684 lines
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684 lines
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<a href="#pub-methods">Public Member Functions</a> |
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<a href="#pri-attribs">Private Attributes</a> |
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<div class="title">mlpack::nca::SoftmaxErrorFunction< MetricType > Class Template Reference</div> </div>
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<p>The "softmax" stochastic neighbor assignment probability function.
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<a href="classmlpack_1_1nca_1_1SoftmaxErrorFunction.html#details">More...</a></p>
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<table class="memberdecls">
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<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="pub-methods"></a>
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Public Member Functions</h2></td></tr>
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<tr class="memitem:ae91e96bf31977e0ca88c7805a7d7e978"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="classmlpack_1_1nca_1_1SoftmaxErrorFunction.html#ae91e96bf31977e0ca88c7805a7d7e978">SoftmaxErrorFunction</a> (const arma::mat &<a class="el" href="classmlpack_1_1nca_1_1SoftmaxErrorFunction.html#a9b4a79cbe8a8be32c8972b01ee2073d9">dataset</a>, const arma::Row< size_t > &<a class="el" href="classmlpack_1_1nca_1_1SoftmaxErrorFunction.html#afe9c3d9edb331845c0b6ce28444cc484">labels</a>, MetricType <a class="el" href="classmlpack_1_1nca_1_1SoftmaxErrorFunction.html#abb8345f6eaaebc416f8e57808785e034">metric</a>=MetricType())</td></tr>
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<tr class="memdesc:ae91e96bf31977e0ca88c7805a7d7e978"><td class="mdescLeft"> </td><td class="mdescRight">Initialize with the given kernel; useful when the kernel has some state to store, which is set elsewhere. <a href="#ae91e96bf31977e0ca88c7805a7d7e978">More...</a><br /></td></tr>
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<tr class="separator:ae91e96bf31977e0ca88c7805a7d7e978"><td class="memSeparator" colspan="2"> </td></tr>
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<tr class="memitem:a60fcd093a9a44400cfcd6d259357d26e"><td class="memItemLeft" align="right" valign="top">double </td><td class="memItemRight" valign="bottom"><a class="el" href="classmlpack_1_1nca_1_1SoftmaxErrorFunction.html#a60fcd093a9a44400cfcd6d259357d26e">Evaluate</a> (const arma::mat &covariance)</td></tr>
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<tr class="memdesc:a60fcd093a9a44400cfcd6d259357d26e"><td class="mdescLeft"> </td><td class="mdescRight">Evaluate the softmax function for the given covariance matrix. <a href="#a60fcd093a9a44400cfcd6d259357d26e">More...</a><br /></td></tr>
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<tr class="separator:a60fcd093a9a44400cfcd6d259357d26e"><td class="memSeparator" colspan="2"> </td></tr>
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<tr class="memitem:aab64c0f64a6ba941e930551faa7a65b6"><td class="memItemLeft" align="right" valign="top">double </td><td class="memItemRight" valign="bottom"><a class="el" href="classmlpack_1_1nca_1_1SoftmaxErrorFunction.html#aab64c0f64a6ba941e930551faa7a65b6">Evaluate</a> (const arma::mat &covariance, const size_t i)</td></tr>
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<tr class="memdesc:aab64c0f64a6ba941e930551faa7a65b6"><td class="mdescLeft"> </td><td class="mdescRight">Evaluate the softmax objective function for the given covariance matrix on only one point of the dataset. <a href="#aab64c0f64a6ba941e930551faa7a65b6">More...</a><br /></td></tr>
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<tr class="separator:aab64c0f64a6ba941e930551faa7a65b6"><td class="memSeparator" colspan="2"> </td></tr>
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<tr class="memitem:a46a4bfef990e09a9e44d63750386829b"><td class="memItemLeft" align="right" valign="top">const arma::mat </td><td class="memItemRight" valign="bottom"><a class="el" href="classmlpack_1_1nca_1_1SoftmaxErrorFunction.html#a46a4bfef990e09a9e44d63750386829b">GetInitialPoint</a> () const </td></tr>
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<tr class="memdesc:a46a4bfef990e09a9e44d63750386829b"><td class="mdescLeft"> </td><td class="mdescRight">Get the initial point. <a href="#a46a4bfef990e09a9e44d63750386829b">More...</a><br /></td></tr>
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<tr class="separator:a46a4bfef990e09a9e44d63750386829b"><td class="memSeparator" colspan="2"> </td></tr>
