public static IList <double> Sample(IList <double> means, IList <double> precs)
{
SparseList <double> sample = SparseList <double> .FromSize(means.Count);
sample.SetToFunction(means, precs, (m, p) => Gaussian.Sample(m, p));
return(sample);
}
public static IList <TResult> ListZip <TFirst, TSecond, TThird, TFourth, TResult>(
this IList <TFirst> first,
IList <TSecond> second,
IList <TThird> third,
IList <TFourth> fourth,
Func <TFirst, TSecond, TThird, TFourth, TResult> fun)
//where TResult : Diffable
{
if (first is ISparseList <TFirst> &&
second is ISparseList <TSecond> &&
third is ISparseList <TThird> &&
fourth is ISparseList <TFourth> )
{
var result = SparseList <TResult> .FromSize(first.Count);
//var result = ApproximateSparseList<TResult>.FromSize(first.Count, 1e-6);
result.SetToFunction((ISparseList <TFirst>)first, (ISparseList <TSecond>)second, (ISparseList <TThird>)third, (ISparseList <TFourth>)fourth, fun);
return(result);
}
else
{
var result = new List <TResult>();
using (IEnumerator <TFirst> e1 = first.GetEnumerator())
using (IEnumerator <TSecond> e2 = second.GetEnumerator())
using (IEnumerator <TThird> e3 = third.GetEnumerator())
using (IEnumerator <TFourth> e4 = fourth.GetEnumerator())
while (e1.MoveNext() && e2.MoveNext() && e3.MoveNext() && e4.MoveNext())
{
result.Add(fun(e1.Current, e2.Current, e3.Current, e4.Current));
}
return(result);
}
}
public static IList <TResult> ListZip <TFirst, TSecond, TThird, TResult>(
this IList <TFirst> first,
IEnumerable <TSecond> second,
IEnumerable <TThird> third,
Func <TFirst, TSecond, TThird, TResult> fun)
{
if (first is ISparseEnumerable <TFirst> &&
second is ISparseEnumerable <TSecond> &&
third is ISparseEnumerable <TThird> )
{
var result = SparseList <TResult> .FromSize(first.Count);
result.SetToFunction((SparseList <TFirst>)first, (SparseList <TSecond>)second, (SparseList <TThird>)third, fun);
return(result);
}
else
{
var result = new List <TResult>();
using (IEnumerator <TFirst> e1 = first.GetEnumerator())
using (IEnumerator <TSecond> e2 = second.GetEnumerator())
using (IEnumerator <TThird> e3 = third.GetEnumerator())
while (e1.MoveNext() && e2.MoveNext() && e3.MoveNext())
{
result.Add(fun(e1.Current, e2.Current, e3.Current));
}
return(result);
}
}
public static ISparseList <double> Sample(ISparseList <double> trueCounts, ISparseList <double> falseCounts)
{
SparseList <double> sample = SparseList <double> .FromSize(trueCounts.Count);
sample.SetToFunction(trueCounts, falseCounts, (tc, fc) => Beta.Sample(tc, fc));
return(sample);
}
public static ISparseList <double> Sample(ISparseList <double> shapes, ISparseList <double> rates)
{
SparseList <double> sample = SparseList <double> .FromSize(shapes.Count);
sample.SetToFunction(shapes, rates, (s, r) => Gamma.Sample(s, r));
return(sample);
}
public static ISparseList <bool> Sample(ISparseList <double> probTrue)
{
SparseList <bool> sample = SparseList <bool> .FromSize(probTrue.Count);
sample.SetToFunction(probTrue, p => Bernoulli.Sample(p));
return(sample);
}
/// <summary>
/// Similar to LINQ Select, except it takes and returns a list.
/// If the supplied list is sparse, the returned list will also be sparse.
/// </summary>
/// <typeparam name="T">The type of this list</typeparam>
/// <typeparam name="T2">The target element type</typeparam>
/// <param name="source">This list</param>
/// <param name="converter">The converter</param>
/// <returns></returns>
public static IList <T2> ListSelect <T, T2>(this IList <T> source, Func <T, T2> converter)
{
if (source is SparseList <T> )
{
var target = SparseList <T2> .FromSize(source.Count);
target.SetToFunction <T>(source, converter);
return(target);
}
return(source.Select(converter).ToList());
}
/// <summary>
/// Converts index subset to a sparse list of boolean values.
/// </summary>
/// <param name="indices">The indices.</param>
/// <param name="length">The length of the boolean list.</param>
/// <returns>The equivalent sparse list of boolean values.</returns>
public static ISparseList <bool> SubsetToList(IList <int> indices, int length)
{
var result = SparseList <bool> .Constant(length, false);
foreach (int index in indices)
{
result[index] = true;
}
return(result);
}
/// <summary>
/// Gets the log probability of the given value under this distribution.
/// </summary>
/// <param name="value">The value.</param>
/// <returns>The log probability of the given value under this distribution.</returns>
public double GetLogProb(IList <int> value)
{
var boolValue = SparseList <bool> .Constant(this.SparseBernoulliList.Count, false);
foreach (int index in value)
{
boolValue[index] = true;
}
return(this.SparseBernoulliList.GetLogProb(boolValue));
}
/// <summary>
/// Gets the variance as a sparse list.
/// </summary>
/// <returns>The variance of this distribution.</returns>
public ISparseList <double> GetVariance()
{
ISparseList <double> result = SparseList <double> .Constant(Count, CommonValue.GetVariance());
var sen = GetSparseEnumerator();
while (sen.MoveNext())
{
result[sen.CurrentIndex] = sen.Current.GetVariance();
}
return(result);
}
// Returns array of triangle lists (stored as arrays)
// This requires 2 passes over mesh, but each pass is linear
public static int[][] FindTriangleSetsByGroup(NGonsCore.geometry3Sharp.mesh.DMesh3 mesh, int ignoreGID = int.MinValue)
{
if (!mesh.HasTriangleGroups)
{
return(new int[0][]);
}
// find # of groups and triangle count for each
SparseList <int> counts = CountAllGroups(mesh);
List <int> GroupIDs = new List <int>();
foreach (var idxval in counts.Values())
{
if (idxval.Key != ignoreGID && idxval.Value > 0)
{
GroupIDs.Add(idxval.Key);
}
}
GroupIDs.Sort(); // might as well sort ascending...
SparseList <int> groupMap = new SparseList <int>(mesh.MaxGroupID, GroupIDs.Count, -1);
// allocate sets
int[][] sets = new int[GroupIDs.Count][];
int[] counters = new int[GroupIDs.Count];
for (int i = 0; i < GroupIDs.Count; ++i)
{
int gid = GroupIDs[i];
sets[i] = new int[counts[gid]];
counters[i] = 0;
groupMap[gid] = i;
}
// accumulate triangles
int NT = mesh.MaxTriangleID;
for (int tid = 0; tid < NT; ++tid)
{
if (mesh.IsTriangle(tid))
{
int gid = mesh.GetTriangleGroup(tid);
int i = groupMap[gid];
if (i >= 0)
{
int k = counters[i]++;
sets[i][k] = tid;
}
}
}
return(sets);
}
/// <summary>
/// Sums the elements of this list.
/// </summary>
/// <param name="source"></param>
/// <returns></returns>
public static int ListSum(this IList <int> source)
{
if (source is SparseList <int> )
{
SparseList <int> sl = (SparseList <int>)source;
int sum = sl.CommonValue * (sl.Count - sl.SparseValues.Count);
foreach (var sel in sl.SparseValues)
{
sum += sel.Value;
}
return(sum);
}
return(source.Sum());
}
// returned pairs are [group_id, tri_count]
public static SparseList <int> CountAllGroups(NGonsCore.geometry3Sharp.mesh.DMesh3 mesh)
{
SparseList <int> GroupCounts = new SparseList <int>(mesh.MaxGroupID, 0, 0);
if (mesh.HasTriangleGroups)
{
int NT = mesh.MaxTriangleID;
for (int tid = 0; tid < NT; ++tid)
{
if (mesh.IsTriangle(tid))
{
int gid = mesh.GetTriangleGroup(tid);
GroupCounts[gid] = GroupCounts[gid] + 1;
}
}
}
return(GroupCounts);
}
public SparseListCleaningEnumerator(SparseList <T> list)
{
this.list = list;
this.version = list.version;
//while (Current == null && MoveNext()) ;
}
/// <summary>
/// Samples from this sparse Gaussian list
/// </summary>
/// <returns></returns>
/// <remarks>This method is inefficient in that the result will be dense even though the return type is sparse.</remarks>
public IList <double> Sample()
{
SparseList <double> sv = SparseList <double> .FromSize(Count);
return(Sample(sv));
}
public void Dispose()
{
list = null;
}
public void SoftmaxTest()
{
// Check sparse versus dense.
// Check situation where some entries are positive or negative infinity
// Check effects of different common values
double trueCV = -1;
double[][] array = new double[][]
{
new double[] { trueCV, trueCV, 5, 4, trueCV, -3 },
new double[] { trueCV, trueCV, double.NegativeInfinity, 5, trueCV, 7 },
new double[] { trueCV, trueCV, double.PositiveInfinity, 5, trueCV, double.PositiveInfinity },
new double[] { trueCV, trueCV, double.NegativeInfinity, 5, trueCV, double.PositiveInfinity },
new double[]
{ double.NegativeInfinity, double.NegativeInfinity, double.NegativeInfinity, double.NegativeInfinity, double.NegativeInfinity, double.NegativeInfinity }
};
double[] forcedCVArr = new double[]
{
trueCV, 0, double.PositiveInfinity, double.NegativeInfinity
};
double[][] expected = new double[][]
{
new double[] { 0.001802, 0.001802, 0.7269, 0.2674, 0.001802, 0.0002439 },
new double[] { 0.0002952, 0.0002952, 0, 0.1191, 0.0002952, 0.88 },
new double[] { 0, 0, 0.5, 0, 0, 0.5 },
new double[] { 0, 0, 0, 0, 0, 1 },
new double[] { 0.1667, 0.1667, 0.1667, 0.1667, 0.1667, 0.1667 }
};
for (int pass = 0; pass < 2; pass++)
{
for (int i = 0; i < array.Length; i++)
{
if (pass == 0)
{
Vector sma = MMath.Softmax(new List <double>(array[i]));
for (int e = 0; e < sma.Count; e++)
{
Assert.Equal(expected[i][e], sma[e], 1e-4);
}
Console.WriteLine(sma);
}
else
{
for (int k = 0; k < forcedCVArr.Length; k++)
{
var lst = SparseList <double> .Constant(array[i].Length, forcedCVArr[k]);
for (int j = 0; j < array[i].Length; j++)
{
if (array[i][j] != forcedCVArr[k])
{
lst.SparseValues.Add(new ValueAtIndex <double>(j, array[i][j]));
}
}
Vector sma = MMath.Softmax(lst);
for (int e = 0; e < sma.Count; e++)
{
Assert.Equal(expected[i][e], sma[e], 1e-4);
}
Console.WriteLine(sma);
}
}
}
}
}
/// <summary>
/// Samples from this sparse distribution list.
