move hardcore defs to a different folder, use stdlib math

Makes this way faster. Don't roll your stdlib, Nuño

wip/nim/hardcore/README.md

@@ -0,0 +1,4 @@
+This is a version of time-to-botec for nim for which I define logarithms, normals & lognormals pretty much 
+from scratch, without relying on libraries except for random numbers, sqrts, or taking a to the power of b.
+
+This is interesting because it leads to better understanding. But it doesn't help compare the efficiency of various languages. So I'm setting it aside for now, even though I think it was really interesting.

wip/nim/hardcore/makefile

@@ -0,0 +1,11 @@
+SHELL := /bin/bash
+
+build: samples.nim
+	nim c --verbosity:0 samples.nim
+
+run: samples 
+	./samples --verbosity:0
+
+examine: samples
+	# nim c --verbosity:0 --opt:speed -d:release -d:danger --checks:off samples.nim && time ./samples --verbosity:0 --checks:off
+	nim c -d:release samples.nim && time ./samples

wip/nim/hardcore/samples.nim

@@ -0,0 +1,145 @@
+import std/math
+import std/sugar
+import std/random
+import std/sequtils
+
+randomize()
+
+## Basic math functions
+proc factorial(n: int): int = 
+  if n == 0 or n < 0: 
+    return 1
+  else:
+    return n * factorial(n - 1)
+
+proc sine(x: float): float = 
+  let n = 8
+  # ^ Taylor will converge really quickly
+  # notice that the factorial of 17 is 
+  # already pretty gigantic
+  var acc = 0.0
+  for i in 0..n:
+    var k = 2*i + 1
+    var taylor = pow(-1, i.float) * pow(x, k.float) / factorial(k).float 
+    acc = acc + taylor
+  return acc 
+
+## Log function
+## <https://en.wikipedia.org/wiki/Natural_logarithm#High_precision>
+
+## Arithmetic-geomtric mean
+proc ag(x: float, y: float): float = 
+  let n = 16 # just some high number
+  var a = (x + y)/2.0
+  var b = sqrt(x * y)
+  for i in 0..n:
+    let temp = a
+    a = (a+b)/2.0
+    b = sqrt(b*temp)
+  return a
+
+## Find m such that x * 2^m > 2^precision/2
+proc find_m(x:float): float = 
+  var m = 0.0;
+  let precision = 32 # bits
+  let c = pow(2.0, precision.float / 2.0)
+  while x * pow(2.0, m) < c:
+    m = m + 1
+  return m
+
+proc log(x: float): float = 
+  let m = find_m(x)
+  let s = x * pow(2.0, m)
+  let ln2 = 0.6931471805599453
+  return ( PI / (2.0 * ag(1, 4.0/s)) ) - m * ln2
+
+## Test these functions
+## echo factorial(5)
+## echo sine(1.0)
+## echo log(0.1)
+## echo log(2.0)
+## echo log(3.0)
+## echo pow(2.0, 32.float)
+   
+## Distribution functions
+
+## Normal
+## <https://en.wikipedia.org/wiki/Box%E2%80%93Muller_transform#Basic_form>
+proc ur_normal(): float = 
+  let u1 = rand(1.0)
+  let u2 = rand(1.0)
+  let z = sqrt(-2.0 * log(u1)) * sine(2 * PI * u2)
+  return z
+
+proc normal(mean: float, sigma: float): float = 
+  return (mean + sigma * ur_normal())
+
+proc lognormal(logmean: float, logsigma: float): float =
+  let answer = pow(E, normal(logmean, logsigma))
+  return answer
+
+proc to(low: float, high: float): float = 
+  let normal95confidencePoint = 1.6448536269514722
+  let loglow = log(low)
+  let loghigh = log(high)
+  let logmean = (loglow + loghigh)/2
+  let logsigma = (loghigh - loglow) / (2.0 * normal95confidencePoint);
+  return lognormal(logmean, logsigma)
+
+## echo ur_normal()
+## echo normal(10, 20)
+## echo lognormal(2, 4)
+## echo to(10, 90)
+
+## Manipulate samples
+
+proc make_samples(f: () -> float, n: int): seq[float] = 
+  result = toSeq(1..n).map(_ => f())
+  return result
+
+proc mixture(sxs: seq[seq[float]], ps: seq[float], n: int): seq[float] = 
+  
+  assert sxs.len == ps.len
+
+  var ws: seq[float]
+  var sum = 0.0
+  for i, p in ps:
+    sum = sum + p
+    ws.add(sum)
+  ws = ws.map(w => w/sum)
+  
+  proc get_mixture_sample(): float = 
+    let r = rand(1.0)
+    var i = ws.len - 1
+    for j, w in ws:
+      if r < w:
+        i = j
+        break
+    ## only occasion when ^ doesn't assign i
+    ## is when r is exactly 1
+    ## which would correspond to choosing the last item in ws
+    ## which is why i is initialized to ws.len
+    let xs = sxs[i]
+    let l = xs.len-1
+    let k = rand(0..l)
+    return xs[k]
+  
+  return toSeq(1..n).map(_ => get_mixture_sample())
+
+## Actual model
+
+let n = 1000000
+
+let p_a = 0.8
+let p_b = 0.5
+let p_c = p_a * p_b
+
+let weights = @[ 1.0 - p_c, p_c/2.0, p_c/4.0, p_c/4.0 ]
+
+let fs = [ () => 0.0, () => 1.0, () => to(1.0, 3.0), () => to(2.0, 10.0)  ]
+let dists = fs.map(f => make_samples(f, n))
+let result = mixture(dists, weights, n)
+let mean_result = foldl(result, a + b, 0.0) / float(result.len)
+
+# echo result
+echo mean_result

