public static void GlmPoisson1()
{
UnitTestEqualityComparer comparer = new UnitTestEqualityComparer(8);
double[][] input =
GravityCourseData.Select(
x => new double[]
{
Math.Log(x.Distance),
x.CommonBorder,
x.CommonLanguage,
x.ColonialRelationship,
x.Importer.GetHashCode()
})
.ToArray()
.Indicate(4);
double[] response =
GravityCourseData.Select(x => x.Trade)
.ToArray();
double[] weights =
Enumerable.Repeat(1.0, response.Length)
.ToArray();
GeneralizedLinearModel <double> generalized =
GeneralizedLinearModel.PoissonRegression(input, response, weights);
int n = input.Length;
int k = input[0].Length + 1;
int df = n - k;
double[] coefficients = new double[] { 13.728590, -0.772651, 0.180037, -0.876162, -0.078427 };
//
// double[] varianceOls = new double[] { 4.110142e-07, 5.480129e-07, 1.331497e-06 };
// double[] varianceHC0 = new double[] { 0.005335, 0.011331, 0.028898 };
// double[] varianceHC1 = new double[] { 0.005335, 0.011331, 0.028898 };
//
// double[] standardErrorsOls = varianceOls.Select(Math.Sqrt).ToArray();
// double[] standardErrorsHC0 = varianceHC0.Select(Math.Sqrt).ToArray();
// double[] standardErrorsHC1 = varianceHC1.Select(Math.Sqrt).ToArray();
Assert.Equal(n, generalized.ObservationCount);
Assert.Equal(k, generalized.VariableCount);
Assert.Equal(df, generalized.DegreesOfFreedom);
Assert.Equal(coefficients, generalized.Coefficients, comparer);
//
// Assert.Equal(varianceOls, generalized.VarianceOls, comparer);
// Assert.Equal(varianceHC0, generalized.VarianceHC0, comparer);
// Assert.Equal(varianceHC1, generalized.VarianceHC1, comparer);
//
// Assert.Equal(standardErrorsOls, generalized.StandardErrorsOls, comparer);
// Assert.Equal(standardErrorsHC0, generalized.StandardErrorsHC0, comparer);
// Assert.Equal(standardErrorsHC1, generalized.StandardErrorsHC1, comparer);
}
public static void GlmPoisson0()
{
UnitTestEqualityComparer comparer = new UnitTestEqualityComparer(8);
double[][] input =
GravityCourseData.Select(
x => new double[]
{
Math.Log(x.Distance),
x.CommonBorder,
x.CommonLanguage,
x.ColonialRelationship
})
.ToArray();
double[] response =
GravityCourseData.Select(x => x.Trade)
.ToArray();
double[] weights =
Enumerable.Repeat(1.0, response.Length)
.ToArray();
GeneralizedLinearModel <double> generalized =
GeneralizedLinearModel.PoissonRegression(input, response, weights);
int n = input.Length;
int k = input[0].Length + 1;
int df = n - k;
double[] coefficients = new double[] { 14.34011355, -0.77265135, 0.18003745, -0.87616156, -0.07842665 };
double[] varianceOls = new double[] { 2.47622006e-08, 4.80378670e-10, 5.14379692e-08, 5.98550930e-08, 1.97414603e-07 };
double[] varianceHC0 = new double[] { 0.01842182, 0.00027302, 0.0054894, 0.00458959, 0.00451205 };
double[] varianceHC1 = new double[] { 0.01842182, 0.00027302, 0.0054894, 0.00458959, 0.00451205 };
double[] standardErrorsOls = varianceOls.Select(Math.Sqrt).ToArray();
double[] standardErrorsHC0 = varianceHC0.Select(Math.Sqrt).ToArray();
double[] standardErrorsHC1 = varianceHC1.Select(Math.Sqrt).ToArray();
Assert.Equal(n, generalized.ObservationCount);
Assert.Equal(k, generalized.VariableCount);
Assert.Equal(df, generalized.DegreesOfFreedom);
Assert.Equal(coefficients, generalized.Coefficients, comparer);
Assert.Equal(varianceOls, generalized.VarianceOls, comparer);
Assert.Equal(varianceHC0, generalized.VarianceHC0, comparer);
Assert.Equal(varianceHC1, generalized.VarianceHC1, comparer);
Assert.Equal(standardErrorsOls, generalized.StandardErrorsOls, comparer);
Assert.Equal(standardErrorsHC0, generalized.StandardErrorsHC0, comparer);
