core

Core package

import "github.com/Applifier/go-xgboost/core"

Example

// create the training data
cols := 3
rows := 5
trainData := make([]float32, cols*rows)
for i := 0; i < rows; i++ {
    for j := 0; j < cols; j++ {
        trainData[(i*cols)+j] = float32((i + 1) * (j + 1))
    }
}

trainLabels := make([]float32, rows)
for i := 0; i < rows; i++ {
    trainLabels[i] = float32(1 + i*i*i)
}

// Create XGDMatrix for training data
matrix, _ := core.XGDMatrixCreateFromMat(trainData, rows, cols, -1)

// Set training labels
matrix.SetFloatInfo("label", trainLabels)

// Create booster
booster, _ := core.XGBoosterCreate([]*core.XGDMatrix{matrix})

// Set booster parameters
booster.SetParam("booster", "gbtree")
booster.SetParam("objective", "reg:linear")
booster.SetParam("max_depth", "5")
booster.SetParam("eta", "0.1")
booster.SetParam("min_child_weight", "1")
booster.SetParam("subsample", "0.5")
booster.SetParam("colsample_bytree", "1")
booster.SetParam("num_parallel_tree", "1")

// perform 200 learning iterations
for iter := 0; iter < 200; iter++ {
    booster.UpdateOneIter(iter, matrix)
}

testData := make([]float32, cols*rows)
for i := 0; i < rows; i++ {
    for j := 0; j < cols; j++ {
        testData[(i*cols)+j] = float32((i + 1) * (j + 1))
    }
}

// Create XGDMatrix for test data
testmat, _ := core.XGDMatrixCreateFromMat(testData, rows, cols, -1)

// Predict
res, _ := booster.Predict(testmat, 0, 0)

fmt.Printf("%+v\n", res)
// output: [1.08002 2.5686886 7.86032 29.923136 63.76062]