Merge pull request #2946 from rcurtin/static-code-analysis-fix

Try to fix the static code analysis issue

src/mlpack/methods/ann/layer/recurrent_attention_impl.hpp

@@ -28,11 +28,11 @@ namespace ann /** Artificial Neural Network. */ {
 
 template<typename InputDataType, typename OutputDataType>
 RecurrentAttention<InputDataType, OutputDataType>::RecurrentAttention() :
+    outSize(0),
     rho(0),
     forwardStep(0),
     backwardStep(0),
-    deterministic(false),
-    outSize(0)
+    deterministic(false)
 {
   // Nothing to do.
 }

src/mlpack/methods/reinforcement_learning/q_networks/categorical_dqn.hpp

@@ -37,7 +37,7 @@ using namespace mlpack::ann;
  *   url     = {http://arxiv.org/abs/1707.06887}
  * }
  * @endcode
- * 
+ *
  * @tparam OutputLayerType The output layer type of the network.
  * @tparam InitType The initialization type used for the network.
  * @tparam NetworkType The type of network used for simple dqn.
@@ -53,7 +53,8 @@ class CategoricalDQN
   /**
    * Default constructor.
    */
-  CategoricalDQN() : network(), isNoisy(false), atomSize(0), vMin(0.0), vMax(0.0)
+  CategoricalDQN() :
+      network(), atomSize(0), vMin(0.0), vMax(0.0), isNoisy(false)
   { /* Nothing to do here. */ }
 
   /**
@@ -101,7 +102,8 @@ class CategoricalDQN
   }
 
   /**
-   * Construct an instance of CategoricalDQN class from a pre-constructed network.
+   * Construct an instance of CategoricalDQN class from a pre-constructed
+   * network.
    *
    * @param network The network to be used by CategoricalDQN class.
    * @param config Hyper-parameters for categorical dqn.

src/mlpack/tests/decision_tree_test.cpp

@@ -462,12 +462,12 @@ TEST_CASE("RandomBinaryNumericSplitNoGainTest", "[DecisionTreeTest]")
   for (int i = 0; i < 5; ++i)
   {
     // Call BestBinaryNumericSplit to do the splitting.
-    double gain = BestBinaryNumericSplit<GiniGain>::SplitIfBetter<false>(
+    (void) BestBinaryNumericSplit<GiniGain>::SplitIfBetter<false>(
         bestGain, values, labels, 2, weights, 3, 1e-7, classProbabilities,
         aux);
 
     // Call RandomBinaryNumericSplit to do the splitting.
-    gain = RandomBinaryNumericSplit<GiniGain>::SplitIfBetter<false>(
+    (void) RandomBinaryNumericSplit<GiniGain>::SplitIfBetter<false>(
         bestGain, values, labels, 2, weights, 3, 1e-7, classProbabilities1,
         aux1);
 

src/mlpack/tests/lsh_test.cpp

@@ -985,7 +985,6 @@ TEST_CASE("SparseLSHTest", "[LSHTest]")
   // Make sure that sparse LSH distances aren't garbage.
   for (size_t i = 0; i < sparseNeighbors.n_elem; ++i)
   {
-    REQUIRE(sparseNeighbors[i] >= 0);
     REQUIRE(sparseNeighbors[i] < rdata.n_cols);
     REQUIRE(sparseDistances[i] >= 0.0);
     REQUIRE(!std::isinf(sparseDistances[i]));

src/mlpack/tests/main_tests/hoeffding_tree_test.cpp

@@ -398,7 +398,7 @@ TEST_CASE_METHOD(HoeffdingTreeTestFixture, "HoeffdingMinSamplesTest",
 
   // Check that small min_samples creates larger model.
   REQUIRE((IO::GetParam<HoeffdingTreeModel*>("output_model"))->NumNodes() <
-      nodes);
+      (size_t) nodes);
 }
 
