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<div id=Index><div class=IPageTitle>Index</div><div class=INavigationBar>$#! · 0-9 · <a href="General.html#A">A</a> · B · <a href="General.html#C">C</a> · <a href="General.html#D">D</a> · <a href="General.html#E">E</a> · <a href="General2.html#F">F</a> · <a href="#G">G</a> · H · <a href="#I">I</a> · J · K · <a href="#L">L</a> · <a href="#M">M</a> · <a href="#N">N</a> · O · <a href="#P">P</a> · Q · <a href="#R">R</a> · <a href="General4.html#S">S</a> · <a href="General5.html#T">T</a> · U · V · W · X · Y · Z</div><table border=0 cellspacing=0 cellpadding=0><tr><td class=IHeading id=IFirstHeading><a name="G"></a>G</td><td></td></tr><tr><td class=ISymbolPrefix id=IFirstSymbolPrefix> </td><td class=IEntry><a href="../files/fann_cpp-h.html#neural_net.get_activation_function" id=link239 onMouseOver="ShowTip(event, 'tt239', 'link239')" onMouseOut="HideTip('tt239')" class=ISymbol>get_activation_function</a>, <span class=IParent>neural_net</span></td></tr><tr><td class=ISymbolPrefix> </td><td class=IEntry><a href="../files/fann_cpp-h.html#neural_net.get_activation_steepness" id=link240 onMouseOver="ShowTip(event, 'tt240', 'link240')" onMouseOut="HideTip('tt240')" class=ISymbol>get_activation_steepness</a>, <span class=IParent>neural_net</span></td></tr><tr><td class=ISymbolPrefix> </td><td class=IEntry><a href="../files/fann_cpp-h.html#neural_net.get_bias_array" id=link241 onMouseOver="ShowTip(event, 'tt241', 'link241')" onMouseOut="HideTip('tt241')" class=ISymbol>get_bias_array</a>, <span class=IParent>neural_net</span></td></tr><tr><td class=ISymbolPrefix> </td><td class=IEntry><a href="../files/fann_cpp-h.html#neural_net.get_bit_fail" id=link242 onMouseOver="ShowTip(event, 'tt242', 'link242')" onMouseOut="HideTip('tt242')" class=ISymbol>get_bit_fail</a>, <span class=IParent>neural_net</span></td></tr><tr><td class=ISymbolPrefix> </td><td class=IEntry><a href="../files/fann_cpp-h.html#neural_net.get_bit_fail_limit" id=link243 onMouseOver="ShowTip(event, 'tt243', 'link243')" onMouseOut="HideTip('tt243')" class=ISymbol>get_bit_fail_limit</a>, <span class=IParent>neural_net</span></td></tr><tr><td class=ISymbolPrefix> </td><td class=IEntry><a href="../files/fann_cpp-h.html#neural_net.get_cascade_activation_functions" id=link244 onMouseOver="ShowTip(event, 'tt244', 'link244')" onMouseOut="HideTip('tt244')" class=ISymbol>get_cascade_activation_functions</a>, <span class=IParent>neural_net</span></td></tr><tr><td class=ISymbolPrefix> </td><td class=IEntry><a href="../files/fann_cpp-h.html#neural_net.get_cascade_activation_functions_count" id=link245 onMouseOver="ShowTip(event, 'tt245', 'link245')" onMouseOut="HideTip('tt245')" class=ISymbol>get_cascade_activation_functions_count</a>, <span class=IParent>neural_net</span></td></tr><tr><td class=ISymbolPrefix> </td><td class=IEntry><a href="../files/fann_cpp-h.html#neural_net.get_cascade_activation_steepnesses" id=link246 onMouseOver="ShowTip(event, 'tt246', 'link246')" onMouseOut="HideTip('tt246')" class=ISymbol>get_cascade_activation_steepnesses</a>, <span class=IParent>neural_net</span></td></tr><tr><td class=ISymbolPrefix> </td><td class=IEntry><a href="../files/fann_cpp-h.html#neural_net.get_cascade_activation_steepnesses_count" id=link247 onMouseOver="ShowTip(event, 'tt247', 'link247')" onMouseOut="HideTip('tt247')" class=ISymbol>get_cascade_activation_steepnesses_count</a>, <span class=IParent>neural_net</span></td></tr><tr><td class=ISymbolPrefix> </td><td class=IEntry><a href="../files/fann_cpp-h.html#neural_net.get_cascade_candidate_change_fraction" id=link248 onMouseOver="ShowTip(event, 'tt248', 'link248')" onMouseOut="HideTip('tt248')" class=ISymbol>get_cascade_candidate_change_fraction</a>, <span class=IParent>neural_net</span></td></tr><tr><td class=ISymbolPrefix> </td><td class=IEntry><a href="../files/fann_cpp-h.html#neural_net.get_cascade_candidate_limit" id=link249 onMouseOver="ShowTip(event, 'tt249', 'link249')" onMouseOut="HideTip('tt249')" class=ISymbol>get_cascade_candidate_limit</a>, <span class=IParent>neural_net</span></td></tr><tr><td class=ISymbolPrefix> </td><td class=IEntry><a href="../files/fann_cpp-h.html#neural_net.get_cascade_candidate_stagnation_epochs" id=link250 onMouseOver="ShowTip(event, 'tt250', 'link250')" onMouseOut="HideTip('tt250')" class=ISymbol>get_cascade_candidate_stagnation_epochs</a>, <span class=IParent>neural_net</span></td></tr><tr><td class=ISymbolPrefix> </td><td class=IEntry><a href="../files/fann_cpp-h.html#neural_net.get_cascade_max_cand_epochs" id=link251 onMouseOver="ShowTip(event, 'tt251', 'link251')" onMouseOut="HideTip('tt251')" class=ISymbol>get_cascade_max_cand_epochs</a>, <span class=IParent>neural_net</span></td></tr><tr><td class=ISymbolPrefix> </td><td class=IEntry><a href="../files/fann_cpp-h.html#neural_net.get_cascade_max_out_epochs" id=link252 onMouseOver="ShowTip(event, 'tt252', 'link252')" onMouseOut="HideTip('tt252')" class=ISymbol>get_cascade_max_out_epochs</a>, <span class=IParent>neural_net</span></td></tr><tr><td class=ISymbolPrefix> </td><td class=IEntry><a href="../files/fann_cpp-h.html#neural_net.get_cascade_num_candidate_groups" id=link253 onMouseOver="ShowTip(event, 'tt253', 'link253')" onMouseOut="HideTip('tt253')" class=ISymbol>get_cascade_num_candidate_groups</a>, <span class=IParent>neural_net</span></td></tr><tr><td class=ISymbolPrefix> </td><td class=IEntry><a href="../files/fann_cpp-h.html#neural_net.get_cascade_num_candidates" id=link254 onMouseOver="ShowTip(event, 'tt254', 'link254')" onMouseOut="HideTip('tt254')" class=ISymbol>get_cascade_num_candidates</a>, <span class=IParent>neural_net</span></td></tr><tr><td class=ISymbolPrefix> </td><td class=IEntry><a href="../files/fann_cpp-h.html#neural_net.get_cascade_output_change_fraction" id=link255 onMouseOver="ShowTip(event, 'tt255', 'link255')" onMouseOut="HideTip('tt255')" class=ISymbol>get_cascade_output_change_fraction</a>, <span class=IParent>neural_net</span></td></tr><tr><td class=ISymbolPrefix> </td><td class=IEntry><a href="../files/fann_cpp-h.html#neural_net.get_cascade_output_stagnation_epochs" id=link256 onMouseOver="ShowTip(event, 'tt256', 'link256')" onMouseOut="HideTip('tt256')" class=ISymbol>get_cascade_output_stagnation_epochs</a>, <span class=IParent>neural_net</span></td></tr><tr><td class=ISymbolPrefix> </td><td class=IEntry><a href="../files/fann_cpp-h.html#neural_net.get_cascade_weight_multiplier" id=link257 onMouseOver="ShowTip(event, 'tt257', 'link257')" onMouseOut="HideTip('tt257')" class=ISymbol>get_cascade_weight_multiplier</a>, <span class=IParent>neural_net</span></td></tr><tr><td class=ISymbolPrefix> </td><td class=IEntry><a href="../files/fann_cpp-h.html#neural_net.get_connection_array" id=link258 onMouseOver="ShowTip(event, 'tt258', 'link258')" onMouseOut="HideTip('tt258')" class=ISymbol>get_connection_array</a>, <span class=IParent>neural_net</span></td></tr><tr><td class=ISymbolPrefix> </td><td class=IEntry><a href="../files/fann_cpp-h.html#neural_net.get_connection_rate" id=link259 onMouseOver="ShowTip(event, 'tt259', 'link259')" onMouseOut="HideTip('tt259')" class=ISymbol>get_connection_rate</a>, <span class=IParent>neural_net</span></td></tr><tr><td class=ISymbolPrefix> </td><td class=IEntry><a href="../files/fann_cpp-h.html#neural_net.get_decimal_point" id=link260 onMouseOver="ShowTip(event, 'tt260', 'link260')" onMouseOut="HideTip('tt260')" class=ISymbol>get_decimal_point</a>, <span class=IParent>neural_net</span></td></tr><tr><td class=ISymbolPrefix> </td><td class=IEntry><a href="../files/fann_cpp-h.html#neural_net.get_errno" id=link261 onMouseOver="ShowTip(event, 'tt261', 'link261')" onMouseOut="HideTip('tt261')" class=ISymbol>get_errno</a>, <span class=IParent>neural_net</span></td></tr><tr><td class=ISymbolPrefix> </td><td class=IEntry><a href="../files/fann_cpp-h.html#neural_net.get_errstr" id=link262 onMouseOver="ShowTip(event, 'tt262', 'link262')" onMouseOut="HideTip('tt262')" class=ISymbol>get_errstr</a>, <span class=IParent>neural_net</span></td></tr><tr><td class=ISymbolPrefix> </td><td class=IEntry><a href="../files/fann_cpp-h.html#training_data.get_input" id=link263 onMouseOver="ShowTip(event, 'tt263', 'link263')" onMouseOut="HideTip('tt263')" class=ISymbol>get_input</a>, <span class=IParent>training_data</span></td></tr><tr><td class=ISymbolPrefix> </td><td class=IEntry><a href="../files/fann_cpp-h.html#neural_net.get_layer_array" id=link264 onMouseOver="ShowTip(event, 'tt264', 'link264')" onMouseOut="HideTip('tt264')" class=ISymbol>get_layer_array</a>, <span class=IParent>neural_net</span></td></tr><tr><td class=ISymbolPrefix> </td><td class=IEntry><a href="../files/fann_cpp-h.html#neural_net.get_learning_momentum" id=link265 onMouseOver="ShowTip(event, 'tt265', 'link265')" onMouseOut="HideTip('tt265')" class=ISymbol>get_learning_momentum</a>, <span class=IParent>neural_net</span></td></tr><tr><td class=ISymbolPrefix> </td><td class=IEntry><a href="../files/fann_cpp-h.html#neural_net.get_learning_rate" id=link266 onMouseOver="ShowTip(event, 'tt266', 'link266')" onMouseOut="HideTip('tt266')" class=ISymbol>get_learning_rate</a>, <span class=IParent>neural_net</span></td></tr><tr><td class=ISymbolPrefix> </td><td class=IEntry><a href="../files/fann_cpp-h.html#neural_net.get_MSE" id=link267 onMouseOver="ShowTip(event, 'tt267', 'link267')" onMouseOut="HideTip('tt267')" class=ISymbol>get_MSE</a>, <span class=IParent>neural_net</span></td></tr><tr><td class=ISymbolPrefix> </td><td class=IEntry><a href="../files/fann_cpp-h.html#neural_net.get_multiplier" id=link268 onMouseOver="ShowTip(event, 'tt268', 'link268')" onMouseOut="HideTip('tt268')" class=ISymbol>get_multiplier</a>, <span class=IParent>neural_net</span></td></tr><tr><td class=ISymbolPrefix> </td><td class=IEntry><a href="../files/fann_cpp-h.html#neural_net.get_network_type" id=link269 onMouseOver="ShowTip(event, 'tt269', 'link269')" onMouseOut="HideTip('tt269')" class=ISymbol>get_network_type</a>, <span class=IParent>neural_net</span></td></tr><tr><td class=ISymbolPrefix> </td><td class=IEntry><a href="../files/fann_cpp-h.html#neural_net.get_num_input" id=link270 onMouseOver="ShowTip(event, 'tt270', 'link270')" onMouseOut="HideTip('tt270')" class=ISymbol>get_num_input</a>, <span