The key macro features are:
– Easy integration with external libraries (NumPy, Matplotlib, Pandas, Scikit-Beach, Selenium and more…)
– Object oriented programming and configuration
– Extended explanation of each algorithm
– All the model implementation and data training can be done using python classes (with only a few lines of code).
– Many algorithms, models and datasets can be used using python classes.
– Configuration through a special configuration file
– Easy model selection
– Easy configuration on the fly
– Easy back-testing
– Reducing model search
– Easy hyper-parameter tuning

Lifetime License:
– Free, Open Source under the MIT License (Apache 2.0)

@author: S├ębastien Voisin
This script is based on the codebase at
Some of the script was developed by Sebastien Voisin.
Original version

This script implements a version of the fast SVD method for clustering. The objective of this algorithm is to approximate the eigenvalue decomposition of the covariance matrix of the data, in a few operations.
The method works by fast constructing a set of orthogonal vectors, called principal components, which can be thought as being the result of a projection of the data points on the space spanned by the first k principal components.
With this procedure we can consider clustering as a projection in a multidimensional space of the data.
It is important to note that this algorithm requires the user to provide the number k, the desired number of clusters.
We can also specify k and its value for the eigenvalues.
The algorithm works in two steps:
– Eigenvalue decomposition of the covariance matrix of the data
– Selection of the principal components, with k coefficients and a number of eigenvalues (k is a hyperparameter)
Finally the distance between the data points is computed as the norm of the projection of the data points on the principal components. 4f8c9c8613

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