Class: Rubystats::ProbabilityDistribution

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

Overview

The ProbabilityDistribution superclass provides an object for encapsulating probability distributions.

Author: Jaco van Kooten Author: Mark Hale Author: Paul Meagher Author: Jesus Castagnetto Author: Bryan Donovan (port from PHPmath to Ruby)

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 Attribute Summary

Attributes included from SpecialMath

#log_beta_cache_p, #log_beta_cache_q, #log_beta_cache_res, #log_gamma_cache_res, #log_gamma_cache_x

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, #orig_gamma

Constructor Details

#initialize ⇒ ProbabilityDistribution

Returns a new instance of ProbabilityDistribution.



18
19
 
# File 'lib/rubystats/probability_distribution.rb', line 18

def initialize
end

Instance Method Details

#cdf(x) ⇒ Object

Cummulative distribution function



45
46
47
48
49
50
51
52
53
54
55
 
# File 'lib/rubystats/probability_distribution.rb', line 45

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

check that variable is between lo and hi limits. lo default is 0.0 and hi default is 1.0

Raises:

  • (ArgumentError) 


122
123
124
125
126
127
 
# File 'lib/rubystats/probability_distribution.rb', line 122

def check_range(x, lo=0.0, hi=1.0)
  raise ArgumentError.new("x cannot be nil") if x.nil?
  if x < lo or x > hi
    raise ArgumentError.new("x must be greater than lo (#{lo}) and less than hi (#{hi})") 
  end
end

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



137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
 
# File 'lib/rubystats/probability_distribution.rb', line 137

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_factorial(n) ⇒ Object 



129
130
131
132
133
134
135
 
# File 'lib/rubystats/probability_distribution.rb', line 129

def get_factorial(n)
  if n <= 1
    return 1
  else
    return n.downto(1).reduce(:*)
  end
end

#icdf(p) ⇒ Object

Inverse CDF



58
59
60
61
62
63
64
65
66
67
68
 
# File 'lib/rubystats/probability_distribution.rb', line 58

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

returns the distribution mean



22
23
24
 
# File 'lib/rubystats/probability_distribution.rb', line 22

def mean
  get_mean
end

#pdf(x) ⇒ Object

Probability density function



32
33
34
35
36
37
38
39
40
41
42
 
# File 'lib/rubystats/probability_distribution.rb', line 32

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

Returns random number(s) using subclass’s get_rng method



71
72
73
74
75
76
77
78
79
80
81
82
83
84
 
# File 'lib/rubystats/probability_distribution.rb', line 71

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

#variance ⇒ Object

returns distribution variance



27
28
29
 
# File 'lib/rubystats/probability_distribution.rb', line 27

def variance
  get_variance
end