Documentations and examples prioritized in this Major release

-> I have been receiving complaints that the library is hard to use as it is not well documented for people with no ML background and coming across this repo from Github, There are plenty of README files to explain each model inside its folder

-> Some might still find the wiki useful

-> Compatibility Checked!! users may not be able to find errors during compilation on this release than the previous ones

-> KMeans class updates and version compatibility

-> Dimensionality reduction Base class renamed to BaseDimRed to avoid conflicts with other base classes when multiple files are imported

-> Kohonen Maps PlotScatterCurveMatrix compatibility fixed, this method has been revoked matrix of maps or clusters can be plotted as follows

-> Kohonen maps fit & predicts methods

-> Metrics | scaler save methods for each scaler to csv files | Will be updated to binaries in the next version
-> RegressionMetrics for metric.mqh static class

    CColorGenerator clr;
    
    plt.Plot("kom", x, plotmatrix.Col(0), "map", "clusters","cluster"+string(1),CURVE_POINTS,clr.Next()); //plot the first cluster
    for (ulong i=1; i<plotmatrix.Cols(); i++) //start at the second column in the matrix | the second cluster
      {
        plt.AddPlot(plotmatrix.Col(i), "cluster"+string(i+1),clr.Next()); //Add the rest of clusters to the existing plot 
      }

-> fit() and fit_transform for all the prediction models and transformation models respectively implemented for all models

Python Like MALE5

The introduction of fit | fit_transofm, predict, transform, and predict_proba that is commonly used is Python Scikit learn and Tensorflow models have been introduced, This aids Machine learning coders from Python to find the library easy to use since these methods works the same in python and MQL5, even though they may lead to different outcomes.

Major Updates

• There is no longer a CMatrixUtils class; It has been replaced by the class MatrixExtend which is a static class making it easy to access matrix extension functions without the need to call the instance of a class.
• There is no CPreprocessing class; Each normalization technique can be called an independent class, such as StandardizationScaler, MinMaxScaler etc.
• CLabelEncoder class introduced; CSV files will be opened with the encoding process forcibly applied.
• CMetrics converted into Metrics static class, there is no need to call the entire instance of CMetrics class to use any of the metrics found inside the library

BugFixes

** Matrix manipulation bugs fixed
** Normalization techniques bugfixed