class=IParent>neural_net</span></td></tr><tr><td class=ISymbolPrefix> </td><td class=IEntry><a href="../files/fann_cpp-h.html#neural_net.get_num_layers" id=link271 onMouseOver="ShowTip(event, 'tt271', 'link271')" onMouseOut="HideTip('tt271')" class=ISymbol>get_num_layers</a>, <span class=IParent>neural_net</span></td></tr><tr><td class=ISymbolPrefix> </td><td class=IEntry><a href="../files/fann_cpp-h.html#neural_net.get_num_output" id=link272 onMouseOver="ShowTip(event, 'tt272', 'link272')" onMouseOut="HideTip('tt272')" class=ISymbol>get_num_output</a>, <span class=IParent>neural_net</span></td></tr><tr><td class=ISymbolPrefix> </td><td class=IEntry><a href="../files/fann_cpp-h.html#training_data.get_output" id=link273 onMouseOver="ShowTip(event, 'tt273', 'link273')" onMouseOut="HideTip('tt273')" class=ISymbol>get_output</a>, <span class=IParent>training_data</span></td></tr><tr><td class=ISymbolPrefix> </td><td class=IEntry><a href="../files/fann_cpp-h.html#neural_net.get_quickprop_decay" id=link274 onMouseOver="ShowTip(event, 'tt274', 'link274')" onMouseOut="HideTip('tt274')" class=ISymbol>get_quickprop_decay</a>, <span class=IParent>neural_net</span></td></tr><tr><td class=ISymbolPrefix> </td><td class=IEntry><a href="../files/fann_cpp-h.html#neural_net.get_quickprop_mu" id=link275 onMouseOver="ShowTip(event, 'tt275', 'link275')" onMouseOut="HideTip('tt275')" class=ISymbol>get_quickprop_mu</a>, <span class=IParent>neural_net</span></td></tr><tr><td class=ISymbolPrefix> </td><td class=IEntry><a href="../files/fann_cpp-h.html#neural_net.get_rprop_decrease_factor" id=link276 onMouseOver="ShowTip(event, 'tt276', 'link276')" onMouseOut="HideTip('tt276')" class=ISymbol>get_rprop_decrease_factor</a>, <span class=IParent>neural_net</span></td></tr><tr><td class=ISymbolPrefix> </td><td class=IEntry><a href="../files/fann_cpp-h.html#neural_net.get_rprop_delta_max" id=link277 onMouseOver="ShowTip(event, 'tt277', 'link277')" onMouseOut="HideTip('tt277')" class=ISymbol>get_rprop_delta_max</a>, <span class=IParent>neural_net</span></td></tr><tr><td class=ISymbolPrefix> </td><td class=IEntry><a href="../files/fann_cpp-h.html#neural_net.get_rprop_delta_min" id=link278 onMouseOver="ShowTip(event, 'tt278', 'link278')" onMouseOut="HideTip('tt278')" class=ISymbol>get_rprop_delta_min</a>, <span class=IParent>neural_net</span></td></tr><tr><td class=ISymbolPrefix> </td><td class=IEntry><a href="../files/fann_cpp-h.html#neural_net.get_rprop_increase_factor" id=link279 onMouseOver="ShowTip(event, 'tt279', 'link279')" onMouseOut="HideTip('tt279')" class=ISymbol>get_rprop_increase_factor</a>, <span 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class=ISymbolPrefix id=IFirstSymbolPrefix> </td><td class=IEntry><a href="../files/fann_cpp-h.html#FANN.LAYER" id=link286 onMouseOver="ShowTip(event, 'tt286', 'link286')" onMouseOut="HideTip('tt286')" class=ISymbol>LAYER</a>, <span class=IParent>FANN</span></td></tr><tr><td class=ISymbolPrefix id=ILastSymbolPrefix> </td><td class=IEntry><a href="../files/fann_cpp-h.html#training_data.length_train_data" id=link287 onMouseOver="ShowTip(event, 'tt287', 'link287')" onMouseOut="HideTip('tt287')" class=ISymbol>length_train_data</a>, <span class=IParent>training_data</span></td></tr><tr><td class=IHeading><a name="M"></a>M</td><td></td></tr><tr><td class=ISymbolPrefix id=IOnlySymbolPrefix> </td><td class=IEntry><a href="../files/fann_cpp-h.html#training_data.merge_train_data" id=link288 onMouseOver="ShowTip(event, 'tt288', 'link288')" onMouseOut="HideTip('tt288')" class=ISymbol>merge_train_data</a>, <span class=IParent>training_data</span></td></tr><tr><td class=IHeading><a name="N"></a>N</td><td></td></tr><tr><td class=ISymbolPrefix id=IFirstSymbolPrefix> </td><td class=IEntry><a href="../files/fann_cpp-h.html#FANN.network_type_enum" id=link289 onMouseOver="ShowTip(event, 'tt289', 'link289')" onMouseOut="HideTip('tt289')" class=ISymbol>network_type_enum</a>, <span class=IParent>FANN</span></td></tr><tr><td class=ISymbolPrefix> </td><td class=IEntry><span class=ISymbol>neural_net</span><div class=ISubIndex><a href="../files/fann_cpp-h.html#neural_net" id=link290 onMouseOver="ShowTip(event, 'tt290', 'link290')" onMouseOut="HideTip('tt290')" class=IParent>Global</a><a href="../files/fann_cpp-h.html#neural_net.neural_net" id=link291 onMouseOver="ShowTip(event, 'tt291', 'link291')" onMouseOut="HideTip('tt291')" class=IParent>neural_net</a></div></td></tr><tr><td class=ISymbolPrefix>~</td><td class=IEntry><a href="../files/fann_cpp-h.html#neural_net.