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public static StdDev ( Matrix source, VectorType t = VectorType.Col ) : |
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| source | Matrix | |
| t | VectorType | Use column or row (default: Col) |
| return | ||
/// <summary>Generate Linear Regression model based on a set of examples.</summary>
/// <param name="x">The Matrix to process.</param>
/// <param name="y">The Vector to process.</param>
/// <returns>Model.</returns>
public override IModel Generate(Matrix x, Vector y)
{
// create initial theta
Vector theta = Vector.Ones(x.Cols + 1);
Matrix copy = x.Copy();
// normalise features
for (int i = 0; i < copy.Cols; i++)
{
var j = FeatureNormalizer.FeatureScale(copy[i, VectorType.Col]);
for (int k = 0; k < copy.Rows; k++)
{
copy[k, i] = j[k];
}
}
// add intercept term
copy = copy.Insert(Vector.Ones(copy.Rows), 0, VectorType.Col);
// run gradient descent
var run = GradientDescent.Run(theta, copy, y, MaxIterations, LearningRate, new LinearCostFunction(),
Lambda, new Regularization());
// once converged create model and apply theta
LinearRegressionModel model = new LinearRegressionModel(x.Mean(VectorType.Row), x.StdDev(VectorType.Row))
{
Descriptor = Descriptor,
Theta = run.Item2
};
return model;
}
/// <summary>
/// Summarizes a given Matrix.
/// </summary>
/// <param name="matrix">Matrix to summarize.</param>
/// <param name="byVector">Indicates which direction to summarize, default is <see cref="VectorType.Row"/> indicating top-down.</param>
/// <returns></returns>
public static Summary Summarize(Matrix matrix, VectorType byVector = VectorType.Row)
{
return new Summary()
{
Average = matrix.Mean(byVector),
StandardDeviation = matrix.StdDev(byVector),
Minimum = matrix.Min(byVector),
Maximum = matrix.Max(byVector),
Median = matrix.Median(byVector)
};
}
/// <summary>
/// Computes the standard deviation of the given matrix
/// </summary>
/// <param name="source"></param>
/// <param name="t">Return a Row or Column vector</param>
/// <returns></returns>
public static Vector StdDev(this Matrix source, VectorType t)
{
return(Matrix.StdDev(source, t));
}
