https://github.com/chriso345/golab
Machine Learning and Databases for Go
https://github.com/chriso345/golab
Last synced: 3 months ago
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Machine Learning and Databases for Go
- Host: GitHub
- URL: https://github.com/chriso345/golab
- Owner: ChrisO345
- License: mit
- Created: 2024-11-12T07:08:09.000Z (8 months ago)
- Default Branch: master
- Last Pushed: 2024-12-29T05:40:16.000Z (7 months ago)
- Last Synced: 2025-02-09T02:16:07.897Z (5 months ago)
- Language: Go
- Size: 111 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# golab
*Machine Learning in Go*
**golab** is a machine learning library for Go that provides tools for building and training machine learning models. It includes a variety of models, such as decision trees, linear regression, and k-means clustering, as well as tools for data preprocessing and evaluation.
___
## Installation
To install **golab**, you need to have Go installed on your machine. Then, you can run the following command:
```bash
go get github.com/chriso345/golab
```
___
## Usage
Here is an example using the `DecisionTreeClassifier` model to make predictions on a dataset:
```go
package main
import (
"fmt"
"github.com/chriso345/golab/dataframe"
"github.com/chriso345/golab/dataframe/series"
"github.com/chriso345/golab/tree"
)
func main() {
// Define the training dataframe
dfX := dataframe.New(
series.New([]float64{0.1245, 0.6589, 0.4487, 0.4578, 0.5978, 0.2534, 0.4356, 0.3215}, series.Float, "Feature1"),
series.New([]float64{0.2523, 0.8767, 0.1786, 0.5978, 0.9873, 0.5768, 0.3987, 0.1394}, series.Float, "Feature2"),
series.New([]int{1, 0, 1, 1, 0, 1, 0, 1}, series.Int, "Target"),
)
// Drop the target column from the dataframe
target := dfX.Drop("Target")
// Create the DecisionTreeClassifier
dtc := tree.NewDecisionTreeClassifier()
// Set the criterion to entropy
dtc.SetCriterion("entropy")
// Fit the model to the training data
dtc.Fit(dfX, target)
// Define the prediction dataframe
dfPredict := dataframe.New(
series.New([]float64{0.3276, 0.2345, 0.6789, 0.1234, 0.5678, 0.9876, 0.3456, 0.4567}, series.Float, "Feature1"),
series.New([]float64{0.47, 0.89, 0.12, 0.34, 0.56, 0.78, 0.23, 0.45}, series.Float, "Feature2"),
)
// Make predictions
predictions := dtc.Predict(dfPredict)
fmt.Println(predictions)
// {Target [1 1 0 1 1 0 1 1] int}
}
```
___
## License
This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for more details.