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code/src/GalaktaGlareDATA/GalaktaGlareDATA.go

@@ -0,0 +1,123 @@
+package GalaktaGlareDATA
+
+import (
+    "math"
+    "sort"
+)
+
+func Mean(numbers []float64) float64 {
+    total := 0.0
+    for _, num := range numbers {
+        total += num
+    }
+    return total / float64(len(numbers))
+}
+
+func Median(numbers []float64) float64 {
+    sort.Float64s(numbers)
+    mid := len(numbers) / 2
+    if len(numbers)%2 == 0 {
+        return (numbers[mid-1] + numbers[mid]) / 2
+    }
+    return numbers[mid]
+}
+
+func StandardDeviation(numbers []float64) float64 {
+    mean := Mean(numbers)
+    variance := 0.0
+    for _, num := range numbers {
+        variance += math.Pow(num-mean, 2)
+    }
+    variance /= float64(len(numbers))
+    return math.Sqrt(variance)
+}
+
+func Max(numbers []float64) float64 {
+    max := numbers[0]
+    for _, num := range numbers {
+        if num > max {
+            max = num
+        }
+    }
+    return max
+}
+
+func Min(numbers []float64) float64 {
+    min := numbers[0]
+    for _, num := range numbers {
+        if num < min {
+            min = num
+        }
+    }
+    return min
+}
+
+func Mode(numbers []float64) []float64 {
+    frequency := make(map[float64]int)
+    maxFrequency := 0
+    for _, num := range numbers {
+        frequency[num]++
+        if frequency[num] > maxFrequency {
+            maxFrequency = frequency[num]
+        }
+    }
+    var modes []float64
+    for num, freq := range frequency {
+        if freq == maxFrequency {
+            modes = append(modes, num)
+        }
+    }
+    return modes
+}
+
+func Quartiles(numbers []float64) (float64, float64, float64) {
+    sort.Float64s(numbers)
+    n := len(numbers)
+    q1 := Median(numbers[:n/2])
+    q2 := Median(numbers)
+    q3 := Median(numbers[(n + 1) / 2:])
+    return q1, q2, q3
+}
+
+func Percentile(numbers []float64, p float64) float64 {
+    sort.Float64s(numbers)
+    n := float64(len(numbers))
+    rank := p / 100 * (n - 1)
+    lower := math.Floor(rank)
+    upper := math.Ceil(rank)
+    if lower == upper {
+        return numbers[int(rank)]
+    }
+    return numbers[int(lower)] + (rank - lower) * (numbers[int(upper)] - numbers[int(lower)])
+}
+
+func Correlation(x, y []float64) float64 {
+    if len(x) != len(y) {
+        panic("Tamanho das listas de números não corresponde")
+    }
+    n := len(x)
+    sumX, sumY, sumXY, sumXSquare, sumYSquare := 0.0, 0.0, 0.0, 0.0, 0.0
+    for i := 0; i < n; i++ {
+        sumX += x[i]
+        sumY += y[i]
+        sumXY += x[i] * y[i]
+        sumXSquare += math.Pow(x[i], 2)
+        sumYSquare += math.Pow(y[i], 2)
+    }
+    numerator := float64(n)*sumXY - sumX*sumY
+    denominator := math.Sqrt((float64(n)*sumXSquare - math.Pow(sumX, 2)) * (float64(n)*sumYSquare - math.Pow(sumY, 2)))
+    return numerator / denominator
+}
+
+func DetectAnomalies(numbers []float64, threshold float64) []float64 {
+    anomalies := make([]float64, 0)
+    mean := Mean(numbers)
+    stdDev := StandardDeviation(numbers)
+    for _, num := range numbers {
+        zScore := (num - mean) / stdDev
+        if math.Abs(zScore) > threshold {
+            anomalies = append(anomalies, num)
+        }
+    }
+    return anomalies
+}

code/src/GalaktaGlareDATA/go.mod

@@ -0,0 +1,3 @@
+module github.com/simplyYan/GalaktaGlare/src/GalaktaGlareDATA
+
+go 1.22.4

