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<div id=Index><div class=IPageTitle>Index</div><div class=INavigationBar>$#! &middot; 0-9 &middot; <a href="#A">A</a> &middot; B &middot; <a href="#C">C</a> &middot; <a href="#D">D</a> &middot; <a href="#E">E</a> &middot; <a href="General2.html#F">F</a> &middot; <a href="General3.html#G">G</a> &middot; H &middot; <a href="General3.html#I">I</a> &middot; J &middot; K &middot; <a href="General3.html#L">L</a> &middot; <a href="General3.html#M">M</a> &middot; <a href="General3.html#N">N</a> &middot; O &middot; <a href="General3.html#P">P</a> &middot; Q &middot; <a href="General3.html#R">R</a> &middot; <a href="General4.html#S">S</a> &middot; <a href="General5.html#T">T</a> &middot; U &middot; V &middot; W &middot; X &middot; Y &middot; Z</div><table border=0 cellspacing=0 cellpadding=0><tr><td class=IHeading id=IFirstHeading><a name="A"></a>A</td><td></td></tr><tr><td class=ISymbolPrefix id=IOnlySymbolPrefix>&nbsp;</td><td class=IEntry><a href="../files/fann_cpp-h.html#FANN.activation_function_enum" id=link1 onMouseOver="ShowTip(event, 'tt1', 'link1')" onMouseOut="HideTip('tt1')" class=ISymbol>activation_function_enum</a>, <span class=IParent>FANN</span></td></tr><tr><td class=IHeading><a name="C"></a>C</td><td></td></tr><tr><td class=ISymbolPrefix id=IFirstSymbolPrefix>&nbsp;</td><td class=IEntry><a href="../files/fann_cpp-h.html#FANN.callback_type" id=link2 onMouseOver="ShowTip(event, 'tt2', 'link2')" onMouseOut="HideTip('tt2')" class=ISymbol>callback_type</a>, <span class=IParent>FANN</span></td></tr><tr><td class=ISymbolPrefix>&nbsp;</td><td class=IEntry><a href="../files/fann_cascade-h.html#Cascade_Training"  class=ISymbol>Cascade Training</a></td></tr><tr><td class=ISymbolPrefix>&nbsp;</td><td class=IEntry><a href="../files/fann_cpp-h.html#neural_net.cascadetrain_on_data" id=link3 onMouseOver="ShowTip(event, 'tt3', 'link3')" onMouseOut="HideTip('tt3')" class=ISymbol>cascadetrain_on_data</a>, <span class=IParent>neural_net</span></td></tr><tr><td class=ISymbolPrefix>&nbsp;</td><td class=IEntry><a href="../files/fann_cpp-h.html#neural_net.cascadetrain_on_file" id=link4 onMouseOver="ShowTip(event, 'tt4', 'link4')" onMouseOut="HideTip('tt4')" class=ISymbol>cascadetrain_on_file</a>, <span class=IParent>neural_net</span></td></tr><tr><td class=ISymbolPrefix>&nbsp;</td><td class=IEntry><a href="../files/fann_cpp-h.html#neural_net.clear_scaling_params" id=link5 onMouseOver="ShowTip(event, 'tt5', 'link5')" onMouseOut="HideTip('tt5')" class=ISymbol>clear_scaling_params</a>, <span class=IParent>neural_net</span></td></tr><tr><td class=ISymbolPrefix>&nbsp;</td><td class=IEntry><a href="../files/fann_cpp-h.html#FANN.connection" id=link6 onMouseOver="ShowTip(event, 'tt6', 'link6')" onMouseOut="HideTip('tt6')" class=ISymbol>connection</a>, <span class=IParent>FANN</span></td></tr><tr><td class=ISymbolPrefix>&nbsp;</td><td class=IEntry><a href="../files/fann_cpp-h.html#neural_net.copy_from_struct_fann" id=link7 onMouseOver="ShowTip(event, 'tt7', 'link7')" onMouseOut="HideTip('tt7')" class=ISymbol>copy_from_struct_fann</a>, <span class=IParent>neural_net</span></td></tr><tr><td class=ISymbolPrefix>&nbsp;</td><td class=IEntry><a href="../files/fann_cpp-h.html#neural_net.create_from_file" id=link8 onMouseOver="ShowTip(event, 'tt8', 'link8')" onMouseOut="HideTip('tt8')" class=ISymbol>create_from_file</a>, <span class=IParent>neural_net</span></td></tr><tr><td class=ISymbolPrefix>&nbsp;</td><td class=IEntry><a