringliteral">"mog.get_means() = "</span> << endl << mog.get_means() << endl;
cout << <span class="stringliteral">"mog.get_diag_covs() = "</span> << endl << mog.get_diag_covs() << endl;
cout << <span class="stringliteral">"mog.get_weights() = "</span> << endl << mog.get_weights() << endl;
cout << endl;
cout << <span class="stringliteral">"mog.avg_log_lhood(X) = "</span> << mog.avg_log_lhood(X) << endl;
<span class="keywordflow">return</span> 0;
}
</pre></div> </div></div>
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