Curt TB2 overview page, amend other Guides so they appear in SC browser as Guides
parent
ed4f0641c5
commit
6072eddddc
@ -0,0 +1,64 @@
|
|||||||
|
title:: The Fluid Corpus Manipulation Data Tools
|
||||||
|
summary:: Tools for organising, exploring and querying corpora
|
||||||
|
categories:: Libraries>FluidDecomposition,Guides>FluCoMa
|
||||||
|
related:: Guides/FluCoMa, Guides/FluidDecomposition, Classes/FluidDataSet,Classes/FluidLabelSet
|
||||||
|
|
||||||
|
The suite of Fluid Corpus Manipulation data tools offer facilities for building, exploring, transforming and playing with corpora. The tools are built around two container classes, link::Classes/FluidDataSet:: and link::Classes/FluidLabelSet::, which provides a way to build up and stored collections of labelled data, and a suite of objects that act on these containers.
|
||||||
|
|
||||||
|
The design and interface of many of these objects is heavily based on the Python library link::https://scikit-learn.org/stable/##scikit-learn::, a mature and well developed machine learning toolkit that is comparatively quick to get going with. As our documentation continues to develop, we will also lean quite heavily on sci-learn's!
|
||||||
|
|
||||||
|
section:: Containers
|
||||||
|
|
||||||
|
Map id labels to data points, or to other labels
|
||||||
|
|
||||||
|
link::Classes/FluidDataSet::
|
||||||
|
link::Classes/FluidLabelSet::
|
||||||
|
|
||||||
|
|
||||||
|
section:: DataSet Filtering
|
||||||
|
|
||||||
|
Select and filter items from FluidDataSet by building queries
|
||||||
|
|
||||||
|
link::Classes/FluidDataSetQuery::
|
||||||
|
|
||||||
|
section:: Data Structure
|
||||||
|
|
||||||
|
Perform nearest neighbour searches
|
||||||
|
|
||||||
|
link::Classes/FluidKDTree::
|
||||||
|
|
||||||
|
section:: Data Conditioning
|
||||||
|
|
||||||
|
Pre-process data
|
||||||
|
|
||||||
|
link::Classes/FluidNormalize::
|
||||||
|
|
||||||
|
link::Classes/FluidStandardize::
|
||||||
|
|
||||||
|
section:: Dimension Reduction
|
||||||
|
|
||||||
|
Compress data to fewer dimensions for visualisation / efficiency / preprocessing
|
||||||
|
|
||||||
|
link::Classes/FluidPCA::
|
||||||
|
|
||||||
|
link::Classes/FluidMDS::
|
||||||
|
|
||||||
|
section:: Supervised Learning
|
||||||
|
|
||||||
|
Train supervised learning models using either K nearest neighbours or a simple neural network
|
||||||
|
|
||||||
|
subsection:: Classification
|
||||||
|
|
||||||
|
Map input data points to categories
|
||||||
|
|
||||||
|
link::Classes/FluidKNNClassifier::
|
||||||
|
|
||||||
|
link::Classes/FluidMLPClassifier::
|
||||||
|
|
||||||
|
subsection:: Regression
|
||||||
|
|
||||||
|
Map input data points to continuous output
|
||||||
|
|
||||||
|
link::Classes/FluidKNNRegressor::
|
||||||
|
|
||||||
|
link::Classes/FluidMLPRegressor::
|
||||||
Loading…
Reference in New Issue