/// <summary>
/// Fits the underlying distribution to a given set of observations.
/// </summary>
///
/// <param name="observations">The array of observations to fit the model against.
/// The array elements can be either of type double (for univariate data) or type
/// double[] (for multivariate data).
/// </param>
/// <param name="weights">The weight vector containing the weight for each of the samples.</param>
/// <param name="options">Optional arguments which may be used during fitting,
/// such as regularization constants and additional parameters.</param>
///
public void Fit(double[] observations, int[] weights, TriangularOptions options)
{
bool fixMax = true;
bool fixMin = true;
bool sorted = false;
int imax = -1;
int imin = -1;
TriangularEstimationMethod method = TriangularEstimationMethod.MeanMaxMin;
if (options != null)
{
fixMax = options.FixMax;
fixMin = options.FixMin;
method = options.Method;
sorted = options.IsSorted;
imin = options.MinIndex;
imax = options.MaxIndex;
}
double min = this.Min;
double max = this.Max;
double mode = this.Mode;
if (!sorted)
{
Array.Sort(observations, weights);
}
if (!fixMin)
{
min = GetMin(observations, weights, out imin);
}
if (!fixMax)
{
max = GetMax(observations, weights, out imax);
}
if (imin == -1)
{
imin = FindMin(observations, min);
}
if (imax == -1)
{
imax = FindMax(observations, max);
}
mode = GetMode(observations, weights, method, min, max, mode, imax, imin);
initialize(min, max, mode);
}
/// <summary>
/// Fits the underlying distribution to a given set of observations.
/// </summary>
///
/// <param name="observations">The array of observations to fit the model against.
/// The array elements can be either of type double (for univariate data) or type
/// double[] (for multivariate data).
/// </param>
/// <param name="weights">The weight vector containing the weight for each of the samples.</param>
/// <param name="options">Optional arguments which may be used during fitting,
/// such as regularization constants and additional parameters.</param>
///
public void Fit(double[] observations, double[] weights, TriangularOptions options)
{
bool fixMax = false;
bool fixMin = false;
bool sorted = false;
TriangularEstimationMethod method = TriangularEstimationMethod.MeanMaxMin;
if (options != null)
{
fixMax = options.FixMax;
fixMin = options.FixMin;
method = options.Method;
sorted = options.IsSorted;
}
double min = this.A;
double max = this.B;
double mode = this.C;
int imax = -1;
int imin = -1;
if (!sorted)
{
Array.Sort(observations, weights);
}
if (!fixMin)
{
if (weights == null)
{
imin = 0;
}
else
{
// Look for the first value whose weight is different from zero
for (int i = 0; i < weights.Length; i++)
{
if (weights[i] > 0)
{
imin = i;
break;
}
}
}
min = observations[imin];
}
if (!fixMax)
{
if (weights == null)
{
imax = observations.Length - 1;
}
else
{
// Look for the last value whose weight is different from zero
for (int i = weights.Length - 1; i >= 0; i--)
{
if (weights[i] > 0)
{
imax = i;
break;
}
}
}
max = observations[imax];
}
if (method == TriangularEstimationMethod.MeanMaxMin)
{
if (imin == -1)
{
for (int i = 0; i < observations.Length; i++)
{
if (observations[i] >= min)
{
imin = i;
break;
}
}
}
if (imax == -1)
{
for (int i = observations.Length - 1; i >= 0; i--)
{
if (observations[i] <= max)
{
imax = i;
break;
}
}
}
double mean;
if (weights == null)
{
double sum = 0;
for (int i = imin; i < imax; i++)
{
sum += observations[i];
}
mean = sum / (imax - imin);
}
else
{
double sum = 0;
double weightSum = 0;
for (int i = imin; i < imax; i++)
{
sum += weights[i] * observations[i];
weightSum += weights[i];
}
mean = sum / weightSum;
}
mode = 3 * mean - max - min;
initialize(min, max, mode);
}
else
{
throw new NotImplementedException();
}
}
