Methods dedicated to neural networks

Functions
PMMLLIB_EXPORT void	PMMLlib::PMMLlib::AddNeuralNetwork (std::string modelName, PMMLMiningFunction functionName)
	Specific to NeuralNetwork. More...
PMMLLIB_EXPORT void	PMMLlib::PMMLlib::AddNeuralInput (int id, std::string inputName, std::string optype, std::string dataType, double orig1, double norm1, double orig2, double norm2)
	Specific to NeuralNetwork. More...
PMMLLIB_EXPORT void	PMMLlib::PMMLlib::AddNeuralLayer (PMMLActivationFunction activationFunction)
	Specific to NeuralNetwork. More...
PMMLLIB_EXPORT void	PMMLlib::PMMLlib::AddNeuron (int id, double bias, int conNb, int firstFrom, std::vector< double > weights)
	Specific to NeuralNetwork. More...
PMMLLIB_EXPORT void	PMMLlib::PMMLlib::AddNeuralOutput (int outputNeuron, std::string outputName, std::string optype, std::string dataType, double orig1, double norm1, double orig2, double norm2)
	Specific to NeuralNetwork. More...
PMMLLIB_EXPORT int	PMMLlib::PMMLlib::GetNbInputs ()
	Specific to NeuralNetwork. More...
PMMLLIB_EXPORT int	PMMLlib::PMMLlib::GetNbOutputs ()
	Specific to NeuralNetwork. More...
PMMLLIB_EXPORT std::string	PMMLlib::PMMLlib::GetNameInput (int input_index)
	Specific to NeuralNetwork. More...
PMMLLIB_EXPORT std::string	PMMLlib::PMMLlib::GetNameOutput (int output_index)
	Specific to NeuralNetwork. More...
PMMLLIB_EXPORT int	PMMLlib::PMMLlib::GetNormalizationType ()
	Specific to NeuralNetwork. More...
PMMLLIB_EXPORT void	PMMLlib::PMMLlib::GetNormalisationInput (int input_index, double *dnorm)
	Specific to NeuralNetwork. More...
PMMLLIB_EXPORT void	PMMLlib::PMMLlib::GetNormalisationOutput (int output_index, double *dnorm)
	Specific to NeuralNetwork. More...
PMMLLIB_EXPORT int	PMMLlib::PMMLlib::GetNbHiddenLayers ()
	Specific to NeuralNetwork. More...
PMMLLIB_EXPORT int	PMMLlib::PMMLlib::GetNbLayers ()
PMMLLIB_EXPORT int	PMMLlib::PMMLlib::GetNbNeuronsAtLayer (int layer_index)
PMMLLIB_EXPORT double	PMMLlib::PMMLlib::GetNeuronBias (int layer_index, int neu_index)
	Specific to NeuralNetwork. More...
PMMLLIB_EXPORT double	PMMLlib::PMMLlib::GetPrecNeuronSynapse (int layer_index, int neu_index, int prec_index)
	Specific to NeuralNetwork. More...
PMMLLIB_EXPORT void	PMMLlib::PMMLlib::SetNeuralNetName (int ann_index, std::string ann_name)
	Not tested. More...
PMMLLIB_EXPORT std::string	PMMLlib::PMMLlib::ReadNetworkStructure ()
	Specific to NeuralNetwork. More...
xmlNodePtr	PMMLlib::PMMLlib::GetNeuralNetPtr (std::string ann_name)
xmlNodePtr	PMMLlib::PMMLlib::GetNeuralNetPtr (int ann_index)
void	PMMLlib::PMMLlib::CheckNeuralNetwork ()
	Called in all methods specific to the NeuralNetwork model. More...

Detailed Description

Methods dedicated to neural networks

Function Documentation

AddNeuralInput()

void PMMLlib::PMMLlib::AddNeuralInput	(	int	id,
		std::string	inputName,
		std::string	optype,
		std::string	dataType,
		double	orig1,
		double	norm1,
		double	orig2,
		double	norm2
	)

Specific to NeuralNetwork.

Add a NeuralInput node to the current model.

Parameters

id	Id of the input
inputName	Name of the input
optype	Value of property "optype"
dataType	Value of property "dataType"
orig1	Value of the first origin
norm1	Value of the first norm
orig2	Value of the second origin
norm2	Value of the second norm

Definition at line 1394 of file PMMLlib.cxx.