~neural_net" id=link292 onMouseOver="ShowTip(event, 'tt292', 'link292')" onMouseOut="HideTip('tt292')" class=ISymbol>neural_net</a>, <span class=IParent>neural_net</span></td></tr><tr><td class=ISymbolPrefix> </td><td class=IEntry><a href="../files/fann_cpp-h.html#training_data.num_input_train_data" id=link293 onMouseOver="ShowTip(event, 'tt293', 'link293')" onMouseOut="HideTip('tt293')" class=ISymbol>num_input_train_data</a>, <span class=IParent>training_data</span></td></tr><tr><td class=ISymbolPrefix id=ILastSymbolPrefix> </td><td class=IEntry><a href="../files/fann_cpp-h.html#training_data.num_output_train_data" id=link294 onMouseOver="ShowTip(event, 'tt294', 'link294')" onMouseOut="HideTip('tt294')" class=ISymbol>num_output_train_data</a>, <span class=IParent>training_data</span></td></tr><tr><td class=IHeading><a name="P"></a>P</td><td></td></tr><tr><td class=ISymbolPrefix id=IFirstSymbolPrefix> </td><td class=IEntry><span class=ISymbol>Parameters</span><div class=ISubIndex><a href="../files/fann-h.html#Parameters" class=IFile>fann.h</a><a 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class=IParent>neural_net</span></td></tr><tr><td class=ISymbolPrefix> </td><td class=IEntry><a href="../files/fann_cpp-h.html#neural_net.reset_errstr" id=link301 onMouseOver="ShowTip(event, 'tt301', 'link301')" onMouseOut="HideTip('tt301')" class=ISymbol>reset_errstr</a>, <span class=IParent>neural_net</span></td></tr><tr><td class=ISymbolPrefix> </td><td class=IEntry><a href="../files/fann_cpp-h.html#neural_net.reset_MSE" id=link302 onMouseOver="ShowTip(event, 'tt302', 'link302')" onMouseOut="HideTip('tt302')" class=ISymbol>reset_MSE</a>, <span class=IParent>neural_net</span></td></tr><tr><td class=ISymbolPrefix id=ILastSymbolPrefix> </td><td class=IEntry><a href="../files/fann_cpp-h.html#neural_net.run" id=link303 onMouseOver="ShowTip(event, 'tt303', 'link303')" onMouseOut="HideTip('tt303')" class=ISymbol>run</a>, <span class=IParent>neural_net</span></td></tr></table>
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<div class=CToolTip id="tt239"><div class=CFunction><blockquote><table border=0 cellspacing=0 cellpadding=0 class="Prototype"><tr><td><table border=0 cellspacing=0 cellpadding=0><tr><td class="PBeforeParameters prettyprint "nowrap>activation_function_enum get_activation_function(</td><td class="PType prettyprint " nowrap>int </td><td class="PParameter prettyprint " nowrap>layer,</td></tr><tr><td></td><td class="PType prettyprint " nowrap>int </td><td class="PParameter prettyprint " nowrap>neuron</td><td class="PAfterParameters prettyprint "nowrap>)</td></tr></table></td></tr></table></blockquote>Get the activation function for neuron number <b>neuron</b> in layer number <b>layer</b>, counting the input layer as layer 0.</div></div><div class=CToolTip id="tt240"><div class=CFunction><blockquote><table border=0 cellspacing=0 cellpadding=0 class="Prototype"><tr><td><table border=0 cellspacing=0 cellpadding=0><tr><td class="PBeforeParameters prettyprint "nowrap>fann_type get_activation_steepness(</td><td class="PType prettyprint " nowrap>int </td><td class="PParameter prettyprint " nowrap>layer,</td></tr><tr><td></td><td class="PType prettyprint " nowrap>int </td><td class="PParameter prettyprint " nowrap>neuron</td><td class="PAfterParameters prettyprint "nowrap>)</td></tr></table></td></tr></table></blockquote>Get the activation steepness for neuron number <b>neuron</b> in layer number <b>layer</b>, counting the input layer as layer 0.</div></div><div class=CToolTip id="tt241"><div class=CFunction><blockquote><table border=0 cellspacing=0 cellpadding=0 class="Prototype"><tr><td><table border=0 cellspacing=0 cellpadding=0><tr><td class="PBeforeParameters prettyprint "nowrap>void get_bias_array(</td><td class="PTypePrefix prettyprint " nowrap>unsigned </td><td class="PType prettyprint " nowrap>int </td><td class="PParameterPrefix prettyprint " nowrap>*</td><td class="PParameter prettyprint " nowrap>bias</td><td class="PAfterParameters prettyprint "nowrap>)</td></tr></table></td></tr></table></blockquote>Get the number of bias in each layer in the network.</div></div><div class=CToolTip id="tt242"><div class=CFunction><blockquote><table border=0 cellspacing=0 cellpadding=0 class="Prototype"><tr><td class="prettyprint">unsigned int get_bit_fail()</td></tr></table></blockquote>The number of fail bits; means the number of output neurons which differ more than the bit fail limit (see get_bit_fail_limit, set_bit_fail_limit). </div></div><div class=CToolTip id="tt243"><div class=CFunction><blockquote><table border=0 cellspacing=0 cellpadding=0 class="Prototype"><tr><td class="prettyprint">fann_type get_bit_fail_limit()</td></tr></table></blockquote>Returns the bit fail limit used during training.</div></div><div class=CToolTip id="tt244"><div class=CFunction><blockquote><table border=0 cellspacing=0 cellpadding=0 class="Prototype"><tr><td class="prettyprint">activation_function_enum * get_cascade_activation_functions()</td></tr></table></blockquote>The cascade activation functions array is an array of the different activation functions used by the candidates.</div></div><div class=CToolTip id="tt245"><div class=CFunction><blockquote><table border=0 cellspacing=0 cellpadding=0 class="Prototype"><tr><td class="prettyprint">unsigned int get_cascade_activation_functions_count()</td></tr></table></blockquote>The number of activation functions in the get_cascade_activation_functions array.</div></div><div class=CToolTip id="tt246"><div class=CFunction><blockquote><table border=0 cellspacing=0 cellpadding=0 class="Prototype"><tr><td class="prettyprint">fann_type *get_cascade_activation_steepnesses()</td></tr></table></blockquote>The cascade activation steepnesses array is an array of the different activation functions used by the candidates.