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<div class="headertitle"><div class="title">machine_learning Namespace Reference<div class="ingroups"><a class="el" href="../../d9/d66/group__machine__learning.html">Machine Learning Algorithms</a></div></div></div>
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<p><a href="https://en.wikipedia.org/wiki/A*_search_algorithm" target="_blank">A* search algorithm</a>
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Classes</h2></td></tr>
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<tr class="memitem:adaline" id="r_adaline"><td class="memItemLeft" align="right" valign="top">class  </td><td class="memItemRight" valign="bottom"><a class="el" href="../../d6/d30/classmachine__learning_1_1adaline.html">adaline</a></td></tr>
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<tr class="heading"><td colspan="2"><h2 id="header-func-members" class="groupheader"><a id="func-members" name="func-members"></a>
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Functions</h2></td></tr>
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<tr class="memitem:aa72a53c88203fde278f1fe6c3afe5b07" id="r_aa72a53c88203fde278f1fe6c3afe5b07"><td class="memItemLeft" align="right" valign="top">int </td><td class="memItemRight" valign="bottom"><a class="el" href="#aa72a53c88203fde278f1fe6c3afe5b07">save_u_matrix</a> (const char *fname, const std::vector< std::vector< std::valarray< double > > > &W)</td></tr>
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<tr class="memitem:ae868ad43698a1d69ba46ea3827d7d2c3" id="r_ae868ad43698a1d69ba46ea3827d7d2c3"><td class="memItemLeft" align="right" valign="top">double </td><td class="memItemRight" valign="bottom"><a class="el" href="#ae868ad43698a1d69ba46ea3827d7d2c3">update_weights</a> (const std::valarray< double > &X, std::vector< std::vector< std::valarray< double > > > *W, std::vector< std::valarray< double > > *D, double alpha, int R)</td></tr>
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<tr class="memitem:ac43d294e21a0c4fa33c53757df054576" id="r_ac43d294e21a0c4fa33c53757df054576"><td class="memItemLeft" align="right" valign="top">void </td><td class="memItemRight" valign="bottom"><a class="el" href="#ac43d294e21a0c4fa33c53757df054576">kohonen_som</a> (const std::vector< std::valarray< double > > &X, std::vector< std::vector< std::valarray< double > > > *W, double alpha_min)</td></tr>
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<tr class="memitem:aa6aac06ccf128b0a9c55c9ee1a8e5631" id="r_aa6aac06ccf128b0a9c55c9ee1a8e5631"><td class="memItemLeft" align="right" valign="top">void </td><td class="memItemRight" valign="bottom"><a class="el" href="#aa6aac06ccf128b0a9c55c9ee1a8e5631">update_weights</a> (const std::valarray< double > &x, std::vector< std::valarray< double > > *W, std::valarray< double > *D, double alpha, int R)</td></tr>
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<tr class="memitem:a042f435bca0839e721fc1574a61e8da3" id="r_a042f435bca0839e721fc1574a61e8da3"><td class="memItemLeft" align="right" valign="top">void </td><td class="memItemRight" valign="bottom"><a class="el" href="#a042f435bca0839e721fc1574a61e8da3">kohonen_som_tracer</a> (const std::vector< std::valarray< double > > &X, std::vector< std::valarray< double > > *W, double alpha_min)</td></tr>
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<tr class="memitem:a84260cb1be9b63d6e38107000ac4b7e7" id="r_a84260cb1be9b63d6e38107000ac4b7e7"><td class="memTemplParams" colspan="2">template<typename T> </td></tr>
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<tr class="memitem:a84260cb1be9b63d6e38107000ac4b7e7 template"><td class="memItemLeft" align="right" valign="top">std::ostream & </td><td class="memItemRight" valign="bottom"><a class="el" href="#a84260cb1be9b63d6e38107000ac4b7e7">operator<<</a> (std::ostream &out, std::vector< std::valarray< T > > const &A)</td></tr>
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<tr class="memitem:af4986b23760039711848155739c31b35" id="r_af4986b23760039711848155739c31b35"><td class="memTemplParams" colspan="2">template<typename T> </td></tr>
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<tr class="memitem:af4986b23760039711848155739c31b35 template"><td class="memItemLeft" align="right" valign="top">std::ostream & </td><td class="memItemRight" valign="bottom"><a class="el" href="#af4986b23760039711848155739c31b35">operator<<</a> (std::ostream &out, const std::pair< T, T > &A)</td></tr>
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<tr class="memitem:a54bf1f3c43271a5fc93101f6ae2e6269" id="r_a54bf1f3c43271a5fc93101f6ae2e6269"><td class="memTemplParams" colspan="2">template<typename T> </td></tr>
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<tr class="memitem:a54bf1f3c43271a5fc93101f6ae2e6269 template"><td class="memItemLeft" align="right" valign="top">std::ostream & </td><td class="memItemRight" valign="bottom"><a class="el" href="#a54bf1f3c43271a5fc93101f6ae2e6269">operator<<</a> (std::ostream &out, const std::valarray< T > &A)</td></tr>
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<tr class="memitem:a496302e3371aa7b478cb7d5917904bdd" id="r_a496302e3371aa7b478cb7d5917904bdd"><td class="memTemplParams" colspan="2">template<typename T> </td></tr>
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<tr class="memitem:a496302e3371aa7b478cb7d5917904bdd template"><td class="memItemLeft" align="right" valign="top">std::valarray< T > </td><td class="memItemRight" valign="bottom"><a class="el" href="#a496302e3371aa7b478cb7d5917904bdd">insert_element</a> (const std::valarray< T > &A, const T &ele)</td></tr>
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<tr class="memitem:a912cf68863063a38d6e63545be5eb093" id="r_a912cf68863063a38d6e63545be5eb093"><td class="memTemplParams" colspan="2">template<typename T> </td></tr>
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<tr class="memitem:a912cf68863063a38d6e63545be5eb093 template"><td class="memItemLeft" align="right" valign="top">std::valarray< T > </td><td class="memItemRight" valign="bottom"><a class="el" href="#a912cf68863063a38d6e63545be5eb093">pop_front</a> (const std::valarray< T > &A)</td></tr>
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<tr class="memitem:ae10178b082f0205c326550877d998e5d" id="r_ae10178b082f0205c326550877d998e5d"><td class="memTemplParams" colspan="2">template<typename T> </td></tr>
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<tr class="memitem:ae10178b082f0205c326550877d998e5d template"><td class="memItemLeft" align="right" valign="top">std::valarray< T > </td><td class="memItemRight" valign="bottom"><a class="el" href="#ae10178b082f0205c326550877d998e5d">pop_back</a> (const std::valarray< T > &A)</td></tr>
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<tr class="memitem:af801bf30591ca6b2c38ff4fed0ded23f" id="r_af801bf30591ca6b2c38ff4fed0ded23f"><td class="memTemplParams" colspan="2">template<typename T> </td></tr>
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<tr class="memitem:af801bf30591ca6b2c38ff4fed0ded23f template"><td class="memItemLeft" align="right" valign="top">void </td><td class="memItemRight" valign="bottom"><a class="el" href="#af801bf30591ca6b2c38ff4fed0ded23f">equal_shuffle</a> (std::vector< std::vector< std::valarray< T > > > &A, std::vector< std::vector< std::valarray< T > > > &B)</td></tr>
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<tr class="memitem:abee7b35403af3612222d3b7a53074905" id="r_abee7b35403af3612222d3b7a53074905"><td class="memTemplParams" colspan="2">template<typename T> </td></tr>
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<tr class="memitem:abee7b35403af3612222d3b7a53074905 template"><td class="memItemLeft" align="right" valign="top">void </td><td class="memItemRight" valign="bottom"><a class="el" href="#abee7b35403af3612222d3b7a53074905">uniform_random_initialization</a> (std::vector< std::valarray< T > > &A, const std::pair< size_t, size_t > &shape, const T &low, const T &high)</td></tr>
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<tr class="memitem:a8dd3f1ffbc2f26a3c88da1b1f8b7e9c4" id="r_a8dd3f1ffbc2f26a3c88da1b1f8b7e9c4"><td class="memTemplParams" colspan="2">template<typename T> </td></tr>
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<tr class="memitem:a8dd3f1ffbc2f26a3c88da1b1f8b7e9c4 template"><td class="memItemLeft" align="right" valign="top">void </td><td class="memItemRight" valign="bottom"><a class="el" href="#a8dd3f1ffbc2f26a3c88da1b1f8b7e9c4">unit_matrix_initialization</a> (std::vector< std::valarray< T > > &A, const std::pair< size_t, size_t > &shape)</td></tr>
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<tr class="memitem:ac1bdaa2a724b4ce6a6bb371a5dbe2e7e" id="r_ac1bdaa2a724b4ce6a6bb371a5dbe2e7e"><td class="memTemplParams" colspan="2">template<typename T> </td></tr>
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<tr class="memitem:ac1bdaa2a724b4ce6a6bb371a5dbe2e7e template"><td class="memItemLeft" align="right" valign="top">void </td><td class="memItemRight" valign="bottom"><a class="el" href="#ac1bdaa2a724b4ce6a6bb371a5dbe2e7e">zeroes_initialization</a> (std::vector< std::valarray< T > > &A, const std::pair< size_t, size_t > &shape)</td></tr>
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<tr class="memitem:a6f1c98c016ad34ff3d9f39372161bd35" id="r_a6f1c98c016ad34ff3d9f39372161bd35"><td class="memTemplParams" colspan="2">template<typename T> </td></tr>
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<tr class="memitem:a6f1c98c016ad34ff3d9f39372161bd35 template"><td class="memItemLeft" align="right" valign="top">T </td><td class="memItemRight" valign="bottom"><a class="el" href="#a6f1c98c016ad34ff3d9f39372161bd35">sum</a> (const std::vector< std::valarray< T > > &A)</td></tr>
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<tr class="memitem:aa4bbf61e65f8cd297255fa94b983d078" id="r_aa4bbf61e65f8cd297255fa94b983d078"><td class="memTemplParams" colspan="2">template<typename T> </td></tr>
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<tr class="memitem:aa4bbf61e65f8cd297255fa94b983d078 template"><td class="memItemLeft" align="right" valign="top">std::pair< size_t, size_t > </td><td class="memItemRight" valign="bottom"><a class="el" href="#aa4bbf61e65f8cd297255fa94b983d078">get_shape</a> (const std::vector< std::valarray< T > > &A)</td></tr>
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<tr class="memitem:ac332d152078e96311e43ac5e7183ea26" id="r_ac332d152078e96311e43ac5e7183ea26"><td class="memTemplParams" colspan="2">template<typename T> </td></tr>
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<tr class="memitem:ac332d152078e96311e43ac5e7183ea26 template"><td class="memItemLeft" align="right" valign="top">std::vector< std::vector< std::valarray< T > > > </td><td class="memItemRight" valign="bottom"><a class="el" href="#ac332d152078e96311e43ac5e7183ea26">minmax_scaler</a> (const std::vector< std::vector< std::valarray< T > > > &A, const T &low, const T &high)</td></tr>
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<tr class="memitem:a50480fccfb39de20ca47f1bf51ecb6ec" id="r_a50480fccfb39de20ca47f1bf51ecb6ec"><td class="memTemplParams" colspan="2">template<typename T> </td></tr>
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<tr class="memitem:a50480fccfb39de20ca47f1bf51ecb6ec template"><td class="memItemLeft" align="right" valign="top">size_t </td><td class="memItemRight" valign="bottom"><a class="el" href="#a50480fccfb39de20ca47f1bf51ecb6ec">argmax</a> (const std::vector< std::valarray< T > > &A)</td></tr>
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<tr class="memitem:ad0bdc88e5f1be47c46c0f0c8ebf754bb" id="r_ad0bdc88e5f1be47c46c0f0c8ebf754bb"><td class="memTemplParams" colspan="2">template<typename T> </td></tr>
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<tr class="memitem:ad0bdc88e5f1be47c46c0f0c8ebf754bb template"><td class="memItemLeft" align="right" valign="top">std::vector< std::valarray< T > > </td><td class="memItemRight" valign="bottom"><a class="el" href="#ad0bdc88e5f1be47c46c0f0c8ebf754bb">apply_function</a> (const std::vector< std::valarray< T > > &A, T(*func)(const T &))</td></tr>
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<tr class="memitem:a16f34574b7e0dd51bc3b3fda37446695" id="r_a16f34574b7e0dd51bc3b3fda37446695"><td class="memTemplParams" colspan="2">template<typename T> </td></tr>
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<tr class="memitem:a16f34574b7e0dd51bc3b3fda37446695 template"><td class="memItemLeft" align="right" valign="top">std::vector< std::valarray< T > > </td><td class="memItemRight" valign="bottom"><a class="el" href="#a16f34574b7e0dd51bc3b3fda37446695">operator*</a> (const std::vector< std::valarray< T > > &A, const T &val)</td></tr>
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<tr class="memitem:ae6ec42318d172b97fbdf45638d09d7b5" id="r_ae6ec42318d172b97fbdf45638d09d7b5"><td class="memTemplParams" colspan="2">template<typename T> </td></tr>
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<tr class="memitem:ae6ec42318d172b97fbdf45638d09d7b5 template"><td class="memItemLeft" align="right" valign="top">std::vector< std::valarray< T > > </td><td class="memItemRight" valign="bottom"><a class="el" href="#ae6ec42318d172b97fbdf45638d09d7b5">operator/</a> (const std::vector< std::valarray< T > > &A, const T &val)</td></tr>
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<tr class="memitem:a89fde571b38f9483576594f66572958a" id="r_a89fde571b38f9483576594f66572958a"><td class="memTemplParams" colspan="2">template<typename T> </td></tr>
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<tr class="memitem:a89fde571b38f9483576594f66572958a template"><td class="memItemLeft" align="right" valign="top">std::vector< std::valarray< T > > </td><td class="memItemRight" valign="bottom"><a class="el" href="#a89fde571b38f9483576594f66572958a">transpose</a> (const std::vector< std::valarray< T > > &A)</td></tr>
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<tr class="memitem:a2466857dab977a49f117029835b3b6d2" id="r_a2466857dab977a49f117029835b3b6d2"><td class="memTemplParams" colspan="2">template<typename T> </td></tr>
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<tr class="memitem:a2466857dab977a49f117029835b3b6d2 template"><td class="memItemLeft" align="right" valign="top">std::vector< std::valarray< T > > </td><td class="memItemRight" valign="bottom"><a class="el" href="#a2466857dab977a49f117029835b3b6d2">operator+</a> (const std::vector< std::valarray< T > > &A, const std::vector< std::valarray< T > > &B)</td></tr>
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<tr class="memitem:a0cc29566568e0383dd7d374068cbe6b3" id="r_a0cc29566568e0383dd7d374068cbe6b3"><td class="memTemplParams" colspan="2">template<typename T> </td></tr>
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<tr class="memitem:a0cc29566568e0383dd7d374068cbe6b3 template"><td class="memItemLeft" align="right" valign="top">std::vector< std::valarray< T > > </td><td class="memItemRight" valign="bottom"><a class="el" href="#a0cc29566568e0383dd7d374068cbe6b3">operator-</a> (const std::vector< std::valarray< T > > &A, const std::vector< std::valarray< T > > &B)</td></tr>
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<tr class="memitem:a5342906d42b80fc6b6b3ad17bf00fcb9" id="r_a5342906d42b80fc6b6b3ad17bf00fcb9"><td class="memTemplParams" colspan="2">template<typename T> </td></tr>
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<tr class="memitem:a5342906d42b80fc6b6b3ad17bf00fcb9 template"><td class="memItemLeft" align="right" valign="top">std::vector< std::valarray< T > > </td><td class="memItemRight" valign="bottom"><a class="el" href="#a5342906d42b80fc6b6b3ad17bf00fcb9">multiply</a> (const std::vector< std::valarray< T > > &A, const std::vector< std::valarray< T > > &B)</td></tr>
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<tr class="memitem:acafa3e62b686aebdbad81c4f89913f43" id="r_acafa3e62b686aebdbad81c4f89913f43"><td class="memTemplParams" colspan="2">template<typename T> </td></tr>
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<tr class="memitem:acafa3e62b686aebdbad81c4f89913f43 template"><td class="memItemLeft" align="right" valign="top">std::vector< std::valarray< T > > </td><td class="memItemRight" valign="bottom"><a class="el" href="#acafa3e62b686aebdbad81c4f89913f43">hadamard_product</a> (const std::vector< std::valarray< T > > &A, const std::vector< std::valarray< T > > &B)</td></tr>
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</table><table class="memberdecls">
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<tr class="heading"><td colspan="2"><h2 id="header-var-members" class="groupheader"><a id="var-members" name="var-members"></a>
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Variables</h2></td></tr>
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<tr class="memitem:a7220dbb7fa896d83bfb7a50e4fce1786" id="r_a7220dbb7fa896d83bfb7a50e4fce1786"><td class="memItemLeft" align="right" valign="top">constexpr double </td><td class="memItemRight" valign="bottom"><a class="el" href="#a7220dbb7fa896d83bfb7a50e4fce1786">MIN_DISTANCE</a> = 1e-4</td></tr>
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</table>
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<a name="details" id="details"></a><h2 id="header-details" class="groupheader">Detailed Description</h2>
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<div class="textblock"><p><a href="https://en.wikipedia.org/wiki/A*_search_algorithm" target="_blank">A* search algorithm</a> </p>
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<p>Machine Learning algorithms.</p>
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<p>for std::vector</p>
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<p>Machine learning algorithms.</p>
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<p>A* is an informed search algorithm, or a best-first search, meaning that it is formulated in terms of weighted graphs: starting from a specific starting node of a graph (initial state), it aims to find a path to the given goal node having the smallest cost (least distance travelled, shortest time, etc.). It evaluates by maintaining a tree of paths originating at the start node and extending those paths one edge at a time until it reaches the final state. The weighted edges (or cost) is evaluated on two factors, G score (cost required from starting node or initial state to current state) and H score (cost required from current state to final state). The F(state), then is evaluated as: F(state) = G(state) + H(state).</p>
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<p>To solve the given search with shortest cost or path possible is to inspect values having minimum F(state). </p><dl class="section author"><dt>Author</dt><dd><a href="https://github.com/AshishYUO" target="_blank">Ashish Daulatabad</a> for <span class="tt">std::reverse</span> function for <span class="tt">std::array</span>, representing <span class="tt">EightPuzzle</span> board for <span class="tt">assert</span> for <span class="tt">std::uint32_t</span> for <span class="tt">std::function</span> STL for IO operations for <span class="tt">std::map</span> STL for <span class="tt">std::shared_ptr</span> for <span class="tt">std::set</span> STL for <span class="tt">std::vector</span> STL</dd></dl>
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<p>Machine learning algorithms</p>
