public void QuadraticConstraintConstructorTest()
{
IObjectiveFunction objective = null;
double[,] quadraticTerms =
{
{ 1, 2, 3 },
{ 4, 5, 6 },
{ 7, 8, 9 },
};
double[] linearTerms = { 1, 2, 3 };
objective = new NonlinearObjectiveFunction(3, f => f[0] + f[1] + f[2]);
QuadraticConstraint target = new QuadraticConstraint(objective,
quadraticTerms, linearTerms,
ConstraintType.LesserThanOrEqualTo, 0);
var function = target.Function;
var gradient = target.Gradient;
FiniteDifferences fd = new FiniteDifferences(3, function);
double[][] x =
{
new double[] { 1, 2, 3 },
new double[] { 3, 1, 4 },
new double[] { -6 , 5, 9 },
new double[] { 31, 25, 246 },
new double[] { -0.102, 0, 10 },
};
{ // Function test
for (int i = 0; i < x.Length; i++)
{
double expected =
(x[i].Multiply(quadraticTerms)).InnerProduct(x[i])
+ linearTerms.InnerProduct(x[i]);
double actual = function(x[i]);
Assert.AreEqual(expected, actual, 1e-8);
}
}
{ // Gradient test
for (int i = 0; i < x.Length; i++)
{
double[] expected = fd.Compute(x[i]);
double[] actual = gradient(x[i]);
for (int j = 0; j < actual.Length; j++)
Assert.AreEqual(expected[j], actual[j], 1e-8);
}
}
}
public void ComputeTest()
{
int numberOfParameters = 2;
FiniteDifferences target = new FiniteDifferences(numberOfParameters);
double[] inputs = { -1, 0.4 };
target.Function = BroydenFletcherGoldfarbShannoTest.rosenbrockFunction;
double[] expected = BroydenFletcherGoldfarbShannoTest.rosenbrockGradient(inputs);
double[] actual = target.Compute(inputs);
Assert.IsTrue(expected.IsEqual(actual, 0.05));
}
public void ComputeTest2()
{
// Create a simple function with two parameters: f(x,y) = x² + y
Func<double[], double> function = x => Math.Pow(x[0], 2) + x[1];
// The gradient w.r.t to x should be 2x,
// the gradient w.r.t to y should be 1
// Create a new finite differences calculator
var calculator = new FiniteDifferences(2, function);
// Evaluate the gradient function at the point (2, -1)
double[] result = calculator.Compute(2, -1); // answer is (4, 1)
Assert.AreEqual(4, result[0], 1e-10);
Assert.AreEqual(1, result[1], 1e-10);
Assert.IsFalse(Double.IsNaN(result[0]));
Assert.IsFalse(Double.IsNaN(result[1]));
}
public void GradientTest()
{
for (double a = 0.1; a < 3; a += 0.1)
{
for (double b = 0.1; b < 3; b += 0.1)
{
var target = new BetaDistribution(a, b);
Assert.AreEqual(a, target.Alpha);
Assert.AreEqual(b, target.Beta);
FiniteDifferences fd = new FiniteDifferences(2);
fd.Function = (double[] parameters) => BetaDistribution.LogLikelihood(samples, parameters[0], parameters[1]);
double[] expected = fd.Compute(a, b);
double[] actual = BetaDistribution.Gradient(samples, a, b);
Assert.IsTrue(expected[0].IsRelativelyEqual(actual[0], 0.05));
Assert.IsTrue(expected[1].IsRelativelyEqual(actual[1], 0.05));
}
}
}
public void QuadraticConstructorTest()
