tweak: save some progress.
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wip/nim/makefile
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2
wip/nim/makefile
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@ -0,0 +1,2 @@
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build: samples.nim
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nim c samples.nim
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BIN
wip/nim/samples
BIN
wip/nim/samples
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@ -1,32 +1,64 @@
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import std/random
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import std/math
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import std/random
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# randomize()
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proc pow2(x:float, y:int): float =
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return pow(x, float(y))
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## Basic math functions
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proc factorial(n: int): int =
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if n == 0 or n < 0:
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return 1
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else:
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return n * factorial(n - 1)
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proc sine(x: float): float =
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let n = 100
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let n = 8
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# ^ Taylor will converge really quickly
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# notice that the factorial of 17 is
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# already pretty gigantic
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var acc = 0.0
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for i in 0..n:
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let k = 2*n + 1
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let taylor = pow2(x, k) / float(k)
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var k = 2*i + 1
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var taylor = pow(-1, i.float) * pow(x, k.float) / factorial(k).float
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acc = acc + taylor
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return acc
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# Helpers for calculating the log function
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## Arithmetic-geomtric mean
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proc ag(x: float, y: float): float =
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let n = 100
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var a = (x + y)/2.0
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var b = sqrt(x * y)
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for i in 0..n:
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let temp = a
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a = (a+b)/2.0
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b = sqrt(b*temp)
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return a
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## Find m such that x * 2^m > 2^100
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proc log_slow(x: float): float =
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# See: <https://en.wikipedia.org/wiki/Natural_logarithm#High_precision>
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var y = x - 1
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let n = 100000000
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var acc = 0.0
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for i in 1..n:
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let taylor = pow(-1.0, float(i+1)) * pow(y, i.float) / i.float
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acc = acc + taylor
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return acc
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proc log(x: float): float =
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var y = x - 1
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let n = 1000
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for i in 1..n:
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let taylor = pow2(-1.0, n+1) * pow2(y, n) / float(n)
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result = result + taylor
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return result
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return 1
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## Test these functions
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echo factorial(5)
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echo sine(1.0)
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echo log(1.0)
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echo log(2.0)
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## Distribution functions
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proc normal(): float =
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let u1 = rand(1.0)
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let u2 = rand(1.0)
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let z = 1
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# see https://en.wikipedia.org/wiki/Box%E2%80%93Muller_transform#Basic_form
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echo sine(0.1)
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@ -1,5 +1,4 @@
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import std/math
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# randomize()
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proc factorial(n: int): int =
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if n == 0 or n < 0:
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@ -19,5 +18,5 @@ proc sine(x: float): float =
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acc = acc + taylor
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return acc
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# echo factorial(17)
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echo factorial(17)
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echo sine(1.0)
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