1 files changed, 65 insertions, 0 deletions
diff --git a/ml/dlib/tools/python/test/test_svm_c_trainer.py b/ml/dlib/tools/python/test/test_svm_c_trainer.py
new file mode 100644
index 000000000..ba9392e08
--- /dev/null
+++ b/ml/dlib/tools/python/test/test_svm_c_trainer.py
@@ -0,0 +1,65 @@
+from __future__ import division
+
+import pytest
+from random import Random
+from dlib import (vectors, vector, sparse_vectors, sparse_vector, pair, array,
+                  cross_validate_trainer,
+                  svm_c_trainer_radial_basis,
+                  svm_c_trainer_sparse_radial_basis,
+                  svm_c_trainer_histogram_intersection,
+                  svm_c_trainer_sparse_histogram_intersection,
+                  svm_c_trainer_linear,
+                  svm_c_trainer_sparse_linear,
+                  rvm_trainer_radial_basis,
+                  rvm_trainer_sparse_radial_basis,
+                  rvm_trainer_histogram_intersection,
+                  rvm_trainer_sparse_histogram_intersection,
+                  rvm_trainer_linear,
+                  rvm_trainer_sparse_linear)
+
+
+@pytest.fixture
+def training_data():
+    r = Random(0)
+    predictors = vectors()
+    sparse_predictors = sparse_vectors()
+    response = array()
+    for i in range(30):
+        for c in [-1, 1]:
+            response.append(c)
+            values = [r.random() + c * 0.5 for _ in range(3)]
+            predictors.append(vector(values))
+            sp = sparse_vector()
+            for i, v in enumerate(values):
+                sp.append(pair(i, v))
+            sparse_predictors.append(sp)
+    return predictors, sparse_predictors, response
+
+
+@pytest.mark.parametrize('trainer, class1_accuracy, class2_accuracy', [
+    (svm_c_trainer_radial_basis, 1.0, 1.0),
+    (svm_c_trainer_sparse_radial_basis, 1.0, 1.0),
+    (svm_c_trainer_histogram_intersection, 1.0, 1.0),
+    (svm_c_trainer_sparse_histogram_intersection, 1.0, 1.0),
+    (svm_c_trainer_linear, 1.0, 23 / 30),
+    (svm_c_trainer_sparse_linear, 1.0, 23 / 30),
+    (rvm_trainer_radial_basis, 1.0, 1.0),
+    (rvm_trainer_sparse_radial_basis, 1.0, 1.0),
+    (rvm_trainer_histogram_intersection, 1.0, 1.0),
+    (rvm_trainer_sparse_histogram_intersection, 1.0, 1.0),
+    (rvm_trainer_linear, 1.0, 0.6),
+    (rvm_trainer_sparse_linear, 1.0, 0.6)
+])
+def test_trainers(training_data, trainer, class1_accuracy, class2_accuracy):
+    predictors, sparse_predictors, response = training_data
+    if 'sparse' in trainer.__name__:
+        predictors = sparse_predictors
+    cv = cross_validate_trainer(trainer(), predictors, response, folds=10)
+    assert cv.class1_accuracy == pytest.approx(class1_accuracy)
+    assert cv.class2_accuracy == pytest.approx(class2_accuracy)
+
+    decision_function = trainer().train(predictors, response)
+    assert decision_function(predictors[2]) < 0
+    assert decision_function(predictors[3]) > 0
+    if 'linear' in trainer.__name__:
+        assert len(decision_function.weights) == 3