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GoLearn
=======
<img src="http://talks.golang.org/2013/advconc/gopherhat.jpg" width=125><br>
This is the GoLearn Wiki (welcome!). GoLearn is a "batteries included" machine learning library written for and in Go. [Go check us out on Github](https://github.com/sjwhitworth/golearn).
[Installation](Installation) |
[Loading data](Instances) |
[Filtering](Filtering) |
Classification ([KNN](KNN) | [Trees](Trees) | [liblinear](liblinear)) | [Regression](Regression)
## Quick start
* [Using CSV files](Parsing CSV files)
* [Dividing data into training and test sets](TrainTestSplit) (not written)
## Code excerpts and examples
* [Sorting data](Instances)
* [Reading CSV files](Parsing CSV files)
* [Histogram binning](Filtering)
* [Chi-Merge binning](Filtering)
* [Setting the precision of a `FloatAttribute`](FloatAttribute precision)
* [Adding an Attribute](Adding Attributes)
* [Retrieving Attribute values](Attribute Specifications)
## Future
[Contributing](Contributing)
### The wish-list
* Expectation maximisation
* Native guided tree structures
* Support for time series processing
* Support for disk-backed `DenseInstances`
* SoftMax neural networks
* Deep-learning primitives (recursive neural networks)
* Recurrent neural networks
* Image manipulation
* Relational and pointer Attributes
* Support for sparse binary spaces
GoLearn
=======
<img src="http://talks.golang.org/2013/advconc/gopherhat.jpg" width=125><br>
This is the GoLearn Wiki (welcome!). GoLearn is a "batteries included" machine learning library written for and in Go. [Go check us out on Github](https://github.com/sjwhitworth/golearn).
[Installation](Installation) |
[Loading data](Instances) |
[Filtering](Filtering) |
Classification ([KNN](KNN) | [Trees](Trees) | [liblinear](liblinear)) | [Regression](Regression)
## Quick start
* [Using CSV files](Parsing CSV files)
* [Dividing data into training and test sets](TrainTestSplit) (not written)
## Code excerpts and examples
* [Sorting data](Instances)
* [Reading CSV files](Parsing CSV files)
* [Histogram binning](Filtering)
* [Chi-Merge binning](Filtering)
* [Setting the precision of a `FloatAttribute`](FloatAttribute precision)
* [Adding an Attribute](Adding Attributes)
* [Retrieving Attribute values](Attribute Specifications)
* [Implementing a custom `DataGrid`](Custom DataGrids)
## Future
[Contributing](Contributing)
### The wish-list
* Expectation maximisation
* Native guided tree structures
* Support for time series processing
* Support for disk-backed `DenseInstances`
* SoftMax neural networks
* Deep-learning primitives (recursive neural networks)
* Recurrent neural networks
* Image manipulation
* Relational and pointer Attributes
* Support for sparse binary spaces
* Support for arbitrary insertion and deletion of Attributes