/// <summary>
/// Sets the current array of distributions to the given array of distributions
/// </summary>
/// <param name="value"></param>
public void SetTo(DistributionArray <T> value)
{
if (IsValueType)
{
// Array.Copy does not work properly with reference types.
if (value.Count != this.Count)
{
throw new ArgumentException("value.Count (" + value.Count + ") != this.Count (" + this.Count + ")");
}
Array.Copy(value.array, array, array.Length);
}
else
{
ModifyAll(value.array, delegate(T item, T aItem)
{
if (object.ReferenceEquals(item, null))
{
return((T)aItem.Clone());
}
else
{
item.SetTo(aItem);
} return(item);
});
}
}
public static double EvaluateLn <DistributionType, DomainType>(DistributionArray <DistributionType> darray, DomainType[,] array)
where DistributionType : CanGetLogProb <DomainType>,
ICloneable,
SettableTo <DistributionType>,
SettableToUniform,
Diffable,
SettableToProduct <DistributionType>,
SettableToRatio <DistributionType>,
SettableToPower <DistributionType>,
SettableToWeightedSum <DistributionType>,
CanGetLogAverageOf <DistributionType>,
CanGetAverageLog <DistributionType>
{
if (darray.array2D == null)
{
throw new ArgumentException("DistributionArray has rank " + darray.array.Rank + ", point has rank " + array.Rank);
}
if (darray.array2D.GetLength(0) != array.GetLength(0))
{
throw new ArgumentException("DistributionArray lengths (" +
StringUtil.CollectionToString(darray.GetLengths(), ",") + ") do not match array lengths (" +
StringUtil.CollectionToString(StringUtil.ArrayDimensions(array), ",") + ")");
}
double sum = 0.0;
for (int i = 0; i < array.GetLength(0); i++)
{
for (int j = 0; j < array.GetLength(1); j++)
{
sum += darray[i, j].GetLogProb(array[i, j]);
}
}
return(sum);
}
public static DistributionArray <T> FromCloning(T value, params int[] lengths)
{
DistributionArray <T> result = FromLength(lengths);
result.InitializeTo(value);
return(result);
}
/// <summary>
/// Clones the array and all items in the array.
/// </summary>
/// <returns>A new DistributionArray.</returns>
public object Clone()
{
Array newArray = (Array)array.Clone();
DistributionArray <T> result = new DistributionArray <T>(newArray);
result.ModifyAll(delegate(T item) { return((T)item.Clone()); });
return(result);
}
/// <summary>
/// Sets the current instance to an array of distributions each element
/// of which is a power of the corresponding element in a source distribution array
/// </summary>
/// <param name="a">The source distribution array</param>
/// <param name="exponent">The exponent</param>
public void SetToPower(DistributionArray <T> a, double exponent)
{
ModifyAll(a.array,
delegate(T item, T aItem)
{
item.SetToPower(aItem, exponent);
return(item);
});
}
/// <summary>
/// Sets the current instance to an array of distributions each element
/// of which is a product of the corresponding distributions in two given
/// distribution arrays
/// </summary>
/// <param name="a">The first distribution array</param>
/// <param name="b">The second distribution array</param>
public void SetToProduct(DistributionArray <T> a, DistributionArray <T> b)
{
ModifyAll(a.array, b.array,
delegate(T item, T aItem, T bItem)
{
item.SetToProduct(aItem, bItem);
return(item);
});
}
/// <summary>
/// Sets the current instance to an array of distributions each element
/// of which is a weighted sum of the corresponding distributions in two given
/// distribution arrays
/// </summary>
/// <param name="weight1">The first weight</param>
/// <param name="a">The first distribution array</param>
/// <param name="weight2">The second weight</param>
/// <param name="b">The second distribution array</param>
public void SetToSum(double weight1, DistributionArray <T> a, double weight2, DistributionArray <T> b)
{
ModifyAll(a.array, b.array,
delegate(T item, T aItem, T bItem)
{
item.SetToSum(weight1, aItem, weight2, bItem);
return(item);
});
}
/// <summary>
/// Sets the current instance to an array of distributions each element
/// of which is a ratio of the corrresponding distributions in two given
/// distribution arrays
/// </summary>
/// <param name="numerator">The numerator distribution array</param>
/// <param name="denominator">The denominator distribution array</param>
public void SetToRatio(DistributionArray <T> numerator, DistributionArray <T> denominator)
{
ModifyAll(numerator.array, denominator.array,
delegate(T item, T aItem, T bItem)
{
item.SetToRatio(aItem, bItem);
return(item);
});
}
/// <summary>
/// The maximum difference in parameter values between this distribution and that distribution
/// </summary>
/// <param name="that">That distribution</param>
/// <returns>The maximum difference</returns>
public virtual double MaxDiff(object that)
{
DistributionArray <T> thatd = that as DistributionArray <T>;
if ((object)thatd == null)
{
return(Double.PositiveInfinity);
}
return(Distribution.MaxDiff(this.array, thatd.array));
}
public static DistributionArray <T> CloneElementsOf(Array array)
{
DistributionArray <T> result = new DistributionArray <T>(StringUtil.ArrayDimensions(array));
result.ModifyAll(array, delegate(T item, T aItem)
{
return((T)aItem.Clone());
});
return(result);
}
/// <summary>
/// The expected logarithm of that distribution under this distribution.
/// </summary>
/// <param name="that">The distribution to take the logarithm of.</param>
/// <returns><c>sum_x this.Evaluate(x)*Math.Log(that.Evaluate(x))</c></returns>
/// <remarks>This is also known as the cross entropy.
