Fix CI build errors and warnings

Author: kaingwade

modules/ml/CMakeLists.txt

@@ -7,4 +7,4 @@ ocv_create_module()
 
 ocv_add_accuracy_tests()
 ocv_add_perf_tests()
-ocv_add_samples(opencv_imgproc opencv_imgcodecs opencv_objdetect opencv_videoio opencv_video)
+ocv_add_samples(opencv_imgproc opencv_objdetect opencv_video)

modules/ml/doc/ml.bib

@@ -0,0 +1,60 @@
+@inproceedings{RPROP93,
+  author = {Riedmiller, Martin and Braun, Heinrich},
+  title = {A direct adaptive method for faster backpropagation learning: The RPROP algorithm},
+  booktitle = {Neural Networks, 1993., IEEE International Conference on},
+  year = {1993},
+  pages = {586--591},
+  publisher = {IEEE}
+}
+@article{Kirkpatrick83,
+  author = {Kirkpatrick, S. and Gelatt, C. D. Jr and Vecchi, M. P.},
+  title = {Optimization by Simulated Annealing},
+  year = {1983},
+  pages = {671--680},
+  journal = {Science},
+  volume = {220},
+  number = {4598},
+  publisher = {American Association for the Advancement of Science},
+  url = {http://sci2s.ugr.es/sites/default/files/files/Teaching/GraduatesCourses/Metaheuristicas/Bibliography/1983-Science-Kirkpatrick-sim_anneal.pdf}
+}
+@incollection{bottou2010large,
+  title = {Large-scale machine learning with stochastic gradient descent},
+  author = {Bottou, L{\'e}on},
+  booktitle = {Proceedings of COMPSTAT'2010},
+  pages = {177--186},
+  year = {2010},
+  publisher = {Springer}
+}
+@article{LibSVM,
+  author = {Chang, Chih-Chung and Lin, Chih-Jen},
+  title = {LIBSVM: a library for support vector machines},
+  year = {2011},
+  pages = {27},
+  journal = {ACM Transactions on Intelligent Systems and Technology (TIST)},
+  volume = {2},
+  number = {3},
+  publisher = {ACM}
+}
+@book{Breiman84,
+  title = {Classification and regression trees},
+  author = {Breiman, Leo and Friedman, Jerome and Stone, Charles J and Olshen, Richard A},
+  year = {1984},
+  publisher = {CRC press},
+  url = {https://projecteuclid.org/download/pdf_1/euclid.aos/1016218223}
+}
+@article{HTF01,
+  author = {Trevor, Hastie and Robert, Tibshirani and Jerome, Friedman},
+  title = {The elements of statistical learning: data mining, inference and prediction},
+  year = {2001},
+  pages = {371--406},
+  journal = {New York: Springer-Verlag},
+  volume = {1},
+  number = {8},
+  url = {http://www.stat.auckland.ac.nz/~yee/784/files/ch09AdditiveModelsTrees.pdf}
+}
+@article{FHT98,
+  author = {Friedman, Jerome and Hastie, Trevor and Tibshirani, Robert},
+  title = {Additive Logistic Regression: a Statistical View of Boosting},
+  year = {1998},
+  url = {https://projecteuclid.org/download/pdf_1/euclid.aos/1016218223}
+}

modules/ml/doc/ml_intro.markdown

@@ -476,6 +476,6 @@ training error and ensuring high training accuracy:
     "setMiniBatchSize".
 
 A sample set of training parameters for the Logistic Regression classifier can be initialized as follows:
-@snippet samples/cpp/logistic_regression.cpp init
+@snippet samples/logistic_regression.cpp init
 
 @sa cv::ml::LogisticRegression

modules/ml/misc/python/test/test_gaussian_mix.py

@@ -0,0 +1,64 @@
+#!/usr/bin/env python
+
+# Python 2/3 compatibility
+from __future__ import print_function
+import sys
+PY3 = sys.version_info[0] == 3
+
+if PY3:
+    xrange = range
+
+import numpy as np
+from numpy import random
+import cv2 as cv
+
+def make_gaussians(cluster_n, img_size):
+    points = []
+    ref_distrs = []
+    for _ in xrange(cluster_n):
+        mean = (0.1 + 0.8*random.rand(2)) * img_size
+        a = (random.rand(2, 2)-0.5)*img_size*0.1
+        cov = np.dot(a.T, a) + img_size*0.05*np.eye(2)
+        n = 100 + random.randint(900)
+        pts = random.multivariate_normal(mean, cov, n)
+        points.append( pts )
+        ref_distrs.append( (mean, cov) )
+    points = np.float32( np.vstack(points) )
+    return points, ref_distrs
+
+from tests_common import NewOpenCVTests
+
+class gaussian_mix_test(NewOpenCVTests):
+
+    def test_gaussian_mix(self):
+
+        np.random.seed(10)
+        cluster_n = 5
+        img_size = 512
+
+        points, ref_distrs = make_gaussians(cluster_n, img_size)
+
+        em = cv.ml.EM_create()
+        em.setClustersNumber(cluster_n)
+        em.setCovarianceMatrixType(cv.ml.EM_COV_MAT_GENERIC)
+        em.trainEM(points)
+        means = em.getMeans()
+        covs = em.getCovs()  # Known bug: https://github.com/opencv/opencv/pull/4232
+        #found_distrs = zip(means, covs)
+
+        matches_count = 0
+
+        meanEps = 0.05
+        covEps = 0.1
+
+        for i in range(cluster_n):
+            for j in range(cluster_n):
+                if (cv.norm(means[i] - ref_distrs[j][0], cv.NORM_L2) / cv.norm(ref_distrs[j][0], cv.NORM_L2) < meanEps and
+                    cv.norm(covs[i] - ref_distrs[j][1], cv.NORM_L2) / cv.norm(ref_distrs[j][1], cv.NORM_L2) < covEps):
+                    matches_count += 1
+
+        self.assertEqual(matches_count, cluster_n)
+
+
+if __name__ == '__main__':
+    NewOpenCVTests.bootstrap()

modules/ml/misc/python/test/test_ml.py

@@ -0,0 +1,31 @@
+#!/usr/bin/env python
+from __future__ import print_function
+
+import numpy as np
+import cv2 as cv
+
+from tests_common import NewOpenCVTests
+
+class Bindings(NewOpenCVTests):
+
+    def test_inheritance(self):
+
+        boost = cv.ml.Boost_create()
+        boost.getBoostType()  # from ml::Boost
+        boost.getMaxDepth()  # from ml::DTrees
+        boost.isClassifier()  # from ml::StatModel
+
+class Arguments(NewOpenCVTests):
+
+    def test_class_from_submodule_has_global_alias(self):
+        self.assertTrue(hasattr(cv.ml, "Boost"),
+                        msg="Class is not registered in the submodule")
+        self.assertTrue(hasattr(cv, "ml_Boost"),
+                        msg="Class from submodule doesn't have alias in the "
+                        "global module")
+        self.assertEqual(cv.ml.Boost, cv.ml_Boost,
+                         msg="Classes from submodules and global module don't refer "
+                         "to the same type")
+        
+if __name__ == '__main__':
+    NewOpenCVTests.bootstrap()