public void new_api_test()
{
#region doc_learn
// Fix the random number generator
Accord.Math.Random.Generator.Seed = 0;
double[,] data = // This is the same data used in the RANSAC sample app
{
{ 1.0, 0.79 }, { 3, 2.18 }, { 5, 5.99 }, { 7.0, 7.65 },
{ 9.0, 9.55 }, { 11, 11.89 }, { 13, 13.73 }, { 15.0, 14.77 },
{ 17.0, 18.00 }, { 1.2, 1.45 }, { 1.5, 1.18 }, { 1.8, 1.92 },
{ 2.1, 1.47 }, { 2.4, 2.41 }, { 2.7, 2.35 }, { 3.0, 3.41 },
{ 3.3, 3.78 }, { 3.6, 3.21 }, { 3.9, 4.76 }, { 4.2, 5.03 },
{ 4.5, 4.19 }, { 4.8, 3.81 }, { 5.1, 6.07 }, { 5.4, 5.74 },
{ 5.7, 6.39 }, { 6, 6.11 }, { 6.3, 6.86 }, { 6.6, 6.35 },
{ 6.9, 7.9 }, { 7.2, 8.04 }, { 7.5, 8.48 }, { 7.8, 8.07 },
{ 8.1, 8.22 }, { 8.4, 8.41 }, { 8.7, 9.4 }, { 9, 8.8 },
{ 9.3, 8.44 }, { 9.6, 9.32 }, { 9.9, 9.18 }, { 10.2, 9.86 },
{ 10.5, 10.16 }, { 10.8, 10.28 }, { 11.1, 11.07 }, { 11.4, 11.66 },
{ 11.7, 11.13 }, { 12, 11.55 }, { 12.3, 12.62 }, { 12.6, 12.27 },
{ 12.9, 12.33 }, { 13.2, 12.37 }, { 13.5, 12.75 }, { 13.8, 14.44 },
{ 14.1, 14.71 }, { 14.4, 13.72 }, { 14.7, 14.54 }, { 15, 14.67 },
{ 15.3, 16.04 }, { 15.6, 15.21 }, { 1, 3.9 }, { 2, 11.5 },
{ 3.0, 13.0 }, { 4, 0.9 }, { 5, 5.5 }, { 6, 16.2 },
{ 7.0, 0.8 }, { 8, 9.4 }, { 9, 9.5 }, { 10, 17.5 },
{ 11.0, 6.3 }, { 12, 12.6 }, { 13, 1.5 }, { 14, 1.5 },
{ 2.0, 10 }, { 3, 9 }, { 15, 2 }, { 15.5, 1.2 },
};
// First, fit simple linear regression directly for comparison reasons.
double[] x = data.GetColumn(0); // Extract the independent variable
double[] y = data.GetColumn(1); // Extract the dependent variable
// Use Ordinary Least Squares to learn the regression
OrdinaryLeastSquares ols = new OrdinaryLeastSquares();
// Estimate a line passing through the (x, y) points
SimpleLinearRegression regression = ols.Learn(x, y);
// Now, compute the values predicted by the
// regression for the original input points
double[] commonOutput = regression.Transform(x);
// Now, fit simple linear regression using RANSAC
int maxTrials = 1000;
int minSamples = 20;
double probability = 0.950;
double errorThreshold = 1000;
// Create a RANSAC algorithm to fit a simple linear regression
var ransac = new RANSAC<SimpleLinearRegression>(minSamples)
{
Probability = probability,
Threshold = errorThreshold,
MaxEvaluations = maxTrials,
// Define a fitting function
Fitting = (int[] sample) =>
{
// Build a Simple Linear Regression model
return new OrdinaryLeastSquares()
.Learn(x.Get(sample), y.Get(sample));
},
// Define a inlier detector function
Distances = (SimpleLinearRegression r, double threshold) =>
{
var inliers = new List<int>();
for (int i = 0; i < x.Length; i++)
{
// Compute error for each point
double error = r.Transform(x[i]) - y[i];
// If the square error is low enough,
if (error * error < threshold)
inliers.Add(i); // the point is considered an inlier.
}
return inliers.ToArray();
}
};
// Now that the RANSAC hyperparameters have been specified, we can
// compute another regression model using the RANSAC algorithm:
int[] inlierIndices;
SimpleLinearRegression robustRegression = ransac.Compute(data.Rows(), out inlierIndices);
// Compute the output of the model fitted by RANSAC
double[] ransacOutput = robustRegression.Transform(x);
#endregion
Assert.AreEqual(ransac.TrialsNeeded, 0);
Assert.AreEqual(ransac.TrialsPerformed, 1);
string a = inlierIndices.ToCSharp();
string b = ransacOutput.ToCSharp();
int[] expectedInliers = new int[] { 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75 };
double[] expectedOutput = new double[] { 1.96331236445045, 3.42042856976283, 4.87754477507521, 6.33466098038758, 7.79177718569996, 9.24889339101234, 10.7060095963247, 12.1631258016371, 13.6202420069495, 2.10902398498169, 2.32759141577855, 2.5461588465754, 2.76472627737226, 2.98329370816912, 3.20186113896597, 3.42042856976283, 3.63899600055969, 3.85756343135654, 4.0761308621534, 4.29469829295026, 4.51326572374711, 4.73183315454397, 4.95040058534082, 5.16896801613768, 5.38753544693454, 5.6061028777314, 5.82467030852825, 6.04323773932511, 6.26180517012196, 6.48037260091882, 6.69894003171568, 6.91750746251253, 7.13607489330939, 7.35464232410625, 7.5732097549031, 7.79177718569996, 8.01034461649682, 8.22891204729367, 8.44747947809053, 8.66604690888738, 8.88461433968424, 9.1031817704811, 9.32174920127795, 9.54031663207481, 9.75888406287167, 9.97745149366852, 10.1960189244654, 10.4145863552622, 10.6331537860591, 10.8517212168559, 11.0702886476528, 11.2888560784497, 11.5074235092465, 11.7259909400434, 11.9445583708402, 12.1631258016371, 12.3816932324339, 12.6002606632308, 1.96331236445045, 2.69187046710664, 3.42042856976283, 4.14898667241902, 4.87754477507521, 5.6061028777314, 6.33466098038758, 7.06321908304377, 7.79177718569996, 8.52033528835615, 9.24889339101234, 9.97745149366852, 10.7060095963247, 11.4345676989809, 2.69187046710664, 3.42042856976283, 12.1631258016371, 12.5274048529652 };
Assert.IsTrue(inlierIndices.IsEqual(expectedInliers));
Assert.IsTrue(ransacOutput.IsEqual(expectedOutput, 1e-10));
}
