egutierrez
fc734029c1
12 funciones puras con implementación real: Standardize, MinMaxScale, Clip, RollingWindow, ZipSlices, GroupBy, Histogram, Pearson, Autocorrelation, FFT (Cooley-Tukey), DetectOutliers, Impute 3 funciones impuras (stubs): LoadCSV, LoadParquet, FetchDataFrame Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
36 lines
654 B
Go
36 lines
654 B
Go
package datascience
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import "math"
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// Standardize aplica Z-score normalización a un slice de float64.
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// Cada valor se transforma a (x - mean) / stddev.
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// Si stddev es 0, retorna un slice de ceros.
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func Standardize(data []float64) []float64 {
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n := len(data)
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if n == 0 {
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return []float64{}
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}
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var sum float64
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for _, v := range data {
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sum += v
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}
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mean := sum / float64(n)
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var sqSum float64
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for _, v := range data {
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d := v - mean
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sqSum += d * d
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}
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stddev := math.Sqrt(sqSum / float64(n))
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result := make([]float64, n)
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if stddev == 0 {
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return result
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}
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for i, v := range data {
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result[i] = (v - mean) / stddev
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}
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return result
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}
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