public SimpleSequentialGaussianProposal(double[] sigma)
{
this.sigma = sigma;
gen = new Troschuetz.Random.Generators.MT19937Generator();
gDist = new Troschuetz.Random.Distributions.Continuous.NormalDistribution(gen, 0, 1);
dim = sigma.Length;
duDist = new Troschuetz.Random.Distributions.Discrete.DiscreteUniformDistribution(gen, 0, dim - 1);
}
public ParallelHierarchicalSampler(int _total, int _burnIn, int _numSubSpaces, int _dim)
{
OutputDirectory = Directory.GetCurrentDirectory();
numAuxiliary = _numSubSpaces;
dim = _dim;
total = _total;
burnIn = _burnIn;
mean = new double[dim];
if (!SkipCovarianceComputation)
{
covariance = new double[dim, dim];
}
CI = new double[dim];
mcmc = new AuxiliaryChain[numAuxiliary];
for (int i = 0; i < numAuxiliary; i++)
{
mcmc[i] = new AuxiliaryChain();
}
gen = new Troschuetz.Random.Generators.MT19937Generator();
discreteUniformDist = new Troschuetz.Random.Distributions.Discrete.DiscreteUniformDistribution(gen, 0, numAuxiliary - 1);
}
public static Tuple <List <Ball <T> >, bool, int> Cluster2FixedNumberOfBalls(List <T> elements, Metric metric, int nBalls, int maxNumSteps)
{
if (elements.Count <= nBalls)
{
// nothing to do
return(new Tuple <List <Ball <T> >, bool, int>(null, false, 0));
}
List <Ball <T> > balls = new List <Ball <T> >();
for (int i = 0; i < nBalls; i++)
{
balls.Add(new Ball <T>(metric));
}
Troschuetz.Random.Generators.MT19937Generator gen = new Troschuetz.Random.Generators.MT19937Generator();
Troschuetz.Random.Distributions.Discrete.DiscreteUniformDistribution dud = new Troschuetz.Random.Distributions.Discrete.DiscreteUniformDistribution(gen);
dud.Alpha = 0;
dud.Beta = nBalls - 1;
// start by assigning elements to balls at random
Dictionary <T, Ball <T> > assigmnent = new Dictionary <T, Ball <T> >();
foreach (T element in elements)
{
balls[dud.Next()].AddElement(element);
}
// check for empty balls
for (int i = 0; i < nBalls; i++)
{
if (balls[i].Elements.Count == 0)
{
// select another ball at random
bool done = false;
while (!done)
{
int iBall = dud.Next();
if (balls[iBall].Elements.Count > 1)
{
T elementToMove = balls[iBall].Elements[0];
balls[iBall].RemoveElement(elementToMove);
balls[i].AddElement(elementToMove);
done = true;
}
}
}
}
foreach (Ball <T> ball in balls)
{
foreach (T element in ball.elements)
{
assigmnent.Add(element, ball);
}
}
int nStepsTaken = maxNumSteps;
bool converged = false;
for (int iStep = 0; iStep < maxNumSteps; iStep++)
{
int nChanged = 0;
foreach (T element in elements)
{
double smallestDistance = double.MaxValue;
Ball <T> nearestBall = null;
foreach (Ball <T> ball in balls)
{
double dist = metric(element, ball.Center);
if (dist < smallestDistance)
{
smallestDistance = dist;
nearestBall = ball;
}
}
if (nearestBall.Elements.Contains(element))
{
continue;
}
nearestBall.AddElement(element);
assigmnent[element].RemoveElement(element);
assigmnent.Remove(element);
assigmnent.Add(element, nearestBall);
nChanged++;
}
if (nChanged == 0)
{
nStepsTaken = iStep;
converged = true;
break;
}
}
return(new Tuple <List <Ball <T> >, bool, int>(balls, converged, nStepsTaken));
}
public static Tuple <List <Ball <T> >, bool, int> ClusterFixedNumberOfBalls(List <T> elements, Metric metric, int nBalls, int maxNumSteps)
{
if (elements.Count <= nBalls)
{
// nothing to do
return(new Tuple <List <Ball <T> >, bool, int>(null, false, 0));
}
List <Ball <T> > balls = new List <Ball <T> >();
for (int i = 0; i < nBalls; i++)
{
balls.Add(new Ball <T>(metric));
}
Troschuetz.Random.Generators.MT19937Generator gen = new Troschuetz.Random.Generators.MT19937Generator();
Troschuetz.Random.Distributions.Discrete.DiscreteUniformDistribution dud = new Troschuetz.Random.Distributions.Discrete.DiscreteUniformDistribution(gen);
dud.Alpha = 0;
dud.Beta = nBalls - 1;
// start by assigning elements to balls at random
Dictionary <T, Ball <T> > assigmnent = new Dictionary <T, Ball <T> >();
foreach (T element in elements)
{
balls[dud.Next()].AddElement(element);
}
// check for empty balls
for (int i = 0; i < nBalls; i++)
{
if (balls[i].Elements.Count == 0)
{
// select another ball at random
bool done = false;
while (!done)
{
int iBall = dud.Next();
if (balls[iBall].Elements.Count > 1)
{
T elementToMove = balls[iBall].Elements[0];
balls[iBall].RemoveElement(elementToMove);
balls[i].AddElement(elementToMove);
done = true;
}
}
}
}
foreach (Ball <T> ball in balls)
{
foreach (T element in ball.elements)
{
assigmnent.Add(element, ball);
}
}
Dictionary <T, double> distanceToCenter = new Dictionary <T, double>();
double greatestRadius = double.MinValue;
Ball <T> ballWithGreatestRadius = null;
foreach (Ball <T> ball in balls)
{
if (ball.radius > greatestRadius)
{
greatestRadius = ball.radius;
ballWithGreatestRadius = ball;
}
}
int nStepsTaken = maxNumSteps;
bool converged = false;
for (int iStep = 0; iStep < maxNumSteps; iStep++)
{
T outlier = ballWithGreatestRadius.MostDistantElement;
double smallestDistance = double.MaxValue;
Ball <T> nearestBall = null;
foreach (Ball <T> ball in balls)
{
double dist = metric(outlier, ball.Center);
if (dist < smallestDistance)
{
smallestDistance = dist;
nearestBall = ball;
}
}
// if the closest center is closer than current center, move it. Else end.
if (smallestDistance < ballWithGreatestRadius.radius)
{
// move outlier
ballWithGreatestRadius.RemoveElement(outlier);
nearestBall.AddElement(outlier);
}
else
{
converged = true;
nStepsTaken = iStep;
break;
}
// find the next outlier
greatestRadius = double.MinValue;
foreach (Ball <T> ball in balls)
{
if (ball.radius > greatestRadius)
{
greatestRadius = ball.radius;
ballWithGreatestRadius = ball;
}
}
}
return(new Tuple <List <Ball <T> >, bool, int>(balls, converged, nStepsTaken));
}
