Extract class docs.

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Relintai 2023-02-12 19:17:06 +01:00
parent eaf3abcea5
commit 5a148683ad
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doc_classes/MLPPANN.xml Normal file
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<?xml version="1.0" encoding="UTF-8" ?>
<class name="MLPPANN" inherits="Reference" version="3.11">
<brief_description>
</brief_description>
<description>
</description>
<tutorials>
</tutorials>
<methods>
</methods>
<constants>
</constants>
</class>

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<?xml version="1.0" encoding="UTF-8" ?>
<class name="MLPPAutoEncoder" inherits="Reference" version="3.11">
<brief_description>
</brief_description>
<description>
</description>
<tutorials>
</tutorials>
<methods>
</methods>
<members>
<member name="input_set" type="MLPPMatrix" setter="set_input_set" getter="get_input_set">
</member>
<member name="n_hidden" type="int" setter="set_n_hidden" getter="get_n_hidden" default="0">
</member>
</members>
<constants>
</constants>
</class>

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<?xml version="1.0" encoding="UTF-8" ?>
<class name="MLPPBernoulliNB" inherits="Reference" version="3.11">
<brief_description>
</brief_description>
<description>
</description>
<tutorials>
</tutorials>
<methods>
</methods>
<constants>
</constants>
</class>

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<?xml version="1.0" encoding="UTF-8" ?>
<class name="MLPPCLogLogReg" inherits="Reference" version="3.11">
<brief_description>
</brief_description>
<description>
</description>
<tutorials>
</tutorials>
<methods>
</methods>
<constants>
</constants>
</class>

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<?xml version="1.0" encoding="UTF-8" ?>
<class name="MLPPConvolutions" inherits="Reference" version="3.11">
<brief_description>
</brief_description>
<description>
</description>
<tutorials>
</tutorials>
<methods>
</methods>
<constants>
</constants>
</class>

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<?xml version="1.0" encoding="UTF-8" ?>
<class name="MLPPCost" inherits="Reference" version="3.11">
<brief_description>
</brief_description>
<description>
</description>
<tutorials>
</tutorials>
<methods>
<method name="cross_entropy_derivm">
<return type="MLPPMatrix" />
<argument index="0" name="y_hat" type="MLPPMatrix" />
<argument index="1" name="y" type="MLPPMatrix" />
<description>
</description>
</method>
<method name="cross_entropy_derivv">
<return type="MLPPVector" />
<argument index="0" name="y_hat" type="MLPPVector" />
<argument index="1" name="y" type="MLPPVector" />
<description>
</description>
</method>
<method name="cross_entropym">
<return type="float" />
<argument index="0" name="y_hat" type="MLPPMatrix" />
<argument index="1" name="y" type="MLPPMatrix" />
<description>
</description>
</method>
<method name="cross_entropyv">
<return type="float" />
<argument index="0" name="y_hat" type="MLPPVector" />
<argument index="1" name="y" type="MLPPVector" />
<description>
</description>
</method>
<method name="dual_form_svm">
<return type="float" />
<argument index="0" name="alpha" type="MLPPVector" />
<argument index="1" name="X" type="MLPPMatrix" />
<argument index="2" name="y" type="MLPPVector" />
<description>
</description>
</method>
<method name="dual_form_svm_deriv">
<return type="MLPPVector" />
<argument index="0" name="alpha" type="MLPPVector" />
<argument index="1" name="X" type="MLPPMatrix" />
<argument index="2" name="y" type="MLPPVector" />
<description>
</description>
</method>
<method name="hinge_loss_derivm">
<return type="MLPPMatrix" />
<argument index="0" name="y_hat" type="MLPPMatrix" />
<argument index="1" name="y" type="MLPPMatrix" />
<description>
</description>
</method>
<method name="hinge_loss_derivv">
<return type="MLPPVector" />
<argument index="0" name="y_hat" type="MLPPVector" />
<argument index="1" name="y" type="MLPPVector" />
<description>
</description>
</method>
<method name="hinge_loss_derivwm">
<return type="MLPPMatrix" />
<argument index="0" name="y_hat" type="MLPPMatrix" />
<argument index="1" name="y" type="MLPPMatrix" />
<argument index="2" name="C" type="float" />
<description>
</description>
</method>
<method name="hinge_loss_derivwv">
<return type="MLPPVector" />
<argument index="0" name="y_hat" type="MLPPVector" />
<argument index="1" name="y" type="MLPPVector" />
<argument index="2" name="C" type="float" />
<description>
</description>
</method>
<method name="hinge_lossm">
<return type="float" />
<argument index="0" name="y_hat" type="MLPPMatrix" />
<argument index="1" name="y" type="MLPPMatrix" />
<description>
</description>
</method>
<method name="hinge_lossv">
<return type="float" />
<argument index="0" name="y_hat" type="MLPPVector" />
<argument index="1" name="y" type="MLPPVector" />
<description>
</description>
</method>
<method name="hinge_losswm">
<return type="float" />
<argument index="0" name="y_hat" type="MLPPMatrix" />
<argument index="1" name="y" type="MLPPMatrix" />
<argument index="2" name="arg2" type="MLPPMatrix" />
<argument index="3" name="arg3" type="float" />
<description>
</description>
</method>
<method name="hinge_losswv">
<return type="float" />
<argument index="0" name="y_hat" type="MLPPVector" />
<argument index="1" name="y" type="MLPPVector" />
<argument index="2" name="arg2" type="MLPPVector" />
<argument index="3" name="arg3" type="float" />
<description>
</description>
</method>
<method name="huber_loss_derivm">
<return type="MLPPMatrix" />
<argument index="0" name="y_hat" type="MLPPMatrix" />
<argument index="1" name="y" type="MLPPMatrix" />
<argument index="2" name="arg2" type="float" />
<description>
</description>
</method>
<method name="huber_loss_derivv">
<return type="MLPPVector" />
<argument index="0" name="y_hat" type="MLPPVector" />
<argument index="1" name="y" type="MLPPVector" />
<argument index="2" name="arg2" type="float" />
<description>
</description>
</method>
<method name="huber_lossm">
<return type="float" />
<argument index="0" name="y_hat" type="MLPPMatrix" />
<argument index="1" name="y" type="MLPPMatrix" />
<argument index="2" name="arg2" type="float" />
<description>
</description>
</method>
<method name="huber_lossv">
<return type="float" />
<argument index="0" name="y_hat" type="MLPPVector" />
<argument index="1" name="y" type="MLPPVector" />
<argument index="2" name="arg2" type="float" />
<description>
</description>
</method>
<method name="log_loss_derivm">
<return type="MLPPMatrix" />
<argument index="0" name="y_hat" type="MLPPMatrix" />
<argument index="1" name="y" type="MLPPMatrix" />
<description>
</description>
</method>
<method name="log_loss_derivv">
<return type="MLPPVector" />
<argument index="0" name="y_hat" type="MLPPVector" />
<argument index="1" name="y" type="MLPPVector" />
<description>
</description>
</method>
<method name="log_lossm">
<return type="float" />
<argument index="0" name="y_hat" type="MLPPMatrix" />
<argument index="1" name="y" type="MLPPMatrix" />
<description>
</description>
</method>
<method name="log_lossv">
<return type="float" />
<argument index="0" name="y_hat" type="MLPPVector" />
<argument index="1" name="y" type="MLPPVector" />
<description>
</description>
</method>
<method name="mae_derivm">
<return type="MLPPMatrix" />
<argument index="0" name="y_hat" type="MLPPMatrix" />
<argument index="1" name="y" type="MLPPMatrix" />
<description>
</description>
</method>
<method name="mae_derivv">
<return type="MLPPVector" />
<argument index="0" name="y_hat" type="MLPPVector" />
<argument index="1" name="y" type="MLPPVector" />
<description>
</description>
</method>
<method name="maem">
<return type="float" />
<argument index="0" name="y_hat" type="MLPPMatrix" />
<argument index="1" name="y" type="MLPPMatrix" />
<description>
</description>
</method>
<method name="maev">
<return type="float" />
<argument index="0" name="y_hat" type="MLPPVector" />
<argument index="1" name="y" type="MLPPVector" />
<description>
</description>
</method>
<method name="mbe_derivm">
<return type="MLPPMatrix" />
<argument index="0" name="y_hat" type="MLPPMatrix" />
<argument index="1" name="y" type="MLPPMatrix" />
<description>
</description>
</method>
<method name="mbe_derivv">
<return type="MLPPVector" />
<argument index="0" name="y_hat" type="MLPPVector" />
<argument index="1" name="y" type="MLPPVector" />
<description>
</description>
</method>
<method name="mbem">
<return type="float" />
<argument index="0" name="y_hat" type="MLPPMatrix" />
<argument index="1" name="y" type="MLPPMatrix" />
<description>
</description>
</method>
<method name="mbev">
<return type="float" />
<argument index="0" name="y_hat" type="MLPPVector" />
<argument index="1" name="y" type="MLPPVector" />
<description>
</description>
</method>
<method name="mse_derivm">
<return type="MLPPMatrix" />
<argument index="0" name="y_hat" type="MLPPMatrix" />
<argument index="1" name="y" type="MLPPMatrix" />
<description>
</description>
</method>
<method name="mse_derivv">
<return type="MLPPVector" />
<argument index="0" name="y_hat" type="MLPPVector" />
<argument index="1" name="y" type="MLPPVector" />
<description>
</description>
</method>
<method name="msem">
<return type="float" />
<argument index="0" name="y_hat" type="MLPPMatrix" />
<argument index="1" name="y" type="MLPPMatrix" />
<description>
</description>
</method>
<method name="msev">
<return type="float" />
<argument index="0" name="y_hat" type="MLPPVector" />
<argument index="1" name="y" type="MLPPVector" />
<description>
</description>
</method>
<method name="rmse_derivm">
<return type="MLPPMatrix" />
<argument index="0" name="y_hat" type="MLPPMatrix" />
<argument index="1" name="y" type="MLPPMatrix" />
<description>
</description>
</method>
<method name="rmse_derivv">
<return type="MLPPVector" />
<argument index="0" name="y_hat" type="MLPPVector" />
<argument index="1" name="y" type="MLPPVector" />
<description>
</description>
</method>
<method name="rmsem">
<return type="float" />
<argument index="0" name="y_hat" type="MLPPMatrix" />
<argument index="1" name="y" type="MLPPMatrix" />
<description>
</description>
</method>
<method name="rmsev">
<return type="float" />
<argument index="0" name="y_hat" type="MLPPVector" />
<argument index="1" name="y" type="MLPPVector" />
<description>
</description>
</method>
<method name="run_cost_deriv_matrix">
<return type="MLPPMatrix" />
<argument index="0" name="cost" type="int" enum="MLPPCost.CostTypes" />
<argument index="1" name="y_hat" type="MLPPMatrix" />
<argument index="2" name="y" type="MLPPMatrix" />
<description>
</description>
</method>
<method name="run_cost_deriv_vector">
<return type="MLPPVector" />
<argument index="0" name="cost" type="int" enum="MLPPCost.CostTypes" />
<argument index="1" name="y_hat" type="MLPPVector" />
<argument index="2" name="y" type="MLPPVector" />
<description>
</description>
</method>
<method name="run_cost_norm_matrix">
<return type="float" />
<argument index="0" name="cost" type="int" enum="MLPPCost.CostTypes" />
<argument index="1" name="y_hat" type="MLPPMatrix" />
<argument index="2" name="y" type="MLPPMatrix" />
<description>
</description>
</method>
<method name="run_cost_norm_vector">
<return type="float" />
<argument index="0" name="cost" type="int" enum="MLPPCost.CostTypes" />
<argument index="1" name="y_hat" type="MLPPVector" />
<argument index="2" name="y" type="MLPPVector" />
<description>
</description>
</method>
<method name="wasserstein_loss_derivm">
<return type="MLPPMatrix" />
<argument index="0" name="y_hat" type="MLPPMatrix" />
<argument index="1" name="y" type="MLPPMatrix" />
<description>
</description>
</method>
<method name="wasserstein_loss_derivv">
<return type="MLPPVector" />
<argument index="0" name="y_hat" type="MLPPVector" />
<argument index="1" name="y" type="MLPPVector" />
<description>
</description>
</method>
<method name="wasserstein_lossm">
<return type="float" />
<argument index="0" name="y_hat" type="MLPPMatrix" />
<argument index="1" name="y" type="MLPPMatrix" />
<description>
</description>
</method>
<method name="wasserstein_lossv">
<return type="float" />
<argument index="0" name="y_hat" type="MLPPVector" />
<argument index="1" name="y" type="MLPPVector" />
<description>
</description>
</method>
</methods>
<constants>
<constant name="COST_TYPE_MSE" value="0" enum="CostTypes">
</constant>
<constant name="COST_TYPE_RMSE" value="1" enum="CostTypes">
</constant>
<constant name="COST_TYPE_MAE" value="2" enum="CostTypes">
</constant>
<constant name="COST_TYPE_MBE" value="3" enum="CostTypes">
</constant>
<constant name="COST_TYPE_LOGISTIC_LOSS" value="4" enum="CostTypes">
</constant>
<constant name="COST_TYPE_CROSS_ENTROPY" value="5" enum="CostTypes">
</constant>
<constant name="COST_TYPE_HINGE_LOSS" value="6" enum="CostTypes">
</constant>
<constant name="COST_TYPE_WASSERSTEIN_LOSS" value="7" enum="CostTypes">
</constant>
</constants>
</class>

