add library

ANN/apply_ANN_weights.h

@@ -115,13 +115,13 @@ int ANNModelWeights(std::map<FTYPE, DataVector>
     softmaxF(X3);
 
     auto max = std::max_element(std::begin(X3), std::end(X3));
-    for (size_t i = 0; i < X3.size(); i++)
+    for (int i = 0; i < X3.size(); i++)
     {
         if (*max == X3[i])
         {
             return i;
         }
     }
-    return 0;
+    return 10;
 }
 #endif
\ No newline at end of file

ANN/main.cpp

@@ -16,6 +16,6 @@ int main(int argc, char **argv)
     }
     std::map<FTYPE, DataVector> features = compute_features_for(file_path);
     int result = ANNModelWeights(features);
-    std::cout << result;
+    std::cout << genres[result];
     return 0;
 }
\ No newline at end of file

Helpers/etypes.h

@@ -7,7 +7,8 @@
 
 #include <complex>
 #include <vector>
-
+#include <map>
+#include <string>
 typedef double real;
 typedef std::complex<real> Complex;
 typedef std::vector<real> DataVector;

Helpers/globals.h

@@ -3,14 +3,14 @@
 
 #include "etypes.h"
 
-constexpr std::size_t N = 512;      // WINDOW SIZE
-constexpr real Fs = 22050.0;        // SAMPLING FREQUENCY
+constexpr std::size_t N = 512; // WINDOW SIZE
+constexpr real Fs = 22050.0;   // SAMPLING FREQUENCY
 const std::size_t FFT_SIZE = N / 2;
-constexpr std::size_t CLASS_N = 10; // CLASS NUMBER
-constexpr std::size_t FEAT_N = 512; // FEATURES NUMBER STFT (if AVG and STD)
+constexpr std::size_t CLASS_N = 10;                              // CLASS NUMBER
+constexpr std::size_t FEAT_N = 512;                              // FEATURES NUMBER STFT (if AVG and STD)
 constexpr std::size_t OVO_CLASS_N = CLASS_N * (CLASS_N - 1) / 2; // One Versus One CLASS NUMBER
 constexpr std::size_t MEL_N = 26;
 constexpr std::size_t MEL_FILTERS_N = MEL_N - 2;
+std::map<int, std::string> genres = {{0, "blues"}, {1, "classical"}, {2, "country"}, {3, "disco"}, {4, "hiphop"}, {5, "jazz"}, {6, "metal"}, {7, "pop"}, {8, "reggae"}, {9, "rock"}, {10, "none"}};
 
-
-#endif //GLOBAL_H
+#endif // GLOBAL_H

SVM/apply_SVM_algo.h

@@ -1039,7 +1039,7 @@ int SVMModelAlgo(std::map<FTYPE, DataVector> &features)
     featureVector.insert(featureVector.end(), features[FTYPE::BINSTDEV].begin(), features[FTYPE::BINSTDEV].end());
     std::transform(featureVector.cbegin(), featureVector.cend(), featureVector.begin(), [&, indexM = -1, indexS = -1](real c) mutable
                    {indexM++;indexS++;return (c - meanNorm[indexM]) / stdNorm[indexS]; });
-    int nbClass = 1000;
+    int nbClass = 10;
     real hyperplanResult;
     for (size_t i = 0; i < featureVector.size(); i++)
     {

SVM/apply_SVM_weights.h

@@ -27,7 +27,7 @@ int SVMModelWeights(std::map<FTYPE, DataVector>
                        return (c - meanFeature[indexM]) / stdFeature[indexS];
                    });
     // SVM
-    int nbClass = 1000;
+    int nbClass = 10;
     for (size_t i = 0; i < coefs.size(); i++)
     {
         //  foreach class ie ligne coef avec intercep (ordonee)

SVM/main.cpp

@@ -17,8 +17,8 @@ int main(int argc, char **argv)
     }
     std::map<FTYPE, DataVector> features = compute_features_for(file_path);
     int result2 = SVMModelWeights(features);
-    std::cout << result2 << std::endl;
+    std::cout << genres[result2] << std::endl;
     int result3 = SVMModelAlgo(features);
-    std::cout << result3 << std::endl;
+    std::cout << genres[result3] << std::endl;
     return 0;
 }
\ No newline at end of file