Class: Rubystats::ProbabilityDistribution

Inherits:
Object
  • Object
show all
Includes:
ExtraMath, NumericalConstants, SpecialMath
Defined in:
lib/rubystats/probability_distribution.rb

Constant Summary

Constants included from NumericalConstants

NumericalConstants::Eps, NumericalConstants::Gamma, NumericalConstants::Gamma_x_max_value, NumericalConstants::Golden_ratio, NumericalConstants::Log_gamma_x_max_value, NumericalConstants::Max_float, NumericalConstants::Max_iterations, NumericalConstants::Max_value, NumericalConstants::Precision, NumericalConstants::Sqrt2, NumericalConstants::Sqrt2pi, NumericalConstants::Two_pi, NumericalConstants::Xminin

Instance Method Summary collapse

Methods included from ExtraMath

#binomial

Methods included from SpecialMath

#beta, #beta_fraction, #complementary_error, #error, #gamma, #gamma_fraction, #gamma_series_expansion, #incomplete_beta, #incomplete_gamma, #log_beta, #log_gamma

Constructor Details

#initialize ⇒ ProbabilityDistribution

Returns a new instance of ProbabilityDistribution.



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# File 'lib/rubystats/probability_distribution.rb', line 9

def initialize
end

Instance Method Details

#cdf(x) ⇒ Object 



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# File 'lib/rubystats/probability_distribution.rb', line 35

def cdf(x)
  if x.class == Array
    cdf_vals = []
    for i in (0...x.size)
      cdf_vals[i] = get_cdf(x[i])
    end
    return cdf_vals
  else
    return get_cdf(x)
  end
end

#check_range(x, lo = 0.0, hi = 1.0) ⇒ Object 



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# File 'lib/rubystats/probability_distribution.rb', line 83

def check_range(x, lo=0.0, hi=1.0)
  if (x < lo) || (x > hi)
    return "error"
  end
end

#find_root(prob, guess, x_lo, x_hi) ⇒ Object 



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# File 'lib/rubystats/probability_distribution.rb', line 97

def find_root (prob, guess, x_lo, x_hi) 
  accuracy = 1.0e-10
  max_iteration = 150
  x 		= guess
  x_new = guess
  error = 0.0
  pdf 	= 0.0
  dx 		= 1000.0
  i 		= 0
  while ( dx.abs > accuracy && (i += 1) < max_iteration )
    #Apply Newton-Raphson step
    error = cdf(x) - prob
    if error < 0.0
      x_lo = x
    else
      x_hi = x
    end
    pdf = pdf(x)
    if pdf != 0.0
      dx = error / pdf
      x_new = x -dx
    end
    # If the NR fails to converge (which for example may be the 
    # case if the initial guess is too rough) we apply a bisection
    # step to determine a more narrow interval around the root.
    if  x_new < x_lo || x_new > x_hi || pdf == 0.0
      x_new = (x_lo + x_hi) / 2.0
      dx = x_new - x
    end
    x = x_new
  end
  return x
end

#get_cdf(x) ⇒ Object 



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# File 'lib/rubystats/probability_distribution.rb', line 47

def get_cdf(x)
end

#get_factorial(n) ⇒ Object 



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# File 'lib/rubystats/probability_distribution.rb', line 89

def get_factorial(n)
  if n <= 1
    return 1
  else 
    return n * get_factorial(n-1)
  end
end

#get_icdf(p) ⇒ Object 



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# File 'lib/rubystats/probability_distribution.rb', line 62

def get_icdf(p)
end

#get_mean ⇒ Object 



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# File 'lib/rubystats/probability_distribution.rb', line 16

def get_mean
end

#get_pdf(x) ⇒ Object 



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# File 'lib/rubystats/probability_distribution.rb', line 32

def get_pdf(x)
end

#get_rng ⇒ Object 



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# File 'lib/rubystats/probability_distribution.rb', line 80

def get_rng() 
end

#icdf(p) ⇒ Object 



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# File 'lib/rubystats/probability_distribution.rb', line 50

def icdf(p)
  if p.class == Array
    inv_vals = []
    for i in (0..p.length)
      inv_vals[i] = get_icdf(p[i])
    end
    return inv_vals
  else
    return get_icdf(p)
  end
end

#mean ⇒ Object 



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# File 'lib/rubystats/probability_distribution.rb', line 12

def mean
  get_mean
end

#pdf(x) ⇒ Object 



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# File 'lib/rubystats/probability_distribution.rb', line 20

def pdf(x) 
  if x.class == Array
    pdf_vals = []
    for i in (0..x.length)
      pdf_vals[i] = get_pdf(x[i])
    end
    return pdf_vals
  else
    return get_pdf(x)
  end
end

#rng(n = 1) ⇒ Object 



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# File 'lib/rubystats/probability_distribution.rb', line 65

def rng(n=1)
  if n < 1
    return "Number of random numbers to return must be 1 or greater"
  end
  if (n > 1)
    rnd_vals = []
    for i in (0..n)
      rnd_vals[i] = get_rng()
    end
    return rnd_vals
  else
    return get_rng()
  end
end