// Fit sets the parameters of the probability distribution from the
// data samples x with relative weights w.
// If weights is nil, then all the weights are 1.
// If weights is not nil, then the len(weights) must equal len(samples).
//
// Note: Laplace distribution has no FitPrior because it has no sufficient
// statistics.
func (l *Laplace) Fit(samples, weights []float64) {
if len(samples) != len(weights) {
panic("dist: length of samples and weights must match")
}
if len(samples) == 0 {
panic("dist: must have at least one sample")
}
if len(samples) == 1 {
l.Mu = samples[0]
l.Scale = 0
return
}
var (
sortedSamples []float64
sortedWeights []float64
)
if sort.Float64sAreSorted(samples) {
sortedSamples = samples
sortedWeights = weights
} else {
// Need to copy variables so the input variables aren't effected by the sorting
sortedSamples = make([]float64, len(samples))
copy(sortedSamples, samples)
sortedWeights := make([]float64, len(samples))
copy(sortedWeights, weights)
stat.SortWeighted(sortedSamples, sortedWeights)
}
// The (weighted) median of the samples is the maximum likelihood estimate
// of the mean parameter
// TODO: Rethink quantile type when stat has more options
l.Mu = stat.Quantile(0.5, stat.Empirical, sortedSamples, sortedWeights)
sumWeights := floats.Sum(weights)
// The scale parameter is the average absolute distance
// between the sample and the mean
absError := stat.Moment(1, samples, l.Mu, weights)
l.Scale = absError / sumWeights
}
// SuffStat computes the sufficient statistics of a set of samples to update
// the distribution. The sufficient statistics are stored in place, and the
// effective number of samples are returned.
//
// The normal distribution has two sufficient statistics, the mean of the samples
// and the standard deviation of the samples.
//
// If weights is nil, the weights are assumed to be 1, otherwise panics if
// len(samples) != len(weights). Panics if len(suffStat) != 2.
func (Normal) SuffStat(samples, weights, suffStat []float64) (nSamples float64) {
lenSamp := len(samples)
if len(weights) != 0 && len(samples) != len(weights) {
panic("dist: slice size mismatch")
}
if len(suffStat) != 2 {
panic("dist: incorrect suffStat length")
}
if len(weights) == 0 {
nSamples = float64(lenSamp)
} else {
nSamples = floats.Sum(weights)
}
mean := stat.Mean(samples, weights)
suffStat[0] = mean
// Use Moment and not StdDev because we want it to be uncorrected
variance := stat.Moment(2, samples, mean, weights)
suffStat[1] = math.Sqrt(variance)
return nSamples
}