{
    CheckNeuralNetwork();
 
    xmlNodePtr netNode = _currentModelNode; 
    // if 'NeuralInputs' node does not exist, create it
    xmlNodePtr neuralInputsNode = GetChildByName(netNode, "NeuralInputs");
    if(!neuralInputsNode)
    {
        neuralInputsNode = xmlNewChild(netNode, 0, (const xmlChar*)"NeuralInputs", 0);
        xmlNewProp(neuralInputsNode, (const xmlChar*)"numberOfInputs", (const xmlChar*)"0" );
    }
    // increment the number of inputs
    string numberOfInputsStr = _getProp(neuralInputsNode, string("numberOfInputs"));
    int numberOfInputs;
    istringstream( numberOfInputsStr ) >> numberOfInputs;        
    numberOfInputs++;
    stringstream ss;
    ss << numberOfInputs;
    xmlSetProp(neuralInputsNode, (const xmlChar*)"numberOfInputs", (const xmlChar*)(ss.str().c_str()) );
    // then append the node and its children
    xmlNodePtr neuralInputNode = xmlNewChild(neuralInputsNode, 0, (const xmlChar*)"NeuralInput", 0);
    ss.str(""); ss << id;
    xmlNewProp(neuralInputNode, (const xmlChar*)"id", (const xmlChar*)(ss.str().c_str()) );
 
    xmlNodePtr derivedFieldNode = xmlNewChild(neuralInputNode, 0, (const xmlChar*)"DerivedField", 0);
    xmlNewProp(derivedFieldNode, (const xmlChar*)"optype", (const xmlChar*)(optype.c_str()) );
    xmlNewProp(derivedFieldNode, (const xmlChar*)"dataType", (const xmlChar*)(dataType.c_str()) );
 
    xmlNodePtr normcontNode = xmlNewChild(derivedFieldNode, 0, (const xmlChar*)"NormContinuous", 0);
    xmlNewProp(normcontNode, (const xmlChar*)"field", (const xmlChar*)(inputName.c_str()) );
 
    xmlNodePtr node_linearnorm1 = xmlNewChild(normcontNode, 0, (const xmlChar*)"LinearNorm", 0);
    ss.str(""); ss << scientific << orig1;
    xmlNewProp(node_linearnorm1, (const xmlChar*)"orig", (const xmlChar*)(ss.str().c_str()) );
    ss.str(""); ss << scientific << norm1;
    xmlNewProp(node_linearnorm1, (const xmlChar*)"norm", (const xmlChar*)(ss.str().c_str()) );
    xmlNodePtr node_linearnorm2 = xmlNewChild(normcontNode, 0, (const xmlChar*)"LinearNorm", 0);
    ss.str(""); ss << scientific << orig2;
    xmlNewProp(node_linearnorm2, (const xmlChar*)"orig", (const xmlChar*)(ss.str().c_str()) );
    ss.str(""); ss << scientific << norm2;
    xmlNewProp(node_linearnorm2, (const xmlChar*)"norm", (const xmlChar*)(ss.str().c_str()) );
}

AddNeuralLayer()

void PMMLlib::PMMLlib::AddNeuralLayer

(

PMMLActivationFunction

activationFunction

)

Specific to NeuralNetwork.

Add a NeuralLayer node to the current model.

Parameters

activationFunction

Activation function. One of kIDENTITY, kTANH, kLOGISTIC.

Definition at line 1510 of file PMMLlib.cxx.

{
    CheckNeuralNetwork();
    
    string functionName;
    switch(activationFunction)
    {
    case kIDENTITY:
        functionName = "identity";
        break;
    case kTANH:
        functionName = "tanh";
        break;
    case kLOGISTIC:
        functionName = "logistic";
        break;
    }
    xmlNodePtr netNode = _currentModelNode; 
    // Increment the number of layers
    string numberOfLayersStr = _getProp(_currentModelNode, string("numberOfLayers"));
    int numberOfLayers;
    istringstream( numberOfLayersStr ) >> numberOfLayers;       
    numberOfLayers++;
    stringstream ss;
    ss << numberOfLayers;
    xmlSetProp(netNode, (const xmlChar*)"numberOfLayers", (const xmlChar*)(ss.str().c_str()) );
    // Add the neural layer node
    xmlNodePtr neuralLayerNode = xmlNewChild(netNode, 0, (const xmlChar*)"NeuralLayer", 0);
    xmlNewProp(neuralLayerNode, (const xmlChar*)"activationFunction", (const xmlChar*)(functionName.c_str()) );
    xmlNewProp(neuralLayerNode, (const xmlChar*)"numberOfNeurons", (const xmlChar*)"0" );
    // Save the current layer in the _currentNode attribute
    _currentNode = neuralLayerNode;
}

References PMMLlib::kIDENTITY, PMMLlib::kLOGISTIC, and PMMLlib::kTANH.