href="../files/fann_cpp-h.html#neural_net.create_shortcut" id=link9 onMouseOver="ShowTip(event, 'tt9', 'link9')" onMouseOut="HideTip('tt9')" class=ISymbol>create_shortcut</a>, <span class=IParent>neural_net</span></td></tr><tr><td class=ISymbolPrefix>&nbsp;</td><td class=IEntry><a href="../files/fann_cpp-h.html#neural_net.create_shortcut_array" id=link10 onMouseOver="ShowTip(event, 'tt10', 'link10')" onMouseOut="HideTip('tt10')" class=ISymbol>create_shortcut_array</a>, <span class=IParent>neural_net</span></td></tr><tr><td class=ISymbolPrefix>&nbsp;</td><td class=IEntry><a href="../files/fann_cpp-h.html#neural_net.create_sparse" id=link11 onMouseOver="ShowTip(event, 'tt11', 'link11')" onMouseOut="HideTip('tt11')" class=ISymbol>create_sparse</a>, <span class=IParent>neural_net</span></td></tr><tr><td class=ISymbolPrefix>&nbsp;</td><td class=IEntry><a href="../files/fann_cpp-h.html#neural_net.create_sparse_array" id=link12 onMouseOver="ShowTip(event, 'tt12', 'link12')" onMouseOut="HideTip('tt12')" class=ISymbol>create_sparse_array</a>, <span class=IParent>neural_net</span></td></tr><tr><td class=ISymbolPrefix>&nbsp;</td><td class=IEntry><a href="../files/fann_cpp-h.html#neural_net.create_standard" id=link13 onMouseOver="ShowTip(event, 'tt13', 'link13')" onMouseOut="HideTip('tt13')" class=ISymbol>create_standard</a>, <span class=IParent>neural_net</span></td></tr><tr><td class=ISymbolPrefix>&nbsp;</td><td class=IEntry><a href="../files/fann_cpp-h.html#neural_net.create_standard_array" id=link14 onMouseOver="ShowTip(event, 'tt14', 'link14')" onMouseOut="HideTip('tt14')" class=ISymbol>create_standard_array</a>, <span class=IParent>neural_net</span></td></tr><tr><td class=ISymbolPrefix>&nbsp;</td><td class=IEntry><a href="../files/fann_cpp-h.html#training_data.create_train_from_callback" id=link15 onMouseOver="ShowTip(event, 'tt15', 'link15')" onMouseOut="HideTip('tt15')" class=ISymbol>create_train_from_callback</a>, <span class=IParent>training_data</span></td></tr><tr><td class=ISymbolPrefix id=ILastSymbolPrefix>&nbsp;</td><td class=IEntry><a href="../files/fann-h.html#Creation,DestructionExecution"  class=ISymbol>Creation,Destruction&amp;Execution</a></td></tr><tr><td class=IHeading><a name="D"></a>D</td><td></td></tr><tr><td class=ISymbolPrefix id=IFirstSymbolPrefix>&nbsp;</td><td class=IEntry><a href="../files/fann_cpp-h.html#neural_net.descale_input" id=link16 onMouseOver="ShowTip(event, 'tt16', 'link16')" onMouseOut="HideTip('tt16')" class=ISymbol>descale_input</a>, <span class=IParent>neural_net</span></td></tr><tr><td class=ISymbolPrefix>&nbsp;</td><td class=IEntry><a href="../files/fann_cpp-h.html#neural_net.descale_output" id=link17 onMouseOver="ShowTip(event, 'tt17', 'link17')" onMouseOut="HideTip('tt17')" class=ISymbol>descale_output</a>, <span class=IParent>neural_net</span></td></tr><tr><td class=ISymbolPrefix>&nbsp;</td><td class=IEntry><a href="../files/fann_cpp-h.html#neural_net.descale_train" id=link18 onMouseOver="ShowTip(event, 'tt18', 'link18')" onMouseOut="HideTip('tt18')" class=ISymbol>descale_train</a>, <span class=IParent>neural_net</span></td></tr><tr><td class=ISymbolPrefix id=ILastSymbolPrefix>&nbsp;</td><td class=IEntry><span class=ISymbol>destroy</span><div class=ISubIndex><a href="../files/fann_cpp-h.html#neural_net.destroy" id=link19 onMouseOver="ShowTip(event, 'tt19', 'link19')" onMouseOut="HideTip('tt19')" class=IParent>neural_net</a><a href="../files/fann_cpp-h.html#training_data.destroy" id=link20 