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<?xml version="1.0" encoding="UTF-8" ?>
<class name="MLPPData" inherits="Reference" version="3.11">
<brief_description>
</brief_description>
<description>
</description>
<tutorials>
</tutorials>
<methods>
<method name="load_breast_cancer">
<return type="MLPPDataSimple" />
<argument index="0" name="path" type="String" />
<description>
</description>
</method>
<method name="load_breast_cancer_svc">
<return type="MLPPDataSimple" />
<argument index="0" name="path" type="String" />
<description>
</description>
</method>
<method name="load_california_housing">
<return type="MLPPDataSimple" />
<argument index="0" name="path" type="String" />
<description>
</description>
</method>
<method name="load_fires_and_crime">
<return type="MLPPDataESimple" />
<argument index="0" name="path" type="String" />
<description>
</description>
</method>
<method name="load_iris">
<return type="MLPPDataComplex" />
<argument index="0" name="path" type="String" />
<description>
</description>
</method>
<method name="load_mnist_test">
<return type="MLPPDataComplex" />
<argument index="0" name="path" type="String" />
<description>
</description>
</method>
<method name="load_mnist_train">
<return type="MLPPDataComplex" />
<argument index="0" name="path" type="String" />
<description>
</description>
</method>
<method name="load_wine">
<return type="MLPPDataComplex" />
<argument index="0" name="path" type="String" />
<description>
</description>
</method>
<method name="train_test_split">
<return type="Array" />
<argument index="0" name="data" type="MLPPDataComplex" />
<argument index="1" name="test_size" type="float" />
<description>
</description>
</method>
</methods>
<constants>
</constants>
</class>

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<?xml version="1.0" encoding="UTF-8" ?>
<class name="MLPPDataComplex" inherits="Reference" version="3.11">
<brief_description>
</brief_description>
<description>
</description>
<tutorials>
</tutorials>
<methods>
<method name="instance_data">
<return type="void" />
<description>
</description>
</method>
</methods>
<members>
<member name="input" type="MLPPMatrix" setter="set_input" getter="get_input">
</member>
<member name="output" type="MLPPMatrix" setter="set_output" getter="get_output">
</member>
</members>
<constants>
</constants>
</class>

