Merge pull request #61 from MatthieuHernandez/Improve_code_quality

Add CodeFactor
MatthieuHernandez · Dec 9, 2023 · 2c1b7e4 · 2c1b7e4
2 parents 50afbd9 + 7b7df1d
commit 2c1b7e4
Show file tree

Hide file tree

Showing 17 changed files with 24 additions and 37 deletions.
diff --git a/README.md b/README.md
@@ -7,6 +7,7 @@
 ![](https://github.com/MatthieuHernandez/StraightforwardNeuralNetwork/workflows/Unit%20tests%20Linux/badge.svg?barnch=master)
 ![](https://github.com/MatthieuHernandez/StraightforwardNeuralNetwork/workflows/Unit%20tests%20Windows/badge.svg?barnch=master)
 ![](https://github.com/MatthieuHernandez/StraightforwardNeuralNetwork/workflows/Dataset%20tests/badge.svg?barnch=master)
+![](https://www.codefactor.io/repository/github/matthieuhernandez/straightforwardneuralnetwork/badge/master)
 
 **Straightforward Neural Network** is an open source neural network library in C++20 optimized for CPU. The goal of this library is to make the use of neural networks as easy as possible.
 

diff --git a/src/data/CompositeForTimeSeries.cpp b/src/data/CompositeForTimeSeries.cpp
@@ -56,7 +56,6 @@ void CompositeForTimeSeries::shuffle()
         {
             for (int j = 0; j < this->numberOfRecurrences + 1; ++j)
             {
-
                 const int index = iForIndex * (this->numberOfRecurrences + 1) + j + offset;
                 this->sets[training].shuffledIndexes[index] = this->indexesForShuffling[i] * (this->numberOfRecurrences + 1) + j + offset;
 

diff --git a/src/data/Data.hpp b/src/data/Data.hpp
@@ -104,12 +104,9 @@ namespace snn
         [[nodiscard]] const std::vector<float>& getOutputs(set set, int index);
         [[nodiscard]] int getLabel(set set, int index) const;
 
-
         [[nodiscard]] float getSeparator() const;
         void setSeparator(float value);
         [[nodiscard]] float getPrecision() const;
         void setPrecision(float value);
-
-
     };
 }
diff --git a/src/neural_network/layer/FilterLayer.hpp b/src/neural_network/layer/FilterLayer.hpp
@@ -33,8 +33,8 @@ namespace snn::internal
         virtual ~FilterLayer() = default;
         FilterLayer(const FilterLayer&) = default;
 
-        [[nodiscard]] std::vector<int> getShapeOfInput() const override final;
-        [[nodiscard]] std::vector<int> getShapeOfOutput() const override final;
+        [[nodiscard]] std::vector<int> getShapeOfInput() const final;
+        [[nodiscard]] std::vector<int> getShapeOfOutput() const final;
         [[nodiscard]] int getKernelSize() const;
         [[nodiscard]] int isValid() const override;
 

diff --git a/src/neural_network/layer/Layer.hpp b/src/neural_network/layer/Layer.hpp
@@ -41,20 +41,20 @@ namespace snn::internal
         std::vector<N> neurons;
         std::vector<std::unique_ptr<LayerOptimizer>> optimizers;
 
-        std::vector<float> output(const std::vector<float>& inputs, bool temporalReset) override final;
-        std::vector<float> outputForTraining(const std::vector<float>& inputs, bool temporalReset) override final;
-        std::vector<float> backOutput(std::vector<float>& inputErrors) override final;
+        std::vector<float> output(const std::vector<float>& inputs, bool temporalReset) final;
+        std::vector<float> outputForTraining(const std::vector<float>& inputs, bool temporalReset) final;
+        std::vector<float> backOutput(std::vector<float>& inputErrors) final;
 
-        [[nodiscard]] void* getNeuron(int index) override final;
-        [[nodiscard]] float getAverageOfAbsNeuronWeights() const override final;
-        [[nodiscard]] float getAverageOfSquareNeuronWeights() const override final;
-        [[nodiscard]] int getNumberOfInputs() const override final;
-        [[nodiscard]] int getNumberOfNeurons() const override final;
-        [[nodiscard]] int getNumberOfParameters() const override final;
+        [[nodiscard]] void* getNeuron(int index) final;
+        [[nodiscard]] float getAverageOfAbsNeuronWeights() const final;
+        [[nodiscard]] float getAverageOfSquareNeuronWeights() const final;
+        [[nodiscard]] int getNumberOfInputs() const final;
+        [[nodiscard]] int getNumberOfNeurons() const final;
+        [[nodiscard]] int getNumberOfParameters() const final;
         [[nodiscard]] std::vector<int> getShapeOfInput() const override = 0;
         [[nodiscard]] std::vector<int> getShapeOfOutput() const override = 0;
 