onMouseOver="ShowTip(event, 'tt20', 'link20')" onMouseOut="HideTip('tt20')" class=IParent>training_data</a></div></td></tr><tr><td class=IHeading><a name="E"></a>E</td><td></td></tr><tr><td class=ISymbolPrefix id=IFirstSymbolPrefix>&nbsp;</td><td class=IEntry><a href="../files/fann_cpp-h.html#FANN.Enumerations"  class=ISymbol>Enumerations</a>, <span class=IParent>FANN</span></td></tr><tr><td class=ISymbolPrefix>&nbsp;</td><td class=IEntry><a href="../files/fann_data-h.html#Enumerations_and_Constants"  class=ISymbol>Enumerations and Constants</a></td></tr><tr><td class=ISymbolPrefix>&nbsp;</td><td class=IEntry><a href="../files/fann_error-h.html#Error_Handling"  class=ISymbol>Error Handling</a></td></tr><tr><td class=ISymbolPrefix>&nbsp;</td><td class=IEntry><a href="../files/fann_cpp-h.html#FANN.error_function_enum" id=link21 onMouseOver="ShowTip(event, 'tt21', 'link21')" onMouseOut="HideTip('tt21')" class=ISymbol>error_function_enum</a>, <span class=IParent>FANN</span></td></tr><tr><td class=ISymbolPrefix>&nbsp;</td><td class=IEntry><a href="../files/fann_cpp-h.html#FANN.ERRORFUNC_LINEAR" id=link22 onMouseOver="ShowTip(event, 'tt22', 'link22')" onMouseOut="HideTip('tt22')" class=ISymbol>ERRORFUNC_LINEAR</a>, <span class=IParent>FANN</span></td></tr><tr><td class=ISymbolPrefix id=ILastSymbolPrefix>&nbsp;</td><td class=IEntry><a href="../files/fann_cpp-h.html#FANN.ERRORFUNC_TANH" id=link23 onMouseOver="ShowTip(event, 'tt23', 'link23')" onMouseOut="HideTip('tt23')" class=ISymbol>ERRORFUNC_TANH</a>, <span class=IParent>FANN</span></td></tr></table>
<!--START_ND_TOOLTIPS-->
<div class=CToolTip id="tt1"><div class=CType>The activation functions used for the neurons during training. </div></div><!--END_ND_TOOLTIPS-->


<!--START_ND_TOOLTIPS-->
<div class=CToolTip id="tt2"><div class=CType>This callback function can be called during training when using neural_net::train_on_data, neural_net::train_on_file or neural_net::cascadetrain_on_data.</div></div><div class=CToolTip id="tt3"><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 cascadetrain_on_data(</td><td class="PTypePrefix  prettyprint " nowrap>const&nbsp;</td><td class="PType  prettyprint " nowrap>training_data&nbsp;</td><td class="PParameterPrefix  prettyprint " nowrap>&amp;</td><td class="PParameter  prettyprint " nowrap>data,</td></tr><tr><td></td><td class="PTypePrefix  prettyprint " nowrap>unsigned&nbsp;</td><td class="PType  prettyprint " nowrap>int&nbsp;</td><td class="PParameterPrefix  prettyprint " nowrap></td><td class="PParameter  prettyprint " nowrap>max_neurons,</td></tr><tr><td></td><td class="PTypePrefix  prettyprint " nowrap>unsigned&nbsp;</td><td class="PType  prettyprint " nowrap>int&nbsp;</td><td class="PParameterPrefix  prettyprint " nowrap></td><td class="PParameter  prettyprint " nowrap>neurons_between_reports,</td></tr><tr><td></td><td class="PTypePrefix  prettyprint " nowrap></td><td class="PType  prettyprint " nowrap>float&nbsp;</td><td class="PParameterPrefix  prettyprint " nowrap></td><td class="PParameter  prettyprint " nowrap>desired_error</td><td class="PAfterParameters  prettyprint "nowrap>)</td></tr></table></td></tr></table></blockquote>Trains on an entire dataset, for a period of time using the Cascade2 training algorithm. </div></div><div class=CToolTip id="tt4"><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 cascadetrain_on_file(</td><td class="PTypePrefix  prettyprint " nowrap>const std::</td><td class="PType  prettyprint " nowrap>string&nbsp;</td><td