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<tr class="memitem:a98d77dc2745f01a14aed76aa4735155f"><td class="memItemLeft" align="right" valign="top">void </td><td class="memItemRight" valign="bottom"><a class="el" href="classmlpack_1_1nca_1_1SoftmaxErrorFunction.html#a98d77dc2745f01a14aed76aa4735155f">Gradient</a> (const arma::mat &covariance, arma::mat &gradient)</td></tr>
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<tr class="memdesc:a98d77dc2745f01a14aed76aa4735155f"><td class="mdescLeft"> </td><td class="mdescRight">Evaluate the gradient of the softmax function for the given covariance matrix. <a href="#a98d77dc2745f01a14aed76aa4735155f">More...</a><br /></td></tr>
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<tr class="separator:a98d77dc2745f01a14aed76aa4735155f"><td class="memSeparator" colspan="2"> </td></tr>
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<tr class="memitem:a706948e7cba12f1840dc4c98cb7b1367"><td class="memItemLeft" align="right" valign="top">void </td><td class="memItemRight" valign="bottom"><a class="el" href="classmlpack_1_1nca_1_1SoftmaxErrorFunction.html#a706948e7cba12f1840dc4c98cb7b1367">Gradient</a> (const arma::mat &covariance, const size_t i, arma::mat &gradient)</td></tr>
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<tr class="memdesc:a706948e7cba12f1840dc4c98cb7b1367"><td class="mdescLeft"> </td><td class="mdescRight">Evaluate the gradient of the softmax function for the given covariance matrix on only one point of the dataset. <a href="#a706948e7cba12f1840dc4c98cb7b1367">More...</a><br /></td></tr>
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<tr class="separator:a706948e7cba12f1840dc4c98cb7b1367"><td class="memSeparator" colspan="2"> </td></tr>
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<tr class="memitem:a899db3723fbab16e64238a4d567afa05"><td class="memItemLeft" align="right" valign="top">size_t </td><td class="memItemRight" valign="bottom"><a class="el" href="classmlpack_1_1nca_1_1SoftmaxErrorFunction.html#a899db3723fbab16e64238a4d567afa05">NumFunctions</a> () const </td></tr>
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<tr class="memdesc:a899db3723fbab16e64238a4d567afa05"><td class="mdescLeft"> </td><td class="mdescRight">Get the number of functions the objective function can be decomposed into. <a href="#a899db3723fbab16e64238a4d567afa05">More...</a><br /></td></tr>
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<tr class="separator:a899db3723fbab16e64238a4d567afa05"><td class="memSeparator" colspan="2"> </td></tr>
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</table><table class="memberdecls">
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<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="pri-methods"></a>
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Private Member Functions</h2></td></tr>
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<tr class="memitem:a436b90591cf003ba82b8a4953b7eadaf"><td class="memItemLeft" align="right" valign="top">void </td><td class="memItemRight" valign="bottom"><a class="el" href="classmlpack_1_1nca_1_1SoftmaxErrorFunction.html#a436b90591cf003ba82b8a4953b7eadaf">Precalculate</a> (const arma::mat &coordinates)</td></tr>
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<tr class="memdesc:a436b90591cf003ba82b8a4953b7eadaf"><td class="mdescLeft"> </td><td class="mdescRight">Precalculate the denominators and numerators that will make up the p_ij, but only if the coordinates matrix is different than the last coordinates the <a class="el" href="classmlpack_1_1nca_1_1SoftmaxErrorFunction.html#a436b90591cf003ba82b8a4953b7eadaf" title="Precalculate the denominators and numerators that will make up the p_ij, but only if the coordinates ...">Precalculate()</a> method was run with. <a href="#a436b90591cf003ba82b8a4953b7eadaf">More...</a><br /></td></tr>
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<tr class="separator:a436b90591cf003ba82b8a4953b7eadaf"><td class="memSeparator" colspan="2"> </td></tr>
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</table><table class="memberdecls">
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<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="pri-attribs"></a>
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Private Attributes</h2></td></tr>
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<tr class="memitem:a9b4a79cbe8a8be32c8972b01ee2073d9"><td class="memItemLeft" align="right" valign="top">const arma::mat & </td><td class="memItemRight" valign="bottom"><a class="el" href="classmlpack_1_1nca_1_1SoftmaxErrorFunction.html#a9b4a79cbe8a8be32c8972b01ee2073d9">dataset</a></td></tr>
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<tr class="memdesc:a9b4a79cbe8a8be32c8972b01ee2073d9"><td class="mdescLeft"> </td><td class="mdescRight">The dataset. <a href="#a9b4a79cbe8a8be32c8972b01ee2073d9">More...</a><br /></td></tr>