AddNeuralNetwork()

void PMMLlib::PMMLlib::AddNeuralNetwork	(	std::string	modelName,
		PMMLMiningFunction	functionName
	)

Specific to NeuralNetwork.

Add a NeuralNetwork node to the root node

Parameters

modelName	Model name
functionName	PMMLMiningFunction. One of : kREGRESSION.

Definition at line 1359 of file PMMLlib.cxx.

{
    _currentModelType = kANN;
    _currentModelName = modelName;
    
    CheckNeuralNetwork();
    
    string function;
    switch(functionName)
    {
    case kREGRESSION:
        function = "regression"; 
        break;
    }
 
    xmlNodePtr netNode = xmlNewChild(_rootNode, 0, (const xmlChar*)"NeuralNetwork", 0);
    xmlNewProp(netNode, (const xmlChar*)"modelName", (const xmlChar*)(_currentModelName.c_str()) );
    xmlNewProp(netNode, (const xmlChar*)"functionName", (const xmlChar*)(function.c_str()) );
    xmlNewProp(netNode, (const xmlChar*)"numberOfLayers", (const xmlChar*)"0" );
    _currentModelNode = netNode;
}

References PMMLlib::kANN, and PMMLlib::kREGRESSION.

AddNeuralOutput()

void PMMLlib::PMMLlib::AddNeuralOutput	(	int	outputNeuron,
		std::string	outputName,
		std::string	optype,
		std::string	dataType,
		double	orig1,
		double	norm1,
		double	orig2,
		double	norm2
	)

Specific to NeuralNetwork.

Add a NeuralOutput node to the current model.

Parameters

outputNeuron	Id of the output
outputName	Name of the output
optype	Value of property "optype"
dataType	Value of property "dataType"
orig1	Value of the first origin
norm1	Value of the first norm
orig2	Value of the second origin
norm2	Value of the second norm

Definition at line 1455 of file PMMLlib.cxx.

{
    CheckNeuralNetwork();
 
     xmlNodePtr netNode = _currentModelNode; 
    // if 'NeuralOutputs' node does not exist, create it
    xmlNodePtr neuralOutputsNode = GetChildByName(netNode, "NeuralOutputs");
    if(!neuralOutputsNode)
    {
        neuralOutputsNode = xmlNewChild(netNode, 0, (const xmlChar*)"NeuralOutputs", 0);
        xmlNewProp(neuralOutputsNode, (const xmlChar*)"numberOfOutputs", (const xmlChar*)"0" );
    }
    // increment the number of inputs
    string numberOfOutputsStr = _getProp(neuralOutputsNode, string("numberOfOutputs"));
    int numberOfOutputs;
    istringstream( numberOfOutputsStr ) >> numberOfOutputs;       
    numberOfOutputs++;
    stringstream ss;
    ss << numberOfOutputs;
    xmlSetProp(neuralOutputsNode, (const xmlChar*)"numberOfOutputs", (const xmlChar*)(ss.str().c_str()) );
 
    // then append the node and its children
    xmlNodePtr neuralOutputNode = xmlNewChild(neuralOutputsNode, 0, (const xmlChar*)"NeuralOutput", 0);
    ss.str(""); ss << outputNeuron;
    xmlNewProp(neuralOutputNode, (const xmlChar*)"outputNeuron", (const xmlChar*)(ss.str().c_str()) );
 
    xmlNodePtr derivedFieldNode = xmlNewChild(neuralOutputNode, 0, (const xmlChar*)"DerivedField", 0);
    xmlNewProp(derivedFieldNode, (const xmlChar*)"optype", (const xmlChar*)(optype.c_str()) );
    xmlNewProp(derivedFieldNode, (const xmlChar*)"dataType", (const xmlChar*)(dataType.c_str()) );
 
    xmlNodePtr normcontNode = xmlNewChild(derivedFieldNode, 0, (const xmlChar*)"NormContinuous", 0);
    xmlNewProp(normcontNode, (const xmlChar*)"field", (const xmlChar*)(outputName.c_str()) );
 
    xmlNodePtr node_linearnorm1 = xmlNewChild(normcontNode, 0, (const xmlChar*)"LinearNorm", 0);
    ss.str(""); ss << scientific << orig1;
    xmlNewProp(node_linearnorm1, (const xmlChar*)"orig", (const xmlChar*)(ss.str().c_str()) );
    ss.str(""); ss << scientific << norm1;
    xmlNewProp(node_linearnorm1, (const xmlChar*)"norm", (const xmlChar*)(ss.str().c_str()) );
    xmlNodePtr node_linearnorm2 = xmlNewChild(normcontNode, 0, (const xmlChar*)"LinearNorm", 0);
    ss.str(""); ss << scientific << orig2;
    xmlNewProp(node_linearnorm2, (const xmlChar*)"orig", (const xmlChar*)(ss.str().c_str()) );
    ss.str(""); ss << scientific << norm2;
    xmlNewProp(node_linearnorm2, (const xmlChar*)"norm", (const xmlChar*)(ss.str().c_str()) );
}

