Class: SimpleRandom

Inherits:
Object
  • Object
show all
Defined in:
lib/simple-random/simple_random.rb

Direct Known Subclasses

MultiThreadedSimpleRandom

Instance Method Summary collapse

Constructor Details

#initialize ⇒ SimpleRandom

Returns a new instance of SimpleRandom.



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# File 'lib/simple-random/simple_random.rb', line 2

def initialize
  @m_w = 521288629
  @m_z = 362436069
end

Instance Method Details

#beta(a, b) ⇒ Object 



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# File 'lib/simple-random/simple_random.rb', line 95

def beta(a, b)
  raise "Alpha and beta parameters must be positive. Received a = #{a} and b = #{b}." unless a > 0 && b > 0
  u = gamma(a, 1)
  v = gamma(b, 1)
  u / (u + v)
end

#cauchy(median, scale) ⇒ Object 



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# File 'lib/simple-random/simple_random.rb', line 108

def cauchy(median, scale)
  raise 'Scale must be positive' if scale <= 0

  median + scale * Math.tan(Math::PI * (uniform - 0.5))
end

#chi_square(degrees_of_freedom) ⇒ Object 



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# File 'lib/simple-random/simple_random.rb', line 87

def chi_square(degrees_of_freedom)
  gamma(0.5 * degrees_of_freedom, 2.0)
end

#dirichlet(*parameters) ⇒ Object 



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# File 'lib/simple-random/simple_random.rb', line 129

def dirichlet(*parameters)
  sample = parameters.map { |a| gamma(a, 1) }
  sum = sample.inject(0.0) { |sum, g| sum + g }
  sample.map { |g| g / sum }
end

#exponential(mean = 1) ⇒ Object

Get exponential random sample with specified mean



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# File 'lib/simple-random/simple_random.rb', line 41

def exponential(mean = 1)
  raise 'Mean must be positive' if mean <= 0
  -1.0 * mean * Math.log(uniform)
end

#gamma(shape, scale) ⇒ Object

Implementation based on “A Simple Method for Generating Gamma Variables” by George Marsaglia and Wai Wan Tsang. ACM Transactions on Mathematical Software Vol 26, No 3, September 2000, pages 363-372.



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# File 'lib/simple-random/simple_random.rb', line 62

def gamma(shape, scale)
  if shape >= 1.0
    d = shape - 1.0 / 3.0
    c = 1 / ((9 * d) ** 0.5)
    while true
      v = 0.0
      while v <= 0.0
        x = normal
        v = 1.0 + c * x
      end
      v = v ** 3
      u = uniform
      if u < (1.0 - 0.0331 * (x ** 4)) || Math.log(u) < (0.5 * (x ** 2) + d * (1.0 - v + Math.log(v)))
        return scale * d * v
      end
    end
  elsif shape <= 0.0
    raise 'Shape must be positive'
  else
    g = gamma(shape + 1.0, 1.0)
    w = uniform
    return scale * g * (w ** (1.0 / shape))
  end
end

#inverse_gamma(shape, scale) ⇒ Object 



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# File 'lib/simple-random/simple_random.rb', line 91

def inverse_gamma(shape, scale)
  1.0 / gamma(shape, 1.0 / scale)
end

#laplace(mean, scale) ⇒ Object 



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# File 'lib/simple-random/simple_random.rb', line 120

def laplace(mean, scale)
  u = uniform
  mean + Math.log(2) + ((u < 0.5 ? 1 : -1) * scale * Math.log(u < 0.5 ? u : 1 - u))
end

#log_normal(mu, sigma) ⇒ Object 



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# File 'lib/simple-random/simple_random.rb', line 125

def log_normal(mu, sigma)
  Math.exp(normal(mu, sigma))
end

#normal(mean = 0.0, standard_deviation = 1.0) ⇒ Object

Sample normal distribution with given mean and standard deviation



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# File 'lib/simple-random/simple_random.rb', line 35

def normal(mean = 0.0, standard_deviation = 1.0)
  raise 'Invalid standard deviation' if standard_deviation <= 0
  mean + standard_deviation * ((-2.0 * Math.log(uniform)) ** 0.5) * Math.sin(2.0 * Math::PI * uniform)
end

#set_seed(*args) ⇒ Object 



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# File 'lib/simple-random/simple_random.rb', line 7

def set_seed(*args)
  if args.size > 1
    @m_w = args.first.to_i if args.first.to_i != 0
    @m_z = args.last.to_i if args.last.to_i != 0
  elsif args.first.is_a?(Numeric)
    @m_z = args.first.to_i if args.first.to_i != 0
  elsif args.first.is_a?(Time)
    x = (args.first.to_f * 1000000).to_i
    @m_w = x >> 16
    @m_z = x % 4294967296 # 2 ** 32
  else
    x = (Time.now.to_f * 1000000).to_i
    @m_w = x >> 16
    @m_z = x % 4294967296 # 2 ** 32
  end

  @m_w %= 4294967296
  @m_z %= 4294967296
end

#student_t(degrees_of_freedom) ⇒ Object 



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# File 'lib/simple-random/simple_random.rb', line 114

def student_t(degrees_of_freedom)
  raise 'Degrees of freedom must be positive' if degrees_of_freedom <= 0

  normal / ((chi_square(degrees_of_freedom) / degrees_of_freedom) ** 0.5)
end

#triangular(lower, mode, upper) ⇒ Object

Get triangular random sample with specified lower limit, mode, upper limit



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# File 'lib/simple-random/simple_random.rb', line 47

def triangular(lower, mode, upper)
  raise 'Upper limit must be larger than lower limit' if upper < lower
  raise 'Mode must lie between the upper and lower limits' if (mode < lower || mode > upper)
  f_c = (mode - lower) / (upper - lower)
  uniform_rand_num = uniform
  if uniform_rand_num < f_c
    lower + Math.sqrt(uniform_rand_num * (upper - lower) * (mode - lower))
  else
    upper - Math.sqrt((1 - uniform_rand_num) * (upper - lower) * (upper - mode))
  end
end

#uniform(lower = 0, upper = 1) ⇒ Object

Produce a uniform random sample from the open interval (lower, upper). The method will not return either end point.



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# File 'lib/simple-random/simple_random.rb', line 29

def uniform(lower = 0, upper = 1)
  raise 'Invalid range' if upper <= lower
  ((get_unsigned_int + 1) * (upper - lower) / 4294967296.0) + lower
end

#weibull(shape, scale) ⇒ Object 



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# File 'lib/simple-random/simple_random.rb', line 102

def weibull(shape, scale)
  raise 'Shape and scale must be positive' if shape <= 0.0 || scale <= 0.0

  scale * ((-Math.log(uniform)) ** (1.0 / shape))
end