/// <summary>
/// Computes the gradient of the compiled term at the given point.
/// </summary>
/// <param name="arg">The point at which to differentiate.</param>
/// <returns>A tuple, where the first item is the gradient at <paramref name="arg"/> and the second item is
/// the value at <paramref name="arg"/>. That is, the second value is the same as running <see cref="Evaluate"/> on
/// <paramref name="arg"/>.</returns>
/// <remarks>The number at <c>arg[i]</c> is the value assigned to the variable <c>Variables[i]</c>.</remarks>
public static Tuple <double[], double> Differentiate(this ICompiledTerm term, IReadOnlyList <double> arg)
{
var grad = new double[term.Variables.Count];
var val = term.Differentiate(arg, grad);
return(Tuple.Create(grad, val));
}
public Tuple <double[], double> Differentiate(double[] arg, double[] parameters)
{
var combinedArg = arg.Concat(parameters).ToArray();
var diffResult = compiledTerm.Differentiate(combinedArg);
var partialGradient = new double[arg.Length];
Array.Copy(diffResult.Item1, partialGradient, partialGradient.Length);
return(Tuple.Create(partialGradient, diffResult.Item2));
}
static double[] GradientDescent(ICompiledTerm func, double[] init, double stepSize, int iterations)
{
// clone the initial argument
var x = (double[])init.Clone();
// perform the iterations
for (int i = 0; i < iterations; ++i)
{
// compute the gradient
var gradient = func.Differentiate(x).Item1;
// perform a descent step
for (int j = 0; j < x.Length; ++j)
x[j] -= stepSize * gradient[j];
}
return x;
}
public double Differentiate(IReadOnlyList <double> arg, IReadOnlyList <double> parameters, IList <double> grad)
{
Guard.NotNull(arg, nameof(arg));
Guard.MustHold(arg.Count == Variables.Count, ErrorMessages.ArgLength);
Guard.NotNull(grad, nameof(grad));
Guard.MustHold(grad.Count == Variables.Count, ErrorMessages.GradLength);
Guard.NotNull(parameters, nameof(parameters));
Guard.MustHold(parameters.Count == Parameters.Count, ErrorMessages.ParamLength);
var combinedArg = arg.Concat(parameters).ToArray();
var combinedGrad = new double[combinedArg.Length];
var val = compiledTerm.Differentiate(combinedArg, combinedGrad);
for (var i = 0; i < arg.Count; ++i)
{
grad[i] = combinedGrad[i];
}
return(val);
}
static double[] GradientDescent(ICompiledTerm func, double[] init, double stepSize, int iterations)
{
// clone the initial argument
var x = (double[])init.Clone();
var gradient = new double[x.Length];
// perform the iterations
for (int i = 0; i < iterations; ++i)
{
// compute the gradient - fill the gradient array
func.Differentiate(x, gradient);
// perform a descent step
for (int j = 0; j < x.Length; ++j)
{
x[j] -= stepSize * gradient[j];
}
}
return(x);
}
static void RunBenchmark(TextWriter resultWriter, TextWriter logWriter)
{
var fac = new CsvHelper.CsvFactory();
using (var csvWriter = fac.CreateWriter(resultWriter))
{
csvWriter.WriteHeader <BenchmarkResult>();
int[] sizes = { 1000, 2000, 3000, 4000, 5000, 6000, 7000, 8000, 9000, 10000 };
foreach (int sz in sizes)
{
var row = new BenchmarkResult();
var termsCount = sz; row.NumberOfTerms = termsCount;
var varsCount = sz; row.NumberOfVars = varsCount;
var grad = new double[sz];
logWriter.WriteLine("Benchmark for {0} terms and {1} variables", termsCount, varsCount);
logWriter.Write("\tConstructing coefficients ...");
var coefficients = GenerateCoefficients(termsCount, varsCount);
logWriter.WriteLine(" done");
// generate variables
var vars = new Variable[varsCount];
for (var j = 0; j < sz; ++j)
{
vars[j] = new Variable();
}
logWriter.Write("\tGenerating input data ...");
var inputData = new double[1000][];
for (var j = 0; j < inputData.Length; ++j)
{
inputData[j] = RandomDoubles(varsCount);
}
logWriter.WriteLine(" done");
GC.Collect(GC.MaxGeneration, GCCollectionMode.Default, true);
ICompiledTerm compiledTerm = null;
row.CompileMilliseconds = MeasureMsec("Constructing compiled term", logWriter,
() => compiledTerm = ConstructTerm(coefficients, vars));
row.MillisecondsPerManualEval = MeasureMsecPerOp("manual evaluation",
logWriter, inputData.Length, () => inputData.Sum(array => NativeEvaluate(coefficients, array)));
row.MillisecondsPerCompiledEval = MeasureMsecPerOp("AutoDiff compiled evaluation",
logWriter, inputData.Length, () => inputData.Sum(array => compiledTerm.Evaluate(array)));
row.MillisecondsPerCompiledDiff = MeasureMsecPerOp("compiled differentiation",
logWriter, inputData.Length, () =>
{
var sum = 0.0;
foreach (var array in inputData)
{
var val = compiledTerm.Differentiate(array, grad);
sum += val + grad.Sum();
}
return(sum);
});
csvWriter.WriteRecord(row);
}
}
}
