gistfile1.py

from bayesian.bbn import build_bbn

def f_sex(sex):
  return 0.5


def f_waist(waist):
  return 0.33333333


def f_bmi(sex, bmi):
  if sex == 'f':
    if bmi == 'lt25':
      return 0.78
    if bmi == 'r25_30':
      return 0.145
    if bmi == 'mt30':
      return 0.072
  else:
    if bmi == 'lt25':
      return 0.7
    if bmi == 'r25_30':
      return 0.241
    if bmi == 'mt30':
      return 0.059

def f_shape(bmi, waist, shape):
  if bmi == 'lt25':
    if waist == 'mt102':
      if shape == 'fit':
        return 0.43
      if shape == 'unfit':
        return 0.57
    if waist == 'r94_102':
      if shape == 'fit':
        return 0.57
      if shape == 'unfit':
        return 0.43
    if waist == 'lt94':
      if shape == 'fit':
        return 1.0
      if shape == 'unfit':
        return 0.0
  if bmi == 'r25_30':
    if waist == 'mt102':
      if shape == 'fit':
        return 0.29
      if shape == 'unfit':
        return 0.71
    if waist == 'r94_102':
      if shape == 'fit':
        return 0.43
      if shape == 'unfit':
        return 0.57
    if waist == 'lt94':
      if shape == 'fit':
        return 0.86
      if shape == 'unfit':
        return 0.14
  if bmi == 'mt30':
    if waist == 'mt102':
      if shape == 'fit':
        return 0.0
      if shape == 'unfit':
        return 1.0
    if waist == 'r94_102':
      if shape == 'fit':
        return 0.14
      if shape == 'unfit':
        return 0.86
    if waist == 'lt94':
      if shape == 'fit':
        return 0.57
      if shape == 'unfit':
        return 0.43

if __name__ == '__main__':

  g = build_bbn(
    f_sex,
    f_waist,
    f_bmi,
    f_shape,
    domains=dict(
      sex=['f', 'm'],
      waist=['lt94', 'r94_102', 'mt102'],
      bmi=['lt25', 'r25_30', 'mt30'],
      shape=['fit', 'unfit']))