The data set class

Roberto Lopez.
[email protected]
Artelnics - Making intelligent use of data

The data set contains the information for creating the model. It comprises a data matrix in which columns represent variables and rows represent instances.

1. BasicTheory.
2. Software model.
3. Main classes.

Basic theory

The data set contains the information for creating the model. It comprises a data matrix in which columns represent variables and rows represent instances. The data is contained in a file with the following format:

d_1_1   d_1_2   ...   d_1_q
...     ...     ...   ...
d_p_1   d_p_2   ...   d_p_q

Here the number of instances is denoted p, while the number of variables is denoted q.

• The inputs will be the independent variables in the model.
• The targets will be the dependent variables in the model.
• The unused variables will neither be used as inputs nor as targets.

• Training instances, which are used to construct the model.
• Generalization instances, which are used for selecting the optimal order of the model.
• Testing instances, which are used to validate the functioning of the model.
• Unused instances, which are not used at all.

Software model

To do.

Main classes

The data set is represented in OpenNN by means of the DataSet class. It contains a data matrix, and information about the variables, the instances and the missing values.

Members

• A data matrix.
• A variables object.
• An instances object.
• A missing values object object.

All that members are declared as private, and they can only be used with their corresponding get or set methods.

Constructors

There are several constructors for the DataSet class.

The easiest way of creating a DataSet object is by means of the default constructor, which creates an empty data set.

DataSet ds;

It is possible to construct a data set by loading its members from a XML file. That is done in the following way,

DataSet ds(`data_set.xml');

Methods

The most important methods are used to load the data matrix from a file.

DataSet ds;
ds.set_data_file_name("data.dat");

ds.load_data();

To calculate the output Vector of the network in response to an input Vector we use the method calculate_outputs(). For instance, the following sentence returns the neural network output value for an input value.

Vector<double> inputs(1);
inputs[0] = 0.5;

Vector<double> outputs = nn.calculate_outputs(inputs);

To calculate the statistics of the data (minimums, maximums, means and standard deviations) the following sentences can be used.

Vector< Statistics<double> > data_statistics = calculate_data_statistics();

We can save a data set object to a data file by using the method save. For instance, the next code saves the data set object to the file data_set.xml.

ds.save(`data_set.xml');

We can also load a data set object from a data file by using the method load. Indeed, the following sentence loads the neural network object from the file neural_network.xml.

ds.load(`data_set.xml');

Bibliography

• C. Bishop. Neural Networks for Pattern Recognition. Oxford University Press, 1995.
• H. Demuth, M. Beale, and M. Hagan. Neural Network Toolbox User's Gide. The MathWorks, Inc., 2009.
• S. Haykin. Neural Networks: A Comprehensive Foundation. Prentice Hall.
• R. Lopez. Neural Networks for Variational Problems in Engineering. PhD Thesis, Technical University of Catalonia, 2008.

OpenNN Copyright © 2014 Roberto Lopez (Artelnics)