Add get and set for num_folds for cross validation

acletournel · Feb 15, 2019 · bf3c140 · bf3c140
1 parent 5c20523
commit bf3c140
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Showing 2 changed files with 16 additions and 41 deletions.
diff --git a/sources/classification/ml_svm.cpp b/sources/classification/ml_svm.cpp
@@ -139,7 +139,7 @@ namespace ml
             FLEXT_CADDATTR_SET(c, "cost", set_cost);
             FLEXT_CADDATTR_SET(c, "nu", set_nu);
             FLEXT_CADDATTR_SET(c, "probs", set_probs);
-            FLEXT_CADDATTR_SET(c, "mode", set_kfold_value);
+            FLEXT_CADDATTR_SET(c, "num_folds", set_kfold_value);
             FLEXT_CADDATTR_SET(c, "enable_cross_validation", set_enable_cross_validation);
 
             FLEXT_CADDATTR_GET(c, "type", get_type);
@@ -150,7 +150,7 @@ namespace ml
             FLEXT_CADDATTR_GET(c, "cost", get_cost);
             FLEXT_CADDATTR_GET(c, "nu", get_nu);
             FLEXT_CADDATTR_GET(c, "probs", get_probs);
-            FLEXT_CADDATTR_GET(c, "mode", get_kfold_value);
+            FLEXT_CADDATTR_GET(c, "num_folds", get_kfold_value);
 
             FLEXT_CADDMETHOD_(c, 0, "cross_validation", cross_validation);
 
@@ -297,6 +297,11 @@ namespace ml
 
     }
 
+    void svm::get_kfold_value(int &mode) const
+    {
+        mode = grt_svm.getKFoldCrossValidationValue();
+    }
+
     void svm::get_degree(int &degree) const
     {
         degree = grt_svm.getDegree();

diff --git a/sources/ml_doc_populate.cpp b/sources/ml_doc_populate.cpp
@@ -392,41 +392,19 @@ namespace ml_doc
                                               0.5
                                               );
 
-        ranged_message_descriptor<float> epsilon(
-                                            "epsilon",
-                                            "set the epsilon in loss function of epsilon-SVR",
-                                            INFINITY * -1.f, INFINITY,
-                                            0.1
-                                            );
-
-        ranged_message_descriptor<int> cachesize(
-                                                 "cachesize",
-                                                 "set cache memory size in MB",
-                                                 8,
-                                                 1024,
-                                                 100
-                                                 );
-
-        ranged_message_descriptor<float> tolerance(
-                                                   "tolerance",
-                                                   "set tolerance of termination criterion",
-                                                   INFINITY * -1.f, INFINITY,
-                                                   0.001
-                                                   );
-
-        valued_message_descriptor<bool> shrinking(
-                                                   "shrinking",
-                                                   "whether to use the shrinking heuristics",
-                                                  {0, 1},
-                                                  1
-                                                   );
-
         message_descriptor cross_validation(
                                             "cross_validation",
                                             "perform cross validation"
                                             );
 
-        descriptors[ml::k_svm].add_message_descriptor(cross_validation, type, kernel, degree, svm_gamma, coef0, cost, nu, epsilon, cachesize, tolerance, shrinking);
+        ranged_message_descriptor<int> num_folds(
+                                                 "num_folds",
+                                                 "set the number of folds used for cross validation",
+                                                 1, 100,
+                                                 10
+                                                 );
+
+        descriptors[ml::k_svm].add_message_descriptor(cross_validation, num_folds, type, kernel, degree, svm_gamma, coef0, cost, nu);
 
         //---- ml.adaboost        
         ranged_message_descriptor<int> num_boosting_iterations(
@@ -524,14 +502,6 @@ namespace ml_doc
                                                   5
                                                   );
 
-        ranged_message_descriptor<int> num_symbols(
-                                                  "num_symbols",
-                                                  "sets the number of symbols in the model",
-                                                  0,
-                                                  100,
-                                                  10
-                                                  );
-
         valued_message_descriptor<int> model_type(
                                                   "model_type",
                                                   "set the model type used, 0:ERGODIC, 1:LEFTRIGHT",
@@ -588,7 +558,7 @@ namespace ml_doc
                                                       );
 
         descriptors[ml::k_hmm].insert_message_descriptor(record);
-        descriptors[ml::k_hmm].add_message_descriptor(num_states, num_symbols, model_type, delta, max_num_iterations, num_random_training_iterations, min_improvement, committee_size, downsample_factor);
+        descriptors[ml::k_hmm].add_message_descriptor(num_states, model_type, delta, max_num_iterations, num_random_training_iterations, min_improvement, committee_size, downsample_factor);
 
         //---- ml.softmax