def _build_model(self, data):
data = _data_df2dict(data)
with pm.Model() as model:
# Priors
# NOTE: we need another variable if we deal with losses, which goes
# to the value function
β = pm.Bound(pm.Normal, lower=0)('beta', mu=1, sd=1000)
γ = pm.Bound(pm.Normal, lower=0)('gamma', mu=0, sd=1000)
τ = pm.Bound(pm.Normal, lower=0)('tau', mu=0, sd=1000)
# TODO: pay attention to the choice function & it's params
α = pm.Exponential('alpha', lam=1)
ϵ = 0.01
value_diff = (self._value_function(γ, data['B']) -
self._value_function(γ, data['A']))
time_diff = (self._time_weighing_function(τ, data['DB']) -
self._time_weighing_function(τ, data['DA']))
diff = value_diff - β * time_diff
# Choice function: psychometric
P_chooseB = pm.Deterministic('P_chooseB',
choice_func_psychometric2(α, ϵ, diff))
# Likelihood of observations
r_likelihood = pm.Bernoulli('r_likelihood',
p=P_chooseB,
observed=data['R'])
return model
def _build_model(self, data):
data = _data_df2dict(data)
with pm.Model() as model:
# Priors
k = pm.Normal('k',
mu=0.01,
sd=1.,
shape=2,
transform=Ordered(),
testval=[0.01, 0.02])
p = pm.Beta('p', alpha=1 + 4, beta=1 + 4)
α = pm.Exponential('alpha', lam=1)
ϵ = 0.01
# Value functions
VA = pm.Deterministic('VA', data['A'] * self._df(k, p, data['DA']))
VB = pm.Deterministic('VB', data['B'] * self._df(k, p, data['DB']))
# Choice function: psychometric
P_chooseB = pm.Deterministic(
'P_chooseB', choice_func_psychometric(α, ϵ, VA, VB))
# Likelihood of observations
r_likelihood = pm.Bernoulli('r_likelihood',
p=P_chooseB,
observed=data['R'])
return model
def _build_model(self, data):
data = _data_df2dict(data)
with pm.Model() as model:
# having p separate from P_chooseB is intentional, to ensure trace
# of P_chooseB is the same shape as the other models
p = pm.Beta('p', alpha=1 + 1, beta=1 + 1) # prior
P_chooseB = pm.Deterministic('P_chooseB',
p * np.ones(data['R'].shape))
# Likelihood of observations
r_likelihood = pm.Bernoulli('r_likelihood',
p=P_chooseB,
observed=data['R'])
return model
def _build_model(self, data):
data = _data_df2dict(data)
with pm.Model() as model:
# Priors
k = pm.Bound(pm.Normal, lower=-0.005)('k', mu=0.001, sd=0.5)
α = pm.Exponential('alpha', lam=1)
ϵ = 0.01
# Value functions
VA = pm.Deterministic('VA', data['A'] * self._df(k, data['DA']))
VB = pm.Deterministic('VB', data['B'] * self._df(k, data['DB']))
# Choice function: psychometric
P_chooseB = pm.Deterministic(
'P_chooseB', choice_func_psychometric(α, ϵ, VA, VB))
# Likelihood of observations
r_likelihood = pm.Bernoulli('r_likelihood',
p=P_chooseB,
observed=data['R'])
return model
def _build_model(self, data):
data = _data_df2dict(data)
with pm.Model() as model:
# Priors
logk = pm.Normal('logk', mu=-4, sd=5)
α = pm.Exponential('alpha', lam=1)
# ϵ = pm.Beta('epsilon', alpha=1.1, beta=10.9)
ϵ = 0.01
# Value functions
VA = pm.Deterministic('VA', data['A'] * self._df(logk, data['DA']))
VB = pm.Deterministic('VB', data['B'] * self._df(logk, data['DB']))
# Choice function: psychometric
P_chooseB = pm.Deterministic(
'P_chooseB', choice_func_psychometric(α, ϵ, VA, VB))
# Likelihood of observations
r_likelihood = pm.Bernoulli('r_likelihood',
p=P_chooseB,
observed=data['R'])
return model
def _build_model(self, data):
data = _data_df2dict(data)
with pm.Model() as model:
# Priors
β = pm.Normal('beta', mu=0, sd=1, shape=5)
α = pm.Exponential('alpha', lam=1)
ϵ = 0.01
A = data['B'] - data['A']
B = (data['B'] - data['A']) / ((data['B'] + data['A']) / 2)
C = data['DB'] - data['DA']
D = (data['DB'] - data['DA']) / ((data['DB'] + data['DA']) / 2)
diff = β[0] + β[1] * A + β[2] * B + β[3] * C + β[4] * D
# Choice function: psychometric
P_chooseB = pm.Deterministic('P_chooseB',
choice_func_psychometric2(α, ϵ, diff))
# Likelihood of observations
r_likelihood = pm.Bernoulli('r_likelihood',
p=P_chooseB,
observed=data['R'])
return model
def _build_model(self, data):
data = _data_df2dict(data)
with pm.Model() as model:
# Priors
β = pm.Normal('beta', mu=0, sd=10, shape=4)
α = pm.Exponential('alpha', lam=1)
ϵ = 0.01
D = data['B'] - data['A']
R = (data['B'] - data['A']) / data['A']
T = data['DB'] - data['DA']
I = ((data['B'] / data['A'])**(1. /
(data['DB'] - data['DA']))) - 1.
diff = β[0] + β[0] * D + β[1] * R + β[2] * I + β[3] * T
# Choice function: psychometric
P_chooseB = pm.Deterministic('P_chooseB',
choice_func_psychometric2(α, ϵ, diff))
# Likelihood of observations
r_likelihood = pm.Bernoulli('r_likelihood',
p=P_chooseB,
observed=data['R'])
return model