/// </summary>
/// <returns>A sample from this distribution.</returns>
/// <remarks>This method is inefficient in that the result will be dense even though the return type is sparse.</remarks>
public ISparseList <TDomain> Sample()
{
ISparseList <TDomain> sv = SparseList <TDomain> .FromSize(Count);
return(this.Sample(sv));
}
public void SparseBernoulliFromBetaFactor()
{
var calcSuffix = ": calculation differs between sparse and dense";
var sparsitySuffix = ": result is not sparse as expected";
var calcErrMsg = "";
var sparsityErrMsg = "";
var tolerance = 1e-10;
Rand.Restart(12347);
int listSize = 50;
var sparseProbTrueDist = SparseBetaList.Constant(listSize, new Beta(1, 2));
sparseProbTrueDist[3] = new Beta(4, 5);
sparseProbTrueDist[6] = new Beta(7, 8);
var probTrueDist = sparseProbTrueDist.ToArray();
var sparseProbTruePoint = SparseList <double> .Constant(listSize, 0.1);
sparseProbTruePoint[3] = 0.7;
sparseProbTruePoint[6] = 0.8;
var probTruePoint = sparseProbTruePoint.ToArray();
var sparseSampleDist = SparseBernoulliList.Constant(listSize, new Bernoulli(0.1));
sparseSampleDist[3] = new Bernoulli(0.8);
sparseSampleDist[9] = new Bernoulli(0.9);
var sampleDist = sparseSampleDist.ToArray();
var sparseSamplePoint = SparseList <bool> .Constant(listSize, false);
sparseSamplePoint[3] = true;
sparseSamplePoint[9] = true;
var samplePoint = sparseSamplePoint.ToArray();
var toSparseSampleDist = SparseBernoulliList.Constant(listSize, new Bernoulli(0.1));
toSparseSampleDist[3] = new Bernoulli(0.4);
toSparseSampleDist[4] = new Bernoulli(0.8);
var toSampleDist = toSparseSampleDist.ToArray();
// ---------------------------
// Check average log factor
// ---------------------------
calcErrMsg = "Average log factor" + calcSuffix;
// Dist, dist
var sparseAvgLog = SparseBernoulliFromBetaOp.AverageLogFactor(sparseSampleDist, sparseProbTrueDist);
var avgLog = Util.ArrayInit(listSize, i => BernoulliFromBetaOp.AverageLogFactor(sampleDist[i], probTrueDist[i])).Sum();
TAssert.True(System.Math.Abs(avgLog - sparseAvgLog) < tolerance, calcErrMsg);
// Dist, point
sparseAvgLog = SparseBernoulliFromBetaOp.AverageLogFactor(sparseSampleDist, sparseProbTruePoint);
avgLog = Util.ArrayInit(listSize, i => BernoulliFromBetaOp.AverageLogFactor(sampleDist[i], probTruePoint[i])).Sum();
TAssert.True(System.Math.Abs(avgLog - sparseAvgLog) < tolerance, calcErrMsg);
// Point, dist
sparseAvgLog = SparseBernoulliFromBetaOp.AverageLogFactor(sparseSamplePoint, sparseProbTrueDist);
avgLog = Util.ArrayInit(listSize, i => BernoulliFromBetaOp.AverageLogFactor(samplePoint[i], probTrueDist[i])).Sum();
TAssert.True(System.Math.Abs(avgLog - sparseAvgLog) < tolerance, calcErrMsg);
// Point, point
sparseAvgLog = SparseBernoulliFromBetaOp.AverageLogFactor(sparseSamplePoint, sparseProbTruePoint);
avgLog = Util.ArrayInit(listSize, i => BernoulliFromBetaOp.AverageLogFactor(samplePoint[i], probTruePoint[i])).Sum();
TAssert.True(System.Math.Abs(avgLog - sparseAvgLog) < tolerance, calcErrMsg);
// ---------------------------
// Check log average factor
// ---------------------------
calcErrMsg = "Log average factor" + calcSuffix;
var sparseLogAvg = SparseBernoulliFromBetaOp.LogAverageFactor(sparseSampleDist, toSparseSampleDist);
var logAvg = Util.ArrayInit(listSize, i => BernoulliFromBetaOp.LogAverageFactor(sampleDist[i], toSampleDist[i])).Sum();
TAssert.True(System.Math.Abs(logAvg - sparseLogAvg) < tolerance, calcErrMsg);
sparseLogAvg = SparseBernoulliFromBetaOp.LogAverageFactor(sparseSamplePoint, sparseProbTrueDist);
logAvg = Util.ArrayInit(listSize, i => BernoulliFromBetaOp.LogAverageFactor(samplePoint[i], probTrueDist[i])).Sum();
TAssert.True(System.Math.Abs(logAvg - sparseLogAvg) < tolerance, calcErrMsg);
sparseLogAvg = SparseBernoulliFromBetaOp.LogAverageFactor(sparseSamplePoint, sparseProbTruePoint);
logAvg = Util.ArrayInit(listSize, i => BernoulliFromBetaOp.LogAverageFactor(samplePoint[i], probTruePoint[i])).Sum();
TAssert.True(System.Math.Abs(logAvg - sparseLogAvg) < tolerance, calcErrMsg);
// ---------------------------
// Check log evidence ratio
// ---------------------------
calcErrMsg = "Log evidence ratio" + calcSuffix;
// Dist, dist
var sparseEvidRat = SparseBernoulliFromBetaOp.LogEvidenceRatio(sparseSampleDist, sparseProbTrueDist);
var evidRat = Util.ArrayInit(listSize, i => BernoulliFromBetaOp.LogEvidenceRatio(sampleDist[i], probTrueDist[i])).Sum();
TAssert.True(System.Math.Abs(evidRat - sparseEvidRat) < tolerance, calcErrMsg);
// Dist, point
sparseEvidRat = SparseBernoulliFromBetaOp.LogEvidenceRatio(sparseSampleDist, sparseProbTruePoint);
evidRat = Util.ArrayInit(listSize, i => BernoulliFromBetaOp.LogEvidenceRatio(sampleDist[i], probTruePoint[i])).Sum();
TAssert.True(System.Math.Abs(evidRat - sparseEvidRat) < tolerance, calcErrMsg);
// Point, dist
sparseEvidRat = SparseBernoulliFromBetaOp.LogEvidenceRatio(sparseSamplePoint, sparseProbTrueDist);
evidRat = Util.ArrayInit(listSize, i => BernoulliFromBetaOp.LogEvidenceRatio(samplePoint[i], probTrueDist[i])).Sum();
TAssert.True(System.Math.Abs(evidRat - sparseEvidRat) < tolerance, calcErrMsg);
