train(mlpose = this, options = {}) 
	{
		if (mlpose.settings.brain.type == 'featureextractor'){
			mlpose.brain.train((lossValue) => { 
				if (!lossValue) mlpose.finishedTraining(mlpose); 
			});
		} else {
			mlpose.brain.normalizeData();
			mlpose.brain.train(options, () => mlpose.finishedTraining(mlpose));
		}
	}
/* 
    * Trains the integrated model
	* @options: defines the options of the neural networks: inputs, outputs, debug mode...
	* @mlpose: MlBody object is passed to the method
    */
Trains the integrated model
@options: defines the options of the neural networks: inputs, outputs, debug mode
@mlpose: MlBody object is passed to the method
Trains the integrated model @options : defines the options of the neural networks : inputs outputs debug mode @mlpose : MlBody object is passed to the method
train(mlpose = this, options = {}) 
	{
		if (mlpose.settings.brain.type == 'featureextractor'){
			mlpose.brain.train((lossValue) => { 
				if (!lossValue) mlpose.finishedTraining(mlpose); 
			});
		} else {
			mlpose.brain.normalizeData();
			mlpose.brain.train(options, () => mlpose.finishedTraining(mlpose));
		}
	}
train ( mlpose = this , options = { } ) { if ( mlpose . settings . brain . type == 'featureextractor' ) { mlpose . brain . train ( ( lossValue ) => { if ( ! lossValue ) mlpose . finishedTraining ( mlpose ) ; } ) ; } else { mlpose . brain . normalizeData ( ) ; mlpose . brain . train ( options , ( ) => mlpose . finishedTraining ( mlpose ) ) ; } }
Trains the integrated model
 @options: defines the options of the neural networks: inputs, outputs, debug mode...
 @mlpose: MlBody object is passed to the method
Trains the integrated model @options : defines the options of the neural networks : inputs outputs debug mode ... @mlpose : MlBody object is passed to the method