def gen_items(space, covariate) :
# Create an item bank to store the items.
# Setting the property "sizeHint" increases allocation efficiency
bank = gobject.new(oscats.ItemBank, sizeHint=N_ITEMS)
# Create the items
for i in range(N_ITEMS) :
# First we create an IRT model container for our item
# We have to specify which dimensions to be used with the "dims" array
# (in this case, we use both of the dimensions of the space)
model = gobject.new(oscats.ModelL2p, space=space, dims=dims, covariates=covariate)
# Then, set the parameters. Here there are 4:
# Discrimination on two dimensions, difficulty, and covariate coef.
model.set_param_by_name("Diff", oscats.oscats_rnd_normal(sqrt(3)))
model.set_param_by_name("Discr.Cont.1",
oscats.oscats_rnd_uniform_range(0, 1))
model.set_param_by_name("Discr.Cont.2",
oscats.oscats_rnd_uniform_range(0, 2))
model.set_param_by_name(COVARIATE_NAME,
oscats.oscats_rnd_uniform_range(0.5, 1.5))
# Create an item based on this model
item = gobject.new(oscats.Item)
item.set_model(item.get_default_model(), model)
# Add the item to the item bank
bank.add_item(item)
# Since Python is garbage collected, we don't have to worry about
# reference counting.
return bank
def gen_items(space, covariate):
# Create an item bank to store the items.
# Setting the property "sizeHint" increases allocation efficiency
bank = gobject.new(oscats.ItemBank, sizeHint=N_ITEMS)
# Create the items
for i in range(N_ITEMS):
# First we create an IRT model container for our item
# We have to specify which dimensions to be used with the "dims" array
# (in this case, we use both of the dimensions of the space)
model = gobject.new(oscats.ModelL2p,
space=space,
dims=dims,
covariates=covariate)
# Then, set the parameters. Here there are 4:
# Discrimination on two dimensions, difficulty, and covariate coef.
model.set_param_by_name("Diff", oscats.oscats_rnd_normal(sqrt(3)))
model.set_param_by_name("Discr.Cont.1",
oscats.oscats_rnd_uniform_range(0, 1))
model.set_param_by_name("Discr.Cont.2",
oscats.oscats_rnd_uniform_range(0, 2))
model.set_param_by_name(COVARIATE_NAME,
oscats.oscats_rnd_uniform_range(0.5, 1.5))
# Create an item based on this model
item = gobject.new(oscats.Item)
item.set_model(item.get_default_model(), model)
# Add the item to the item bank
bank.add_item(item)
# Since Python is garbage collected, we don't have to worry about
# reference counting.
return bank
def gen_items(space):
bank = oscats.ItemBank(sizeHint=N_ITEMS)
for i in range(N_ITEMS):
model = gobject.new(oscats.ModelL3p, space=space)
model.set_param_by_name("Diff", oscats.oscats_rnd_uniform_range(-3, 3))
model.set_param_by_name("Discr.Cont.1", oscats.oscats_rnd_uniform_range(0.8, 2.0))
model.set_param_by_name("Guess", oscats.oscats_rnd_uniform_range(0.1, 0.3))
item = oscats.Item(0, model)
bank.add_item(item)
return bank
def gen_items(space):
bank = oscats.ItemBank(sizeHint=N_ITEMS)
for i in range(N_ITEMS):
model = gobject.new(oscats.ModelL3p, space=space)
model.set_param_by_name("Diff", oscats.oscats_rnd_uniform_range(-3, 3))
model.set_param_by_name("Discr.Cont.1",
oscats.oscats_rnd_uniform_range(0.8, 2.0))
model.set_param_by_name("Guess",
oscats.oscats_rnd_uniform_range(0.1, 0.3))
item = oscats.Item(0, model)
bank.add_item(item)
return bank