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2 changes: 1 addition & 1 deletion consts.html
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Expand Up @@ -3089,7 +3089,7 @@ <h1>Constants</h1><p>This module provides a collection of physical constants in
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Expand Up @@ -212,7 +212,7 @@ <h1>🚀 Numerical Differentiation</h1><p>This module provides functions for com
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<li class="open"><a href="#backward">fn backward</a> <ul>
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Expand Up @@ -204,7 +204,7 @@ <h1>🧮 Numerical Differentiation</h1><p>This module equips you with powerful f
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Expand Up @@ -248,7 +248,7 @@ <h1>Probability Distributions algorithms</h1><p>This module provides functions f
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Expand Up @@ -448,7 +448,7 @@ <h1>Easing Functions</h1><p>This is a pure V module that provides easing functio
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Expand Up @@ -204,7 +204,7 @@ <h1>Fast Fourier Transform</h1><p>The <code>fft</code> package is a wrapper of t
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Expand Up @@ -531,7 +531,7 @@ <h1>Geometry algorithms and structures</h1><p>This package provides some functio
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Expand Up @@ -305,7 +305,7 @@ <h1>Graph theory structures and algorithms</h1><p>This package implements algori
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Expand Up @@ -295,7 +295,7 @@ <h1>HDF5</h1><p>The functions described in this chapter will read or write data
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<li class="open"><a href="#open_file">fn open_file</a> <ul>
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Expand Up @@ -556,7 +556,7 @@ <h2>LogIter.new returns an iterator of <code>len</code> numbers evenly spaced on
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</section>


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<div class="doc-toc"><ul><li class="open"><a href="#den_solve">fn den_solve</a> <ul>
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<div class="doc-content">
<section id="readme_ml" class="doc-node">
<div class="title"><h1> ml <a href="#readme_ml">#</a></h1></div>

<h1>VSL Machine Learning (vsl.ml)</h1><p>VSL aims to provide a robust set of tools for scientific computing with an emphasis on performance and ease of use. In the <code>vsl.ml</code> module, some machine learning models are designed as observers of data, meaning they re-train automatically when data changes, while others do not require this functionality.</p><h2>Key Features</h2><ul><li><strong>Observers of Data</strong>: Some machine learning models in VSL act as observers,re-training automatically when data changes.- <strong>High Performance</strong>: Leverages V’s performance optimizations and can integratewith C and Fortran libraries like Open BLAS and LAPACK.- <strong>Versatile Algorithms</strong>: Supports a variety of machine learning algorithms andmodels.</li></ul><h2>Usage</h2><h3>Loading Data</h3><p>The <code>Data</code> struct in <code>vsl.ml</code> is designed to hold data in matrix format for machine learning tasks. Here's a brief overview of how to use it:</p><h4>Creating a Data Object</h4><p>You can create a <code>Data</code> object using the following methods:</p><ul><li><code>Data.new</code>: Creates a new <code>Data</code> object with specified dimensions.</li><li><code>Data.from_raw_x</code>: Creates a <code>Data</code> object from raw x values (without y values).</li><li><code>Data.from_raw_xy</code>: Creates a <code>Data</code> object from raw x and y values combined in a single matrix.</li><li><code>Data.from_raw_xy_sep</code>: Creates a <code>Data</code> object from separate x and y raw values.</li></ul><h3>Data Methods</h3><p>The <code>Data</code> struct has several key methods to manage and manipulate data:</p><ul><li><code>set(x, y)</code>: Sets the x matrix and y vector and notifies observers.</li><li><code>set_y(y)</code>: Sets the y vector and notifies observers.</li><li><code>set_x(x)</code>: Sets the x matrix and notifies observers.</li><li><code>split(ratio)</code>: Splits the data into two parts based on the given ratio.</li><li><code>clone()</code>: Returns a deep copy of the Data object without observers.</li><li><code>clone_with_same_x()</code>: Returns a deep copy of the Data object but shares the same x reference.</li><li><code>add_observer(obs)</code>: Adds an observer to the data object.</li><li><code>notify_update()</code>: Notifies observers of data changes.</li></ul><h3>Stat Observer</h3><p>The <code>Stat</code> struct is an observer of <code>Data</code>, providing statistical analysis of the data it observes. It automatically updates its statistics when the underlying data changes.</p><h2>Observer Models</h2><p>The following machine learning models in VSL are compatible with the <code>Observer</code> pattern. This means they can observe data changes and automatically update themselves.</p><h3>K-Means Clustering</h3><p>K-Means Clustering is used for unsupervised learning to group data points into clusters. As an observer model, it re-trains automatically when the data changes, which is useful for dynamic datasets that require continuous updates.</p><h3>K-Nearest Neighbors (KNN)</h3><p>K-Nearest Neighbors (KNN) is used for classification tasks where the target variable is categorical. As an observer model, it re-trains automatically when the data changes, which is beneficial for datasets that are frequently updated.</p><h2>Non-Observer Models</h2><p>The following machine learning models in VSL do not require the observer pattern and are trained once on a dataset without continuous updates.</p><h3>Linear Regression</h3><p>Linear Regression is used for predicting a continuous target variable based on one or more predictor variables. It is typically trained once on a dataset and used to make predictions without requiring continuous updates. Hence, it is not implemented as an observer model.</p>

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<div class="doc-toc"><ul><li class="open"><a href="#Data.from_raw_x">fn Data.from_raw_x</a> <ul>
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<li class="open"><a href="#Data.from_raw_x">fn Data.from_raw_x</a> <ul>
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<li class="open"><a href="#Data.from_raw_xy">fn Data.from_raw_xy</a> <ul>
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2 changes: 1 addition & 1 deletion ml.nlp.html
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Expand Up @@ -435,7 +435,7 @@ <h1>Natural Language Processing Tools</h1><p>This submodule offers tools for Nat
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Expand Up @@ -816,7 +816,7 @@ <h1>Message Passing Interface for parallel computing</h1><p>The <code>mpi</code>
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Expand Up @@ -255,7 +255,7 @@ <h1>noise</h1><p>This module aims to to implement noise algorithms.</p><p>It use
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Expand Up @@ -611,7 +611,7 @@ <h1>VSL Plot</h1><p>This library implements high-level functions to generate plo
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Expand Up @@ -264,7 +264,7 @@ <h1>Polynomials</h1><p>This chapter describes functions for evaluating and solvi
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2 changes: 1 addition & 1 deletion quaternion.html
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Expand Up @@ -674,7 +674,7 @@ <h1>Quaternions</h1><p>The functions provided by this module add support for qua
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Expand Up @@ -309,7 +309,7 @@ <h1>One Dimensional Root-Finding</h1><p>The module <code>vsl.roots</code> contai
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["","internal.prec.html"],
["This module provides two different ways of managing combinatorics. Let see an ex","iter.html"],
["","la.html"],
["","ml.html"],
["VSL aims to provide a robust set of tools for scientific computing with an empha","ml.html"],
["This submodule offers tools for Natural Language Processing.","ml.nlp.html"],
["The `mpi` package is a simplified wrapper to the [OpenMPI](https://www.open-mpi.","mpi.html"],
["This module aims to to implement noise algorithms.","noise.html"],
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</section>


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Expand Up @@ -694,7 +694,7 @@ <h1>V Computing Language</h1><p>VCL is a high level way of writing programs with
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2 changes: 1 addition & 1 deletion vlas.html
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Expand Up @@ -544,7 +544,7 @@ <h1>V Linear Algebra System</h1><p>This package implements BLAS and LAPACKE func
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