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<tr class="separator:a9b4a79cbe8a8be32c8972b01ee2073d9"><td class="memSeparator" colspan="2"> </td></tr>
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<tr class="memitem:a63f3fc50872eabdcfe5ab9eb8a6af22b"><td class="memItemLeft" align="right" valign="top">arma::vec </td><td class="memItemRight" valign="bottom"><a class="el" href="classmlpack_1_1nca_1_1SoftmaxErrorFunction.html#a63f3fc50872eabdcfe5ab9eb8a6af22b">denominators</a></td></tr>
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<tr class="memdesc:a63f3fc50872eabdcfe5ab9eb8a6af22b"><td class="mdescLeft"> </td><td class="mdescRight">Holds denominators for calculation of p_ij, for the non-separable <a class="el" href="classmlpack_1_1nca_1_1SoftmaxErrorFunction.html#a60fcd093a9a44400cfcd6d259357d26e" title="Evaluate the softmax function for the given covariance matrix. ">Evaluate()</a> and <a class="el" href="classmlpack_1_1nca_1_1SoftmaxErrorFunction.html#a98d77dc2745f01a14aed76aa4735155f" title="Evaluate the gradient of the softmax function for the given covariance matrix. ">Gradient()</a>. <a href="#a63f3fc50872eabdcfe5ab9eb8a6af22b">More...</a><br /></td></tr>
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<tr class="separator:a63f3fc50872eabdcfe5ab9eb8a6af22b"><td class="memSeparator" colspan="2"> </td></tr>
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<tr class="memitem:afe9c3d9edb331845c0b6ce28444cc484"><td class="memItemLeft" align="right" valign="top">const arma::Row< size_t > & </td><td class="memItemRight" valign="bottom"><a class="el" href="classmlpack_1_1nca_1_1SoftmaxErrorFunction.html#afe9c3d9edb331845c0b6ce28444cc484">labels</a></td></tr>
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<tr class="memdesc:afe9c3d9edb331845c0b6ce28444cc484"><td class="mdescLeft"> </td><td class="mdescRight">Labels for each point in the dataset. <a href="#afe9c3d9edb331845c0b6ce28444cc484">More...</a><br /></td></tr>
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<tr class="separator:afe9c3d9edb331845c0b6ce28444cc484"><td class="memSeparator" colspan="2"> </td></tr>
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<tr class="memitem:a68407bb96a6b4a3e3b92d3661c177f88"><td class="memItemLeft" align="right" valign="top">arma::mat </td><td class="memItemRight" valign="bottom"><a class="el" href="classmlpack_1_1nca_1_1SoftmaxErrorFunction.html#a68407bb96a6b4a3e3b92d3661c177f88">lastCoordinates</a></td></tr>
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<tr class="memdesc:a68407bb96a6b4a3e3b92d3661c177f88"><td class="mdescLeft"> </td><td class="mdescRight">Last coordinates. Used for the non-separable <a class="el" href="classmlpack_1_1nca_1_1SoftmaxErrorFunction.html#a60fcd093a9a44400cfcd6d259357d26e" title="Evaluate the softmax function for the given covariance matrix. ">Evaluate()</a> and <a class="el" href="classmlpack_1_1nca_1_1SoftmaxErrorFunction.html#a98d77dc2745f01a14aed76aa4735155f" title="Evaluate the gradient of the softmax function for the given covariance matrix. ">Gradient()</a>. <a href="#a68407bb96a6b4a3e3b92d3661c177f88">More...</a><br /></td></tr>
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<tr class="separator:a68407bb96a6b4a3e3b92d3661c177f88"><td class="memSeparator" colspan="2"> </td></tr>
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<tr class="memitem:abb8345f6eaaebc416f8e57808785e034"><td class="memItemLeft" align="right" valign="top">MetricType </td><td class="memItemRight" valign="bottom"><a class="el" href="classmlpack_1_1nca_1_1SoftmaxErrorFunction.html#abb8345f6eaaebc416f8e57808785e034">metric</a></td></tr>
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<tr class="memdesc:abb8345f6eaaebc416f8e57808785e034"><td class="mdescLeft"> </td><td class="mdescRight">The instantiated metric. <a href="#abb8345f6eaaebc416f8e57808785e034">More...</a><br /></td></tr>
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<tr class="separator:abb8345f6eaaebc416f8e57808785e034"><td class="memSeparator" colspan="2"> </td></tr>
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<tr class="memitem:a768e2c60c0f491e57d8a9d3e240f3c0f"><td class="memItemLeft" align="right" valign="top">arma::vec </td><td class="memItemRight" valign="bottom"><a class="el" href="classmlpack_1_1nca_1_1SoftmaxErrorFunction.html#a768e2c60c0f491e57d8a9d3e240f3c0f">p</a></td></tr>
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<tr class="memdesc:a768e2c60c0f491e57d8a9d3e240f3c0f"><td class="mdescLeft"> </td><td class="mdescRight">Holds calculated p_i, for the non-separable <a class="el" href="classmlpack_1_1nca_1_1SoftmaxErrorFunction.html#a60fcd093a9a44400cfcd6d259357d26e" title="Evaluate the softmax function for the given covariance matrix. ">Evaluate()</a> and <a class="el" href="classmlpack_1_1nca_1_1SoftmaxErrorFunction.html#a98d77dc2745f01a14aed76aa4735155f" title="Evaluate the gradient of the softmax function for the given covariance matrix. ">Gradient()</a>. <a href="#a768e2c60c0f491e57d8a9d3e240f3c0f">More...</a><br /></td></tr>