AddNeuron()

void PMMLlib::PMMLlib::AddNeuron	(	int	id,
		double	bias,
		int	conNb,
		int	firstFrom,
		std::vector< double >	weights
	)

Specific to NeuralNetwork.

Add a NeuralLayer node to the current model.

Parameters

id	Id of the layer
bias	Value of property "bias"
conNb	Number of Con nodes
firstFrom	Value of property "from" for the first Con
weights	Vector of weights (One per Con node)

Definition at line 1553 of file PMMLlib.cxx.

{
    CheckNeuralNetwork();
    
    stringstream ss;
 
    // increment the number of neurons
    string numberOfNeuronsStr = _getProp(_currentNode, string("numberOfNeurons"));
    int numberOfNeurons;
    istringstream( numberOfNeuronsStr ) >> numberOfNeurons;    
    numberOfNeurons++;
    ss << numberOfNeurons;
    xmlSetProp(_currentNode, (const xmlChar*)"numberOfNeurons", (const xmlChar*)(ss.str().c_str()) );
 
    // append a neuron
    xmlNodePtr neuronNode = xmlNewChild(_currentNode, 0, (const xmlChar*)"Neuron", 0);
    ss.str(""); ss << id;
    xmlNewProp(neuronNode, (const xmlChar*)"id", (const xmlChar*)(ss.str().c_str()) );
    ss.str(""); ss << scientific << bias;
    xmlNewProp(neuronNode, (const xmlChar*)"bias", (const xmlChar*)(ss.str().c_str()) );
 
    // append multiple 'Con' to the neuron
    for(int k=0 ; k<conNb ; k++)
    {
        xmlNodePtr conNode = xmlNewChild(neuronNode, 0, (const xmlChar*)"Con", 0);
        ss.str(""); ss << firstFrom+k;
        xmlNewProp(conNode, (const xmlChar*)"from", (const xmlChar*)(ss.str().c_str()) ); // !!! ce n'est pas k !!!
        ss.str(""); ss << scientific << weights[k];
        xmlNewProp(conNode, (const xmlChar*)"weight", (const xmlChar*)(ss.str().c_str()) );
    }
}

CheckNeuralNetwork()

void PMMLlib::PMMLlib::CheckNeuralNetwork ( )

private

Called in all methods specific to the NeuralNetwork model.

Check if the current model type is kANN.

Throw an exception if the model type is not kANN.

Definition at line 728 of file PMMLlib.cxx.

{
    if ( _currentModelType != kANN )
        throw string("Use this method with NeuralNetwork models.");
}

References PMMLlib::kANN.

GetNameInput()

std::string PMMLlib::PMMLlib::GetNameInput

(

int

index

)

Specific to NeuralNetwork.

Recovery of the name of an input in the current model.

Parameters

index

Index of the input

Returns

Name of the input

Definition at line 897 of file PMMLlib.cxx.

{
    CheckNeuralNetwork();  
    
    string name("");
    xmlNodePtr node_inputs = GetChildByName(_currentModelNode,"NeuralInputs");
    if ( node_inputs == NULL )
        return name;   
    
    node_inputs = node_inputs->children;
    if ( node_inputs == NULL )
        return name; 
    
    for(int i = 0;i<index;i++)
    {
        node_inputs = node_inputs->next;
        if ( node_inputs == NULL )
            return name;         
    }
 
    node_inputs = node_inputs->children;
    if ( node_inputs == NULL )
        return name; 
    
    node_inputs = node_inputs->children;
    if ( node_inputs == NULL )
        return name;     
 
    name = _getProp(node_inputs, string("field"));
    
    return name;
}

References yacsorb.CORBAEngineTest::i.

GetNameOutput()

std::string PMMLlib::PMMLlib::GetNameOutput

(

int

index

)

Specific to NeuralNetwork.

Get the name of an output in the current model.

Parameters

index

Index of the output

Returns

Name of the output

Definition at line 936 of file PMMLlib.cxx.