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<?xml version="1.0" encoding="UTF-8" ?>
<class name="MLPPDataESimple" inherits="Reference" version="3.11">
<brief_description>
</brief_description>
<description>
</description>
<tutorials>
</tutorials>
<methods>
<method name="instance_data">
<return type="void" />
<description>
</description>
</method>
</methods>
<members>
<member name="input" type="MLPPVector" setter="set_input" getter="get_input">
</member>
<member name="output" type="MLPPVector" setter="set_output" getter="get_output">
</member>
</members>
<constants>
</constants>
</class>

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<?xml version="1.0" encoding="UTF-8" ?>
<class name="MLPPDataSimple" inherits="Reference" version="3.11">
<brief_description>
</brief_description>
<description>
</description>
<tutorials>
</tutorials>
<methods>
<method name="instance_data">
<return type="void" />
<description>
</description>
</method>
</methods>
<members>
<member name="input" type="MLPPMatrix" setter="set_input" getter="get_input">
</member>
<member name="output" type="MLPPMatrix" setter="set_output" getter="get_output">
</member>
</members>
<constants>
</constants>
</class>

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<?xml version="1.0" encoding="UTF-8" ?>
<class name="MLPPDualSVC" inherits="Reference" version="3.11">
<brief_description>
</brief_description>
<description>
</description>
<tutorials>
</tutorials>
<methods>
</methods>
<constants>
</constants>
</class>

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<?xml version="1.0" encoding="UTF-8" ?>
<class name="MLPPExpReg" inherits="Reference" version="3.11">
<brief_description>
</brief_description>
<description>
</description>
<tutorials>
</tutorials>
<methods>
</methods>
<constants>
</constants>
</class>

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doc_classes/MLPPGAN.xml Normal file
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<?xml version="1.0" encoding="UTF-8" ?>
<class name="MLPPGAN" inherits="Reference" version="3.11">
<brief_description>
</brief_description>
<description>
</description>
<tutorials>
</tutorials>
<methods>
</methods>
<constants>
</constants>
</class>

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<?xml version="1.0" encoding="UTF-8" ?>
<class name="MLPPGaussMarkovChecker" inherits="Reference" version="3.11">
<brief_description>
</brief_description>
<description>
</description>
<tutorials>
</tutorials>
<methods>
</methods>
<constants>
</constants>
</class>

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<?xml version="1.0" encoding="UTF-8" ?>
<class name="MLPPGaussianNB" inherits="Reference" version="3.11">
<brief_description>
</brief_description>
<description>
</description>
<tutorials>
</tutorials>
<methods>
</methods>
<constants>
</constants>
</class>

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<?xml version="1.0" encoding="UTF-8" ?>
<class name="MLPPHiddenLayer" inherits="Reference" version="3.11">
<brief_description>
</brief_description>
<description>
</description>
<tutorials>
</tutorials>
<methods>
<method name="forward_pass">
<return type="void" />
<description>
</description>
</method>
<method name="initialize">
<return type="void" />
<description>
</description>
</method>
<method name="is_initialized">
<return type="bool" />
<description>
</description>
</method>
<method name="test">
<return type="void" />
<argument index="0" name="x" type="MLPPVector" />
<description>
</description>
</method>
</methods>
<members>
<member name="a" type="MLPPMatrix" setter="set_a" getter="get_a">
</member>
<member name="a_test" type="MLPPVector" setter="set_a_test" getter="get_a_test">
</member>
<member name="activation" type="int" setter="set_activation" getter="get_activation" enum="MLPPActivation.ActivationFunction" default="0">
</member>
<member name="alpha" type="float" setter="set_alpha" getter="get_alpha" default="0.0">
</member>
<member name="bias" type="MLPPVector" setter="set_bias" getter="get_bias">
</member>
<member name="delta" type="MLPPMatrix" setter="set_delta" getter="get_delta">
</member>
<member name="input" type="MLPPMatrix" setter="set_input" getter="get_input">
</member>
<member name="lambda" type="float" setter="set_lambda" getter="get_lambda" default="0.0">
</member>
<member name="n_hidden" type="int" setter="set_n_hidden" getter="get_n_hidden" default="0">
</member>
<member name="reg" type="int" setter="set_reg" getter="get_reg" enum="MLPPReg.RegularizationType" default="136122720">
</member>
<member name="set_weight_init" type="int" setter="set_weight_init" getter="get_weight_init" enum="MLPPUtilities.WeightDistributionType" default="0">
</member>
<member name="weights" type="MLPPMatrix" setter="set_weights" getter="get_weights">
</member>
<member name="z" type="MLPPMatrix" setter="set_z" getter="get_z">
</member>
<member name="z_test" type="MLPPVector" setter="set_z_test" getter="get_z_test">
</member>
</members>
<constants>
</constants>
</class>

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<?xml version="1.0" encoding="UTF-8" ?>
<class name="MLPPHypothesisTesting" inherits="Reference" version="3.11">
<brief_description>
</brief_description>
<description>
</description>
<tutorials>
</tutorials>
<methods>
</methods>
<constants>
</constants>
</class>

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<?xml version="1.0" encoding="UTF-8" ?>
<class name="MLPPKMeans" inherits="Reference" version="3.11">
<brief_description>
</brief_description>
<description>
</description>
<tutorials>
</tutorials>
<methods>
<method name="initialize">
<return type="void" />
<description>
</description>
</method>
<method name="model_set_test">
<return type="MLPPMatrix" />
<argument index="0" name="X" type="MLPPMatrix" />
<description>
</description>
</method>
<method name="model_test">
<return type="MLPPVector" />
<argument index="0" name="x" type="MLPPVector" />
<description>
</description>
</method>
<method name="score">
<return type="float" />
<description>
</description>
</method>
<method name="silhouette_scores">
<return type="MLPPVector" />
<description>
</description>
</method>
<method name="train">
<return type="void" />
<argument index="0" name="epoch_num" type="int" />
<argument index="1" name="UI" type="bool" default="false" />
<description>
</description>
</method>
</methods>
<members>
<member name="input_set" type="MLPPMatrix" setter="set_input_set" getter="get_input_set">
</member>
<member name="k" type="int" setter="set_k" getter="get_k" default="0">
</member>
<member name="mean_type" type="int" setter="set_mean_type" getter="get_mean_type" enum="MLPPKMeans.MeanType" default="0">
</member>
</members>
<constants>
<constant name="MEAN_TYPE_CENTROID" value="0" enum="MeanType">
</constant>
<constant name="MEAN_TYPE_KMEANSPP" value="1" enum="MeanType">
</constant>
</constants>
</class>

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<?xml version="1.0" encoding="UTF-8" ?>
<class name="MLPPKNN" inherits="Reference" version="3.11">
<brief_description>
</brief_description>
<description>
</description>
<tutorials>
</tutorials>
<methods>
<method name="model_set_test">
<return type="PoolIntArray" />
<argument index="0" name="X" type="MLPPMatrix" />
<description>
</description>
</method>
<method name="model_test">
<return type="int" />
<argument index="0" name="x" type="MLPPVector" />
<description>
</description>
</method>
<method name="score">
<return type="float" />
<description>
</description>
</method>
</methods>
<members>
<member name="input_set" type="MLPPMatrix" setter="set_input_set" getter="get_input_set">
</member>
<member name="k" type="int" setter="set_k" getter="get_k" default="0">
</member>
<member name="output_set" type="MLPPVector" setter="set_output_set" getter="get_output_set">
</member>
</members>
<constants>
</constants>
</class>

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<?xml version="1.0" encoding="UTF-8" ?>
<class name="MLPPLinAlg" inherits="Reference" version="3.11">
<brief_description>
</brief_description>
<description>
</description>
<tutorials>
</tutorials>
<methods>
</methods>
<constants>
</constants>
</class>

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<?xml version="1.0" encoding="UTF-8" ?>
<class name="MLPPLinReg" inherits="Reference" version="3.11">
<brief_description>
</brief_description>
<description>
</description>
<tutorials>
</tutorials>
<methods>
</methods>
<constants>
</constants>
</class>