Assert.Equal(standardErrorsHC1, generalized.StandardErrorsHC1, comparer);
}
public static void Main()
{
double[][] input =
GeneralizedLinearModelTests.GravityCourseData
.Select(
x => new double[]
{
Math.Log(x.Distance),
x.CommonBorder,
x.CommonLanguage,
x.ColonialRelationship
})
.ToArray();
double[] response =
GeneralizedLinearModelTests.GravityCourseData
.Select(x => x.Trade)
.ToArray();
double[] weights =
Enumerable.Repeat(1.0, response.Length)
.ToArray();
GeneralizedLinearModel <double> generalized =
GeneralizedLinearModel.PoissonRegression(input, response, weights);
const int count = 10;
IDistribution <double> distribution = new PoissonDistribution(new LogLinkFunction());
Stopwatch sw = new Stopwatch();
sw.Start();
for (int i = 0; i < count; i++)
{
generalized = new GeneralizedLinearModel <double>(input, response, weights, distribution, true);
}
sw.Stop();
System.Console.WriteLine(generalized);
System.Console.WriteLine((double)sw.ElapsedMilliseconds / count);
ParallelOptions options = new ParallelOptions();
sw.Restart();
for (int i = 0; i < count; i++)
{
generalized = new GeneralizedLinearModel <double>(input, response, weights, distribution, true, options);
}
sw.Stop();
System.Console.WriteLine(generalized);
System.Console.WriteLine((double)sw.ElapsedMilliseconds / count);
}
public static void GlmPoisson2()
{
UnitTestEqualityComparer comparer = new UnitTestEqualityComparer(8);
double[][] groupMeans =
GravityCourseData.GroupBy(
x => new
{
x.Importer,
x.Exporter,
x.Year
})
.Select(
x => new
{
x,
dist = x.Average(y => y.Distance),
trade = x.Average(y => y.Trade)
})
.Select(
x =>
x.x.SelectMany(
y => new double[]
{
x.dist,
x.trade
})
.ToArray())
.ToArray();
double[] grandMeans =
new double[]
{
groupMeans.Average(x => x[0]),
groupMeans.Average(x => x[1])
};
double[][] input =
GravityCourseData.Select(
(x, i) => new double[]
{
Math.Log(x.Distance) - Math.Log(groupMeans[i][0]) + Math.Log(grandMeans[0]),
x.CommonBorder,
x.CommonLanguage,
x.ColonialRelationship
})
.ToArray();
double[] response =
GravityCourseData.Select(
(x, i) => x.Trade - groupMeans[i][1] + grandMeans[1])
.ToArray();
double[] weights =
Enumerable.Repeat(1.0, response.Length)
.ToArray();
GeneralizedLinearModel <double> generalized =
GeneralizedLinearModel.PoissonRegression(input, response, weights, new System.Threading.Tasks.ParallelOptions());
int n = input.Length;
int k = input[0].Length + 1;
int df = n - k;
double[] coefficients = new double[] { 13.728590, -0.772651, 0.180037, -0.876162, -0.078427 };
//
// double[] varianceOls = new double[] { 4.110142e-07, 5.480129e-07, 1.331497e-06 };
// double[] varianceHC0 = new double[] { 0.005335, 0.011331, 0.028898 };
// double[] varianceHC1 = new double[] { 0.005335, 0.011331, 0.028898 };
//
// double[] standardErrorsOls = varianceOls.Select(Math.Sqrt).ToArray();
// double[] standardErrorsHC0 = varianceHC0.Select(Math.Sqrt).ToArray();
// double[] standardErrorsHC1 = varianceHC1.Select(Math.Sqrt).ToArray();
Assert.Equal(n, generalized.ObservationCount);
Assert.Equal(k, generalized.VariableCount);
Assert.Equal(df, generalized.DegreesOfFreedom);
Assert.Equal(coefficients, generalized.Coefficients, comparer);
//
// Assert.Equal(varianceOls, generalized.VarianceOls, comparer);
// Assert.Equal(varianceHC0, generalized.VarianceHC0, comparer);
// Assert.Equal(varianceHC1, generalized.VarianceHC1, comparer);
//
// Assert.Equal(standardErrorsOls, generalized.StandardErrorsOls, comparer);
// Assert.Equal(standardErrorsHC0, generalized.StandardErrorsHC0, comparer);
// Assert.Equal(standardErrorsHC1, generalized.StandardErrorsHC1, comparer);
}
