def choose_spouse(person, eligible_mates):
"""
Once lists of marrying men and women are created, this function chooses a
wife for a particular male based on the age differential between the man
and each woman, based on observed data.
"""
sp_probs = []
for eligible_mate in eligible_mates:
if person.get_sex == "male":
agediff = person.get_age_years() - eligible_mate.get_age_years()
else:
agediff = eligible_mate.get_age_years() - person.get_age_years()
if person.get_sex() == eligible_mate.get_sex() or \
person.get_ethnicity() != eligible_mate.get_ethnicity() or \
person.is_sibling(eligible_mate):
sp_probs.append(0)
else:
sp_probs.append(calc_prob_from_prob_dist(rcParams['spousechoice.male.agediff'], agediff))
#print "f", eligible_mate.get_age_years(),
#print "m", male.get_age_years(), "|",
if sum(sp_probs) == 0:
# In this case NONE of the eligible_mates are eligible (all of different
# ethnicities than the person).
return None
num = np.random.rand() * np.sum(sp_probs)
sp_probs = np.cumsum(sp_probs)
n = 0
for problim in sp_probs[0:-1]:
if num <= problim:
break
n += 1
return eligible_mates[n]
def calc_education_level(person):
"""
Calculate education level for person, based on results of empirical analysis of CVFS panel data.
"""
levels = rcParams['education.depvar_levels']
intercepts = [rcParams['education.coef.y>=gt0lt4'],
rcParams['education.coef.y>=gt4lt8'],
rcParams['education.coef.y>=gt8lt11'],
rcParams['education.coef.y>=gt11']]
prob_y_gte_j = np.zeros(len(levels) - 1) # probability y >= j
for n in np.arange(len(prob_y_gte_j)):
intercept = intercepts[n]
xb_sum = 0
# Individual-level characteristics
if person.get_sex() == "female":
xb_sum += rcParams['education.coef.gender=female']
if person.get_ethnicity() == "HighHindu":
# This was the reference class
pass
elif person.get_ethnicity() == "HillTibeto":
xb_sum += rcParams['education.coef.ethnic=HillTibeto']
elif person.get_ethnicity() == "LowHindu":
xb_sum += rcParams['education.coef.ethnic=LowHindu']
elif person.get_ethnicity() == "Newar":
xb_sum += rcParams['education.coef.ethnic=Newar']
elif person.get_ethnicity() == "TeraiTibeto":
xb_sum += rcParams['education.coef.ethnic=TeraiTibeto']
else:
raise StatisticsError("No coefficient was specified for ethnicity '%s'"%person.get_ethnicity())
# Neighborhood-level characteristics
neighborhood = person.get_parent_agent().get_parent_agent()
xb_sum += rcParams['education.coef.avg_yrs_services_lt15'] * \
neighborhood._avg_yrs_services_lt15
prob_y_gte_j[n] = 1. / (1 + np.exp(-(intercept + xb_sum)))
prob_y_eq_j = np.zeros(4) # probability y == j
prob_y_eq_j[0] = 1 - prob_y_gte_j[0]
# Loop over all but the first cell of prob_y_eq_j
for j in np.arange(1, len(prob_y_gte_j)):
prob_y_lt_j = np.sum(prob_y_eq_j[0:j])
prob_y_eq_j[j] = 1 - prob_y_gte_j[j] - prob_y_lt_j
prob_cutoffs = np.cumsum(prob_y_eq_j)
prob_cutoffs[-1] = 1
# Code for testing only:
#print prob_cutoffs
rand = np.random.rand()
for n in np.arange(len(prob_cutoffs)):
if rand <= prob_cutoffs[n]:
return levels[n]
# Should never reach the next line.
raise StatisticsError("Check level calculation - no class predicted")