Sha256: 3e23cfb90b2746ed709fd8945fed0d85dfc60b2729e9f4cb8a7618a19cdfd25a
Contents?: true
Size: 1.48 KB
Versions: 6
Compression:
Stored size: 1.48 KB
Contents
module Statistics
module Distribution
class Weibull
attr_accessor :shape, :scale # k and lambda
def initialize(k, lamb)
self.shape = k.to_f
self.scale = lamb.to_f
end
def cumulative_function(random_value)
return 0 if random_value < 0
1 - Math.exp(-((random_value/scale) ** shape))
end
def density_function(value)
return if shape <= 0 || scale <= 0
return 0 if value < 0
left = shape/scale
center = (value/scale)**(shape - 1)
right = Math.exp(-((value/scale)**shape))
left * center * right
end
def mean
scale * Math.gamma(1 + (1/shape))
end
def mode
return 0 if shape <= 1
scale * (((shape - 1)/shape) ** (1/shape))
end
def variance
left = Math.gamma(1 + (2/shape))
right = Math.gamma(1 + (1/shape)) ** 2
(scale ** 2) * (left - right)
end
# Using the inverse CDF function, also called quantile, we can calculate
# a random sample that follows a weibull distribution.
#
# Formula extracted from http://www.stat.yale.edu/Courses/1997-98/101/chigf.htm
def random(elements: 1, seed: Random.new_seed)
results = []
srand(seed)
elements.times do
results << ((-1/scale) * Math.log(1 - rand)) ** (1/shape)
end
if elements == 1
results.first
else
results
end
end
end
end
end
Version data entries
6 entries across 6 versions & 1 rubygems