// Point, point
sparseEvidRat = SparseBernoulliFromBetaOp.LogEvidenceRatio(sparseSamplePoint, sparseProbTruePoint);
evidRat = Util.ArrayInit(listSize, i => BernoulliFromBetaOp.LogEvidenceRatio(samplePoint[i], probTruePoint[i])).Sum();
TAssert.True(System.Math.Abs(evidRat - sparseEvidRat) < tolerance, calcErrMsg);
// ---------------------------
// Check SampleConditional
// ---------------------------
calcErrMsg = "SampleConditional" + calcSuffix;
sparsityErrMsg = "SampleConditional" + sparsitySuffix;
// Use different common value to ensure this gets properly set
var sparseSampleConditional = SparseBernoulliList.Constant(listSize, new Bernoulli(0.5));
sparseSampleConditional = SparseBernoulliFromBetaOp.SampleConditional(sparseProbTruePoint, sparseSampleConditional);
var sampleConditional = Util.ArrayInit(listSize, i => BernoulliFromBetaOp.SampleConditional(probTruePoint[i]));
TAssert.True(2 == sparseSampleConditional.SparseCount, sparsityErrMsg);
TAssert.True(sparseSampleConditional.MaxDiff(sampleConditional) < tolerance, calcErrMsg);
// ---------------------------
// Check SampleAverageConditional
// ---------------------------
calcErrMsg = "SampleAverageConditional" + calcSuffix;
sparsityErrMsg = "SampleAverageConditional" + sparsitySuffix;
// Use different common value to ensure this gets properly set
var sparseSampleAvgConditional = SparseBernoulliList.Constant(listSize, new Bernoulli(0.5));
sparseSampleAvgConditional = SparseBernoulliFromBetaOp.SampleAverageConditional(sparseProbTrueDist, sparseSampleAvgConditional);
var sampleAvgConditional = Util.ArrayInit(listSize, i => BernoulliFromBetaOp.SampleAverageConditional(probTrueDist[i]));
TAssert.True(2 == sparseSampleAvgConditional.SparseCount, sparsityErrMsg);
TAssert.True(sparseSampleAvgConditional.MaxDiff(sampleAvgConditional) < tolerance, calcErrMsg);
sparseSampleAvgConditional = SparseBernoulliList.Constant(listSize, new Bernoulli(0.5));
sparseSampleAvgConditional = SparseBernoulliFromBetaOp.SampleAverageConditional(sparseProbTruePoint, sparseSampleAvgConditional);
sampleAvgConditional = Util.ArrayInit(listSize, i => BernoulliFromBetaOp.SampleAverageConditional(probTruePoint[i]));
TAssert.True(2 == sparseSampleAvgConditional.SparseCount, sparsityErrMsg);
TAssert.True(sparseSampleAvgConditional.MaxDiff(sampleAvgConditional) < tolerance, calcErrMsg);
// ---------------------------
// Check ProbTrueConditional
// ---------------------------
calcErrMsg = "ProbTrueConditional" + calcSuffix;
sparsityErrMsg = "ProbTrueConditional" + sparsitySuffix;
// Use different common value to ensure this gets properly set
var sparseProbTrueConditional = SparseBetaList.Constant(listSize, new Beta(1.1, 2.2));
sparseProbTrueConditional = SparseBernoulliFromBetaOp.ProbTrueConditional(sparseSamplePoint, sparseProbTrueConditional);
var probTrueConditional = Util.ArrayInit(listSize, i => BernoulliFromBetaOp.ProbTrueConditional(samplePoint[i]));
TAssert.True(2 == sparseProbTrueConditional.SparseCount, sparsityErrMsg);
TAssert.True(sparseProbTrueConditional.MaxDiff(probTrueConditional) < tolerance, calcErrMsg);
// ---------------------------
// Check ProbTrueAverageConditional
// ---------------------------
calcErrMsg = "ProbTrueAverageConditional" + calcSuffix;
sparsityErrMsg = "ProbTrueAverageConditional" + sparsitySuffix;
// Use different common value to ensure this gets properly set
var sparseProbTrueAvgConditional = SparseBetaList.Constant(listSize, new Beta(1.1, 2.2));
sparseProbTrueAvgConditional = SparseBernoulliFromBetaOp.ProbTrueAverageConditional(sparseSampleDist, sparseProbTrueDist, sparseProbTrueAvgConditional);
var probTrueAvgConditional = Util.ArrayInit(listSize, i => BernoulliFromBetaOp.ProbTrueAverageConditional(sampleDist[i], probTrueDist[i]));
TAssert.True(2 == sparseProbTrueAvgConditional.SparseCount, sparsityErrMsg);
TAssert.True(sparseProbTrueAvgConditional.MaxDiff(probTrueAvgConditional) < tolerance, calcErrMsg);
sparseProbTrueAvgConditional = SparseBetaList.Constant(listSize, new Beta(1.1, 2.2));
sparseProbTrueAvgConditional = SparseBernoulliFromBetaOp.ProbTrueAverageConditional(sparseSamplePoint, sparseProbTrueAvgConditional);
probTrueAvgConditional = Util.ArrayInit(listSize, i => BernoulliFromBetaOp.ProbTrueAverageConditional(samplePoint[i]));
TAssert.True(2 == sparseProbTrueAvgConditional.SparseCount, sparsityErrMsg);
TAssert.True(sparseProbTrueAvgConditional.MaxDiff(probTrueAvgConditional) < tolerance, calcErrMsg);
// ---------------------------
// Check SampleAverageLogarithm
// ---------------------------
calcErrMsg = "SampleAverageLogarithm" + calcSuffix;
sparsityErrMsg = "SampleAverageLogarithm" + sparsitySuffix;
// Use different common value to ensure this gets properly set
var sparseSampleAvgLogarithm = SparseBernoulliList.Constant(listSize, new Bernoulli(0.5));
sparseSampleAvgLogarithm = SparseBernoulliFromBetaOp.SampleAverageLogarithm(sparseProbTrueDist, sparseSampleAvgLogarithm);
var sampleAvgLogarithm = Util.ArrayInit(listSize, i => BernoulliFromBetaOp.SampleAverageLogarithm(probTrueDist[i]));