 /**
@@ -466,7 +466,7 @@ TEST_CASE_METHOD(HoeffdingTreeTestFixture, "HoeffdingMaxSamplesTest",
   mlpackMain();
 
   // Check that large max_samples creates smaller model.
-  REQUIRE(nodes <
+  REQUIRE((size_t) nodes <
       (IO::GetParam<HoeffdingTreeModel*>("output_model"))->NumNodes());
 }
 
@@ -533,7 +533,7 @@ TEST_CASE_METHOD(HoeffdingTreeTestFixture, "HoeffdingConfidenceTest",
 
   mlpackMain();
   // Check that higher confidence creates smaller tree.
-  REQUIRE(nodes <
+  REQUIRE((size_t) nodes <
       (IO::GetParam<HoeffdingTreeModel*>("output_model"))->NumNodes());
 }
 
@@ -601,7 +601,7 @@ TEST_CASE_METHOD(HoeffdingTreeTestFixture, "HoeffdingPassesTest",
   mlpackMain();
 
   // Check that model with larger number of passes has greater number of nodes.
-  REQUIRE(nodes <
+  REQUIRE((size_t) nodes <
       (IO::GetParam<HoeffdingTreeModel*>("output_model"))->NumNodes());
 }
 
@@ -715,7 +715,7 @@ TEST_CASE_METHOD(HoeffdingTreeTestFixture,
 
   // Check that both models have different number of nodes.
   CHECK((IO::GetParam<HoeffdingTreeModel*>("output_model"))->NumNodes() !=
-      nodes);
+      (size_t) nodes);
 }
 
 /**

src/mlpack/tests/main_tests/kmeans_test.cpp

@@ -153,7 +153,7 @@ TEST_CASE_METHOD(KmTestFixture, "KmClusteringSizeCheck",
   REQUIRE(IO::GetParam<arma::mat>("output").n_rows == row+1);
   REQUIRE(IO::GetParam<arma::mat>("output").n_cols == col);
   REQUIRE(IO::GetParam<arma::mat>("centroid").n_rows == row);
-  REQUIRE(IO::GetParam<arma::mat>("centroid").n_cols == c);
+  REQUIRE(IO::GetParam<arma::mat>("centroid").n_cols == (arma::uword) c);
 }
 
 /**
@@ -179,7 +179,7 @@ TEST_CASE_METHOD(KmTestFixture, "KmClusteringSizeCheckLabelOnly",
   REQUIRE(IO::GetParam<arma::mat>("output").n_rows == 1);
   REQUIRE(IO::GetParam<arma::mat>("output").n_cols == col);
   REQUIRE(IO::GetParam<arma::mat>("centroid").n_rows == row);
-  REQUIRE(IO::GetParam<arma::mat>("centroid").n_cols == c);
+  REQUIRE(IO::GetParam<arma::mat>("centroid").n_cols == (arma::uword) c);
 }
 
 

src/mlpack/tests/main_tests/local_coordinate_coding_test.cpp

@@ -62,10 +62,10 @@ TEST_CASE_METHOD(LCCTestFixture, "LCCDimensionsTest",
   mlpackMain();
 
   // Check that the output has correct dimensions.
-  REQUIRE(IO::GetParam<arma::mat>("codes").n_rows == atoms);
-  REQUIRE(IO::GetParam<arma::mat>("codes").n_cols == cols);
-  REQUIRE(IO::GetParam<arma::mat>("dictionary").n_rows == rows);
-  REQUIRE(IO::GetParam<arma::mat>("dictionary").n_cols == atoms);
+  REQUIRE(IO::GetParam<arma::mat>("codes").n_rows == (arma::uword) atoms);
+  REQUIRE(IO::GetParam<arma::mat>("codes").n_cols == (arma::uword) cols);
+  REQUIRE(IO::GetParam<arma::mat>("dictionary").n_rows == (arma::uword) rows);
+  REQUIRE(IO::GetParam<arma::mat>("dictionary").n_cols == (arma::uword) atoms);
 }
 
