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>
40 lines
748 B
Go
40 lines
748 B
Go
package datascience
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import "math"
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// Pearson calcula el coeficiente de correlación de Pearson entre dos slices.
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// Si los slices tienen distinta longitud, usa la longitud mínima.
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// Retorna 0 si alguna desviación estándar es 0.
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func Pearson(xs, ys []float64) float64 {
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n := len(xs)
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if len(ys) < n {
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n = len(ys)
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}
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if n == 0 {
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return 0
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}
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var sumX, sumY float64
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for i := 0; i < n; i++ {
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sumX += xs[i]
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sumY += ys[i]
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}
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meanX := sumX / float64(n)
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meanY := sumY / float64(n)
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var num, denomX, denomY float64
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for i := 0; i < n; i++ {
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dx := xs[i] - meanX
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dy := ys[i] - meanY
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num += dx * dy
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denomX += dx * dx
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denomY += dy * dy
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}
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denom := math.Sqrt(denomX * denomY)
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if denom == 0 {
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return 0
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}
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return num / denom
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}
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