Fixing errors occuring when training, and deprecated warning

dependencies/jsoncpp/include/reader.h

@@ -28,7 +28,7 @@ namespace Json {
 /** \brief Unserialize a <a HREF="http://www.json.org">JSON</a> document into a
  *Value.
  *
- * \deprecated Use CharReader and CharReaderBuilder.
+ *  Use CharReader and CharReaderBuilder.
  */
 class JSON_API Reader {
   public:
@@ -111,7 +111,7 @@ class JSON_API Reader {
      * \deprecated Use getFormattedErrorMessages() instead (typo fix).
      */
     JSONCPP_DEPRECATED("Use getFormattedErrorMessages() instead.")
-    std::string getFormatedErrorMessages() const;
+    std::string getFormatedErrorMessages() __deprecated; //const;
 
     /** \brief Returns a user friendly string that list errors in the parsed
      * document.
@@ -120,7 +120,7 @@ class JSON_API Reader {
      * in
      *         the parsed document. An empty string is returned if no error
      * occurred
-     *         during parsing.
+     *         during parsing. 
      */
     std::string getFormattedErrorMessages() const;
 

dependencies/jsoncpp/include/value.h

@@ -483,11 +483,11 @@ Json::Value obj_value(Json::objectValue); // {}
   /// \pre type() is objectValue or nullValue
   /// \post type() is unchanged
   /// \deprecated
-  Value removeMember(const char* key);
+    Value removeMember(const char* key) __deprecated;
   /// Same as removeMember(const char*)
   /// \param key may contain embedded nulls.
   /// \deprecated
-  Value removeMember(const std::string& key);
+    Value removeMember(const std::string& key) __deprecated;
   /// Same as removeMember(const char* begin, const char* end, Value* removed),
   /// but 'key' is null-terminated.
   bool removeMember(const char* key, Value* removed);
@@ -535,7 +535,7 @@ Json::Value obj_value(Json::objectValue); // {}
 
   /// \deprecated Always pass len.
   JSONCPP_DEPRECATED("Use setComment(std::string const&) instead.")
-  void setComment(const char* comment, CommentPlacement placement);
+    void setComment(const char* comment, CommentPlacement placement) __deprecated;
   /// Comments must be //... or /* ... */
   void setComment(const char* comment, size_t len, CommentPlacement placement);
   /// Comments must be //... or /* ... */

dependencies/jsoncpp/include/writer.h

@@ -136,7 +136,7 @@ class JSON_API StreamWriterBuilder : public StreamWriter::Factory {
 };
 
 /** \brief Abstract class for writers.
- * \deprecated Use StreamWriter. (And really, this is an implementation detail.)
+ *  Use StreamWriter. (And really, this is an implementation detail.)
  */
 class JSON_API Writer {
 public:
@@ -152,7 +152,7 @@ class JSON_API Writer {
  *consumption,
  * but may be usefull to support feature such as RPC where bandwith is limited.
  * \sa Reader, Value
- * \deprecated Use StreamWriterBuilder.
+ *  Use StreamWriterBuilder.
  */
 class JSON_API FastWriter : public Writer {
 
@@ -205,7 +205,7 @@ class JSON_API FastWriter : public Writer {
  *#CommentPlacement.
  *
  * \sa Reader, Value, Value::setComment()
- * \deprecated Use StreamWriterBuilder.
+ *  Use StreamWriterBuilder.
  */
 class JSON_API StyledWriter : public Writer {
 public:
@@ -265,9 +265,9 @@ class JSON_API StyledWriter : public Writer {
  * If the Value have comments then they are outputed according to their
  #CommentPlacement.
  *
- * \param indentation Each level will be indented by this amount extra.
+ *  indentation Each level will be indented by this amount extra.
  * \sa Reader, Value, Value::setComment()
- * \deprecated Use StreamWriterBuilder.
+ *  Use StreamWriterBuilder.
  */
 class JSON_API StyledStreamWriter {
 public:

src/core/model/xmmModel.hpp

@@ -251,13 +251,14 @@ class Model : public Writable {
         is_training_ = true;
 