{
double[,] quadraticTerms =
{
{ 1, 2, 3 },
{ 2, 5, 6 },
{ 3, 6, 9 },
};
double[] linearTerms = { 1, 2, 3 };
var target = new QuadraticObjectiveFunction(quadraticTerms, linearTerms);
var function = target.Function;
var gradient = target.Gradient;
FiniteDifferences fd = new FiniteDifferences(3, function);
double[][] x =
{
new double[] { 1, 2, 3 },
new double[] { 3, 1, 4 },
new double[] { -6 , 5, 9 },
new double[] { 31, 25, 246 },
new double[] { -0.102, 0, 10 },
};
{ // Function test
for (int i = 0; i < x.Length; i++)
{
double expected = 0.5 *
(x[i].Multiply(quadraticTerms)).InnerProduct(x[i])
+ linearTerms.InnerProduct(x[i]);
double actual = function(x[i]);
Assert.AreEqual(expected, actual, 1e-8);
}
}
{ // Gradient test
for (int i = 0; i < x.Length; i++)
{
double[] expected = fd.Compute(x[i]);
double[] actual = gradient(x[i]);
for (int j = 0; j < actual.Length; j++)
Assert.AreEqual(expected[j], actual[j], 1e-8);
}
}
}
public void HomogeneousTest2()
{
double[,] quadraticTerms =
{
{ 1, 0, 1 },
{ 0, 2, 0 },
{ 1, 0, 1 },
};
double[] linearTerms = { 0, 0, 0 };
var target = new QuadraticObjectiveFunction(quadraticTerms, linearTerms);
var function = target.Function;
var gradient = target.Gradient;
FiniteDifferences fd = new FiniteDifferences(3, function);
double[][] x =
{
new double[] { 1, 2, 3 },
new double[] { 3, 1, 4 },
new double[] { -6 , 5, 9 },
new double[] { 31, 25, 246 },
new double[] { -0.102, 0, 10 },
};
{ // Gradient test
for (int i = 0; i < x.Length; i++)
{
double[] expected = fd.Compute(x[i]);
double[] actual = gradient(x[i]);
for (int j = 0; j < actual.Length; j++)
Assert.AreEqual(expected[j], actual[j], 1e-8);
}
}
}
private static double[] finiteDifferences(double[][] input, double[] output, bool stochastic)
{
LogisticRegression regression;
LogisticGradientDescent teacher;
regression = new LogisticRegression(inputs: 2);
teacher = new LogisticGradientDescent(regression)
{
Stochastic = stochastic,
LearningRate = 1e-4,
};
FiniteDifferences diff = new FiniteDifferences(3);
diff.Function = (x) =>
{
for (int i = 0; i < x.Length; i++)
regression.Coefficients[i] = x[i];
return regression.GetLogLikelihood(input, output);
};
return diff.Compute(regression.Coefficients);
}
private static double[] finiteDifferences(double[][] input, double[] output, bool stochastic)
{
LogisticRegression regression;
regression = new LogisticRegression(inputs: 2);
FiniteDifferences diff = new FiniteDifferences(3);
diff.Function = (x) =>
{
for (int i = 0; i < x.Length; i++)
regression.Coefficients[i] = x[i];
return regression.GetLogLikelihood(input, output);
};
return diff.Compute(regression.Coefficients);
}
public void GradientTest()
{
// Creates a sequence classifier containing 2 hidden Markov Models
// with 2 states and an underlying Normal distribution as density.
