private void init(double[][] inputs, double[] outputs)
{
if (inputs == null)
{
throw new ArgumentNullException("inputs");
}
if (outputs == null)
{
throw new ArgumentNullException("outputs");
}
if (inputs.Length != outputs.Length)
{
throw new ArgumentException("The number of rows in the input array must match the number of given outputs.");
}
this.NumberOfInputs = inputs[0].Length;
this.NumberOfOutputs = 1;
for (int i = 0; i < inputs.Length; i++)
{
if (inputs[i].Length != NumberOfInputs)
{
throw new ArgumentException("All input vectors must have the same length.");
}
}
// Create the linear regression
regression = new MultipleLinearRegression()
{
NumberOfInputs = NumberOfInputs
};
// Create additional structures
int coefficientCount = NumberOfInputs + 1;
this.standardErrors = new double[coefficientCount];
this.confidences = new DoubleRange[coefficientCount];
this.ftests = new FTest[coefficientCount];
this.ttests = new TTest[coefficientCount];
if (outputName == null)
{
this.outputName = "Output";
}
if (inputNames == null)
{
this.inputNames = new string[NumberOfInputs];
for (int i = 0; i < inputNames.Length; i++)
{
inputNames[i] = "Input " + i;
}
}
// Create object-oriented structure to represent the analysis
var coefs = new LinearRegressionCoefficient[coefficientCount];
for (int i = 0; i < coefs.Length; i++)
{
coefs[i] = new LinearRegressionCoefficient(this, i);
}
this.coefficientCollection = new LinearRegressionCoefficientCollection(this, coefs);
}
/// <summary>
/// Constructs a Multiple Linear Regression Analysis.
/// </summary>
///
/// <param name="inputs">The input data for the analysis.</param>
/// <param name="outputs">The output data for the analysis.</param>
/// <param name="intercept">True to use an intercept term, false otherwise. Default is false.</param>
///
public MultipleLinearRegressionAnalysis(double[][] inputs, double[] outputs, bool intercept = false)
{
// Initial argument checking
if (inputs == null)
{
throw new ArgumentNullException("inputs");
}
if (outputs == null)
{
throw new ArgumentNullException("outputs");
}
if (inputs.Length != outputs.Length)
{
throw new ArgumentException("The number of rows in the input array must match the number of given outputs.");
}
this.inputCount = inputs[0].Length;
for (int i = 0; i < inputs.Length; i++)
{
if (inputs[i].Length != inputCount)
{
throw new ArgumentException("All input vectors must have the same length.");
}
}
// Store data sets
this.inputData = inputs;
this.outputData = outputs;
// Create the linear regression
regression = new MultipleLinearRegression(inputCount, intercept: intercept);
// Create additional structures
this.coefficientCount = regression.Coefficients.Length;
this.standardErrors = new double[coefficientCount];
this.confidences = new DoubleRange[coefficientCount];
this.ftests = new FTest[coefficientCount];
this.ttests = new TTest[coefficientCount];
this.outputName = "Output";
this.inputNames = new string[inputCount];
for (int i = 0; i < inputNames.Length; i++)
{
inputNames[i] = "Input " + i;
}
// Create object-oriented structure to represent the analysis
var coefs = new LinearRegressionCoefficient[coefficientCount];
for (int i = 0; i < coefs.Length; i++)
{
coefs[i] = new LinearRegressionCoefficient(this, i);
}
this.coefficientCollection = new LinearRegressionCoefficientCollection(this, coefs);
this.Source = inputs.ToMatrix();
}