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<?xml version="1.0" encoding="UTF-8" ?>
<class name="MLPPLogReg" inherits="Reference" version="3.11">
<brief_description>
</brief_description>
<description>
</description>
<tutorials>
</tutorials>
<methods>
</methods>
<constants>
</constants>
</class>

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<?xml version="1.0" encoding="UTF-8" ?>
<class name="MLPPMANN" inherits="Reference" version="3.11">
<brief_description>
</brief_description>
<description>
</description>
<tutorials>
</tutorials>
<methods>
</methods>
<constants>
</constants>
</class>

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<?xml version="1.0" encoding="UTF-8" ?>
<class name="MLPPMLP" inherits="Reference" version="3.11">
<brief_description>
</brief_description>
<description>
</description>
<tutorials>
</tutorials>
<methods>
<method name="gradient_descent">
<return type="void" />
<argument index="0" name="learning_rate" type="float" />
<argument index="1" name="max_epoch" type="int" />
<argument index="2" name="UI" type="bool" default="false" />
<description>
</description>
</method>
<method name="initialize">
<return type="void" />
<description>
</description>
</method>
<method name="is_initialized">
<return type="bool" />
<description>
</description>
</method>
<method name="mbgd">
<return type="void" />
<argument index="0" name="learning_rate" type="float" />
<argument index="1" name="max_epoch" type="int" />
<argument index="2" name="mini_batch_size" type="int" />
<argument index="3" name="UI" type="bool" default="false" />
<description>
</description>
</method>
<method name="model_set_test">
<return type="MLPPVector" />
<argument index="0" name="X" type="MLPPMatrix" />
<description>
</description>
</method>
<method name="model_test">
<return type="float" />
<argument index="0" name="x" type="MLPPVector" />
<description>
</description>
</method>
<method name="save">
<return type="void" />
<argument index="0" name="file_name" type="String" />
<description>
</description>
</method>
<method name="score">
<return type="float" />
<description>
</description>
</method>
<method name="sgd">
<return type="void" />
<argument index="0" name="learning_rate" type="float" />
<argument index="1" name="max_epoch" type="int" />
<argument index="2" name="UI" type="bool" default="false" />
<description>
</description>
</method>
</methods>
<members>
<member name="alpha" type="float" setter="set_alpha" getter="get_alpha" default="0.5">
</member>
<member name="input_set" type="MLPPMatrix" setter="set_input_set" getter="get_input_set">
</member>
<member name="lambda" type="float" setter="set_lambda" getter="get_lambda" default="0.5">
</member>
<member name="n_hidden" type="int" setter="set_n_hidden" getter="get_n_hidden" default="0">
</member>
<member name="output_set" type="MLPPVector" setter="set_output_set" getter="get_output_set">
</member>
<member name="reg" type="int" setter="set_reg" getter="get_reg" enum="MLPPReg.RegularizationType" default="0">
</member>
</members>
<constants>
</constants>
</class>

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<?xml version="1.0" encoding="UTF-8" ?>
<class name="MLPPMatrix" inherits="Reference" version="3.11">
<brief_description>
</brief_description>
<description>
</description>
<tutorials>
</tutorials>
<methods>
<method name="add_row">
<return type="void" />
<argument index="0" name="row" type="PoolRealArray" />
<description>
</description>
</method>
<method name="add_row_mlpp_vector">
<return type="void" />
<argument index="0" name="row" type="MLPPVector" />
<description>
</description>
</method>
<method name="add_rows_mlpp_matrix">
<return type="void" />
<argument index="0" name="other" type="MLPPMatrix" />
<description>
</description>
</method>
<method name="clear">
<return type="void" />
<description>
</description>
</method>
<method name="data_size" qualifiers="const">
<return type="int" />
<description>
</description>
</method>
<method name="duplicate" qualifiers="const">
<return type="MLPPMatrix" />
<description>
</description>
</method>
<method name="empty" qualifiers="const">
<return type="bool" />
<description>
</description>
</method>
<method name="fill">
<return type="void" />
<argument index="0" name="val" type="float" />
<description>
</description>
</method>
<method name="get_element" qualifiers="const">
<return type="float" />
<argument index="0" name="index_x" type="int" />
<argument index="1" name="index_y" type="int" />
<description>
</description>
</method>
<method name="get_row_into_mlpp_vector" qualifiers="const">
<return type="void" />
<argument index="0" name="index_y" type="int" />
<argument index="1" name="target" type="MLPPVector" />
<description>
</description>
</method>
<method name="get_row_mlpp_vector">
<return type="MLPPVector" />
<argument index="0" name="index_y" type="int" />
<description>
</description>
</method>
<method name="get_row_pool_vector">
<return type="PoolRealArray" />
<argument index="0" name="index_y" type="int" />
<description>
</description>
</method>
<method name="is_equal_approx" qualifiers="const">
<return type="bool" />
<argument index="0" name="with" type="MLPPMatrix" />
<argument index="1" name="tolerance" type="float" default="1e-05" />
<description>
</description>
</method>
<method name="remove_row">
<return type="void" />
<argument index="0" name="index" type="float" />
<description>
</description>
</method>
<method name="remove_row_unordered">
<return type="void" />
<argument index="0" name="index" type="int" />
<description>
</description>
</method>
<method name="reset">
<return type="void" />
<description>
</description>
</method>
<method name="resize">
<return type="void" />
<argument index="0" name="size" type="Vector2i" />
<description>
</description>
</method>
<method name="set_element">
<return type="void" />
<argument index="0" name="index_x" type="int" />
<argument index="1" name="index_y" type="int" />
<argument index="2" name="val" type="float" />
<description>
</description>
</method>
<method name="set_from_arrays">
<return type="void" />
<argument index="0" name="from" type="Array" />
<description>
</description>
</method>
<method name="set_from_mlpp_matrix">
<return type="void" />
<argument index="0" name="from" type="MLPPMatrix" />
<description>
</description>
</method>
<method name="set_from_mlpp_vectors_array">
<return type="void" />
<argument index="0" name="from" type="Array" />
<description>
</description>
</method>
<method name="set_row_mlpp_vector">
<return type="void" />
<argument index="0" name="index_y" type="int" />
<argument index="1" name="row" type="MLPPVector" />
<description>
</description>
</method>
<method name="set_row_pool_vector">
<return type="void" />
<argument index="0" name="index_y" type="int" />
<argument index="1" name="row" type="PoolRealArray" />
<description>
</description>
</method>
<method name="size" qualifiers="const">
<return type="Vector2i" />
<description>
</description>
</method>
<method name="swap_row">
<return type="void" />
<argument index="0" name="index_1" type="int" />
<argument index="1" name="index_2" type="int" />
<description>
</description>
</method>
<method name="to_flat_byte_array" qualifiers="const">
<return type="PoolByteArray" />
<description>
</description>
</method>
<method name="to_flat_pool_vector" qualifiers="const">
<return type="PoolRealArray" />
<description>
</description>
</method>
</methods>
<constants>
</constants>
</class>