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<p>for std::transform and std::sort for assert for std::pow and std::sqrt for std::cout for std::accumulate for std::unordered_map</p>
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<p>Machine learning algorithms </p>
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</div><a name="doc-func-members" id="doc-func-members"></a><h2 id="header-doc-func-members" class="groupheader">Function Documentation</h2>
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<a id="ad0bdc88e5f1be47c46c0f0c8ebf754bb" name="ad0bdc88e5f1be47c46c0f0c8ebf754bb"></a>
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<h2 class="memtitle"><span class="permalink"><a href="#ad0bdc88e5f1be47c46c0f0c8ebf754bb">◆ </a></span>apply_function()</h2>
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<div class="memitem">
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<div class="memproto">
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<div class="memtemplate">
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template<typename T> </div>
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<td class="memname">std::vector< std::valarray< T > > machine_learning::apply_function </td>
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<td>(</td>
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<td class="paramtype">const std::vector< std::valarray< T > > &</td> <td class="paramname"><span class="paramname"><em>A</em></span>, </td>
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<td class="paramkey"></td>
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<td></td>
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<td class="paramtype">T(*</td> <td class="paramname"><span class="paramname"><em>func </em></span>)(const T &) )</td>
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</tr>
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</div><div class="memdoc">
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<p>Function which applys supplied function to every element of 2D vector </p><dl class="tparams"><dt>Template Parameters</dt><dd>
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<table class="tparams">
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<tr><td class="paramname">T</td><td>typename of the vector </td></tr>
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</table>
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</dd>
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</dl>
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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">A</td><td>2D vector on which function will be applied </td></tr>
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<tr><td class="paramname">func</td><td>Function to be applied </td></tr>
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</table>
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</dd>
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</dl>
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<dl class="section return"><dt>Returns</dt><dd>new resultant vector </dd></dl>
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<p class="definition">Definition at line <a class="el" href="../../d8/d95/vector__ops_8hpp_source.html#l00329">329</a> of file <a class="el" href="../../d8/d95/vector__ops_8hpp_source.html">vector_ops.hpp</a>.</p>
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<div class="fragment"><div class="line"><span class="lineno"> 330</span> {</div>
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<div class="line"><span class="lineno"> 331</span> std::vector<std::valarray<double>> B =</div>
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<div class="line"><span class="lineno"> 332</span> A; <span class="comment">// New vector to store resultant vector</span></div>
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<div class="line"><span class="lineno"> 333</span> <span class="keywordflow">for</span> (<span class="keyword">auto</span> &b : B) { <span class="comment">// For every row in vector</span></div>
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<div class="line"><span class="lineno"> 334</span> b = b.apply(func); <span class="comment">// Apply function to that row</span></div>
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<div class="line"><span class="lineno"> 335</span> }</div>
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<div class="line"><span class="lineno"> 336</span> <span class="keywordflow">return</span> B; <span class="comment">// Return new resultant 2D vector</span></div>
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<div class="line"><span class="lineno"> 337</span>}</div>
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</div><!-- fragment -->
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</div>
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</div>
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<a id="a50480fccfb39de20ca47f1bf51ecb6ec" name="a50480fccfb39de20ca47f1bf51ecb6ec"></a>
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<h2 class="memtitle"><span class="permalink"><a href="#a50480fccfb39de20ca47f1bf51ecb6ec">◆ </a></span>argmax()</h2>
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<div class="memitem">
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<div class="memproto">
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<div class="memtemplate">
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template<typename T> </div>
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<tr>
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<td class="memname">size_t machine_learning::argmax </td>
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<td>(</td>
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<td class="paramtype">const std::vector< std::valarray< T > > &</td> <td class="paramname"><span class="paramname"><em>A</em></span></td><td>)</td>
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<td></td>
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</div><div class="memdoc">
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<p>Function to get index of maximum element in 2D vector </p><dl class="tparams"><dt>Template Parameters</dt><dd>
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<table class="tparams">
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<tr><td class="paramname">T</td><td>typename of the vector </td></tr>
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</table>
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</dd>
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</dl>
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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">A</td><td>2D vector for which maximum index is required </td></tr>
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</table>
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</dd>
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</dl>
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<dl class="section return"><dt>Returns</dt><dd>index of maximum element </dd></dl>
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<p class="definition">Definition at line <a class="el" href="../../d8/d95/vector__ops_8hpp_source.html#l00307">307</a> of file <a class="el" href="../../d8/d95/vector__ops_8hpp_source.html">vector_ops.hpp</a>.</p>
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<div class="fragment"><div class="line"><span class="lineno"> 307</span> {</div>
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<div class="line"><span class="lineno"> 308</span> <span class="keyword">const</span> <span class="keyword">auto</span> shape = <a class="code hl_function" href="#aa4bbf61e65f8cd297255fa94b983d078">get_shape</a>(A);</div>
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<div class="line"><span class="lineno"> 309</span> <span class="comment">// As this function is used on predicted (or target) vector, shape should be</span></div>
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<div class="line"><span class="lineno"> 310</span> <span class="comment">// (1, X)</span></div>
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<div class="line"><span class="lineno"> 311</span> <span class="keywordflow">if</span> (shape.first != 1) {</div>
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<div class="line"><span class="lineno"> 312</span> std::cerr << <span class="stringliteral">"ERROR ("</span> << __func__ << <span class="stringliteral">") : "</span>;</div>
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<div class="line"><span class="lineno"> 313</span> std::cerr << <span class="stringliteral">"Supplied vector is ineligible for argmax"</span> << std::endl;</div>
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<div class="line"><span class="lineno"> 314</span> std::exit(EXIT_FAILURE);</div>
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<div class="line"><span class="lineno"> 315</span> }</div>
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<div class="line"><span class="lineno"> 316</span> <span class="comment">// Return distance of max element from first element (i.e. index)</span></div>
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<div class="line"><span class="lineno"> 317</span> <span class="keywordflow">return</span> std::distance(std::begin(A[0]),</div>
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<div class="line"><span class="lineno"> 318</span> std::max_element(std::begin(A[0]), std::end(A[0])));</div>
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<div class="line"><span class="lineno"> 319</span>}</div>
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<div class="ttc" id="anamespacemachine__learning_html_aa4bbf61e65f8cd297255fa94b983d078"><div class="ttname"><a href="#aa4bbf61e65f8cd297255fa94b983d078">machine_learning::get_shape</a></div><div class="ttdeci">std::pair< size_t, size_t > get_shape(const std::vector< std::valarray< T > > &A)</div><div class="ttdef"><b>Definition</b> <a href="../../d8/d95/vector__ops_8hpp_source.html#l00247">vector_ops.hpp:247</a></div></div>
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</div>
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</div>
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<a id="af801bf30591ca6b2c38ff4fed0ded23f" name="af801bf30591ca6b2c38ff4fed0ded23f"></a>
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<h2 class="memtitle"><span class="permalink"><a href="#af801bf30591ca6b2c38ff4fed0ded23f">◆ </a></span>equal_shuffle()</h2>
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<div class="memitem">
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<div class="memproto">
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template<typename T> </div>
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<td class="memname">void machine_learning::equal_shuffle </td>
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<td>(</td>
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<td class="paramtype">std::vector< std::vector< std::valarray< T > > > &</td> <td class="paramname"><span class="paramname"><em>A</em></span>, </td>
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<td class="paramkey"></td>
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<td></td>
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<td class="paramtype">std::vector< std::vector< std::valarray< T > > > &</td> <td class="paramname"><span class="paramname"><em>B</em></span> )</td>
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<p>Function to equally shuffle two 3D vectors (used for shuffling training data) </p><dl class="tparams"><dt>Template Parameters</dt><dd>
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<table class="tparams">
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<tr><td class="paramname">T</td><td>typename of the vector </td></tr>
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</table>
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</dd>
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</dl>
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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">A</td><td>First 3D vector </td></tr>
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<tr><td class="paramname">B</td><td>Second 3D vector </td></tr>
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</table>
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</dd>
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</dl>
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<p class="definition">Definition at line <a class="el" href="../../d8/d95/vector__ops_8hpp_source.html#l00136">136</a> of file <a class="el" href="../../d8/d95/vector__ops_8hpp_source.html">vector_ops.hpp</a>.</p>
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<div class="fragment"><div class="line"><span class="lineno"> 137</span> {</div>
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<div class="line"><span class="lineno"> 138</span> <span class="comment">// If two vectors have different sizes</span></div>
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<div class="line"><span class="lineno"> 139</span> <span class="keywordflow">if</span> (A.size() != B.size()) {</div>
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<div class="line"><span class="lineno"> 140</span> std::cerr << <span class="stringliteral">"ERROR ("</span> << __func__ << <span class="stringliteral">") : "</span>;</div>
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<div class="line"><span class="lineno"> 141</span> std::cerr</div>
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<div class="line"><span class="lineno"> 142</span> << <span class="stringliteral">"Can not equally shuffle two vectors with different sizes: "</span>;</div>
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<div class="line"><span class="lineno"> 143</span> std::cerr << A.size() << <span class="stringliteral">" and "</span> << B.size() << std::endl;</div>
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<div class="line"><span class="lineno"> 144</span> std::exit(EXIT_FAILURE);</div>
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<div class="line"><span class="lineno"> 145</span> }</div>
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<div class="line"><span class="lineno"> 146</span> <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> i = 0; i < A.size(); i++) { <span class="comment">// For every element in A and B</span></div>
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<div class="line"><span class="lineno"> 147</span> <span class="comment">// Genrating random index < size of A and B</span></div>
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<div class="line"><span class="lineno"> 148</span> std::srand(std::chrono::system_clock::now().time_since_epoch().count());</div>
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<div class="line"><span class="lineno"> 149</span> <span class="keywordtype">size_t</span> random_index = std::rand() % A.size();</div>
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<div class="line"><span class="lineno"> 150</span> <span class="comment">// Swap elements in both A and B with same random index</span></div>
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<div class="line"><span class="lineno"> 151</span> std::swap(A[i], A[random_index]);</div>
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<div class="line"><span class="lineno"> 152</span> std::swap(B[i], B[random_index]);</div>
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<div class="line"><span class="lineno"> 153</span> }</div>
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<div class="line"><span class="lineno"> 154</span> <span class="keywordflow">return</span>;</div>
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<div class="line"><span class="lineno"> 155</span>}</div>
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<h2 class="memtitle"><span class="permalink"><a href="#aa4bbf61e65f8cd297255fa94b983d078">◆ </a></span>get_shape()</h2>
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template<typename T> </div>
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<td class="memname">std::pair< size_t, size_t > machine_learning::get_shape </td>
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<td>(</td>
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<td class="paramtype">const std::vector< std::valarray< T > > &</td> <td class="paramname"><span class="paramname"><em>A</em></span></td><td>)</td>
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<td></td>
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<p>Function to get shape of given 2D vector </p><dl class="tparams"><dt>Template Parameters</dt><dd>
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<table class="tparams">
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<tr><td class="paramname">T</td><td>typename of the vector </td></tr>
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</dl>
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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">A</td><td>2D vector for which shape is required </td></tr>
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</table>
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</dd>
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</dl>
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<dl class="section return"><dt>Returns</dt><dd>shape as pair </dd></dl>
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<p class="definition">Definition at line <a class="el" href="../../d8/d95/vector__ops_8hpp_source.html#l00247">247</a> of file <a class="el" href="../../d8/d95/vector__ops_8hpp_source.html">vector_ops.hpp</a>.</p>
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<div class="fragment"><div class="line"><span class="lineno"> 247</span> {</div>
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<div class="line"><span class="lineno"> 248</span> <span class="keyword">const</span> <span class="keywordtype">size_t</span> sub_size = (*A.begin()).size();</div>
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<div class="line"><span class="lineno"> 249</span> <span class="keywordflow">for</span> (<span class="keyword">const</span> <span class="keyword">auto</span> &a : A) {</div>
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<div class="line"><span class="lineno"> 250</span> <span class="comment">// If supplied vector don't have same shape in all rows</span></div>
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<div class="line"><span class="lineno"> 251</span> <span class="keywordflow">if</span> (a.size() != sub_size) {</div>
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<div class="line"><span class="lineno"> 252</span> std::cerr << <span class="stringliteral">"ERROR ("</span> << __func__ << <span class="stringliteral">") : "</span>;</div>
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<div class="line"><span class="lineno"> 253</span> std::cerr << <span class="stringliteral">"Supplied vector is not 2D Matrix"</span> << std::endl;</div>
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<div class="line"><span class="lineno"> 254</span> std::exit(EXIT_FAILURE);</div>
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<div class="line"><span class="lineno"> 255</span> }</div>
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<div class="line"><span class="lineno"> 256</span> }</div>
