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588 lines
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<div class="textblock">Here are the classes, structs, unions and interfaces with brief descriptions:</div><div class="directory">
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<div class="levels">[detail level <span onclick="javascript:toggleLevel(1);">1</span><span onclick="javascript:toggleLevel(2);">2</span><span onclick="javascript:toggleLevel(3);">3</span><span onclick="javascript:toggleLevel(4);">4</span><span onclick="javascript:toggleLevel(5);">5</span>]</div><table class="directory">
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<tr id="row_0_" class="even"><td class="entry"><span style="width:0px;display:inline-block;"> </span><span id="arr_0_" class="arrow" onclick="toggleFolder('0_')">▼</span><span class="icona"><span class="icon">N</span></span><a class="el" href="namespaceboost.html" target="_self">boost</a></td><td class="desc"></td></tr>
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<tr id="row_1_"><td class="entry"><span style="width:0px;display:inline-block;"> </span><span id="arr_1_" class="arrow" onclick="toggleFolder('1_')">▼</span><span class="icona"><span class="icon">N</span></span><a class="el" href="namespacemlpack.html" target="_self">mlpack</a></td><td class="desc">Linear algebra utility functions, generally performed on matrices or vectors </td></tr>
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<tr id="row_1_0_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span id="arr_1_0_" class="arrow" onclick="toggleFolder('1_0_')">►</span><span class="icona"><span class="icon">N</span></span><a class="el" href="namespacemlpack_1_1adaboost.html" target="_self">adaboost</a></td><td class="desc"></td></tr>
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<tr id="row_1_0_0_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1adaboost_1_1AdaBoost.html" target="_self">AdaBoost</a></td><td class="desc">The <a class="el" href="classmlpack_1_1adaboost_1_1AdaBoost.html" title="The AdaBoost class. ">AdaBoost</a> class </td></tr>
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<tr id="row_1_0_1_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1adaboost_1_1AdaBoostModel.html" target="_self">AdaBoostModel</a></td><td class="desc">The model to save to disk </td></tr>
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<tr id="row_1_1_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span id="arr_1_1_" class="arrow" onclick="toggleFolder('1_1_')">►</span><span class="icona"><span class="icon">N</span></span><a class="el" href="namespacemlpack_1_1amf.html" target="_self">amf</a></td><td class="desc">Alternating Matrix Factorization </td></tr>
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<tr id="row_1_1_0_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1amf_1_1AMF.html" target="_self">AMF</a></td><td class="desc">This class implements <a class="el" href="classmlpack_1_1amf_1_1AMF.html" title="This class implements AMF (alternating matrix factorization) on the given matrix V. ">AMF</a> (alternating matrix factorization) on the given matrix V </td></tr>
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<tr id="row_1_1_1_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1amf_1_1AverageInitialization.html" target="_self">AverageInitialization</a></td><td class="desc">This initialization rule initializes matrix W and H to root of the average of V, perturbed with uniform noise </td></tr>
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<tr id="row_1_1_2_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1amf_1_1CompleteIncrementalTermination.html" target="_self">CompleteIncrementalTermination</a></td><td class="desc">This class acts as a wrapper for basic termination policies to be used by <a class="el" href="classmlpack_1_1amf_1_1SVDCompleteIncrementalLearning.html" title="This class computes SVD using complete incremental batch learning, as described in the following pape...">SVDCompleteIncrementalLearning</a> </td></tr>
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<tr id="row_1_1_3_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1amf_1_1GivenInitialization.html" target="_self">GivenInitialization</a></td><td class="desc">This initialization rule for <a class="el" href="classmlpack_1_1amf_1_1AMF.html" title="This class implements AMF (alternating matrix factorization) on the given matrix V. ">AMF</a> simply fills the W and H matrices with the matrices given to the constructor of this object </td></tr>
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<tr id="row_1_1_4_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1amf_1_1IncompleteIncrementalTermination.html" target="_self">IncompleteIncrementalTermination</a></td><td class="desc">This class acts as a wrapper for basic termination policies to be used by <a class="el" href="classmlpack_1_1amf_1_1SVDIncompleteIncrementalLearning.html" title="This class computes SVD using incomplete incremental batch learning, as described in the following pa...">SVDIncompleteIncrementalLearning</a> </td></tr>
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<tr id="row_1_1_5_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1amf_1_1MaxIterationTermination.html" target="_self">MaxIterationTermination</a></td><td class="desc">This termination policy only terminates when the maximum number of iterations has been reached </td></tr>
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<tr id="row_1_1_6_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1amf_1_1NMFALSUpdate.html" target="_self">NMFALSUpdate</a></td><td class="desc">This class implements a method titled 'Alternating Least Squares' described in the following paper: </td></tr>
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<tr id="row_1_1_7_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1amf_1_1NMFMultiplicativeDistanceUpdate.html" target="_self">NMFMultiplicativeDistanceUpdate</a></td><td class="desc">The multiplicative distance update rules for matrices W and H </td></tr>
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<tr id="row_1_1_8_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1amf_1_1NMFMultiplicativeDivergenceUpdate.html" target="_self">NMFMultiplicativeDivergenceUpdate</a></td><td class="desc">This follows a method described in the paper 'Algorithms for Non-negative </td></tr>
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<tr id="row_1_1_9_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1amf_1_1RandomAcolInitialization.html" target="_self">RandomAcolInitialization</a></td><td class="desc">This class initializes the W matrix of the <a class="el" href="classmlpack_1_1amf_1_1AMF.html" title="This class implements AMF (alternating matrix factorization) on the given matrix V. ">AMF</a> algorithm by averaging p randomly chosen columns of V </td></tr>
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<tr id="row_1_1_10_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1amf_1_1RandomInitialization.html" target="_self">RandomInitialization</a></td><td class="desc">This initialization rule for <a class="el" href="classmlpack_1_1amf_1_1AMF.html" title="This class implements AMF (alternating matrix factorization) on the given matrix V. ">AMF</a> simply fills the W and H matrices with uniform random noise in [0, 1] </td></tr>
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<tr id="row_1_1_11_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1amf_1_1SimpleResidueTermination.html" target="_self">SimpleResidueTermination</a></td><td class="desc">This class implements a simple residue-based termination policy </td></tr>
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<tr id="row_1_1_12_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1amf_1_1SimpleToleranceTermination.html" target="_self">SimpleToleranceTermination</a></td><td class="desc">This class implements residue tolerance termination policy </td></tr>
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<tr id="row_1_1_13_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1amf_1_1SVDBatchLearning.html" target="_self">SVDBatchLearning</a></td><td class="desc">This class implements SVD batch learning with momentum </td></tr>
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<tr id="row_1_1_14_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1amf_1_1SVDCompleteIncrementalLearning.html" target="_self">SVDCompleteIncrementalLearning</a></td><td class="desc">This class computes SVD using complete incremental batch learning, as described in the following paper: </td></tr>
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<tr id="row_1_1_15_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1amf_1_1SVDCompleteIncrementalLearning_3_01arma_1_1sp__mat_01_4.html" target="_self">SVDCompleteIncrementalLearning< arma::sp_mat ></a></td><td class="desc">TODO : Merge this template specialized function for sparse matrix using common row_col_iterator </td></tr>
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<tr id="row_1_1_16_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1amf_1_1SVDIncompleteIncrementalLearning.html" target="_self">SVDIncompleteIncrementalLearning</a></td><td class="desc">This class computes SVD using incomplete incremental batch learning, as described in the following paper: </td></tr>
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<tr id="row_1_1_17_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1amf_1_1ValidationRMSETermination.html" target="_self">ValidationRMSETermination</a></td><td class="desc">This class implements validation termination policy based on RMSE index </td></tr>
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<tr id="row_1_2_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span id="arr_1_2_" class="arrow" onclick="toggleFolder('1_2_')">►</span><span class="icona"><span class="icon">N</span></span><a class="el" href="namespacemlpack_1_1ann.html" target="_self">ann</a></td><td class="desc">Artificial Neural Network </td></tr>
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<tr id="row_1_2_0_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1ann_1_1Add.html" target="_self">Add</a></td><td class="desc">Implementation of the <a class="el" href="classmlpack_1_1ann_1_1Add.html" title="Implementation of the Add module class. ">Add</a> module class </td></tr>
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<tr id="row_1_2_1_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1ann_1_1AddMerge.html" target="_self">AddMerge</a></td><td class="desc">Implementation of the <a class="el" href="classmlpack_1_1ann_1_1AddMerge.html" title="Implementation of the AddMerge module class. ">AddMerge</a> module class </td></tr>
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<tr id="row_1_2_2_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1ann_1_1AddVisitor.html" target="_self">AddVisitor</a></td><td class="desc"><a class="el" href="classmlpack_1_1ann_1_1AddVisitor.html" title="AddVisitor exposes the Add() method of the given module. ">AddVisitor</a> exposes the Add() method of the given module </td></tr>
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<tr id="row_1_2_3_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1ann_1_1BackwardVisitor.html" target="_self">BackwardVisitor</a></td><td class="desc"><a class="el" href="classmlpack_1_1ann_1_1BackwardVisitor.html" title="BackwardVisitor executes the Backward() function given the input, error and delta parameter...">BackwardVisitor</a> executes the Backward() function given the input, error and delta parameter </td></tr>
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<tr id="row_1_2_4_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1ann_1_1BaseLayer.html" target="_self">BaseLayer</a></td><td class="desc">Implementation of the base layer </td></tr>
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<tr id="row_1_2_5_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1ann_1_1Concat.html" target="_self">Concat</a></td><td class="desc">Implementation of the <a class="el" href="classmlpack_1_1ann_1_1Concat.html" title="Implementation of the Concat class. ">Concat</a> class </td></tr>
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<tr id="row_1_2_6_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1ann_1_1ConcatPerformance.html" target="_self">ConcatPerformance</a></td><td class="desc">Implementation of the concat performance class </td></tr>
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<tr id="row_1_2_7_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1ann_1_1Constant.html" target="_self">Constant</a></td><td class="desc">Implementation of the constant layer </td></tr>
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<tr id="row_1_2_8_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1ann_1_1Convolution.html" target="_self">Convolution</a></td><td class="desc">Implementation of the <a class="el" href="classmlpack_1_1ann_1_1Convolution.html" title="Implementation of the Convolution class. ">Convolution</a> class </td></tr>
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<tr id="row_1_2_9_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1ann_1_1DeleteVisitor.html" target="_self">DeleteVisitor</a></td><td class="desc"><a class="el" href="classmlpack_1_1ann_1_1DeleteVisitor.html" title="DeleteVisitor executes the destructor of the instantiated object. ">DeleteVisitor</a> executes the destructor of the instantiated object </td></tr>
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<tr id="row_1_2_10_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1ann_1_1DeltaVisitor.html" target="_self">DeltaVisitor</a></td><td class="desc"><a class="el" href="classmlpack_1_1ann_1_1DeltaVisitor.html" title="DeltaVisitor exposes the delta parameter of the given module. ">DeltaVisitor</a> exposes the delta parameter of the given module </td></tr>
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<tr id="row_1_2_11_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1ann_1_1DeterministicSetVisitor.html" target="_self">DeterministicSetVisitor</a></td><td class="desc"><a class="el" href="classmlpack_1_1ann_1_1DeterministicSetVisitor.html" title="DeterministicSetVisitor set the deterministic parameter given the deterministic value. ">DeterministicSetVisitor</a> set the deterministic parameter given the deterministic value </td></tr>
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<tr id="row_1_2_12_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1ann_1_1DropConnect.html" target="_self">DropConnect</a></td><td class="desc">The <a class="el" href="classmlpack_1_1ann_1_1DropConnect.html" title="The DropConnect layer is a regularizer that randomly with probability ratio sets the connection value...">DropConnect</a> layer is a regularizer that randomly with probability ratio sets the connection values to zero and scales the remaining elements by factor 1 /(1 - ratio) </td></tr>
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<tr id="row_1_2_13_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1ann_1_1Dropout.html" target="_self">Dropout</a></td><td class="desc">The dropout layer is a regularizer that randomly with probability ratio sets input values to zero and scales the remaining elements by factor 1 / (1 - ratio) </td></tr>
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<tr id="row_1_2_14_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1ann_1_1ELU.html" target="_self">ELU</a></td><td class="desc">The <a class="el" href="classmlpack_1_1ann_1_1ELU.html" title="The ELU activation function, defined by. ">ELU</a> activation function, defined by </td></tr>
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<tr id="row_1_2_15_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1ann_1_1FFN.html" target="_self">FFN</a></td><td class="desc">Implementation of a standard feed forward network </td></tr>
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<tr id="row_1_2_16_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1ann_1_1FFTConvolution.html" target="_self">FFTConvolution</a></td><td class="desc">Computes the two-dimensional convolution through fft </td></tr>
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<tr id="row_1_2_17_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1ann_1_1ForwardVisitor.html" target="_self">ForwardVisitor</a></td><td class="desc"><a class="el" href="classmlpack_1_1ann_1_1ForwardVisitor.html" title="ForwardVisitor executes the Forward() function given the input and output parameter. ">ForwardVisitor</a> executes the Forward() function given the input and output parameter </td></tr>
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<tr id="row_1_2_18_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1ann_1_1FullConvolution.html" target="_self">FullConvolution</a></td><td class="desc"></td></tr>
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<tr id="row_1_2_19_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1ann_1_1Glimpse.html" target="_self">Glimpse</a></td><td class="desc">The glimpse layer returns a retina-like representation (down-scaled cropped images) of increasing scale around a given location in a given image </td></tr>
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<tr id="row_1_2_20_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1ann_1_1GradientSetVisitor.html" target="_self">GradientSetVisitor</a></td><td class="desc"><a class="el" href="classmlpack_1_1ann_1_1GradientSetVisitor.html" title="GradientSetVisitor update the gradient parameter given the gradient set. ">GradientSetVisitor</a> update the gradient parameter given the gradient set </td></tr>
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<tr id="row_1_2_21_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1ann_1_1GradientUpdateVisitor.html" target="_self">GradientUpdateVisitor</a></td><td class="desc"><a class="el" href="classmlpack_1_1ann_1_1GradientUpdateVisitor.html" title="GradientUpdateVisitor update the gradient parameter given the gradient set. ">GradientUpdateVisitor</a> update the gradient parameter given the gradient set </td></tr>
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<tr id="row_1_2_22_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1ann_1_1GradientVisitor.html" target="_self">GradientVisitor</a></td><td class="desc">SearchModeVisitor executes the Gradient() method of the given module using the input and delta parameter </td></tr>
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<tr id="row_1_2_23_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1ann_1_1GradientZeroVisitor.html" target="_self">GradientZeroVisitor</a></td><td class="desc"></td></tr>
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<tr id="row_1_2_24_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1ann_1_1HardTanH.html" target="_self">HardTanH</a></td><td class="desc">The Hard Tanh activation function, defined by </td></tr>
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<tr id="row_1_2_25_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1ann_1_1IdentityFunction.html" target="_self">IdentityFunction</a></td><td class="desc">The identity function, defined by </td></tr>
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<tr id="row_1_2_26_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1ann_1_1Join.html" target="_self">Join</a></td><td class="desc">Implementation of the <a class="el" href="classmlpack_1_1ann_1_1Join.html" title="Implementation of the Join module class. ">Join</a> module class </td></tr>
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<tr id="row_1_2_27_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1ann_1_1KathirvalavakumarSubavathiInitialization.html" target="_self">KathirvalavakumarSubavathiInitialization</a></td><td class="desc">This class is used to initialize the weight matrix with the method proposed by T </td></tr>
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<tr id="row_1_2_28_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1ann_1_1LayerTraits.html" target="_self">LayerTraits</a></td><td class="desc">This is a template class that can provide information about various layers </td></tr>
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<tr id="row_1_2_29_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1ann_1_1LeakyReLU.html" target="_self">LeakyReLU</a></td><td class="desc">The <a class="el" href="classmlpack_1_1ann_1_1LeakyReLU.html" title="The LeakyReLU activation function, defined by. ">LeakyReLU</a> activation function, defined by </td></tr>
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<tr id="row_1_2_30_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1ann_1_1Linear.html" target="_self">Linear</a></td><td class="desc">Implementation of the <a class="el" href="classmlpack_1_1ann_1_1Linear.html" title="Implementation of the Linear layer class. ">Linear</a> layer class </td></tr>
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<tr id="row_1_2_31_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1ann_1_1LinearNoBias.html" target="_self">LinearNoBias</a></td><td class="desc">Implementation of the <a class="el" href="classmlpack_1_1ann_1_1LinearNoBias.html" title="Implementation of the LinearNoBias class. ">LinearNoBias</a> class </td></tr>
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<tr id="row_1_2_32_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1ann_1_1LoadOutputParameterVisitor.html" target="_self">LoadOutputParameterVisitor</a></td><td class="desc"><a class="el" href="classmlpack_1_1ann_1_1LoadOutputParameterVisitor.html" title="LoadOutputParameterVisitor restores the output parameter using the given parameter set...">LoadOutputParameterVisitor</a> restores the output parameter using the given parameter set </td></tr>
