[SFD] respect naming guidelines and small const extraction refactor

src/shared/algorithms/Filters/LowPass.h

@@ -48,10 +48,7 @@ public:
     float filter(float input);
 
 private:
-    float cutoffFreq;
-    float lambda;
-    float gain;
-    float output;
+    float gain, cutoffFreq, lambda, output;
 };
 
 }  // namespace Boardcore

src/shared/algorithms/SFD/SFDAscent.cpp

@@ -29,7 +29,7 @@
 namespace Boardcore
 {
 
-SFDAscent::SFDAscent(const SFDAConfig& config) : svm(config.modelParameters) {}
+SFDAscent::SFDAscent(const SFDAscentConfig& config) : svm(config.modelParameters) {}
 
 SFDAscent::FeaturesVec SFDAscent::getFeatures(const VectorIn& input)
 {
@@ -41,17 +41,17 @@ SFDAscent::FeaturesVec SFDAscent::getFeatures(const VectorIn& input)
     min   = input.minCoeff();
     max   = input.maxCoeff();
     delta = max - min;
-    for (int i = 0; i < lenChunk; i++)
+    for (int i = 0; i < LEN_CHUNK; i++)
         data(i) = (input(i) - min) / (std::max(delta, 1e-25f) * 2) - 1;
     u   = data.mean();
     x0  = data - u * VectorIn::Ones();
-    var = x0.squaredNorm() / lenChunk;
+    var = x0.squaredNorm() / LEN_CHUNK;
     s2  = x0.array().pow(2).mean();
     m4  = x0.array().pow(4).mean();
 
     rfourier = FFT32::fft(data).real();  // TODO: fix complex -> float
     rfmean   = rfourier.mean();
-    rfvar    = (rfourier - rfmean * VectorIn::Ones()).squaredNorm() / lenChunk;
+    rfvar    = (rfourier - rfmean * VectorIn::Ones()).squaredNorm() / LEN_CHUNK;
 
     features(0) = delta;
     features(1) = var;

src/shared/algorithms/SFD/SFDAscent.h

@@ -28,25 +28,26 @@
 
 #include <Eigen/Core>
 
+#include "SFDCommon.h"
+
 namespace Boardcore
 {
 
 class SFDAscent
 {
 public:
-    static constexpr int numFeatures = 6;
-    static constexpr int lenChunk    = 32;
+    static constexpr int NUM_FEATURES = 6;
 
-    using SVMn        = SVM<numFeatures>;
-    using FeaturesVec = Eigen::Vector<float, numFeatures>;
-    using VectorIn    = Eigen::Vector<float, lenChunk>;
+    using SVMn        = SVM<NUM_FEATURES>;
+    using FeaturesVec = Eigen::Vector<float, NUM_FEATURES>;
+    using VectorIn    = Eigen::Vector<float, LEN_CHUNK>;
 
-    struct SFDAConfig
+    struct SFDAscentConfig
     {
         SVMn::SVMConfig modelParameters;
     };
 
-    explicit SFDAscent(const SFDAConfig& config);
+    explicit SFDAscent(const SFDAscentConfig& config);
 
     bool classify(const VectorIn& input);
 

src/shared/algorithms/SFD/SFDCommon.h

+
+/* Copyright (c) 2023 Skyward Experimental Rocketry
+ * Author: Federico Lolli
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to deal
+ * in the Software without restriction, including without limitation the rights
+ * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+ * copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in
+ * all copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
+ * THE SOFTWARE.
+ */
+
+#pragma once
+
+static constexpr int LEN_CHUNK = 32;

src/shared/algorithms/SFD/SFDDescent.cpp

@@ -27,7 +27,7 @@
 namespace Boardcore
 {
 
-SFDDescent::SFDDescent(const SFDDConfig& config) : svm(config.modelParameters)
+SFDDescent::SFDDescent(const SFDDescentConfig& config) : svm(config.modelParameters)
 {
 }
 
@@ -41,7 +41,7 @@ SFDDescent::FeaturesVec SFDDescent::getFeatures(const VectorIn& input)
     min   = input.minCoeff();
     max   = input.maxCoeff();
     delta = max - min;
-    for (int i = 0; i < lenChunk; i++)
+    for (int i = 0; i < LEN_CHUNK; i++)
         data(i) = (input(i) - min) / (std::max(delta, 1e-25f) * 2) - 1;
     u   = data.mean();
     x0  = data - u * VectorIn::Ones();

src/shared/algorithms/SFD/SFDDescent.h

@@ -28,25 +28,26 @@
 
 #include <Eigen/Core>
 
+#include "SFDCommon.h"
+
 namespace Boardcore
 {
 
 class SFDDescent
 {
 public:
-    static constexpr int numFeatures = 5;
-    static constexpr int lenChunk    = 32;
+    static constexpr int NUM_FEATURES = 5;
 
-    using SVMn        = SVM<numFeatures>;
-    using FeaturesVec = Eigen::Vector<float, numFeatures>;
-    using VectorIn    = Eigen::Vector<float, lenChunk>;
+    using SVMn        = SVM<NUM_FEATURES>;
+    using FeaturesVec = Eigen::Vector<float, NUM_FEATURES>;
+    using VectorIn    = Eigen::Vector<float, LEN_CHUNK>;
 
-    struct SFDDConfig
+    struct SFDDescentConfig
     {
         SVMn::SVMConfig modelParameters;
     };
 
-    explicit SFDDescent(const SFDDConfig& config);
+    explicit SFDDescent(const SFDDescentConfig& config);
 
     bool classify(const VectorIn& input);