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<div class="line"><span class="lineno"> 257</span> <span class="keywordflow">return</span> std::make_pair(A.size(), sub_size); <span class="comment">// Return shape as pair</span></div>
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<div class="line"><span class="lineno"> 258</span>}</div>
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<h2 class="memtitle"><span class="permalink"><a href="#acafa3e62b686aebdbad81c4f89913f43">◆ </a></span>hadamard_product()</h2>
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<td class="memname">std::vector< std::valarray< T > > machine_learning::hadamard_product </td>
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<td>(</td>
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<td class="paramtype">const std::vector< std::valarray< T > > &</td> <td class="paramname"><span class="paramname"><em>A</em></span>, </td>
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</div><div class="memdoc">
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<p>Function to get hadamard product of two 2D vectors </p><dl class="tparams"><dt>Template Parameters</dt><dd>
|
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<table class="tparams">
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<tr><td class="paramname">T</td><td>typename of the vector </td></tr>
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</table>
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</dd>
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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">A</td><td>First 2D vector </td></tr>
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<tr><td class="paramname">B</td><td>Second 2D vector </td></tr>
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</table>
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</dd>
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<dl class="section return"><dt>Returns</dt><dd>new resultant vector </dd></dl>
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<p class="definition">Definition at line <a class="el" href="../../d8/d95/vector__ops_8hpp_source.html#l00494">494</a> of file <a class="el" href="../../d8/d95/vector__ops_8hpp_source.html">vector_ops.hpp</a>.</p>
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<div class="fragment"><div class="line"><span class="lineno"> 496</span> {</div>
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<div class="line"><span class="lineno"> 497</span> <span class="keyword">const</span> <span class="keyword">auto</span> shape_a = <a class="code hl_function" href="#aa4bbf61e65f8cd297255fa94b983d078">get_shape</a>(A);</div>
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<div class="line"><span class="lineno"> 498</span> <span class="keyword">const</span> <span class="keyword">auto</span> shape_b = <a class="code hl_function" href="#aa4bbf61e65f8cd297255fa94b983d078">get_shape</a>(B);</div>
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<div class="line"><span class="lineno"> 499</span> <span class="comment">// If vectors are not eligible for hadamard product</span></div>
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<div class="line"><span class="lineno"> 500</span> <span class="keywordflow">if</span> (shape_a.first != shape_b.first || shape_a.second != shape_b.second) {</div>
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<div class="line"><span class="lineno"> 501</span> std::cerr << <span class="stringliteral">"ERROR ("</span> << __func__ << <span class="stringliteral">") : "</span>;</div>
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<div class="line"><span class="lineno"> 502</span> std::cerr << <span class="stringliteral">"Vectors have different shapes "</span>;</div>
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<div class="line"><span class="lineno"> 503</span> std::cerr << shape_a << <span class="stringliteral">" and "</span> << shape_b << std::endl;</div>
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<div class="line"><span class="lineno"> 504</span> std::exit(EXIT_FAILURE);</div>
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<div class="line"><span class="lineno"> 505</span> }</div>
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<div class="line"><span class="lineno"> 506</span> std::vector<std::valarray<T>> C; <span class="comment">// Vector to store result</span></div>
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<div class="line"><span class="lineno"> 507</span> <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> i = 0; i < A.size(); i++) {</div>
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<div class="line"><span class="lineno"> 508</span> C.push_back(A[i] * B[i]); <span class="comment">// Elementwise multiplication</span></div>
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<div class="line"><span class="lineno"> 509</span> }</div>
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<div class="line"><span class="lineno"> 510</span> <span class="keywordflow">return</span> C; <span class="comment">// Return new resultant 2D vector</span></div>
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<div class="line"><span class="lineno"> 511</span>}</div>
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<h2 class="memtitle"><span class="permalink"><a href="#a496302e3371aa7b478cb7d5917904bdd">◆ </a></span>insert_element()</h2>
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template<typename T> </div>
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<td class="memname">std::valarray< T > machine_learning::insert_element </td>
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<td>(</td>
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<td class="paramtype">const std::valarray< T > &</td> <td class="paramname"><span class="paramname"><em>A</em></span>, </td>
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<p>Function to insert element into 1D vector </p><dl class="tparams"><dt>Template Parameters</dt><dd>
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<table class="tparams">
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<tr><td class="paramname">T</td><td>typename of the 1D vector and the element </td></tr>
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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">A</td><td>1D vector in which element will to be inserted </td></tr>
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<tr><td class="paramname">ele</td><td>element to be inserted </td></tr>
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<dl class="section return"><dt>Returns</dt><dd>new resultant vector </dd></dl>
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<p class="definition">Definition at line <a class="el" href="../../d8/d95/vector__ops_8hpp_source.html#l00085">85</a> of file <a class="el" href="../../d8/d95/vector__ops_8hpp_source.html">vector_ops.hpp</a>.</p>
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<div class="fragment"><div class="line"><span class="lineno"> 85</span> {</div>
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<div class="line"><span class="lineno"> 86</span> std::valarray<T> B; <span class="comment">// New 1D vector to store resultant vector</span></div>
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<div class="line"><span class="lineno"> 87</span> B.resize(A.size() + 1); <span class="comment">// Resizing it accordingly</span></div>
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<div class="line"><span class="lineno"> 88</span> <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> i = 0; i < A.size(); i++) { <span class="comment">// For every element in A</span></div>
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<div class="line"><span class="lineno"> 89</span> B[i] = A[i]; <span class="comment">// Copy element in B</span></div>
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<div class="line"><span class="lineno"> 90</span> }</div>
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<div class="line"><span class="lineno"> 91</span> B[B.size() - 1] = ele; <span class="comment">// Inserting new element in last position</span></div>
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<div class="line"><span class="lineno"> 92</span> <span class="keywordflow">return</span> B; <span class="comment">// Return resultant vector</span></div>
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<div class="line"><span class="lineno"> 93</span>}</div>
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<h2 class="memtitle"><span class="permalink"><a href="#ac43d294e21a0c4fa33c53757df054576">◆ </a></span>kohonen_som()</h2>
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<td class="memname">void machine_learning::kohonen_som </td>
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<td>(</td>
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<td class="paramtype">const std::vector< std::valarray< double > > &</td> <td class="paramname"><span class="paramname"><em>X</em></span>, </td>
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<td class="paramtype">std::vector< std::vector< std::valarray< double > > > *</td> <td class="paramname"><span class="paramname"><em>W</em></span>, </td>
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<td class="paramtype">double</td> <td class="paramname"><span class="paramname"><em>alpha_min</em></span> )</td>
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<p>Apply incremental algorithm with updating neighborhood and learning rates on all samples in the given datset.</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="paramdir">[in]</td><td class="paramname">X</td><td>data set </td></tr>
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<tr><td class="paramdir">[in,out]</td><td class="paramname">W</td><td>weights matrix </td></tr>
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<tr><td class="paramdir">[in]</td><td class="paramname">alpha_min</td><td>terminal value of alpha </td></tr>
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</table>
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<p class="definition">Definition at line <a class="el" href="../../d4/def/kohonen__som__topology_8cpp_source.html#l00269">269</a> of file <a class="el" href="../../d4/def/kohonen__som__topology_8cpp_source.html">kohonen_som_topology.cpp</a>.</p>
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<div class="fragment"><div class="line"><span class="lineno"> 271</span> {</div>
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<div class="line"><span class="lineno"> 272</span> <span class="keywordtype">size_t</span> num_samples = X.size(); <span class="comment">// number of rows</span></div>
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<div class="line"><span class="lineno"> 273</span> <span class="comment">// size_t num_features = X[0].size(); // number of columns</span></div>
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<div class="line"><span class="lineno"> 274</span> <span class="keywordtype">size_t</span> num_out = W->size(); <span class="comment">// output matrix size</span></div>
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<div class="line"><span class="lineno"> 275</span> <span class="keywordtype">size_t</span> R = num_out >> 2, iter = 0;</div>
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<div class="line"><span class="lineno"> 276</span> <span class="keywordtype">double</span> alpha = 1.f;</div>
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<div class="line"><span class="lineno"> 277</span> </div>
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<div class="line"><span class="lineno"> 278</span> std::vector<std::valarray<double>> D(num_out);</div>
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<div class="line"><span class="lineno"> 279</span> <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i < num_out; i++) D[i] = std::valarray<double>(num_out);</div>
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<div class="line"><span class="lineno"> 280</span> </div>
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<div class="line"><span class="lineno"> 281</span> <span class="keywordtype">double</span> dmin = 1.f; <span class="comment">// average minimum distance of all samples</span></div>
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<div class="line"><span class="lineno"> 282</span> <span class="keywordtype">double</span> past_dmin = 1.f; <span class="comment">// average minimum distance of all samples</span></div>
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<div class="line"><span class="lineno"> 283</span> <span class="keywordtype">double</span> dmin_ratio = 1.f; <span class="comment">// change per step</span></div>
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<div class="line"><span class="lineno"> 284</span> </div>
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<div class="line"><span class="lineno"> 285</span> <span class="comment">// Loop alpha from 1 to slpha_min</span></div>
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<div class="line"><span class="lineno"> 286</span> <span class="keywordflow">for</span> (; alpha > 0 && dmin_ratio > 1e-5; alpha -= 1e-4, iter++) {</div>
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<div class="line"><span class="lineno"> 287</span> <span class="comment">// Loop for each sample pattern in the data set</span></div>
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<div class="line"><span class="lineno"> 288</span> <span class="keywordflow">for</span> (<span class="keywordtype">int</span> sample = 0; sample < num_samples; sample++) {</div>
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<div class="line"><span class="lineno"> 289</span> <span class="comment">// update weights for the current input pattern sample</span></div>
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<div class="line"><span class="lineno"> 290</span> dmin += <a class="code hl_function" href="#ae868ad43698a1d69ba46ea3827d7d2c3">update_weights</a>(X[sample], W, &D, alpha, R);</div>
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<div class="line"><span class="lineno"> 291</span> }</div>
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<div class="line"><span class="lineno"> 292</span> </div>
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<div class="line"><span class="lineno"> 293</span> <span class="comment">// every 100th iteration, reduce the neighborhood range</span></div>
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<div class="line"><span class="lineno"> 294</span> <span class="keywordflow">if</span> (iter % 300 == 0 && R > 1) {</div>
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<div class="line"><span class="lineno"> 295</span> R--;</div>
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<div class="line"><span class="lineno"> 296</span> }</div>
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<div class="line"><span class="lineno"> 297</span> </div>
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<div class="line"><span class="lineno"> 298</span> dmin /= num_samples;</div>
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<div class="line"><span class="lineno"> 299</span> </div>
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<div class="line"><span class="lineno"> 300</span> <span class="comment">// termination condition variable -> % change in minimum distance</span></div>
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<div class="line"><span class="lineno"> 301</span> dmin_ratio = (past_dmin - dmin) / past_dmin;</div>
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<div class="line"><span class="lineno"> 302</span> <span class="keywordflow">if</span> (dmin_ratio < 0) {</div>
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<div class="line"><span class="lineno"> 303</span> dmin_ratio = 1.f;</div>
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<div class="line"><span class="lineno"> 304</span> }</div>
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<div class="line"><span class="lineno"> 305</span> past_dmin = dmin;</div>
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<div class="line"><span class="lineno"> 306</span> </div>
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<div class="line"><span class="lineno"> 307</span> std::cout << <span class="stringliteral">"iter: "</span> << iter << <span class="stringliteral">"\t alpha: "</span> << alpha << <span class="stringliteral">"\t R: "</span> << R</div>
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<div class="line"><span class="lineno"> 308</span> << <span class="stringliteral">"\t d_min: "</span> << dmin_ratio << <span class="stringliteral">"\r"</span>;</div>
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<div class="line"><span class="lineno"> 309</span> }</div>
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<div class="line"><span class="lineno"> 310</span> </div>
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<div class="line"><span class="lineno"> 311</span> std::cout << <span class="stringliteral">"\n"</span>;</div>
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<div class="line"><span class="lineno"> 312</span>}</div>
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<div class="ttc" id="anamespacemachine__learning_html_ae868ad43698a1d69ba46ea3827d7d2c3"><div class="ttname"><a href="#ae868ad43698a1d69ba46ea3827d7d2c3">machine_learning::update_weights</a></div><div class="ttdeci">double update_weights(const std::valarray< double > &X, std::vector< std::vector< std::valarray< double > > > *W, std::vector< std::valarray< double > > *D, double alpha, int R)</div><div class="ttdef"><b>Definition</b> <a href="../../d4/def/kohonen__som__topology_8cpp_source.html#l00200">kohonen_som_topology.cpp:200</a></div></div>
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<h2 class="memtitle"><span class="permalink"><a href="#a042f435bca0839e721fc1574a61e8da3">◆ </a></span>kohonen_som_tracer()</h2>
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<div class="memitem">
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<td class="memname">void machine_learning::kohonen_som_tracer </td>
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<td>(</td>
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<td class="paramtype">const std::vector< std::valarray< double > > &</td> <td class="paramname"><span class="paramname"><em>X</em></span>, </td>
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<td class="paramtype">std::vector< std::valarray< double > > *</td> <td class="paramname"><span class="paramname"><em>W</em></span>, </td>
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<td class="paramkey"></td>
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<td></td>
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<td class="paramtype">double</td> <td class="paramname"><span class="paramname"><em>alpha_min</em></span> )</td>
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</tr>
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</table>
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</div><div class="memdoc">
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<p>Apply incremental algorithm with updating neighborhood and learning rates on all samples in the given datset.</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="paramdir">[in]</td><td class="paramname">X</td><td>data set </td></tr>
