1 files changed, 83 insertions, 0 deletions
diff --git a/ml/dlib/dlib/svm/cross_validate_sequence_labeler_abstract.h b/ml/dlib/dlib/svm/cross_validate_sequence_labeler_abstract.h
new file mode 100644
index 000000000..3d2409b28
--- /dev/null
+++ b/ml/dlib/dlib/svm/cross_validate_sequence_labeler_abstract.h
@@ -0,0 +1,83 @@
+// Copyright (C) 2011  Davis E. King (davis@dlib.net)
+// License: Boost Software License   See LICENSE.txt for the full license.
+#undef DLIB_CROSS_VALIDATE_SEQUENCE_LABeLER_ABSTRACT_Hh_
+#ifdef DLIB_CROSS_VALIDATE_SEQUENCE_LABeLER_ABSTRACT_Hh_
+
+#include <vector>
+#include "../matrix.h"
+#include "svm.h"
+
+
+namespace dlib
+{
+
+// ----------------------------------------------------------------------------------------
+
+    template <
+        typename sequence_labeler_type,
+        typename sequence_type 
+        >
+    const matrix<double> test_sequence_labeler (
+        const sequence_labeler_type& labeler,
+        const std::vector<sequence_type>& samples,
+        const std::vector<std::vector<unsigned long> >& labels
+    );
+    /*!
+        requires
+            - is_sequence_labeling_problem(samples, labels)
+            - sequence_labeler_type == dlib::sequence_labeler or an object with a 
+              compatible interface.
+        ensures
+            - Tests labeler against the given samples and labels and returns a confusion 
+              matrix summarizing the results.
+            - The confusion matrix C returned by this function has the following properties.
+                - C.nc() == labeler.num_labels()
+                - C.nr() == labeler.num_labels() 
+                - C(T,P) == the number of times a sequence element with label T was predicted
+                  to have a label of P.
+            - Any samples with a label value >= labeler.num_labels() are ignored.  That 
+              is, samples with labels the labeler hasn't ever seen before are ignored.
+    !*/
+
+// ----------------------------------------------------------------------------------------
+
+    template <
+        typename trainer_type,
+        typename sequence_type
+        >
+    const matrix<double> cross_validate_sequence_labeler (
+        const trainer_type& trainer,
+        const std::vector<sequence_type>& samples,
+        const std::vector<std::vector<unsigned long> >& labels,
+        const long folds
+    );
+    /*!
+        requires
+            - is_sequence_labeling_problem(samples, labels)
+            - 1 < folds <= samples.size()
+            - for all valid i and j: labels[i][j] < trainer.num_labels()
+            - trainer_type == dlib::structural_sequence_labeling_trainer or an object
+              with a compatible interface.
+        ensures
+            - performs k-fold cross validation by using the given trainer to solve the
+              given sequence labeling problem for the given number of folds.  Each fold 
+              is tested using the output of the trainer and the confusion matrix from all 
+              folds is summed and returned.
+            - The total confusion matrix is computed by running test_sequence_labeler()
+              on each fold and summing its output.
+            - The number of folds used is given by the folds argument.
+            - The confusion matrix C returned by this function has the following properties.
+                - C.nc() == trainer.num_labels()
+                - C.nr() == trainer.num_labels() 
+                - C(T,P) == the number of times a sequence element with label T was predicted
+                  to have a label of P.
+    !*/
+
+// ----------------------------------------------------------------------------------------
+
+}
+
+#endif // DLIB_CROSS_VALIDATE_SEQUENCE_LABeLER_ABSTRACT_Hh_
+
+
+