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<tr id="row_1_2_33_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1ann_1_1LogisticFunction.html" target="_self">LogisticFunction</a></td><td class="desc">The logistic function, defined by </td></tr>
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<tr id="row_1_2_34_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1ann_1_1LogSoftMax.html" target="_self">LogSoftMax</a></td><td class="desc">Implementation of the log softmax layer </td></tr>
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<tr id="row_1_2_35_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1ann_1_1Lookup.html" target="_self">Lookup</a></td><td class="desc">Implementation of the <a class="el" href="classmlpack_1_1ann_1_1Lookup.html" title="Implementation of the Lookup class. ">Lookup</a> class </td></tr>
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<tr id="row_1_2_36_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1ann_1_1LSTM.html" target="_self">LSTM</a></td><td class="desc">An implementation of a lstm network layer </td></tr>
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<tr id="row_1_2_37_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1ann_1_1MaxPooling.html" target="_self">MaxPooling</a></td><td class="desc">Implementation of the <a class="el" href="classmlpack_1_1ann_1_1MaxPooling.html" title="Implementation of the MaxPooling layer. ">MaxPooling</a> layer </td></tr>
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<tr id="row_1_2_38_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1ann_1_1MaxPoolingRule.html" target="_self">MaxPoolingRule</a></td><td class="desc"></td></tr>
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<tr id="row_1_2_39_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1ann_1_1MeanPooling.html" target="_self">MeanPooling</a></td><td class="desc">Implementation of the <a class="el" href="classmlpack_1_1ann_1_1MeanPooling.html" title="Implementation of the MeanPooling. ">MeanPooling</a> </td></tr>
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<tr id="row_1_2_40_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1ann_1_1MeanPoolingRule.html" target="_self">MeanPoolingRule</a></td><td class="desc"></td></tr>
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<tr id="row_1_2_41_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1ann_1_1MeanSquaredError.html" target="_self">MeanSquaredError</a></td><td class="desc">The mean squared error performance function measures the network's performance according to the mean of squared errors </td></tr>
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<tr id="row_1_2_42_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1ann_1_1MultiplyConstant.html" target="_self">MultiplyConstant</a></td><td class="desc">Implementation of the multiply constant layer </td></tr>
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<tr id="row_1_2_43_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1ann_1_1NaiveConvolution.html" target="_self">NaiveConvolution</a></td><td class="desc">Computes the two-dimensional convolution </td></tr>
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<tr id="row_1_2_44_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1ann_1_1NegativeLogLikelihood.html" target="_self">NegativeLogLikelihood</a></td><td class="desc">Implementation of the negative log likelihood layer </td></tr>
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<tr id="row_1_2_45_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1ann_1_1NguyenWidrowInitialization.html" target="_self">NguyenWidrowInitialization</a></td><td class="desc">This class is used to initialize the weight matrix with the Nguyen-Widrow method </td></tr>
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<tr id="row_1_2_46_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1ann_1_1OivsInitialization.html" target="_self">OivsInitialization</a></td><td class="desc">This class is used to initialize the weight matrix with the oivs method </td></tr>
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<tr id="row_1_2_47_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1ann_1_1OrthogonalInitialization.html" target="_self">OrthogonalInitialization</a></td><td class="desc">This class is used to initialize the weight matrix with the orthogonal matrix initialization </td></tr>
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<tr id="row_1_2_48_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1ann_1_1OutputHeightVisitor.html" target="_self">OutputHeightVisitor</a></td><td class="desc"><a class="el" href="classmlpack_1_1ann_1_1OutputWidthVisitor.html" title="OutputWidthVisitor exposes the OutputWidth() method of the given module. ">OutputWidthVisitor</a> exposes the OutputHeight() method of the given module </td></tr>
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<tr id="row_1_2_49_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1ann_1_1OutputParameterVisitor.html" target="_self">OutputParameterVisitor</a></td><td class="desc"><a class="el" href="classmlpack_1_1ann_1_1OutputParameterVisitor.html" title="OutputParameterVisitor exposes the output parameter of the given module. ">OutputParameterVisitor</a> exposes the output parameter of the given module </td></tr>
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<tr id="row_1_2_50_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1ann_1_1OutputWidthVisitor.html" target="_self">OutputWidthVisitor</a></td><td class="desc"><a class="el" href="classmlpack_1_1ann_1_1OutputWidthVisitor.html" title="OutputWidthVisitor exposes the OutputWidth() method of the given module. ">OutputWidthVisitor</a> exposes the OutputWidth() method of the given module </td></tr>
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<tr id="row_1_2_51_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1ann_1_1ParametersSetVisitor.html" target="_self">ParametersSetVisitor</a></td><td class="desc"><a class="el" href="classmlpack_1_1ann_1_1ParametersSetVisitor.html" title="ParametersSetVisitor update the parameters set using the given matrix. ">ParametersSetVisitor</a> update the parameters set using the given matrix </td></tr>
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<tr id="row_1_2_52_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1ann_1_1ParametersVisitor.html" target="_self">ParametersVisitor</a></td><td class="desc"><a class="el" href="classmlpack_1_1ann_1_1ParametersVisitor.html" title="ParametersVisitor exposes the parameters set of the given module and stores the parameters set into t...">ParametersVisitor</a> exposes the parameters set of the given module and stores the parameters set into the given matrix </td></tr>
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<tr id="row_1_2_53_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1ann_1_1PReLU.html" target="_self">PReLU</a></td><td class="desc">The <a class="el" href="classmlpack_1_1ann_1_1PReLU.html" title="The PReLU activation function, defined by (where alpha is trainable) ">PReLU</a> activation function, defined by (where alpha is trainable) </td></tr>
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<tr id="row_1_2_54_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1ann_1_1RandomInitialization.html" target="_self">RandomInitialization</a></td><td class="desc">This class is used to initialize randomly the weight matrix </td></tr>
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<tr id="row_1_2_55_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1ann_1_1RectifierFunction.html" target="_self">RectifierFunction</a></td><td class="desc">The rectifier function, defined by </td></tr>
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<tr id="row_1_2_56_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1ann_1_1Recurrent.html" target="_self">Recurrent</a></td><td class="desc">Implementation of the RecurrentLayer class </td></tr>
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<tr id="row_1_2_57_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1ann_1_1RecurrentAttention.html" target="_self">RecurrentAttention</a></td><td class="desc">This class implements the <a class="el" href="classmlpack_1_1ann_1_1Recurrent.html" title="Implementation of the RecurrentLayer class. ">Recurrent</a> Model for Visual Attention, using a variety of possible layer implementations </td></tr>
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<tr id="row_1_2_58_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1ann_1_1ReinforceNormal.html" target="_self">ReinforceNormal</a></td><td class="desc">Implementation of the reinforce normal layer </td></tr>
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<tr id="row_1_2_59_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1ann_1_1ResetVisitor.html" target="_self">ResetVisitor</a></td><td class="desc"><a class="el" href="classmlpack_1_1ann_1_1ResetVisitor.html" title="ResetVisitor executes the Reset() function. ">ResetVisitor</a> executes the Reset() function </td></tr>
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<tr id="row_1_2_60_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1ann_1_1RewardSetVisitor.html" target="_self">RewardSetVisitor</a></td><td class="desc"><a class="el" href="classmlpack_1_1ann_1_1RewardSetVisitor.html" title="RewardSetVisitor set the reward parameter given the reward value. ">RewardSetVisitor</a> set the reward parameter given the reward value </td></tr>
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<tr id="row_1_2_61_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1ann_1_1RNN.html" target="_self">RNN</a></td><td class="desc">Implementation of a standard recurrent neural network container </td></tr>
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<tr id="row_1_2_62_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1ann_1_1SaveOutputParameterVisitor.html" target="_self">SaveOutputParameterVisitor</a></td><td class="desc"><a class="el" href="classmlpack_1_1ann_1_1SaveOutputParameterVisitor.html" title="SaveOutputParameterVisitor saves the output parameter into the given parameter set. ">SaveOutputParameterVisitor</a> saves the output parameter into the given parameter set </td></tr>
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<tr id="row_1_2_63_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1ann_1_1Select.html" target="_self">Select</a></td><td class="desc">The select module selects the specified column from a given input matrix </td></tr>
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<tr id="row_1_2_64_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1ann_1_1Sequential.html" target="_self">Sequential</a></td><td class="desc">Implementation of the <a class="el" href="classmlpack_1_1ann_1_1Sequential.html" title="Implementation of the Sequential class. ">Sequential</a> class </td></tr>
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<tr id="row_1_2_65_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1ann_1_1SetInputHeightVisitor.html" target="_self">SetInputHeightVisitor</a></td><td class="desc"><a class="el" href="classmlpack_1_1ann_1_1SetInputHeightVisitor.html" title="SetInputHeightVisitor updates the input height parameter with the given input height. ">SetInputHeightVisitor</a> updates the input height parameter with the given input height </td></tr>
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<tr id="row_1_2_66_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1ann_1_1SetInputWidthVisitor.html" target="_self">SetInputWidthVisitor</a></td><td class="desc"><a class="el" href="classmlpack_1_1ann_1_1SetInputWidthVisitor.html" title="SetInputWidthVisitor updates the input width parameter with the given input width. ">SetInputWidthVisitor</a> updates the input width parameter with the given input width </td></tr>
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<tr id="row_1_2_67_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1ann_1_1SoftplusFunction.html" target="_self">SoftplusFunction</a></td><td class="desc">The softplus function, defined by </td></tr>
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<tr id="row_1_2_68_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1ann_1_1SoftsignFunction.html" target="_self">SoftsignFunction</a></td><td class="desc">The softsign function, defined by </td></tr>
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<tr id="row_1_2_69_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1ann_1_1SVDConvolution.html" target="_self">SVDConvolution</a></td><td class="desc">Computes the two-dimensional convolution using singular value decomposition </td></tr>
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<tr id="row_1_2_70_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1ann_1_1TanhFunction.html" target="_self">TanhFunction</a></td><td class="desc">The tanh function, defined by </td></tr>
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<tr id="row_1_2_71_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1ann_1_1ValidConvolution.html" target="_self">ValidConvolution</a></td><td class="desc"></td></tr>
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<tr id="row_1_2_72_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1ann_1_1VRClassReward.html" target="_self">VRClassReward</a></td><td class="desc">Implementation of the variance reduced classification reinforcement layer </td></tr>
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<tr id="row_1_2_73_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1ann_1_1WeightSetVisitor.html" target="_self">WeightSetVisitor</a></td><td class="desc"><a class="el" href="classmlpack_1_1ann_1_1WeightSetVisitor.html" title="WeightSetVisitor update the module parameters given the parameters set. ">WeightSetVisitor</a> update the module parameters given the parameters set </td></tr>
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<tr id="row_1_2_74_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1ann_1_1WeightSizeVisitor.html" target="_self">WeightSizeVisitor</a></td><td class="desc"><a class="el" href="classmlpack_1_1ann_1_1WeightSizeVisitor.html" title="WeightSizeVisitor returns the number of weights of the given module. ">WeightSizeVisitor</a> returns the number of weights of the given module </td></tr>
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<tr id="row_1_2_75_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1ann_1_1ZeroInitialization.html" target="_self">ZeroInitialization</a></td><td class="desc">This class is used to initialize randomly the weight matrix </td></tr>
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<tr id="row_1_3_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span id="arr_1_3_" class="arrow" onclick="toggleFolder('1_3_')">►</span><span class="icona"><span class="icon">N</span></span><a class="el" href="namespacemlpack_1_1bound.html" target="_self">bound</a></td><td class="desc"></td></tr>
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<tr id="row_1_3_0_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span id="arr_1_3_0_" class="arrow" onclick="toggleFolder('1_3_0_')">►</span><span class="icona"><span class="icon">N</span></span><a class="el" href="namespacemlpack_1_1bound_1_1meta.html" target="_self">meta</a></td><td class="desc">Metaprogramming utilities </td></tr>
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<tr id="row_1_3_0_0_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structmlpack_1_1bound_1_1meta_1_1IsLMetric.html" target="_self">IsLMetric</a></td><td class="desc">Utility struct where Value is true if and only if the argument is of type LMetric </td></tr>
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<tr id="row_1_3_0_1_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structmlpack_1_1bound_1_1meta_1_1IsLMetric_3_01metric_1_1LMetric_3_01Power_00_01TakeRoot_01_4_01_4.html" target="_self">IsLMetric< metric::LMetric< Power, TakeRoot > ></a></td><td class="desc">Specialization for <a class="el" href="structmlpack_1_1bound_1_1meta_1_1IsLMetric.html" title="Utility struct where Value is true if and only if the argument is of type LMetric. ">IsLMetric</a> when the argument is of type LMetric </td></tr>
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<tr id="row_1_3_1_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1bound_1_1BallBound.html" target="_self">BallBound</a></td><td class="desc">Ball bound encloses a set of points at a specific distance (radius) from a specific point (center) </td></tr>
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<tr id="row_1_3_2_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structmlpack_1_1bound_1_1BoundTraits.html" target="_self">BoundTraits</a></td><td class="desc">A class to obtain compile-time traits about BoundType classes </td></tr>
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<tr id="row_1_3_3_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structmlpack_1_1bound_1_1BoundTraits_3_01BallBound_3_01MetricType_00_01VecType_01_4_01_4.html" target="_self">BoundTraits< BallBound< MetricType, VecType > ></a></td><td class="desc">A specialization of <a class="el" href="structmlpack_1_1bound_1_1BoundTraits.html" title="A class to obtain compile-time traits about BoundType classes. ">BoundTraits</a> for this bound type </td></tr>
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<tr id="row_1_3_4_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structmlpack_1_1bound_1_1BoundTraits_3_01CellBound_3_01MetricType_00_01ElemType_01_4_01_4.html" target="_self">BoundTraits< CellBound< MetricType, ElemType > ></a></td><td class="desc"></td></tr>
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<tr id="row_1_3_5_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structmlpack_1_1bound_1_1BoundTraits_3_01HollowBallBound_3_01MetricType_00_01ElemType_01_4_01_4.html" target="_self">BoundTraits< HollowBallBound< MetricType, ElemType > ></a></td><td class="desc">A specialization of <a class="el" href="structmlpack_1_1bound_1_1BoundTraits.html" title="A class to obtain compile-time traits about BoundType classes. ">BoundTraits</a> for this bound type </td></tr>
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<tr id="row_1_3_6_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structmlpack_1_1bound_1_1BoundTraits_3_01HRectBound_3_01MetricType_00_01ElemType_01_4_01_4.html" target="_self">BoundTraits< HRectBound< MetricType, ElemType > ></a></td><td class="desc"></td></tr>
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<tr id="row_1_3_7_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1bound_1_1CellBound.html" target="_self">CellBound</a></td><td class="desc">The <a class="el" href="classmlpack_1_1bound_1_1CellBound.html" title="The CellBound class describes a bound that consists of a number of hyperrectangles. ">CellBound</a> class describes a bound that consists of a number of hyperrectangles </td></tr>
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<tr id="row_1_3_8_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1bound_1_1HollowBallBound.html" target="_self">HollowBallBound</a></td><td class="desc">Hollow ball bound encloses a set of points at a specific distance (radius) from a specific point (center) except points at a specific distance from another point (the center of the hole) </td></tr>
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<tr id="row_1_3_9_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1bound_1_1HRectBound.html" target="_self">HRectBound</a></td><td class="desc">Hyper-rectangle bound for an L-metric </td></tr>
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<tr id="row_1_4_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span id="arr_1_4_" class="arrow" onclick="toggleFolder('1_4_')">►</span><span class="icona"><span class="icon">N</span></span><a class="el" href="namespacemlpack_1_1cf.html" target="_self">cf</a></td><td class="desc">Collaborative filtering </td></tr>
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<tr id="row_1_4_0_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span id="arr_1_4_0_" class="arrow" onclick="toggleFolder('1_4_0_')">►</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1cf_1_1CF.html" target="_self">CF</a></td><td class="desc">This class implements Collaborative Filtering (<a class="el" href="classmlpack_1_1cf_1_1CF.html" title="This class implements Collaborative Filtering (CF). ">CF</a>) </td></tr>
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<tr id="row_1_4_0_0_" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structmlpack_1_1cf_1_1CF_1_1CandidateCmp.html" target="_self">CandidateCmp</a></td><td class="desc">Compare two candidates based on the value </td></tr>
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<tr id="row_1_4_1_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1cf_1_1DummyClass.html" target="_self">DummyClass</a></td><td class="desc">This class acts as a dummy class for passing as template parameter </td></tr>
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<tr id="row_1_4_2_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structmlpack_1_1cf_1_1FactorizerTraits.html" target="_self">FactorizerTraits</a></td><td class="desc">Template class for factorizer traits </td></tr>
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<tr id="row_1_4_3_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1cf_1_1FactorizerTraits_3_01mlpack_1_1svd_1_1RegularizedSVD_3_4_01_4.html" target="_self">FactorizerTraits< mlpack::svd::RegularizedSVD<> ></a></td><td class="desc">Factorizer traits of Regularized SVD </td></tr>
