Andromeda is a Golang library that focuses on implementing machine learning and deep learning algorithms with an emphasis on speed compared to traditional implementations in Python. Currently, the library supports the K-means clustering algorithm, and additional algorithms will be added in future releases.
To use Andromeda in your Go project, you can simply use the go get
command:
go get -u github.com/lodjim/andromeda
Here is an example of using Andromeda to perform K-means clustering on a dataset:
package main
import (
"fmt"
"os"
"encoding/csv"
"strconv"
"gonum.org/v1/gonum/mat"
"github.com/yourusername/andromeda/Kmean"
)
func main() {
// Open and read the CSV file
file, err := os.Open("iris.csv")
if err != nil {
fmt.Println("Error opening CSV file:", err)
return
}
defer file.Close()
// Parse the CSV records
reader := csv.NewReader(file)
records, err := reader.ReadAll()
if err != nil {
fmt.Println("Error reading CSV records:", err)
return
}
// Prepare the data for K-means clustering
var data []float64
for _, record := range records[1:] {
for _, value := range record[0 : len(record)-1] {
floatValue, _ := strconv.ParseFloat(value, 64)
data = append(data, floatValue)
}
}
x := mat.NewDense(len(data)/4, 4, data)
// Create a K-means model with 3 clusters
model := &kmean.Kmean{3}
// Fit the model to the data
centroids, _ := model.Fit(x)
// Predict the label and minimum distance for a new data point
label, distanceMin := model.Predict([]float64{5.4, 3, 4.5, 1.5}, centroids)
// Print the results
fmt.Println("Predicted Label:", label)
fmt.Println("Minimum Distance:", distanceMin)
}
Contributions to Andromeda are welcome! If you have ideas for new algorithms, optimizations, or improvements, feel free to open an issue or submit a pull request on the GitHub repository.
This project is licensed under the MIT License - see the LICENSE file for details.