 /**

src/mlpack/tests/main_tests/mean_shift_test.cpp

@@ -118,9 +118,10 @@ TEST_CASE_METHOD(
   mlpackMain();
 
   // Now check that the output has 1 extra row for labels.
-  REQUIRE(IO::GetParam<arma::mat>("output").n_rows == numRows + 1);
+  REQUIRE(IO::GetParam<arma::mat>("output").n_rows ==
+      (arma::uword) (numRows + 1));
   // Check number of output points are the same.
-  REQUIRE(IO::GetParam<arma::mat>("output").n_cols == numCols);
+  REQUIRE(IO::GetParam<arma::mat>("output").n_cols == (arma::uword) numCols);
 }
 
 /**

src/mlpack/tests/main_tests/preprocess_split_test.cpp

@@ -168,11 +168,12 @@ TEST_CASE_METHOD(
   mlpackMain();
 
   // Now check that the output has desired dimensions.
-  REQUIRE(IO::GetParam<arma::mat>("training").n_cols == inputSize);
+  REQUIRE(IO::GetParam<arma::mat>("training").n_cols ==
+      (arma::uword) inputSize);
   REQUIRE(IO::GetParam<arma::mat>("test").n_cols == 0);
 
   REQUIRE(IO::GetParam<arma::Mat<size_t>>("training_labels").n_cols ==
-      labelSize);
+      (arma::uword) labelSize);
   REQUIRE(IO::GetParam<arma::Mat<size_t>>("test_labels").n_cols == 0);
 }
 
@@ -205,10 +206,11 @@ TEST_CASE_METHOD(
 
   // Now check that the output has desired dimensions.
   REQUIRE(IO::GetParam<arma::mat>("training").n_cols == 0);
-  REQUIRE(IO::GetParam<arma::mat>("test").n_cols == inputSize);
+  REQUIRE(IO::GetParam<arma::mat>("test").n_cols == (arma::uword) inputSize);
 
   REQUIRE(IO::GetParam<arma::Mat<size_t>>("training_labels").n_cols == 0);
-  REQUIRE(IO::GetParam<arma::Mat<size_t>>("test_labels").n_cols == labelSize);
+  REQUIRE(IO::GetParam<arma::Mat<size_t>>("test_labels").n_cols ==
+      (arma::uword) labelSize);
 }
 
 /**
@@ -275,11 +277,12 @@ TEST_CASE_METHOD(
   mlpackMain();
 
   // Now check that the output has desired dimensions.
-  REQUIRE(IO::GetParam<arma::mat>("training").n_cols == inputSize);
+  REQUIRE(IO::GetParam<arma::mat>("training").n_cols ==
+      (arma::uword) inputSize);
   REQUIRE(IO::GetParam<arma::mat>("test").n_cols == 0);
 
   REQUIRE(IO::GetParam<arma::Mat<size_t>>("training_labels").n_cols ==
-      labelSize);
+      (arma::uword) labelSize);
   REQUIRE(IO::GetParam<arma::Mat<size_t>>("test_labels").n_cols == 0);
 }
 
@@ -314,10 +317,11 @@ TEST_CASE_METHOD(
 
   // Now check that the output has desired dimensions.
   REQUIRE(IO::GetParam<arma::mat>("training").n_cols == 0);
-  REQUIRE(IO::GetParam<arma::mat>("test").n_cols == inputSize);
+  REQUIRE(IO::GetParam<arma::mat>("test").n_cols == (arma::uword) inputSize);
 
   REQUIRE(IO::GetParam<arma::Mat<size_t>>("training_labels").n_cols == 0);
-  REQUIRE(IO::GetParam<arma::Mat<size_t>>("test_labels").n_cols == labelSize);
+  REQUIRE(IO::GetParam<arma::Mat<size_t>>("test_labels").n_cols ==
+      (arma::uword) labelSize);
 }
 