         // Fetch training set parameters
-        shared_parameters->dimension.set(trainingSet->dimension.get());
+        shared_parameters->dimension = trainingSet->dimension.get();
         if (shared_parameters->bimodal.get()) {
             shared_parameters->dimension_input.set(
                 trainingSet->dimension_input.get());
         }
+        shared_parameters->column_names.resize(trainingSet->dimension.get());
         shared_parameters->column_names.set(trainingSet->column_names.get());
-
+        
         // Update models
         bool contLoop(true);
         while (contLoop) {
@@ -275,7 +276,6 @@ class Model : public Writable {
              it != trainingSet->labels().end(); ++it) {
             addModelForClass(*it);
         }
-
         // Start class training
         for (auto it = this->models.begin(); it != this->models.end(); ++it) {
             it->second.is_training_ = true;
@@ -285,9 +285,10 @@ class Model : public Writable {
                  MultithreadingMode::Parallel) ||
                 (configuration.multithreading ==
                  MultithreadingMode::Background)) {
-                training_threads_[it->first] =
+                    training_threads_.insert(std::pair<std::string, std::thread>(
+                    it->first,
                     std::thread(&SingleClassModel::train, &it->second,
-                                trainingSet->getPhrasesOfClass(it->first));
+                                trainingSet->getPhrasesOfClass(it->first))));
             } else {
                 it->second.train(trainingSet->getPhrasesOfClass(it->first));
             }
@@ -332,21 +333,22 @@ class Model : public Writable {
         is_training_ = true;
 
         // Fetch training set parameters
-        shared_parameters->dimension.set(trainingSet->dimension.get());
+        shared_parameters->dimension = trainingSet->dimension.get();
         if (shared_parameters->bimodal.get()) {
             shared_parameters->dimension_input.set(
                 trainingSet->dimension_input.get());
         }
+        shared_parameters->column_names.resize(trainingSet->dimension.get());
         shared_parameters->column_names.set(trainingSet->column_names.get());
-
+        
         addModelForClass(label);
 
         // Start class training
         models[label].is_training_ = true;
         models[label].cancel_training_ = false;
         models_still_training_++;
         if (configuration.multithreading == MultithreadingMode::Sequential) {
-            models[label].train(trainingSet->getPhrasesOfClass(label));
+            models[label].xmm::SingleClassProbabilisticModel::train(trainingSet->getPhrasesOfClass(label));
         } else {
             training_threads_[label] =
                 std::thread(&SingleClassModel::train, &(this->models[label]),

src/core/model/xmmModelSingleClass.cpp

@@ -90,7 +90,7 @@ operator=(SingleClassProbabilisticModel const& src) {
             throw std::runtime_error(
                 "Cannot copy: source model is still training");
         label = src.label;
-        training_status = TrainingEvent(this, label),
+        training_status = TrainingEvent(this, label);
         shared_parameters = src.shared_parameters;
         is_training_ = false;
         cancel_training_ = false;
@@ -235,4 +235,4 @@ Json::Value xmm::SingleClassProbabilisticModel::toJson() const {
     Json::Value root;
     root["label"] = label;
     return root;
-}
+}

src/models/gmm/xmmGmmSingleClass.cpp

@@ -224,12 +224,10 @@ void xmm::SingleClassGMM::fromJson(Json::Value const& root) {
 void xmm::SingleClassGMM::allocate() {
     mixture_coeffs.resize(parameters.gaussians.get());
     beta.resize(parameters.gaussians.get());
-    components.assign(
-        parameters.gaussians.get(),
-        GaussianDistribution(shared_parameters->bimodal.get(),
-                             shared_parameters->dimension.get(),
-                             shared_parameters->dimension_input.get(),
-                             parameters.covariance_mode.get()));
+
+    GaussianDistribution mgaus(shared_parameters->bimodal.get(),
+                         shared_parameters->dimension.get());
+    components.assign(parameters.gaussians.get(),mgaus);
 }
 
 double xmm::SingleClassGMM::obsProb(const float* observation,

src/models/hmm/xmmHierarchicalHmm.cpp

@@ -466,14 +466,20 @@ void xmm::HierarchicalHMM::updateResults() {
     double normconst_instant(0.0);
     double normconst_smoothed(0.0);
     int i(0);
+    results.instant_likelihoods.resize(shared_parameters->dimension.get());
+    results.smoothed_log_likelihoods.resize(shared_parameters->dimension.get());
+    results.smoothed_likelihoods.resize(shared_parameters->dimension.get());
+    results.instant_normalized_likelihoods.resize(shared_parameters->dimension.get());
+    results.smoothed_normalized_likelihoods.resize(shared_parameters->dimension.get());
+
     for (auto &model : models) {
         results.instant_likelihoods[i] =
             model.second.results.instant_likelihood;
         results.smoothed_log_likelihoods[i] =
             model.second.results.log_likelihood;
         results.smoothed_likelihoods[i] =
             exp(results.smoothed_log_likelihoods[i]);
-
+    
         results.instant_normalized_likelihoods[i] =
             results.instant_likelihoods[i];
         results.smoothed_normalized_likelihoods[i] =