/// <summary>
/// Calculates a weighted mean average of the set of numbers.
/// See:
/// http://en.wikipedia.org/wiki/Weighted_mean
/// </summary>
/// <param name="weights">The weights</param>
/// <returns>Weighted mean</returns>
public double Mean(Statistics weights)
{
try
{
double sum = 0;
for (int i = 0; i < _list.Length; i++)
sum += _list[i] * weights._list[i];
return sum / weights.Sum();
}
catch (Exception)
{
return double.NaN;
}
}
/// <summary>
/// Calculates the a term of the quadratic regression function (y = a*x^2 + b*x + c)
/// given the Y and X values.
/// </summary>
/// <param name="y">Y values</param>
/// <param name="x">X values</param>
/// <returns>The a term of y = a*x^2 + b*x + c</returns>
public static double ATerm3(Statistics y, Statistics x)
{
if (x.Length < 3)
throw new InvalidOperationException("Insufficient pairs of co-ordinates"); // Not L10N
//notation sjk to mean the sum of x_i^j*y_i^k.
double s40 = x._list.Sum(v => v*v*v*v); //sum of x^4
double s30 = x._list.Sum(v => v*v*v); //sum of x^3
double s20 = x._list.Sum(v => v*v); //sum of x^2
double s10 = x.Sum(); //sum of x
double s00 = x.Length;
//sum of x^0 * y^0 ie 1 * number of entries
double s21 = x._list.Select((v, i) => v*v*y._list[i]).Sum(); //sum of x^2*y
double s11 = x._list.Select((v, i) => v*y._list[i]).Sum(); //sum of x*y
double s01 = y.Sum(); //sum of y
//a = Da/D
return (s21 * (s20 * s00 - s10 * s10) -
s11 * (s30 * s00 - s10 * s20) +
s01 * (s30 * s10 - s20 * s20))
/
(s40 * (s20 * s00 - s10 * s10) -
s30 * (s30 * s00 - s10 * s20) +
s20 * (s30 * s10 - s20 * s20));
}
public static RatioValue Calculate(IList<double> numerators, IList<double> denominators)
{
if (numerators.Count != denominators.Count)
{
throw new ArgumentException();
}
if (numerators.Count == 0)
{
return null;
}
if (numerators.Count == 1)
{
return new RatioValue(numerators.First()/denominators.First());
}
var statsNumerators = new Statistics(numerators);
var statsDenominators = new Statistics(denominators);
var ratios = new Statistics(numerators.Select((value, index) => value/denominators[index]));
// The mean ratio is the average of "ratios" weighted by "statsDenominators".
// It's also equal to the sum of the numerators divided by the sum of the denominators.
var meanRatio = statsNumerators.Sum()/statsDenominators.Sum();
// Helpers.Assume(Math.Abs(mean - stats.Mean(statsW)) < 0.0001);
// Make sure the value does not exceed the bounds of a float.
float meanRatioFloat = (float)Math.Min(float.MaxValue, Math.Max(float.MinValue, meanRatio));
return new RatioValue
{
Ratio = meanRatioFloat,
StdDev = (float) ratios.StdDev(statsDenominators),
DotProduct = (float) statsNumerators.Angle(statsDenominators),
};
}