</div></div><div class=CToolTip id="tt247"><div class=CFunction><blockquote><table border=0 cellspacing=0 cellpadding=0 class="Prototype"><tr><td class="prettyprint">unsigned int get_cascade_activation_steepnesses_count()</td></tr></table></blockquote>The number of activation steepnesses in the get_cascade_activation_functions array.</div></div><div class=CToolTip id="tt248"><div class=CFunction><blockquote><table border=0 cellspacing=0 cellpadding=0 class="Prototype"><tr><td class="prettyprint">float get_cascade_candidate_change_fraction()</td></tr></table></blockquote>The cascade candidate change fraction is a number between 0 and 1 determining how large a fraction the get_MSE value should change within get_cascade_candidate_stagnation_epochs during training of the candidate neurons, in order for the training not to stagnate. </div></div><div class=CToolTip id="tt249"><div class=CFunction><blockquote><table border=0 cellspacing=0 cellpadding=0 class="Prototype"><tr><td class="prettyprint">fann_type get_cascade_candidate_limit()</td></tr></table></blockquote>The candidate limit is a limit for how much the candidate neuron may be trained. </div></div><div class=CToolTip id="tt250"><div class=CFunction><blockquote><table border=0 cellspacing=0 cellpadding=0 class="Prototype"><tr><td class="prettyprint">unsigned int get_cascade_candidate_stagnation_epochs()</td></tr></table></blockquote>The number of cascade candidate stagnation epochs determines the number of epochs training is allowed to continue without changing the MSE by a fraction of get_cascade_candidate_change_fraction.</div></div><div class=CToolTip id="tt251"><div class=CFunction><blockquote><table border=0 cellspacing=0 cellpadding=0 class="Prototype"><tr><td class="prettyprint">unsigned int get_cascade_max_cand_epochs()</td></tr></table></blockquote>The maximum candidate epochs determines the maximum number of epochs the input connections to the candidates may be trained before adding a new candidate neuron.</div></div><div class=CToolTip id="tt252"><div class=CFunction><blockquote><table border=0 cellspacing=0 cellpadding=0 class="Prototype"><tr><td class="prettyprint">unsigned int get_cascade_max_out_epochs()</td></tr></table></blockquote>The maximum out epochs determines the maximum number of epochs the output connections may be trained after adding a new candidate neuron.</div></div><div class=CToolTip id="tt253"><div class=CFunction><blockquote><table border=0 cellspacing=0 cellpadding=0 class="Prototype"><tr><td class="prettyprint">unsigned int get_cascade_num_candidate_groups()</td></tr></table></blockquote>The number of candidate groups is the number of groups of identical candidates which will be used during training.</div></div><div class=CToolTip id="tt254"><div class=CFunction><blockquote><table border=0 cellspacing=0 cellpadding=0 class="Prototype"><tr><td class="prettyprint">unsigned int get_cascade_num_candidates()</td></tr></table></blockquote>The number of candidates used during training (calculated by multiplying get_cascade_activation_functions_count, get_cascade_activation_steepnesses_count and get_cascade_num_candidate_groups).</div></div><div class=CToolTip id="tt255"><div class=CFunction><blockquote><table border=0 cellspacing=0 cellpadding=0 class="Prototype"><tr><td class="prettyprint">float get_cascade_output_change_fraction()</td></tr></table></blockquote>The cascade output change fraction is a number between 0 and 1 determining how large a fraction the get_MSE value should change within get_cascade_output_stagnation_epochs during training of the output connections, in order for the training not to stagnate. </div></div><div class=CToolTip id="tt256"><div class=CFunction><blockquote><table border=0 cellspacing=0 cellpadding=0 class="Prototype"><tr><td class="prettyprint">unsigned int get_cascade_output_stagnation_epochs()</td></tr></table></blockquote>The number of cascade output stagnation epochs determines the number of epochs training is allowed to continue without changing the MSE by a fraction of get_cascade_output_change_fraction.</div></div><div class=CToolTip id="tt257"><div class=CFunction><blockquote><table border=0 cellspacing=0 cellpadding=0 class="Prototype"><tr><td class="prettyprint">fann_type get_cascade_weight_multiplier()</td></tr></table></blockquote>The weight multiplier is a parameter which is used to multiply the weights from the candidate neuron before adding the neuron to the neural network. </div></div><div class=CToolTip id="tt258"><div class=CFunction><blockquote><table border=0 cellspacing=0 cellpadding=0 class="Prototype"><tr><td><table border=0 cellspacing=0 cellpadding=0><tr><td class="PBeforeParameters prettyprint "nowrap>void get_connection_array(</td><td class="PType prettyprint " nowrap>connection </td><td class="PParameterPrefix prettyprint " nowrap>*</td><td class="PParameter prettyprint " nowrap>connections</td><td class="PAfterParameters prettyprint "nowrap>)</td></tr></table></td></tr></table></blockquote>Get the connections in the network.