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<tr class="separator:a768e2c60c0f491e57d8a9d3e240f3c0f"><td class="memSeparator" colspan="2"> </td></tr>
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<tr class="memitem:a7bc17888d9ab1ce6920c72d85f618ff7"><td class="memItemLeft" align="right" valign="top">bool </td><td class="memItemRight" valign="bottom"><a class="el" href="classmlpack_1_1nca_1_1SoftmaxErrorFunction.html#a7bc17888d9ab1ce6920c72d85f618ff7">precalculated</a></td></tr>
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<tr class="memdesc:a7bc17888d9ab1ce6920c72d85f618ff7"><td class="mdescLeft"> </td><td class="mdescRight">False if nothing has ever been precalculated (only at construction time). <a href="#a7bc17888d9ab1ce6920c72d85f618ff7">More...</a><br /></td></tr>
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<tr class="separator:a7bc17888d9ab1ce6920c72d85f618ff7"><td class="memSeparator" colspan="2"> </td></tr>
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<tr class="memitem:a11759ec30b7853559df670867e57ceb4"><td class="memItemLeft" align="right" valign="top">arma::mat </td><td class="memItemRight" valign="bottom"><a class="el" href="classmlpack_1_1nca_1_1SoftmaxErrorFunction.html#a11759ec30b7853559df670867e57ceb4">stretchedDataset</a></td></tr>
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<tr class="memdesc:a11759ec30b7853559df670867e57ceb4"><td class="mdescLeft"> </td><td class="mdescRight">Stretched dataset. Kept internal to avoid memory reallocations. <a href="#a11759ec30b7853559df670867e57ceb4">More...</a><br /></td></tr>
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<tr class="separator:a11759ec30b7853559df670867e57ceb4"><td class="memSeparator" colspan="2"> </td></tr>
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</table>
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<a name="details" id="details"></a><h2 class="groupheader">Detailed Description</h2>
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<div class="textblock"><h3>template<typename MetricType = metric::SquaredEuclideanDistance><br />
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class mlpack::nca::SoftmaxErrorFunction< MetricType ></h3>
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<p>The "softmax" stochastic neighbor assignment probability function. </p>
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<p>The actual function is</p>
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<p>p_ij = (exp(-|| A x_i - A x_j || ^ 2)) / (sum_{k != i} (exp(-|| A x_i - A x_k || ^ 2)))</p>
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<p>where x_n represents a point and A is the current scaling matrix.</p>
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<p>This class is more flexible than the original paper, allowing an arbitrary metric function to be used in place of || A x_i - A x_j ||^2, meaning that the squared Euclidean distance is not the only allowed metric for <a class="el" href="classmlpack_1_1nca_1_1NCA.html" title="An implementation of Neighborhood Components Analysis, both a linear dimensionality reduction techniq...">NCA</a>. However, that is probably the best way to use this class.</p>
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<p>In addition to the standard <a class="el" href="classmlpack_1_1nca_1_1SoftmaxErrorFunction.html#a60fcd093a9a44400cfcd6d259357d26e" title="Evaluate the softmax function for the given covariance matrix. ">Evaluate()</a> and <a class="el" href="classmlpack_1_1nca_1_1SoftmaxErrorFunction.html#a98d77dc2745f01a14aed76aa4735155f" title="Evaluate the gradient of the softmax function for the given covariance matrix. ">Gradient()</a> functions which mlpack optimizers use, overloads of <a class="el" href="classmlpack_1_1nca_1_1SoftmaxErrorFunction.html#a60fcd093a9a44400cfcd6d259357d26e" title="Evaluate the softmax function for the given covariance matrix. ">Evaluate()</a> and <a class="el" href="classmlpack_1_1nca_1_1SoftmaxErrorFunction.html#a98d77dc2745f01a14aed76aa4735155f" title="Evaluate the gradient of the softmax function for the given covariance matrix. ">Gradient()</a> are given which only operate on one point in the dataset. This is useful for optimizers like stochastic gradient descent (see <a class="el" href="classmlpack_1_1optimization_1_1SGD.html" title="Stochastic Gradient Descent is a technique for minimizing a function which can be expressed as a sum ...">mlpack::optimization::SGD</a>). </p>
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<p>Definition at line <a class="el" href="nca__softmax__error__function_8hpp_source.html#l00042">42</a> of file <a class="el" href="nca__softmax__error__function_8hpp_source.html">nca_softmax_error_function.hpp</a>.</p>
