fa998619dc
Centralizes common math operations used across core/go-ai, core/go, and core/mining into Poindexter as the math pillar (alongside Borg=data, Enchantrix=encryption). New modules: - stats: Sum, Mean, Variance, StdDev, MinMax, IsUnderrepresented - scale: Lerp, InverseLerp, Remap, RoundToN, Clamp, MinMaxScale - epsilon: ApproxEqual, ApproxZero - score: WeightedScore, Ratio, Delta, DeltaPercent - signal: RampUp, SineWave, Oscillate, Noise (seeded RNG) 235 LOC implementation, 509 LOC tests, zero external deps, WASM-safe. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
63 lines
1.3 KiB
Go
63 lines
1.3 KiB
Go
package poindexter
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import "math"
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// Sum returns the sum of all values. Returns 0 for empty slices.
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func Sum(data []float64) float64 {
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var s float64
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for _, v := range data {
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s += v
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}
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return s
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}
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// Mean returns the arithmetic mean. Returns 0 for empty slices.
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func Mean(data []float64) float64 {
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if len(data) == 0 {
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return 0
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}
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return Sum(data) / float64(len(data))
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}
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// Variance returns the population variance. Returns 0 for empty slices.
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func Variance(data []float64) float64 {
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if len(data) == 0 {
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return 0
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}
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m := Mean(data)
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var ss float64
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for _, v := range data {
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d := v - m
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ss += d * d
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}
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return ss / float64(len(data))
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}
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// StdDev returns the population standard deviation.
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func StdDev(data []float64) float64 {
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return math.Sqrt(Variance(data))
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}
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// MinMax returns the minimum and maximum values.
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// Returns (0, 0) for empty slices.
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func MinMax(data []float64) (min, max float64) {
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if len(data) == 0 {
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return 0, 0
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}
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min, max = data[0], data[0]
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for _, v := range data[1:] {
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if v < min {
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min = v
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}
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if v > max {
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max = v
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}
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}
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return min, max
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}
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// IsUnderrepresented returns true if val is below threshold fraction of avg.
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// For example, IsUnderrepresented(3, 10, 0.5) returns true because 3 < 10*0.5.
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func IsUnderrepresented(val, avg, threshold float64) bool {
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return val < avg*threshold
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}
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