class="PParameterPrefix  prettyprint " nowrap>&amp;</td><td class="PParameter  prettyprint " nowrap>filename,</td></tr><tr><td></td><td class="PTypePrefix  prettyprint " nowrap>unsigned&nbsp;</td><td class="PType  prettyprint " nowrap>int&nbsp;</td><td class="PParameterPrefix  prettyprint " nowrap></td><td class="PParameter  prettyprint " nowrap>max_neurons,</td></tr><tr><td></td><td class="PTypePrefix  prettyprint " nowrap>unsigned&nbsp;</td><td class="PType  prettyprint " nowrap>int&nbsp;</td><td class="PParameterPrefix  prettyprint " nowrap></td><td class="PParameter  prettyprint " nowrap>neurons_between_reports,</td></tr><tr><td></td><td class="PTypePrefix  prettyprint " nowrap></td><td class="PType  prettyprint " nowrap>float&nbsp;</td><td class="PParameterPrefix  prettyprint " nowrap></td><td class="PParameter  prettyprint " nowrap>desired_error</td><td class="PAfterParameters  prettyprint "nowrap>)</td></tr></table></td></tr></table></blockquote>Does the same as cascadetrain_on_data, but reads the training data directly from a file.</div></div><div class=CToolTip id="tt5"><div class=CFunction><blockquote><table border=0 cellspacing=0 cellpadding=0 class="Prototype"><tr><td class="prettyprint">bool clear_scaling_params()</td></tr></table></blockquote>Clears scaling parameters.</div></div><div class=CToolTip id="tt6"><div class=CType>Describes a connection between two neurons and its weight</div></div><div class=CToolTip id="tt7"><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 copy_from_struct_fann(</td><td class="PTypePrefix  prettyprint " nowrap>struct&nbsp;</td><td class="PType  prettyprint " nowrap>fann&nbsp;</td><td class="PParameterPrefix  prettyprint " nowrap>*</td><td class="PParameter  prettyprint " nowrap>other</td><td class="PAfterParameters  prettyprint "nowrap>)</td></tr></table></td></tr></table></blockquote>Set the internal fann struct to a copy of other</div></div><div class=CToolTip id="tt8"><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 create_from_file(</td><td class="PTypePrefix  prettyprint " nowrap>const std::</td><td class="PType  prettyprint " nowrap>string&nbsp;</td><td class="PParameterPrefix  prettyprint " nowrap>&amp;</td><td class="PParameter  prettyprint " nowrap>configuration_file</td><td class="PAfterParameters  prettyprint "nowrap>)</td></tr></table></td></tr></table></blockquote>Constructs a backpropagation neural network from a configuration file, which have been saved by save.</div></div><div class=CToolTip id="tt9"><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 create_shortcut(</td><td class="PTypePrefix  prettyprint " nowrap>unsigned&nbsp;</td><td class="PType  prettyprint " nowrap>int&nbsp;</td><td class="PParameter  prettyprint " nowrap>num_layers,</td></tr><tr><td></td><td class="PTypePrefix  prettyprint " nowrap></td><td class="PType  prettyprint " nowrap>&nbsp;</td><td class="PParameter  prettyprint " nowrap>...</td><td class="PAfterParameters  prettyprint "nowrap>)</td></tr></table></td></tr></table></blockquote>Creates a standard backpropagation neural network, which is not fully connected and which also has shortcut connections.</div></div><div class=CToolTip id="tt10"><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 create_shortcut_array(</td><td class="PTypePrefix  prettyprint " nowrap>unsigned&nbsp;</td><td class="PType  prettyprint " nowrap>int&nbsp;</td><td class="PParameterPrefix  prettyprint " nowrap></td><td class="PParameter  prettyprint " nowrap>num_layers,</td></tr><tr><td></td><td class="PTypePrefix  prettyprint " nowrap>const unsigned&nbsp;</td><td class="PType  prettyprint " nowrap>int&nbsp;</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>Just like create_shortcut, but with an array of layer sizes instead of individual parameters.