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<?xml version="1.0" encoding="UTF-8" ?>
<class name="MLPPMultiOutputLayer" inherits="Reference" version="3.11">
<brief_description>
</brief_description>
<description>
</description>
<tutorials>
</tutorials>
<methods>
<method name="forward_pass">
<return type="void" />
<description>
</description>
</method>
<method name="test">
<return type="void" />
<argument index="0" name="x" type="MLPPVector" />
<description>
</description>
</method>
</methods>
<members>
<member name="a" type="MLPPMatrix" setter="set_a" getter="get_a">
</member>
<member name="a_test" type="MLPPVector" setter="set_a_test" getter="get_a_test">
</member>
<member name="activation" type="int" setter="set_activation" getter="get_activation" enum="MLPPActivation.ActivationFunction" default="0">
</member>
<member name="alpha" type="float" setter="set_alpha" getter="get_alpha" default="0.0">
</member>
<member name="bias" type="MLPPVector" setter="set_bias" getter="get_bias">
</member>
<member name="cost" type="int" setter="set_cost" getter="get_cost" enum="MLPPCost.CostTypes" default="0">
</member>
<member name="delta" type="MLPPMatrix" setter="set_delta" getter="get_delta">
</member>
<member name="input" type="MLPPMatrix" setter="set_input" getter="get_input">
</member>
<member name="lambda" type="float" setter="set_lambda" getter="get_lambda" default="0.0">
</member>
<member name="n_hidden" type="int" setter="set_n_hidden" getter="get_n_hidden" default="0">
</member>
<member name="n_output" type="int" setter="set_n_output" getter="get_n_output" default="0">
</member>
<member name="reg" type="int" setter="set_reg" getter="get_reg" enum="MLPPReg.RegularizationType" default="0">
</member>
<member name="set_weight_init" type="int" setter="set_weight_init" getter="get_weight_init" enum="MLPPUtilities.WeightDistributionType" default="0">
</member>
<member name="weights" type="MLPPMatrix" setter="set_weights" getter="get_weights">
</member>
<member name="z" type="MLPPMatrix" setter="set_z" getter="get_z">
</member>
<member name="z_test" type="MLPPVector" setter="set_z_test" getter="get_z_test">
</member>
</members>
<constants>
</constants>
</class>

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<?xml version="1.0" encoding="UTF-8" ?>
<class name="MLPPMultinomialNB" inherits="Reference" version="3.11">
<brief_description>
</brief_description>
<description>
</description>
<tutorials>
</tutorials>
<methods>
</methods>
<constants>
</constants>
</class>

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<?xml version="1.0" encoding="UTF-8" ?>
<class name="MLPPNumericalAnalysis" inherits="Reference" version="3.11">
<brief_description>
</brief_description>
<description>
</description>
<tutorials>
</tutorials>
<methods>
</methods>
<constants>
</constants>
</class>

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<?xml version="1.0" encoding="UTF-8" ?>
<class name="MLPPOutlierFinder" inherits="Reference" version="3.11">
<brief_description>
</brief_description>
<description>
</description>
<tutorials>
</tutorials>
<methods>
<method name="model_set_test">
<return type="Array" />
<argument index="0" name="input_set" type="MLPPMatrix" />
<description>
</description>
</method>
<method name="model_set_test_indices">
<return type="PoolVector2iArray" />
<argument index="0" name="input_set" type="MLPPMatrix" />
<description>
</description>
</method>
<method name="model_test">
<return type="PoolRealArray" />
<argument index="0" name="input_set" type="MLPPVector" />
<description>
</description>
</method>
</methods>
<members>
<member name="threshold" type="float" setter="set_threshold" getter="get_threshold" default="0.0">
</member>
</members>
<constants>
</constants>
</class>

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<?xml version="1.0" encoding="UTF-8" ?>
<class name="MLPPOutputLayer" inherits="Reference" version="3.11">
<brief_description>
</brief_description>
<description>
</description>
<tutorials>
</tutorials>
<methods>
<method name="forward_pass">
<return type="void" />
<description>
</description>
</method>
<method name="initialize">
<return type="void" />
<description>
</description>
</method>
<method name="is_initialized">
<return type="bool" />
<description>
</description>
</method>
<method name="test">
<return type="void" />
<argument index="0" name="x" type="MLPPVector" />
<description>
</description>
</method>
</methods>
<members>
<member name="a" type="MLPPVector" setter="set_a" getter="get_a">
</member>
<member name="a_test" type="MLPPVector" setter="set_a_test" getter="get_a_test">
</member>
<member name="activation" type="int" setter="set_activation" getter="get_activation" enum="MLPPActivation.ActivationFunction" default="0">
</member>
<member name="alpha" type="float" setter="set_alpha" getter="get_alpha" default="0.0">
</member>
<member name="bias" type="float" setter="set_bias" getter="get_bias" default="0.0">
</member>
<member name="cost" type="int" setter="set_cost" getter="get_cost" enum="MLPPCost.CostTypes" default="0">
</member>
<member name="delta" type="MLPPVector" setter="set_delta" getter="get_delta">
</member>
<member name="input" type="MLPPMatrix" setter="set_input" getter="get_input">
</member>
<member name="lambda" type="float" setter="set_lambda" getter="get_lambda" default="0.0">
</member>
<member name="n_hidden" type="int" setter="set_n_hidden" getter="get_n_hidden" default="0">
</member>
<member name="reg" type="int" setter="set_reg" getter="get_reg" enum="MLPPReg.RegularizationType" default="0">
</member>
<member name="set_weight_init" type="int" setter="set_weight_init" getter="get_weight_init" enum="MLPPUtilities.WeightDistributionType" default="0">
</member>
<member name="weights" type="MLPPVector" setter="set_weights" getter="get_weights">
</member>
<member name="z" type="MLPPVector" setter="set_z" getter="get_z">
</member>
<member name="z_test" type="MLPPVector" setter="set_z_test" getter="get_z_test">
</member>
</members>
<constants>
</constants>
</class>

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<?xml version="1.0" encoding="UTF-8" ?>
<class name="MLPPPCA" inherits="Reference" version="3.11">
<brief_description>
</brief_description>
<description>
</description>
<tutorials>
</tutorials>
<methods>
<method name="principal_components">
<return type="MLPPMatrix" />
<description>
</description>
</method>
<method name="score">
<return type="float" />
<description>
</description>
</method>
</methods>
<members>
<member name="get_input_set" type="MLPPMatrix" setter="set_input_set" getter="get_input_set">
</member>
<member name="k" type="int" setter="set_k" getter="get_k" default="0">
</member>
</members>
<constants>
</constants>
</class>

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<?xml version="1.0" encoding="UTF-8" ?>
<class name="MLPPProbitReg" inherits="Reference" version="3.11">
<brief_description>
</brief_description>
<description>
</description>
<tutorials>
</tutorials>
<methods>
<method name="gradient_descent">
<return type="void" />
<argument index="0" name="learning_rate" type="float" />
<argument index="1" name="max_epoch" type="int" default="0" />
<argument index="2" name="ui" type="bool" default="false" />
<description>
</description>
</method>
<method name="initialize">
<return type="void" />
<description>
</description>
</method>
<method name="is_initialized">
<return type="bool" />
<description>
</description>
</method>
<method name="mbgd">
<return type="void" />
<argument index="0" name="learning_rate" type="float" />
<argument index="1" name="max_epoch" type="int" />
<argument index="2" name="mini_batch_size" type="int" />
<argument index="3" name="ui" type="bool" default="false" />
<description>
</description>
</method>
<method name="mle">
<return type="void" />
<argument index="0" name="learning_rate" type="float" />
<argument index="1" name="max_epoch" type="int" default="0" />
<argument index="2" name="ui" type="bool" default="false" />
<description>
</description>
</method>
<method name="model_set_test">
<return type="MLPPVector" />
<argument index="0" name="X" type="MLPPMatrix" />
<description>
</description>
</method>
<method name="model_test">
<return type="float" />
<argument index="0" name="x" type="MLPPVector" />
<description>
</description>
</method>
<method name="save">
<return type="void" />
<argument index="0" name="file_name" type="String" />
<description>
</description>
</method>
<method name="score">
<return type="float" />
<description>
</description>
</method>
<method name="sgd">
<return type="void" />
<argument index="0" name="learning_rate" type="float" />
<argument index="1" name="max_epoch" type="int" default="0" />
<argument index="2" name="ui" type="bool" default="false" />
<description>
</description>
</method>
</methods>
<members>
<member name="alpha" type="float" setter="set_alpha" getter="get_alpha" default="0.5">
</member>
<member name="input_set" type="MLPPMatrix" setter="set_input_set" getter="get_input_set">
</member>
<member name="lambda" type="float" setter="set_lambda" getter="get_lambda" default="0.5">
</member>
<member name="output_set" type="MLPPVector" setter="set_output_set" getter="get_output_set">
</member>
<member name="reg" type="int" setter="set_reg" getter="get_reg" enum="MLPPReg.RegularizationType" default="0">
</member>
</members>
<constants>
</constants>
</class>