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<tr><td class="paramdir">[in,out]</td><td class="paramname">W</td><td>weights matrix </td></tr>
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<tr><td class="paramdir">[in]</td><td class="paramname">alpha_min</td><td>terminal value of alpha </td></tr>
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<p class="definition">Definition at line <a class="el" href="../../d9/d49/kohonen__som__trace_8cpp_source.html#l00149">149</a> of file <a class="el" href="../../d9/d49/kohonen__som__trace_8cpp_source.html">kohonen_som_trace.cpp</a>.</p>
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<div class="fragment"><div class="line"><span class="lineno"> 151</span> {</div>
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<div class="line"><span class="lineno"> 152</span> <span class="keywordtype">int</span> num_samples = X.size(); <span class="comment">// number of rows</span></div>
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<div class="line"><span class="lineno"> 153</span> <span class="comment">// int num_features = X[0].size(); // number of columns</span></div>
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<div class="line"><span class="lineno"> 154</span> <span class="keywordtype">int</span> num_out = W->size(); <span class="comment">// number of rows</span></div>
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<div class="line"><span class="lineno"> 155</span> <span class="keywordtype">int</span> R = num_out >> 2, iter = 0;</div>
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<div class="line"><span class="lineno"> 156</span> <span class="keywordtype">double</span> alpha = 1.f;</div>
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<div class="line"><span class="lineno"> 157</span> </div>
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<div class="line"><span class="lineno"> 158</span> std::valarray<double> D(num_out);</div>
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<div class="line"><span class="lineno"> 159</span> </div>
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<div class="line"><span class="lineno"> 160</span> <span class="comment">// Loop alpha from 1 to slpha_min</span></div>
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<div class="line"><span class="lineno"> 161</span> <span class="keywordflow">do</span> {</div>
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<div class="line"><span class="lineno"> 162</span> <span class="comment">// Loop for each sample pattern in the data set</span></div>
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<div class="line"><span class="lineno"> 163</span> <span class="keywordflow">for</span> (<span class="keywordtype">int</span> sample = 0; sample < num_samples; sample++) {</div>
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<div class="line"><span class="lineno"> 164</span> <span class="comment">// update weights for the current input pattern sample</span></div>
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<div class="line"><span class="lineno"> 165</span> <a class="code hl_function" href="#ae868ad43698a1d69ba46ea3827d7d2c3">update_weights</a>(X[sample], W, &D, alpha, R);</div>
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<div class="line"><span class="lineno"> 166</span> }</div>
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<div class="line"><span class="lineno"> 167</span> </div>
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<div class="line"><span class="lineno"> 168</span> <span class="comment">// every 10th iteration, reduce the neighborhood range</span></div>
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<div class="line"><span class="lineno"> 169</span> <span class="keywordflow">if</span> (iter % 10 == 0 && R > 1) {</div>
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<div class="line"><span class="lineno"> 170</span> R--;</div>
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<div class="line"><span class="lineno"> 171</span> }</div>
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<div class="line"><span class="lineno"> 172</span> </div>
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<div class="line"><span class="lineno"> 173</span> alpha -= 0.01;</div>
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<div class="line"><span class="lineno"> 174</span> iter++;</div>
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<div class="line"><span class="lineno"> 175</span> } <span class="keywordflow">while</span> (alpha > alpha_min);</div>
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<div class="line"><span class="lineno"> 176</span>}</div>
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<h2 class="memtitle"><span class="permalink"><a href="#ac332d152078e96311e43ac5e7183ea26">◆ </a></span>minmax_scaler()</h2>
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<td class="memname">std::vector< std::vector< std::valarray< T > > > machine_learning::minmax_scaler </td>
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<td>(</td>
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<td class="paramtype">const std::vector< std::vector< std::valarray< T > > > &</td> <td class="paramname"><span class="paramname"><em>A</em></span>, </td>
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<p>Function to scale given 3D vector using min-max scaler </p><dl class="tparams"><dt>Template Parameters</dt><dd>
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<table class="tparams">
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<tr><td class="paramname">T</td><td>typename of the vector </td></tr>
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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">A</td><td>3D vector which will be scaled </td></tr>
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<tr><td class="paramname">low</td><td>new minimum value </td></tr>
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<tr><td class="paramname">high</td><td>new maximum value </td></tr>
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</table>
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<dl class="section return"><dt>Returns</dt><dd>new scaled 3D vector </dd></dl>
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<p class="definition">Definition at line <a class="el" href="../../d8/d95/vector__ops_8hpp_source.html#l00269">269</a> of file <a class="el" href="../../d8/d95/vector__ops_8hpp_source.html">vector_ops.hpp</a>.</p>
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<div class="fragment"><div class="line"><span class="lineno"> 271</span> {</div>
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<div class="line"><span class="lineno"> 272</span> std::vector<std::vector<std::valarray<T>>> B =</div>
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<div class="line"><span class="lineno"> 273</span> A; <span class="comment">// Copying into new vector B</span></div>
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<div class="line"><span class="lineno"> 274</span> <span class="keyword">const</span> <span class="keyword">auto</span> shape = <a class="code hl_function" href="#aa4bbf61e65f8cd297255fa94b983d078">get_shape</a>(B[0]); <span class="comment">// Storing shape of B's every element</span></div>
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<div class="line"><span class="lineno"> 275</span> <span class="comment">// As this function is used for scaling training data vector should be of</span></div>
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<div class="line"><span class="lineno"> 276</span> <span class="comment">// shape (1, X)</span></div>
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<div class="line"><span class="lineno"> 277</span> <span class="keywordflow">if</span> (shape.first != 1) {</div>
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<div class="line"><span class="lineno"> 278</span> std::cerr << <span class="stringliteral">"ERROR ("</span> << __func__ << <span class="stringliteral">") : "</span>;</div>
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<div class="line"><span class="lineno"> 279</span> std::cerr</div>
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<div class="line"><span class="lineno"> 280</span> << <span class="stringliteral">"Supplied vector is not supported for minmax scaling, shape: "</span>;</div>
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<div class="line"><span class="lineno"> 281</span> std::cerr << shape << std::endl;</div>
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<div class="line"><span class="lineno"> 282</span> std::exit(EXIT_FAILURE);</div>
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<div class="line"><span class="lineno"> 283</span> }</div>
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<div class="line"><span class="lineno"> 284</span> <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> i = 0; i < shape.second; i++) {</div>
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<div class="line"><span class="lineno"> 285</span> T min = B[0][0][i], max = B[0][0][i];</div>
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<div class="line"><span class="lineno"> 286</span> <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> j = 0; j < B.size(); j++) {</div>
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<div class="line"><span class="lineno"> 287</span> <span class="comment">// Updating minimum and maximum values</span></div>
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<div class="line"><span class="lineno"> 288</span> min = std::min(min, B[j][0][i]);</div>
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<div class="line"><span class="lineno"> 289</span> max = std::max(max, B[j][0][i]);</div>
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<div class="line"><span class="lineno"> 290</span> }</div>
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<div class="line"><span class="lineno"> 291</span> <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> j = 0; j < B.size(); j++) {</div>
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<div class="line"><span class="lineno"> 292</span> <span class="comment">// Applying min-max scaler formula</span></div>
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<div class="line"><span class="lineno"> 293</span> B[j][0][i] =</div>
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<div class="line"><span class="lineno"> 294</span> ((B[j][0][i] - min) / (max - min)) * (high - low) + low;</div>
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<div class="line"><span class="lineno"> 295</span> }</div>
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<div class="line"><span class="lineno"> 296</span> }</div>
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<div class="line"><span class="lineno"> 297</span> <span class="keywordflow">return</span> B; <span class="comment">// Return new resultant 3D vector</span></div>
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<div class="line"><span class="lineno"> 298</span>}</div>
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<h2 class="memtitle"><span class="permalink"><a href="#a5342906d42b80fc6b6b3ad17bf00fcb9">◆ </a></span>multiply()</h2>
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<td class="memname">std::vector< std::valarray< T > > machine_learning::multiply </td>
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<td class="paramtype">const std::vector< std::valarray< T > > &</td> <td class="paramname"><span class="paramname"><em>A</em></span>, </td>
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<p>Function to multiply two 2D vectors </p><dl class="tparams"><dt>Template Parameters</dt><dd>
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<tr><td class="paramname">T</td><td>typename of the vector </td></tr>
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<dl class="params"><dt>Parameters</dt><dd>
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<tr><td class="paramname">A</td><td>First 2D vector </td></tr>
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<tr><td class="paramname">B</td><td>Second 2D vector </td></tr>
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<dl class="section return"><dt>Returns</dt><dd>new resultant vector </dd></dl>
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<p class="definition">Definition at line <a class="el" href="../../d8/d95/vector__ops_8hpp_source.html#l00460">460</a> of file <a class="el" href="../../d8/d95/vector__ops_8hpp_source.html">vector_ops.hpp</a>.</p>
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<div class="fragment"><div class="line"><span class="lineno"> 461</span> {</div>
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<div class="line"><span class="lineno"> 462</span> <span class="keyword">const</span> <span class="keyword">auto</span> shape_a = <a class="code hl_function" href="#aa4bbf61e65f8cd297255fa94b983d078">get_shape</a>(A);</div>
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<div class="line"><span class="lineno"> 463</span> <span class="keyword">const</span> <span class="keyword">auto</span> shape_b = <a class="code hl_function" href="#aa4bbf61e65f8cd297255fa94b983d078">get_shape</a>(B);</div>
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<div class="line"><span class="lineno"> 464</span> <span class="comment">// If vectors are not eligible for multiplication</span></div>
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<div class="line"><span class="lineno"> 465</span> <span class="keywordflow">if</span> (shape_a.second != shape_b.first) {</div>
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<div class="line"><span class="lineno"> 466</span> std::cerr << <span class="stringliteral">"ERROR ("</span> << __func__ << <span class="stringliteral">") : "</span>;</div>
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<div class="line"><span class="lineno"> 467</span> std::cerr << <span class="stringliteral">"Vectors are not eligible for multiplication "</span>;</div>
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<div class="line"><span class="lineno"> 468</span> std::cerr << shape_a << <span class="stringliteral">" and "</span> << shape_b << std::endl;</div>
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<div class="line"><span class="lineno"> 469</span> std::exit(EXIT_FAILURE);</div>
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<div class="line"><span class="lineno"> 470</span> }</div>
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<div class="line"><span class="lineno"> 471</span> std::vector<std::valarray<T>> C; <span class="comment">// Vector to store result</span></div>
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<div class="line"><span class="lineno"> 472</span> <span class="comment">// Normal matrix multiplication</span></div>
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<div class="line"><span class="lineno"> 473</span> <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> i = 0; i < shape_a.first; i++) {</div>
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<div class="line"><span class="lineno"> 474</span> std::valarray<T> row;</div>
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<div class="line"><span class="lineno"> 475</span> row.resize(shape_b.second);</div>
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<div class="line"><span class="lineno"> 476</span> <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> j = 0; j < shape_b.second; j++) {</div>
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<div class="line"><span class="lineno"> 477</span> <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> k = 0; k < shape_a.second; k++) {</div>
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<div class="line"><span class="lineno"> 478</span> row[j] += A[i][k] * B[k][j];</div>
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<div class="line"><span class="lineno"> 479</span> }</div>
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<div class="line"><span class="lineno"> 480</span> }</div>
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<div class="line"><span class="lineno"> 481</span> C.push_back(row);</div>
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<div class="line"><span class="lineno"> 482</span> }</div>
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<div class="line"><span class="lineno"> 483</span> <span class="keywordflow">return</span> C; <span class="comment">// Return new resultant 2D vector</span></div>
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<div class="line"><span class="lineno"> 484</span>}</div>
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<h2 class="memtitle"><span class="permalink"><a href="#a16f34574b7e0dd51bc3b3fda37446695">◆ </a></span>operator*()</h2>
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<p>Overloaded operator "*" to multiply given 2D vector with scaler </p><dl class="tparams"><dt>Template Parameters</dt><dd>
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<table class="tparams">
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<tr><td class="paramname">T</td><td>typename of both vector and the scaler </td></tr>
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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">A</td><td>2D vector to which scaler will be multiplied </td></tr>
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<tr><td class="paramname">val</td><td>Scaler value which will be multiplied </td></tr>
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<dl class="section return"><dt>Returns</dt><dd>new resultant vector </dd></dl>
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<p class="definition">Definition at line <a class="el" href="../../d8/d95/vector__ops_8hpp_source.html#l00347">347</a> of file <a class="el" href="../../d8/d95/vector__ops_8hpp_source.html">vector_ops.hpp</a>.</p>
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<div class="fragment"><div class="line"><span class="lineno"> 348</span> {</div>
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<div class="line"><span class="lineno"> 349</span> std::vector<std::valarray<double>> B =</div>
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<div class="line"><span class="lineno"> 350</span> A; <span class="comment">// New vector to store resultant vector</span></div>
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<div class="line"><span class="lineno"> 351</span> <span class="keywordflow">for</span> (<span class="keyword">auto</span> &b : B) { <span class="comment">// For every row in vector</span></div>
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<div class="line"><span class="lineno"> 352</span> b = b * val; <span class="comment">// Multiply row with scaler</span></div>
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<div class="line"><span class="lineno"> 353</span> }</div>
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<div class="line"><span class="lineno"> 354</span> <span class="keywordflow">return</span> B; <span class="comment">// Return new resultant 2D vector</span></div>
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<div class="line"><span class="lineno"> 355</span>}</div>
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<h2 class="memtitle"><span class="permalink"><a href="#a2466857dab977a49f117029835b3b6d2">◆ </a></span>operator+()</h2>
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template<typename T> </div>