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</div><h2 class="groupheader">Constructor & Destructor Documentation</h2>
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template<typename MetricType = metric::SquaredEuclideanDistance> </div>
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<td class="memname"><a class="el" href="classmlpack_1_1nca_1_1SoftmaxErrorFunction.html">mlpack::nca::SoftmaxErrorFunction</a>< MetricType >::<a class="el" href="classmlpack_1_1nca_1_1SoftmaxErrorFunction.html">SoftmaxErrorFunction</a> </td>
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<td>(</td>
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<td class="paramtype">const arma::mat & </td>
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<td class="paramname"><em>dataset</em>, </td>
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<td class="paramtype">const arma::Row< size_t > & </td>
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<p>Initialize with the given kernel; useful when the kernel has some state to store, which is set elsewhere. </p>
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<p>If no kernel is given, an empty kernel is used; this way, you can call the constructor with no arguments. A reference to the dataset we will be optimizing over is also required.</p>
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<dl class="params"><dt>Parameters</dt><dd>
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<table class="params">
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<tr><td class="paramname">dataset</td><td>Matrix containing the dataset. </td></tr>
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<tr><td class="paramname">labels</td><td>Vector of class labels for each point in the dataset. </td></tr>
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<tr><td class="paramname">kernel</td><td>Instantiated kernel (optional). </td></tr>
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<h2 class="groupheader">Member Function Documentation</h2>
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template<typename MetricType = metric::SquaredEuclideanDistance> </div>
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<td class="memname">double <a class="el" href="classmlpack_1_1nca_1_1SoftmaxErrorFunction.html">mlpack::nca::SoftmaxErrorFunction</a>< MetricType >::Evaluate </td>
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<td>(</td>
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<td class="paramtype">const arma::mat & </td>
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<p>Evaluate the softmax function for the given covariance matrix. </p>
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<p>This is the non-separable implementation, where the objective function is not decomposed into the sum of several objective functions.</p>
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<dl class="params"><dt>Parameters</dt><dd>
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<table class="params">
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<tr><td class="paramname">covariance</td><td>Covariance matrix of Mahalanobis distance. </td></tr>
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</dl>
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template<typename MetricType = metric::SquaredEuclideanDistance> </div>
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<td class="memname">double <a class="el" href="classmlpack_1_1nca_1_1SoftmaxErrorFunction.html">mlpack::nca::SoftmaxErrorFunction</a>< MetricType >::Evaluate </td>
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<td>(</td>
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<td class="paramtype">const arma::mat & </td>
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<td class="paramname"><em>covariance</em>, </td>
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<td class="paramkey"></td>
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<td></td>
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<p>Evaluate the softmax objective function for the given covariance matrix on only one point of the dataset. </p>
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<p>This is the separable implementation, where the objective function is decomposed into the sum of many objective functions, and here, only one of those constituent objective functions is returned.</p>
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<dl class="params"><dt>Parameters</dt><dd>
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<table class="params">
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<tr><td class="paramname">covariance</td><td>Covariance matrix of Mahalanobis distance. </td></tr>
