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Add float array operations across JVM (SIMD), JS, and Native platforms (#66)
* Initial plan * Add comprehensive float array operations for JVM (SIMD), JS, and Native platforms Agent-Logs-Url: https://github.com/Quafadas/vecxt/sessions/13e4bb2a-657d-47a6-a532-f7a905bd6a42 Co-authored-by: Quafadas <24899792+Quafadas@users.noreply.github.com> * Fix -=scalar tail loop bug in float JVM implementation and add coverage test Agent-Logs-Url: https://github.com/Quafadas/vecxt/sessions/13e4bb2a-657d-47a6-a532-f7a905bd6a42 Co-authored-by: Quafadas <24899792+Quafadas@users.noreply.github.com> --------- Co-authored-by: copilot-swe-agent[bot] <198982749+Copilot@users.noreply.github.com> Co-authored-by: Quafadas <24899792+Quafadas@users.noreply.github.com>
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‎vecxt/src-js-native/floatarray.scala‎

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‎vecxt/src-js/array.scala‎

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@@ -16,6 +16,7 @@ end arrayUtil
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object arrays:
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export vecxt.JsNativeDoubleArrays.*
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export vecxt.JsNativeFloatArrays.*
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extension (d: Float64Array) def printArr: String = d.mkString("[", ",", "]")
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end extension
@@ -491,6 +492,252 @@ object arrays:
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// val t = js.Math.max( vec.toArray: _* )
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end extension
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extension (vec: Array[Float])
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inline def apply(index: Array[Boolean])(using inline boundsCheck: BoundsCheck.BoundsCheck): Array[Float] =
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dimCheck(vec, index)
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val trues = index.trues
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val newVec = Array.ofDim[Float](trues)
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var j = 0
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for i <- 0 until index.length do
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if index(i) then
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newVec(j) = vec(i)
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j = 1 + j
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end for
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newVec
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end apply
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inline def mean: Float =
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var sum = 0.0
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var i = 0
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while i < vec.length do
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sum += vec(i)
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i += 1
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end while
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(sum / vec.length).toFloat
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end mean
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inline def variance: Float = variance(VarianceMode.Population)
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inline def variance(mode: VarianceMode): Float =
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meanAndVariance(mode).variance
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end variance
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inline def meanAndVariance: (mean: Float, variance: Float) =
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meanAndVariance(VarianceMode.Population)
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inline def meanAndVariance(mode: VarianceMode): (mean: Float, variance: Float) =
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var mean = 0.0
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var m2 = 0.0
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var i = 0
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while i < vec.length do
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val n = i + 1
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val delta = vec(i).toDouble - mean
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mean += delta / n
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val delta2 = vec(i).toDouble - mean
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m2 += delta * delta2
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i += 1
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end while
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val denom = mode match
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case VarianceMode.Population => vec.length.toDouble
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case VarianceMode.Sample => (vec.length - 1).toDouble
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(mean.toFloat, (m2 / denom).toFloat)
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end meanAndVariance
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inline def std: Float = std(VarianceMode.Population)
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inline def std(mode: VarianceMode): Float =
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Math.sqrt(vec.variance(mode).toDouble).toFloat
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inline def stdDev: Float = stdDev(VarianceMode.Population)
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inline def stdDev(mode: VarianceMode): Float = std(mode)
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inline def dot(v1: Array[Float])(using inline boundsCheck: BoundsCheck): Float =
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dimCheck(vec, v1)
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var product = 0.0
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var i = 0
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while i < vec.length do
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product = product + vec(i) * v1(i)
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i = i + 1
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end while
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product.toFloat
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end dot
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inline def norm: Float =
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Math.sqrt(vec.dot(vec)(using vecxt.BoundsCheck.DoBoundsCheck.no).toDouble).toFloat
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end norm
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inline def +(d: Float): Array[Float] =
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vec.clone().tap(_ += d)
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inline def +=(d: Float): Unit =
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var i = 0
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while i < vec.length do
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vec(i) = vec(i) + d
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i = i + 1
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end while
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end +=
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inline def -(d: Float): Array[Float] =
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vec.clone().tap(_ -= d)
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end -
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inline def -=(d: Float): Unit =
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var i = 0
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while i < vec.length do
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vec(i) = vec(i) - d
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i = i + 1
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end while
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end -=
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inline def -(vec2: Array[Float])(using inline boundsCheck: BoundsCheck.BoundsCheck): Array[Float] =
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dimCheck(vec, vec2)
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vec.clone().tap(_ -= vec2)
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end -
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inline def -=(vec2: Array[Float])(using inline boundsCheck: BoundsCheck.BoundsCheck): Unit =
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dimCheck(vec, vec2)
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var i = 0
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while i < vec.length do
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vec(i) = vec(i) - vec2(i)
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i = i + 1
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end while
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end -=
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inline def +(vec2: Array[Float])(using inline boundsCheck: BoundsCheck.BoundsCheck): Array[Float] =
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dimCheck(vec, vec2)
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vec.clone().tap(_ += vec2)
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end +
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inline def +=(vec2: Array[Float])(using inline boundsCheck: BoundsCheck.BoundsCheck): Unit =
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dimCheck(vec, vec2)
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var i = 0