</div></div><div class=CToolTip id="tt259"><div class=CFunction><blockquote><table border=0 cellspacing=0 cellpadding=0 class="Prototype"><tr><td class="prettyprint">float get_connection_rate()</td></tr></table></blockquote>Get the connection rate used when the network was created</div></div><div class=CToolTip id="tt260"><div class=CFunction><blockquote><table border=0 cellspacing=0 cellpadding=0 class="Prototype"><tr><td class="prettyprint">unsigned int get_decimal_point()</td></tr></table></blockquote>Returns the position of the decimal point in the ann.</div></div><div class=CToolTip id="tt261"><div class=CFunction><blockquote><table border=0 cellspacing=0 cellpadding=0 class="Prototype"><tr><td class="prettyprint">unsigned int get_errno()</td></tr></table></blockquote>Returns the last error number.</div></div><div class=CToolTip id="tt262"><div class=CFunction><blockquote><table border=0 cellspacing=0 cellpadding=0 class="Prototype"><tr><td class="prettyprint">std::string get_errstr()</td></tr></table></blockquote>Returns the last errstr.</div></div><div class=CToolTip id="tt263"><div class=CFunction><blockquote><table border=0 cellspacing=0 cellpadding=0 class="Prototype"><tr><td class="prettyprint">fann_type **get_input()</td></tr></table></blockquote>A pointer to the array of input training data</div></div><div class=CToolTip id="tt264"><div class=CFunction><blockquote><table border=0 cellspacing=0 cellpadding=0 class="Prototype"><tr><td><table border=0 cellspacing=0 cellpadding=0><tr><td class="PBeforeParameters prettyprint "nowrap>void get_layer_array(</td><td class="PTypePrefix prettyprint " nowrap>unsigned </td><td class="PType prettyprint " nowrap>int </td><td class="PParameterPrefix prettyprint " nowrap>*</td><td class="PParameter prettyprint " nowrap>layers</td><td class="PAfterParameters prettyprint "nowrap>)</td></tr></table></td></tr></table></blockquote>Get the number of neurons in each layer in the network.</div></div><div class=CToolTip id="tt265"><div class=CFunction><blockquote><table border=0 cellspacing=0 cellpadding=0 class="Prototype"><tr><td class="prettyprint">float get_learning_momentum()</td></tr></table></blockquote>Get the learning momentum.</div></div><div class=CToolTip id="tt266"><div class=CFunction><blockquote><table border=0 cellspacing=0 cellpadding=0 class="Prototype"><tr><td class="prettyprint">float get_learning_rate()</td></tr></table></blockquote>Return the learning rate.</div></div><div class=CToolTip id="tt267"><div class=CFunction><blockquote><table border=0 cellspacing=0 cellpadding=0 class="Prototype"><tr><td class="prettyprint">float get_MSE()</td></tr></table></blockquote>Reads the mean square error from the network.</div></div><div class=CToolTip id="tt268"><div class=CFunction><blockquote><table border=0 cellspacing=0 cellpadding=0 class="Prototype"><tr><td class="prettyprint">unsigned int get_multiplier()</td></tr></table></blockquote>Returns the multiplier that fix point data is multiplied with.</div></div><div class=CToolTip id="tt269"><div class=CFunction><blockquote><table border=0 cellspacing=0 cellpadding=0 class="Prototype"><tr><td class="prettyprint">network_type_enum get_network_type()</td></tr></table></blockquote>Get the type of neural network it was created as.</div></div><div class=CToolTip id="tt270"><div class=CFunction><blockquote><table border=0 cellspacing=0 cellpadding=0 class="Prototype"><tr><td class="prettyprint">unsigned int get_num_input()</td></tr></table></blockquote>Get the number of input neurons.</div></div><div class=CToolTip id="tt271"><div class=CFunction><blockquote><table border=0 cellspacing=0 cellpadding=0 class="Prototype"><tr><td class="prettyprint">unsigned int get_num_layers()</td></tr></table></blockquote>Get the number of layers in the network</div></div><div class=CToolTip id="tt272"><div class=CFunction><blockquote><table border=0 cellspacing=0 cellpadding=0 class="Prototype"><tr><td class="prettyprint">unsigned int get_num_output()</td></tr></table></blockquote>Get the number of output neurons.</div></div><div class=CToolTip id="tt273"><div class=CFunction><blockquote><table border=0 cellspacing=0 cellpadding=0 class="Prototype"><tr><td class="prettyprint">fann_type **get_output()</td></tr></table></blockquote>A pointer to the array of output training data</div></div><div class=CToolTip id="tt274"><div class=CFunction><blockquote><table border=0 cellspacing=0 cellpadding=0 class="Prototype"><tr><td class="prettyprint">float get_quickprop_decay()</td></tr></table></blockquote>The decay is a small negative valued number which is the factor that the weights should become smaller in each iteration during quickprop training. </div></div><div class=CToolTip id="tt275"><div class=CFunction><blockquote><table border=0 cellspacing=0 cellpadding=0 class="Prototype"><tr><td class="prettyprint">float get_quickprop_mu()</td></tr></table></blockquote>The mu factor is used to increase and decrease the step-size during quickprop training. </div></div><div class=CToolTip id="tt276"><div class=CFunction><blockquote><table border=0 cellspacing=0 cellpadding=0 class="Prototype"><tr><td class="prettyprint">float get_rprop_decrease_factor()</td></tr></table></blockquote>The decrease factor is a value smaller than 1, which is used to decrease the step-size during RPROP training.