/// For a DistributionArray, this specializes to:
/// <c>sum_i sum_x this[i].Evaluate(x)*Math.Log(that[i].Evaluate(x))</c>
/// = <c>sum_i this[i].GetAverageLog(that[i])</c>
/// </remarks>
public double GetAverageLog(DistributionArray <T> that)
{
double sum = 0.0;
ForEach(that.array, delegate(T item, T thatItem)
{
sum += item.GetAverageLog(thatItem);
});
return(sum);
}
public static DistributionArray <T> CloneElementsOfCollection(ICollection <T> array)
{
DistributionArray <T> result = new DistributionArray <T>(array.Count);
int i = 0;
foreach (T item in array)
{
result.array1D[i++] = (T)item.Clone();
}
return(result);
}
/// <summary>
/// The maximum difference across all corresponding distributions in
/// this distribution array and that distribution array
/// </summary>
/// <param name="that">That distribution array</param>
/// <returns>The maximum difference</returns>
public double MaxDiff(object that)
{
DistributionArray <T> thatd = that as DistributionArray <T>;
if ((object)thatd == null)
{
return(Double.PositiveInfinity);
}
try {
double diff = 0.0;
ForEach(thatd.array,
delegate(T item, T bItem)
{
diff = Math.Max(diff, item.MaxDiff(bItem));
});
return(diff);
} catch {
return(Double.PositiveInfinity);
}
}
/// <summary>
/// Sets the current instance to an array of distributions each element
/// of which is a power of the corresponding element in a source distribution array
/// </summary>
/// <param name="a">The source distribution array</param>
/// <param name="exponent">The exponent</param>
public void SetToPower(DistributionArray <T> a, double exponent)
{
Distribution.SetToPower(array, a.array, exponent);
}
/// <summary>
/// Sets the current instance to an array of distributions each element
/// of which is a product of the corresponding distributions in two given
/// distribution arrays
/// </summary>
/// <param name="a">The first distribution array</param>
/// <param name="b">The second distribution array</param>
public void SetToProduct(DistributionArray <T> a, DistributionArray <T> b)
{
Distribution.SetToProduct(array, a.array, b.array);
}
/// <summary>
/// The expected logarithm of that distribution under this distribution.
/// </summary>
/// <param name="that">The distribution to take the logarithm of.</param>
/// <returns><c>sum_x this.Evaluate(x)*Math.Log(that.Evaluate(x))</c></returns>
/// <remarks>This is also known as the cross entropy.
/// For a DistributionArray, this specializes to:
/// <c>sum_i sum_x this[i].Evaluate(x)*Math.Log(that[i].Evaluate(x))</c>
/// = <c>sum_i this[i].GetAverageLog(that[i])</c>
/// </remarks>
public double GetAverageLog(DistributionArray <T> that)
{
return(Distribution.GetAverageLog(array, that.array));
}
/// <summary>
/// Copy constructor.
/// </summary>
/// <param name="that"></param>
public DistributionArray(DistributionArray <T> that)
: this(that.GetLengths())
{
InitializeTo(that);
}
/// <summary>
/// Sets the current instance to an array of distributions each element
/// of which is a weighted sum of the corresponding distributions in two given
/// distribution arrays
/// </summary>
/// <param name="weight1">The first weight</param>
/// <param name="a">The first distribution array</param>
/// <param name="weight2">The second weight</param>
/// <param name="b">The second distribution array</param>
public void SetToSum(double weight1, DistributionArray <T> a, double weight2, DistributionArray <T> b)
{
Distribution.SetToSum(array, weight1, a.array, weight2, b.array);
}
public void InitializeTo(DistributionArray <T> that)
{
if (IsValueType)
{
//ModifyAll(that.array, delegate(T item, T thatItem) { return thatItem; });
if (array1D != null)
{
for (int i = 0; i < array1D.Length; i++)
{
array1D[i] = that.array1D[i];
}
}
else if (array2D != null)
{
for (int i = 0; i < array2D.GetLength(0); i++)
{
for (int j = 0; j < array2D.GetLength(1); j++)
{
array2D[i, j] = that.array2D[i, j];
}
}
}
else
{
int[] mIndex = new int[array.Rank];
for (int index = 0; index < Count; index++)
{
StringUtil.LinearIndexToMultidimensionalIndex(index, strides, mIndex);
T item = (T)that.array.GetValue(mIndex);
array.SetValue(item, mIndex);
}
}
}
else
{
//ModifyAll(that.array, delegate(T item, T thatItem) { return (T)thatItem.Clone(); });
if (array1D != null)
{
for (int i = 0; i < array1D.Length; i++)
{
array1D[i] = (T)that.array1D[i].Clone();
}
}
else if (array2D != null)
{
for (int i = 0; i < array2D.GetLength(0); i++)
{
for (int j = 0; j < array2D.GetLength(1); j++)
{
array2D[i, j] = (T)that.array2D[i, j].Clone();
}
}
}
else
{
int[] mIndex = new int[array.Rank];
for (int index = 0; index < Count; index++)
{
StringUtil.LinearIndexToMultidimensionalIndex(index, strides, mIndex);
T item = (T)that.array.GetValue(mIndex);
array.SetValue((T)item.Clone(), mIndex);
}
}
}
}
public void SetTo(DistributionArray <T> that)
{
base.SetTo(that);
}
/// <summary>
/// Sets the current instance to an array of distributions each element
/// of which is a ratio of the corrresponding distributions in two given
/// distribution arrays
/// </summary>
/// <param name="numerator">The numerator distribution array</param>
/// <param name="denominator">The denominator distribution array</param>
public void SetToRatio(DistributionArray <T> numerator, DistributionArray <T> denominator)
{
Distribution.SetToRatio(array, numerator.array, denominator.array);
}