-        void train(std::vector<float>& inputErrors) override final;
+        void train(std::vector<float>& inputErrors) final;
 
         [[nodiscard]] int isValid() const override;
 

diff --git a/src/neural_network/layer/LayerFactory.hpp b/src/neural_network/layer/LayerFactory.hpp
@@ -133,7 +133,6 @@ namespace snn
             kernelSize,
             std::vector<int>(),
             std::vector<LayerOptimizerModel>()
-
         };
         return model;
     }
@@ -161,7 +160,6 @@ namespace snn
             kernelSize,
             std::vector<int>(),
             {static_cast<LayerOptimizerModel>(optimizers) ...}
-
         };
         return model;
     }

diff --git a/src/neural_network/layer/MaxPooling1D.cpp b/src/neural_network/layer/MaxPooling1D.cpp
@@ -46,7 +46,6 @@ void MaxPooling1D::buildKernelIndexes()
                 this->kernelIndexes[k][kernelIndex] = inputIndex;
             else
                 this->kernelIndexes[k][kernelIndex] = -1;
-
         }
     }
 }

diff --git a/src/neural_network/layer/SimpleLayer.hpp b/src/neural_network/layer/SimpleLayer.hpp
@@ -18,9 +18,9 @@ namespace snn::internal
         void serialize(Archive& ar, unsigned version);
 
     protected:
-        [[nodiscard]] std::vector<float> computeBackOutput(std::vector<float>& inputErrors) override final;
-        [[nodiscard]] std::vector<float> computeOutput(const std::vector<float>& inputs, bool temporalReset) override final;
-        virtual void computeTrain(std::vector<float>& inputErrors) override final;
+        [[nodiscard]] std::vector<float> computeBackOutput(std::vector<float>& inputErrors) final;
+        [[nodiscard]] std::vector<float> computeOutput(const std::vector<float>& inputs, bool temporalReset) final;
+        virtual void computeTrain(std::vector<float>& inputErrors) final;
 
     public:
         SimpleLayer() = default;  // use restricted to Boost library only
@@ -29,12 +29,12 @@ namespace snn::internal
         virtual ~SimpleLayer() = default;
         [[nodiscard]] std::unique_ptr<BaseLayer> clone(std::shared_ptr<NeuralNetworkOptimizer> optimizer) const override;
 
-        [[nodiscard]] std::vector<int> getShapeOfInput() const override final;
-        [[nodiscard]] std::vector<int> getShapeOfOutput() const override final;
-        [[nodiscard]] int isValid() const override final;
+        [[nodiscard]] std::vector<int> getShapeOfInput() const final;
+        [[nodiscard]] std::vector<int> getShapeOfOutput() const final;
+        [[nodiscard]] int isValid() const final;
 
-        bool operator==(const BaseLayer& layer) const override final;
-        bool operator!=(const BaseLayer& layer) const override final;
+        bool operator==(const BaseLayer& layer) const final;
+        bool operator!=(const BaseLayer& layer) const final;
     };
 
     template <BaseNeuron N>

diff --git a/src/neural_network/layer/neuron/Neuron.hpp b/src/neural_network/layer/neuron/Neuron.hpp
@@ -20,7 +20,6 @@ namespace snn::internal
         void serialize(Archive& ar, unsigned version);
 
     protected:
-
         int numberOfInputs;
         int batchSize;
         std::vector<float> weights;

diff --git a/src/neural_network/layer/neuron/activation_function/ActivationFunction.hpp b/src/neural_network/layer/neuron/activation_function/ActivationFunction.hpp
@@ -21,7 +21,6 @@ namespace snn::internal
     class ActivationFunction
     {
     private:
-
         friend class boost::serialization::access;
         template <class Archive>
         void serialize(Archive& ar, unsigned version);

diff --git a/src/neural_network/optimizer/Softmax.cpp b/src/neural_network/optimizer/Softmax.cpp
@@ -45,7 +45,6 @@ void Softmax::computeSoftmax(std::vector<float>& outputs)
         else
             output = value;
     }
-
 }
 
 inline

diff --git a/tests/dataset_tests/audio-cats-and-dogs/AudioCatsAndDogsTest.cpp b/tests/dataset_tests/audio-cats-and-dogs/AudioCatsAndDogsTest.cpp
@@ -38,7 +38,6 @@ TEST_F(AudioCatsAndDogsTest, loadData)
 