</div></div><div class=CToolTip id="tt277"><div class=CFunction><blockquote><table border=0 cellspacing=0 cellpadding=0 class="Prototype"><tr><td class="prettyprint">float get_rprop_delta_max()</td></tr></table></blockquote>The maximum step-size is a positive number determining how large the maximum step-size may be.</div></div><div class=CToolTip id="tt278"><div class=CFunction><blockquote><table border=0 cellspacing=0 cellpadding=0 class="Prototype"><tr><td class="prettyprint">float get_rprop_delta_min()</td></tr></table></blockquote>The minimum step-size is a small positive number determining how small the minimum step-size may be.</div></div><div class=CToolTip id="tt279"><div class=CFunction><blockquote><table border=0 cellspacing=0 cellpadding=0 class="Prototype"><tr><td class="prettyprint">float get_rprop_increase_factor()</td></tr></table></blockquote>The increase factor is a value larger than 1, which is used to increase the step-size during RPROP training.</div></div><div class=CToolTip id="tt280"><div class=CFunction><blockquote><table border=0 cellspacing=0 cellpadding=0 class="Prototype"><tr><td class="prettyprint">unsigned int get_total_connections()</td></tr></table></blockquote>Get the total number of connections in the entire network.</div></div><div class=CToolTip id="tt281"><div class=CFunction><blockquote><table border=0 cellspacing=0 cellpadding=0 class="Prototype"><tr><td class="prettyprint">unsigned int get_total_neurons()</td></tr></table></blockquote>Get the total number of neurons in the entire network. </div></div><div class=CToolTip id="tt282"><div class=CFunction><blockquote><table border=0 cellspacing=0 cellpadding=0 class="Prototype"><tr><td class="prettyprint">error_function_enum get_train_error_function()</td></tr></table></blockquote>Returns the error function used during training.</div></div><div class=CToolTip id="tt283"><div class=CFunction><blockquote><table border=0 cellspacing=0 cellpadding=0 class="Prototype"><tr><td class="prettyprint">stop_function_enum get_train_stop_function()</td></tr></table></blockquote>Returns the the stop function used during training.</div></div><div class=CToolTip id="tt284"><div class=CFunction><blockquote><table border=0 cellspacing=0 cellpadding=0 class="Prototype"><tr><td class="prettyprint">training_algorithm_enum get_training_algorithm()</td></tr></table></blockquote>Return the training algorithm as described by FANN::training_algorithm_enum. </div></div><!--END_ND_TOOLTIPS-->
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<div class=CToolTip id="tt285"><div class=CFunction><blockquote><table border=0 cellspacing=0 cellpadding=0 class="Prototype"><tr><td><table border=0 cellspacing=0 cellpadding=0><tr><td class="PBeforeParameters prettyprint "nowrap>void init_weights(</td><td class="PTypePrefix prettyprint " nowrap>const </td><td class="PType prettyprint " nowrap>training_data </td><td class="PParameterPrefix prettyprint " nowrap>&</td><td class="PParameter prettyprint " nowrap>data</td><td class="PAfterParameters prettyprint "nowrap>)</td></tr></table></td></tr></table></blockquote>Initialize the weights using Widrow + Nguyen’s algorithm.</div></div><!--END_ND_TOOLTIPS-->
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<div class=CToolTip id="tt286"><div class=CConstant>Each layer only has connections to the next layer</div></div><div class=CToolTip id="tt287"><div class=CFunction><blockquote><table border=0 cellspacing=0 cellpadding=0 class="Prototype"><tr><td class="prettyprint">unsigned int length_train_data()</td></tr></table></blockquote>Returns the number of training patterns in the training_data.</div></div><!--END_ND_TOOLTIPS-->
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<div class=CToolTip id="tt288"><div class=CFunction><blockquote><table border=0 cellspacing=0 cellpadding=0 class="Prototype"><tr><td><table border=0 cellspacing=0 cellpadding=0><tr><td class="PBeforeParameters prettyprint "nowrap>void merge_train_data(</td><td class="PTypePrefix prettyprint " nowrap>const </td><td class="PType prettyprint " nowrap>training_data </td><td class="PParameterPrefix prettyprint " nowrap>&</td><td class="PParameter prettyprint " nowrap>data</td><td class="PAfterParameters prettyprint "nowrap>)</td></tr></table></td></tr></table></blockquote>Merges the data into the data contained in the training_data.</div></div><!--END_ND_TOOLTIPS-->
