internal ScaleOffsetExpression(VectorParameterExpression operand, T scale, T offset)
: base(UnaryElementWiseOperation.ScaleOffset, operand)
{
Operand = operand;
Scale = scale;
Offset = offset;
}
public void AddBinaryElementWiseOperation <T>(VectorParameterExpression a, VectorParameterExpression b,
VectorParameterExpression result, ExpressionType operation)
{
_operations.Add(
Expression.Assign(result,
Expression.MakeBinary(operation, a, b))
);
}
protected Expression VisitParameter(VectorParameterExpression variable, StringBuilder builder)
{
Write(variable.Name, builder);
if (variable.ParameterType == Expressions.ParameterType.Argument)
{
Write(indexing, builder);
}
return(variable);
}
public Expression AddInverseExpression(VectorParameterExpression a)
{
var paramA = a as ReferencingVectorParameterExpression <double>;
if (paramA.IsScalar)
{
return(new ConstantExpression <double>(1 / paramA.ScalarValue));
}
return(AddLocalAssignment <double>(new ScaleInverseExpression <double>(a, 1)));
}
public Expression AddHalfInverseSquareRootExpression(VectorParameterExpression a)
{
var paramA = a as ReferencingVectorParameterExpression <double>;
if (paramA.IsScalar)
{
return(new ConstantExpression <double>(0.5 / Math.Sqrt(paramA.ScalarValue)));
}
var newLocal = AddLocalAssignment <double>(new ScaleInverseExpression <double>(a, 1));
return(AddLocalAssignment <double>(new ScaleOffsetExpression <double>(newLocal, 0.5, 0)));
}
/// <summary>
/// Performs reverse-mode (adjoint) automatic differentiation
/// </summary>
/// <param name="builder"></param>
/// <param name="dependentVariable"></param>
/// <param name="independentVariables"></param>
public static void Differentiate(BlockExpressionBuilder builder,
out IList<Expression> derivativeExpressions,
VectorParameterExpression dependentVariable,
params VectorParameterExpression[] independentVariables)
{
if (independentVariables.Length == 0)
{
derivativeExpressions = null;
return;
}
var block = builder.ToBlock();
// change the numbering of variables; arguments first, then locals
List<int>[] jLookup;
Function[] f;
// we want a list of functions which can be unary or binary (in principle high order if it makes sense) and a look-up
// each function is associated with a parameter, which is either an argument or a local
// for each function index we return a list of the indices of the functions that reference the parameter
GetFunctions(block, out f, out jLookup);
int N = dependentVariable.Index;
bool[] derivativeRequired; int[] derivativeExpressionIndex;
IdentifyNeccesaryDerivatives(N, jLookup, independentVariables, out derivativeRequired, out derivativeExpressionIndex);
// the list of operations needed to calculate the derivatives (*all* derivatives)
derivativeExpressions = new Expression[independentVariables.Length];
var dxNdx = new Expression[N + 1];
dxNdx[N] = new ConstantExpression<double>(1.0);
for (int i = N - 1; i >= 0; i--)
{
if (!derivativeRequired[i]) continue;
// dxN / dxi
// need to find all operation indices j such that p(j) contains i
// that is, all functions that have xi as an argument (these must therefore have a higher index than i)
VectorParameterExpression total = new ConstantExpression<double>(0);
var xi = f[i].Parameter;
//need sum of dfj/dxi dXN/dxj
foreach (var j in jLookup[i])
{
var fj = f[j];
var dfjdxi = Differentiate(fj, xi, builder); // dfj/dxi
var dXNdxj = dxNdx[j]; // dXN/dxj
var product = builder.AddProductExpression(dfjdxi, dXNdxj);
total = builder.AddAdditionExpression(total, product);
}
dxNdx[i] = total;
int targetIndex = derivativeExpressionIndex[i];