</div></div><div class=CToolTip id="tt11"><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 create_sparse(</td><td class="PTypePrefix  prettyprint " nowrap></td><td class="PType  prettyprint " nowrap>float&nbsp;</td><td class="PParameter  prettyprint " nowrap>connection_rate,</td></tr><tr><td></td><td class="PTypePrefix  prettyprint " nowrap>unsigned&nbsp;</td><td class="PType  prettyprint " nowrap>int&nbsp;</td><td class="PParameter  prettyprint " nowrap>num_layers,</td></tr><tr><td></td><td class="PTypePrefix  prettyprint " nowrap></td><td class="PType  prettyprint " nowrap>&nbsp;</td><td class="PParameter  prettyprint " nowrap>...</td><td class="PAfterParameters  prettyprint "nowrap>)</td></tr></table></td></tr></table></blockquote>Creates a standard backpropagation neural network, which is not fully connected.</div></div><div class=CToolTip id="tt12"><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 create_sparse_array(</td><td class="PTypePrefix  prettyprint " nowrap></td><td class="PType  prettyprint " nowrap>float&nbsp;</td><td class="PParameterPrefix  prettyprint " nowrap></td><td class="PParameter  prettyprint " nowrap>connection_rate,</td></tr><tr><td></td><td class="PTypePrefix  prettyprint " nowrap>unsigned&nbsp;</td><td class="PType  prettyprint " nowrap>int&nbsp;</td><td class="PParameterPrefix  prettyprint " nowrap></td><td class="PParameter  prettyprint " nowrap>num_layers,</td></tr><tr><td></td><td class="PTypePrefix  prettyprint " nowrap>const unsigned&nbsp;</td><td class="PType  prettyprint " nowrap>int&nbsp;</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>Just like create_sparse, but with an array of layer sizes instead of individual parameters.</div></div><div class=CToolTip id="tt13"><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 create_standard(</td><td class="PTypePrefix  prettyprint " nowrap>unsigned&nbsp;</td><td class="PType  prettyprint " nowrap>int&nbsp;</td><td class="PParameter  prettyprint " nowrap>num_layers,</td></tr><tr><td></td><td class="PTypePrefix  prettyprint " nowrap></td><td class="PType  prettyprint " nowrap>&nbsp;</td><td class="PParameter  prettyprint " nowrap>...</td><td class="PAfterParameters  prettyprint "nowrap>)</td></tr></table></td></tr></table></blockquote>Creates a standard fully connected backpropagation neural network.</div></div><div class=CToolTip id="tt14"><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 create_standard_array(</td><td class="PTypePrefix  prettyprint " nowrap>unsigned&nbsp;</td><td class="PType  prettyprint " nowrap>int&nbsp;</td><td class="PParameterPrefix  prettyprint " nowrap></td><td class="PParameter  prettyprint " nowrap>num_layers,</td></tr><tr><td></td><td class="PTypePrefix  prettyprint " nowrap>const unsigned&nbsp;</td><td class="PType  prettyprint " nowrap>int&nbsp;</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>Just like create_standard, but with an array of layer sizes instead of individual parameters.