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<tr><td class="paramname">i</td><td>Index of point to use for objective function. </td></tr>
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template<typename MetricType = metric::SquaredEuclideanDistance> </div>
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<td class="memname">const arma::mat <a class="el" href="classmlpack_1_1nca_1_1SoftmaxErrorFunction.html">mlpack::nca::SoftmaxErrorFunction</a>< MetricType >::GetInitialPoint </td>
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<td>(</td>
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<p>Get the initial point. </p>
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template<typename MetricType = metric::SquaredEuclideanDistance> </div>
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<td class="memname">void <a class="el" href="classmlpack_1_1nca_1_1SoftmaxErrorFunction.html">mlpack::nca::SoftmaxErrorFunction</a>< MetricType >::Gradient </td>
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<td>(</td>
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<td class="paramtype">const arma::mat & </td>
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<td class="paramname"><em>covariance</em>, </td>
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<p>Evaluate the gradient of the softmax function for the given covariance matrix. </p>
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<p>This is the non-separable implementation, where the objective function is not decomposed into the sum of several objective functions.</p>
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<dl class="params"><dt>Parameters</dt><dd>
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<table class="params">
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<tr><td class="paramname">covariance</td><td>Covariance matrix of Mahalanobis distance. </td></tr>
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<tr><td class="paramname">gradient</td><td>Matrix to store the calculated gradient in. </td></tr>
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template<typename MetricType = metric::SquaredEuclideanDistance> </div>
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<td class="memname">void <a class="el" href="classmlpack_1_1nca_1_1SoftmaxErrorFunction.html">mlpack::nca::SoftmaxErrorFunction</a>< MetricType >::Gradient </td>
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<td>(</td>
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<td class="paramtype">const arma::mat & </td>
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<td class="paramname"><em>covariance</em>, </td>
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<td class="paramkey"></td>
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<td></td>
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<td class="paramkey"></td>
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<td class="paramtype">arma::mat & </td>
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<td class="paramname"><em>gradient</em> </td>
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<tr>
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<td></td>
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<td>)</td>
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<p>Evaluate the gradient of the softmax function for the given covariance matrix on only one point of the dataset. </p>
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<p>This is the separable implementation, where the objective function is decomposed into the sum of many objective functions, and here, only one of those constituent objective functions is returned.</p>
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<dl class="params"><dt>Parameters</dt><dd>
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<table class="params">
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<tr><td class="paramname">covariance</td><td>Covariance matrix of Mahalanobis distance. </td></tr>
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<tr><td class="paramname">i</td><td>Index of point to use for objective function. </td></tr>
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<tr><td class="paramname">gradient</td><td>Matrix to store the calculated gradient in. </td></tr>
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</table>
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</dd>
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</dl>
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template<typename MetricType = metric::SquaredEuclideanDistance> </div>
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<td class="memname">size_t <a class="el" href="classmlpack_1_1nca_1_1SoftmaxErrorFunction.html">mlpack::nca::SoftmaxErrorFunction</a>< MetricType >::NumFunctions </td>