code/src/GalaktaGlareDT/GalaktaGlareDT.go

@@ -0,0 +1,161 @@
+package GalaktaGlareDT
+
+import (
+	"errors"
+	"math"
+)
+
+type Node struct {
+	IsLeaf       bool
+	Prediction   interface{}
+	SplitFeature int
+	SplitValue   interface{}
+	Left         *Node
+	Right        *Node
+}
+
+type DecisionTree struct {
+	Root *Node
+}
+
+func NewDecisionTree() *DecisionTree {
+	return &DecisionTree{}
+}
+
+func (dt *DecisionTree) Fit(data [][]interface{}, labels []interface{}, features []int, maxDepth int) error {
+	if len(data) == 0 || len(data) != len(labels) {
+		return errors.New("dados ou rótulos inválidos")
+	}
+	dt.Root = dt.buildTree(data, labels, features, maxDepth, 1)
+	return nil
+}
+
+func (dt *DecisionTree) buildTree(data [][]interface{}, labels []interface{}, features []int, maxDepth, currentDepth int) *Node {
+	if len(data) == 0 || currentDepth >= maxDepth {
+		return &Node{IsLeaf: true, Prediction: majorityVote(labels)}
+	}
+
+	bestFeature, bestValue := chooseBestFeatureToSplit(data, labels, features)
+	leftData, leftLabels, rightData, rightLabels := splitData(data, labels, bestFeature, bestValue)
+
+	leftChild := dt.buildTree(leftData, leftLabels, features, maxDepth, currentDepth+1)
+	rightChild := dt.buildTree(rightData, rightLabels, features, maxDepth, currentDepth+1)
+
+	return &Node{
+		SplitFeature: bestFeature,
+		SplitValue:   bestValue,
+		Left:         leftChild,
+		Right:        rightChild,
+	}
+}
+
+func (dt *DecisionTree) Predict(input []interface{}) (interface{}, error) {
+	currentNode := dt.Root
+	for !currentNode.IsLeaf {
+		value := input[currentNode.SplitFeature]
+		if compareValues(value, currentNode.SplitValue) {
+			currentNode = currentNode.Left
+		} else {
+			currentNode = currentNode.Right
+		}
+	}
+	return currentNode.Prediction, nil
+}
+
+func allSame(items []interface{}) bool {
+	first := items[0]
+	for _, item := range items[1:] {
+		if item != first {
+			return false
+		}
+	}
+	return true
+}
+
+func majorityVote(items []interface{}) interface{} {
+	counts := make(map[interface{}]int)
+	for _, item := range items {
+		counts[item]++
+	}
+	var majority interface{}
+	maxCount := 0
+	for key, count := range counts {
+		if count > maxCount {
+			majority = key
+			maxCount = count
+		}
+	}
+	return majority
+}
+
+func chooseBestFeatureToSplit(data [][]interface{}, labels []interface{}, features []int) (int, interface{}) {
+	bestFeature := -1
+	var bestValue interface{}
+	bestImpurity := math.Inf(1)
+
+	for _, featureIndex := range features {
+		for _, row := range data {
+			value := row[featureIndex]
+			_, leftLabels, _, rightLabels := splitData(data, labels, featureIndex, value)
+			impurity := calculateImpurity(leftLabels, rightLabels)
+
+			if impurity < bestImpurity {
+				bestImpurity = impurity
+				bestFeature = featureIndex
+				bestValue = value
+			}
+		}
+	}
+
+	return bestFeature, bestValue
+}
+
+func calculateImpurity(labels ...[]interface{}) float64 {
+	totalSamples := 0
+	labelCounts := make(map[interface{}]int)
+
+	for _, labelSet := range labels {
+		for _, label := range labelSet {
+			totalSamples++
+			labelCounts[label]++
+		}
+	}
+
+	entropy := 0.0
+	for _, count := range labelCounts {
+		probability := float64(count) / float64(totalSamples)
+		entropy -= probability * math.Log2(probability)
+	}
+
+	return entropy
+}
+
+func splitData(data [][]interface{}, labels []interface{}, featureIndex int, value interface{}) ([][]interface{}, []interface{}, [][]interface{}, []interface{}) {
+	leftData, rightData := [][]interface{}{}, [][]interface{}{}
+	leftLabels, rightLabels := []interface{}{}, []interface{}{}
+	for i, row := range data {
+		if compareValues(row[featureIndex], value) {
+			leftData = append(leftData, row)
+			leftLabels = append(leftLabels, labels[i])
+		} else {
+			rightData = append(rightData, row)
+			rightLabels = append(rightLabels, labels[i])
+		}
+	}
+	return leftData, leftLabels, rightData, rightLabels
+}
+
+func compareValues(a, b interface{}) bool {
+	switch a.(type) {
+	case int:
+		return a.(int) < b.(int)
+	case float64:
+		return a.(float64) < b.(float64)
+	case string:
+		return a.(string) < b.(string)
+	case bool:
+		return !a.(bool) && b.(bool) 
+	default:
+		return false 
+	}
+}