MultivariateNormalDistribution density = new MultivariateNormalDistribution(3);
var hmm = new HiddenMarkovClassifier<MultivariateNormalDistribution>(2, new Ergodic(2), density);
double[][][] inputs =
{
new [] { new double[] { 0, 1, 0 }, new double[] { 0, 1, 0 }, new double[] { 0, 1, 0 } },
new [] { new double[] { 1, 6, 2 }, new double[] { 2, 1, 6 }, new double[] { 1, 1, 0 } },
new [] { new double[] { 9, 1, 0 }, new double[] { 0, 1, 5 }, new double[] { 0, 0, 0 } },
};
int[] outputs =
{
0, 0, 1
};
var function = new MarkovMultivariateFunction(hmm);
var model = new HiddenConditionalRandomField<double[]>(function);
var target = new ForwardBackwardGradient<double[]>(model);
FiniteDifferences diff = new FiniteDifferences(function.Weights.Length);
diff.Function = parameters => func(model, parameters, inputs, outputs);
double[] expected = diff.Compute(function.Weights);
double[] actual = target.Gradient(function.Weights, inputs, outputs);
for (int i = 0; i < actual.Length; i++)
{
Assert.AreEqual(expected[i], actual[i], 0.05);
Assert.IsFalse(double.IsNaN(actual[i]));
Assert.IsFalse(double.IsNaN(expected[i]));
}
}
public void GradientTest4()
{
var hmm = IndependentMarkovClassifierPotentialFunctionTest.CreateModel2();
var function = new MarkovMultivariateFunction(hmm);
var model = new HiddenConditionalRandomField<double[]>(function);
var target = new ForwardBackwardGradient<double[]>(model);
target.Regularization = 0;
FiniteDifferences diff = new FiniteDifferences(function.Weights.Length);
diff.Function = parameters => func(model, parameters,
IndependentMarkovClassifierPotentialFunctionTest.sequences,
IndependentMarkovClassifierPotentialFunctionTest.labels);
double[] expected = diff.Compute(function.Weights);
double[] actual = target.Gradient(function.Weights,
IndependentMarkovClassifierPotentialFunctionTest.sequences,
IndependentMarkovClassifierPotentialFunctionTest.labels);
for (int i = 0; i < actual.Length; i++)
{
if (double.IsNaN(expected[i]))
continue;
Assert.AreEqual(expected[i], actual[i], 1e-5);
Assert.IsFalse(double.IsNaN(actual[i]));
}
}
public void GradientTest2()
{
HiddenMarkovClassifier hmm = HiddenMarkovClassifierPotentialFunctionTest.CreateModel1();
var function = new MarkovDiscreteFunction(hmm);
var model = new HiddenConditionalRandomField<int>(function);
var target = new ForwardBackwardGradient<int>(model);
FiniteDifferences diff = new FiniteDifferences(function.Weights.Length);
diff.Function = parameters => func(model, parameters);
double[] expected = diff.Compute(function.Weights);
double[] actual = target.Gradient(function.Weights, inputs, outputs);
for (int i = 0; i < actual.Length; i++)
{
Assert.AreEqual(expected[i], actual[i], 1e-5);
Assert.IsFalse(double.IsNaN(actual[i]));
Assert.IsFalse(double.IsNaN(expected[i]));
}
}
public void GradientTest2()
{
var hmm = CreateModel3();
var function = new MarkovMultivariateFunction(hmm);
var model = new HiddenConditionalRandomField<double[]>(function);
var target = new ForwardBackwardGradient<double[]>(model);
var inputs = sequences2;
var outputs = labels2;
double[] actual = target.Gradient(function.Weights, inputs, outputs);
FiniteDifferences diff = new FiniteDifferences(function.Weights.Length);
diff.Function = parameters => func(model, parameters, inputs, outputs);
double[] expected = diff.Compute(function.Weights);
for (int i = 0; i < actual.Length; i++)
{
Assert.AreEqual(expected[i], actual[i], 1e-3);
Assert.IsFalse(double.IsNaN(actual[i]));
Assert.IsFalse(double.IsNaN(expected[i]));
}
}
public void GradientDeoptimizeTest3()
{
double[][][] sequences2;
int[] labels2;
var hmm = CreateModel3(out sequences2, out labels2);
var function = new MarkovMultivariateFunction(hmm);
#pragma warning disable 0618
function.Deoptimize();
#pragma warning restore 0618
var model = new HiddenConditionalRandomField<double[]>(function);
var target = new ForwardBackwardGradient<double[]>(model);