TAssert.True(2 == sparseSampleAvgLogarithm.SparseCount, sparsityErrMsg);
TAssert.True(sparseSampleAvgLogarithm.MaxDiff(sampleAvgLogarithm) < tolerance, calcErrMsg);
sparseSampleAvgLogarithm = SparseBernoulliList.Constant(listSize, new Bernoulli(0.5));
sparseSampleAvgLogarithm = SparseBernoulliFromBetaOp.SampleAverageLogarithm(sparseProbTruePoint, sparseSampleAvgLogarithm);
sampleAvgLogarithm = Util.ArrayInit(listSize, i => BernoulliFromBetaOp.SampleAverageLogarithm(probTruePoint[i]));
TAssert.True(2 == sparseSampleAvgLogarithm.SparseCount, sparsityErrMsg);
TAssert.True(sparseSampleAvgLogarithm.MaxDiff(sampleAvgLogarithm) < tolerance, calcErrMsg);
// ---------------------------
// Check ProbTrueAverageLogarithm
// ---------------------------
calcErrMsg = "ProbTrueAverageLogarithm" + calcSuffix;
sparsityErrMsg = "ProbTrueAverageLogarithm" + sparsitySuffix;
// Use different common value to ensure this gets properly set
var sparseProbTrueAvgLogarithm = SparseBetaList.Constant(listSize, new Beta(1.1, 2.2));
sparseProbTrueAvgLogarithm = SparseBernoulliFromBetaOp.ProbTrueAverageLogarithm(sparseSampleDist, sparseProbTrueAvgLogarithm);
var probTrueAvgLogarithm = Util.ArrayInit(listSize, i => BernoulliFromBetaOp.ProbTrueAverageLogarithm(sampleDist[i]));
TAssert.True(2 == sparseProbTrueAvgLogarithm.SparseCount, sparsityErrMsg);
TAssert.True(sparseProbTrueAvgLogarithm.MaxDiff(probTrueAvgLogarithm) < tolerance, calcErrMsg);
sparseProbTrueAvgLogarithm = SparseBetaList.Constant(listSize, new Beta(1.1, 2.2));
sparseProbTrueAvgLogarithm = SparseBernoulliFromBetaOp.ProbTrueAverageLogarithm(sparseSamplePoint, sparseProbTrueAvgLogarithm);
probTrueAvgLogarithm = Util.ArrayInit(listSize, i => BernoulliFromBetaOp.ProbTrueAverageLogarithm(samplePoint[i]));
TAssert.True(2 == sparseProbTrueAvgLogarithm.SparseCount, sparsityErrMsg);
TAssert.True(sparseProbTrueAvgLogarithm.MaxDiff(probTrueAvgLogarithm) < tolerance, calcErrMsg);
}
public void SparseGaussianListFactor()
{
SparseGaussianList.DefaultTolerance = 1e-10;
var calcSuffix = ": calculation differs between sparse and dense";
var sparsitySuffix = ": result is not sparse as expected";
var calcErrMsg = "";
var sparsityErrMsg = "";
var tolerance = 1e-10;
Rand.Restart(12347);
int listSize = 50;
// True distribution for the means
var sparseMeanDist = SparseGaussianList.Constant(listSize, Gaussian.FromMeanAndPrecision(1, 2), tolerance);
sparseMeanDist[3] = Gaussian.FromMeanAndPrecision(4, 5);
sparseMeanDist[6] = Gaussian.FromMeanAndPrecision(7, 8);
var meanDist = sparseMeanDist.ToArray();
var sparseMeanPoint = SparseList <double> .Constant(listSize, 0.1);
sparseMeanPoint[3] = 0.7;
sparseMeanPoint[6] = 0.8;
var meanPoint = sparseMeanPoint.ToArray();
// True distribution for the precisions
var sparsePrecDist = SparseGammaList.Constant(listSize, Gamma.FromShapeAndRate(1.1, 1.2), tolerance);
sparsePrecDist[3] = Gamma.FromShapeAndRate(2.3, 2.4);
sparsePrecDist[6] = Gamma.FromShapeAndRate(3.4, 4.5);
var precDist = sparsePrecDist.ToArray();
var sparsePrecPoint = SparseList <double> .Constant(listSize, 0.1);
sparsePrecPoint[3] = 5.6;
sparsePrecPoint[6] = 0.5;
var precPoint = sparsePrecPoint.ToArray();
var sparseSampleDist = SparseGaussianList.Constant(listSize, Gaussian.FromMeanAndPrecision(0.5, 1.5), tolerance);
sparseSampleDist[3] = Gaussian.FromMeanAndPrecision(-0.5, 2.0);
sparseSampleDist[9] = Gaussian.FromMeanAndPrecision(1.6, 0.4);
var sampleDist = sparseSampleDist.ToArray();
var sparseSamplePoint = SparseList <double> .Constant(listSize, 0.5);
sparseSamplePoint[3] = 0.1;
sparseSamplePoint[9] = 2.3;
var samplePoint = sparseSamplePoint.ToArray();
var toSparseSampleDist = SparseGaussianList.Constant(listSize, Gaussian.FromMeanAndPrecision(-0.2, 0.3), tolerance);
toSparseSampleDist[3] = Gaussian.FromMeanAndPrecision(2.1, 3.2);
toSparseSampleDist[4] = Gaussian.FromMeanAndPrecision(1.3, 0.7);
var toSampleDist = toSparseSampleDist.ToArray();
var toSparsePrecDist = SparseGammaList.Constant(listSize, Gamma.FromShapeAndRate(2.3, 3.4), tolerance);
toSparsePrecDist[3] = Gamma.FromShapeAndRate(3.4, 4.5);
toSparsePrecDist[4] = Gamma.FromShapeAndRate(5.6, 6.7);
var toPrecDist = toSparsePrecDist.ToArray();
// ---------------------------
// Check average log factor
// ---------------------------
calcErrMsg = "Average log factor" + calcSuffix;
// Dist, dist, dist
var sparseAvgLog = SparseGaussianListOp.AverageLogFactor(sparseSampleDist, sparseMeanDist, sparsePrecDist);
var avgLog = Util.ArrayInit(listSize, i => GaussianOp.AverageLogFactor(sampleDist[i], meanDist[i], precDist[i])).Sum();
TAssert.True(System.Math.Abs(avgLog - sparseAvgLog) < tolerance, calcErrMsg);
// Dist, dist, point
sparseAvgLog = SparseGaussianListOp.AverageLogFactor(sparseSampleDist, sparseMeanDist, sparsePrecPoint);
avgLog = Util.ArrayInit(listSize, i => GaussianOp.AverageLogFactor(sampleDist[i], meanDist[i], precPoint[i])).Sum();
TAssert.True(System.Math.Abs(avgLog - sparseAvgLog) < tolerance, calcErrMsg);
// Dist, point, dist
sparseAvgLog = SparseGaussianListOp.AverageLogFactor(sparseSampleDist, sparseMeanPoint, sparsePrecDist);
