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<para>Variables initialized in <ref refid="classforpy_1_1_i_thresh_opt_1a938786b8aa4163d1af10bf46d1e7cf7a" kindref="member">IThreshOpt::full_entropy</ref>. </para>        </briefdescription>
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<para>Variables initialized in <ref refid="classforpy_1_1_i_thresh_opt_1a938786b8aa4163d1af10bf46d1e7cf7a" kindref="member">IThreshOpt::full_entropy</ref>. </para>        </briefdescription>
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<para>Variables initialized in <ref refid="classforpy_1_1_i_thresh_opt_1a938786b8aa4163d1af10bf46d1e7cf7a" kindref="member">IThreshOpt::full_entropy</ref>. </para>        </briefdescription>
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<para>Variables initialized in <ref refid="classforpy_1_1_i_thresh_opt_1a938786b8aa4163d1af10bf46d1e7cf7a" kindref="member">IThreshOpt::full_entropy</ref>. </para>        </briefdescription>
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<para>Variables initialized in <ref refid="classforpy_1_1_i_thresh_opt_1a938786b8aa4163d1af10bf46d1e7cf7a" kindref="member">IThreshOpt::full_entropy</ref>. </para>        </briefdescription>
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<para>Variables initialized in <ref refid="classforpy_1_1_i_thresh_opt_1a938786b8aa4163d1af10bf46d1e7cf7a" kindref="member">IThreshOpt::full_entropy</ref>. </para>        </briefdescription>
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<para>Variables initialized in <ref refid="classforpy_1_1_i_thresh_opt_1a938786b8aa4163d1af10bf46d1e7cf7a" kindref="member">IThreshOpt::full_entropy</ref>. </para>        </briefdescription>
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<para>Return values from <ref refid="classforpy_1_1_i_thresh_opt_1aa7b9f1c4109199a9122788687756731c" kindref="member">IThreshOpt::optimize</ref>. </para>        </briefdescription>
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<para>Return values from <ref refid="classforpy_1_1_i_thresh_opt_1aa7b9f1c4109199a9122788687756731c" kindref="member">IThreshOpt::optimize</ref>. </para>        </briefdescription>
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<para>Return values from <ref refid="classforpy_1_1_i_thresh_opt_1aa7b9f1c4109199a9122788687756731c" kindref="member">IThreshOpt::optimize</ref>. </para>        </briefdescription>
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<para>Return values from <ref refid="classforpy_1_1_i_thresh_opt_1aa7b9f1c4109199a9122788687756731c" kindref="member">IThreshOpt::optimize</ref>. </para>        </briefdescription>
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        <definition>id_t forpy::DeciderDesk::right_id</definition>
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<para>Return values from <ref refid="classforpy_1_1_i_thresh_opt_1aa7b9f1c4109199a9122788687756731c" kindref="member">IThreshOpt::optimize</ref>. </para>        </briefdescription>
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        <briefdescription>
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<para>Must be initialized before calling <ref refid="classforpy_1_1_i_thresh_opt_1aa7b9f1c4109199a9122788687756731c" kindref="member">IThreshOpt::optimize</ref>. Points to the a selected feature vector (full samples, so even if for this node only samples 1:3 are needed, this points to the feature for sample 0) with stride 1. This can be done using <ref refid="classforpy_1_1_i_data_provider_1acf4a872308c8fb12b2f07d1e6e4c97be" kindref="member">IDataProvider::get_feature</ref> . </para>        </detaileddescription>
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<para>Stores the number of elements in a vector of feature IDs that have been determined as invalid (e.g., because they are constant). For these values to work correctly, only DFS may be used (otherwise, the vector of features may be re-sorted and counts may become invalid before the node optimization reaches relevant nodes). </para>        </detaileddescription>
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<para>Pointer to a shared vector of a mapping node_id-&gt;feature. Since multiple threads never write to the same node and the vector is guaranteed to be large enough, concurrent writes can be performed. </para>        </detaileddescription>
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        <detaileddescription>
<para>Pointer to a shared vector of a mapping node_id-&gt;threshold. Since multiple threads never write to the same node and the vector is guaranteed to be large enough, concurrent writes can be performed. </para>        </detaileddescription>
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    <briefdescription>
<para><ref refid="structforpy_1_1_desk" kindref="compound">Desk</ref> for decider training. </para>    </briefdescription>
    <detaileddescription>
<para>Storage container for threaded decider training (see also <ref refid="group__forpydesk_group" kindref="compound">Desk Implementations</ref>). </para>    </detaileddescription>
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