{
    CheckNeuralNetwork();  
    
    string name("");
    xmlNodePtr node_outputs = GetChildByName(_currentModelNode,"NeuralOutputs");
    if ( node_outputs == NULL )
      return name;       
    node_outputs = node_outputs->children;
    if ( node_outputs == NULL )
      return name;   
    for(int i = 0;i<index;i++)
    {
        node_outputs = node_outputs->next;
        if ( node_outputs == NULL )
          return name;          
    }
 
    node_outputs = node_outputs->children;
    if ( node_outputs == NULL )
      return name;  
    node_outputs = node_outputs->children;
    if ( node_outputs == NULL )
      return name;      
 
    name = _getProp(node_outputs, string("field") );    
 
    return name;
}

References yacsorb.CORBAEngineTest::i.

GetNbHiddenLayers()

int PMMLlib::PMMLlib::GetNbHiddenLayers

(

)

Specific to NeuralNetwork.

Get the number of hidden layers

Returns

Number of hidden layers

Definition at line 1137 of file PMMLlib.cxx.

{
    CheckNeuralNetwork();
    
    int nb_layers = 0;  
    xmlNodePtr node_layers = GetChildByName(_currentModelNode,"NeuralLayer");
    if ( node_layers == NULL )
      return nb_layers;
    
    while (string((const char*)(node_layers->name)) == "NeuralLayer")
    {
        nb_layers++;
        node_layers = node_layers->next;
        if ( node_layers == NULL )
          return nb_layers;    
    }
    return nb_layers;
}

GetNbInputs()

int PMMLlib::PMMLlib::GetNbInputs

(

)

Specific to NeuralNetwork.

Get the number of inputs, ie the number of NeuralInputs nodes.

Returns

Number of input nodes

Definition at line 847 of file PMMLlib.cxx.

{
    CheckNeuralNetwork();  
    
    int nb=0;
    xmlNodePtr node_inputs = GetChildByName(_currentModelNode,"NeuralInputs");
    if ( node_inputs == NULL )
      return nb;
    
    node_inputs = node_inputs->children;
    while (node_inputs != NULL)
    {
        nb++;
        node_inputs = node_inputs->next;
    }
 
    return nb;
}

GetNbLayers()

int PMMLlib::PMMLlib::GetNbLayers

(

)

Get the total number of layers

Returns

Total number of layers

Definition at line 1160 of file PMMLlib.cxx.

{
    return (GetNbHiddenLayers() + 2);
}

GetNbNeuronsAtLayer()

int PMMLlib::PMMLlib::GetNbNeuronsAtLayer

(

int

index

)

Get the number of neurons at a given layer

Parameters

index

Index of the layer

Returns

Number of neurons at given layer

Definition at line 1170 of file PMMLlib.cxx.

{
    CheckNeuralNetwork();  
    
    int nb_neurons = 0;
    xmlNodePtr node_layers = GetChildByName(_currentModelNode,"NeuralLayer");
    if ( node_layers == NULL )
        return nb_neurons;  
 
    // Positionnement à la bonne couche
    for(int i=0;i<index;i++)
    {
        node_layers = node_layers->next;
        if ( node_layers == NULL )
            return nb_neurons;         
    }
 
    xmlNodePtr node_neurons = GetChildByName(node_layers,"Neuron");
    while(node_neurons != NULL)
    {
        nb_neurons++;
        node_neurons = node_neurons->next;
    }
 
    return nb_neurons;
}

References yacsorb.CORBAEngineTest::i.

GetNbOutputs()

int PMMLlib::PMMLlib::GetNbOutputs

(

)

Specific to NeuralNetwork.

Recover the number of outputs

Returns

Number of outputs

Definition at line 871 of file PMMLlib.cxx.

{
    CheckNeuralNetwork();   
    
    int nb=0;
    xmlNodePtr node_outputs = GetChildByName(_currentModelNode,"NeuralOutputs");
    if ( node_outputs == NULL )
      return nb;    
    
    node_outputs = node_outputs->children;
 
    while (node_outputs != NULL)
    {
        nb++;
        node_outputs = node_outputs->next;
    }
 
    return nb;
}

GetNeuralNetPtr() [1/2]

xmlNodePtr PMMLlib::PMMLlib::GetNeuralNetPtr ( int  index )

private

Get the XML node of a given network from the index

Parameters

index

Index of the neural network

Returns

Pointer to the XML node

Definition at line 739 of file PMMLlib.cxx.