avgLog = Util.ArrayInit(listSize, i => GaussianOp.AverageLogFactor(sampleDist[i], meanPoint[i], precDist[i])).Sum();
TAssert.True(System.Math.Abs(avgLog - sparseAvgLog) < tolerance, calcErrMsg);
// Dist, point, point
sparseAvgLog = SparseGaussianListOp.AverageLogFactor(sparseSampleDist, sparseMeanPoint, sparsePrecPoint);
avgLog = Util.ArrayInit(listSize, i => GaussianOp.AverageLogFactor(sampleDist[i], meanPoint[i], precPoint[i])).Sum();
TAssert.True(System.Math.Abs(avgLog - sparseAvgLog) < tolerance, calcErrMsg);
// Point, dist, dist
sparseAvgLog = SparseGaussianListOp.AverageLogFactor(sparseSamplePoint, sparseMeanDist, sparsePrecDist);
avgLog = Util.ArrayInit(listSize, i => GaussianOp.AverageLogFactor(samplePoint[i], meanDist[i], precDist[i])).Sum();
TAssert.True(System.Math.Abs(avgLog - sparseAvgLog) < tolerance, calcErrMsg);
// Point, dist, point
sparseAvgLog = SparseGaussianListOp.AverageLogFactor(sparseSamplePoint, sparseMeanDist, sparsePrecPoint);
avgLog = Util.ArrayInit(listSize, i => GaussianOp.AverageLogFactor(samplePoint[i], meanDist[i], precPoint[i])).Sum();
TAssert.True(System.Math.Abs(avgLog - sparseAvgLog) < tolerance, calcErrMsg);
// Point, point, dist
sparseAvgLog = SparseGaussianListOp.AverageLogFactor(sparseSamplePoint, sparseMeanPoint, sparsePrecDist);
avgLog = Util.ArrayInit(listSize, i => GaussianOp.AverageLogFactor(samplePoint[i], meanPoint[i], precDist[i])).Sum();
TAssert.True(System.Math.Abs(avgLog - sparseAvgLog) < tolerance, calcErrMsg);
// Point, point, point
sparseAvgLog = SparseGaussianListOp.AverageLogFactor(sparseSamplePoint, sparseMeanPoint, sparsePrecPoint);
avgLog = Util.ArrayInit(listSize, i => GaussianOp.AverageLogFactor(samplePoint[i], meanPoint[i], precPoint[i])).Sum();
TAssert.True(System.Math.Abs(avgLog - sparseAvgLog) < tolerance, calcErrMsg);
// ---------------------------
// Check log average factor
// ---------------------------
calcErrMsg = "Log average factor" + calcSuffix;
var sparseLogAvg = SparseGaussianListOp.LogAverageFactor(sparseSampleDist, sparseMeanDist, sparsePrecDist, toSparsePrecDist);
var logAvg = Util.ArrayInit(listSize, i => GaussianOp.LogAverageFactor(sampleDist[i], meanDist[i], precDist[i], toPrecDist[i])).Sum();
TAssert.True(System.Math.Abs(logAvg - sparseLogAvg) < tolerance, calcErrMsg);
// Dist, dist, point
sparseLogAvg = SparseGaussianListOp.LogAverageFactor(sparseSampleDist, sparseMeanDist, sparsePrecPoint);
logAvg = Util.ArrayInit(listSize, i => GaussianOp.LogAverageFactor(sampleDist[i], meanDist[i], precPoint[i])).Sum();
TAssert.True(System.Math.Abs(logAvg - sparseLogAvg) < tolerance, calcErrMsg);
// Dist, point, dist
sparseLogAvg = SparseGaussianListOp.LogAverageFactor(sparseSampleDist, sparseMeanPoint, sparsePrecDist, toSparsePrecDist);
logAvg = Util.ArrayInit(listSize, i => GaussianOp.LogAverageFactor(sampleDist[i], meanPoint[i], precDist[i], toPrecDist[i])).Sum();
TAssert.True(System.Math.Abs(logAvg - sparseLogAvg) < tolerance, calcErrMsg);
// Dist, point, point
sparseLogAvg = SparseGaussianListOp.LogAverageFactor(sparseSampleDist, sparseMeanPoint, sparsePrecPoint);
logAvg = Util.ArrayInit(listSize, i => GaussianOp.LogAverageFactor(sampleDist[i], meanPoint[i], precPoint[i])).Sum();
TAssert.True(System.Math.Abs(logAvg - sparseLogAvg) < tolerance, calcErrMsg);
// Point, dist, dist
sparseLogAvg = SparseGaussianListOp.LogAverageFactor(sparseSamplePoint, sparseMeanDist, sparsePrecDist, toSparsePrecDist);
logAvg = Util.ArrayInit(listSize, i => GaussianOp.LogAverageFactor(samplePoint[i], meanDist[i], precDist[i], toPrecDist[i])).Sum();
TAssert.True(System.Math.Abs(logAvg - sparseLogAvg) < tolerance, calcErrMsg);
// Point, dist, point
sparseLogAvg = SparseGaussianListOp.LogAverageFactor(sparseSamplePoint, sparseMeanDist, sparsePrecPoint);
logAvg = Util.ArrayInit(listSize, i => GaussianOp.LogAverageFactor(samplePoint[i], meanDist[i], precPoint[i])).Sum();
TAssert.True(System.Math.Abs(logAvg - sparseLogAvg) < tolerance, calcErrMsg);
// Point, point, dist
sparseLogAvg = SparseGaussianListOp.LogAverageFactor(sparseSamplePoint, sparseMeanPoint, sparsePrecDist);
logAvg = Util.ArrayInit(listSize, i => GaussianOp.LogAverageFactor(samplePoint[i], meanPoint[i], precDist[i])).Sum();
TAssert.True(System.Math.Abs(logAvg - sparseLogAvg) < tolerance, calcErrMsg);
// Point, point, point
sparseLogAvg = SparseGaussianListOp.LogAverageFactor(sparseSamplePoint, sparseMeanPoint, sparsePrecPoint);
logAvg = Util.ArrayInit(listSize, i => GaussianOp.LogAverageFactor(samplePoint[i], meanPoint[i], precPoint[i])).Sum();
TAssert.True(System.Math.Abs(logAvg - sparseLogAvg) < tolerance, calcErrMsg);
// ---------------------------
// Check log evidence ratio
// ---------------------------
calcErrMsg = "Log evidence ratio" + calcSuffix;
var sparseEvidRat = SparseGaussianListOp.LogEvidenceRatio(sparseSampleDist, sparseMeanDist, sparsePrecDist, toSparseSampleDist, toSparsePrecDist);
var evidRat = Util.ArrayInit(listSize, i => GaussianOp.LogEvidenceRatio(sampleDist[i], meanDist[i], precDist[i], toSampleDist[i], toPrecDist[i])).Sum();
