private void Check(Func <double, double> function, double min, double max, double x)
{
var inverseFunction = new InverseMonotonousFunction(function, min, max);
double y = function(x);
double z = inverseFunction.GetValue(y);
output.WriteLine($"x = {x}");
output.WriteLine($"F(x) = {y}");
output.WriteLine($"F^{{-1}}(F(x)) = {z}");
Assert.Equal(x, z, comparer);
}
public MixtureDistribution([NotNull, ItemNotNull] IReadOnlyList <IContinuousDistribution> distributions,
[CanBeNull] IReadOnlyList <double> weights = null)
{
Assertion.NotNullOrEmpty(nameof(distributions), distributions);
Assertion.ItemNotNull(nameof(distributions), distributions);
bool isWeighted = weights != null;
weights ??= GetDefaultWeights(distributions);
Assertion.NotNullOrEmpty(nameof(weights), weights);
Assertion.Equal($"{nameof(distributions)}.Length", distributions.Count, $"{nameof(weights)}.Length", weights.Count);
Assertion.Positive(nameof(weights), weights);
double totalWeight = weights.Sum();
if (Math.Abs(totalWeight - 1) < 1e-9)
{
weights = weights.Select(w => w / totalWeight).ToArray();
}
n = distributions.Count;
this.distributions = distributions;
this.weights = weights;
inverseCdf = new InverseMonotonousFunction(Cdf);
Median = Quantile(0.5);
Mean = Aggregate(d => d.Mean);
Variance = Aggregate(d => d.Variance + d.Mean.Sqr()) - Mean * Mean;
StandardDeviation = Variance.Sqrt();
lazyToString = new Lazy <string>(() =>
{
var builder = new StringBuilder();
builder.Append("Mix(");
for (int i = 0; i < distributions.Count; i++)
{
if (i != 0)
{
builder.Append(";");
}
builder.Append(distributions[i]);
if (isWeighted)
{
builder.Append("|");
builder.Append(weights[i].ToStringInvariant());
}
}
builder.Append(")");
return(builder.ToString());
});
}
public MixtureDistribution([NotNull, ItemNotNull] IReadOnlyList <IDistribution> distributions,
[CanBeNull] IReadOnlyList <double> weights = null)
{
Assertion.NotNullOrEmpty(nameof(distributions), distributions);
Assertion.ItemNotNull(nameof(distributions), distributions);
weights ??= GetDefaultWeights(distributions);
Assertion.NotNullOrEmpty(nameof(weights), weights);
Assertion.Equal($"{nameof(distributions)}.Length", distributions.Count, $"{nameof(weights)}.Length", weights.Count);
Assertion.Equal($"sum({nameof(weights)})", weights.Sum(), 1);
n = distributions.Count;
this.distributions = distributions;
this.weights = weights;
inverseCdf = new InverseMonotonousFunction(Cdf);
Median = Quantile(0.5);
Mean = Aggregate(d => d.Mean);
Variance = Aggregate(d => d.Variance + d.Mean.Sqr()) - Mean * Mean;
StandardDeviation = Variance.Sqrt();
}