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<tr id="row_1_4_4_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1cf_1_1SVDWrapper.html" target="_self">SVDWrapper</a></td><td class="desc">This class acts as the wrapper for all SVD factorizers which are incompatible with <a class="el" href="classmlpack_1_1cf_1_1CF.html" title="This class implements Collaborative Filtering (CF). ">CF</a> module </td></tr>
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<tr id="row_1_5_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span id="arr_1_5_" class="arrow" onclick="toggleFolder('1_5_')">►</span><span class="icona"><span class="icon">N</span></span><a class="el" href="namespacemlpack_1_1data.html" target="_self">data</a></td><td class="desc">Functions to load and save matrices and models </td></tr>
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<tr id="row_1_5_0_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1data_1_1CustomImputation.html" target="_self">CustomImputation</a></td><td class="desc">A simple custom imputation class </td></tr>
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<tr id="row_1_5_1_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1data_1_1DatasetMapper.html" target="_self">DatasetMapper</a></td><td class="desc">Auxiliary information for a dataset, including mappings to/from strings and the datatype of each dimension </td></tr>
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<tr id="row_1_5_2_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structmlpack_1_1data_1_1FirstArrayShim.html" target="_self">FirstArrayShim</a></td><td class="desc">A first shim for arrays </td></tr>
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<tr id="row_1_5_3_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structmlpack_1_1data_1_1FirstNormalArrayShim.html" target="_self">FirstNormalArrayShim</a></td><td class="desc">A first shim for arrays without a Serialize() method </td></tr>
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<tr id="row_1_5_4_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structmlpack_1_1data_1_1FirstShim.html" target="_self">FirstShim</a></td><td class="desc">The first shim: simply holds the object and its name </td></tr>
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<tr id="row_1_5_5_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span id="arr_1_5_5_" class="arrow" onclick="toggleFolder('1_5_5_')">►</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structmlpack_1_1data_1_1HasSerialize.html" target="_self">HasSerialize</a></td><td class="desc"></td></tr>
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<tr id="row_1_5_5_0_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structmlpack_1_1data_1_1HasSerialize_1_1check.html" target="_self">check</a></td><td class="desc"></td></tr>
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<tr id="row_1_5_6_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structmlpack_1_1data_1_1HasSerializeFunction.html" target="_self">HasSerializeFunction</a></td><td class="desc"></td></tr>
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<tr id="row_1_5_7_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1data_1_1Imputer.html" target="_self">Imputer</a></td><td class="desc">Given a dataset of a particular datatype, replace user-specified missing value with a variable dependent on the StrategyType and MapperType </td></tr>
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<tr id="row_1_5_8_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1data_1_1IncrementPolicy.html" target="_self">IncrementPolicy</a></td><td class="desc"><a class="el" href="classmlpack_1_1data_1_1IncrementPolicy.html" title="IncrementPolicy is used as a helper class for DatasetMapper. ">IncrementPolicy</a> is used as a helper class for <a class="el" href="classmlpack_1_1data_1_1DatasetMapper.html" title="Auxiliary information for a dataset, including mappings to/from strings and the datatype of each dime...">DatasetMapper</a> </td></tr>
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<tr id="row_1_5_9_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1data_1_1ListwiseDeletion.html" target="_self">ListwiseDeletion</a></td><td class="desc">A complete-case analysis to remove the values containing mappedValue </td></tr>
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<tr id="row_1_5_10_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span id="arr_1_5_10_" class="arrow" onclick="toggleFolder('1_5_10_')">►</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1data_1_1LoadCSV.html" target="_self">LoadCSV</a></td><td class="desc">Load the csv file.This class use boost::spirit to implement the parser, please refer to following link <a href="http://theboostcpplibraries.com/boost.spirit">http://theboostcpplibraries.com/boost.spirit</a> for quick review </td></tr>
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<tr id="row_1_5_10_0_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structmlpack_1_1data_1_1LoadCSV_1_1ElemParser.html" target="_self">ElemParser</a></td><td class="desc"></td></tr>
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<tr id="row_1_5_11_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1data_1_1MeanImputation.html" target="_self">MeanImputation</a></td><td class="desc">A simple mean imputation class </td></tr>
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<tr id="row_1_5_12_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1data_1_1MedianImputation.html" target="_self">MedianImputation</a></td><td class="desc">This is a class implementation of simple median imputation </td></tr>
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<tr id="row_1_5_13_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1data_1_1MissingPolicy.html" target="_self">MissingPolicy</a></td><td class="desc"><a class="el" href="classmlpack_1_1data_1_1MissingPolicy.html" title="MissingPolicy is used as a helper class for DatasetMapper. ">MissingPolicy</a> is used as a helper class for <a class="el" href="classmlpack_1_1data_1_1DatasetMapper.html" title="Auxiliary information for a dataset, including mappings to/from strings and the datatype of each dime...">DatasetMapper</a> </td></tr>
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<tr id="row_1_5_14_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structmlpack_1_1data_1_1PointerShim.html" target="_self">PointerShim</a></td><td class="desc">A shim for pointers </td></tr>
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<tr id="row_1_5_15_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structmlpack_1_1data_1_1SecondArrayShim.html" target="_self">SecondArrayShim</a></td><td class="desc">A shim for objects in an array; this is basically like the <a class="el" href="structmlpack_1_1data_1_1SecondShim.html" title="The second shim: wrap the call to Serialize() inside of a serialize() function, so that an archive ty...">SecondShim</a>, but for arrays that hold objects that have Serialize() methods instead of <a class="el" href="structmlpack_1_1data_1_1SecondArrayShim.html#a9b7f34ee88e73a99ced7ca803c6c010d" title="A wrapper for Serialize() for each element. ">serialize()</a> methods </td></tr>
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<tr id="row_1_5_16_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structmlpack_1_1data_1_1SecondNormalArrayShim.html" target="_self">SecondNormalArrayShim</a></td><td class="desc">A shim for objects in an array which do not have a Serialize() function </td></tr>
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<tr id="row_1_5_17_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structmlpack_1_1data_1_1SecondShim.html" target="_self">SecondShim</a></td><td class="desc">The second shim: wrap the call to Serialize() inside of a <a class="el" href="structmlpack_1_1data_1_1SecondShim.html#af876ca6368e66bbfebbfa8ca25ad3b45" title="A wrapper for t.Serialize(). ">serialize()</a> function, so that an archive type can call <a class="el" href="structmlpack_1_1data_1_1SecondShim.html#af876ca6368e66bbfebbfa8ca25ad3b45" title="A wrapper for t.Serialize(). ">serialize()</a> on a <a class="el" href="structmlpack_1_1data_1_1SecondShim.html" title="The second shim: wrap the call to Serialize() inside of a serialize() function, so that an archive ty...">SecondShim</a> object and this gets forwarded correctly to our object's Serialize() function </td></tr>
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<tr id="row_1_6_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span id="arr_1_6_" class="arrow" onclick="toggleFolder('1_6_')">►</span><span class="icona"><span class="icon">N</span></span><a class="el" href="namespacemlpack_1_1dbscan.html" target="_self">dbscan</a></td><td class="desc"></td></tr>
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<tr id="row_1_6_0_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1dbscan_1_1DBSCAN.html" target="_self">DBSCAN</a></td><td class="desc"><a class="el" href="classmlpack_1_1dbscan_1_1DBSCAN.html" title="DBSCAN (Density-Based Spatial Clustering of Applications with Noise) is a clustering technique descri...">DBSCAN</a> (Density-Based Spatial Clustering of Applications with Noise) is a clustering technique described in the following paper: </td></tr>
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<tr id="row_1_6_1_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1dbscan_1_1RandomPointSelection.html" target="_self">RandomPointSelection</a></td><td class="desc">This class can be used to randomly select the next point to use for <a class="el" href="classmlpack_1_1dbscan_1_1DBSCAN.html" title="DBSCAN (Density-Based Spatial Clustering of Applications with Noise) is a clustering technique descri...">DBSCAN</a> </td></tr>
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<tr id="row_1_7_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span id="arr_1_7_" class="arrow" onclick="toggleFolder('1_7_')">►</span><span class="icona"><span class="icon">N</span></span><a class="el" href="namespacemlpack_1_1decision__stump.html" target="_self">decision_stump</a></td><td class="desc"></td></tr>
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<tr id="row_1_7_0_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1decision__stump_1_1DecisionStump.html" target="_self">DecisionStump</a></td><td class="desc">This class implements a decision stump </td></tr>
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<tr id="row_1_8_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span id="arr_1_8_" class="arrow" onclick="toggleFolder('1_8_')">►</span><span class="icona"><span class="icon">N</span></span><a class="el" href="namespacemlpack_1_1det.html" target="_self">det</a></td><td class="desc">Density Estimation Trees </td></tr>
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<tr id="row_1_8_0_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1det_1_1DTree.html" target="_self">DTree</a></td><td class="desc">A density estimation tree is similar to both a decision tree and a space partitioning tree (like a kd-tree) </td></tr>
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<tr id="row_1_9_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span id="arr_1_9_" class="arrow" onclick="toggleFolder('1_9_')">►</span><span class="icona"><span class="icon">N</span></span><a class="el" href="namespacemlpack_1_1distribution.html" target="_self">distribution</a></td><td class="desc">Probability distributions </td></tr>
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<tr id="row_1_9_0_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1distribution_1_1DiscreteDistribution.html" target="_self">DiscreteDistribution</a></td><td class="desc">A discrete distribution where the only observations are discrete observations </td></tr>
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<tr id="row_1_9_1_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1distribution_1_1GammaDistribution.html" target="_self">GammaDistribution</a></td><td class="desc">This class represents the Gamma distribution </td></tr>
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<tr id="row_1_9_2_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1distribution_1_1GaussianDistribution.html" target="_self">GaussianDistribution</a></td><td class="desc">A single multivariate Gaussian distribution </td></tr>
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<tr id="row_1_9_3_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1distribution_1_1LaplaceDistribution.html" target="_self">LaplaceDistribution</a></td><td class="desc">The multivariate Laplace distribution centered at 0 has pdf </td></tr>
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<tr id="row_1_9_4_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1distribution_1_1RegressionDistribution.html" target="_self">RegressionDistribution</a></td><td class="desc">A class that represents a univariate conditionally Gaussian distribution </td></tr>
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<tr id="row_1_10_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span id="arr_1_10_" class="arrow" onclick="toggleFolder('1_10_')">►</span><span class="icona"><span class="icon">N</span></span><a class="el" href="namespacemlpack_1_1emst.html" target="_self">emst</a></td><td class="desc">Euclidean Minimum Spanning Trees </td></tr>
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<tr id="row_1_10_0_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1emst_1_1DTBRules.html" target="_self">DTBRules</a></td><td class="desc"></td></tr>
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<tr id="row_1_10_1_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1emst_1_1DTBStat.html" target="_self">DTBStat</a></td><td class="desc">A statistic for use with mlpack trees, which stores the upper bound on distance to nearest neighbors and the component which this node belongs to </td></tr>
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<tr id="row_1_10_2_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span id="arr_1_10_2_" class="arrow" onclick="toggleFolder('1_10_2_')">►</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1emst_1_1DualTreeBoruvka.html" target="_self">DualTreeBoruvka</a></td><td class="desc">Performs the MST calculation using the Dual-Tree Boruvka algorithm, using any type of tree </td></tr>
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<tr id="row_1_10_2_0_" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structmlpack_1_1emst_1_1DualTreeBoruvka_1_1SortEdgesHelper.html" target="_self">SortEdgesHelper</a></td><td class="desc">For sorting the edge list after the computation </td></tr>
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<tr id="row_1_10_3_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1emst_1_1EdgePair.html" target="_self">EdgePair</a></td><td class="desc">An edge pair is simply two indices and a distance </td></tr>
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<tr id="row_1_10_4_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1emst_1_1UnionFind.html" target="_self">UnionFind</a></td><td class="desc">A Union-Find data structure </td></tr>
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<tr id="row_1_11_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span id="arr_1_11_" class="arrow" onclick="toggleFolder('1_11_')">►</span><span class="icona"><span class="icon">N</span></span><a class="el" href="namespacemlpack_1_1fastmks.html" target="_self">fastmks</a></td><td class="desc">Fast max-kernel search </td></tr>
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<tr id="row_1_11_0_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span id="arr_1_11_0_" class="arrow" onclick="toggleFolder('1_11_0_')">►</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1fastmks_1_1FastMKS.html" target="_self">FastMKS</a></td><td class="desc">An implementation of fast exact max-kernel search </td></tr>
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<tr id="row_1_11_0_0_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structmlpack_1_1fastmks_1_1FastMKS_1_1CandidateCmp.html" target="_self">CandidateCmp</a></td><td class="desc">Compare two candidates based on the value </td></tr>
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<tr id="row_1_11_1_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1fastmks_1_1FastMKSModel.html" target="_self">FastMKSModel</a></td><td class="desc">A utility struct to contain all the possible <a class="el" href="classmlpack_1_1fastmks_1_1FastMKS.html" title="An implementation of fast exact max-kernel search. ">FastMKS</a> models, for use by the mlpack_fastmks program </td></tr>
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<tr id="row_1_11_2_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span id="arr_1_11_2_" class="arrow" onclick="toggleFolder('1_11_2_')">►</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1fastmks_1_1FastMKSRules.html" target="_self">FastMKSRules</a></td><td class="desc">The <a class="el" href="classmlpack_1_1fastmks_1_1FastMKSRules.html" title="The FastMKSRules class is a template helper class used by FastMKS class when performing exact max-ker...">FastMKSRules</a> class is a template helper class used by <a class="el" href="classmlpack_1_1fastmks_1_1FastMKS.html" title="An implementation of fast exact max-kernel search. ">FastMKS</a> class when performing exact max-kernel search </td></tr>
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<tr id="row_1_11_2_0_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structmlpack_1_1fastmks_1_1FastMKSRules_1_1CandidateCmp.html" target="_self">CandidateCmp</a></td><td class="desc">Compare two candidates based on the value </td></tr>
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<tr id="row_1_11_3_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1fastmks_1_1FastMKSStat.html" target="_self">FastMKSStat</a></td><td class="desc">The statistic used in trees with <a class="el" href="classmlpack_1_1fastmks_1_1FastMKS.html" title="An implementation of fast exact max-kernel search. ">FastMKS</a> </td></tr>
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<tr id="row_1_12_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span id="arr_1_12_" class="arrow" onclick="toggleFolder('1_12_')">►</span><span class="icona"><span class="icon">N</span></span><a class="el" href="namespacemlpack_1_1gmm.html" target="_self">gmm</a></td><td class="desc">Gaussian Mixture Models </td></tr>
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<tr id="row_1_12_0_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1gmm_1_1DiagonalConstraint.html" target="_self">DiagonalConstraint</a></td><td class="desc">Force a covariance matrix to be diagonal </td></tr>
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<tr id="row_1_12_1_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1gmm_1_1EigenvalueRatioConstraint.html" target="_self">EigenvalueRatioConstraint</a></td><td class="desc">Given a vector of eigenvalue ratios, ensure that the covariance matrix always has those eigenvalue ratios </td></tr>
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<tr id="row_1_12_2_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1gmm_1_1EMFit.html" target="_self">EMFit</a></td><td class="desc">This class contains methods which can fit a <a class="el" href="classmlpack_1_1gmm_1_1GMM.html" title="A Gaussian Mixture Model (GMM). ">GMM</a> to observations using the EM algorithm </td></tr>
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<tr id="row_1_12_3_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1gmm_1_1GMM.html" target="_self">GMM</a></td><td class="desc">A Gaussian Mixture Model (<a class="el" href="classmlpack_1_1gmm_1_1GMM.html" title="A Gaussian Mixture Model (GMM). ">GMM</a>) </td></tr>
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<tr id="row_1_12_4_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1gmm_1_1NoConstraint.html" target="_self">NoConstraint</a></td><td class="desc">This class enforces no constraint on the covariance matrix </td></tr>
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<tr id="row_1_12_5_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1gmm_1_1PositiveDefiniteConstraint.html" target="_self">PositiveDefiniteConstraint</a></td><td class="desc">Given a covariance matrix, force the matrix to be positive definite </td></tr>
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<tr id="row_1_13_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span id="arr_1_13_" class="arrow" onclick="toggleFolder('1_13_')">►</span><span class="icona"><span class="icon">N</span></span><a class="el" href="namespacemlpack_1_1hmm.html" target="_self">hmm</a></td><td class="desc">Hidden Markov Models </td></tr>
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<tr id="row_1_13_0_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1hmm_1_1HMM.html" target="_self">HMM</a></td><td class="desc">A class that represents a Hidden Markov Model with an arbitrary type of emission distribution </td></tr>
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<tr id="row_1_13_1_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1hmm_1_1HMMModel.html" target="_self">HMMModel</a></td><td class="desc">A serializable <a class="el" href="classmlpack_1_1hmm_1_1HMM.html" title="A class that represents a Hidden Markov Model with an arbitrary type of emission distribution. ">HMM</a> model that also stores the type </td></tr>
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<tr id="row_1_13_2_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1hmm_1_1HMMRegression.html" target="_self">HMMRegression</a></td><td class="desc">A class that represents a Hidden Markov Model Regression (HMMR) </td></tr>
