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@ClassificationKNN/predict: fix minor gub in input parsing
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pr0m1th3as committed Dec 29, 2023
1 parent bd79bb7 commit a6c4db7
Showing 1 changed file with 22 additions and 22 deletions.
44 changes: 22 additions & 22 deletions inst/@ClassificationKNN/predict.m
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Original file line number Diff line number Diff line change
Expand Up @@ -48,11 +48,11 @@

## Check for sufficient input arguments
if (nargin < 2)
error ("@ClassificationKNN/predict: Too few arguments.");
error ("@ClassificationKNN/predict: too few arguments.");
endif

## Check for obj being a ClasifficationKNN object
if (! strcmp (class (obj), "ClasifficationKNN"))
if (! strcmp (class (obj), "ClassificationKNN"))
error (strcat (["@ClassificationKNN/predict: OBJ"], ...
[" must be a ClasifficationKNN class object."]));
endif
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%! y = species;
%!test
%! xc = [min(x); mean(x); max(x)];
%! obj = ClassificationKNN (x, y, "k", 5);
%! obj = fitcknn (x, y, "k", 5);
%! [l, s, c] = predict (obj, xc);
%! assert (l, {"setosa"; "versicolor"; "virginica"})
%! assert (s, [1, 0, 0; 0, 1, 0; 0, 0, 1])
%! assert (c, [0, 1, 1; 1, 0, 1; 1, 1, 0])
%!test
%! xc = [min(x); mean(x); max(x)];
%! obj = ClassificationKNN (x, y, "k", 10, "distance", "mahalanobis");
%! obj = fitcknn (x, y, "k", 10, "distance", "mahalanobis");
%! [l, s, c] = predict (obj, xc);
%! assert (s, [0.3, 0.7, 0; 0, 0.9, 0.1; 0.2, 0.2, 0.6], 1e-4)
%! assert (c, [0.7, 0.3, 1; 1, 0.1, 0.9; 0.8, 0.8, 0.4], 1e-4)
%!test
%! xc = [min(x); mean(x); max(x)];
%! obj = ClassificationKNN (x, y, "k", 10, "distance", "cosine");
%! obj = fitcknn (x, y, "k", 10, "distance", "cosine");
%! [l, s, c] = predict (obj, xc);
%! assert (l, {"setosa"; "versicolor"; "virginica"})
%! assert (s, [1, 0, 0; 0, 1, 0; 0, 0.3, 0.7], 1e-4)
%! assert (c, [0, 1, 1; 1, 0, 1; 1, 0.7, 0.3], 1e-4)
%!test
%! xc = [5.2, 4.1, 1.5, 0.1; 5.1, 3.8, 1.9, 0.4; ...
%! 5.1, 3.8, 1.5, 0.3; 4.9, 3.6, 1.4, 0.1];
%! obj = ClassificationKNN (x, y, "k", 5);
%! obj = fitcknn (x, y, "k", 5);
%! [l, s, c] = predict (obj, xc);
%! assert (l, {"setosa"; "setosa"; "setosa"; "setosa"})
%! assert (s, [1, 0, 0; 1, 0, 0; 1, 0, 0; 1, 0, 0])
%! assert (c, [0, 1, 1; 0, 1, 1; 0, 1, 1; 0, 1, 1])
%!test
%! xc = [5, 3, 5, 1.45];
%! obj = ClassificationKNN (x, y, "k", 5);
%! obj = fitcknn (x, y, "k", 5);
%! [l, s, c] = predict (obj, xc);
%! assert (l, {"versicolor"})
%! assert (s, [0, 0.6, 0.4], 1e-4)
%! assert (c, [1, 0.4, 0.6], 1e-4)
%!test
%! xc = [5, 3, 5, 1.45];
%! obj = ClassificationKNN (x, y, "k", 10, "distance", "minkowski", "P", 5);
%! obj = fitcknn (x, y, "k", 10, "distance", "minkowski", "P", 5);
%! [l, s, c] = predict (obj, xc);
%! assert (l, {"versicolor"})
%! assert (s, [0, 0.5, 0.5], 1e-4)
%! assert (c, [1, 0.5, 0.5], 1e-4)
%!test
%! xc = [5, 3, 5, 1.45];
%! obj = ClassificationKNN (x, y, "k", 10, "distance", "jaccard");
%! obj = fitcknn (x, y, "k", 10, "distance", "jaccard");
%! [l, s, c] = predict (obj, xc);
%! assert (l, {"setosa"})
%! assert (s, [0.9, 0.1, 0], 1e-4)
%! assert (c, [0.1, 0.9, 1], 1e-4)
%!test
%! xc = [5, 3, 5, 1.45];
%! obj = ClassificationKNN (x, y, "k", 10, "distance", "mahalanobis");
%! obj = fitcknn (x, y, "k", 10, "distance", "mahalanobis");
%! [l, s, c] = predict (obj, xc);
%! assert (l, {"versicolor"})
%! assert (s, [0.1000, 0.5000, 0.4000], 1e-4)
%! assert (c, [0.9000, 0.5000, 0.6000], 1e-4)
%!test
%! xc = [5, 3, 5, 1.45];