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<td class="memname">std::vector< std::valarray< T > > machine_learning::operator+ </td>
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<td>(</td>
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<td class="paramtype">const std::vector< std::valarray< T > > &</td> <td class="paramname"><span class="paramname"><em>A</em></span>, </td>
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<td class="paramtype">const std::vector< std::valarray< T > > &</td> <td class="paramname"><span class="paramname"><em>B</em></span> )</td>
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<p>Overloaded operator "+" to add two 2D vectors </p><dl class="tparams"><dt>Template Parameters</dt><dd>
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<table class="tparams">
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<tr><td class="paramname">T</td><td>typename of the vector </td></tr>
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<dl class="params"><dt>Parameters</dt><dd>
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<tr><td class="paramname">A</td><td>First 2D vector </td></tr>
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<tr><td class="paramname">B</td><td>Second 2D vector </td></tr>
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<dl class="section return"><dt>Returns</dt><dd>new resultant vector </dd></dl>
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<p class="definition">Definition at line <a class="el" href="../../d8/d95/vector__ops_8hpp_source.html#l00406">406</a> of file <a class="el" href="../../d8/d95/vector__ops_8hpp_source.html">vector_ops.hpp</a>.</p>
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<div class="fragment"><div class="line"><span class="lineno"> 408</span> {</div>
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<div class="line"><span class="lineno"> 409</span> <span class="keyword">const</span> <span class="keyword">auto</span> shape_a = <a class="code hl_function" href="#aa4bbf61e65f8cd297255fa94b983d078">get_shape</a>(A);</div>
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<div class="line"><span class="lineno"> 410</span> <span class="keyword">const</span> <span class="keyword">auto</span> shape_b = <a class="code hl_function" href="#aa4bbf61e65f8cd297255fa94b983d078">get_shape</a>(B);</div>
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<div class="line"><span class="lineno"> 411</span> <span class="comment">// If vectors don't have equal shape</span></div>
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<div class="line"><span class="lineno"> 412</span> <span class="keywordflow">if</span> (shape_a.first != shape_b.first || shape_a.second != shape_b.second) {</div>
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<div class="line"><span class="lineno"> 413</span> std::cerr << <span class="stringliteral">"ERROR ("</span> << __func__ << <span class="stringliteral">") : "</span>;</div>
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<div class="line"><span class="lineno"> 414</span> std::cerr << <span class="stringliteral">"Supplied vectors have different shapes "</span>;</div>
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<div class="line"><span class="lineno"> 415</span> std::cerr << shape_a << <span class="stringliteral">" and "</span> << shape_b << std::endl;</div>
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<div class="line"><span class="lineno"> 416</span> std::exit(EXIT_FAILURE);</div>
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<div class="line"><span class="lineno"> 417</span> }</div>
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<div class="line"><span class="lineno"> 418</span> std::vector<std::valarray<T>> C;</div>
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<div class="line"><span class="lineno"> 419</span> <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> i = 0; i < A.size(); i++) { <span class="comment">// For every row</span></div>
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<div class="line"><span class="lineno"> 420</span> C.push_back(A[i] + B[i]); <span class="comment">// Elementwise addition</span></div>
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<div class="line"><span class="lineno"> 421</span> }</div>
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<div class="line"><span class="lineno"> 422</span> <span class="keywordflow">return</span> C; <span class="comment">// Return new resultant 2D vector</span></div>
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<div class="line"><span class="lineno"> 423</span>}</div>
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<h2 class="memtitle"><span class="permalink"><a href="#a0cc29566568e0383dd7d374068cbe6b3">◆ </a></span>operator-()</h2>
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<p>Overloaded operator "-" to add subtract 2D vectors </p><dl class="tparams"><dt>Template Parameters</dt><dd>
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<tr><td class="paramname">T</td><td>typename of the vector </td></tr>
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<tr><td class="paramname">A</td><td>First 2D vector </td></tr>
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<tr><td class="paramname">B</td><td>Second 2D vector </td></tr>
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<dl class="section return"><dt>Returns</dt><dd>new resultant vector </dd></dl>
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<p class="definition">Definition at line <a class="el" href="../../d8/d95/vector__ops_8hpp_source.html#l00433">433</a> of file <a class="el" href="../../d8/d95/vector__ops_8hpp_source.html">vector_ops.hpp</a>.</p>
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<div class="fragment"><div class="line"><span class="lineno"> 435</span> {</div>
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<div class="line"><span class="lineno"> 436</span> <span class="keyword">const</span> <span class="keyword">auto</span> shape_a = <a class="code hl_function" href="#aa4bbf61e65f8cd297255fa94b983d078">get_shape</a>(A);</div>
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<div class="line"><span class="lineno"> 437</span> <span class="keyword">const</span> <span class="keyword">auto</span> shape_b = <a class="code hl_function" href="#aa4bbf61e65f8cd297255fa94b983d078">get_shape</a>(B);</div>
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<div class="line"><span class="lineno"> 438</span> <span class="comment">// If vectors don't have equal shape</span></div>
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<div class="line"><span class="lineno"> 439</span> <span class="keywordflow">if</span> (shape_a.first != shape_b.first || shape_a.second != shape_b.second) {</div>
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<div class="line"><span class="lineno"> 440</span> std::cerr << <span class="stringliteral">"ERROR ("</span> << __func__ << <span class="stringliteral">") : "</span>;</div>
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<div class="line"><span class="lineno"> 441</span> std::cerr << <span class="stringliteral">"Supplied vectors have different shapes "</span>;</div>
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<div class="line"><span class="lineno"> 442</span> std::cerr << shape_a << <span class="stringliteral">" and "</span> << shape_b << std::endl;</div>
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<div class="line"><span class="lineno"> 443</span> std::exit(EXIT_FAILURE);</div>
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<div class="line"><span class="lineno"> 444</span> }</div>
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<div class="line"><span class="lineno"> 445</span> std::vector<std::valarray<T>> C; <span class="comment">// Vector to store result</span></div>
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<div class="line"><span class="lineno"> 446</span> <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> i = 0; i < A.size(); i++) { <span class="comment">// For every row</span></div>
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<div class="line"><span class="lineno"> 447</span> C.push_back(A[i] - B[i]); <span class="comment">// Elementwise substraction</span></div>
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<div class="line"><span class="lineno"> 448</span> }</div>
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<div class="line"><span class="lineno"> 449</span> <span class="keywordflow">return</span> C; <span class="comment">// Return new resultant 2D vector</span></div>
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<div class="line"><span class="lineno"> 450</span>}</div>
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<h2 class="memtitle"><span class="permalink"><a href="#ae6ec42318d172b97fbdf45638d09d7b5">◆ </a></span>operator/()</h2>
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<p>Overloaded operator "/" to divide given 2D vector with scaler </p><dl class="tparams"><dt>Template Parameters</dt><dd>
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<table class="tparams">
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<tr><td class="paramname">T</td><td>typename of the vector and the scaler </td></tr>
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</dd>
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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">A</td><td>2D vector to which scaler will be divided </td></tr>
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<tr><td class="paramname">val</td><td>Scaler value which will be divided </td></tr>
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</table>
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</dd>
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<dl class="section return"><dt>Returns</dt><dd>new resultant vector </dd></dl>
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<p class="definition">Definition at line <a class="el" href="../../d8/d95/vector__ops_8hpp_source.html#l00365">365</a> of file <a class="el" href="../../d8/d95/vector__ops_8hpp_source.html">vector_ops.hpp</a>.</p>
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<div class="fragment"><div class="line"><span class="lineno"> 366</span> {</div>
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<div class="line"><span class="lineno"> 367</span> std::vector<std::valarray<double>> B =</div>
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<div class="line"><span class="lineno"> 368</span> A; <span class="comment">// New vector to store resultant vector</span></div>
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<div class="line"><span class="lineno"> 369</span> <span class="keywordflow">for</span> (<span class="keyword">auto</span> &b : B) { <span class="comment">// For every row in vector</span></div>
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<div class="line"><span class="lineno"> 370</span> b = b / val; <span class="comment">// Divide row with scaler</span></div>
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<div class="line"><span class="lineno"> 371</span> }</div>
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<div class="line"><span class="lineno"> 372</span> <span class="keywordflow">return</span> B; <span class="comment">// Return new resultant 2D vector</span></div>
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<div class="line"><span class="lineno"> 373</span>}</div>
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<h2 class="memtitle"><span class="permalink"><a href="#af4986b23760039711848155739c31b35">◆ </a></span>operator<<() <span class="overload">[1/3]</span></h2>
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template<typename T> </div>
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<td class="memname">std::ostream & machine_learning::operator<< </td>
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<td>(</td>
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<td class="paramtype">std::ostream &</td> <td class="paramname"><span class="paramname"><em>out</em></span>, </td>
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<p>Overloaded operator "<<" to print a pair </p><dl class="tparams"><dt>Template Parameters</dt><dd>
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<table class="tparams">
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<tr><td class="paramname">T</td><td>typename of the pair </td></tr>
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<dl class="params"><dt>Parameters</dt><dd>
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<tr><td class="paramname">out</td><td>std::ostream to output </td></tr>
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<tr><td class="paramname">A</td><td>Pair to be printed </td></tr>
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<p class="definition">Definition at line <a class="el" href="../../d8/d95/vector__ops_8hpp_source.html#l00052">52</a> of file <a class="el" href="../../d8/d95/vector__ops_8hpp_source.html">vector_ops.hpp</a>.</p>
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<div class="fragment"><div class="line"><span class="lineno"> 52</span> {</div>
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<div class="line"><span class="lineno"> 53</span> <span class="comment">// Setting output precision to 4 in case of floating point numbers</span></div>
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<div class="line"><span class="lineno"> 54</span> out.precision(4);</div>
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<div class="line"><span class="lineno"> 55</span> <span class="comment">// printing pair in the form (p, q)</span></div>
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<div class="line"><span class="lineno"> 56</span> std::cout << <span class="stringliteral">"("</span> << A.first << <span class="stringliteral">", "</span> << A.second << <span class="stringliteral">")"</span>;</div>
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<div class="line"><span class="lineno"> 57</span> <span class="keywordflow">return</span> out;</div>
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<div class="line"><span class="lineno"> 58</span>}</div>
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<h2 class="memtitle"><span class="permalink"><a href="#a54bf1f3c43271a5fc93101f6ae2e6269">◆ </a></span>operator<<() <span class="overload">[2/3]</span></h2>
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<td class="paramtype">std::ostream &</td> <td class="paramname"><span class="paramname"><em>out</em></span>, </td>
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<p>Overloaded operator "<<" to print a 1D vector </p><dl class="tparams"><dt>Template Parameters</dt><dd>
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<table class="tparams">
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<tr><td class="paramname">T</td><td>typename of the vector </td></tr>
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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">out</td><td>std::ostream to output </td></tr>
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<tr><td class="paramname">A</td><td>1D vector to be printed </td></tr>
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<p class="definition">Definition at line <a class="el" href="../../d8/d95/vector__ops_8hpp_source.html#l00067">67</a> of file <a class="el" href="../../d8/d95/vector__ops_8hpp_source.html">vector_ops.hpp</a>.</p>
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<div class="fragment"><div class="line"><span class="lineno"> 67</span> {</div>
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<div class="line"><span class="lineno"> 68</span> <span class="comment">// Setting output precision to 4 in case of floating point numbers</span></div>
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<div class="line"><span class="lineno"> 69</span> out.precision(4);</div>
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<div class="line"><span class="lineno"> 70</span> <span class="keywordflow">for</span> (<span class="keyword">const</span> <span class="keyword">auto</span> &a : A) { <span class="comment">// For every element in the vector.</span></div>
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<div class="line"><span class="lineno"> 71</span> std::cout << a << <span class="charliteral">' '</span>; <span class="comment">// Print element</span></div>
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<div class="line"><span class="lineno"> 72</span> }</div>
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<div class="line"><span class="lineno"> 73</span> std::cout << std::endl;</div>
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<div class="line"><span class="lineno"> 74</span> <span class="keywordflow">return</span> out;</div>
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<div class="line"><span class="lineno"> 75</span>}</div>
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<h2 class="memtitle"><span class="permalink"><a href="#a84260cb1be9b63d6e38107000ac4b7e7">◆ </a></span>operator<<() <span class="overload">[3/3]</span></h2>
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template<typename T> </div>
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<td class="memname">std::ostream & machine_learning::operator<< </td>
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<td>(</td>
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<td class="paramtype">std::ostream &</td> <td class="paramname"><span class="paramname"><em>out</em></span>, </td>
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<td class="paramkey"></td>
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<td></td>
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<td class="paramtype">std::vector< std::valarray< T > > const &</td> <td class="paramname"><span class="paramname"><em>A</em></span> )</td>
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</div><div class="memdoc">
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<p>Overloaded operator "<<" to print 2D vector </p><dl class="tparams"><dt>Template Parameters</dt><dd>
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<table class="tparams">
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<tr><td class="paramname">T</td><td>typename of the vector </td></tr>
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</dl>
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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">out</td><td>std::ostream to output </td></tr>
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<tr><td class="paramname">A</td><td>2D vector to be printed </td></tr>
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<p class="definition">Definition at line <a class="el" href="../../d8/d95/vector__ops_8hpp_source.html#l00032">32</a> of file <a class="el" href="../../d8/d95/vector__ops_8hpp_source.html">vector_ops.hpp</a>.</p>
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<div class="fragment"><div class="line"><span class="lineno"> 33</span> {</div>
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<div class="line"><span class="lineno"> 34</span> <span class="comment">// Setting output precision to 4 in case of floating point numbers</span></div>
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<div class="line"><span class="lineno"> 35</span> out.precision(4);</div>
|