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while i < vec.length do
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vec(i) = vec(i) + vec2(i)
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i = i + 1
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end while
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end +=
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inline def +:+(d: Float): Array[Float] =
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vec.clone().tap(_ +:+= d)
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end +:+
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inline def +:+=(d: Float): Unit =
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var i = 0
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while i < vec.length do
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vec(i) = vec(i) + d
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i = i + 1
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end while
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end +:+=
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inline def *=(d: Float): Unit =
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var i = 0
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while i < vec.length do
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vec(i) = vec(i) * d
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i = i + 1
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end while
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end *=
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inline def *(d: Float): Array[Float] =
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vec.clone().tap(_ *= d)
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end *
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inline def /=(d: Float): Unit =
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var i = 0
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while i < vec.length do
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vec(i) = vec(i) / d
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i = i + 1
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end while
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end /=
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inline def /(d: Float): Array[Float] =
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vec.clone().tap(_ /= d)
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end /
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inline def pearsonCorrelationCoefficient(thatVector: Array[Float])(using
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inline boundsCheck: BoundsCheck.BoundsCheck
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): Float =
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dimCheck(vec, thatVector)
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val n = vec.length
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var i = 0
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var sum_x = 0.0
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var sum_y = 0.0
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var sum_xy = 0.0
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var sum_x2 = 0.0
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var sum_y2 = 0.0
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while i < n do
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sum_x = sum_x + vec(i)
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sum_y = sum_y + thatVector(i)
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sum_xy = sum_xy + vec(i) * thatVector(i)
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sum_x2 = sum_x2 + vec(i) * vec(i)
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sum_y2 = sum_y2 + thatVector(i) * thatVector(i)
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i = i + 1
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end while
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((n * sum_xy - (sum_x * sum_y)) / Math.sqrt(
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(sum_x2 * n - sum_x * sum_x) * (sum_y2 * n - sum_y * sum_y)
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)).toFloat
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end pearsonCorrelationCoefficient
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inline def spearmansRankCorrelation(thatVector: Array[Float])(using
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inline boundsCheck: BoundsCheck.BoundsCheck
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): Float =
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dimCheck(vec, thatVector)
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val theseRanks = vec.elementRanks
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val thoseRanks = thatVector.elementRanks
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theseRanks.pearsonCorrelationCoefficient(thoseRanks)
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end spearmansRankCorrelation
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inline def corr(thatVector: Array[Float])(using inline boundsCheck: BoundsCheck.BoundsCheck): Float =
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pearsonCorrelationCoefficient(thatVector)
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def elementRanks: Array[Float] =
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val indexed1 = vec.zipWithIndex
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val indexed = indexed1.toArray.sorted(using Ordering.by(_._1))
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val ranks: Array[Float] = new Array(vec.length)
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ranks(indexed.last._2) = vec.length.toFloat
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var currentValue: Float = indexed(0)._1
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var r0: Int = 0
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var rank: Int = 1
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while rank < vec.length do
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val temp: Float = indexed(rank)._1
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val end: Int =
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if temp != currentValue then rank
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else if rank == vec.length - 1 then rank + 1
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else -1
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if end > -1 then
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val avg: Float = ((1.0 + (end + r0)) / 2.0).toFloat
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var i: Int = r0;
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while i < end do
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ranks(indexed(i)._2) = avg
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i += 1
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end while
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r0 = rank
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currentValue = temp
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end if
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rank += 1
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end while
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ranks
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end elementRanks
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def covariance(thatVector: Array[Float]): Float =
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val μThis = vec.mean
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val μThat = thatVector.mean
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var cv: Double = 0
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var i: Int = 0
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while i < vec.length do
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cv += (vec(i) - μThis) * (thatVector(i) - μThat)
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i += 1
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end while
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(cv / (vec.length - 1)).toFloat
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end covariance
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end extension
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extension (vec: Array[Array[Double]])
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inline def horizontalSum: Array[Double] =
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val out = Array.ofDim[Double](vec.head.length)

‎vecxt/src-js/dimCheck.scala‎

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@@ -38,4 +38,10 @@ protected[vecxt] object dimCheck:
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inline def apply(a: Array[Int], b: Array[Boolean])(using inline doCheck: BoundsCheck) =
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inline if doCheck then if a.length != b.length then throw VectorDimensionMismatch(a.length, b.length)
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inline def apply(a: Array[Float], b: Array[Float])(using inline doCheck: BoundsCheck) =
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inline if doCheck then if a.length != b.length then throw VectorDimensionMismatch(a.length, b.length)
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inline def apply(a: Array[Float], b: Array[Boolean])(using inline doCheck: BoundsCheck) =
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inline if doCheck then if a.length != b.length then throw VectorDimensionMismatch(a.length, b.length)
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end dimCheck

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