%! obj = ClassificationKNN (x, y, "k", 5, "distance", "jaccard");
%! obj = fitcknn (x, y, "k", 5, "distance", "jaccard");
%! [l, s, c] = predict (obj, xc);
%! assert (l, {"setosa"})
%! assert (s, [0.8, 0.2, 0], 1e-4)
%! assert (c, [0.2, 0.8, 1], 1e-4)
%!test
%! xc = [5, 3, 5, 1.45];
%! obj = ClassificationKNN (x, y, "k", 5, "distance", "seuclidean");
%! obj = fitcknn (x, y, "k", 5, "distance", "seuclidean");
%! [l, s, c] = predict (obj, xc);
%! assert (l, {"versicolor"})
%! assert (s, [0, 1, 0], 1e-4)
%! assert (c, [1, 0, 1], 1e-4)
%!test
%! xc = [5, 3, 5, 1.45];
%! obj = ClassificationKNN (x, y, "k", 10, "distance", "chebychev");
%! obj = fitcknn (x, y, "k", 10, "distance", "chebychev");
%! [l, s, c] = predict (obj, xc);
%! assert (l, {"versicolor"})
%! assert (s, [0, 0.7, 0.3], 1e-4)
%! assert (c, [1, 0.3, 0.7], 1e-4)
%!test
%! xc = [5, 3, 5, 1.45];
%! obj = ClassificationKNN (x, y, "k", 10, "distance", "cityblock");
%! obj = fitcknn (x, y, "k", 10, "distance", "cityblock");
%! [l, s, c] = predict (obj, xc);
%! assert (l, {"versicolor"})
%! assert (s, [0, 0.6, 0.4], 1e-4)
%! assert (c, [1, 0.4, 0.6], 1e-4)
%!test
%! xc = [5, 3, 5, 1.45];
%! obj = ClassificationKNN (x, y, "k", 10, "distance", "manhattan");
%! obj = fitcknn (x, y, "k", 10, "distance", "manhattan");
%! [l, s, c] = predict (obj, xc);
%! assert (l, {"versicolor"})
%! assert (s, [0, 0.6, 0.4], 1e-4)
%! assert (c, [1, 0.4, 0.6], 1e-4)
%!test
%! xc = [5, 3, 5, 1.45];
%! obj = ClassificationKNN (x, y, "k", 10, "distance", "cosine");
%! obj = fitcknn (x, y, "k", 10, "distance", "cosine");
%! [l, s, c] = predict (obj, xc);
%! assert (l, {"virginica"})
%! assert (s, [0, 0.1, 0.9], 1e-4)
%! assert (c, [1, 0.9, 0.1], 1e-4)
%!test
%! xc = [5, 3, 5, 1.45];
%! obj = ClassificationKNN (x, y, "k", 10, "distance", "correlation");
%! obj = fitcknn (x, y, "k", 10, "distance", "correlation");
%! [l, s, c] = predict (obj, xc);
%! assert (l, {"virginica"})
%! assert (s, [0, 0.1, 0.9], 1e-4)
%! assert (c, [1, 0.9, 0.1], 1e-4)
%!test
%! xc = [5, 3, 5, 1.45];
%! obj = ClassificationKNN (x, y, "k", 30, "distance", "spearman");
%! obj = fitcknn (x, y, "k", 30, "distance", "spearman");
%! [l, s, c] = predict (obj, xc);
%! assert (l, {"versicolor"})
%! assert (s, [0, 1, 0], 1e-4)
%! assert (c, [1, 0, 1], 1e-4)
%!test
%! xc = [5, 3, 5, 1.45];
%! obj = ClassificationKNN (x, y, "k", 30, "distance", "hamming");
%! obj = fitcknn (x, y, "k", 30, "distance", "hamming");
%! [l, s, c] = predict (obj, xc);
%! assert (l, {"setosa"})
%! assert (s, [0.4333, 0.3333, 0.2333], 1e-4)
%! assert (c, [0.5667, 0.6667, 0.7667], 1e-4)
%!test
%! xc = [5, 3, 5, 1.45];
%! obj = ClassificationKNN (x, y, "k", 5, "distance", "hamming");
%! obj = fitcknn (x, y, "k", 5, "distance", "hamming");
%! [l, s, c] = predict (obj, xc);
%! assert (l, {"setosa"})
%! assert (s, [0.8, 0.2, 0], 1e-4)
%! assert (c, [0.2, 0.8, 1], 1e-4)
%!test
%! xc = [min(x); mean(x); max(x)];
%! obj = ClassificationKNN (x, y, "k", 10, "distance", "correlation");
%! obj = fitcknn (x, y, "k", 10, "distance", "correlation");
%! [l, s, c] = predict (obj, xc);
%! assert (l, {"setosa"; "versicolor"; "virginica"})
%! assert (s, [1, 0, 0; 0, 1, 0; 0, 0.4, 0.6], 1e-4)
%! assert (c, [0, 1, 1; 1, 0, 1; 1, 0.6, 0.4], 1e-4)
%!test
%! xc = [min(x); mean(x); max(x)];
%! obj = ClassificationKNN (x, y, "k", 10, "distance", "hamming");
%! obj = fitcknn (x, y, "k", 10, "distance", "hamming");
%! [l, s, c] = predict (obj, xc);
%! assert (l, {"setosa";"setosa";"setosa"})
%! assert (s, [0.9, 0.1, 0; 1, 0, 0; 0.5, 0, 0.5], 1e-4)
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