{  
    return GetPtr(index, GetTypeString() );
}

GetNeuralNetPtr() [2/2]

xmlNodePtr PMMLlib::PMMLlib::GetNeuralNetPtr ( std::string  name )

private

Get the XML node of a given network model

Parameters

name	Name of the neural network

Returns

Pointer to the XML node

Definition at line 749 of file PMMLlib.cxx.

{   
    return GetPtr(name, GetTypeString() );
}

GetNeuronBias()

double PMMLlib::PMMLlib::GetNeuronBias	(	int	layer_index,
		int	neu_index
	)

Specific to NeuralNetwork.

Get the bias of a neuron

Parameters

layer_index	Index of the layer to get bias
neu_index	Index of the neuron

Returns

Bias of the specified neuron

Definition at line 1204 of file PMMLlib.cxx.

{
    CheckNeuralNetwork();  
    
    double bias = 0.;
    xmlNodePtr node_layers = GetChildByName(_currentModelNode,"NeuralLayer");
    if ( node_layers == NULL )
        return bias;  
    // Positionnement a la bonne couche
    for(int i=0;i<layer_index;i++)
    {
        node_layers = node_layers->next;
        if ( node_layers == NULL )
            return bias;          
    }
    xmlNodePtr node_neurons = GetChildByName(node_layers,"Neuron");
    // Positionnement sur le bon neurone
    for(int j=0;j<neu_index;j++)
    {
        node_neurons = node_neurons->next;
        if ( node_neurons == NULL )
            return bias;           
    }
    string str_tmp  = _getProp(node_neurons, string("bias"));
    bias = atof(str_tmp.c_str());
    return bias;
}

References yacsorb.CORBAEngineTest::i.

GetNormalisationInput()

void PMMLlib::PMMLlib::GetNormalisationInput	(	int	index,
		double *	dnorm
	)

Specific to NeuralNetwork.

Get the input parameters on the normalization

Parameters

	node_ann	Neural network node
	index	Index of the input
[out]	dnorm	Array that contains the mean and the standard deviation

Definition at line 1016 of file PMMLlib.cxx.

{
    CheckNeuralNetwork();     
    dnorm[0] = 0.0;
    dnorm[1] = 0.0;
    xmlNodePtr node_inputs = GetChildByName(_currentModelNode,"NeuralInputs");
    if ( node_inputs == NULL )
        return ;       
    node_inputs = GetChildByName(node_inputs,"NeuralInput");
    if ( node_inputs == NULL )
        return ;    
    // Positionnement sur la bonne entree
    for(int i=0;i<index;i++)
    {
        node_inputs = node_inputs->next;
        if ( node_inputs == NULL )
            return ;  
    }
    xmlNodePtr tmpNode = GetChildByName(node_inputs,"DerivedField");
    if ( tmpNode == NULL )
        return ; 
    xmlNodePtr node_field = GetChildByName(tmpNode,"NormContinuous");
    if ( node_field == NULL )
        return ; 
    if (string((const char*)(node_field->name)) == "NormContinuous")
    {
        //Get mean and standard deviation
        string str_tmp;        
        xmlNodePtr node_linearnorm = node_field->children;
        str_tmp = _getProp(node_linearnorm, string("orig"));
        double dorig1 = atof(str_tmp.c_str());
        str_tmp = _getProp(node_linearnorm, string("norm"));
        double dnorm1 = atof(str_tmp.c_str());
        node_linearnorm = node_linearnorm->next;
        str_tmp = _getProp(node_linearnorm, string("orig"));
        double dorig2 = atof(str_tmp.c_str());
        str_tmp = _getProp(node_linearnorm, string("norm"));
        double dnorm2 = atof(str_tmp.c_str());
        if ( dnorm1 * dnorm2  < -0.5 ) // <=> GetNormalizationType == 0
        {
            // case of kMinusOneOne
            dnorm[0] = dorig1;
            dnorm[1] = dorig2;
        } 
        else // <=> GetNormalizationType == 1
        {
            // case of kCR, kZeroOne
            dnorm[0] = dorig2;
            dnorm[1] = -1.0 * dnorm1 * dorig2; //dorig2 / dnorm1; 
        }
    }
}

References yacsorb.CORBAEngineTest::i.

GetNormalisationOutput()

void PMMLlib::PMMLlib::GetNormalisationOutput	(	int	index,
		double *	dnorm
	)

Specific to NeuralNetwork.

Get the parameters on the normalization of an output for the current model.

Parameters

	index	Output index
[out]	dnorm	Array that contains the mean and the standard deviation

Definition at line 1076 of file PMMLlib.cxx.