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<?xml version="1.0" encoding="UTF-8" ?>
<class name="MLPPReg" inherits="Reference" version="3.11">
<brief_description>
</brief_description>
<description>
</description>
<tutorials>
</tutorials>
<methods>
<method name="reg_deriv_termm">
<return type="MLPPMatrix" />
<argument index="0" name="weights" type="MLPPMatrix" />
<argument index="1" name="lambda" type="float" />
<argument index="2" name="alpha" type="float" />
<argument index="3" name="reg" type="int" enum="MLPPReg.RegularizationType" />
<description>
</description>
</method>
<method name="reg_deriv_termv">
<return type="MLPPVector" />
<argument index="0" name="weights" type="MLPPVector" />
<argument index="1" name="lambda" type="float" />
<argument index="2" name="alpha" type="float" />
<argument index="3" name="reg" type="int" enum="MLPPReg.RegularizationType" />
<description>
</description>
</method>
<method name="reg_termm">
<return type="float" />
<argument index="0" name="weights" type="MLPPMatrix" />
<argument index="1" name="lambda" type="float" />
<argument index="2" name="alpha" type="float" />
<argument index="3" name="reg" type="int" enum="MLPPReg.RegularizationType" />
<description>
</description>
</method>
<method name="reg_termv">
<return type="float" />
<argument index="0" name="weights" type="MLPPVector" />
<argument index="1" name="lambda" type="float" />
<argument index="2" name="alpha" type="float" />
<argument index="3" name="reg" type="int" enum="MLPPReg.RegularizationType" />
<description>
</description>
</method>
<method name="reg_weightsm">
<return type="MLPPMatrix" />
<argument index="0" name="weights" type="MLPPMatrix" />
<argument index="1" name="lambda" type="float" />
<argument index="2" name="alpha" type="float" />
<argument index="3" name="reg" type="int" enum="MLPPReg.RegularizationType" />
<description>
</description>
</method>
<method name="reg_weightsv">
<return type="MLPPVector" />
<argument index="0" name="weights" type="MLPPVector" />
<argument index="1" name="lambda" type="float" />
<argument index="2" name="alpha" type="float" />
<argument index="3" name="reg" type="int" enum="MLPPReg.RegularizationType" />
<description>
</description>
</method>
</methods>
<constants>
<constant name="REGULARIZATION_TYPE_NONE" value="0" enum="RegularizationType">
</constant>
<constant name="REGULARIZATION_TYPE_RIDGE" value="1" enum="RegularizationType">
</constant>
<constant name="REGULARIZATION_TYPE_LASSO" value="2" enum="RegularizationType">
</constant>
<constant name="REGULARIZATION_TYPE_ELASTIC_NET" value="3" enum="RegularizationType">
</constant>
<constant name="REGULARIZATION_TYPE_WEIGHT_CLIPPING" value="4" enum="RegularizationType">
</constant>
</constants>
</class>

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<?xml version="1.0" encoding="UTF-8" ?>
<class name="MLPPSVC" inherits="Reference" version="3.11">
<brief_description>
</brief_description>
<description>
</description>
<tutorials>
</tutorials>
<methods>
<method name="gradient_descent">
<return type="void" />
<argument index="0" name="learning_rate" type="float" />
<argument index="1" name="max_epoch" type="int" />
<argument index="2" name="ui" type="bool" default="false" />
<description>
</description>
</method>
<method name="initialize">
<return type="void" />
<description>
</description>
</method>
<method name="is_initialized">
<return type="bool" />
<description>
</description>
</method>
<method name="mbgd">
<return type="void" />
<argument index="0" name="learning_rate" type="float" />
<argument index="1" name="max_epoch" type="int" />
<argument index="2" name="mini_batch_size" type="int" />
<argument index="3" name="ui" type="bool" default="false" />
<description>
</description>
</method>
<method name="model_set_test">
<return type="MLPPVector" />
<argument index="0" name="X" type="MLPPMatrix" />
<description>
</description>
</method>
<method name="model_test">
<return type="float" />
<argument index="0" name="x" type="MLPPVector" />
<description>
</description>
</method>
<method name="save">
<return type="void" />
<argument index="0" name="file_name" type="String" />
<description>
</description>
</method>
<method name="score">
<return type="float" />
<description>
</description>
</method>
<method name="sgd">
<return type="void" />
<argument index="0" name="learning_rate" type="float" />
<argument index="1" name="max_epoch" type="int" />
<argument index="2" name="ui" type="bool" default="false" />
<description>
</description>
</method>
</methods>
<members>
<member name="c" type="float" setter="set_c" getter="get_c" default="0.0">
</member>
<member name="input_set" type="MLPPMatrix" setter="set_input_set" getter="get_input_set">
</member>
<member name="output_set" type="MLPPVector" setter="set_output_set" getter="get_output_set">
</member>
</members>
<constants>
</constants>
</class>

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<?xml version="1.0" encoding="UTF-8" ?>
<class name="MLPPSoftmaxNet" inherits="Reference" version="3.11">
<brief_description>
</brief_description>
<description>
</description>
<tutorials>
</tutorials>
<methods>
</methods>
<constants>
</constants>
</class>

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<?xml version="1.0" encoding="UTF-8" ?>
<class name="MLPPSoftmaxReg" inherits="Reference" version="3.11">
<brief_description>
</brief_description>
<description>
</description>
<tutorials>
</tutorials>
<methods>
<method name="gradient_descent">
<return type="void" />
<argument index="0" name="learning_rate" type="float" />
<argument index="1" name="max_epoch" type="int" />
<argument index="2" name="ui" type="bool" default="false" />
<description>
</description>
</method>
<method name="initialize">
<return type="void" />
<description>
</description>
</method>
<method name="is_initialized">
<return type="bool" />
<description>
</description>
</method>
<method name="mbgd">
<return type="void" />
<argument index="0" name="learning_rate" type="float" />
<argument index="1" name="max_epoch" type="int" />
<argument index="2" name="mini_batch_size" type="int" />
<argument index="3" name="ui" type="bool" default="false" />
<description>
</description>
</method>
<method name="model_set_test">
<return type="MLPPMatrix" />
<argument index="0" name="X" type="MLPPMatrix" />
<description>
</description>
</method>
<method name="model_test">
<return type="MLPPVector" />
<argument index="0" name="x" type="MLPPVector" />
<description>
</description>
</method>
<method name="save">
<return type="void" />
<argument index="0" name="file_name" type="String" />
<description>
</description>
</method>
<method name="score">
<return type="float" />
<description>
</description>
</method>
<method name="sgd">
<return type="void" />
<argument index="0" name="learning_rate" type="float" />
<argument index="1" name="max_epoch" type="int" />
<argument index="2" name="ui" type="bool" default="false" />
<description>
</description>
</method>
</methods>
<members>
<member name="alpha" type="float" setter="set_alpha" getter="get_alpha" default="0.5">
</member>
<member name="input_set" type="MLPPMatrix" setter="set_input_set" getter="get_input_set">
</member>
<member name="lambda" type="float" setter="set_lambda" getter="get_lambda" default="0.5">
</member>
<member name="output_set" type="MLPPMatrix" setter="set_output_set" getter="get_output_set">
</member>
<member name="reg" type="int" setter="set_reg" getter="get_reg" enum="MLPPReg.RegularizationType" default="0">
</member>
</members>
<constants>
</constants>
</class>

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<?xml version="1.0" encoding="UTF-8" ?>
<class name="MLPPStat" inherits="Reference" version="3.11">
<brief_description>
</brief_description>
<description>
</description>
<tutorials>
</tutorials>
<methods>
</methods>
<constants>
</constants>
</class>

