1 files changed, 0 insertions, 65 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
deleted file mode 100644
index ba9392e08..000000000
--- a/ml/dlib/tools/python/test/test_svm_c_trainer.py
+++ /dev/null
@@ -1,65 +0,0 @@
-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