{
    CheckNeuralNetwork();     
    dnorm[0] = 0.0;
    dnorm[1] = 0.0;
    
    xmlNodePtr node_outputs = GetChildByName(_currentModelNode,"NeuralOutputs");
    if ( node_outputs == NULL )
        return ;      
    node_outputs = GetChildByName(node_outputs,"NeuralOutput");
    if ( node_outputs == NULL )
        return ;   
    // Positionnement sur la bonne sortie
    for(int i=0;i< index;i++)
    {
        node_outputs = node_outputs->next;
        if ( node_outputs == NULL )
            return ;        
    }
    xmlNodePtr tmpNode = GetChildByName(node_outputs,"DerivedField");
    if ( tmpNode == NULL )
        return ;   
    xmlNodePtr node_field = GetChildByName(tmpNode,"NormContinuous"); 
    if ( node_field == NULL )
        return ;    
 
    if (string((const char*)(node_field->name)) == "NormContinuous")
    {
        // Recuperation de la moyenne et de l'ecart type
        string str_tmp;
        xmlNodePtr node_linearnorm = node_field->children;
        str_tmp = _getProp(node_linearnorm, string("orig"));
        double dorig1 = atof(str_tmp.c_str());
        str_tmp = _getProp(node_linearnorm, string("norm"));
        double dnorm1 = atof(str_tmp.c_str());
        node_linearnorm = node_linearnorm->next;
        str_tmp = _getProp(node_linearnorm,string("orig"));
        double dorig2 = atof(str_tmp.c_str());
        str_tmp = _getProp(node_linearnorm, string("norm"));
        double dnorm2 = atof(str_tmp.c_str());
        if ( dnorm1 * dnorm2  < -0.5 ) 
        {
            // case of kMinusOneOne
            dnorm[0] = dorig1;
            dnorm[1] = dorig2;
        } 
        else 
        {
            // case of kCR, kZeroOne
            dnorm[0] = dorig2;
            dnorm[1] = -1.0 * dorig2 * dnorm1; //-1.0 * dorig2 / dnorm1;
        }
    }
}

References yacsorb.CORBAEngineTest::i.

GetNormalizationType()

int PMMLlib::PMMLlib::GetNormalizationType

(

)

Specific to NeuralNetwork.

Get the normalization type of the current model

Returns

Normalization type of the neural network

Definition at line 971 of file PMMLlib.cxx.

{
    CheckNeuralNetwork();  
    
    xmlNodePtr node_inputs = GetChildByName(_currentModelNode,"NeuralInputs");
    node_inputs = GetChildByName(node_inputs,"NeuralInput");
    xmlNodePtr nodeTmp = GetChildByName(node_inputs,"DerivedField");
    xmlNodePtr node_field = nodeTmp->children;
    xmlNodePtr node_linearnorm;
    string str_tmp;
    double dorig1, dnorm1;
    double dorig2, dnorm2;
    if (string((const char*)(node_field->name)) == "NormContinuous")
    {
        // Get mean and standard deviation
        node_linearnorm = node_field->children;
        str_tmp = _getProp(node_linearnorm, string("orig"));
        dorig1 = atof(str_tmp.c_str());
        str_tmp = _getProp(node_linearnorm, string("norm"));
        dnorm1 = atof(str_tmp.c_str());
        node_linearnorm = node_linearnorm->next;
        str_tmp = _getProp(node_linearnorm, string("orig"));
        dorig2 = atof(str_tmp.c_str());
        str_tmp = _getProp(node_linearnorm, string("norm"));
        dnorm2 = atof(str_tmp.c_str());
        if ( dnorm1 * dnorm2  < -0.5 )
        {   // case of kMinusOneOne
            return 0;
        }
        else
        {   // case of kCR, kZeroOne
            return 1;
        }
    }
    string msg("Unable to retrieve the normalization type.");
    throw msg;
}

GetPrecNeuronSynapse()

double PMMLlib::PMMLlib::GetPrecNeuronSynapse	(	int	layer_index,
		int	neu_index,
		int	prec_index
	)

Specific to NeuralNetwork.

Get the synaptic weight

Parameters

layer_index	Index of the layer to get synaptic weight
neu_index	Index of the neuron
prec_index	Index of the synapse

Returns

Synaptic weight

Definition at line 1241 of file PMMLlib.cxx.