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<?xml version="1.0" encoding="UTF-8" ?>
<class name="MLPPTanhReg" inherits="Reference" version="3.11">
<brief_description>
</brief_description>
<description>
</description>
<tutorials>
</tutorials>
<methods>
</methods>
<constants>
</constants>
</class>

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<?xml version="1.0" encoding="UTF-8" ?>
<class name="MLPPTests" inherits="Reference" version="3.11">
<brief_description>
</brief_description>
<description>
</description>
<tutorials>
</tutorials>
<methods>
<method name="test_ann">
<return type="void" />
<argument index="0" name="ui" type="bool" default="false" />
<description>
</description>
</method>
<method name="test_autoencoder">
<return type="void" />
<argument index="0" name="ui" type="bool" default="false" />
<description>
</description>
</method>
<method name="test_c_log_log_regression">
<return type="void" />
<argument index="0" name="ui" type="bool" default="false" />
<description>
</description>
</method>
<method name="test_convolution_tensors_etc">
<return type="void" />
<description>
</description>
</method>
<method name="test_dynamically_sized_ann">
<return type="void" />
<argument index="0" name="ui" type="bool" default="false" />
<description>
</description>
</method>
<method name="test_dynamically_sized_mann">
<return type="void" />
<argument index="0" name="ui" type="bool" default="false" />
<description>
</description>
</method>
<method name="test_exp_reg_regression">
<return type="void" />
<argument index="0" name="ui" type="bool" default="false" />
<description>
</description>
</method>
<method name="test_k_means">
<return type="void" />
<argument index="0" name="ui" type="bool" default="false" />
<description>
</description>
</method>
<method name="test_knn">
<return type="void" />
<argument index="0" name="ui" type="bool" default="false" />
<description>
</description>
</method>
<method name="test_linear_algebra">
<return type="void" />
<description>
</description>
</method>
<method name="test_logistic_regression">
<return type="void" />
<argument index="0" name="ui" type="bool" default="false" />
<description>
</description>
</method>
<method name="test_mlp">
<return type="void" />
<argument index="0" name="ui" type="bool" default="false" />
<description>
</description>
</method>
<method name="test_mlpp_matrix">
<return type="void" />
<description>
</description>
</method>
<method name="test_mlpp_vector">
<return type="void" />
<description>
</description>
</method>
<method name="test_multivariate_linear_regression_adam">
<return type="void" />
<description>
</description>
</method>
<method name="test_multivariate_linear_regression_epochs_gradient_descent">
<return type="void" />
<argument index="0" name="ui" type="bool" default="false" />
<description>
</description>
</method>
<method name="test_multivariate_linear_regression_gradient_descent">
<return type="void" />
<argument index="0" name="ui" type="bool" default="false" />
<description>
</description>
</method>
<method name="test_multivariate_linear_regression_mbgd">
<return type="void" />
<argument index="0" name="ui" type="bool" default="false" />
<description>
</description>
</method>
<method name="test_multivariate_linear_regression_newton_raphson">
<return type="void" />
<argument index="0" name="ui" type="bool" default="false" />
<description>
</description>
</method>
<method name="test_multivariate_linear_regression_normal_equation">
<return type="void" />
<argument index="0" name="ui" type="bool" default="false" />
<description>
</description>
</method>
<method name="test_multivariate_linear_regression_score_sgd_adam">
<return type="void" />
<argument index="0" name="ui" type="bool" default="false" />
<description>
</description>
</method>
<method name="test_multivariate_linear_regression_sgd">
<return type="void" />
<argument index="0" name="ui" type="bool" default="false" />
<description>
</description>
</method>
<method name="test_naive_bayes">
<return type="void" />
<description>
</description>
</method>
<method name="test_new_math_functions">
<return type="void" />
<description>
</description>
</method>
<method name="test_nlp_and_data">
<return type="void" />
<argument index="0" name="ui" type="bool" default="false" />
<description>
</description>
</method>
<method name="test_numerical_analysis">
<return type="void" />
<description>
</description>
</method>
<method name="test_outlier_finder">
<return type="void" />
<argument index="0" name="ui" type="bool" default="false" />
<description>
</description>
</method>
<method name="test_pca_svd_eigenvalues_eigenvectors">
<return type="void" />
<argument index="0" name="ui" type="bool" default="false" />
<description>
</description>
</method>
<method name="test_positive_definiteness_checker">
<return type="void" />
<description>
</description>
</method>
<method name="test_probit_regression">
<return type="void" />
<argument index="0" name="ui" type="bool" default="false" />
<description>
</description>
</method>
<method name="test_soft_max_network">
<return type="void" />
<argument index="0" name="ui" type="bool" default="false" />
<description>
</description>
</method>
<method name="test_softmax_regression">
<return type="void" />
<argument index="0" name="ui" type="bool" default="false" />
<description>
</description>
</method>
<method name="test_statistics">
<return type="void" />
<description>
</description>
</method>
<method name="test_support_vector_classification">
<return type="void" />
<argument index="0" name="ui" type="bool" default="false" />
<description>
</description>
</method>
<method name="test_support_vector_classification_kernel">
<return type="void" />
<argument index="0" name="ui" type="bool" default="false" />
<description>
</description>
</method>
<method name="test_tanh_regression">
<return type="void" />
<argument index="0" name="ui" type="bool" default="false" />
<description>
</description>
</method>
<method name="test_train_test_split_mann">
<return type="void" />
<argument index="0" name="ui" type="bool" default="false" />
<description>
</description>
</method>
<method name="test_univariate_linear_regression">
<return type="void" />
<description>
</description>
</method>
<method name="test_wgan">
<return type="void" />
<argument index="0" name="ui" type="bool" default="false" />
<description>
</description>
</method>
<method name="test_wgan_old">
<return type="void" />
<argument index="0" name="ui" type="bool" default="false" />
<description>
</description>
</method>
</methods>
<constants>
</constants>
</class>

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<?xml version="1.0" encoding="UTF-8" ?>
<class name="MLPPTransforms" inherits="Reference" version="3.11">
<brief_description>
</brief_description>
<description>
</description>
<tutorials>
</tutorials>
<methods>
</methods>
<constants>
</constants>
</class>

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<?xml version="1.0" encoding="UTF-8" ?>
<class name="MLPPUniLinReg" inherits="Reference" version="3.11">
<brief_description>
</brief_description>
<description>
</description>
<tutorials>
</tutorials>
<methods>
<method name="get_b0">
<return type="float" />
<description>
</description>
</method>
<method name="get_b1">
<return type="float" />
<description>
</description>
</method>
<method name="initialize">
<return type="void" />
<description>
</description>
</method>
<method name="model_set_test">
<return type="MLPPVector" />
<argument index="0" name="x" type="MLPPVector" />
<description>
</description>
</method>
<method name="model_test">
<return type="float" />
<argument index="0" name="x" type="float" />
<description>
</description>
</method>
</methods>
<members>
<member name="input_set" type="MLPPVector" setter="set_input_set" getter="get_input_set">
</member>
<member name="output_set" type="MLPPVector" setter="set_output_set" getter="get_output_set">
</member>
</members>
<constants>
</constants>
</class>