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<tr id="row_1_14_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span id="arr_1_14_" class="arrow" onclick="toggleFolder('1_14_')">►</span><span class="icona"><span class="icon">N</span></span><a class="el" href="namespacemlpack_1_1kernel.html" target="_self">kernel</a></td><td class="desc">Kernel functions </td></tr>
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<tr id="row_1_14_0_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1kernel_1_1CosineDistance.html" target="_self">CosineDistance</a></td><td class="desc">The cosine distance (or cosine similarity) </td></tr>
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<tr id="row_1_14_1_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1kernel_1_1EpanechnikovKernel.html" target="_self">EpanechnikovKernel</a></td><td class="desc">The Epanechnikov kernel, defined as </td></tr>
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<tr id="row_1_14_2_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1kernel_1_1ExampleKernel.html" target="_self">ExampleKernel</a></td><td class="desc">An example kernel function </td></tr>
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<tr id="row_1_14_3_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1kernel_1_1GaussianKernel.html" target="_self">GaussianKernel</a></td><td class="desc">The standard Gaussian kernel </td></tr>
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<tr id="row_1_14_4_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1kernel_1_1HyperbolicTangentKernel.html" target="_self">HyperbolicTangentKernel</a></td><td class="desc">Hyperbolic tangent kernel </td></tr>
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<tr id="row_1_14_5_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1kernel_1_1KernelTraits.html" target="_self">KernelTraits</a></td><td class="desc">This is a template class that can provide information about various kernels </td></tr>
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<tr id="row_1_14_6_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1kernel_1_1KernelTraits_3_01CosineDistance_01_4.html" target="_self">KernelTraits< CosineDistance ></a></td><td class="desc">Kernel traits for the cosine distance </td></tr>
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<tr id="row_1_14_7_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1kernel_1_1KernelTraits_3_01EpanechnikovKernel_01_4.html" target="_self">KernelTraits< EpanechnikovKernel ></a></td><td class="desc">Kernel traits for the Epanechnikov kernel </td></tr>
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<tr id="row_1_14_8_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1kernel_1_1KernelTraits_3_01GaussianKernel_01_4.html" target="_self">KernelTraits< GaussianKernel ></a></td><td class="desc">Kernel traits for the Gaussian kernel </td></tr>
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<tr id="row_1_14_9_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1kernel_1_1KernelTraits_3_01LaplacianKernel_01_4.html" target="_self">KernelTraits< LaplacianKernel ></a></td><td class="desc">Kernel traits of the Laplacian kernel </td></tr>
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<tr id="row_1_14_10_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1kernel_1_1KernelTraits_3_01SphericalKernel_01_4.html" target="_self">KernelTraits< SphericalKernel ></a></td><td class="desc">Kernel traits for the spherical kernel </td></tr>
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<tr id="row_1_14_11_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1kernel_1_1KernelTraits_3_01TriangularKernel_01_4.html" target="_self">KernelTraits< TriangularKernel ></a></td><td class="desc">Kernel traits for the triangular kernel </td></tr>
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<tr id="row_1_14_12_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1kernel_1_1KMeansSelection.html" target="_self">KMeansSelection</a></td><td class="desc">Implementation of the kmeans sampling scheme </td></tr>
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<tr id="row_1_14_13_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1kernel_1_1LaplacianKernel.html" target="_self">LaplacianKernel</a></td><td class="desc">The standard Laplacian kernel </td></tr>
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<tr id="row_1_14_14_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1kernel_1_1LinearKernel.html" target="_self">LinearKernel</a></td><td class="desc">The simple linear kernel (dot product) </td></tr>
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<tr id="row_1_14_15_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1kernel_1_1NystroemMethod.html" target="_self">NystroemMethod</a></td><td class="desc"></td></tr>
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<tr id="row_1_14_16_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1kernel_1_1OrderedSelection.html" target="_self">OrderedSelection</a></td><td class="desc"></td></tr>
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<tr id="row_1_14_17_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1kernel_1_1PolynomialKernel.html" target="_self">PolynomialKernel</a></td><td class="desc">The simple polynomial kernel </td></tr>
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<tr id="row_1_14_18_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1kernel_1_1PSpectrumStringKernel.html" target="_self">PSpectrumStringKernel</a></td><td class="desc">The p-spectrum string kernel </td></tr>
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<tr id="row_1_14_19_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1kernel_1_1RandomSelection.html" target="_self">RandomSelection</a></td><td class="desc"></td></tr>
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<tr id="row_1_14_20_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1kernel_1_1SphericalKernel.html" target="_self">SphericalKernel</a></td><td class="desc">The spherical kernel, which is 1 when the distance between the two argument points is less than or equal to the bandwidth, or 0 otherwise </td></tr>
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<tr id="row_1_14_21_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1kernel_1_1TriangularKernel.html" target="_self">TriangularKernel</a></td><td class="desc">The trivially simple triangular kernel, defined by </td></tr>
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<tr id="row_1_15_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span id="arr_1_15_" class="arrow" onclick="toggleFolder('1_15_')">►</span><span class="icona"><span class="icon">N</span></span><a class="el" href="namespacemlpack_1_1kmeans.html" target="_self">kmeans</a></td><td class="desc">K-Means clustering </td></tr>
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<tr id="row_1_15_0_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1kmeans_1_1AllowEmptyClusters.html" target="_self">AllowEmptyClusters</a></td><td class="desc">Policy which allows K-Means to create empty clusters without any error being reported </td></tr>
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<tr id="row_1_15_1_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1kmeans_1_1DualTreeKMeans.html" target="_self">DualTreeKMeans</a></td><td class="desc">An algorithm for an exact Lloyd iteration which simply uses dual-tree nearest-neighbor search to find the nearest centroid for each point in the dataset </td></tr>
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<tr id="row_1_15_2_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1kmeans_1_1DualTreeKMeansRules.html" target="_self">DualTreeKMeansRules</a></td><td class="desc"></td></tr>
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<tr id="row_1_15_3_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1kmeans_1_1DualTreeKMeansStatistic.html" target="_self">DualTreeKMeansStatistic</a></td><td class="desc"></td></tr>
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<tr id="row_1_15_4_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1kmeans_1_1ElkanKMeans.html" target="_self">ElkanKMeans</a></td><td class="desc"></td></tr>
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<tr id="row_1_15_5_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1kmeans_1_1HamerlyKMeans.html" target="_self">HamerlyKMeans</a></td><td class="desc"></td></tr>
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<tr id="row_1_15_6_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1kmeans_1_1KillEmptyClusters.html" target="_self">KillEmptyClusters</a></td><td class="desc">Policy which allows K-Means to "kill" empty clusters without any error being reported </td></tr>
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<tr id="row_1_15_7_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1kmeans_1_1KMeans.html" target="_self">KMeans</a></td><td class="desc">This class implements K-Means clustering, using a variety of possible implementations of Lloyd's algorithm </td></tr>
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<tr id="row_1_15_8_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1kmeans_1_1MaxVarianceNewCluster.html" target="_self">MaxVarianceNewCluster</a></td><td class="desc">When an empty cluster is detected, this class takes the point furthest from the centroid of the cluster with maximum variance as a new cluster </td></tr>
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<tr id="row_1_15_9_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1kmeans_1_1NaiveKMeans.html" target="_self">NaiveKMeans</a></td><td class="desc">This is an implementation of a single iteration of Lloyd's algorithm for k-means </td></tr>
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<tr id="row_1_15_10_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1kmeans_1_1PellegMooreKMeans.html" target="_self">PellegMooreKMeans</a></td><td class="desc">An implementation of Pelleg-Moore's 'blacklist' algorithm for k-means clustering </td></tr>
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<tr id="row_1_15_11_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1kmeans_1_1PellegMooreKMeansRules.html" target="_self">PellegMooreKMeansRules</a></td><td class="desc">The rules class for the single-tree Pelleg-Moore kd-tree traversal for k-means clustering </td></tr>
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<tr id="row_1_15_12_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1kmeans_1_1PellegMooreKMeansStatistic.html" target="_self">PellegMooreKMeansStatistic</a></td><td class="desc">A statistic for trees which holds the blacklist for Pelleg-Moore k-means clustering (which represents the clusters that cannot possibly own any points in a node) </td></tr>
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<tr id="row_1_15_13_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1kmeans_1_1RandomPartition.html" target="_self">RandomPartition</a></td><td class="desc">A very simple partitioner which partitions the data randomly into the number of desired clusters </td></tr>
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<tr id="row_1_15_14_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1kmeans_1_1RefinedStart.html" target="_self">RefinedStart</a></td><td class="desc">A refined approach for choosing initial points for k-means clustering </td></tr>
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<tr id="row_1_15_15_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1kmeans_1_1SampleInitialization.html" target="_self">SampleInitialization</a></td><td class="desc"></td></tr>
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<tr id="row_1_16_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span id="arr_1_16_" class="arrow" onclick="toggleFolder('1_16_')">►</span><span class="icona"><span class="icon">N</span></span><a class="el" href="namespacemlpack_1_1kpca.html" target="_self">kpca</a></td><td class="desc"></td></tr>
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<tr id="row_1_16_0_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1kpca_1_1KernelPCA.html" target="_self">KernelPCA</a></td><td class="desc">This class performs kernel principal components analysis (Kernel PCA), for a given kernel </td></tr>
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<tr id="row_1_16_1_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1kpca_1_1NaiveKernelRule.html" target="_self">NaiveKernelRule</a></td><td class="desc"></td></tr>
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<tr id="row_1_16_2_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1kpca_1_1NystroemKernelRule.html" target="_self">NystroemKernelRule</a></td><td class="desc"></td></tr>
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<tr id="row_1_17_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span id="arr_1_17_" class="arrow" onclick="toggleFolder('1_17_')">►</span><span class="icona"><span class="icon">N</span></span><a class="el" href="namespacemlpack_1_1lcc.html" target="_self">lcc</a></td><td class="desc"></td></tr>
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<tr id="row_1_17_0_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1lcc_1_1LocalCoordinateCoding.html" target="_self">LocalCoordinateCoding</a></td><td class="desc">An implementation of Local Coordinate Coding (LCC) that codes data which approximately lives on a manifold using a variation of l1-norm regularized sparse coding; in LCC, the penalty on the absolute value of each point's coefficient for each atom is weighted by the squared distance of that point to that atom </td></tr>
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<tr id="row_1_18_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span id="arr_1_18_" class="arrow" onclick="toggleFolder('1_18_')">►</span><span class="icona"><span class="icon">N</span></span><a class="el" href="namespacemlpack_1_1math.html" target="_self">math</a></td><td class="desc">Miscellaneous math routines </td></tr>
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<tr id="row_1_18_0_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1math_1_1ColumnsToBlocks.html" target="_self">ColumnsToBlocks</a></td><td class="desc">Transform the columns of the given matrix into a block format </td></tr>
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<tr id="row_1_18_1_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1math_1_1RangeType.html" target="_self">RangeType</a></td><td class="desc">Simple real-valued range </td></tr>
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<tr id="row_1_19_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span id="arr_1_19_" class="arrow" onclick="toggleFolder('1_19_')">►</span><span class="icona"><span class="icon">N</span></span><a class="el" href="namespacemlpack_1_1matrix__completion.html" target="_self">matrix_completion</a></td><td class="desc"></td></tr>
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<tr id="row_1_19_0_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1matrix__completion_1_1MatrixCompletion.html" target="_self">MatrixCompletion</a></td><td class="desc">This class implements the popular nuclear norm minimization heuristic for matrix completion problems </td></tr>
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<tr id="row_1_20_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span id="arr_1_20_" class="arrow" onclick="toggleFolder('1_20_')">►</span><span class="icona"><span class="icon">N</span></span><a class="el" href="namespacemlpack_1_1meanshift.html" target="_self">meanshift</a></td><td class="desc">Mean shift clustering </td></tr>
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<tr id="row_1_20_0_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1meanshift_1_1MeanShift.html" target="_self">MeanShift</a></td><td class="desc">This class implements mean shift clustering </td></tr>
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<tr id="row_1_21_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span id="arr_1_21_" class="arrow" onclick="toggleFolder('1_21_')">►</span><span class="icona"><span class="icon">N</span></span><a class="el" href="namespacemlpack_1_1metric.html" target="_self">metric</a></td><td class="desc"></td></tr>
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<tr id="row_1_21_0_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1metric_1_1IPMetric.html" target="_self">IPMetric</a></td><td class="desc">The inner product metric, <a class="el" href="classmlpack_1_1metric_1_1IPMetric.html" title="The inner product metric, IPMetric, takes a given Mercer kernel (KernelType), and when Evaluate() is ...">IPMetric</a>, takes a given Mercer kernel (KernelType), and when <a class="el" href="classmlpack_1_1metric_1_1IPMetric.html#aeeaaa6e72c5e9c9c5f43fb66a25c2b04" title="Evaluate the metric. ">Evaluate()</a> is called, returns the distance between the two points in kernel space: </td></tr>
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<tr id="row_1_21_1_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1metric_1_1LMetric.html" target="_self">LMetric</a></td><td class="desc">The L_p metric for arbitrary integer p, with an option to take the root </td></tr>
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<tr id="row_1_21_2_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1metric_1_1MahalanobisDistance.html" target="_self">MahalanobisDistance</a></td><td class="desc">The Mahalanobis distance, which is essentially a stretched Euclidean distance </td></tr>
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<tr id="row_1_22_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span id="arr_1_22_" class="arrow" onclick="toggleFolder('1_22_')">►</span><span class="icona"><span class="icon">N</span></span><a class="el" href="namespacemlpack_1_1mvu.html" target="_self">mvu</a></td><td class="desc"></td></tr>
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<tr id="row_1_22_0_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1mvu_1_1MVU.html" target="_self">MVU</a></td><td class="desc">Meant to provide a good abstraction for users </td></tr>
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<tr id="row_1_23_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span id="arr_1_23_" class="arrow" onclick="toggleFolder('1_23_')">►</span><span class="icona"><span class="icon">N</span></span><a class="el" href="namespacemlpack_1_1naive__bayes.html" target="_self">naive_bayes</a></td><td class="desc">The Naive Bayes Classifier </td></tr>
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<tr id="row_1_23_0_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1naive__bayes_1_1NaiveBayesClassifier.html" target="_self">NaiveBayesClassifier</a></td><td class="desc">The simple Naive Bayes classifier </td></tr>
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<tr id="row_1_24_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span id="arr_1_24_" class="arrow" onclick="toggleFolder('1_24_')">►</span><span class="icona"><span class="icon">N</span></span><a class="el" href="namespacemlpack_1_1nca.html" target="_self">nca</a></td><td class="desc">Neighborhood Components Analysis </td></tr>
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<tr id="row_1_24_0_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1nca_1_1NCA.html" target="_self">NCA</a></td><td class="desc">An implementation of Neighborhood Components Analysis, both a linear dimensionality reduction technique and a distance learning technique </td></tr>
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<tr id="row_1_24_1_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1nca_1_1SoftmaxErrorFunction.html" target="_self">SoftmaxErrorFunction</a></td><td class="desc">The "softmax" stochastic neighbor assignment probability function </td></tr>
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<tr id="row_1_25_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span id="arr_1_25_" class="arrow" onclick="toggleFolder('1_25_')">►</span><span class="icona"><span class="icon">N</span></span><a class="el" href="namespacemlpack_1_1neighbor.html" target="_self">neighbor</a></td><td class="desc">Neighbor-search routines </td></tr>
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<tr id="row_1_25_0_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1neighbor_1_1BiSearchVisitor.html" target="_self">BiSearchVisitor</a></td><td class="desc"><a class="el" href="classmlpack_1_1neighbor_1_1BiSearchVisitor.html" title="BiSearchVisitor executes a bichromatic neighbor search on the given NSType. ">BiSearchVisitor</a> executes a bichromatic neighbor search on the given NSType </td></tr>
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<tr id="row_1_25_1_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1neighbor_1_1DeleteVisitor.html" target="_self">DeleteVisitor</a></td><td class="desc"><a class="el" href="classmlpack_1_1neighbor_1_1DeleteVisitor.html" title="DeleteVisitor deletes the given NSType instance. ">DeleteVisitor</a> deletes the given NSType instance </td></tr>
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<tr id="row_1_25_2_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1neighbor_1_1DrusillaSelect.html" target="_self">DrusillaSelect</a></td><td class="desc"></td></tr>
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<tr id="row_1_25_3_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1neighbor_1_1EpsilonVisitor.html" target="_self">EpsilonVisitor</a></td><td class="desc"><a class="el" href="classmlpack_1_1neighbor_1_1EpsilonVisitor.html" title="EpsilonVisitor exposes the Epsilon method of the given NSType. ">EpsilonVisitor</a> exposes the Epsilon method of the given NSType </td></tr>