{
    CheckNeuralNetwork();
    
    double weight = 0.;
    xmlNodePtr node_layers = GetChildByName(_currentModelNode,"NeuralLayer");
    if ( node_layers == NULL )
        return weight;     
    // Positionnement a la bonne couche
    for(int i=0;i<layer_index;i++)
    {
        node_layers = node_layers->next;
        if ( node_layers == NULL )
            return weight;        
    }
    xmlNodePtr node_neurons = GetChildByName(node_layers,"Neuron");
    // Positionnement sur le bon neurone
    for(int i=0;i<neu_index;i++)
    {
        node_neurons = node_neurons->next;
        if ( node_neurons == NULL )
            return weight;          
    }
    xmlNodePtr node_con = GetChildByName(node_neurons,"Con");
    // Positionnement sur la bonne synapse
    for(int i=0;i<prec_index;i++)
    {
        node_con = node_con->next;
        if ( node_con == NULL )
            return weight;          
    }
    string str_tmp  = _getProp(node_con, string("weight"));
    weight = atof(str_tmp.c_str());
    return weight;
}

References yacsorb.CORBAEngineTest::i.

ReadNetworkStructure()

std::string PMMLlib::PMMLlib::ReadNetworkStructure

(

)

Specific to NeuralNetwork.

Read the structure of the network

Returns

Structure read

Definition at line 759 of file PMMLlib.cxx.

{   
    CheckNeuralNetwork(); 
    
    string structure("");
    // Treatment of the input
    xmlNodePtr inputNodes = GetChildByName(_currentModelNode,"NeuralInputs");
    if ( inputNodes != NULL )
    {
        xmlNodePtr inputNode = GetChildByName(inputNodes,"NeuralInput");
        if ( inputNode != NULL ) 
        {
            while (inputNode != NULL)
            {
                xmlNodePtr child = GetChildByName(inputNode,"DerivedField");
                if ( child != NULL )
                {
                    xmlNodePtr fieldName = child->children; // NormContinuous
                    if ( fieldName != NULL )
                    {
                        string field = _getProp(fieldName, string("field"));
                        structure += field;
                        structure += ":";
                    }
                } 
                inputNode = inputNode->next;
            }
            // Delete the last comma
            structure.erase(structure.size()-1);
        }
    }
    // Intermediary layers
    xmlNodePtr node_layer = GetChildByName(_currentModelNode,"NeuralLayer");
    if ( node_layer != NULL )
    {
        string name = string((const char*)(node_layer->name));
        structure += ",";
 
        while ( node_layer != NULL &&
                (string((const char*)(node_layer->name)) == "NeuralLayer") &&
                node_layer->next != NULL &&
                (string((const char*)(node_layer->next->name)) != "NeuralOutputs") )
        {
            // Get the number of neurons of the current layer
            string nbneurons = _getProp(node_layer, string("numberOfNeurons"));
            structure += nbneurons;
            structure += ",";
            node_layer = node_layer->next;
        }
    }
    // Output layers
    xmlNodePtr node_outputs = GetChildByName(_currentModelNode,"NeuralOutputs");
    if ( node_outputs != NULL )
    {    
        xmlNodePtr node_output = GetChildByName(node_outputs,"NeuralOutput");
        if ( node_output != NULL )
        {   
            while (node_output != NULL)
            {
                // Get the input of the current layer
                xmlNodePtr child = GetChildByName(node_output,"DerivedField");
                if ( child != NULL )
                {                
                    xmlNodePtr fieldName = child->children; // NormContinuous
                    if ( fieldName != NULL )
                    {
                        if (string((const char*)(fieldName->name)) == "NormContinuous")
                            structure += "@";
 
                        string field = _getProp(fieldName, string("field"));
                        structure += field;
                        structure += ":"; 
                    }    
                }
                node_output = node_output->next;
            }
            // Delete the last comma
            structure.erase(structure.size()-1);
        }
    }
    return structure;
}

SetNeuralNetName()

void PMMLlib::PMMLlib::SetNeuralNetName	(	int	index,
		std::string	name
	)

Not tested.

Set the name of the neural network

Parameters

index	Neural network index
name	Neural network name to set

Definition at line 1286 of file PMMLlib.cxx.

{
    CheckNeuralNetwork();
    
    int i=0;
    if (_doc != NULL)
    {
        xmlNodePtr node_ann = GetChildByName(_rootNode,"NeuralNetwork");
        while ((i != index) && (node_ann != NULL))
        {
            node_ann = node_ann->next;
            i++;
        }
        xmlNewProp(node_ann, (const xmlChar*)"modelName", (const xmlChar*)(name.c_str()));
    }
    xmlSaveFormatFile( string(_pmmlFile+".pmml").c_str(), _doc, 1);
}

References yacsorb.CORBAEngineTest::i.