https://github.com/roberino/linqinfer
A lightweight and slightly experimental inference library for C# / LINQ
https://github.com/roberino/linqinfer
linq machine-learning neural-networks text-mining
Last synced: 12 months ago
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A lightweight and slightly experimental inference library for C# / LINQ
- Host: GitHub
- URL: https://github.com/roberino/linqinfer
- Owner: roberino
- License: mit
- Created: 2016-02-16T20:38:52.000Z (over 10 years ago)
- Default Branch: master
- Last Pushed: 2022-01-12T13:25:10.000Z (over 4 years ago)
- Last Synced: 2025-06-25T15:16:48.917Z (about 1 year ago)
- Topics: linq, machine-learning, neural-networks, text-mining
- Language: C#
- Homepage:
- Size: 72 MB
- Stars: 15
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# linqinfer
[](https://travis-ci.org/roberino/linqinfer)
Nuget Package: https://www.nuget.org/packages/LinqInfer/
## A lightweight inference library for C# / LINQ
LinqInfer is a data transformation, learning and inference framework.
This library reflects my interest over the years in Bayesian probability,
probabilistic reasoning, classification and other means of inference.
### Some key features / aims of the library
* Data extraction methods for object and text based data sets for converting into vector representations
* Methods for transforming and processing vector models
* Implementations of learning algorithms
* An extensible framework for integrating other vector based algorithms
### Basic library layout
#### Features and Feature Pipelines
Feature extraction is key to many of the classification algorithms.
Feature pipelines represent a pipeline of feature data which can be
transformed and pumped into various forms. There are a few mechanisms
for reducing the dimensionality of the input data, such as principle component
analysis.
When data is extracted, it is represented as an enumeration of column vectors which
can be transformed and filtered before being consumed for classifier training.
Feature pipelines come in [asynchronous](docs/async-pipelines.md) and synchronous flavours.
Async pipelines support a more complex, batch orientated processing model
which allows for parallel processing of data. Create asyncronous enumerators
using the Data.Pipes namespace. You can create from simple enumerators
or from loading functions.
Synchronous pipelines are derived from IQueryable data sets.
#### Learning
This is a collection of machine learning algorithms which are available through extention
functions and operate on IQueryable sets of data or on asyncronous enumerators.
* Self organising feature maps
* Simple statistical classifier
* Multi-layer neural network classifier
##### Example
```cs
var cancel = new CancellationTokenSource();
// MyDataLoader returns an enumeration of data
// There are various other ways to create
// an async enumerator
var data = MyDataLoader().AsAsyncEnumerator();
// Apply transformations and create a set for training
// You need to supply an expression which can classify your data
// for training
var trainingSet = await data
.BuildPipelineAsync(cancel.Token)
.CentreAndScaleAsync(Range.ZeroToOne)
.AsTrainingSetAsync(x => x.classification, cancel.Token);
// Attach a network model to the training set
// You can attach numerous models and train them in parallel
var classifier = trainingSet.AttachMultilayerNetworkClassifier(b =>
{
b.ConfigureSoftmaxNetwork(4, p =>
{
p.LearningRate = 0.005;
});
});
// Run the training procedure (over 550 epochs)
await trainingSet.RunAsync(cancel.Token, 550);
// Test the classifier
var results = classifier.Classify(new
{
x = 10,
y = 10,
classification = "?"
});
// Export the classifier
var exportedNetwork = classifier.ExportData();
```
See also [character learning example] (tests/LinqInfer.ImageLearningTests/ImageLearningExamples.cs)
See more documentation on [Neural Networks](docs/neural-networks.md)
#### Text
Utilities for working with text and text documents.
```cs
var index = docs // enumeration of XDocuments
.AsTokenisedDocuments(d => d.Root.Name.LocalName) // Use the root element name as the doc ID
.CreateIndex();
var results = index.Search("brown fox");
// create training sets
var corpus = File.OpenText("some.txt").CreateCorpus();
var trainingSet = corpus.CreateContinuousBagOfWordsAsyncTrainingSet(index.ExtractKeyTerms(500));
```
See more documentation on [Text](docs/text.md)
#### Maths
The Maths namespace consists of some basic numerical utilities
including numerous forms of vectors and vector manipulation methods.
#### Maths.Graphs
The Maths.Graphs namespace contains useful objects and functions for creating graphical representations of structures.
Graphs can be exported into [GEXF](https://gephi.org/gexf/format/schema.html).
Check out [Sigmajs](http://sigmajs.org/) and [Gephi](https://gephi.org/) for visualising graphs.
#### Maths.Probability
This is a collection of functions and probability "objects" to help solve simple probability problems.
* Sample spaces
* Hypotheses
* Markov chains
* Monte Carlo simulations
##### Examples
```cs
// Sample space
var sampleSpace = queryableSampleDataset.AsSampleSpace();
var p = sampleSpace.ProbabilityOfEvent(p => p.Age > 25);
// Hypotheses
var die = new[] { 4, 6, 8, 12, 20 };
var hypos = die.Select(n => P.Of(n).Is(1).OutOf(die.Length)).AsHypotheses();
hypos.Update(x => x < 6 ? Fraction.Zero : (1).OutOf(x));
hypos.ProbabilityOf(4);
```
#### Data
Serialisation and data storage interfaces.
### Examples
See tests for more usage examples.
It is still a work in progress.