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<tr id="row_1_25_4_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1neighbor_1_1FurthestNeighborSort.html" target="_self">FurthestNeighborSort</a></td><td class="desc">This class implements the necessary methods for the SortPolicy template parameter of the <a class="el" href="classmlpack_1_1neighbor_1_1NeighborSearch.html" title="The NeighborSearch class is a template class for performing distance-based neighbor searches...">NeighborSearch</a> class </td></tr>
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<tr id="row_1_25_5_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span id="arr_1_25_5_" class="arrow" onclick="toggleFolder('1_25_5_')">►</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1neighbor_1_1LSHSearch.html" target="_self">LSHSearch</a></td><td class="desc">The <a class="el" href="classmlpack_1_1neighbor_1_1LSHSearch.html" title="The LSHSearch class; this class builds a hash on the reference set and uses this hash to compute the ...">LSHSearch</a> class; this class builds a hash on the reference set and uses this hash to compute the distance-approximate nearest-neighbors of the given queries </td></tr>
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<tr id="row_1_25_5_0_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structmlpack_1_1neighbor_1_1LSHSearch_1_1CandidateCmp.html" target="_self">CandidateCmp</a></td><td class="desc">Compare two candidates based on the distance </td></tr>
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<tr id="row_1_25_6_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1neighbor_1_1MonoSearchVisitor.html" target="_self">MonoSearchVisitor</a></td><td class="desc"><a class="el" href="classmlpack_1_1neighbor_1_1MonoSearchVisitor.html" title="MonoSearchVisitor executes a monochromatic neighbor search on the given NSType. ">MonoSearchVisitor</a> executes a monochromatic neighbor search on the given NSType </td></tr>
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<tr id="row_1_25_7_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1neighbor_1_1NearestNeighborSort.html" target="_self">NearestNeighborSort</a></td><td class="desc">This class implements the necessary methods for the SortPolicy template parameter of the <a class="el" href="classmlpack_1_1neighbor_1_1NeighborSearch.html" title="The NeighborSearch class is a template class for performing distance-based neighbor searches...">NeighborSearch</a> class </td></tr>
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<tr id="row_1_25_8_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1neighbor_1_1NeighborSearch.html" target="_self">NeighborSearch</a></td><td class="desc">The <a class="el" href="classmlpack_1_1neighbor_1_1NeighborSearch.html" title="The NeighborSearch class is a template class for performing distance-based neighbor searches...">NeighborSearch</a> class is a template class for performing distance-based neighbor searches </td></tr>
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<tr id="row_1_25_9_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span id="arr_1_25_9_" class="arrow" onclick="toggleFolder('1_25_9_')">►</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1neighbor_1_1NeighborSearchRules.html" target="_self">NeighborSearchRules</a></td><td class="desc">The <a class="el" href="classmlpack_1_1neighbor_1_1NeighborSearchRules.html" title="The NeighborSearchRules class is a template helper class used by NeighborSearch class when performing...">NeighborSearchRules</a> class is a template helper class used by <a class="el" href="classmlpack_1_1neighbor_1_1NeighborSearch.html" title="The NeighborSearch class is a template class for performing distance-based neighbor searches...">NeighborSearch</a> class when performing distance-based neighbor searches </td></tr>
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<tr id="row_1_25_9_0_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structmlpack_1_1neighbor_1_1NeighborSearchRules_1_1CandidateCmp.html" target="_self">CandidateCmp</a></td><td class="desc">Compare two candidates based on the distance </td></tr>
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<tr id="row_1_25_10_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1neighbor_1_1NeighborSearchStat.html" target="_self">NeighborSearchStat</a></td><td class="desc">Extra data for each node in the tree </td></tr>
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<tr id="row_1_25_11_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1neighbor_1_1NSModel.html" target="_self">NSModel</a></td><td class="desc">The <a class="el" href="classmlpack_1_1neighbor_1_1NSModel.html" title="The NSModel class provides an easy way to serialize a model, abstracts away the different types of tr...">NSModel</a> class provides an easy way to serialize a model, abstracts away the different types of trees, and also reflects the <a class="el" href="classmlpack_1_1neighbor_1_1NeighborSearch.html" title="The NeighborSearch class is a template class for performing distance-based neighbor searches...">NeighborSearch</a> API </td></tr>
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<tr id="row_1_25_12_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structmlpack_1_1neighbor_1_1NSModelName.html" target="_self">NSModelName</a></td><td class="desc"></td></tr>
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<tr id="row_1_25_13_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structmlpack_1_1neighbor_1_1NSModelName_3_01FurthestNeighborSort_01_4.html" target="_self">NSModelName< FurthestNeighborSort ></a></td><td class="desc"></td></tr>
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<tr id="row_1_25_14_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structmlpack_1_1neighbor_1_1NSModelName_3_01NearestNeighborSort_01_4.html" target="_self">NSModelName< NearestNeighborSort ></a></td><td class="desc"></td></tr>
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<tr id="row_1_25_15_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1neighbor_1_1QDAFN.html" target="_self">QDAFN</a></td><td class="desc"></td></tr>
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<tr id="row_1_25_16_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1neighbor_1_1RAModel.html" target="_self">RAModel</a></td><td class="desc">The <a class="el" href="classmlpack_1_1neighbor_1_1RAModel.html" title="The RAModel class provides an abstraction for the RASearch class, abstracting away the TreeType param...">RAModel</a> class provides an abstraction for the <a class="el" href="classmlpack_1_1neighbor_1_1RASearch.html" title="The RASearch class: This class provides a generic manner to perform rank-approximate search via rando...">RASearch</a> class, abstracting away the TreeType parameter and allowing it to be specified at runtime in this class </td></tr>
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<tr id="row_1_25_17_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1neighbor_1_1RAQueryStat.html" target="_self">RAQueryStat</a></td><td class="desc">Extra data for each node in the tree </td></tr>
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<tr id="row_1_25_18_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1neighbor_1_1RASearch.html" target="_self">RASearch</a></td><td class="desc">The <a class="el" href="classmlpack_1_1neighbor_1_1RASearch.html" title="The RASearch class: This class provides a generic manner to perform rank-approximate search via rando...">RASearch</a> class: This class provides a generic manner to perform rank-approximate search via random-sampling </td></tr>
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<tr id="row_1_25_19_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span id="arr_1_25_19_" class="arrow" onclick="toggleFolder('1_25_19_')">►</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1neighbor_1_1RASearchRules.html" target="_self">RASearchRules</a></td><td class="desc">The <a class="el" href="classmlpack_1_1neighbor_1_1RASearchRules.html" title="The RASearchRules class is a template helper class used by RASearch class when performing rank-approx...">RASearchRules</a> class is a template helper class used by <a class="el" href="classmlpack_1_1neighbor_1_1RASearch.html" title="The RASearch class: This class provides a generic manner to perform rank-approximate search via rando...">RASearch</a> class when performing rank-approximate search via random-sampling </td></tr>
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<tr id="row_1_25_19_0_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structmlpack_1_1neighbor_1_1RASearchRules_1_1CandidateCmp.html" target="_self">CandidateCmp</a></td><td class="desc">Compare two candidates based on the distance </td></tr>
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<tr id="row_1_25_20_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1neighbor_1_1RAUtil.html" target="_self">RAUtil</a></td><td class="desc"></td></tr>
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<tr id="row_1_25_21_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1neighbor_1_1ReferenceSetVisitor.html" target="_self">ReferenceSetVisitor</a></td><td class="desc"><a class="el" href="classmlpack_1_1neighbor_1_1ReferenceSetVisitor.html" title="ReferenceSetVisitor exposes the referenceSet of the given NSType. ">ReferenceSetVisitor</a> exposes the referenceSet of the given NSType </td></tr>
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<tr id="row_1_25_22_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1neighbor_1_1SearchModeVisitor.html" target="_self">SearchModeVisitor</a></td><td class="desc"><a class="el" href="classmlpack_1_1neighbor_1_1SearchModeVisitor.html" title="SearchModeVisitor exposes the SearchMode() method of the given NSType. ">SearchModeVisitor</a> exposes the SearchMode() method of the given NSType </td></tr>
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<tr id="row_1_25_23_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1neighbor_1_1TrainVisitor.html" target="_self">TrainVisitor</a></td><td class="desc"><a class="el" href="classmlpack_1_1neighbor_1_1TrainVisitor.html" title="TrainVisitor sets the reference set to a new reference set on the given NSType. ">TrainVisitor</a> sets the reference set to a new reference set on the given NSType </td></tr>
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<tr id="row_1_26_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span id="arr_1_26_" class="arrow" onclick="toggleFolder('1_26_')">►</span><span class="icona"><span class="icon">N</span></span><a class="el" href="namespacemlpack_1_1nn.html" target="_self">nn</a></td><td class="desc"></td></tr>
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<tr id="row_1_26_0_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1nn_1_1SparseAutoencoder.html" target="_self">SparseAutoencoder</a></td><td class="desc">A sparse autoencoder is a neural network whose aim to learn compressed representations of the data, typically for dimensionality reduction, with a constraint on the activity of the neurons in the network </td></tr>
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<tr id="row_1_26_1_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1nn_1_1SparseAutoencoderFunction.html" target="_self">SparseAutoencoderFunction</a></td><td class="desc">This is a class for the sparse autoencoder objective function </td></tr>
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<tr id="row_1_27_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span id="arr_1_27_" class="arrow" onclick="toggleFolder('1_27_')">►</span><span class="icona"><span class="icon">N</span></span><a class="el" href="namespacemlpack_1_1optimization.html" target="_self">optimization</a></td><td class="desc"></td></tr>
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<tr id="row_1_27_0_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span id="arr_1_27_0_" class="arrow" onclick="toggleFolder('1_27_0_')">►</span><span class="icona"><span class="icon">N</span></span><a class="el" href="namespacemlpack_1_1optimization_1_1test.html" target="_self">test</a></td><td class="desc"></td></tr>
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<tr id="row_1_27_0_0_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1optimization_1_1test_1_1GDTestFunction.html" target="_self">GDTestFunction</a></td><td class="desc">Very, very simple test function which is the composite of three other functions </td></tr>
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<tr id="row_1_27_0_1_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1optimization_1_1test_1_1GeneralizedRosenbrockFunction.html" target="_self">GeneralizedRosenbrockFunction</a></td><td class="desc">The Generalized Rosenbrock function in n dimensions, defined by f(x) = sum_i^{n - 1} (f(i)(x)) f_i(x) = 100 * (x_i^2 - x_{i + 1})^2 + (1 - x_i)^2 x_0 = [-1.2, 1, -1.2, 1, ...] </td></tr>
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<tr id="row_1_27_0_2_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1optimization_1_1test_1_1RosenbrockFunction.html" target="_self">RosenbrockFunction</a></td><td class="desc">The Rosenbrock function, defined by f(x) = f1(x) + f2(x) f1(x) = 100 (x2 - x1^2)^2 f2(x) = (1 - x1)^2 x_0 = [-1.2, 1] </td></tr>
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<tr id="row_1_27_0_3_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1optimization_1_1test_1_1RosenbrockWoodFunction.html" target="_self">RosenbrockWoodFunction</a></td><td class="desc">The Generalized Rosenbrock function in 4 dimensions with the Wood Function in four dimensions </td></tr>
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<tr id="row_1_27_0_4_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1optimization_1_1test_1_1SGDTestFunction.html" target="_self">SGDTestFunction</a></td><td class="desc">Very, very simple test function which is the composite of three other functions </td></tr>
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<tr id="row_1_27_0_5_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1optimization_1_1test_1_1WoodFunction.html" target="_self">WoodFunction</a></td><td class="desc">The Wood function, defined by f(x) = f1(x) + f2(x) + f3(x) + f4(x) + f5(x) + f6(x) f1(x) = 100 (x2 - x1^2)^2 f2(x) = (1 - x1)^2 f3(x) = 90 (x4 - x3^2)^2 f4(x) = (1 - x3)^2 f5(x) = 10 (x2 + x4 - 2)^2 f6(x) = (1 / 10) (x2 - x4)^2 x_0 = [-3, -1, -3, -1] </td></tr>
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<tr id="row_1_27_1_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1optimization_1_1AdaDelta.html" target="_self">AdaDelta</a></td><td class="desc">Adadelta is an optimizer that uses two ideas to improve upon the two main drawbacks of the Adagrad method: </td></tr>
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<tr id="row_1_27_2_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1optimization_1_1Adam.html" target="_self">Adam</a></td><td class="desc"><a class="el" href="classmlpack_1_1optimization_1_1Adam.html" title="Adam is an optimizer that computes individual adaptive learning rates for different parameters from e...">Adam</a> is an optimizer that computes individual adaptive learning rates for different parameters from estimates of first and second moments of the gradients </td></tr>
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<tr id="row_1_27_3_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1optimization_1_1AugLagrangian.html" target="_self">AugLagrangian</a></td><td class="desc">The <a class="el" href="classmlpack_1_1optimization_1_1AugLagrangian.html" title="The AugLagrangian class implements the Augmented Lagrangian method of optimization. ">AugLagrangian</a> class implements the Augmented Lagrangian method of optimization </td></tr>
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<tr id="row_1_27_4_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1optimization_1_1AugLagrangianFunction.html" target="_self">AugLagrangianFunction</a></td><td class="desc">This is a utility class used by <a class="el" href="classmlpack_1_1optimization_1_1AugLagrangian.html" title="The AugLagrangian class implements the Augmented Lagrangian method of optimization. ">AugLagrangian</a>, meant to wrap a LagrangianFunction into a function usable by a simple optimizer like L-BFGS </td></tr>
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<tr id="row_1_27_5_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1optimization_1_1AugLagrangianTestFunction.html" target="_self">AugLagrangianTestFunction</a></td><td class="desc">This function is taken from "Practical Mathematical Optimization" (Snyman), section 5.3.8 ("Application of the Augmented Lagrangian Method") </td></tr>
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<tr id="row_1_27_6_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1optimization_1_1ExponentialSchedule.html" target="_self">ExponentialSchedule</a></td><td class="desc">The exponential cooling schedule cools the temperature T at every step according to the equation </td></tr>
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<tr id="row_1_27_7_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1optimization_1_1GockenbachFunction.html" target="_self">GockenbachFunction</a></td><td class="desc">This function is taken from M </td></tr>
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<tr id="row_1_27_8_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1optimization_1_1GradientDescent.html" target="_self">GradientDescent</a></td><td class="desc">Gradient Descent is a technique to minimize a function </td></tr>
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<tr id="row_1_27_9_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1optimization_1_1L__BFGS.html" target="_self">L_BFGS</a></td><td class="desc">The generic L-BFGS optimizer, which uses a back-tracking line search algorithm to minimize a function </td></tr>
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<tr id="row_1_27_10_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1optimization_1_1LovaszThetaSDP.html" target="_self">LovaszThetaSDP</a></td><td class="desc">This function is the Lovasz-Theta semidefinite program, as implemented in the following paper: </td></tr>
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<tr id="row_1_27_11_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1optimization_1_1LRSDP.html" target="_self">LRSDP</a></td><td class="desc"><a class="el" href="classmlpack_1_1optimization_1_1LRSDP.html" title="LRSDP is the implementation of Monteiro and Burer's formulation of low-rank semidefinite programs (LR...">LRSDP</a> is the implementation of Monteiro and Burer's formulation of low-rank semidefinite programs (LR-SDP) </td></tr>
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<tr id="row_1_27_12_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1optimization_1_1LRSDPFunction.html" target="_self">LRSDPFunction</a></td><td class="desc">The objective function that <a class="el" href="classmlpack_1_1optimization_1_1LRSDP.html" title="LRSDP is the implementation of Monteiro and Burer's formulation of low-rank semidefinite programs (LR...">LRSDP</a> is trying to optimize </td></tr>
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<tr id="row_1_27_13_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1optimization_1_1MiniBatchSGD.html" target="_self">MiniBatchSGD</a></td><td class="desc">Mini-batch Stochastic Gradient Descent is a technique for minimizing a function which can be expressed as a sum of other functions </td></tr>
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<tr id="row_1_27_14_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1optimization_1_1PrimalDualSolver.html" target="_self">PrimalDualSolver</a></td><td class="desc">Interface to a primal dual interior point solver </td></tr>
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<tr id="row_1_27_15_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1optimization_1_1RMSprop.html" target="_self">RMSprop</a></td><td class="desc"><a class="el" href="classmlpack_1_1optimization_1_1RMSprop.html" title="RMSprop is an optimizer that utilizes the magnitude of recent gradients to normalize the gradients...">RMSprop</a> is an optimizer that utilizes the magnitude of recent gradients to normalize the gradients </td></tr>
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<tr id="row_1_27_16_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1optimization_1_1SA.html" target="_self">SA</a></td><td class="desc">Simulated Annealing is an stochastic optimization algorithm which is able to deliver near-optimal results quickly without knowing the gradient of the function being optimized </td></tr>
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<tr id="row_1_27_17_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1optimization_1_1SDP.html" target="_self">SDP</a></td><td class="desc">Specify an <a class="el" href="classmlpack_1_1optimization_1_1SDP.html" title="Specify an SDP in primal form. ">SDP</a> in primal form </td></tr>
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<tr id="row_1_27_18_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1optimization_1_1SGD.html" target="_self">SGD</a></td><td class="desc">Stochastic Gradient Descent is a technique for minimizing a function which can be expressed as a sum of other functions </td></tr>
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<tr id="row_1_27_19_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1optimization_1_1VanillaUpdate.html" target="_self">VanillaUpdate</a></td><td class="desc">Vanilla update policy for Stochastic Gradient Descent (<a class="el" href="classmlpack_1_1optimization_1_1SGD.html" title="Stochastic Gradient Descent is a technique for minimizing a function which can be expressed as a sum ...">SGD</a>) </td></tr>
