/// <summary>Sets all values in a matrix to zero</summary>
/// <param name='c'>a correlation matrix</param>
static public void SetZero(this ICorrelationMatrix c)
{
int size = c.NumberOfRows;
c.Resize(0);
c.Resize(size); // TODO consider doing this without resize
}
public AssetFxModel(TO_AssetFxModel transportObject, ICurrencyProvider currencyProvider, ICalendarProvider calendarProvider)
: this(transportObject.BuildDate, new FundingModel(transportObject.FundingModel, currencyProvider, calendarProvider))
{
_assetCurves = transportObject.AssetCurves.ToDictionary(x => x.Key, x => x.Value.GetPriceCurve(currencyProvider, calendarProvider));
_assetVols = transportObject.AssetVols.ToDictionary(x => new VolSurfaceKey(x.Key, currencyProvider), y => VolSurfaceFactory.GetVolSurface(y.Value, currencyProvider));
_fixings = transportObject.Fixings?.ToDictionary(x => x.Key, x => (IFixingDictionary) new FixingDictionary(x.Value)) ?? new Dictionary <string, IFixingDictionary>();
CorrelationMatrix = transportObject.CorrelationMatrix == null?null:CorrelationMatrixFactory.GetCorrelationMatrix(transportObject.CorrelationMatrix);
}
/// <summary>Sum up the correlations between a given entity and the entities in a collection</summary>
/// <param name="correlation">the correlation matrix</param>
/// <param name="entity_id">the numerical ID of the entity</param>
/// <param name="entities">a collection containing the numerical IDs of the entities to compare to</param>
/// <param name="q">score exponent</param>
/// <returns>the correlation sum</returns>
public static double SumUp(this ICorrelationMatrix correlation, int entity_id, ICollection <int> entities, float q = 1.0f)
{
double result = 0;
foreach (int entity_id2 in entities)
{
result += Math.Pow(correlation[entity_id, entity_id2], q);
}
return(result);
}
public static TO_CorrelationMatrix GetTransportObject(this ICorrelationMatrix matrix)
{
switch (matrix)
{
case CorrelationMatrix cm:
return(cm.GetTransportObject());
case CorrelationTimeVector ctv:
return(ctv.GetTransportObject());
case CorrelationMatrixCollection ctc:
return(ctc.GetTransportObject());
default:
throw new Exception("Unable to serialize correlation object");
}
}
/// <summary>Get all entities that are positively correlated to an entity, sorted by correlation</summary>
/// <param name="c">a correlation matrix</param>
/// <param name="entity_id">the entity ID</param>
/// <returns>a sorted list of all entities that are positively correlated to entitiy_id</returns>
public static IList <int> GetPositivelyCorrelatedEntities(this ICorrelationMatrix c, int entity_id)
{
int num_entities = c.NumberOfRows;
var result = new List <int>();
for (int i = 0; i < num_entities; i++)
{
if (c[i, entity_id] > 0)
{
result.Add(i);
}
}
result.Remove(entity_id);
result.Sort(delegate(int i, int j) { return(c[j, entity_id].CompareTo(c[i, entity_id])); });
return(result);
}
void InitModel()
{
int num_entities = 0;
switch (Correlation)
{
case RatingCorrelationType.BinaryCosine:
correlation_matrix = new BinaryCosine(num_entities);
break;
case RatingCorrelationType.Jaccard:
correlation_matrix = new Jaccard(num_entities);
break;
case RatingCorrelationType.ConditionalProbability:
correlation_matrix = new ConditionalProbability(num_entities);
break;
case RatingCorrelationType.BidirectionalConditionalProbability:
correlation_matrix = new BidirectionalConditionalProbability(num_entities, Alpha);
break;
case RatingCorrelationType.Cooccurrence:
correlation_matrix = new Cooccurrence(num_entities);
break;
case RatingCorrelationType.Pearson:
correlation_matrix = new Pearson(num_entities, Alpha);
break;
case RatingCorrelationType.RatingCosine:
correlation_matrix = new RatingCosine(num_entities, Alpha);
break;
default:
throw new NotImplementedException(string.Format("Support for {0} is not implemented", Correlation));
}
if (correlation_matrix is IBinaryDataCorrelationMatrix)
{
((IBinaryDataCorrelationMatrix)correlation_matrix).Weighted = WeightedBinary;
}
}
/// <summary>Get the k nearest neighbors of a given entity</summary>
/// <param name="c">a correlation matrix</param>
/// <param name="entity_id">the numerical ID of the entity</param>
/// <param name="k">the neighborhood size</param>
/// <returns>a sorted list containing the numerical IDs of the k nearest neighbors</returns>
public static IList <int> GetNearestNeighbors(this ICorrelationMatrix c, int entity_id, uint k)
{
int num_entities = c.NumberOfRows;
var entities = new List <int>();
for (int i = 0; i < num_entities; i++)
{
entities.Add(i);
}
entities.Remove(entity_id);
entities.Sort(delegate(int i, int j) { return(c[j, entity_id].CompareTo(c[i, entity_id])); });
if (k < entities.Count)
{
return(entities.GetRange(0, (int)k).ToArray());
}
else
{
return(entities.ToArray());
}
}
void InitModel()
{
int num_entities = 0;
switch (Correlation)
{
case RatingCorrelationType.BinaryCosine:
correlation_matrix = new BinaryCosine(num_entities);
break;
case RatingCorrelationType.Jaccard:
correlation_matrix = new Jaccard(num_entities);
break;
case RatingCorrelationType.ConditionalProbability:
correlation_matrix = new ConditionalProbability(num_entities);
break;
case RatingCorrelationType.BidirectionalConditionalProbability:
correlation_matrix = new BidirectionalConditionalProbability(num_entities, Alpha);
break;
case RatingCorrelationType.Cooccurrence:
correlation_matrix = new Cooccurrence(num_entities);
break;
case RatingCorrelationType.Pearson:
correlation_matrix = new Pearson(num_entities, Alpha);
break;
case RatingCorrelationType.RatingCosine:
correlation_matrix = new RatingCosine(num_entities, Alpha);
break;
default:
throw new NotImplementedException(string.Format("Support for {0} is not implemented", Correlation));
}
if (correlation_matrix is IBinaryDataCorrelationMatrix)
((IBinaryDataCorrelationMatrix) correlation_matrix).Weighted = WeightedBinary;
}
public CholeskyWithTime(ICorrelationMatrix correlationMatrix, IAssetFxModel model)
{
_matrix = correlationMatrix;
_model = model;
}
public Cholesky(ICorrelationMatrix correlationMatrix)
{
_matrix = correlationMatrix;
}