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<div class="line"><span class="lineno"> 36</span> <span class="keywordflow">for</span> (<span class="keyword">const</span> <span class="keyword">auto</span> &a : A) { <span class="comment">// For each row in A</span></div>
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<div class="line"><span class="lineno"> 37</span> <span class="keywordflow">for</span> (<span class="keyword">const</span> <span class="keyword">auto</span> &x : a) { <span class="comment">// For each element in row</span></div>
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<div class="line"><span class="lineno"> 38</span> std::cout << x << <span class="charliteral">' '</span>; <span class="comment">// print element</span></div>
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<div class="line"><span class="lineno"> 39</span> }</div>
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<div class="line"><span class="lineno"> 40</span> std::cout << std::endl;</div>
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<div class="line"><span class="lineno"> 41</span> }</div>
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<div class="line"><span class="lineno"> 42</span> <span class="keywordflow">return</span> out;</div>
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<div class="line"><span class="lineno"> 43</span>}</div>
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<h2 class="memtitle"><span class="permalink"><a href="#ae10178b082f0205c326550877d998e5d">◆ </a></span>pop_back()</h2>
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<td class="memname">std::valarray< T > machine_learning::pop_back </td>
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<td>(</td>
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<td class="paramtype">const std::valarray< T > &</td> <td class="paramname"><span class="paramname"><em>A</em></span></td><td>)</td>
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<p>Function to remove last element from 1D vector </p><dl class="tparams"><dt>Template Parameters</dt><dd>
|
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<table class="tparams">
|
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<tr><td class="paramname">T</td><td>typename of the vector </td></tr>
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</table>
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</dd>
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</dl>
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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">A</td><td>1D vector from which last element will be removed </td></tr>
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<dl class="section return"><dt>Returns</dt><dd>new resultant vector </dd></dl>
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<p class="definition">Definition at line <a class="el" href="../../d8/d95/vector__ops_8hpp_source.html#l00119">119</a> of file <a class="el" href="../../d8/d95/vector__ops_8hpp_source.html">vector_ops.hpp</a>.</p>
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<div class="fragment"><div class="line"><span class="lineno"> 119</span> {</div>
|
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<div class="line"><span class="lineno"> 120</span> std::valarray<T> B; <span class="comment">// New 1D vector to store resultant vector</span></div>
|
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<div class="line"><span class="lineno"> 121</span> B.resize(A.size() - 1); <span class="comment">// Resizing it accordingly</span></div>
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<div class="line"><span class="lineno"> 122</span> <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> i = 0; i < A.size() - 1;</div>
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<div class="line"><span class="lineno"> 123</span> i++) { <span class="comment">// For every (except last) element in A</span></div>
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<div class="line"><span class="lineno"> 124</span> B[i] = A[i]; <span class="comment">// Copy element in B</span></div>
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<div class="line"><span class="lineno"> 125</span> }</div>
|
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<div class="line"><span class="lineno"> 126</span> <span class="keywordflow">return</span> B; <span class="comment">// Return resultant vector</span></div>
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<div class="line"><span class="lineno"> 127</span>}</div>
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<h2 class="memtitle"><span class="permalink"><a href="#a912cf68863063a38d6e63545be5eb093">◆ </a></span>pop_front()</h2>
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<td class="memname">std::valarray< T > machine_learning::pop_front </td>
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<td class="paramtype">const std::valarray< T > &</td> <td class="paramname"><span class="paramname"><em>A</em></span></td><td>)</td>
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<p>Function to remove first element from 1D vector </p><dl class="tparams"><dt>Template Parameters</dt><dd>
|
|
<table class="tparams">
|
|
<tr><td class="paramname">T</td><td>typename of the vector </td></tr>
|
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<dl class="params"><dt>Parameters</dt><dd>
|
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<tr><td class="paramname">A</td><td>1D vector from which first element will be removed </td></tr>
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|
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|
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<dl class="section return"><dt>Returns</dt><dd>new resultant vector </dd></dl>
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<p class="definition">Definition at line <a class="el" href="../../d8/d95/vector__ops_8hpp_source.html#l00102">102</a> of file <a class="el" href="../../d8/d95/vector__ops_8hpp_source.html">vector_ops.hpp</a>.</p>
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<div class="fragment"><div class="line"><span class="lineno"> 102</span> {</div>
|
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<div class="line"><span class="lineno"> 103</span> std::valarray<T> B; <span class="comment">// New 1D vector to store resultant vector</span></div>
|
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<div class="line"><span class="lineno"> 104</span> B.resize(A.size() - 1); <span class="comment">// Resizing it accordingly</span></div>
|
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<div class="line"><span class="lineno"> 105</span> <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> i = 1; i < A.size();</div>
|
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<div class="line"><span class="lineno"> 106</span> i++) { <span class="comment">// // For every (except first) element in A</span></div>
|
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<div class="line"><span class="lineno"> 107</span> B[i - 1] = A[i]; <span class="comment">// Copy element in B with left shifted position</span></div>
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<div class="line"><span class="lineno"> 108</span> }</div>
|
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<div class="line"><span class="lineno"> 109</span> <span class="keywordflow">return</span> B; <span class="comment">// Return resultant vector</span></div>
|
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<div class="line"><span class="lineno"> 110</span>}</div>
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<h2 class="memtitle"><span class="permalink"><a href="#aa72a53c88203fde278f1fe6c3afe5b07">◆ </a></span>save_u_matrix()</h2>
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|
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<p>Create the distance matrix or <a href="https://en.wikipedia.org/wiki/U-matrix" target="_blank">U-matrix</a> from the trained 3D weiths matrix and save to disk.</p>
|
|
<dl class="params"><dt>Parameters</dt><dd>
|
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<table class="params">
|
|
<tr><td class="paramdir">[in]</td><td class="paramname">fname</td><td>filename to save in (gets overwriten without confirmation) </td></tr>
|
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<tr><td class="paramdir">[in]</td><td class="paramname">W</td><td>model matrix to save </td></tr>
|
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|
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|
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|
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<dl class="section return"><dt>Returns</dt><dd>0 if all ok </dd>
|
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|
|
-1 if file creation failed </dd></dl>
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<p class="definition">Definition at line <a class="el" href="../../d4/def/kohonen__som__topology_8cpp_source.html#l00142">142</a> of file <a class="el" href="../../d4/def/kohonen__som__topology_8cpp_source.html">kohonen_som_topology.cpp</a>.</p>
|
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<div class="fragment"><div class="line"><span class="lineno"> 143</span> {</div>
|
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<div class="line"><span class="lineno"> 144</span> std::ofstream fp(fname);</div>
|
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<div class="line"><span class="lineno"> 145</span> <span class="keywordflow">if</span> (!fp) { <span class="comment">// error with fopen</span></div>
|
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<div class="line"><span class="lineno"> 146</span> std::cerr << <span class="stringliteral">"File error ("</span> << fname << <span class="stringliteral">"): "</span> << std::strerror(errno)</div>
|
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<div class="line"><span class="lineno"> 147</span> << std::endl;</div>
|
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<div class="line"><span class="lineno"> 148</span> <span class="keywordflow">return</span> -1;</div>
|
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<div class="line"><span class="lineno"> 149</span> }</div>
|
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<div class="line"><span class="lineno"> 150</span> </div>
|
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<div class="line"><span class="lineno"> 151</span> <span class="comment">// neighborhood range</span></div>
|
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<div class="line"><span class="lineno"> 152</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> R = 1;</div>
|
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<div class="line"><span class="lineno"> 153</span> </div>
|
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<div class="line"><span class="lineno"> 154</span> <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i < W.size(); i++) { <span class="comment">// for each x</span></div>
|
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<div class="line"><span class="lineno"> 155</span> <span class="keywordflow">for</span> (<span class="keywordtype">int</span> j = 0; j < W[0].size(); j++) { <span class="comment">// for each y</span></div>
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<div class="line"><span class="lineno"> 156</span> <span class="keywordtype">double</span> distance = 0.f;</div>
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<div class="line"><span class="lineno"> 157</span> </div>
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<div class="line"><span class="lineno"> 158</span> <span class="keywordtype">int</span> from_x = std::max<int>(0, i - R);</div>
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<div class="line"><span class="lineno"> 159</span> <span class="keywordtype">int</span> to_x = std::min<int>(W.size(), i + R + 1);</div>
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<div class="line"><span class="lineno"> 160</span> <span class="keywordtype">int</span> from_y = std::max<int>(0, j - R);</div>
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<div class="line"><span class="lineno"> 161</span> <span class="keywordtype">int</span> to_y = std::min<int>(W[0].size(), j + R + 1);</div>
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<div class="line"><span class="lineno"> 162</span> <span class="keywordtype">int</span> l = 0, m = 0;</div>
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<div class="line"><span class="lineno"> 163</span><span class="preprocessor">#ifdef _OPENMP</span></div>
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<div class="line"><span class="lineno"> 164</span><span class="preprocessor">#pragma omp parallel for reduction(+ : distance)</span></div>
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<div class="line"><span class="lineno"> 165</span><span class="preprocessor">#endif</span></div>
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<div class="line"><span class="lineno"> 166</span> <span class="keywordflow">for</span> (l = from_x; l < to_x; l++) { <span class="comment">// scan neighborhoor in x</span></div>
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<div class="line"><span class="lineno"> 167</span> <span class="keywordflow">for</span> (m = from_y; m < to_y; m++) { <span class="comment">// scan neighborhood in y</span></div>
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<div class="line"><span class="lineno"> 168</span> <span class="keyword">auto</span> d = W[i][j] - W[l][m];</div>
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<div class="line"><span class="lineno"> 169</span> <span class="keywordtype">double</span> d2 = std::pow(d, 2).sum();</div>
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<div class="line"><span class="lineno"> 170</span> distance += std::sqrt(d2);</div>
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<div class="line"><span class="lineno"> 171</span> <span class="comment">// distance += d2;</span></div>
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<div class="line"><span class="lineno"> 172</span> }</div>
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<div class="line"><span class="lineno"> 173</span> }</div>
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<div class="line"><span class="lineno"> 174</span> </div>
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<div class="line"><span class="lineno"> 175</span> distance /= R * R; <span class="comment">// mean distance from neighbors</span></div>
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<div class="line"><span class="lineno"> 176</span> fp << distance; <span class="comment">// print the mean separation</span></div>
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<div class="line"><span class="lineno"> 177</span> <span class="keywordflow">if</span> (j < W[0].size() - 1) { <span class="comment">// if not the last column</span></div>
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<div class="line"><span class="lineno"> 178</span> fp << <span class="charliteral">','</span>; <span class="comment">// suffix comma</span></div>
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<div class="line"><span class="lineno"> 179</span> }</div>
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<div class="line"><span class="lineno"> 180</span> }</div>
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<div class="line"><span class="lineno"> 181</span> <span class="keywordflow">if</span> (i < W.size() - 1) { <span class="comment">// if not the last row</span></div>
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<div class="line"><span class="lineno"> 182</span> fp << <span class="charliteral">'\n'</span>; <span class="comment">// start a new line</span></div>
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<div class="line"><span class="lineno"> 183</span> }</div>
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<div class="line"><span class="lineno"> 184</span> }</div>
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<div class="line"><span class="lineno"> 185</span> </div>
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<div class="line"><span class="lineno"> 186</span> fp.close();</div>
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<div class="line"><span class="lineno"> 187</span> <span class="keywordflow">return</span> 0;</div>
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<div class="line"><span class="lineno"> 188</span>}</div>
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<h2 class="memtitle"><span class="permalink"><a href="#a6f1c98c016ad34ff3d9f39372161bd35">◆ </a></span>sum()</h2>
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<td class="memname">T machine_learning::sum </td>
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<td>(</td>
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<td class="paramtype">const std::vector< std::valarray< T > > &</td> <td class="paramname"><span class="paramname"><em>A</em></span></td><td>)</td>
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<td></td>
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<p>Function to get sum of all elements in 2D vector </p><dl class="tparams"><dt>Template Parameters</dt><dd>
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<table class="tparams">
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<tr><td class="paramname">T</td><td>typename of the vector </td></tr>
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</dd>
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</dl>
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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">A</td><td>2D vector for which sum is required </td></tr>
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</table>
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<dl class="section return"><dt>Returns</dt><dd>returns sum of all elements of 2D vector </dd></dl>
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<p class="definition">Definition at line <a class="el" href="../../d8/d95/vector__ops_8hpp_source.html#l00232">232</a> of file <a class="el" href="../../d8/d95/vector__ops_8hpp_source.html">vector_ops.hpp</a>.</p>
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<div class="fragment"><div class="line"><span class="lineno"> 232</span> {</div>
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<div class="line"><span class="lineno"> 233</span> T cur_sum = 0; <span class="comment">// Initially sum is zero</span></div>
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<div class="line"><span class="lineno"> 234</span> <span class="keywordflow">for</span> (<span class="keyword">const</span> <span class="keyword">auto</span> &a : A) { <span class="comment">// For every row in A</span></div>
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<div class="line"><span class="lineno"> 235</span> cur_sum += a.sum(); <span class="comment">// Add sum of that row to current sum</span></div>
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<div class="line"><span class="lineno"> 236</span> }</div>
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<div class="line"><span class="lineno"> 237</span> <span class="keywordflow">return</span> cur_sum; <span class="comment">// Return sum</span></div>
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<div class="line"><span class="lineno"> 238</span>}</div>
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<h2 class="memtitle"><span class="permalink"><a href="#a89fde571b38f9483576594f66572958a">◆ </a></span>transpose()</h2>