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<tr id="row_1_28_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span id="arr_1_28_" class="arrow" onclick="toggleFolder('1_28_')">►</span><span class="icona"><span class="icon">N</span></span><a class="el" href="namespacemlpack_1_1pca.html" target="_self">pca</a></td><td class="desc"></td></tr>
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<tr id="row_1_28_0_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1pca_1_1ExactSVDPolicy.html" target="_self">ExactSVDPolicy</a></td><td class="desc">Implementation of the exact SVD policy </td></tr>
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<tr id="row_1_28_1_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1pca_1_1PCAType.html" target="_self">PCAType</a></td><td class="desc">This class implements principal components analysis (PCA) </td></tr>
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<tr id="row_1_28_2_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1pca_1_1QUICSVDPolicy.html" target="_self">QUICSVDPolicy</a></td><td class="desc">Implementation of the QUIC-SVD policy </td></tr>
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<tr id="row_1_28_3_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1pca_1_1RandomizedSVDPolicy.html" target="_self">RandomizedSVDPolicy</a></td><td class="desc">Implementation of the randomized SVD policy </td></tr>
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<tr id="row_1_29_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span id="arr_1_29_" class="arrow" onclick="toggleFolder('1_29_')">►</span><span class="icona"><span class="icon">N</span></span><a class="el" href="namespacemlpack_1_1perceptron.html" target="_self">perceptron</a></td><td class="desc"></td></tr>
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<tr id="row_1_29_0_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1perceptron_1_1Perceptron.html" target="_self">Perceptron</a></td><td class="desc">This class implements a simple perceptron (i.e., a single layer neural network) </td></tr>
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<tr id="row_1_29_1_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1perceptron_1_1RandomInitialization.html" target="_self">RandomInitialization</a></td><td class="desc">This class is used to initialize weights for the weightVectors matrix in a random manner </td></tr>
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<tr id="row_1_29_2_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1perceptron_1_1SimpleWeightUpdate.html" target="_self">SimpleWeightUpdate</a></td><td class="desc"></td></tr>
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<tr id="row_1_29_3_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1perceptron_1_1ZeroInitialization.html" target="_self">ZeroInitialization</a></td><td class="desc">This class is used to initialize the matrix weightVectors to zero </td></tr>
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<tr id="row_1_30_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span id="arr_1_30_" class="arrow" onclick="toggleFolder('1_30_')">►</span><span class="icona"><span class="icon">N</span></span><a class="el" href="namespacemlpack_1_1radical.html" target="_self">radical</a></td><td class="desc"></td></tr>
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<tr id="row_1_30_0_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1radical_1_1Radical.html" target="_self">Radical</a></td><td class="desc">An implementation of RADICAL, an algorithm for independent component analysis (ICA) </td></tr>
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<tr id="row_1_31_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span id="arr_1_31_" class="arrow" onclick="toggleFolder('1_31_')">►</span><span class="icona"><span class="icon">N</span></span><a class="el" href="namespacemlpack_1_1range.html" target="_self">range</a></td><td class="desc">Range-search routines </td></tr>
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<tr id="row_1_31_0_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1range_1_1BiSearchVisitor.html" target="_self">BiSearchVisitor</a></td><td class="desc"><a class="el" href="classmlpack_1_1range_1_1BiSearchVisitor.html" title="BiSearchVisitor executes a bichromatic range search on the given RSType. ">BiSearchVisitor</a> executes a bichromatic range search on the given RSType </td></tr>
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<tr id="row_1_31_1_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1range_1_1DeleteVisitor.html" target="_self">DeleteVisitor</a></td><td class="desc"><a class="el" href="classmlpack_1_1range_1_1DeleteVisitor.html" title="DeleteVisitor deletes the given RSType instance. ">DeleteVisitor</a> deletes the given RSType instance </td></tr>
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<tr id="row_1_31_2_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1range_1_1MonoSearchVisitor.html" target="_self">MonoSearchVisitor</a></td><td class="desc"><a class="el" href="classmlpack_1_1range_1_1MonoSearchVisitor.html" title="MonoSearchVisitor executes a monochromatic range search on the given RSType. ">MonoSearchVisitor</a> executes a monochromatic range search on the given RSType </td></tr>
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<tr id="row_1_31_3_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1range_1_1NaiveVisitor.html" target="_self">NaiveVisitor</a></td><td class="desc"><a class="el" href="classmlpack_1_1range_1_1NaiveVisitor.html" title="NaiveVisitor exposes the Naive() method of the given RSType. ">NaiveVisitor</a> exposes the Naive() method of the given RSType </td></tr>
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<tr id="row_1_31_4_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1range_1_1RangeSearch.html" target="_self">RangeSearch</a></td><td class="desc">The <a class="el" href="classmlpack_1_1range_1_1RangeSearch.html" title="The RangeSearch class is a template class for performing range searches. ">RangeSearch</a> class is a template class for performing range searches </td></tr>
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<tr id="row_1_31_5_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1range_1_1RangeSearchRules.html" target="_self">RangeSearchRules</a></td><td class="desc">The <a class="el" href="classmlpack_1_1range_1_1RangeSearchRules.html" title="The RangeSearchRules class is a template helper class used by RangeSearch class when performing range...">RangeSearchRules</a> class is a template helper class used by <a class="el" href="classmlpack_1_1range_1_1RangeSearch.html" title="The RangeSearch class is a template class for performing range searches. ">RangeSearch</a> class when performing range searches </td></tr>
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<tr id="row_1_31_6_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1range_1_1RangeSearchStat.html" target="_self">RangeSearchStat</a></td><td class="desc">Statistic class for <a class="el" href="classmlpack_1_1range_1_1RangeSearch.html" title="The RangeSearch class is a template class for performing range searches. ">RangeSearch</a>, to be set to the StatisticType of the tree type that range search is being performed with </td></tr>
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<tr id="row_1_31_7_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1range_1_1ReferenceSetVisitor.html" target="_self">ReferenceSetVisitor</a></td><td class="desc"><a class="el" href="classmlpack_1_1range_1_1ReferenceSetVisitor.html" title="ReferenceSetVisitor exposes the referenceSet of the given RSType. ">ReferenceSetVisitor</a> exposes the referenceSet of the given RSType </td></tr>
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<tr id="row_1_31_8_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1range_1_1RSModel.html" target="_self">RSModel</a></td><td class="desc"></td></tr>
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<tr id="row_1_31_9_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structmlpack_1_1range_1_1RSModelName.html" target="_self">RSModelName</a></td><td class="desc"></td></tr>
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<tr id="row_1_31_10_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1range_1_1SerializeVisitor.html" target="_self">SerializeVisitor</a></td><td class="desc">Exposes the seralize method of the given RSType </td></tr>
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<tr id="row_1_31_11_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1range_1_1SingleModeVisitor.html" target="_self">SingleModeVisitor</a></td><td class="desc"><a class="el" href="classmlpack_1_1range_1_1SingleModeVisitor.html" title="SingleModeVisitor exposes the SingleMode() method of the given RSType. ">SingleModeVisitor</a> exposes the SingleMode() method of the given RSType </td></tr>
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<tr id="row_1_31_12_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1range_1_1TrainVisitor.html" target="_self">TrainVisitor</a></td><td class="desc"><a class="el" href="classmlpack_1_1range_1_1TrainVisitor.html" title="TrainVisitor sets the reference set to a new reference set on the given RSType. ">TrainVisitor</a> sets the reference set to a new reference set on the given RSType </td></tr>
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<tr id="row_1_32_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span id="arr_1_32_" class="arrow" onclick="toggleFolder('1_32_')">►</span><span class="icona"><span class="icon">N</span></span><a class="el" href="namespacemlpack_1_1regression.html" target="_self">regression</a></td><td class="desc">Regression methods </td></tr>
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<tr id="row_1_32_0_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1regression_1_1LARS.html" target="_self">LARS</a></td><td class="desc">An implementation of <a class="el" href="classmlpack_1_1regression_1_1LARS.html" title="An implementation of LARS, a stage-wise homotopy-based algorithm for l1-regularized linear regression...">LARS</a>, a stage-wise homotopy-based algorithm for l1-regularized linear regression (LASSO) and l1+l2 regularized linear regression (Elastic Net) </td></tr>
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<tr id="row_1_32_1_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1regression_1_1LinearRegression.html" target="_self">LinearRegression</a></td><td class="desc">A simple linear regression algorithm using ordinary least squares </td></tr>
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<tr id="row_1_32_2_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1regression_1_1LogisticRegression.html" target="_self">LogisticRegression</a></td><td class="desc">The <a class="el" href="classmlpack_1_1regression_1_1LogisticRegression.html" title="The LogisticRegression class implements an L2-regularized logistic regression model, and supports training with multiple optimizers and classification. ">LogisticRegression</a> class implements an L2-regularized logistic regression model, and supports training with multiple optimizers and classification </td></tr>
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<tr id="row_1_32_3_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1regression_1_1LogisticRegressionFunction.html" target="_self">LogisticRegressionFunction</a></td><td class="desc">The log-likelihood function for the logistic regression objective function </td></tr>
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<tr id="row_1_32_4_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1regression_1_1SoftmaxRegression.html" target="_self">SoftmaxRegression</a></td><td class="desc">Softmax Regression is a classifier which can be used for classification when the data available can take two or more class values </td></tr>
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<tr id="row_1_32_5_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1regression_1_1SoftmaxRegressionFunction.html" target="_self">SoftmaxRegressionFunction</a></td><td class="desc"></td></tr>
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<tr id="row_1_33_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span id="arr_1_33_" class="arrow" onclick="toggleFolder('1_33_')">►</span><span class="icona"><span class="icon">N</span></span><a class="el" href="namespacemlpack_1_1sparse__coding.html" target="_self">sparse_coding</a></td><td class="desc"></td></tr>
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<tr id="row_1_33_0_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1sparse__coding_1_1DataDependentRandomInitializer.html" target="_self">DataDependentRandomInitializer</a></td><td class="desc">A data-dependent random dictionary initializer for <a class="el" href="classmlpack_1_1sparse__coding_1_1SparseCoding.html" title="An implementation of Sparse Coding with Dictionary Learning that achieves sparsity via an l1-norm reg...">SparseCoding</a> </td></tr>
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<tr id="row_1_33_1_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1sparse__coding_1_1NothingInitializer.html" target="_self">NothingInitializer</a></td><td class="desc">A DictionaryInitializer for <a class="el" href="classmlpack_1_1sparse__coding_1_1SparseCoding.html" title="An implementation of Sparse Coding with Dictionary Learning that achieves sparsity via an l1-norm reg...">SparseCoding</a> which does not initialize anything; it is useful for when the dictionary is already known and will be set with <a class="el" href="classmlpack_1_1sparse__coding_1_1SparseCoding.html#ab069f805f1db24a672b6f38a6d9cf755" title="Modify the dictionary. ">SparseCoding::Dictionary()</a> </td></tr>
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<tr id="row_1_33_2_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1sparse__coding_1_1RandomInitializer.html" target="_self">RandomInitializer</a></td><td class="desc">A DictionaryInitializer for use with the <a class="el" href="classmlpack_1_1sparse__coding_1_1SparseCoding.html" title="An implementation of Sparse Coding with Dictionary Learning that achieves sparsity via an l1-norm reg...">SparseCoding</a> class </td></tr>
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<tr id="row_1_33_3_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1sparse__coding_1_1SparseCoding.html" target="_self">SparseCoding</a></td><td class="desc">An implementation of Sparse Coding with Dictionary Learning that achieves sparsity via an l1-norm regularizer on the codes (LASSO) or an (l1+l2)-norm regularizer on the codes (the Elastic Net) </td></tr>
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<tr id="row_1_34_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span id="arr_1_34_" class="arrow" onclick="toggleFolder('1_34_')">►</span><span class="icona"><span class="icon">N</span></span><a class="el" href="namespacemlpack_1_1svd.html" target="_self">svd</a></td><td class="desc"></td></tr>
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<tr id="row_1_34_0_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1svd_1_1QUIC__SVD.html" target="_self">QUIC_SVD</a></td><td class="desc">QUIC-SVD is a matrix factorization technique, which operates in a subspace such that A's approximation in that subspace has minimum error(A being the data matrix) </td></tr>
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<tr id="row_1_34_1_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1svd_1_1RandomizedSVD.html" target="_self">RandomizedSVD</a></td><td class="desc">Randomized SVD is a matrix factorization that is based on randomized matrix approximation techniques, developed in in "Finding structure with randomness:
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Probabilistic algorithms for constructing approximate matrix decompositions" </td></tr>
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<tr id="row_1_34_2_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1svd_1_1RegularizedSVD.html" target="_self">RegularizedSVD</a></td><td class="desc">Regularized SVD is a matrix factorization technique that seeks to reduce the error on the training set, that is on the examples for which the ratings have been provided by the users </td></tr>
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<tr id="row_1_34_3_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1svd_1_1RegularizedSVDFunction.html" target="_self">RegularizedSVDFunction</a></td><td class="desc"></td></tr>
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<tr id="row_1_35_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span id="arr_1_35_" class="arrow" onclick="toggleFolder('1_35_')">►</span><span class="icona"><span class="icon">N</span></span><a class="el" href="namespacemlpack_1_1tree.html" target="_self">tree</a></td><td class="desc">Trees and tree-building procedures </td></tr>
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<tr id="row_1_35_0_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span id="arr_1_35_0_" class="arrow" onclick="toggleFolder('1_35_0_')">►</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1tree_1_1AllCategoricalSplit.html" target="_self">AllCategoricalSplit</a></td><td class="desc">The <a class="el" href="classmlpack_1_1tree_1_1AllCategoricalSplit.html" title="The AllCategoricalSplit is a splitting function that will split categorical features into many childr...">AllCategoricalSplit</a> is a splitting function that will split categorical features into many children: one child for each category </td></tr>
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<tr id="row_1_35_0_0_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1tree_1_1AllCategoricalSplit_1_1AuxiliarySplitInfo.html" target="_self">AuxiliarySplitInfo</a></td><td class="desc"></td></tr>
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<tr id="row_1_35_1_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1tree_1_1AxisParallelProjVector.html" target="_self">AxisParallelProjVector</a></td><td class="desc"><a class="el" href="classmlpack_1_1tree_1_1AxisParallelProjVector.html" title="AxisParallelProjVector defines an axis-parallel projection vector. ">AxisParallelProjVector</a> defines an axis-parallel projection vector </td></tr>
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<tr id="row_1_35_2_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span id="arr_1_35_2_" class="arrow" onclick="toggleFolder('1_35_2_')">►</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1tree_1_1BestBinaryNumericSplit.html" target="_self">BestBinaryNumericSplit</a></td><td class="desc">The <a class="el" href="classmlpack_1_1tree_1_1BestBinaryNumericSplit.html" title="The BestBinaryNumericSplit is a splitting function for decision trees that will exhaustively search a...">BestBinaryNumericSplit</a> is a splitting function for decision trees that will exhaustively search a numeric dimension for the best binary split </td></tr>
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<tr id="row_1_35_2_0_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1tree_1_1BestBinaryNumericSplit_1_1AuxiliarySplitInfo.html" target="_self">AuxiliarySplitInfo</a></td><td class="desc"></td></tr>
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<tr id="row_1_35_3_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1tree_1_1BinaryNumericSplit.html" target="_self">BinaryNumericSplit</a></td><td class="desc">The <a class="el" href="classmlpack_1_1tree_1_1BinaryNumericSplit.html" title="The BinaryNumericSplit class implements the numeric feature splitting strategy devised by Gama...">BinaryNumericSplit</a> class implements the numeric feature splitting strategy devised by Gama, Rocha, and Medas in the following paper: </td></tr>
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<tr id="row_1_35_4_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1tree_1_1BinaryNumericSplitInfo.html" target="_self">BinaryNumericSplitInfo</a></td><td class="desc"></td></tr>
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<tr id="row_1_35_5_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span id="arr_1_35_5_" class="arrow" onclick="toggleFolder('1_35_5_')">►</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1tree_1_1BinarySpaceTree.html" target="_self">BinarySpaceTree</a></td><td class="desc">A binary space partitioning tree, such as a KD-tree or a ball tree </td></tr>
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<tr id="row_1_35_5_0_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1tree_1_1BinarySpaceTree_1_1BreadthFirstDualTreeTraverser.html" target="_self">BreadthFirstDualTreeTraverser</a></td><td class="desc"></td></tr>
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<tr id="row_1_35_5_1_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1tree_1_1BinarySpaceTree_1_1DualTreeTraverser.html" target="_self">DualTreeTraverser</a></td><td class="desc">A dual-tree traverser for binary space trees; see dual_tree_traverser.hpp </td></tr>
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<tr id="row_1_35_5_2_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1tree_1_1BinarySpaceTree_1_1SingleTreeTraverser.html" target="_self">SingleTreeTraverser</a></td><td class="desc">A single-tree traverser for binary space trees; see single_tree_traverser.hpp for implementation </td></tr>
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<tr id="row_1_35_6_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1tree_1_1CategoricalSplitInfo.html" target="_self">CategoricalSplitInfo</a></td><td class="desc"></td></tr>
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<tr id="row_1_35_7_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1tree_1_1CompareCosineNode.html" target="_self">CompareCosineNode</a></td><td class="desc"></td></tr>