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<?xml version="1.0" encoding="UTF-8" ?>
<class name="MLPPUtilities" inherits="Reference" version="3.11">
<brief_description>
</brief_description>
<description>
</description>
<tutorials>
</tutorials>
<methods>
<method name="bias_initializationr">
<return type="float" />
<description>
</description>
</method>
<method name="bias_initializationv">
<return type="void" />
<argument index="0" name="z" type="MLPPVector" />
<description>
</description>
</method>
<method name="create_mini_batchesm">
<return type="Array" />
<argument index="0" name="input_set" type="MLPPMatrix" />
<argument index="1" name="n_mini_batch" type="int" />
<description>
</description>
</method>
<method name="create_mini_batchesmm">
<return type="Array" />
<argument index="0" name="input_set" type="MLPPMatrix" />
<argument index="1" name="output_set" type="MLPPMatrix" />
<argument index="2" name="n_mini_batch" type="int" />
<description>
</description>
</method>
<method name="create_mini_batchesmv">
<return type="Array" />
<argument index="0" name="input_set" type="MLPPMatrix" />
<argument index="1" name="output_set" type="MLPPVector" />
<argument index="2" name="n_mini_batch" type="int" />
<description>
</description>
</method>
<method name="performance_mat">
<return type="float" />
<argument index="0" name="y_hat" type="MLPPMatrix" />
<argument index="1" name="y" type="MLPPMatrix" />
<description>
</description>
</method>
<method name="performance_pool_int_array_vec">
<return type="float" />
<argument index="0" name="y_hat" type="PoolIntArray" />
<argument index="1" name="output_set" type="MLPPVector" />
<description>
</description>
</method>
<method name="performance_vec">
<return type="float" />
<argument index="0" name="y_hat" type="MLPPVector" />
<argument index="1" name="output_set" type="MLPPVector" />
<description>
</description>
</method>
<method name="weight_initializationm">
<return type="void" />
<argument index="0" name="weights" type="MLPPMatrix" />
<argument index="1" name="type" type="int" enum="MLPPUtilities.WeightDistributionType" default="0" />
<description>
</description>
</method>
<method name="weight_initializationv">
<return type="void" />
<argument index="0" name="weights" type="MLPPVector" />
<argument index="1" name="type" type="int" enum="MLPPUtilities.WeightDistributionType" default="0" />
<description>
</description>
</method>
</methods>
<constants>
<constant name="WEIGHT_DISTRIBUTION_TYPE_DEFAULT" value="0" enum="WeightDistributionType">
</constant>
<constant name="WEIGHT_DISTRIBUTION_TYPE_XAVIER_NORMAL" value="1" enum="WeightDistributionType">
</constant>
<constant name="WEIGHT_DISTRIBUTION_TYPE_XAVIER_UNIFORM" value="2" enum="WeightDistributionType">
</constant>
<constant name="WEIGHT_DISTRIBUTION_TYPE_HE_NORMAL" value="3" enum="WeightDistributionType">
</constant>
<constant name="WEIGHT_DISTRIBUTION_TYPE_HE_UNIFORM" value="4" enum="WeightDistributionType">
</constant>
<constant name="WEIGHT_DISTRIBUTION_TYPE_LE_CUN_NORMAL" value="5" enum="WeightDistributionType">
</constant>
<constant name="WEIGHT_DISTRIBUTION_TYPE_LE_CUN_UNIFORM" value="6" enum="WeightDistributionType">
</constant>
<constant name="WEIGHT_DISTRIBUTION_TYPE_UNIFORM" value="7" enum="WeightDistributionType">
</constant>
</constants>
</class>

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<?xml version="1.0" encoding="UTF-8" ?>
<class name="MLPPVector" inherits="Reference" version="3.11">
<brief_description>
</brief_description>
<description>
</description>
<tutorials>
</tutorials>
<methods>
<method name="add_mlpp_vector">
<return type="void" />
<argument index="0" name="other" type="float" />
<description>
</description>
</method>
<method name="clear">
<return type="void" />
<description>
</description>
</method>
<method name="duplicate" qualifiers="const">
<return type="MLPPVector" />
<description>
</description>
</method>
<method name="empty" qualifiers="const">
<return type="bool" />
<description>
</description>
</method>
<method name="erase">
<return type="void" />
<argument index="0" name="val" type="float" />
<description>
</description>
</method>
<method name="erase_multiple_unordered">
<return type="int" />
<argument index="0" name="val" type="float" />
<description>
</description>
</method>
<method name="fill">
<return type="void" />
<argument index="0" name="val" type="float" />
<description>
</description>
</method>
<method name="find" qualifiers="const">
<return type="int" />
<argument index="0" name="val" type="float" />
<argument index="1" name="from" type="int" default="0" />
<description>
</description>
</method>
<method name="get_element" qualifiers="const">
<return type="float" />
<argument index="0" name="index" type="int" />
<description>
</description>
</method>
<method name="insert">
<return type="void" />
<argument index="0" name="pos" type="int" />
<argument index="1" name="val" type="float" />
<description>
</description>
</method>
<method name="invert">
<return type="void" />
<description>
</description>
</method>
<method name="is_equal_approx" qualifiers="const">
<return type="bool" />
<argument index="0" name="with" type="MLPPVector" />
<argument index="1" name="tolerance" type="float" default="1e-05" />
<description>
</description>
</method>
<method name="ordered_insert">
<return type="void" />
<argument index="0" name="val" type="float" />
<description>
</description>
</method>
<method name="push_back">
<return type="void" />
<argument index="0" name="elem" type="float" />
<description>
</description>
</method>
<method name="remove">
<return type="void" />
<argument index="0" name="index" type="float" />
<description>
</description>
</method>
<method name="remove_unordered">
<return type="void" />
<argument index="0" name="index" type="int" />
<description>
</description>
</method>
<method name="reset">
<return type="void" />
<description>
</description>
</method>
<method name="resize">
<return type="void" />
<argument index="0" name="size" type="int" />
<description>
</description>
</method>
<method name="set_element">
<return type="void" />
<argument index="0" name="index" type="int" />
<argument index="1" name="val" type="float" />
<description>
</description>
</method>
<method name="set_from_mlpp_vector">
<return type="void" />
<argument index="0" name="from" type="MLPPVector" />
<description>
</description>
</method>
<method name="set_from_pool_vector">
<return type="void" />
<argument index="0" name="from" type="PoolRealArray" />
<description>
</description>
</method>
<method name="size" qualifiers="const">
<return type="int" />
<description>
</description>
</method>
<method name="sort">
<return type="void" />
<description>
</description>
</method>
<method name="to_byte_array" qualifiers="const">
<return type="PoolByteArray" />
<description>
</description>
</method>
<method name="to_pool_vector" qualifiers="const">
<return type="PoolRealArray" />
<description>
</description>
</method>
</methods>
<constants>
</constants>
</class>

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<?xml version="1.0" encoding="UTF-8" ?>
<class name="MLPPWGAN" inherits="Reference" version="3.11">
<brief_description>
</brief_description>
<description>
</description>
<tutorials>
</tutorials>
<methods>
<method name="add_layer">
<return type="void" />
<argument index="0" name="activation" type="int" />
<argument index="1" name="weight_init" type="int" enum="MLPPActivation.ActivationFunction" />
<argument index="2" name="reg" type="int" enum="MLPPUtilities.WeightDistributionType" default="0" />
<argument index="3" name="lambda" type="int" enum="MLPPReg.RegularizationType" default="0" />
<argument index="4" name="alpha" type="float" default="0.5" />
<argument index="5" name="arg5" type="float" default="0.5" />
<description>
</description>
</method>
<method name="add_output_layer">
<return type="void" />
<argument index="0" name="weight_init" type="int" enum="MLPPUtilities.WeightDistributionType" default="0" />
<argument index="1" name="reg" type="int" enum="MLPPReg.RegularizationType" default="0" />
<argument index="2" name="lambda" type="float" default="0.5" />
<argument index="3" name="alpha" type="float" default="0.5" />
<description>
</description>
</method>
<method name="generate_example">
<return type="MLPPMatrix" />
<argument index="0" name="n" type="int" />
<description>
</description>
</method>
<method name="gradient_descent">
<return type="void" />
<argument index="0" name="learning_rate" type="float" />
<argument index="1" name="max_epoch" type="int" />
<argument index="2" name="ui" type="bool" default="false" />
<description>
</description>
</method>
<method name="save">
<return type="void" />
<argument index="0" name="file_name" type="String" />
<description>
</description>
</method>
<method name="score">
<return type="float" />
<description>
</description>
</method>
</methods>
<members>
<member name="k" type="int" setter="set_k" getter="get_k" default="0">
</member>
<member name="output_set" type="MLPPMatrix" setter="set_output_set" getter="get_output_set">
</member>
</members>
<constants>
</constants>
</class>