</div></div><div class=CToolTip id="tt15"><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 "colspan=4>void create_train_from_callback(</td></tr><tr><td>&nbsp;&nbsp;&nbsp;</td><td class="PTypePrefix  prettyprint " nowrap>unsigned&nbsp;</td><td class="PType  prettyprint " nowrap>int&nbsp;</td><td class="PParameter  prettyprint " nowrap width=100%>num_data,</td></tr><tr><td>&nbsp;&nbsp;&nbsp;</td><td class="PTypePrefix  prettyprint " nowrap>unsigned&nbsp;</td><td class="PType  prettyprint " nowrap>int&nbsp;</td><td class="PParameter  prettyprint " nowrap width=100%>num_input,</td></tr><tr><td>&nbsp;&nbsp;&nbsp;</td><td class="PTypePrefix  prettyprint " nowrap>unsigned&nbsp;</td><td class="PType  prettyprint " nowrap>int&nbsp;</td><td class="PParameter  prettyprint " nowrap width=100%>num_output,</td></tr><tr><td>&nbsp;&nbsp;&nbsp;</td><td class="PTypePrefix  prettyprint " nowrap></td><td class="PType  prettyprint " nowrap>void&nbsp;</td><td class="PParameter  prettyprint " nowrap width=100%>(FANN_API *user_function)( unsigned int, unsigned int, unsigned int, fann_type * , fann_type * )</td></tr><tr><td class="PAfterParameters  prettyprint "colspan=4>)</td></tr></table></td></tr></table></blockquote>Creates the training data struct from a user supplied function. </div></div><!--END_ND_TOOLTIPS-->


<!--START_ND_TOOLTIPS-->
<div class=CToolTip id="tt16"><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 descale_input(</td><td class="PType  prettyprint " nowrap>fann_type&nbsp;</td><td class="PParameterPrefix  prettyprint " nowrap>*</td><td class="PParameter  prettyprint " nowrap>input_vector</td><td class="PAfterParameters  prettyprint "nowrap>)</td></tr></table></td></tr></table></blockquote>Scale data in input vector after get it from ann based on previously calculated parameters.</div></div><div class=CToolTip id="tt17"><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 descale_output(</td><td class="PType  prettyprint " nowrap>fann_type&nbsp;</td><td class="PParameterPrefix  prettyprint " nowrap>*</td><td class="PParameter  prettyprint " nowrap>output_vector</td><td class="PAfterParameters  prettyprint "nowrap>)</td></tr></table></td></tr></table></blockquote>Scale data in output vector after get it from ann based on previously calculated parameters.</div></div><div class=CToolTip id="tt18"><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 descale_train(</td><td class="PType  prettyprint " nowrap>training_data&nbsp;</td><td class="PParameterPrefix  prettyprint " nowrap>&amp;</td><td class="PParameter  prettyprint " nowrap>data</td><td class="PAfterParameters  prettyprint "nowrap>)</td></tr></table></td></tr></table></blockquote>Descale input and output data based on previously calculated parameters.</div></div><div class=CToolTip id="tt19"><div class=CFunction><blockquote><table border=0 cellspacing=0 cellpadding=0 class="Prototype"><tr><td class="prettyprint">void destroy()</td></tr></table></blockquote>Destructs the entire network. </div></div><div class=CToolTip id="tt20"><div class=CFunction><blockquote><table border=0 cellspacing=0 cellpadding=0 class="Prototype"><tr><td class="prettyprint">void destroy_train()</td></tr></table></blockquote>Destructs the training data. </div></div><!--END_ND_TOOLTIPS-->


<!--START_ND_TOOLTIPS-->
<div class=CToolTip id="tt21"><div class=CType>Error function used during training.</div></div><div class=CToolTip id="tt22"><div class=CConstant>Standard linear error function.</div></div><div class=CToolTip id="tt23"><div class=CConstant>Tanh error function, usually better but can require a lower learning rate. </div></div><!--END_ND_TOOLTIPS-->

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