TAssert.True(System.Math.Abs(evidRat - sparseEvidRat) < tolerance, calcErrMsg);
// Dist, dist, point
sparseEvidRat = SparseGaussianListOp.LogEvidenceRatio(sparseSampleDist, sparseMeanDist, sparsePrecPoint);
evidRat = Util.ArrayInit(listSize, i => GaussianOp.LogEvidenceRatio(sampleDist[i], meanDist[i], precPoint[i])).Sum();
TAssert.True(System.Math.Abs(evidRat - sparseEvidRat) < tolerance, calcErrMsg);
// Dist, point, dist
sparseEvidRat = SparseGaussianListOp.LogEvidenceRatio(sparseSampleDist, sparseMeanPoint, sparsePrecDist, toSparseSampleDist, toSparsePrecDist);
evidRat = Util.ArrayInit(listSize, i => GaussianOp.LogEvidenceRatio(sampleDist[i], meanPoint[i], precDist[i], toSampleDist[i], toPrecDist[i])).Sum();
TAssert.True(System.Math.Abs(evidRat - sparseEvidRat) < tolerance, calcErrMsg);
// Dist, point, point
sparseEvidRat = SparseGaussianListOp.LogEvidenceRatio(sparseSampleDist, sparseMeanPoint, sparsePrecPoint);
evidRat = Util.ArrayInit(listSize, i => GaussianOp.LogEvidenceRatio(sampleDist[i], meanPoint[i], precPoint[i])).Sum();
TAssert.True(System.Math.Abs(evidRat - sparseEvidRat) < tolerance, calcErrMsg);
// Point, dist, dist
sparseEvidRat = SparseGaussianListOp.LogEvidenceRatio(sparseSamplePoint, sparseMeanDist, sparsePrecDist, toSparsePrecDist);
evidRat = Util.ArrayInit(listSize, i => GaussianOp.LogEvidenceRatio(samplePoint[i], meanDist[i], precDist[i], toPrecDist[i])).Sum();
TAssert.True(System.Math.Abs(evidRat - sparseEvidRat) < tolerance, calcErrMsg);
// Point, dist, point
sparseEvidRat = SparseGaussianListOp.LogEvidenceRatio(sparseSamplePoint, sparseMeanDist, sparsePrecPoint);
evidRat = Util.ArrayInit(listSize, i => GaussianOp.LogEvidenceRatio(samplePoint[i], meanDist[i], precPoint[i])).Sum();
TAssert.True(System.Math.Abs(evidRat - sparseEvidRat) < tolerance, calcErrMsg);
// Point, point, dist
sparseEvidRat = SparseGaussianListOp.LogEvidenceRatio(sparseSamplePoint, sparseMeanPoint, sparsePrecDist);
evidRat = Util.ArrayInit(listSize, i => GaussianOp.LogEvidenceRatio(samplePoint[i], meanPoint[i], precDist[i])).Sum();
TAssert.True(System.Math.Abs(evidRat - sparseEvidRat) < tolerance, calcErrMsg);
// Point, point, point
sparseEvidRat = SparseGaussianListOp.LogEvidenceRatio(sparseSamplePoint, sparseMeanPoint, sparsePrecPoint);
evidRat = Util.ArrayInit(listSize, i => GaussianOp.LogEvidenceRatio(samplePoint[i], meanPoint[i], precPoint[i])).Sum();
TAssert.True(System.Math.Abs(evidRat - sparseEvidRat) < tolerance, calcErrMsg);
// ---------------------------
// Check SampleAverageConditional
// ---------------------------
calcErrMsg = "SampleAverageConditional" + calcSuffix;
sparsityErrMsg = "SampleAverageConditional" + sparsitySuffix;
// Use different common value to ensure this gets properly set
var sparseSampleAvgConditional = SparseGaussianList.Constant(listSize, Gaussian.FromMeanAndPrecision(0.5, 0.6), tolerance);
sparseSampleAvgConditional = SparseGaussianListOp.SampleAverageConditional(sparseSampleDist, sparseMeanDist, sparsePrecDist, toSparsePrecDist, sparseSampleAvgConditional);
var sampleAvgConditional = Util.ArrayInit(listSize, i => GaussianOp.SampleAverageConditional(sampleDist[i], meanDist[i], precDist[i], toPrecDist[i]));
TAssert.True(3 == sparseSampleAvgConditional.SparseCount, sparsityErrMsg);
TAssert.True(sparseSampleAvgConditional.MaxDiff(sampleAvgConditional) < tolerance, calcErrMsg);
sparseSampleAvgConditional = SparseGaussianList.Constant(listSize, Gaussian.FromMeanAndPrecision(0.5, 0.6), tolerance);
sparseSampleAvgConditional = SparseGaussianListOp.SampleAverageConditional(sparseSampleDist, sparseMeanPoint, sparsePrecDist, toSparsePrecDist, sparseSampleAvgConditional);
sampleAvgConditional = Util.ArrayInit(listSize, i => GaussianOp.SampleAverageConditional(sampleDist[i], meanPoint[i], precDist[i], toPrecDist[i]));
TAssert.True(3 == sparseSampleAvgConditional.SparseCount, sparsityErrMsg);
TAssert.True(sparseSampleAvgConditional.MaxDiff(sampleAvgConditional) < tolerance, calcErrMsg);
sparseSampleAvgConditional = SparseGaussianList.Constant(listSize, Gaussian.FromMeanAndPrecision(0.5, 0.6), tolerance);
sparseSampleAvgConditional = SparseGaussianListOp.SampleAverageConditional(sparseMeanDist, sparsePrecPoint, sparseSampleAvgConditional);
sampleAvgConditional = Util.ArrayInit(listSize, i => GaussianOp.SampleAverageConditional(meanDist[i], precPoint[i]));
TAssert.True(2 == sparseSampleAvgConditional.SparseCount, sparsityErrMsg);
TAssert.True(sparseSampleAvgConditional.MaxDiff(sampleAvgConditional) < tolerance, calcErrMsg);
sparseSampleAvgConditional = SparseGaussianList.Constant(listSize, Gaussian.FromMeanAndPrecision(0.5, 0.6), tolerance);
sparseSampleAvgConditional = SparseGaussianListOp.SampleAverageConditional(sparseMeanPoint, sparsePrecPoint, sparseSampleAvgConditional);
sampleAvgConditional = Util.ArrayInit(listSize, i => GaussianOp.SampleAverageConditional(meanPoint[i], precPoint[i]));
TAssert.True(2 == sparseSampleAvgConditional.SparseCount, sparsityErrMsg);
TAssert.True(sparseSampleAvgConditional.MaxDiff(sampleAvgConditional) < tolerance, calcErrMsg);