 /**

src/mlpack/tests/random_forest_test.cpp

@@ -514,7 +514,7 @@ TEST_CASE("WarmStartTreesTest", "[RandomForestTest]")
   // Train a random forest.
   RandomForest<> rf(trainingData, di, trainingLabels, 5, 25 /* 25 trees */, 1,
       1e-7, 0, MultipleRandomDimensionSelect(4));
-  
+
   REQUIRE(rf.NumTrees() == 25);
 
   rf.Train(trainingData, di, trainingLabels, 5, 20 /* 20 trees */, 1, 1e-7, 0,
@@ -538,7 +538,7 @@ TEST_CASE("WarmStartTreesPredictionsQualityTest", "[RandomForestTest]")
   // Train a random forest.
   RandomForest<> rf(trainingData, di, trainingLabels, 5, 3 /* 3 trees */, 1,
       1e-7, 0, MultipleRandomDimensionSelect(4));
-  
+
   // Get performance statistics on train data.
   arma::Row<size_t> oldPredictions;
   rf.Classify(trainingData, oldPredictions);
@@ -556,7 +556,7 @@ TEST_CASE("WarmStartTreesPredictionsQualityTest", "[RandomForestTest]")
   // Calculate the number of correct points.
   size_t newCorrect = arma::accu(newPredictions == trainingLabels);
 
-  REQUIRE(newCorrect - oldCorrect >= 0);
+  REQUIRE(newCorrect >= oldCorrect);
 }
 
 /**

src/mlpack/tests/rectangle_tree_test.cpp

@@ -364,7 +364,7 @@ TEST_CASE("TreeBalance", "[RectangleTreeTraitsTest]")
   TreeType tree(dataset, 20, 6, 5, 2, 0);
 
   REQUIRE(GetMinLevel(tree) == GetMaxLevel(tree));
-  REQUIRE(tree.TreeDepth() == GetMinLevel(tree));
+  REQUIRE((int) tree.TreeDepth() == GetMinLevel(tree));
 }
 
 // A test to see if point deletion is working correctly.  We build a tree, then
@@ -982,7 +982,7 @@ TEST_CASE("RPlusTreeOverlapTest", "[RectangleTreeTraitsTest]")
 
   // Ensure that all leaf nodes are at the same level.
   REQUIRE(GetMinLevel(rPlusTree) == GetMaxLevel(rPlusTree));
-  REQUIRE(rPlusTree.TreeDepth() == GetMinLevel(rPlusTree));
+  REQUIRE((int) rPlusTree.TreeDepth() == GetMinLevel(rPlusTree));
 }
 
 
@@ -1099,7 +1099,7 @@ TEST_CASE("RPlusPlusTreeBoundTest", "[RectangleTreeTraitsTest]")
   REQUIRE(b == false);
 
   REQUIRE(GetMinLevel(rPlusPlusTree) == GetMaxLevel(rPlusPlusTree));
-  REQUIRE(rPlusPlusTree.TreeDepth() == GetMinLevel(rPlusPlusTree));
+  REQUIRE((int) rPlusPlusTree.TreeDepth() == GetMinLevel(rPlusPlusTree));
 
   // Check the MinimalSplitsNumberSweep.
   typedef RectangleTree<EuclideanDistance,
@@ -1113,7 +1113,7 @@ TEST_CASE("RPlusPlusTreeBoundTest", "[RectangleTreeTraitsTest]")
   CheckRPlusPlusTreeBound(rPlusPlusTree2);
 
   REQUIRE(GetMinLevel(rPlusPlusTree2) == GetMaxLevel(rPlusPlusTree2));
-  REQUIRE(rPlusPlusTree2.TreeDepth() == GetMinLevel(rPlusPlusTree2));
+  REQUIRE((int) rPlusPlusTree2.TreeDepth() == GetMinLevel(rPlusPlusTree2));
 }
 
 TEST_CASE("RPlusPlusTreeTraverserTest", "[RectangleTreeTraitsTest]")