Certainly! Here's a Go implementation of the same min-max normalization function:
```
import (
"errors"
"math"
"github.com/gonum/stat"
)

func MinMaxNormalization(arr []float64, providedMin, providedMax *float64) ([]float64, error) {
if len(arr) == 0 {
return nil, errors.New("input array cannot be empty")
}
if providedMin != nil && providedMax != nil {
	if *providedMin == *providedMax {
		return nil, errors.New("provided minimum and maximum values cannot be equal")
	}
} else {
	minValue, maxValue := stat.MinMax(arr, nil)
	if minValue == maxValue {
		return nil, errors.New("input array must have at least two distinct values")
	}
	if providedMin == nil {
		providedMin = &minValue
	}
	if providedMax == nil {
		providedMax = &maxValue
	}
}

normalizedArr := make([]float64, len(arr))
for i, x := range arr {
	normalizedArr[i] = (x - *providedMin) / (*providedMax - *providedMin)
}
return normalizedArr, nil
}

// Example usage:
arr := []float64{2, 4, 6, 8, 10}
normalizedArr, err := MinMaxNormalization(arr, nil, nil)
if err != nil {
panic(err)
}
fmt.Println(normalizedArr)

// With provided min and max values:
minValue := 0.0
maxValue := 12.0
normalizedArr2, err := MinMaxNormalization(arr, &minValue, &maxValue)
if err != nil {
panic(err)
}
fmt.Println(normalizedArr2)
```
This implementation uses the Gonum statistics library to calculate the minimum and maximum values of the input array, and then performs min-max normalization using those values or the provided values.

If the provided minimum and maximum values are not equal, it normalizes the input array using those values. If they are not provided, it calculates the minimum and maximum values using the Gonum library. If the minimum and maximum values are equal, it returns an error.

Finally, the function returns the normalized array and an error if any.
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