code/src/GalaktaGlareDT/go.mod

@@ -0,0 +1,3 @@
+module github.com/simplyYan/GalaktaGlare/src/GalaktaGlareDT
+
+go 1.19

code/src/GalaktaGlareDT/go.sum

@@ -0,0 +1,21 @@
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+github.com/hajimehoshi/go-mp3 v0.3.3 h1:cWnfRdpye2m9ElSoVqneYRcpt/l3ijttgjMeQh+r+FE=
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+golang.org/x/image v0.0.0-20190227222117-0694c2d4d067/go.mod h1:kZ7UVZpmo3dzQBMxlp+ypCbDeSB+sBbTgSJuh5dn5js=
+golang.org/x/mobile v0.0.0-20190415191353-3e0bab5405d6/go.mod h1:E/iHnbuqvinMTCcRqshq8CkpyQDoeVncDDYHnLhea+o=
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code/src/GalaktaGlareIMG/GalaktaGlareIMG.go

@@ -0,0 +1,72 @@
+package galaktaglareimg
+
+import (
+	"errors"
+	"image"
+	"image/color"
+	"math"
+
+	"github.com/disintegration/imaging"
+)
+
+func LoadImage(path string) (image.Image, error) {
+	img, err := imaging.Open(path)
+	if err != nil {
+		return nil, err
+	}
+	return img, nil
+}
+
+func SaveImage(img image.Image, path string) error {
+	return imaging.Save(img, path)
+}
+
+func ResizeImage(img image.Image, width, height int) image.Image {
+	return imaging.Resize(img, width, height, imaging.Lanczos)
+}
+
+func ConvertToGrayscale(img image.Image) image.Image {
+	return imaging.Grayscale(img)
+}
+
+func CompareImages(img1, img2 image.Image) (float64, error) {
+	bounds1 := img1.Bounds()
+	bounds2 := img2.Bounds()
+
+	if bounds1 != bounds2 {
+		return 0, errors.New("images must have the same dimensions")
+	}
+
+	var sum float64
+	var count int
+
+	for y := bounds1.Min.Y; y < bounds1.Max.Y; y++ {
+		for x := bounds1.Min.X; x < bounds1.Max.X; x++ {
+			r1, g1, b1, _ := img1.At(x, y).RGBA()
+			r2, g2, b2, _ := img2.At(x, y).RGBA()
+			dr := float64(r1) - float64(r2)
+			dg := float64(g1) - float64(g2)
+			db := float64(b1) - float64(b2)
+			sum += dr*dr + dg*dg + db*db
+			count++
+		}
+	}
+
+	mse := sum / float64(count)
+	return 10 * math.Log10(65535*65535/mse), nil
+}
+
+func ImageToGrayscaleArray(img image.Image) [][]uint8 {
+	bounds := img.Bounds()
+	width, height := bounds.Max.X, bounds.Max.Y
+	grayscaleArray := make([][]uint8, height)
+	for y := 0; y < height; y++ {
+		grayscaleArray[y] = make([]uint8, width)
+		for x := 0; x < width; x++ {
+			r, g, b, _ := img.At(x, y).RGBA()
+			gray := color.GrayModel.Convert(color.RGBA{uint8(r >> 8), uint8(g >> 8), uint8(b >> 8), 255})
+			grayscaleArray[y][x] = gray.(color.Gray).Y
+		}
+	}
+	return grayscaleArray
+}