add new README

CMakeLists.txt

 cmake_minimum_required(VERSION 3.16)
 project(EML)
 # set(CMAKE_C_COMPILER "/usr/bin/gcc-11")
-# set(CMAKE_CXX_COMPILER "/usr/bin/g++-11")
+set(CMAKE_CXX_COMPILER "/usr/bin/g++-11")
 # cmake -DCMAKE_CXX_COMPILER=/usr/bin/g++-11 ..
 set(CMAKE_CXX_STANDARD 20)
 set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS}  -O3  -Wall -Wextra -std=c++20")

READMETOO.md

+## Pre requisites
+Necessary python librairies:
+* keras
+* sklearn
+* graphviz
+* pandas
+* tensorflow
+  
+Donwload the database [here](https://www.kaggle.com/carlthome/gtzan-genre-collection). Then, from the root of the project:
+
+```` 
+cd DATASETS/ 
+````
+
+Then decompress the archive here.
+
+## Compile the projet
+
+From the root of the project:
+````
+mkdir build
+cd build/
+cmake ..
+make
+````
+
+
+## Extract features from database
+
+From the root of the project:
+````
+cd build/Extraction/
+./EXTRACTION
+````
+A file **features.csv** has been written into the folder ```build/Extraction/```.
+
+## Classify data
+
+### Train the classifier
+
+Launch the python file in the folder of the classifier to train.
+A file **file_test.csv** has been written into the folder ```classifier_name/```.
+
+### Apply the classifier
+From the root of the project
+
+* Example with a music sample:
+````
+cd build/RF/
+./RF ../../DATASETS/genres/jazz/jazz.00022.au
+````
+* Example for all the test data:
+````
+cd build/RF/
+./RF
+````
\ No newline at end of file

RF/Python/RF_training.py

@@ -4,37 +4,18 @@ from time import time
 import sys
 import warnings
 import sklearn
-from sklearn.utils.multiclass import unique_labels
-from sklearn.metrics import confusion_matrix
 from sklearn.model_selection import train_test_split
-from sklearn import svm, datasets
-from sklearn.metrics import roc_curve, auc
-from sklearn.preprocessing import label_binarize
-from sklearn.preprocessing import StandardScaler
-from sklearn.base import clone
-from sklearn.preprocessing import LabelEncoder
 from sklearn import preprocessing
 from sklearn import metrics
 import seaborn as sns
 import matplotlib.pyplot as plt
-import numpy as np
-from numpy.random import rand
-import scipy as sp
-from scipy import interp
 import pandas as pd
-from sklearn import linear_model
-from sklearn.neural_network import MLPClassifier
 from sklearn.ensemble import RandomForestClassifier
-from sklearn.svm import SVC
 from sklearn.model_selection import train_test_split
 from sklearn.model_selection import learning_curve
 from sklearn.model_selection import GridSearchCV
-from sklearn.datasets import fetch_lfw_people
-from sklearn.metrics import classification_report
-from sklearn.decomposition import PCA
 from sklearn.model_selection import cross_val_score
 from sklearn.metrics import ConfusionMatrixDisplay
-from sklearn.metrics import accuracy_score
 from sklearn.tree import export_graphviz, export_text
 
 # ---------------------------------------->
@@ -219,17 +200,6 @@ def train_RF(saveAlgo=False, path=""):
     display_model_performance_metrics(
         labelInd_test, prediction_random_best, labelIndices_unique)
 
-    # print(prediction_random_best)
-    # print(model_best.apply(X_train))
-    # print(model_best.decision_path(X_train))
-    # for e in model_best.estimators_:
-    # print(e.tree_)
-    # print(e.classes_)
-    # print(e.n_classes_)
-    # print(e.n_outputs_)
-    # report = export_text(e)
-    # print(report)
-    # help(sklearn.tree._tree.Tree)
     file = path+'RF/RFTrained.h'
     open(file, 'w').close()
     with open(file, 'a') as f: