/// <summary>
/// Calculates the mean of the elements along the specified axis
/// </summary>
/// <param name="axis">The axis to operate along.</param>
/// <param name="source">The NdArray containing the source values.</param>
/// <returns>A new NdArray containing the result of this operation.</returns>
public static NdArray <T> MeanAxis(int axis, NdArray <T> source)
{
var sp = ScalarPrimitivesRegistry.For <T, int>();
var sum = SumAxis(axis, source);
var scalar = NdArray <T> .ScalarLike(source, sp.Convert(source.Shape[axis]));
return(sum / scalar);
}
/// <summary>
/// Calculates the variance of the elements along the specified axis.
/// </summary>
/// <param name="axis">The axis to operate along.</param>
/// <param name="source">The NdArray containing the source values.</param>
/// <param name="ddof">The delta degrees of freedom.</param>
/// <returns>A new NdArray containing the result of this operation.</returns>
public static NdArray <T> VarAxis(int axis, NdArray <T> source, T deltaDegreeOfFreedom)
{
var sp = ScalarPrimitivesRegistry.For <T, int>();
var spc = ScalarPrimitivesRegistry.For <int, T>();
var mean = NdArray <T> .InsertAxis(axis, NdArray <T> .MeanAxis(axis, source));
var v = source - mean;
var n = sp.Convert(source.Shape[axis] - spc.Convert(deltaDegreeOfFreedom));
return(SumAxis(axis, (v * v) / NdArray <T> .ScalarLike(source, n)));
}
private static NdArray <bool> IsCloseWithTolerence <P>(NdArray <P> lhs, NdArray <P> rhs, P absoluteTolerence, P relativeTolerence)
{
var absoluteTolerenceScalar = NdArray <P> .ScalarLike(lhs, absoluteTolerence);
var relativeTolerenceScalar = NdArray <P> .ScalarLike(lhs, relativeTolerence);
/// NOTE This is not symmetric.
/// https://docs.scipy.org/doc/numpy-1.15.0/reference/generated/numpy.isclose.html
var absDiff = NdArray <P> .Abs(lhs - rhs);
var absRhs = NdArray <P> .Abs(rhs);
return(absDiff <= absoluteTolerenceScalar + (relativeTolerenceScalar * absRhs));
}