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template<typename T> </div>
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<td class="memname">std::vector< std::valarray< T > > machine_learning::transpose </td>
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<td>(</td>
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<td class="paramtype">const std::vector< std::valarray< T > > &</td> <td class="paramname"><span class="paramname"><em>A</em></span></td><td>)</td>
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<td></td>
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<p>Function to get transpose of 2D vector </p><dl class="tparams"><dt>Template Parameters</dt><dd>
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<table class="tparams">
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<tr><td class="paramname">T</td><td>typename of the vector </td></tr>
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</dd>
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</dl>
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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">A</td><td>2D vector which will be transposed </td></tr>
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</table>
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<dl class="section return"><dt>Returns</dt><dd>new resultant vector </dd></dl>
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<p class="definition">Definition at line <a class="el" href="../../d8/d95/vector__ops_8hpp_source.html#l00382">382</a> of file <a class="el" href="../../d8/d95/vector__ops_8hpp_source.html">vector_ops.hpp</a>.</p>
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<div class="fragment"><div class="line"><span class="lineno"> 383</span> {</div>
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<div class="line"><span class="lineno"> 384</span> <span class="keyword">const</span> <span class="keyword">auto</span> shape = <a class="code hl_function" href="#aa4bbf61e65f8cd297255fa94b983d078">get_shape</a>(A); <span class="comment">// Current shape of vector</span></div>
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<div class="line"><span class="lineno"> 385</span> std::vector<std::valarray<T>> B; <span class="comment">// New vector to store result</span></div>
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<div class="line"><span class="lineno"> 386</span> <span class="comment">// Storing transpose values of A in B</span></div>
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<div class="line"><span class="lineno"> 387</span> <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> j = 0; j < shape.second; j++) {</div>
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<div class="line"><span class="lineno"> 388</span> std::valarray<T> row;</div>
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<div class="line"><span class="lineno"> 389</span> row.resize(shape.first);</div>
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<div class="line"><span class="lineno"> 390</span> <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> i = 0; i < shape.first; i++) {</div>
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<div class="line"><span class="lineno"> 391</span> row[i] = A[i][j];</div>
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<div class="line"><span class="lineno"> 392</span> }</div>
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<div class="line"><span class="lineno"> 393</span> B.push_back(row);</div>
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<div class="line"><span class="lineno"> 394</span> }</div>
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<div class="line"><span class="lineno"> 395</span> <span class="keywordflow">return</span> B; <span class="comment">// Return new resultant 2D vector</span></div>
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<div class="line"><span class="lineno"> 396</span>}</div>
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<h2 class="memtitle"><span class="permalink"><a href="#abee7b35403af3612222d3b7a53074905">◆ </a></span>uniform_random_initialization()</h2>
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<td class="memname">void machine_learning::uniform_random_initialization </td>
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<td>(</td>
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<td class="paramtype">std::vector< std::valarray< T > > &</td> <td class="paramname"><span class="paramname"><em>A</em></span>, </td>
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<td class="paramkey"></td>
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<td></td>
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<td class="paramtype">const std::pair< size_t, size_t > &</td> <td class="paramname"><span class="paramname"><em>shape</em></span>, </td>
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<td class="paramtype">const T &</td> <td class="paramname"><span class="paramname"><em>low</em></span>, </td>
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<td class="paramtype">const T &</td> <td class="paramname"><span class="paramname"><em>high</em></span> )</td>
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<p>Function to initialize given 2D vector using uniform random initialization </p><dl class="tparams"><dt>Template Parameters</dt><dd>
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<table class="tparams">
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<tr><td class="paramname">T</td><td>typename of the vector </td></tr>
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</table>
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</dl>
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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">A</td><td>2D vector to be initialized </td></tr>
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<tr><td class="paramname">shape</td><td>required shape </td></tr>
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<tr><td class="paramname">low</td><td>lower limit on value </td></tr>
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<tr><td class="paramname">high</td><td>upper limit on value </td></tr>
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</table>
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</dd>
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<p class="definition">Definition at line <a class="el" href="../../d8/d95/vector__ops_8hpp_source.html#l00166">166</a> of file <a class="el" href="../../d8/d95/vector__ops_8hpp_source.html">vector_ops.hpp</a>.</p>
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<div class="fragment"><div class="line"><span class="lineno"> 168</span> {</div>
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<div class="line"><span class="lineno"> 169</span> A.clear(); <span class="comment">// Making A empty</span></div>
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<div class="line"><span class="lineno"> 170</span> <span class="comment">// Uniform distribution in range [low, high]</span></div>
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<div class="line"><span class="lineno"> 171</span> std::default_random_engine generator(</div>
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<div class="line"><span class="lineno"> 172</span> std::chrono::system_clock::now().time_since_epoch().count());</div>
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<div class="line"><span class="lineno"> 173</span> std::uniform_real_distribution<T> distribution(low, high);</div>
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<div class="line"><span class="lineno"> 174</span> <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> i = 0; i < shape.first; i++) { <span class="comment">// For every row</span></div>
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<div class="line"><span class="lineno"> 175</span> std::valarray<T></div>
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<div class="line"><span class="lineno"> 176</span> row; <span class="comment">// Making empty row which will be inserted in vector</span></div>
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<div class="line"><span class="lineno"> 177</span> row.resize(shape.second);</div>
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<div class="line"><span class="lineno"> 178</span> <span class="keywordflow">for</span> (<span class="keyword">auto</span> &r : row) { <span class="comment">// For every element in row</span></div>
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<div class="line"><span class="lineno"> 179</span> r = distribution(generator); <span class="comment">// copy random number</span></div>
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<div class="line"><span class="lineno"> 180</span> }</div>
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<div class="line"><span class="lineno"> 181</span> A.push_back(row); <span class="comment">// Insert new row in vector</span></div>
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<div class="line"><span class="lineno"> 182</span> }</div>
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<div class="line"><span class="lineno"> 183</span> <span class="keywordflow">return</span>;</div>
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<div class="line"><span class="lineno"> 184</span>}</div>
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<a id="a8dd3f1ffbc2f26a3c88da1b1f8b7e9c4" name="a8dd3f1ffbc2f26a3c88da1b1f8b7e9c4"></a>
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<h2 class="memtitle"><span class="permalink"><a href="#a8dd3f1ffbc2f26a3c88da1b1f8b7e9c4">◆ </a></span>unit_matrix_initialization()</h2>
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<td class="memname">void machine_learning::unit_matrix_initialization </td>
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<td>(</td>
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<td class="paramkey"></td>
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<td class="paramtype">const std::pair< size_t, size_t > &</td> <td class="paramname"><span class="paramname"><em>shape</em></span> )</td>
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<p>Function to Intialize 2D vector as unit matrix </p><dl class="tparams"><dt>Template Parameters</dt><dd>
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<table class="tparams">
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<tr><td class="paramname">T</td><td>typename of the vector </td></tr>
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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">A</td><td>2D vector to be initialized </td></tr>
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<tr><td class="paramname">shape</td><td>required shape </td></tr>
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<p class="definition">Definition at line <a class="el" href="../../d8/d95/vector__ops_8hpp_source.html#l00193">193</a> of file <a class="el" href="../../d8/d95/vector__ops_8hpp_source.html">vector_ops.hpp</a>.</p>
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<div class="fragment"><div class="line"><span class="lineno"> 194</span> {</div>
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<div class="line"><span class="lineno"> 195</span> A.clear(); <span class="comment">// Making A empty</span></div>
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<div class="line"><span class="lineno"> 196</span> <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> i = 0; i < shape.first; i++) {</div>
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<div class="line"><span class="lineno"> 197</span> std::valarray<T></div>
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<div class="line"><span class="lineno"> 198</span> row; <span class="comment">// Making empty row which will be inserted in vector</span></div>
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<div class="line"><span class="lineno"> 199</span> row.resize(shape.second);</div>
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<div class="line"><span class="lineno"> 200</span> row[i] = T(1); <span class="comment">// Insert 1 at ith position</span></div>
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<div class="line"><span class="lineno"> 201</span> A.push_back(row); <span class="comment">// Insert new row in vector</span></div>
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<div class="line"><span class="lineno"> 202</span> }</div>
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<div class="line"><span class="lineno"> 203</span> <span class="keywordflow">return</span>;</div>
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<div class="line"><span class="lineno"> 204</span>}</div>
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<h2 class="memtitle"><span class="permalink"><a href="#aa6aac06ccf128b0a9c55c9ee1a8e5631">◆ </a></span>update_weights() <span class="overload">[1/2]</span></h2>
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<td class="memname">void machine_learning::update_weights </td>
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<td>(</td>
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<td class="paramtype">std::vector< std::valarray< double > > *</td> <td class="paramname"><span class="paramname"><em>W</em></span>, </td>
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<td class="paramtype">std::valarray< double > *</td> <td class="paramname"><span class="paramname"><em>D</em></span>, </td>
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<p>Update weights of the SOM using Kohonen algorithm</p>
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<dl class="params"><dt>Parameters</dt><dd>
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<tr><td class="paramdir">[in]</td><td class="paramname">X</td><td>data point </td></tr>
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<tr><td class="paramdir">[in,out]</td><td class="paramname">W</td><td>weights matrix </td></tr>
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<tr><td class="paramdir">[in,out]</td><td class="paramname">D</td><td>temporary vector to store distances </td></tr>
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<tr><td class="paramdir">[in]</td><td class="paramname">alpha</td><td>learning rate \(0<\alpha\le1\) </td></tr>
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<tr><td class="paramdir">[in]</td><td class="paramname">R</td><td>neighborhood range </td></tr>
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<p class="definition">Definition at line <a class="el" href="../../d9/d49/kohonen__som__trace_8cpp_source.html#l00103">103</a> of file <a class="el" href="../../d9/d49/kohonen__som__trace_8cpp_source.html">kohonen_som_trace.cpp</a>.</p>
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<div class="fragment"><div class="line"><span class="lineno"> 105</span> {</div>
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<div class="line"><span class="lineno"> 106</span> <span class="keywordtype">int</span> j = 0;</div>
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<div class="line"><span class="lineno"> 107</span> <span class="keywordtype">int</span> num_out = W->size(); <span class="comment">// number of SOM output nodes</span></div>
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<div class="line"><span class="lineno"> 108</span> <span class="comment">// int num_features = x.size(); // number of data features</span></div>
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<div class="line"><span class="lineno"> 109</span> </div>
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<div class="line"><span class="lineno"> 110</span><span class="preprocessor">#ifdef _OPENMP</span></div>
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<div class="line"><span class="lineno"> 111</span><span class="preprocessor">#pragma omp for</span></div>
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<div class="line"><span class="lineno"> 112</span><span class="preprocessor">#endif</span></div>
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<div class="line"><span class="lineno"> 113</span> <span class="comment">// step 1: for each output point</span></div>
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<div class="line"><span class="lineno"> 114</span> <span class="keywordflow">for</span> (j = 0; j < num_out; j++) {</div>
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<div class="line"><span class="lineno"> 115</span> <span class="comment">// compute Euclidian distance of each output</span></div>
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<div class="line"><span class="lineno"> 116</span> <span class="comment">// point from the current sample</span></div>
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<div class="line"><span class="lineno"> 117</span> (*D)[j] = (((*W)[j] - x) * ((*W)[j] - x)).<a class="code hl_function" href="#a6f1c98c016ad34ff3d9f39372161bd35">sum</a>();</div>
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<div class="line"><span class="lineno"> 118</span> }</div>
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<div class="line"><span class="lineno"> 119</span> </div>
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<div class="line"><span class="lineno"> 120</span> <span class="comment">// step 2: get closest node i.e., node with snallest Euclidian distance to</span></div>
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<div class="line"><span class="lineno"> 121</span> <span class="comment">// the current pattern</span></div>
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<div class="line"><span class="lineno"> 122</span> <span class="keyword">auto</span> result = std::min_element(std::begin(*D), std::end(*D));</div>
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<div class="line"><span class="lineno"> 123</span> <span class="comment">// double d_min = *result;</span></div>
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<div class="line"><span class="lineno"> 124</span> <span class="keywordtype">int</span> d_min_idx = std::distance(std::begin(*D), result);</div>
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<div class="line"><span class="lineno"> 125</span> </div>
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<div class="line"><span class="lineno"> 126</span> <span class="comment">// step 3a: get the neighborhood range</span></div>
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<div class="line"><span class="lineno"> 127</span> <span class="keywordtype">int</span> from_node = std::max(0, d_min_idx - R);</div>
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<div class="line"><span class="lineno"> 128</span> <span class="keywordtype">int</span> to_node = std::min(num_out, d_min_idx + R + 1);</div>
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<div class="line"><span class="lineno"> 129</span> </div>
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<div class="line"><span class="lineno"> 130</span> <span class="comment">// step 3b: update the weights of nodes in the</span></div>
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<div class="line"><span class="lineno"> 131</span> <span class="comment">// neighborhood</span></div>
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<div class="line"><span class="lineno"> 132</span><span class="preprocessor">#ifdef _OPENMP</span></div>