if (targetIndex != -1) derivativeExpressions[targetIndex] = total;
}
}
public Expression AddGaussian(VectorParameterExpression a)
{
var paramA = a as ReferencingVectorParameterExpression <double>;
if (paramA.IsScalar)
{
return(new ConstantExpression <double>(_gaussianScaling * Math.Exp(-paramA.ScalarValue * paramA.ScalarValue / 2)));
}
var newLocal1 = AddLocalAssignment <double>(Expression.MakeBinary(ExpressionType.Multiply, paramA, paramA));
var newLocal2 = AddLocalAssignment <double>(new ScaleOffsetExpression <double>(newLocal1, -0.5, 0));
var newLocal3 = AddLocalAssignment <double>(new UnaryMathsExpression(UnaryElementWiseOperation.Exp, newLocal2));
return(AddLocalAssignment <double>(new ScaleOffsetExpression <double>(newLocal3, _gaussianScaling, 0)));
}
/// <summary>
/// Adds a division and negation expression efficiently; assumes that scalar independent variables can be combined
/// i.e. that any differentiation has already occurred
/// </summary>
public Expression AddNegateDivideExpression(VectorParameterExpression a, VectorParameterExpression b)
{
var paramA = a as ReferencingVectorParameterExpression <double>;
var paramB = b as ReferencingVectorParameterExpression <double>;
if (paramA.IsScalar && paramB.IsScalar)
{
return(new ConstantExpression <double>(
-paramA.ScalarValue /
paramB.ScalarValue));
}
var newLocal = AddLocalAssignment <double>(Expression.Divide(a, b));
return(AddLocalAssignment <double>(new ScaleOffsetExpression <double>(newLocal, -1, 0)));
}
public VectorOperationExpression(VectorParameterExpression result)
{
Result = result;
}
public static ScaleOffsetExpression <T> ScaleOffset <T>(VectorParameterExpression operand, T scale, T offset)
{
return(new ScaleOffsetExpression <T>(operand, scale, offset));
}
public static ScaleInverseExpression <T> ScaleInverse <T>(VectorParameterExpression operand, T scale)
{
return(new ScaleInverseExpression <T>(operand, scale));
}
public static UnaryMathsExpression MakeUnary(UnaryElementWiseOperation unaryType, VectorParameterExpression operand)
{
return(new UnaryMathsExpression(unaryType, operand));
}
internal UnaryMathsExpression(UnaryElementWiseOperation unaryType, VectorParameterExpression operand)
{
UnaryType = unaryType;
Operand = operand;
}
public VectorOperationExpression(VectorParameterExpression result)
{
Result = result;
}
private static void UpdateOperationIndices(List<int>[] operationIndices,
VectorParameterExpression expression, int operationIndex)
{
if (expression.Index != -1) operationIndices[expression.Index].Add(operationIndex); // -1 indices a constant
}
private static void IdentifyNeccesaryDerivatives(int N, List<int>[] jLookup,
VectorParameterExpression[] independentVariables,
out bool[] derivativeRequired, out int[] derivativeExpressionIndex)
{
derivativeRequired = new bool[N + 1]; // true if the derivative expression is required
derivativeExpressionIndex = Enumerable.Repeat(-1, N + 1).ToArray();
// the index of the independent variable that the derivative expression refers to
for (int i = 0; i < independentVariables.Length; ++i)
{
int expessionindex = independentVariables[i].Index;
derivativeExpressionIndex[expessionindex] = i;
derivativeRequired[expessionindex] = true;
}
for (int i = 0; i < N; i++)
{
if (derivativeRequired[i])
{
foreach (int j in jLookup[i])
{
derivativeRequired[j] = true;
}
}
}
}
private static NArray <T> GetArray <T>(VectorParameterExpression expression)
{
return((expression as ReferencingVectorParameterExpression <T>).Array);
}
internal ScaleInverseExpression(VectorParameterExpression operand, T scale)
: base(UnaryElementWiseOperation.ScaleInverse, operand)
{
Scale = scale;
}
