Removed BeamSearch leftovers

src/main/java/de/monticore/lang/monticar/cnnarch/gluongenerator/CNNArch2Gluon.java

@@ -65,9 +65,6 @@ public class CNNArch2Gluon extends CNNArchGenerator {
         temp = controller.process("CNNSupervisedTrainer", Target.PYTHON);
         fileContentMap.put(temp.getKey(), temp.getValue());
 
-        temp = controller.process("BeamSearch", Target.CPP);
-        fileContentMap.put(temp.getKey().replace(".h", ""), temp.getValue());
-
         temp = controller.process("execute", Target.CPP);
         fileContentMap.put(temp.getKey().replace(".h", ""), temp.getValue());
 

src/main/resources/templates/gluon/BeamSearch.ftl

-vector<float> applyBeamSearch(vector<float> input, int depth, int width, int maxDepth, double currProb, int netIndex, vector<float> bestOutput)
-{
-double bestProb = 0.0;
-while (depth < maxDepth){
-    depth ++;
-    int batchIndex = 0;
-    for batchEntry in input:
-        int top_k_indices[width];
-        float top_k_values[width];
-
-        for(int i = 0; i < width; i++){
-            vector<float> batchEntryCopy = batchEntry;
-            std::nth_element(batchEntry.begin(), batchEntry.begin() + i, batchEntry.end());
-            top_k_values[i] = batchEntry[i]
-            std::vector<int>::iterator itr = std::find(batchEntryCopy.begin(), batchEntryCopy.end(), top_k_values[i]);
-            top_k_indices[i] = std::distance(batchEntryCopy.begin(), itr);
-        }
-
-        for(int index = 0; index < width; index++){
-
-            /*print mx.nd.array(top_k_indices[index]) */
-            /*print top_k_values[index] */
-            if (depth == 1){
-                /*print mx.nd.array(top_k_indices[index]) */
-                result = applyBeamSearch(self._networks[netIndex](mx.nd.array(top_k_indices[index])), depth, width, maxDepth,
-                                    currProb * top_k_values[index], netIndex, self._networks[netIndex](mx.nd.array(top_k_indices[index])));
-                _predictor_3_.predict(target_0_, decoder_state_, target_1_, decoder_state_, decoder_output_);
-
-                result = applyBeamSearch(self._networks[netIndex](mx.nd.array(top_k_indices[index])), depth, width, maxDepth,
-                    currProb * top_k_values[index], netIndex, self._networks[netIndex](mx.nd.array(top_k_indices[index])));
-            }else{
-                result = applyBeamSearch(self._networks[netIndex](mx.nd.array(top_k_indices[index])), depth, width, maxDepth,
-                    currProb * top_k_values[index], netIndex, bestOutput);
-            }
-
-            if (depth == maxDepth){
-                /*print currProb */
-                if (currProb > bestProb){
-                    bestProb = currProb;
-                    bestOutput[batchIndex] = result[batchIndex];
-                    /*print "new bestOutput: ", bestOutput */
-                    }
-            }
-
-        batchIndex ++;
-        }
-    }
-/*print bestOutput; */
-/*cout << bestProb; */
-return bestOutput;
-}
\ No newline at end of file

src/main/resources/templates/gluon/elements/BeamSearch.ftl

-<#--                BeamSearchPredictions = [
-<#list tc.architectureOutputs as output_name>
-                    mx.nd.topk(${output_name}, axis=1, k=4)<#sep>,
-</#list>
-                ]
-
-                logging.info("BeamSearch indices: " + str(BeamSearchPredictions))-->
\ No newline at end of file