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<tr id="row_1_35_8_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1tree_1_1CosineTree.html" target="_self">CosineTree</a></td><td class="desc"></td></tr>
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<tr id="row_1_35_9_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span id="arr_1_35_9_" class="arrow" onclick="toggleFolder('1_35_9_')">►</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1tree_1_1CoverTree.html" target="_self">CoverTree</a></td><td class="desc">A cover tree is a tree specifically designed to speed up nearest-neighbor computation in high-dimensional spaces </td></tr>
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<tr id="row_1_35_9_0_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span id="arr_1_35_9_0_" class="arrow" onclick="toggleFolder('1_35_9_0_')">►</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1tree_1_1CoverTree_1_1DualTreeTraverser.html" target="_self">DualTreeTraverser</a></td><td class="desc">A dual-tree cover tree traverser; see dual_tree_traverser.hpp </td></tr>
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<tr id="row_1_35_9_0_0_" class="even" style="display:none;"><td class="entry"><span style="width:80px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structmlpack_1_1tree_1_1CoverTree_1_1DualTreeTraverser_1_1DualCoverTreeMapEntry.html" target="_self">DualCoverTreeMapEntry</a></td><td class="desc">Struct used for traversal </td></tr>
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<tr id="row_1_35_9_1_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1tree_1_1CoverTree_1_1SingleTreeTraverser.html" target="_self">SingleTreeTraverser</a></td><td class="desc">A single-tree cover tree traverser; see single_tree_traverser.hpp for implementation </td></tr>
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<tr id="row_1_35_10_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1tree_1_1DecisionTree.html" target="_self">DecisionTree</a></td><td class="desc">This class implements a generic decision tree learner </td></tr>
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<tr id="row_1_35_11_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1tree_1_1DiscreteHilbertValue.html" target="_self">DiscreteHilbertValue</a></td><td class="desc">The <a class="el" href="classmlpack_1_1tree_1_1DiscreteHilbertValue.html" title="The DiscreteHilbertValue class stores Hilbert values for all of the points in a RectangleTree node...">DiscreteHilbertValue</a> class stores Hilbert values for all of the points in a <a class="el" href="classmlpack_1_1tree_1_1RectangleTree.html" title="A rectangle type tree tree, such as an R-tree or X-tree. ">RectangleTree</a> node, and calculates Hilbert values for new points </td></tr>
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<tr id="row_1_35_12_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1tree_1_1EmptyStatistic.html" target="_self">EmptyStatistic</a></td><td class="desc">Empty statistic if you are not interested in storing statistics in your tree </td></tr>
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<tr id="row_1_35_13_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1tree_1_1ExampleTree.html" target="_self">ExampleTree</a></td><td class="desc">This is not an actual space tree but instead an example tree that exists to show and document all the functions that mlpack trees must implement </td></tr>
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<tr id="row_1_35_14_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1tree_1_1FirstPointIsRoot.html" target="_self">FirstPointIsRoot</a></td><td class="desc">This class is meant to be used as a choice for the policy class RootPointPolicy of the <a class="el" href="classmlpack_1_1tree_1_1CoverTree.html" title="A cover tree is a tree specifically designed to speed up nearest-neighbor computation in high-dimensi...">CoverTree</a> class </td></tr>
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<tr id="row_1_35_15_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1tree_1_1GiniGain.html" target="_self">GiniGain</a></td><td class="desc">The Gini gain, a measure of set purity usable as a fitness function (FitnessFunction) for decision trees </td></tr>
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<tr id="row_1_35_16_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1tree_1_1GiniImpurity.html" target="_self">GiniImpurity</a></td><td class="desc"></td></tr>
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<tr id="row_1_35_17_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1tree_1_1GreedySingleTreeTraverser.html" target="_self">GreedySingleTreeTraverser</a></td><td class="desc"></td></tr>
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<tr id="row_1_35_18_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1tree_1_1HilbertRTreeAuxiliaryInformation.html" target="_self">HilbertRTreeAuxiliaryInformation</a></td><td class="desc"></td></tr>
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<tr id="row_1_35_19_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1tree_1_1HilbertRTreeDescentHeuristic.html" target="_self">HilbertRTreeDescentHeuristic</a></td><td class="desc">This class chooses the best child of a node in a Hilbert R tree when inserting a new point </td></tr>
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<tr id="row_1_35_20_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1tree_1_1HilbertRTreeSplit.html" target="_self">HilbertRTreeSplit</a></td><td class="desc">The splitting procedure for the Hilbert R tree </td></tr>
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<tr id="row_1_35_21_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1tree_1_1HoeffdingCategoricalSplit.html" target="_self">HoeffdingCategoricalSplit</a></td><td class="desc">This is the standard Hoeffding-bound categorical feature proposed in the paper below: </td></tr>
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<tr id="row_1_35_22_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1tree_1_1HoeffdingNumericSplit.html" target="_self">HoeffdingNumericSplit</a></td><td class="desc">The <a class="el" href="classmlpack_1_1tree_1_1HoeffdingNumericSplit.html" title="The HoeffdingNumericSplit class implements the numeric feature splitting strategy alluded to by Domin...">HoeffdingNumericSplit</a> class implements the numeric feature splitting strategy alluded to by Domingos and Hulten in the following paper: </td></tr>
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<tr id="row_1_35_23_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1tree_1_1HoeffdingTree.html" target="_self">HoeffdingTree</a></td><td class="desc">The <a class="el" href="classmlpack_1_1tree_1_1HoeffdingTree.html" title="The HoeffdingTree object represents all of the necessary information for a Hoeffding-bound-based deci...">HoeffdingTree</a> object represents all of the necessary information for a Hoeffding-bound-based decision tree </td></tr>
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<tr id="row_1_35_24_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1tree_1_1HoeffdingTreeModel.html" target="_self">HoeffdingTreeModel</a></td><td class="desc">This class is a serializable Hoeffding tree model that can hold four different types of Hoeffding trees </td></tr>
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<tr id="row_1_35_25_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1tree_1_1HyperplaneBase.html" target="_self">HyperplaneBase</a></td><td class="desc"><a class="el" href="classmlpack_1_1tree_1_1HyperplaneBase.html" title="HyperplaneBase defines a splitting hyperplane based on a projection vector and projection value...">HyperplaneBase</a> defines a splitting hyperplane based on a projection vector and projection value </td></tr>
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<tr id="row_1_35_26_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1tree_1_1InformationGain.html" target="_self">InformationGain</a></td><td class="desc">The standard information gain criterion, used for calculating gain in decision trees </td></tr>
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<tr id="row_1_35_27_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structmlpack_1_1tree_1_1IsSpillTree.html" target="_self">IsSpillTree</a></td><td class="desc"></td></tr>
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<tr id="row_1_35_28_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structmlpack_1_1tree_1_1IsSpillTree_3_01tree_1_1SpillTree_3_01MetricType_00_01StatisticType_00_0d41f2b10e451850b8eb14d3156c51340.html" target="_self">IsSpillTree< tree::SpillTree< MetricType, StatisticType, MatType, HyperplaneType, SplitType > ></a></td><td class="desc"></td></tr>
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<tr id="row_1_35_29_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1tree_1_1MeanSpaceSplit.html" target="_self">MeanSpaceSplit</a></td><td class="desc"></td></tr>
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<tr id="row_1_35_30_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span id="arr_1_35_30_" class="arrow" onclick="toggleFolder('1_35_30_')">►</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1tree_1_1MeanSplit.html" target="_self">MeanSplit</a></td><td class="desc">A binary space partitioning tree node is split into its left and right child </td></tr>
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<tr id="row_1_35_30_0_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structmlpack_1_1tree_1_1MeanSplit_1_1SplitInfo.html" target="_self">SplitInfo</a></td><td class="desc">An information about the partition </td></tr>
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<tr id="row_1_35_31_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1tree_1_1MidpointSpaceSplit.html" target="_self">MidpointSpaceSplit</a></td><td class="desc"></td></tr>
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<tr id="row_1_35_32_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span id="arr_1_35_32_" class="arrow" onclick="toggleFolder('1_35_32_')">►</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1tree_1_1MidpointSplit.html" target="_self">MidpointSplit</a></td><td class="desc">A binary space partitioning tree node is split into its left and right child </td></tr>
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<tr id="row_1_35_32_0_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structmlpack_1_1tree_1_1MidpointSplit_1_1SplitInfo.html" target="_self">SplitInfo</a></td><td class="desc">A struct that contains an information about the split </td></tr>
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<tr id="row_1_35_33_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span id="arr_1_35_33_" class="arrow" onclick="toggleFolder('1_35_33_')">►</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1tree_1_1MinimalCoverageSweep.html" target="_self">MinimalCoverageSweep</a></td><td class="desc">The <a class="el" href="classmlpack_1_1tree_1_1MinimalCoverageSweep.html" title="The MinimalCoverageSweep class finds a partition along which we can split a node according to the cov...">MinimalCoverageSweep</a> class finds a partition along which we can split a node according to the coverage of two resulting nodes </td></tr>
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<tr id="row_1_35_33_0_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structmlpack_1_1tree_1_1MinimalCoverageSweep_1_1SweepCost.html" target="_self">SweepCost</a></td><td class="desc">A struct that provides the type of the sweep cost </td></tr>
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<tr id="row_1_35_34_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span id="arr_1_35_34_" class="arrow" onclick="toggleFolder('1_35_34_')">►</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1tree_1_1MinimalSplitsNumberSweep.html" target="_self">MinimalSplitsNumberSweep</a></td><td class="desc">The <a class="el" href="classmlpack_1_1tree_1_1MinimalSplitsNumberSweep.html" title="The MinimalSplitsNumberSweep class finds a partition along which we can split a node according to the...">MinimalSplitsNumberSweep</a> class finds a partition along which we can split a node according to the number of required splits of the node </td></tr>
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<tr id="row_1_35_34_0_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structmlpack_1_1tree_1_1MinimalSplitsNumberSweep_1_1SweepCost.html" target="_self">SweepCost</a></td><td class="desc">A struct that provides the type of the sweep cost </td></tr>
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<tr id="row_1_35_35_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1tree_1_1NoAuxiliaryInformation.html" target="_self">NoAuxiliaryInformation</a></td><td class="desc"></td></tr>
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<tr id="row_1_35_36_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1tree_1_1NumericSplitInfo.html" target="_self">NumericSplitInfo</a></td><td class="desc"></td></tr>
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<tr id="row_1_35_37_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span id="arr_1_35_37_" class="arrow" onclick="toggleFolder('1_35_37_')">►</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1tree_1_1Octree.html" target="_self">Octree</a></td><td class="desc"></td></tr>
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<tr id="row_1_35_37_0_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1tree_1_1Octree_1_1DualTreeTraverser.html" target="_self">DualTreeTraverser</a></td><td class="desc">A dual-tree traverser; see dual_tree_traverser.hpp </td></tr>
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<tr id="row_1_35_37_1_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1tree_1_1Octree_1_1SingleTreeTraverser.html" target="_self">SingleTreeTraverser</a></td><td class="desc">A single-tree traverser; see single_tree_traverser.hpp </td></tr>
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<tr id="row_1_35_37_2_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structmlpack_1_1tree_1_1Octree_1_1SplitInfo.html" target="_self">SplitInfo</a></td><td class="desc">This is used for sorting points while splitting </td></tr>
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<tr id="row_1_35_38_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1tree_1_1ProjVector.html" target="_self">ProjVector</a></td><td class="desc"><a class="el" href="classmlpack_1_1tree_1_1ProjVector.html" title="ProjVector defines a general projection vector (not necessarily axis-parallel). ">ProjVector</a> defines a general projection vector (not necessarily axis-parallel) </td></tr>
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<tr id="row_1_35_39_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structmlpack_1_1tree_1_1QueueFrame.html" target="_self">QueueFrame</a></td><td class="desc"></td></tr>
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<tr id="row_1_35_40_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span id="arr_1_35_40_" class="arrow" onclick="toggleFolder('1_35_40_')">►</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1tree_1_1RectangleTree.html" target="_self">RectangleTree</a></td><td class="desc">A rectangle type tree tree, such as an R-tree or X-tree </td></tr>
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<tr id="row_1_35_40_0_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span id="arr_1_35_40_0_" class="arrow" onclick="toggleFolder('1_35_40_0_')">►</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1tree_1_1RectangleTree_1_1DualTreeTraverser.html" target="_self">DualTreeTraverser</a></td><td class="desc">A dual tree traverser for rectangle type trees </td></tr>
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<tr id="row_1_35_40_0_0_" class="even" style="display:none;"><td class="entry"><span style="width:80px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structmlpack_1_1tree_1_1RectangleTree_1_1DualTreeTraverser_1_1NodeAndScore.html" target="_self">NodeAndScore</a></td><td class="desc"></td></tr>
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<tr id="row_1_35_40_1_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span id="arr_1_35_40_1_" class="arrow" onclick="toggleFolder('1_35_40_1_')">►</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1tree_1_1RectangleTree_1_1SingleTreeTraverser.html" target="_self">SingleTreeTraverser</a></td><td class="desc">A single traverser for rectangle type trees </td></tr>
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<tr id="row_1_35_40_1_0_" class="even" style="display:none;"><td class="entry"><span style="width:80px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structmlpack_1_1tree_1_1RectangleTree_1_1SingleTreeTraverser_1_1NodeAndScore.html" target="_self">NodeAndScore</a></td><td class="desc"></td></tr>
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<tr id="row_1_35_41_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1tree_1_1RPlusPlusTreeAuxiliaryInformation.html" target="_self">RPlusPlusTreeAuxiliaryInformation</a></td><td class="desc"></td></tr>
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<tr id="row_1_35_42_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1tree_1_1RPlusPlusTreeDescentHeuristic.html" target="_self">RPlusPlusTreeDescentHeuristic</a></td><td class="desc"></td></tr>
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<tr id="row_1_35_43_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1tree_1_1RPlusPlusTreeSplitPolicy.html" target="_self">RPlusPlusTreeSplitPolicy</a></td><td class="desc">The <a class="el" href="classmlpack_1_1tree_1_1RPlusPlusTreeSplitPolicy.html" title="The RPlusPlusTreeSplitPolicy helps to determine the subtree into which we should insert a child of an...">RPlusPlusTreeSplitPolicy</a> helps to determine the subtree into which we should insert a child of an intermediate node that is being split </td></tr>
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<tr id="row_1_35_44_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1tree_1_1RPlusTreeDescentHeuristic.html" target="_self">RPlusTreeDescentHeuristic</a></td><td class="desc"></td></tr>
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<tr id="row_1_35_45_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1tree_1_1RPlusTreeSplit.html" target="_self">RPlusTreeSplit</a></td><td class="desc">The <a class="el" href="classmlpack_1_1tree_1_1RPlusTreeSplit.html" title="The RPlusTreeSplit class performs the split process of a node on overflow. ">RPlusTreeSplit</a> class performs the split process of a node on overflow </td></tr>
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<tr id="row_1_35_46_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1tree_1_1RPlusTreeSplitPolicy.html" target="_self">RPlusTreeSplitPolicy</a></td><td class="desc">The <a class="el" href="classmlpack_1_1tree_1_1RPlusPlusTreeSplitPolicy.html" title="The RPlusPlusTreeSplitPolicy helps to determine the subtree into which we should insert a child of an...">RPlusPlusTreeSplitPolicy</a> helps to determine the subtree into which we should insert a child of an intermediate node that is being split </td></tr>
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<tr id="row_1_35_47_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span id="arr_1_35_47_" class="arrow" onclick="toggleFolder('1_35_47_')">►</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1tree_1_1RPTreeMaxSplit.html" target="_self">RPTreeMaxSplit</a></td><td class="desc">This class splits a node by a random hyperplane </td></tr>
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<tr id="row_1_35_47_0_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structmlpack_1_1tree_1_1RPTreeMaxSplit_1_1SplitInfo.html" target="_self">SplitInfo</a></td><td class="desc">An information about the partition </td></tr>
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<tr id="row_1_35_48_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span id="arr_1_35_48_" class="arrow" onclick="toggleFolder('1_35_48_')">►</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1tree_1_1RPTreeMeanSplit.html" target="_self">RPTreeMeanSplit</a></td><td class="desc">This class splits a binary space tree </td></tr>
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<tr id="row_1_35_48_0_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structmlpack_1_1tree_1_1RPTreeMeanSplit_1_1SplitInfo.html" target="_self">SplitInfo</a></td><td class="desc">An information about the partition </td></tr>
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<tr id="row_1_35_49_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1tree_1_1RStarTreeDescentHeuristic.html" target="_self">RStarTreeDescentHeuristic</a></td><td class="desc">When descending a <a class="el" href="classmlpack_1_1tree_1_1RectangleTree.html" title="A rectangle type tree tree, such as an R-tree or X-tree. ">RectangleTree</a> to insert a point, we need to have a way to choose a child node when the point isn't enclosed by any of them </td></tr>
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<tr id="row_1_35_50_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1tree_1_1RStarTreeSplit.html" target="_self">RStarTreeSplit</a></td><td class="desc">A Rectangle Tree has new points inserted at the bottom </td></tr>
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<tr id="row_1_35_51_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1tree_1_1RTreeDescentHeuristic.html" target="_self">RTreeDescentHeuristic</a></td><td class="desc">When descending a <a class="el" href="classmlpack_1_1tree_1_1RectangleTree.html" title="A rectangle type tree tree, such as an R-tree or X-tree. ">RectangleTree</a> to insert a point, we need to have a way to choose a child node when the point isn't enclosed by any of them </td></tr>