 TEST_F(AudioCatsAndDogsTest, DISABLED_trainBestNeuralNetwork)
 {
-
     StraightforwardNeuralNetwork neuralNetwork({
         Input(sizeOfOneData),
         MaxPooling(1600),

diff --git a/tests/manual_tests/Cifar10SimpleNetwork.py b/tests/manual_tests/Cifar10SimpleNetwork.py
@@ -37,5 +37,5 @@
 plt.ylim([0.5, 1])
 plt.legend(loc='lower right')
 
-test_loss, test_acc = model.evaluate(test_images,  test_labels, verbose=2)
+test_loss, test_acc = model.evaluate(test_images, test_labels, verbose=2)
 print(test_acc)
diff --git a/tests/manual_tests/FashionMnistSimpleNetwork.py b/tests/manual_tests/FashionMnistSimpleNetwork.py
@@ -36,5 +36,5 @@
 plt.ylim([0.5, 1])
 plt.legend(loc='lower right')
 
-test_loss, test_acc = model.evaluate(test_images,  test_labels, verbose=2)
+test_loss, test_acc = model.evaluate(test_images, test_labels, verbose=2)
 print(test_acc)
diff --git a/tests/unit_tests/ConvolutionTests.cpp b/tests/unit_tests/ConvolutionTests.cpp
@@ -64,7 +64,6 @@ TEST(Convolution, LayerConvolution2D)
         324, 324, 795, 795, 1425, 1425, 1137, 1137, 666, 666,
         411, 411, 944, 944, 1607, 1607, 1220, 1220, 687, 687,
         315, 315, 703, 703, 1168, 1168, 865, 865, 477, 477
-
     };
     LayerModel model {
         convolution,

diff --git a/tests/unit_tests/LocallyConnectedTests.cpp b/tests/unit_tests/LocallyConnectedTests.cpp
@@ -93,8 +93,7 @@ TEST(LocallyConnected, ComplexeLayerLocallyConnected2D)
         {73, 75, 77, 79, 81, 83, 85, 87, 89, 91, 93, 95, 97, 99, 101, 103, 105, 107, 1},
         {74, 76, 78, 80, 82, 84, 86, 88, 90, 92, 94, 96, 98, 100, 102, 104, 106, 108, 1},
         {109, 111, 113, 115, 117, 119, 121, 123, 125, 127, 129, 131, 133, 135, 137, 139, 141, 143, 1},
-        {110, 112, 114, 116, 118, 120, 122, 124, 126, 128, 130, 132, 134, 136, 138, 140, 142, 144, 1},
-
+        {110, 112, 114, 116, 118, 120, 122, 124, 126, 128, 130, 132, 134, 136, 138, 140, 142, 144, 1}
     };
     vector<float> error(8);
     std::iota(std::begin(input), std::end(input), 1.0f);

diff --git a/tests/unit_tests/SoftmaxTests.cpp b/tests/unit_tests/SoftmaxTests.cpp
@@ -29,7 +29,6 @@ TEST(Softmax, largeValues)
 
     ASSERT_VECTOR_EQ(values1, expectedValues);
     ASSERT_VECTOR_EQ(values2, expectedValues);
-
 }
 
 TEST(Softmax, smallValues)
-Original file line number
+Diff line change
@@ Expand Up / @@ -56,7 +56,6 @@ void CompositeForTimeSeries::shuffle() @@
             {
                 for (int j = 0; j < this->numberOfRecurrences + 1; ++j)
                 {
                     const int index = iForIndex * (this->numberOfRecurrences + 1) + j + offset;
                     this->sets[training].shuffledIndexes[index] = this->indexesForShuffling[i] * (this->numberOfRecurrences + 1) + j + offset;
@@ Expand Down @@