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<div class=CToolTip id="tt289"><div class=CType>Definition of network types used by neural_net::get_network_type</div></div><div class=CToolTip id="tt290"><div class=CClass><blockquote><table border=0 cellspacing=0 cellpadding=0 class="Prototype"><tr><td class="prettyprint">class neural_net</td></tr></table></blockquote>Encapsulation of a neural network struct fann and associated C API functions.</div></div><div class=CToolTip id="tt291"><div class=CFunction><blockquote><table border=0 cellspacing=0 cellpadding=0 class="Prototype"><tr><td><table border=0 cellspacing=0 cellpadding=0><tr><td class="PBeforeParameters prettyprint "nowrap>neural_net(</td><td class="PParameter prettyprint " nowrap></td><td class="PAfterParameters prettyprint "nowrap>) : ann(NULL)</td></tr></table></td></tr></table></blockquote>Default constructor creates an empty neural net. </div></div><div class=CToolTip id="tt292"><div class=CFunction><blockquote><table border=0 cellspacing=0 cellpadding=0 class="Prototype"><tr><td class="prettyprint">#ifdef USE_VIRTUAL_DESTRUCTOR virtual #endif ~neural_net()</td></tr></table></blockquote>Provides automatic cleanup of data. </div></div><div class=CToolTip id="tt293"><div class=CFunction><blockquote><table border=0 cellspacing=0 cellpadding=0 class="Prototype"><tr><td class="prettyprint">unsigned int num_input_train_data()</td></tr></table></blockquote>Returns the number of inputs in each of the training patterns in the training_data.</div></div><div class=CToolTip id="tt294"><div class=CFunction><blockquote><table border=0 cellspacing=0 cellpadding=0 class="Prototype"><tr><td class="prettyprint">unsigned int num_output_train_data()</td></tr></table></blockquote>Returns the number of outputs in each of the training patterns in the struct fann_train_data.</div></div><!--END_ND_TOOLTIPS-->
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<div class=CToolTip id="tt295"><div class=CFunction><blockquote><table border=0 cellspacing=0 cellpadding=0 class="Prototype"><tr><td class="prettyprint">void print_connections()</td></tr></table></blockquote>Will print the connections of the ann in a compact matrix, for easy viewing of the internals of the ann.</div></div><div class=CToolTip id="tt296"><div class=CFunction><blockquote><table border=0 cellspacing=0 cellpadding=0 class="Prototype"><tr><td class="prettyprint">void print_error()</td></tr></table></blockquote>Prints the last error to stderr.</div></div><div class=CToolTip id="tt297"><div class=CFunction><blockquote><table border=0 cellspacing=0 cellpadding=0 class="Prototype"><tr><td class="prettyprint">void print_parameters()</td></tr></table></blockquote>Prints all of the parameters and options of the neural network</div></div><!--END_ND_TOOLTIPS-->
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<div class=CToolTip id="tt298"><div class=CFunction><blockquote><table border=0 cellspacing=0 cellpadding=0 class="Prototype"><tr><td><table border=0 cellspacing=0 cellpadding=0><tr><td class="PBeforeParameters prettyprint "nowrap>void randomize_weights(</td><td class="PType prettyprint " nowrap>fann_type </td><td class="PParameter prettyprint " nowrap>min_weight,</td></tr><tr><td></td><td class="PType prettyprint " nowrap>fann_type </td><td class="PParameter prettyprint " nowrap>max_weight</td><td class="PAfterParameters prettyprint "nowrap>)</td></tr></table></td></tr></table></blockquote>Give each connection a random weight between <b>min_weight</b> and <b>max_weight</b></div></div><div class=CToolTip id="tt299"><div class=CFunction><blockquote><table border=0 cellspacing=0 cellpadding=0 class="Prototype"><tr><td><table border=0 cellspacing=0 cellpadding=0><tr><td class="PBeforeParameters prettyprint "nowrap>bool read_train_from_file(</td><td class="PTypePrefix prettyprint " nowrap>const std::</td><td class="PType prettyprint " nowrap>string </td><td class="PParameterPrefix prettyprint " nowrap>&</td><td class="PParameter prettyprint " nowrap>filename</td><td class="PAfterParameters prettyprint "nowrap>)</td></tr></table></td></tr></table></blockquote>Reads a file that stores training data.</div></div><div class=CToolTip id="tt300"><div class=CFunction><blockquote><table border=0 cellspacing=0 cellpadding=0 class="Prototype"><tr><td class="prettyprint">void reset_errno()</td></tr></table></blockquote>Resets the last error number.</div></div><div class=CToolTip id="tt301"><div class=CFunction><blockquote><table border=0 cellspacing=0 cellpadding=0 class="Prototype"><tr><td class="prettyprint">void reset_errstr()</td></tr></table></blockquote>Resets the last error string.</div></div><div class=CToolTip id="tt302"><div class=CFunction><blockquote><table border=0 cellspacing=0 cellpadding=0 class="Prototype"><tr><td class="prettyprint">void reset_MSE()</td></tr></table></blockquote>Resets the mean square error from the network.</div></div><div class=CToolTip id="tt303"><div class=CFunction><blockquote><table border=0 cellspacing=0 cellpadding=0 class="Prototype"><tr><td><table border=0 cellspacing=0 cellpadding=0><tr><td class="PBeforeParameters prettyprint "nowrap>fann_type* run(</td><td class="PType prettyprint " nowrap>fann_type </td><td class="PParameterPrefix prettyprint " nowrap>*</td><td class="PParameter prettyprint " nowrap>input</td><td class="PAfterParameters prettyprint "nowrap>)</td></tr></table></td></tr></table></blockquote>Will run input through the neural network, returning an array of outputs, the number of which being equal to the number of neurons in the output layer.</div></div><!--END_ND_TOOLTIPS-->
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