https://github.com/chriso345/golab

Machine Learning and Databases for Go
https://github.com/chriso345/golab

Last synced: 11 days 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 (about 1 month ago)
• Default Branch: master
• Last Pushed: 2024-12-16T10:11:06.000Z (11 days ago)
• Last Synced: 2024-12-16T10:32:56.881Z (11 days ago)
• Language: Go
• Size: 88.9 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*

___

## 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 (

	"GoLab/dataframe/series"

	"fmt"

	"github.com/chriso345/golab/dataframe"

	"github.com/chriso345/golab/tree"

)

func main() {

	// Define the features and target

	df := dataframe.NewDataFrame(

		series.NewSeries([]float64{0.1245, 0.6589, 0.4487, 0.4578, 0.5978, 0.2534, 0.4356, 0.3215}, series.Float, "Feature1"),

		series.NewSeries([]float64{0.2523, 0.8767, 0.1786, 0.5978, 0.9873, 0.5768, 0.3987, 0.1394}, series.Float, "Feature2"),

		series.NewSeries([]int{1, 0, 1, 1, 0, 1, 0, 1}, series.Int, "Target"),

	)

	// Extract the feature column

	target := dfX.Drop("Target")

	// Create a DecisionTreeClassifier model

	dtc := tree.NewDecisionTreeClassifier()

	// Set the hyperparameters

	dtc.SetMaxDepth(3)

	dtc.SetCriterion("entropy")

	// Fit the model

	dtc.Fit(df, target)

	// Defining the prediction set

	dfPredict := dataframe.NewDataFrame(

		series.NewSeries([]float64{0.3276, 0.2345, 0.6789, 0.1234, 0.5678, 0.9876, 0.3456, 0.4567}, series.Float, "Feature1"),

		series.NewSeries([]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.String())

	// Output: {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.