/* Method: MergeTrainData Merges the data into the data contained in the <TrainingData>. This function appears in FANN >= 1.1.0. */ public void MergeTrainData(TrainingData data) { InternalData.merge_train_data(data.InternalData); }
/* Constructor: TrainingData Copy constructor constructs a copy of the training data. Corresponds to the C API <fann_duplicate_train_data at http://libfann.github.io/fann/docs/files/fann_train-h.html#fann_duplicate_train_data> function. */ public TrainingData(TrainingData data) { InternalData = new FannWrapperFixed.training_data(data.InternalData); }
/* Constructor: TrainingData * * Copy constructor constructs a copy of the training data. * Corresponds to the C API <fann_duplicate_train_data at http://libfann.github.io/fann/docs/files/fann_train-h.html#fann_duplicate_train_data> function. */ public TrainingData(TrainingData data) { InternalData = new FannWrapperFixed.training_data(data.InternalData); }
/* Method: MergeTrainData * * Merges the data into the data contained in the <TrainingData>. * * This function appears in FANN >= 1.1.0. */ public void MergeTrainData(TrainingData data) { InternalData.merge_train_data(data.InternalData); }
/* Method: TestData Test a set of training data and calculates the MSE for the training data. This function updates the MSE and the bit fail values. See also: <Test>, <MSE>, <BitFail>, <fann_test_data at http://libfann.github.io/fann/docs/files/fann_train-h.html#fann_test_data> This function appears in FANN >= 1.2.0. */ public float TestData(TrainingData data) { return net.test_data(data.InternalData); }
/* Method: InitWeights Initialize the weights using Widrow + Nguyen's algorithm. This function behaves similarly to <fann_randomize_weights at http://libfann.github.io/fann/docs/files/fann-h.html#fann_randomize_weights>. It will use the algorithm developed by Derrick Nguyen and Bernard Widrow to set the weights in such a way as to speed up training. This technique is not always successful, and in some cases can be less efficient than a purely random initialization. The algorithm requires access to the range of the input data (ie, largest and smallest input), and therefore accepts a second argument, data, which is the training data that will be used to train the network. See also: <RandomizeWeights>, <TrainingData::ReadTrainFromFile>, <fann_init_weights at http://libfann.github.io/fann/docs/files/fann-h.html#fann_init_weights> This function appears in FANN >= 1.1.0. */ public void InitWeights(TrainingData data) { net.init_weights(data.InternalData); }