wip/nim/samples.nim

@@ -5,62 +5,6 @@ import std/sequtils
 
 randomize()
 
-## Basic math functions
-proc factorial(n: int): int = 
-  if n == 0 or n < 0: 
-    return 1
-  else:
-    return n * factorial(n - 1)
-
-proc sine(x: float): float = 
-  let n = 8
-  # ^ Taylor will converge really quickly
-  # notice that the factorial of 17 is 
-  # already pretty gigantic
-  var acc = 0.0
-  for i in 0..n:
-    var k = 2*i + 1
-    var taylor = pow(-1, i.float) * pow(x, k.float) / factorial(k).float 
-    acc = acc + taylor
-  return acc 
-
-## Log function
-## <https://en.wikipedia.org/wiki/Natural_logarithm#High_precision>
-
-## Arithmetic-geomtric mean
-proc ag(x: float, y: float): float = 
-  let n = 16 # just some high number
-  var a = (x + y)/2.0
-  var b = sqrt(x * y)
-  for i in 0..n:
-    let temp = a
-    a = (a+b)/2.0
-    b = sqrt(b*temp)
-  return a
-
-## Find m such that x * 2^m > 2^precision/2
-proc find_m(x:float): float = 
-  var m = 0.0;
-  let precision = 32 # bits
-  let c = pow(2.0, precision.float / 2.0)
-  while x * pow(2.0, m) < c:
-    m = m + 1
-  return m
-
-proc log(x: float): float = 
-  let m = find_m(x)
-  let s = x * pow(2.0, m)
-  let ln2 = 0.6931471805599453
-  return ( PI / (2.0 * ag(1, 4.0/s)) ) - m * ln2
-
-## Test these functions
-## echo factorial(5)
-## echo sine(1.0)
-## echo log(0.1)
-## echo log(2.0)
-## echo log(3.0)
-## echo pow(2.0, 32.float)
-   
 ## Distribution functions
 
 ## Normal
@@ -68,7 +12,7 @@ proc log(x: float): float =
 proc ur_normal(): float = 
   let u1 = rand(1.0)
   let u2 = rand(1.0)
-  let z = sqrt(-2.0 * log(u1)) * sine(2 * PI * u2)
+  let z = sqrt(-2.0 * ln(u1)) * sin(2 * PI * u2)
   return z
 
 proc normal(mean: float, sigma: float): float = 
@@ -80,8 +24,8 @@ proc lognormal(logmean: float, logsigma: float): float =
 
 proc to(low: float, high: float): float = 
   let normal95confidencePoint = 1.6448536269514722
-  let loglow = log(low)
+  let loglow = ln(low)
-  let loghigh = log(high)
+  let loghigh = ln(high)
   let logmean = (loglow + loghigh)/2
   let logsigma = (loghigh - loglow) / (2.0 * normal95confidencePoint);
   return lognormal(logmean, logsigma)