// ---------------------------
// Check MeanAverageConditional
// ---------------------------
calcErrMsg = "MeanAverageConditional" + calcSuffix;
sparsityErrMsg = "MeanAverageConditional" + sparsitySuffix;
// Use different common value to ensure this gets properly set
var sparseMeanAvgConditional = SparseGaussianList.Constant(listSize, Gaussian.FromMeanAndPrecision(0.5, 0.6), tolerance);
sparseMeanAvgConditional = SparseGaussianListOp.MeanAverageConditional(sparseSampleDist, sparseMeanDist, sparsePrecDist, toSparsePrecDist, sparseMeanAvgConditional);
var meanAvgConditional = Util.ArrayInit(listSize, i => GaussianOp.MeanAverageConditional(sampleDist[i], meanDist[i], precDist[i], toPrecDist[i]));
TAssert.True(3 == sparseMeanAvgConditional.SparseCount, sparsityErrMsg);
TAssert.True(sparseMeanAvgConditional.MaxDiff(meanAvgConditional) < tolerance, calcErrMsg);
sparseMeanAvgConditional = SparseGaussianList.Constant(listSize, Gaussian.FromMeanAndPrecision(0.5, 0.6), tolerance);
sparseMeanAvgConditional = SparseGaussianListOp.MeanAverageConditional(sparseSamplePoint, sparseMeanDist, sparsePrecDist, toSparsePrecDist, sparseMeanAvgConditional);
meanAvgConditional = Util.ArrayInit(listSize, i => GaussianOp.MeanAverageConditional(samplePoint[i], meanDist[i], precDist[i], toPrecDist[i]));
TAssert.True(3 == sparseMeanAvgConditional.SparseCount, sparsityErrMsg);
TAssert.True(sparseMeanAvgConditional.MaxDiff(meanAvgConditional) < tolerance, calcErrMsg);
sparseMeanAvgConditional = SparseGaussianList.Constant(listSize, Gaussian.FromMeanAndPrecision(0.5, 0.6), tolerance);
sparseMeanAvgConditional = SparseGaussianListOp.MeanAverageConditional(sparseSampleDist, sparsePrecPoint, sparseMeanAvgConditional);
meanAvgConditional = Util.ArrayInit(listSize, i => GaussianOp.MeanAverageConditional(sampleDist[i], precPoint[i]));
TAssert.True(3 == sparseMeanAvgConditional.SparseCount, sparsityErrMsg);
TAssert.True(sparseMeanAvgConditional.MaxDiff(meanAvgConditional) < tolerance, calcErrMsg);
sparseMeanAvgConditional = SparseGaussianList.Constant(listSize, Gaussian.FromMeanAndPrecision(0.5, 0.6), tolerance);
sparseMeanAvgConditional = SparseGaussianListOp.MeanAverageConditional(sparseSamplePoint, sparsePrecPoint, sparseMeanAvgConditional);
meanAvgConditional = Util.ArrayInit(listSize, i => GaussianOp.MeanAverageConditional(samplePoint[i], precPoint[i]));
TAssert.True(3 == sparseMeanAvgConditional.SparseCount, sparsityErrMsg);
TAssert.True(sparseMeanAvgConditional.MaxDiff(meanAvgConditional) < tolerance, calcErrMsg);
// ---------------------------
// Check PrecisionAverageConditional
// ---------------------------
calcErrMsg = "PrecisionAverageConditional" + calcSuffix;
sparsityErrMsg = "PrecisionAverageConditional" + sparsitySuffix;
// Use different common value to ensure this gets properly set
var sparsePrecAvgConditional = SparseGammaList.Constant(listSize, Gamma.FromShapeAndRate(2.1, 3.2), tolerance);
sparsePrecAvgConditional = SparseGaussianListOp.PrecisionAverageConditional(sparseSampleDist, sparseMeanDist, sparsePrecDist, sparsePrecAvgConditional);
var precAvgConditional = Util.ArrayInit(listSize, i => GaussianOp.PrecisionAverageConditional(sampleDist[i], meanDist[i], precDist[i]));
TAssert.True(3 == sparsePrecAvgConditional.SparseCount, sparsityErrMsg);
TAssert.True(sparsePrecAvgConditional.MaxDiff(precAvgConditional) < tolerance, calcErrMsg);
sparsePrecAvgConditional = SparseGammaList.Constant(listSize, Gamma.FromShapeAndRate(2.1, 3.2), tolerance);
sparsePrecAvgConditional = SparseGaussianListOp.PrecisionAverageConditional(sparseSamplePoint, sparseMeanDist, sparsePrecDist, sparsePrecAvgConditional);
precAvgConditional = Util.ArrayInit(listSize, i => GaussianOp.PrecisionAverageConditional(Gaussian.PointMass(samplePoint[i]), meanDist[i], precDist[i]));
TAssert.True(3 == sparsePrecAvgConditional.SparseCount, sparsityErrMsg);
TAssert.True(sparsePrecAvgConditional.MaxDiff(precAvgConditional) < tolerance, calcErrMsg);
sparsePrecAvgConditional = SparseGammaList.Constant(listSize, Gamma.FromShapeAndRate(2.1, 3.2), tolerance);
sparsePrecAvgConditional = SparseGaussianListOp.PrecisionAverageConditional(sparseSampleDist, sparseMeanPoint, sparsePrecDist, sparsePrecAvgConditional);
precAvgConditional = Util.ArrayInit(listSize, i => GaussianOp.PrecisionAverageConditional(sampleDist[i], Gaussian.PointMass(meanPoint[i]), precDist[i]));
TAssert.True(3 == sparsePrecAvgConditional.SparseCount, sparsityErrMsg);
TAssert.True(sparsePrecAvgConditional.MaxDiff(precAvgConditional) < tolerance, calcErrMsg);
sparsePrecAvgConditional = SparseGammaList.Constant(listSize, Gamma.FromShapeAndRate(2.1, 3.2), tolerance);
sparsePrecAvgConditional = SparseGaussianListOp.PrecisionAverageConditional(sparseSamplePoint, sparseMeanPoint, sparsePrecAvgConditional);
precAvgConditional = Util.ArrayInit(listSize, i => GaussianOp.PrecisionAverageConditional(samplePoint[i], meanPoint[i]));
TAssert.True(3 == sparsePrecAvgConditional.SparseCount, sparsityErrMsg);
TAssert.True(sparsePrecAvgConditional.MaxDiff(precAvgConditional) < tolerance, calcErrMsg);
}
