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88 lines
2.5 KiB
Plaintext
88 lines
2.5 KiB
Plaintext
// Credits to Nickr5 for the useful procs I've taken from his library resource.
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var/const/E = 2.71828183
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var/const/Sqrt2 = 1.41421356
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/proc/Atan2(x, y)
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if(!x && !y) return 0
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var/a = arccos(x / sqrt(x*x + y*y))
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return y >= 0 ? a : -a
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// Greatest Common Divisor - Euclid's algorithm
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/proc/Gcd(a, b)
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return b ? Gcd(b, a % b) : a
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/proc/IsAboutEqual(a, b, deviation = 0.1)
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return abs(a - b) <= deviation
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// Performs a linear interpolation between a and b.
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// Note that amount=0 returns a, amount=1 returns b, and
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// amount=0.5 returns the mean of a and b.
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/proc/Lerp(a, b, amount = 0.5)
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return a + (b - a) * amount
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/proc/Mean(...)
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var/values = 0
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var/sum = 0
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for(var/val in args)
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values++
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sum += val
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return sum / values
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// The quadratic formula. Returns a list with the solutions, or an empty list
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// if they are imaginary.
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/proc/SolveQuadratic(a, b, c)
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ASSERT(a)
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. = list()
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var/d = b*b - 4 * a * c
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var/bottom = 2 * a
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if(d < 0) return
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var/root = sqrt(d)
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. += (-b + root) / bottom
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if(!d) return
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. += (-b - root) / bottom
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// Will filter out extra rotations and negative rotations
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// E.g: 540 becomes 180. -180 becomes 180.
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/proc/SimplifyDegrees(degrees)
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degrees = degrees % 360
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if(degrees < 0)
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degrees += 360
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return degrees
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// min is inclusive, max is exclusive
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/proc/Wrap(val, min, max)
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var/d = max - min
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var/t = Floor((val - min) / d)
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return val - (t * d)
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/proc/RaiseToPower(num, power)
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if(!power) return 1
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return (power-- > 1 ? num * RaiseToPower(num, power) : num)
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//converts a uniform distributed random number into a normal distributed one
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//since this method produces two random numbers, one is saved for subsequent calls
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//(making the cost negligble for every second call)
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//This will return +/- decimals, situated about mean with standard deviation stddev
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//68% chance that the number is within 1stddev
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//95% chance that the number is within 2stddev
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//98% chance that the number is within 3stddev...etc
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var/gaussian_next
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#define ACCURACY 10000
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/proc/gaussian(mean, stddev)
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var/R1;var/R2;var/working
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if(gaussian_next != null)
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R1 = gaussian_next
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gaussian_next = null
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else
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do
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R1 = rand(-ACCURACY,ACCURACY)/ACCURACY
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R2 = rand(-ACCURACY,ACCURACY)/ACCURACY
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working = R1*R1 + R2*R2
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while(working >= 1 || working==0)
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working = sqrt(-2 * log(working) / working)
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R1 *= working
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gaussian_next = R2 * working
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return (mean + stddev * R1)
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#undef ACCURACY
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