target.Regularization = 2;
var inputs = sequences2;
var outputs = labels2;
FiniteDifferences diff = new FiniteDifferences(function.Weights.Length);
diff.Function = parameters => func(model, parameters, inputs, outputs, target.Regularization);
double[] expected = diff.Compute(function.Weights);
double[] actual = target.Gradient(function.Weights, inputs, outputs);
for (int i = 0; i < actual.Length; i++)
{
double e = expected[i];
double a = actual[i];
Assert.AreEqual(e, a, 1e-3);
Assert.IsFalse(double.IsNaN(actual[i]));
Assert.IsFalse(double.IsNaN(expected[i]));
}
}
public void RunTest1()
{
// Example from https://en.wikipedia.org/wiki/Gauss%E2%80%93Newton_algorithm
double[,] data =
{
{ 0.03, 0.1947, 0.425, 0.626, 1.253, 2.500, 3.740 },
{ 0.05, 0.127, 0.094, 0.2122, 0.2729, 0.2665, 0.3317}
};
double[][] inputs = data.GetRow(0).ToArray();
double[] outputs = data.GetRow(1);
RegressionFunction rate = (double[] weights, double[] xi) =>
{
double x = xi[0];
return (weights[0] * x) / (weights[1] + x);
};
RegressionGradientFunction grad = (double[] weights, double[] xi, double[] result) =>
{
double x = xi[0];
FiniteDifferences diff = new FiniteDifferences(2);
diff.Function = (bla) => rate(bla, xi);
double[] compare = diff.Compute(weights);
result[0] = -((-x) / (weights[1] + x));
result[1] = -((weights[0] * x) / Math.Pow(weights[1] + x, 2));
};
NonlinearRegression regression = new NonlinearRegression(2, rate, grad);
NonlinearLeastSquares nls = new NonlinearLeastSquares(regression, new GaussNewton(2));
Assert.IsTrue(nls.Algorithm is GaussNewton);
regression.Coefficients[0] = 0.9; // β1
regression.Coefficients[1] = 0.2; // β2
int iterations = 10;
double[] errors = new double[iterations];
for (int i = 0; i < errors.Length; i++)
errors[i] = nls.Run(inputs, outputs);
double b1 = regression.Coefficients[0];
double b2 = regression.Coefficients[1];
Assert.AreEqual(0.362, b1, 1e-3);
Assert.AreEqual(0.556, b2, 3e-3);
Assert.IsFalse(Double.IsNaN(b1));
Assert.IsFalse(Double.IsNaN(b2));
for (int i = 1; i < errors.Length; i++)
{
Assert.IsFalse(Double.IsNaN(errors[i - 1]));
Assert.IsTrue(errors[i - 1] >= errors[i]);
}
Assert.AreEqual(1.23859, regression.StandardErrors[0], 1e-3);
Assert.AreEqual(6.06352, regression.StandardErrors[1], 3e-3);
}
public void GradientTest3()
{
var hmm = MultivariateNormalHiddenMarkovClassifierPotentialFunctionTest.CreateModel1();
var function = new MarkovMultivariateFunction(hmm);
var model = new HiddenConditionalRandomField<double[]>(function);
var target = new ForwardBackwardGradient<double[]>(model);
target.Regularization = 2;
var inputs = inputs1;
var outputs = outputs1;
FiniteDifferences diff = new FiniteDifferences(function.Weights.Length);
diff.Function = parameters => func(model, parameters, inputs, outputs, target.Regularization);
double[] expected = diff.Compute(function.Weights);
double[] actual = target.Gradient(function.Weights, inputs, outputs);
for (int i = 0; i < actual.Length; i++)
{
Assert.AreEqual(expected[i], actual[i], 1e-3);
Assert.IsFalse(double.IsNaN(actual[i]));
Assert.IsFalse(double.IsNaN(expected[i]));
}
}
static Func<double[], double[]> Grad(int n, Func<double[], double> fn){
var gradient = new FiniteDifferences (n, fn);
return a => gradient.Compute (a);
}
public void GradientTest()
{
var function = new DiscreteMarkovClassifierFunction(2, 2, 2);
var model = new HiddenConditionalRandomField<int>(function);
var target = new QuasiNewtonHiddenLearning<int>(model);
FiniteDifferences diff = new FiniteDifferences(function.Weights.Length);
diff.Function = parameters => func(model, parameters);
double[] expected = diff.Compute(function.Weights);
double[] actual = target.Gradient(function.Weights, inputs, outputs);
for (int i = 0; i < actual.Length; i++)
{
Assert.AreEqual(expected[i], actual[i], 1e-4);
Assert.IsFalse(double.IsNaN(actual[i]));
Assert.IsFalse(double.IsNaN(expected[i]));
}
}