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<div class="line"><span class="lineno"> 133</span><span class="preprocessor">#pragma omp for</span></div>
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<div class="line"><span class="lineno"> 134</span><span class="preprocessor">#endif</span></div>
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<div class="line"><span class="lineno"> 135</span> <span class="keywordflow">for</span> (j = from_node; j < to_node; j++) {</div>
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<div class="line"><span class="lineno"> 136</span> <span class="comment">// update weights of nodes in the neighborhood</span></div>
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<div class="line"><span class="lineno"> 137</span> (*W)[j] += alpha * (x - (*W)[j]);</div>
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<div class="line"><span class="lineno"> 138</span> }</div>
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<div class="line"><span class="lineno"> 139</span>}</div>
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<div class="ttc" id="anamespacemachine__learning_html_a6f1c98c016ad34ff3d9f39372161bd35"><div class="ttname"><a href="#a6f1c98c016ad34ff3d9f39372161bd35">machine_learning::sum</a></div><div class="ttdeci">T sum(const std::vector< std::valarray< T > > &A)</div><div class="ttdef"><b>Definition</b> <a href="../../d8/d95/vector__ops_8hpp_source.html#l00232">vector_ops.hpp:232</a></div></div>
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<h2 class="memtitle"><span class="permalink"><a href="#ae868ad43698a1d69ba46ea3827d7d2c3">◆ </a></span>update_weights() <span class="overload">[2/2]</span></h2>
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<td class="memname">double machine_learning::update_weights </td>
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<td>(</td>
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<td class="paramtype">const std::valarray< double > &</td> <td class="paramname"><span class="paramname"><em>X</em></span>, </td>
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<td class="paramkey"></td>
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<td></td>
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<td class="paramtype">std::vector< std::vector< std::valarray< double > > > *</td> <td class="paramname"><span class="paramname"><em>W</em></span>, </td>
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<td class="paramkey"></td>
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<td></td>
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<td class="paramtype">std::vector< std::valarray< double > > *</td> <td class="paramname"><span class="paramname"><em>D</em></span>, </td>
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<td class="paramkey"></td>
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<td></td>
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<td class="paramtype">double</td> <td class="paramname"><span class="paramname"><em>alpha</em></span>, </td>
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<td class="paramkey"></td>
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<td class="paramtype">int</td> <td class="paramname"><span class="paramname"><em>R</em></span> )</td>
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</div><div class="memdoc">
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<p>Update weights of the SOM using Kohonen algorithm</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="paramdir">[in]</td><td class="paramname">X</td><td>data point - N features </td></tr>
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<tr><td class="paramdir">[in,out]</td><td class="paramname">W</td><td>weights matrix - PxQxN </td></tr>
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<tr><td class="paramdir">[in,out]</td><td class="paramname">D</td><td>temporary vector to store distances PxQ </td></tr>
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<tr><td class="paramdir">[in]</td><td class="paramname">alpha</td><td>learning rate \(0<\alpha\le1\) </td></tr>
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<tr><td class="paramdir">[in]</td><td class="paramname">R</td><td>neighborhood range </td></tr>
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</table>
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</dd>
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</dl>
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<dl class="section return"><dt>Returns</dt><dd>minimum distance of sample and trained weights </dd></dl>
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<p class="definition">Definition at line <a class="el" href="../../d4/def/kohonen__som__topology_8cpp_source.html#l00200">200</a> of file <a class="el" href="../../d4/def/kohonen__som__topology_8cpp_source.html">kohonen_som_topology.cpp</a>.</p>
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<div class="fragment"><div class="line"><span class="lineno"> 203</span> {</div>
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<div class="line"><span class="lineno"> 204</span> <span class="keywordtype">int</span> x = 0, y = 0;</div>
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<div class="line"><span class="lineno"> 205</span> <span class="keywordtype">int</span> num_out_x = <span class="keyword">static_cast<</span><span class="keywordtype">int</span><span class="keyword">></span>(W->size()); <span class="comment">// output nodes - in X</span></div>
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<div class="line"><span class="lineno"> 206</span> <span class="keywordtype">int</span> num_out_y = <span class="keyword">static_cast<</span><span class="keywordtype">int</span><span class="keyword">></span>(W[0][0].size()); <span class="comment">// output nodes - in Y</span></div>
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<div class="line"><span class="lineno"> 207</span> <span class="comment">// int num_features = static_cast<int>(W[0][0][0].size()); // features =</span></div>
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<div class="line"><span class="lineno"> 208</span> <span class="comment">// in Z</span></div>
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<div class="line"><span class="lineno"> 209</span> <span class="keywordtype">double</span> d_min = 0.f;</div>
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<div class="line"><span class="lineno"> 210</span> </div>
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<div class="line"><span class="lineno"> 211</span><span class="preprocessor">#ifdef _OPENMP</span></div>
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<div class="line"><span class="lineno"> 212</span><span class="preprocessor">#pragma omp for</span></div>
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<div class="line"><span class="lineno"> 213</span><span class="preprocessor">#endif</span></div>
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<div class="line"><span class="lineno"> 214</span> <span class="comment">// step 1: for each output point</span></div>
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<div class="line"><span class="lineno"> 215</span> <span class="keywordflow">for</span> (x = 0; x < num_out_x; x++) {</div>
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<div class="line"><span class="lineno"> 216</span> <span class="keywordflow">for</span> (y = 0; y < num_out_y; y++) {</div>
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<div class="line"><span class="lineno"> 217</span> (*D)[x][y] = 0.f;</div>
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<div class="line"><span class="lineno"> 218</span> <span class="comment">// compute Euclidian distance of each output</span></div>
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<div class="line"><span class="lineno"> 219</span> <span class="comment">// point from the current sample</span></div>
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<div class="line"><span class="lineno"> 220</span> <span class="keyword">auto</span> d = ((*W)[x][y] - X);</div>
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<div class="line"><span class="lineno"> 221</span> (*D)[x][y] = (d * d).<a class="code hl_function" href="#a6f1c98c016ad34ff3d9f39372161bd35">sum</a>();</div>
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<div class="line"><span class="lineno"> 222</span> (*D)[x][y] = std::sqrt((*D)[x][y]);</div>
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<div class="line"><span class="lineno"> 223</span> }</div>
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<div class="line"><span class="lineno"> 224</span> }</div>
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<div class="line"><span class="lineno"> 225</span> </div>
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<div class="line"><span class="lineno"> 226</span> <span class="comment">// step 2: get closest node i.e., node with snallest Euclidian distance</span></div>
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<div class="line"><span class="lineno"> 227</span> <span class="comment">// to the current pattern</span></div>
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<div class="line"><span class="lineno"> 228</span> <span class="keywordtype">int</span> d_min_x = 0, d_min_y = 0;</div>
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<div class="line"><span class="lineno"> 229</span> <a class="code hl_function" href="../../d9/d66/group__machine__learning.html#ga60f9186ccb682724a8792a2bf81e9b9e">get_min_2d</a>(*D, &d_min, &d_min_x, &d_min_y);</div>
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<div class="line"><span class="lineno"> 230</span> </div>
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<div class="line"><span class="lineno"> 231</span> <span class="comment">// step 3a: get the neighborhood range</span></div>
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<div class="line"><span class="lineno"> 232</span> <span class="keywordtype">int</span> from_x = std::max(0, d_min_x - R);</div>
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<div class="line"><span class="lineno"> 233</span> <span class="keywordtype">int</span> to_x = std::min(num_out_x, d_min_x + R + 1);</div>
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<div class="line"><span class="lineno"> 234</span> <span class="keywordtype">int</span> from_y = std::max(0, d_min_y - R);</div>
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<div class="line"><span class="lineno"> 235</span> <span class="keywordtype">int</span> to_y = std::min(num_out_y, d_min_y + R + 1);</div>
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<div class="line"><span class="lineno"> 236</span> </div>
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<div class="line"><span class="lineno"> 237</span> <span class="comment">// step 3b: update the weights of nodes in the</span></div>
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<div class="line"><span class="lineno"> 238</span> <span class="comment">// neighborhood</span></div>
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<div class="line"><span class="lineno"> 239</span><span class="preprocessor">#ifdef _OPENMP</span></div>
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<div class="line"><span class="lineno"> 240</span><span class="preprocessor">#pragma omp for</span></div>
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<div class="line"><span class="lineno"> 241</span><span class="preprocessor">#endif</span></div>
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<div class="line"><span class="lineno"> 242</span> <span class="keywordflow">for</span> (x = from_x; x < to_x; x++) {</div>
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<div class="line"><span class="lineno"> 243</span> <span class="keywordflow">for</span> (y = from_y; y < to_y; y++) {</div>
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<div class="line"><span class="lineno"> 244</span> <span class="comment">/* you can enable the following normalization if needed.</span></div>
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<div class="line"><span class="lineno"> 245</span><span class="comment"> personally, I found it detrimental to convergence */</span></div>
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<div class="line"><span class="lineno"> 246</span> <span class="comment">// const double s2pi = sqrt(2.f * M_PI);</span></div>
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<div class="line"><span class="lineno"> 247</span> <span class="comment">// double normalize = 1.f / (alpha * s2pi);</span></div>
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<div class="line"><span class="lineno"> 248</span> </div>
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<div class="line"><span class="lineno"> 249</span> <span class="comment">/* apply scaling inversely proportional to distance from the</span></div>
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<div class="line"><span class="lineno"> 250</span><span class="comment"> current node */</span></div>
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<div class="line"><span class="lineno"> 251</span> <span class="keywordtype">double</span> d2 =</div>
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<div class="line"><span class="lineno"> 252</span> (d_min_x - x) * (d_min_x - x) + (d_min_y - y) * (d_min_y - y);</div>
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<div class="line"><span class="lineno"> 253</span> <span class="keywordtype">double</span> scale_factor = std::exp(-d2 / (2.f * alpha * alpha));</div>
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<div class="line"><span class="lineno"> 254</span> </div>
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<div class="line"><span class="lineno"> 255</span> (*W)[x][y] += (X - (*W)[x][y]) * alpha * scale_factor;</div>
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<div class="line"><span class="lineno"> 256</span> }</div>
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<div class="line"><span class="lineno"> 257</span> }</div>
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<div class="line"><span class="lineno"> 258</span> <span class="keywordflow">return</span> d_min;</div>
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<div class="line"><span class="lineno"> 259</span>}</div>
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<div class="ttc" id="agroup__machine__learning_html_ga60f9186ccb682724a8792a2bf81e9b9e"><div class="ttname"><a href="../../d9/d66/group__machine__learning.html#ga60f9186ccb682724a8792a2bf81e9b9e">get_min_2d</a></div><div class="ttdeci">void get_min_2d(const std::vector< std::valarray< double > > &X, double *val, int *x_idx, int *y_idx)</div><div class="ttdef"><b>Definition</b> <a href="../../d4/def/kohonen__som__topology_8cpp_source.html#l00105">kohonen_som_topology.cpp:105</a></div></div>
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<a id="ac1bdaa2a724b4ce6a6bb371a5dbe2e7e" name="ac1bdaa2a724b4ce6a6bb371a5dbe2e7e"></a>
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<h2 class="memtitle"><span class="permalink"><a href="#ac1bdaa2a724b4ce6a6bb371a5dbe2e7e">◆ </a></span>zeroes_initialization()</h2>
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template<typename T> </div>
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<td class="memname">void machine_learning::zeroes_initialization </td>
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<td>(</td>
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<td class="paramtype">std::vector< std::valarray< T > > &</td> <td class="paramname"><span class="paramname"><em>A</em></span>, </td>
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<p>Function to Intialize 2D vector as zeroes </p><dl class="tparams"><dt>Template Parameters</dt><dd>
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<table class="tparams">
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<tr><td class="paramname">T</td><td>typename of the vector </td></tr>
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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">A</td><td>2D vector to be initialized </td></tr>
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<tr><td class="paramname">shape</td><td>required shape </td></tr>
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<p class="definition">Definition at line <a class="el" href="../../d8/d95/vector__ops_8hpp_source.html#l00213">213</a> of file <a class="el" href="../../d8/d95/vector__ops_8hpp_source.html">vector_ops.hpp</a>.</p>
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<div class="fragment"><div class="line"><span class="lineno"> 214</span> {</div>
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<div class="line"><span class="lineno"> 215</span> A.clear(); <span class="comment">// Making A empty</span></div>
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<div class="line"><span class="lineno"> 216</span> <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> i = 0; i < shape.first; i++) {</div>
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<div class="line"><span class="lineno"> 217</span> std::valarray<T></div>
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<div class="line"><span class="lineno"> 218</span> row; <span class="comment">// Making empty row which will be inserted in vector</span></div>
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<div class="line"><span class="lineno"> 219</span> row.resize(shape.second); <span class="comment">// By default all elements are zero</span></div>
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<div class="line"><span class="lineno"> 220</span> A.push_back(row); <span class="comment">// Insert new row in vector</span></div>
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<div class="line"><span class="lineno"> 221</span> }</div>
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<div class="line"><span class="lineno"> 222</span> <span class="keywordflow">return</span>;</div>
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<div class="line"><span class="lineno"> 223</span>}</div>
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<a name="doc-var-members" id="doc-var-members"></a><h2 id="header-doc-var-members" class="groupheader">Variable Documentation</h2>
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<a id="a7220dbb7fa896d83bfb7a50e4fce1786" name="a7220dbb7fa896d83bfb7a50e4fce1786"></a>
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<h2 class="memtitle"><span class="permalink"><a href="#a7220dbb7fa896d83bfb7a50e4fce1786">◆ </a></span>MIN_DISTANCE</h2>
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<span class="mlabels"><span class="mlabel constexpr">constexpr</span></span> </td>
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<p><a class="el" href="../../d4/d12/namespace_minimum.html" title="Implementation of Minimum Edit Distance algorithm.">Minimum</a> average distance of image nodes </p>
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<p class="definition">Definition at line <a class="el" href="../../d4/def/kohonen__som__topology_8cpp_source.html#l00129">129</a> of file <a class="el" href="../../d4/def/kohonen__som__topology_8cpp_source.html">kohonen_som_topology.cpp</a>.</p>
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</div>
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</body>
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</html>
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