Module: Selector::BNS

Included in:
BiNormalSeperation
Defined in:
lib/svm_helper/selectors/calc.rb

Constant Summary collapse

SQR2 = 
Math.sqrt(2)
SQR2PI = 
Math.sqrt(2.0*Math::PI)
A =

Coefficients in rational approximations.

[0, -3.969683028665376e+01, 2.209460984245205e+02, -2.759285104469687e+02, 1.383577518672690e+02, -3.066479806614716e+01, 2.506628277459239e+00]
B = 
[0, -5.447609879822406e+01, 1.615858368580409e+02, -1.556989798598866e+02, 6.680131188771972e+01, -1.328068155288572e+01]
C = 
[0, -7.784894002430293e-03, -3.223964580411365e-01, -2.400758277161838e+00, -2.549732539343734e+00, 4.374664141464968e+00, 2.938163982698783e+00]
D = 
[0, 7.784695709041462e-03, 3.224671290700398e-01, 2.445134137142996e+00, 3.754408661907416e+00]
P_LOW =

Define break-points.

0.02425
P_HIGH = 
1.0 - P_LOW

Instance Method Summary collapse

Instance Method Details

#bi_normal_seperation(pos, neg, tp, fp) ⇒ Object 



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# File 'lib/svm_helper/selectors/calc.rb', line 21

def bi_normal_seperation pos, neg, tp, fp
  false_prositive_rate = fp.quo(neg)
  true_prositive_rate = tp.quo(pos)
  bns = cdf_inverse(true_prositive_rate) - cdf_inverse(false_prositive_rate)
end

#cdf(z) ⇒ Object

standard normal cumulative distribution function



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# File 'lib/svm_helper/selectors/calc.rb', line 27

def cdf(z)
  0.5 * (1.0 + Math.erf( z.quo(SQR2) ) )
end

#cdf_inverse(p) ⇒ Object 



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# File 'lib/svm_helper/selectors/calc.rb', line 43

def cdf_inverse(p)
  return 0.0 if p < 0 || p > 1 || p == 0.5
  x = 0.0

  if 0.0 < p && p < P_LOW
    # Rational approximation for lower region.
    q = Math.sqrt(-2.0*Math.log(p))
    x = (((((C[1]*q+C[2])*q+C[3])*q+C[4])*q+C[5])*q+C[6]) /
        ((((D[1]*q+D[2])*q+D[3])*q+D[4])*q+1.0)
  elsif P_LOW <= p && p <= P_HIGH
    # Rational approximation for central region.
    q = p - 0.5
    r = q*q
    x = (((((A[1]*r+A[2])*r+A[3])*r+A[4])*r+A[5])*r+A[6])*q /
        (((((B[1]*r+B[2])*r+B[3])*r+B[4])*r+B[5])*r+1.0)
  elsif P_HIGH < p && p < 1.0
    # Rational approximation for upper region.
    q = Math.sqrt(-2.0*Math.log(1.0-p))
    x = -(((((C[1]*q+C[2])*q+C[3])*q+C[4])*q+C[5])*q+C[6]) /
         ((((D[1]*q+D[2])*q+D[3])*q+D[4])*q+1.0)
  end
  if 0 < p && p < 1
    u = cdf(p) * SQR2PI * Math.exp((x**2.0)/2.0)
    x = x - u/(1.0 + x*u/2.0)
  end
  x
end