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<td>(</td>
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<td class="paramname"></td><td>)</td>
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<td> const</td>
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<td class="mlabels-right">
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<span class="mlabels"><span class="mlabel">inline</span></span> </td>
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<p>Get the number of functions the objective function can be decomposed into. </p>
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<p>This is just the number of points in the dataset. </p>
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<p>Definition at line <a class="el" href="nca__softmax__error__function_8hpp_source.html#l00114">114</a> of file <a class="el" href="nca__softmax__error__function_8hpp_source.html">nca_softmax_error_function.hpp</a>.</p>
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<td class="memname">void <a class="el" href="classmlpack_1_1nca_1_1SoftmaxErrorFunction.html">mlpack::nca::SoftmaxErrorFunction</a>< MetricType >::Precalculate </td>
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<td>(</td>
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<td class="paramtype">const arma::mat & </td>
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<td class="paramname"><em>coordinates</em></td><td>)</td>
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<span class="mlabels"><span class="mlabel">private</span></span> </td>
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<p>Precalculate the denominators and numerators that will make up the p_ij, but only if the coordinates matrix is different than the last coordinates the <a class="el" href="classmlpack_1_1nca_1_1SoftmaxErrorFunction.html#a436b90591cf003ba82b8a4953b7eadaf" title="Precalculate the denominators and numerators that will make up the p_ij, but only if the coordinates ...">Precalculate()</a> method was run with. </p>
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<p>This method is only called by the non-separable <a class="el" href="classmlpack_1_1nca_1_1SoftmaxErrorFunction.html#a60fcd093a9a44400cfcd6d259357d26e" title="Evaluate the softmax function for the given covariance matrix. ">Evaluate()</a> and <a class="el" href="classmlpack_1_1nca_1_1SoftmaxErrorFunction.html#a98d77dc2745f01a14aed76aa4735155f" title="Evaluate the gradient of the softmax function for the given covariance matrix. ">Gradient()</a>.</p>
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<p>This will update last_coordinates_ and stretched_dataset_, and also calculate the p_i and denominators_ which are used in the calculation of p_i or p_ij. The calculation will be O((n * (n + 1)) / 2), which is not great.</p>
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<dl class="params"><dt>Parameters</dt><dd>
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<table class="params">
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<h2 class="groupheader">Member Data Documentation</h2>
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<td class="memname">const arma::mat& <a class="el" href="classmlpack_1_1nca_1_1SoftmaxErrorFunction.html">mlpack::nca::SoftmaxErrorFunction</a>< MetricType >::dataset</td>
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<p>The dataset. </p>
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<p>Definition at line <a class="el" href="nca__softmax__error__function_8hpp_source.html#l00118">118</a> of file <a class="el" href="nca__softmax__error__function_8hpp_source.html">nca_softmax_error_function.hpp</a>.</p>
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<td class="memname">arma::vec <a class="el" href="classmlpack_1_1nca_1_1SoftmaxErrorFunction.html">mlpack::nca::SoftmaxErrorFunction</a>< MetricType >::denominators</td>
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<p>Holds denominators for calculation of p_ij, for the non-separable <a class="el" href="classmlpack_1_1nca_1_1SoftmaxErrorFunction.html#a60fcd093a9a44400cfcd6d259357d26e" title="Evaluate the softmax function for the given covariance matrix. ">Evaluate()</a> and <a class="el" href="classmlpack_1_1nca_1_1SoftmaxErrorFunction.html#a98d77dc2745f01a14aed76aa4735155f" title="Evaluate the gradient of the softmax function for the given covariance matrix. ">Gradient()</a>. </p>
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<p>Definition at line <a class="el" href="nca__softmax__error__function_8hpp_source.html#l00133">133</a> of file <a class="el" href="nca__softmax__error__function_8hpp_source.html">nca_softmax_error_function.hpp</a>.</p>
