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diff --git a/lib/std/sums.nim b/lib/std/sums.nim
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-#
-#
-#            Nim's Runtime Library
-#        (c) Copyright 2019 b3liever
-#
-#    See the file "copying.txt", included in this
-#    distribution, for details about the copyright.
-
-## Fast sumation functions.
-
-
-func sumKbn*[T](x: openArray[T]): T =
-  ## Kahan (compensated) summation: O(1) error growth, at the expense
-  ## of a considerable increase in computational expense.
-  if len(x) == 0: return
-  var sum = x[0]
-  var c = T(0)
-  for i in 1 ..< len(x):
-    let xi = x[i]
-    let t = sum + xi
-    if abs(sum) >= abs(xi):
-      c += (sum - t) + xi
-    else:
-      c += (xi - t) + sum
-    sum = t
-  result = sum + c
-
-func sumPairwise[T](x: openArray[T], i0, n: int): T =
-  if n < 128:
-    result = x[i0]
-    for i in i0 + 1 ..< i0 + n:
-      result += x[i]
-  else:
-    let n2 = n div 2
-    result = sumPairwise(x, i0, n2) + sumPairwise(x, i0 + n2, n - n2)
-
-func sumPairs*[T](x: openArray[T]): T =
-  ## Pairwise (cascade) summation of ``x[i0:i0+n-1]``, with O(log n) error growth
-  ## (vs O(n) for a simple loop) with negligible performance cost if
-  ## the base case is large enough.
-  ##
-  ## See, e.g.:
-  ## * http://en.wikipedia.org/wiki/Pairwise_summation
-  ##   Higham, Nicholas J. (1993), "The accuracy of floating point
-  ##   summation", SIAM Journal on Scientific Computing 14 (4): 783–799.
-  ##
-  ## In fact, the root-mean-square error growth, assuming random roundoff
-  ## errors, is only O(sqrt(log n)), which is nearly indistinguishable from O(1)
-  ## in practice. See:
-  ## * Manfred Tasche and Hansmartin Zeuner, Handbook of
-  ##   Analytic-Computational Methods in Applied Mathematics (2000).
-  ##
-  let n = len(x)
-  if n == 0: T(0) else: sumPairwise(x, 0, n)
-
-
-when isMainModule:
-  from math import pow
-
-  var epsilon = 1.0
-  while 1.0 + epsilon != 1.0:
-    epsilon /= 2.0
-  let data = @[1.0, epsilon, -epsilon]
-  assert sumKbn(data) == 1.0
-  assert sumPairs(data) != 1.0 # known to fail
-  assert (1.0 + epsilon) - epsilon != 1.0
-
-  var tc1: seq[float]
-  for n in 1 .. 1000:
-    tc1.add 1.0 / n.float
-  assert sumKbn(tc1) == 7.485470860550345
-  assert sumPairs(tc1) == 7.485470860550345
-
-  var tc2: seq[float]
-  for n in 1 .. 1000:
-    tc2.add pow(-1.0, n.float) / n.float
-  assert sumKbn(tc2) == -0.6926474305598203
-  assert sumPairs(tc2) == -0.6926474305598204