diff --git a/consts.html b/consts.html index 703e6aec6..a6ac67605 100644 --- a/consts.html +++ b/consts.html @@ -3089,7 +3089,7 @@
This module provides a collection of physical constants in -
+This module equips you with powerful f -
+This module provides functions f -
+This is a pure V module that provides easing functio -
+The fft
package is a wrapper of t
-
This package provides some functio -
+The functions described in this chapter will read or write data -
+len
numbers evenly spaced on
-
+
VSL aims to provide a robust set of tools for scientific computing with an emphasis on performance and ease of use. In the vsl.ml
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.
The Data
struct in vsl.ml
is designed to hold data in matrix format for machine learning tasks. Here's a brief overview of how to use it:
You can create a Data
object using the following methods:
Data.new
: Creates a new Data
object with specified dimensions.Data.from_raw_x
: Creates a Data
object from raw x values (without y values).Data.from_raw_xy
: Creates a Data
object from raw x and y values combined in a single matrix.Data.from_raw_xy_sep
: Creates a Data
object from separate x and y raw values.The Data
struct has several key methods to manage and manipulate data:
set(x, y)
: Sets the x matrix and y vector and notifies observers.set_y(y)
: Sets the y vector and notifies observers.set_x(x)
: Sets the x matrix and notifies observers.split(ratio)
: Splits the data into two parts based on the given ratio.clone()
: Returns a deep copy of the Data object without observers.clone_with_same_x()
: Returns a deep copy of the Data object but shares the same x reference.add_observer(obs)
: Adds an observer to the data object.notify_update()
: Notifies observers of data changes.The Stat
struct is an observer of Data
, providing statistical analysis of the data it observes. It automatically updates its statistics when the underlying data changes.
The following machine learning models in VSL are compatible with the Observer
pattern. This means they can observe data changes and automatically update themselves.
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.
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.
The following machine learning models in VSL do not require the observer pattern and are trained once on a dataset without continuous updates.
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.
This submodule offers tools for Nat -
+This module aims to to implement noise algorithms.
It use -
+This library implements high-level functions to generate plo -
+This chapter describes functions for evaluating and solvi -
+The functions provided by this module add support for qua -
+The module vsl.roots
contai
-
VCL is a high level way of writing programs with -
+This package implements BLAS and LAPACKE func -
+