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<td class="memname">const arma::Row<size_t>& <a class="el" href="classmlpack_1_1nca_1_1SoftmaxErrorFunction.html">mlpack::nca::SoftmaxErrorFunction</a>< MetricType >::labels</td>
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<p>Labels for each point in the dataset. </p>
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<p>Definition at line <a class="el" href="nca__softmax__error__function_8hpp_source.html#l00120">120</a> of file <a class="el" href="nca__softmax__error__function_8hpp_source.html">nca_softmax_error_function.hpp</a>.</p>
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<td class="memname">arma::mat <a class="el" href="classmlpack_1_1nca_1_1SoftmaxErrorFunction.html">mlpack::nca::SoftmaxErrorFunction</a>< MetricType >::lastCoordinates</td>
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<p>Last coordinates. Used for the non-separable <a class="el" href="classmlpack_1_1nca_1_1SoftmaxErrorFunction.html#a60fcd093a9a44400cfcd6d259357d26e" title="Evaluate the softmax function for the given covariance matrix. ">Evaluate()</a> and <a class="el" href="classmlpack_1_1nca_1_1SoftmaxErrorFunction.html#a98d77dc2745f01a14aed76aa4735155f" title="Evaluate the gradient of the softmax function for the given covariance matrix. ">Gradient()</a>. </p>
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<p>Definition at line <a class="el" href="nca__softmax__error__function_8hpp_source.html#l00126">126</a> of file <a class="el" href="nca__softmax__error__function_8hpp_source.html">nca_softmax_error_function.hpp</a>.</p>
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template<typename MetricType = metric::SquaredEuclideanDistance> </div>
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<td class="memname">MetricType <a class="el" href="classmlpack_1_1nca_1_1SoftmaxErrorFunction.html">mlpack::nca::SoftmaxErrorFunction</a>< MetricType >::metric</td>
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<p>The instantiated metric. </p>
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<p>Definition at line <a class="el" href="nca__softmax__error__function_8hpp_source.html#l00123">123</a> of file <a class="el" href="nca__softmax__error__function_8hpp_source.html">nca_softmax_error_function.hpp</a>.</p>
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<td class="memname">arma::vec <a class="el" href="classmlpack_1_1nca_1_1SoftmaxErrorFunction.html">mlpack::nca::SoftmaxErrorFunction</a>< MetricType >::p</td>
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<p>Holds calculated p_i, for the non-separable <a class="el" href="classmlpack_1_1nca_1_1SoftmaxErrorFunction.html#a60fcd093a9a44400cfcd6d259357d26e" title="Evaluate the softmax function for the given covariance matrix. ">Evaluate()</a> and <a class="el" href="classmlpack_1_1nca_1_1SoftmaxErrorFunction.html#a98d77dc2745f01a14aed76aa4735155f" title="Evaluate the gradient of the softmax function for the given covariance matrix. ">Gradient()</a>. </p>
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<p>Definition at line <a class="el" href="nca__softmax__error__function_8hpp_source.html#l00130">130</a> of file <a class="el" href="nca__softmax__error__function_8hpp_source.html">nca_softmax_error_function.hpp</a>.</p>
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<td class="memname">bool <a class="el" href="classmlpack_1_1nca_1_1SoftmaxErrorFunction.html">mlpack::nca::SoftmaxErrorFunction</a>< MetricType >::precalculated</td>
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<p>False if nothing has ever been precalculated (only at construction time). </p>
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<p>Definition at line <a class="el" href="nca__softmax__error__function_8hpp_source.html#l00136">136</a> of file <a class="el" href="nca__softmax__error__function_8hpp_source.html">nca_softmax_error_function.hpp</a>.</p>
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<td class="memname">arma::mat <a class="el" href="classmlpack_1_1nca_1_1SoftmaxErrorFunction.html">mlpack::nca::SoftmaxErrorFunction</a>< MetricType >::stretchedDataset</td>
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<p>Stretched dataset. Kept internal to avoid memory reallocations. </p>
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<p>Definition at line <a class="el" href="nca__softmax__error__function_8hpp_source.html#l00128">128</a> of file <a class="el" href="nca__softmax__error__function_8hpp_source.html">nca_softmax_error_function.hpp</a>.</p>
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<hr/>The documentation for this class was generated from the following file:<ul>
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<li>src/mlpack/methods/nca/<a class="el" href="nca__softmax__error__function_8hpp_source.html">nca_softmax_error_function.hpp</a></li>
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