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<tr id="row_1_35_52_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1tree_1_1RTreeSplit.html" target="_self">RTreeSplit</a></td><td class="desc">A Rectangle Tree has new points inserted at the bottom </td></tr>
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<tr id="row_1_35_53_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1tree_1_1SpaceSplit.html" target="_self">SpaceSplit</a></td><td class="desc"></td></tr>
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<tr id="row_1_35_54_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span id="arr_1_35_54_" class="arrow" onclick="toggleFolder('1_35_54_')">►</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1tree_1_1SpillTree.html" target="_self">SpillTree</a></td><td class="desc">A hybrid spill tree is a variant of binary space trees in which the children of a node can "spill over" each other, and contain shared datapoints </td></tr>
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<tr id="row_1_35_54_0_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1tree_1_1SpillTree_1_1SpillDualTreeTraverser.html" target="_self">SpillDualTreeTraverser</a></td><td class="desc">A generic dual-tree traverser for hybrid spill trees; see <a class="el" href="spill__dual__tree__traverser_8hpp.html">spill_dual_tree_traverser.hpp</a> for implementation </td></tr>
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<tr id="row_1_35_54_1_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1tree_1_1SpillTree_1_1SpillSingleTreeTraverser.html" target="_self">SpillSingleTreeTraverser</a></td><td class="desc">A generic single-tree traverser for hybrid spill trees; see <a class="el" href="spill__single__tree__traverser_8hpp.html">spill_single_tree_traverser.hpp</a> for implementation </td></tr>
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<tr id="row_1_35_55_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1tree_1_1TraversalInfo.html" target="_self">TraversalInfo</a></td><td class="desc">The <a class="el" href="classmlpack_1_1tree_1_1TraversalInfo.html" title="The TraversalInfo class holds traversal information which is used in dual-tree (and single-tree) trav...">TraversalInfo</a> class holds traversal information which is used in dual-tree (and single-tree) traversals </td></tr>
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<tr id="row_1_35_56_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1tree_1_1TreeTraits.html" target="_self">TreeTraits</a></td><td class="desc">The <a class="el" href="classmlpack_1_1tree_1_1TreeTraits.html" title="The TreeTraits class provides compile-time information on the characteristics of a given tree type...">TreeTraits</a> class provides compile-time information on the characteristics of a given tree type </td></tr>
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<tr id="row_1_35_57_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1tree_1_1TreeTraits_3_01BinarySpaceTree_3_01MetricType_00_01StatisticType_00_01Mat267d3b8606ae92840ddcba6834055254.html" target="_self">TreeTraits< BinarySpaceTree< MetricType, StatisticType, MatType, bound::BallBound, SplitType > ></a></td><td class="desc">This is a specialization of the TreeType class to the BallTree tree type </td></tr>
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<tr id="row_1_35_58_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1tree_1_1TreeTraits_3_01BinarySpaceTree_3_01MetricType_00_01StatisticType_00_01Mat224e09bac64c8e2ee29120d72866c234.html" target="_self">TreeTraits< BinarySpaceTree< MetricType, StatisticType, MatType, bound::CellBound, SplitType > ></a></td><td class="desc">This is a specialization of the TreeType class to the UBTree tree type </td></tr>
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<tr id="row_1_35_59_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1tree_1_1TreeTraits_3_01BinarySpaceTree_3_01MetricType_00_01StatisticType_00_01Mat5e47ac61d347b64f5768de253cdf2773.html" target="_self">TreeTraits< BinarySpaceTree< MetricType, StatisticType, MatType, bound::HollowBallBound, SplitType > ></a></td><td class="desc">This is a specialization of the TreeType class to an arbitrary tree with HollowBallBound (currently only the vantage point tree is supported) </td></tr>
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<tr id="row_1_35_60_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1tree_1_1TreeTraits_3_01BinarySpaceTree_3_01MetricType_00_01StatisticType_00_01Mat455d0165b2c85743977ec4c0a5dd95ca.html" target="_self">TreeTraits< BinarySpaceTree< MetricType, StatisticType, MatType, BoundType, RPTreeMaxSplit > ></a></td><td class="desc">This is a specialization of the TreeType class to the max-split random projection tree </td></tr>
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<tr id="row_1_35_61_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1tree_1_1TreeTraits_3_01BinarySpaceTree_3_01MetricType_00_01StatisticType_00_01Mat83fa92e671856c0b52f8456f1beaf6c5.html" target="_self">TreeTraits< BinarySpaceTree< MetricType, StatisticType, MatType, BoundType, RPTreeMeanSplit > ></a></td><td class="desc">This is a specialization of the TreeType class to the mean-split random projection tree </td></tr>
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<tr id="row_1_35_62_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1tree_1_1TreeTraits_3_01BinarySpaceTree_3_01MetricType_00_01StatisticType_00_01Matc82955fcc5e17376c7ac825c22d34930.html" target="_self">TreeTraits< BinarySpaceTree< MetricType, StatisticType, MatType, BoundType, SplitType > ></a></td><td class="desc">This is a specialization of the <a class="el" href="classmlpack_1_1tree_1_1TreeTraits.html" title="The TreeTraits class provides compile-time information on the characteristics of a given tree type...">TreeTraits</a> class to the <a class="el" href="classmlpack_1_1tree_1_1BinarySpaceTree.html" title="A binary space partitioning tree, such as a KD-tree or a ball tree. ">BinarySpaceTree</a> tree type </td></tr>
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<tr id="row_1_35_63_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1tree_1_1TreeTraits_3_01CoverTree_3_01MetricType_00_01StatisticType_00_01MatType_00_01RootPointPolicy_01_4_01_4.html" target="_self">TreeTraits< CoverTree< MetricType, StatisticType, MatType, RootPointPolicy > ></a></td><td class="desc">The specialization of the <a class="el" href="classmlpack_1_1tree_1_1TreeTraits.html" title="The TreeTraits class provides compile-time information on the characteristics of a given tree type...">TreeTraits</a> class for the <a class="el" href="classmlpack_1_1tree_1_1CoverTree.html" title="A cover tree is a tree specifically designed to speed up nearest-neighbor computation in high-dimensi...">CoverTree</a> tree type </td></tr>
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<tr id="row_1_35_64_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1tree_1_1TreeTraits_3_01Octree_3_01MetricType_00_01StatisticType_00_01MatType_01_4_01_4.html" target="_self">TreeTraits< Octree< MetricType, StatisticType, MatType > ></a></td><td class="desc">This is a specialization of the <a class="el" href="classmlpack_1_1tree_1_1TreeTraits.html" title="The TreeTraits class provides compile-time information on the characteristics of a given tree type...">TreeTraits</a> class to the <a class="el" href="classmlpack_1_1tree_1_1Octree.html">Octree</a> tree type </td></tr>
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<tr id="row_1_35_65_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1tree_1_1TreeTraits_3_01RectangleTree_3_01MetricType_00_01StatisticType_00_01MatTyd3300c6b7e2f56d4c1027298545eb7bf.html" target="_self">TreeTraits< RectangleTree< MetricType, StatisticType, MatType, RPlusTreeSplit< SplitPolicyType, SweepType >, DescentType, AuxiliaryInformationType > ></a></td><td class="desc">Since the R+/R++ tree can not have overlapping children, we should define traits for the R+/R++ tree </td></tr>
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<tr id="row_1_35_66_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1tree_1_1TreeTraits_3_01RectangleTree_3_01MetricType_00_01StatisticType_00_01MatTy0686cbbcde9440cadacd80904499ea50.html" target="_self">TreeTraits< RectangleTree< MetricType, StatisticType, MatType, SplitType, DescentType, AuxiliaryInformationType > ></a></td><td class="desc">This is a specialization of the TreeType class to the <a class="el" href="classmlpack_1_1tree_1_1RectangleTree.html" title="A rectangle type tree tree, such as an R-tree or X-tree. ">RectangleTree</a> tree type </td></tr>
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<tr id="row_1_35_67_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1tree_1_1TreeTraits_3_01SpillTree_3_01MetricType_00_01StatisticType_00_01MatType_03c639ada9e7ec3c7879b4d5a2cf50982.html" target="_self">TreeTraits< SpillTree< MetricType, StatisticType, MatType, HyperplaneType, SplitType > ></a></td><td class="desc">This is a specialization of the TreeType class to the <a class="el" href="classmlpack_1_1tree_1_1SpillTree.html" title="A hybrid spill tree is a variant of binary space trees in which the children of a node can "spill ove...">SpillTree</a> tree type </td></tr>
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<tr id="row_1_35_68_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1tree_1_1UBTreeSplit.html" target="_self">UBTreeSplit</a></td><td class="desc">Split a node into two parts according to the median address of points contained in the node </td></tr>
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<tr id="row_1_35_69_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span id="arr_1_35_69_" class="arrow" onclick="toggleFolder('1_35_69_')">►</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1tree_1_1VantagePointSplit.html" target="_self">VantagePointSplit</a></td><td class="desc">The class splits a binary space partitioning tree node according to the median distance to the vantage point </td></tr>
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<tr id="row_1_35_69_0_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structmlpack_1_1tree_1_1VantagePointSplit_1_1SplitInfo.html" target="_self">SplitInfo</a></td><td class="desc">A struct that contains an information about the split </td></tr>
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<tr id="row_1_35_70_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span id="arr_1_35_70_" class="arrow" onclick="toggleFolder('1_35_70_')">►</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1tree_1_1XTreeAuxiliaryInformation.html" target="_self">XTreeAuxiliaryInformation</a></td><td class="desc">The <a class="el" href="classmlpack_1_1tree_1_1XTreeAuxiliaryInformation.html" title="The XTreeAuxiliaryInformation class provides information specific to X trees for each node in a Recta...">XTreeAuxiliaryInformation</a> class provides information specific to X trees for each node in a <a class="el" href="classmlpack_1_1tree_1_1RectangleTree.html" title="A rectangle type tree tree, such as an R-tree or X-tree. ">RectangleTree</a> </td></tr>
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<tr id="row_1_35_70_0_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structmlpack_1_1tree_1_1XTreeAuxiliaryInformation_1_1SplitHistoryStruct.html" target="_self">SplitHistoryStruct</a></td><td class="desc">The X tree requires that the tree records it's "split history" </td></tr>
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<tr id="row_1_35_71_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1tree_1_1XTreeSplit.html" target="_self">XTreeSplit</a></td><td class="desc">A Rectangle Tree has new points inserted at the bottom </td></tr>
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<tr id="row_1_36_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span id="arr_1_36_" class="arrow" onclick="toggleFolder('1_36_')">►</span><span class="icona"><span class="icon">N</span></span><a class="el" href="namespacemlpack_1_1util.html" target="_self">util</a></td><td class="desc"></td></tr>
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<tr id="row_1_36_0_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1util_1_1CLIDeleter.html" target="_self">CLIDeleter</a></td><td class="desc">Extremely simple class whose only job is to delete the existing <a class="el" href="classmlpack_1_1CLI.html" title="Parses the command line for parameters and holds user-specified parameters. ">CLI</a> object at the end of execution </td></tr>
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<tr id="row_1_36_1_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structmlpack_1_1util_1_1IsStdVector.html" target="_self">IsStdVector</a></td><td class="desc">Metaprogramming structure for vector detection </td></tr>
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<tr id="row_1_36_2_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structmlpack_1_1util_1_1IsStdVector_3_01std_1_1vector_3_01T_00_01A_01_4_01_4.html" target="_self">IsStdVector< std::vector< T, A > ></a></td><td class="desc">Metaprogramming structure for vector detection </td></tr>
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<tr id="row_1_36_3_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1util_1_1NullOutStream.html" target="_self">NullOutStream</a></td><td class="desc">Used for <a class="el" href="classmlpack_1_1Log.html#a80ba817a1abcf742c7463b1e74bf55da" title="MLPACK_EXPORT is required for global variables, so that they are properly exported by the Windows com...">Log::Debug</a> when not compiled with debugging symbols </td></tr>
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<tr id="row_1_36_4_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1util_1_1Option.html" target="_self">Option</a></td><td class="desc">A static object whose constructor registers a parameter with the <a class="el" href="classmlpack_1_1CLI.html" title="Parses the command line for parameters and holds user-specified parameters. ">CLI</a> class </td></tr>
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<tr id="row_1_36_5_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structmlpack_1_1util_1_1ParamData.html" target="_self">ParamData</a></td><td class="desc">This structure holds all of the information about a single parameter, including its value (which is set when ParseCommandLine() is called) </td></tr>
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<tr id="row_1_36_6_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structmlpack_1_1util_1_1ParameterType.html" target="_self">ParameterType</a></td><td class="desc">Utility struct to return the type that boost::program_options should accept for a given input type </td></tr>
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<tr id="row_1_36_7_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structmlpack_1_1util_1_1ParameterType_3_01arma_1_1Col_3_01eT_01_4_01_4.html" target="_self">ParameterType< arma::Col< eT > ></a></td><td class="desc">For vector types, boost::program_options will accept a std::string, not an arma::Col<eT> (since it is not clear how to specify a vector on the command-line) </td></tr>
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<tr id="row_1_36_8_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structmlpack_1_1util_1_1ParameterType_3_01arma_1_1Mat_3_01eT_01_4_01_4.html" target="_self">ParameterType< arma::Mat< eT > ></a></td><td class="desc">For matrix types, boost::program_options will accept a std::string, not an arma::mat (since it is not clear how to specify a matrix on the command-line) </td></tr>
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<tr id="row_1_36_9_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structmlpack_1_1util_1_1ParameterType_3_01arma_1_1Row_3_01eT_01_4_01_4.html" target="_self">ParameterType< arma::Row< eT > ></a></td><td class="desc">For row vector types, boost::program_options will accept a std::string, not an arma::Row<eT> (since it is not clear how to specify a vector on the command-line) </td></tr>
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<tr id="row_1_36_10_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structmlpack_1_1util_1_1ParameterType_3_01std_1_1tuple_3_01mlpack_1_1data_1_1DatasetMapper_3_01P0117e227090eb0960979e600219ef0b2.html" target="_self">ParameterType< std::tuple< mlpack::data::DatasetMapper< PolicyType >, arma::Mat< eT > > ></a></td><td class="desc">For matrix+dataset info types, we should accept a std::string </td></tr>
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<tr id="row_1_36_11_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structmlpack_1_1util_1_1ParameterTypeDeducer.html" target="_self">ParameterTypeDeducer</a></td><td class="desc"></td></tr>
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<tr id="row_1_36_12_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structmlpack_1_1util_1_1ParameterTypeDeducer_3_01true_00_01T_01_4.html" target="_self">ParameterTypeDeducer< true, T ></a></td><td class="desc"></td></tr>
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<tr id="row_1_36_13_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1util_1_1PrefixedOutStream.html" target="_self">PrefixedOutStream</a></td><td class="desc">Allows us to output to an ostream with a prefix at the beginning of each line, in the same way we would output to cout or cerr </td></tr>
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<tr id="row_1_36_14_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1util_1_1ProgramDoc.html" target="_self">ProgramDoc</a></td><td class="desc">A static object whose constructor registers program documentation with the <a class="el" href="classmlpack_1_1CLI.html" title="Parses the command line for parameters and holds user-specified parameters. ">CLI</a> class </td></tr>
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<tr id="row_1_37_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span id="arr_1_37_" class="arrow" onclick="toggleFolder('1_37_')">►</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1Backtrace.html" target="_self">Backtrace</a></td><td class="desc">Provides a backtrace </td></tr>
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<tr id="row_1_37_0_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structmlpack_1_1Backtrace_1_1Frames.html" target="_self">Frames</a></td><td class="desc"><a class="el" href="classmlpack_1_1Backtrace.html" title="Provides a backtrace. ">Backtrace</a> datastructure </td></tr>
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<tr id="row_1_38_" class="even"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1CLI.html" target="_self">CLI</a></td><td class="desc">Parses the command line for parameters and holds user-specified parameters </td></tr>
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<tr id="row_1_39_"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1Log.html" target="_self">Log</a></td><td class="desc">Provides a convenient way to give formatted output </td></tr>
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<tr id="row_1_40_" class="even"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1Timer.html" target="_self">Timer</a></td><td class="desc">The timer class provides a way for mlpack methods to be timed </td></tr>
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<tr id="row_1_41_"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmlpack_1_1Timers.html" target="_self">Timers</a></td><td class="desc"></td></tr>
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<tr id="row_2_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structIsVector.html" target="_self">IsVector</a></td><td class="desc">If value == true, then VecType is some sort of Armadillo vector or subview </td></tr>
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<tr id="row_3_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structIsVector_3_01arma_1_1Col_3_01eT_01_4_01_4.html" target="_self">IsVector< arma::Col< eT > ></a></td><td class="desc"></td></tr>
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<tr id="row_4_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structIsVector_3_01arma_1_1Row_3_01eT_01_4_01_4.html" target="_self">IsVector< arma::Row< eT > ></a></td><td class="desc"></td></tr>
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<tr id="row_5_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structIsVector_3_01arma_1_1SpCol_3_01eT_01_4_01_4.html" target="_self">IsVector< arma::SpCol< eT > ></a></td><td class="desc"></td></tr>
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<tr id="row_6_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structIsVector_3_01arma_1_1SpRow_3_01eT_01_4_01_4.html" target="_self">IsVector< arma::SpRow< eT > ></a></td><td class="desc"></td></tr>
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<tr id="row_7_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structIsVector_3_01arma_1_1SpSubview_3_01eT_01_4_01_4.html" target="_self">IsVector< arma::SpSubview< eT > ></a></td><td class="desc"></td></tr>
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<tr id="row_8_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structIsVector_3_01arma_1_1subview__col_3_01eT_01_4_01_4.html" target="_self">IsVector< arma::subview_col< eT > ></a></td><td class="desc"></td></tr>
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<tr id="row_9_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structIsVector_3_01arma_1_1subview__row_3_01eT_01_4_01_4.html" target="_self">IsVector< arma::subview_row< eT > ></a></td><td class="desc"></td></tr>
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