Avoid some unnecessary conversions from float to double

Signed-off-by: Stefan Weil <sw@weilnetz.de>
karama300 · Nov 28, 2021 · a1f40ca · a1f40ca
1 parent 5e8d877
commit a1f40ca
Showing 1 changed file with 7 additions and 7 deletions.
diff --git a/src/classify/adaptmatch.cpp b/src/classify/adaptmatch.cpp
@@ -1301,7 +1301,7 @@ int Classify::CharNormTrainingSample(bool pruner_only, int keep_this, const Trai
   std::vector<uint8_t> char_norm_array(unicharset.size());
   auto num_pruner_classes = std::max(static_cast<unsigned>(unicharset.size()), PreTrainedTemplates->NumClasses);
   std::vector<uint8_t> pruner_norm_array(num_pruner_classes);
-  adapt_results->BlobLength = static_cast<int>(ActualOutlineLength(norm_feature) * 20 + 0.5);
+  adapt_results->BlobLength = static_cast<int>(ActualOutlineLength(norm_feature) * 20 + 0.5f);
   ComputeCharNormArrays(norm_feature, PreTrainedTemplates, &char_norm_array[0], &pruner_norm_array[0]);
 
   PruneClasses(PreTrainedTemplates, num_features, keep_this, sample.features(), &pruner_norm_array[0],
@@ -1347,7 +1347,7 @@ int Classify::CharNormTrainingSample(bool pruner_only, int keep_this, const Trai
 void Classify::ClassifyAsNoise(ADAPT_RESULTS *results) {
   float rating = results->BlobLength / matcher_avg_noise_size;
   rating *= rating;
-  rating /= 1.0 + rating;
+  rating /= 1 + rating;
 
   AddNewResult(UnicharRating(UNICHAR_SPACE, 1.0f - rating), results);
 } /* ClassifyAsNoise */
@@ -1783,8 +1783,8 @@ PROTO_ID Classify::MakeNewTempProtos(FEATURE_SET Features, int NumBadFeat, FEATU
       A2 = F2->Params[PicoFeatDir];
 
       AngleDelta = std::fabs(A1 - A2);
-      if (AngleDelta > 0.5) {
-        AngleDelta = 1.0 - AngleDelta;
+      if (AngleDelta > 0.5f) {
+        AngleDelta = 1 - AngleDelta;
       }
 
       if (AngleDelta > matcher_clustering_max_angle_delta || std::fabs(X1 - X2) > SegmentLength ||
@@ -1811,8 +1811,8 @@ PROTO_ID Classify::MakeNewTempProtos(FEATURE_SET Features, int NumBadFeat, FEATU
    instead of the -0.25 to 0.75 used in baseline normalization */
     Proto->Length = SegmentLength;
     Proto->Angle = A1;
-    Proto->X = (X1 + X2) / 2.0;
-    Proto->Y = (Y1 + Y2) / 2.0 - Y_DIM_OFFSET;
+    Proto->X = (X1 + X2) / 2;
+    Proto->Y = (Y1 + Y2) / 2 - Y_DIM_OFFSET;
     FillABC(Proto);
 
     TempProto->ProtoId = Pid;
@@ -2045,7 +2045,7 @@ void Classify::RemoveExtraPuncs(ADAPT_RESULTS *Results) {
  * - matcher_good_threshold default good match rating
  */
 void Classify::SetAdaptiveThreshold(float Threshold) {
-  Threshold = (Threshold == matcher_good_threshold) ? 0.9 : (1.0 - Threshold);
+  Threshold = (Threshold == matcher_good_threshold) ? 0.9f : (1 - Threshold);
   classify_adapt_proto_threshold.set_value(ClipToRange<int>(255 * Threshold, 0, 255));
   classify_adapt_feature_threshold.set_value(ClipToRange<int>(255 * Threshold, 0, 255));
 } /* SetAdaptiveThreshold */