psy.cat package

Submodules

psy.cat.tirt module

class psy.cat.tirt.BaseModel(slop, threshold, init_theta=None, score=None, iter_method=u'newton', sigma=None)

Bases: object

gradient_ascent

newton

基于牛顿迭代的参数估计 :return: ndarray(int|float), 特质向量初值

prob(theta)

score

solve

z(theta)

class psy.cat.tirt.BaseProbitModel(slop, threshold, init_theta=None, score=None, iter_method=u'newton', sigma=None)

Bases: psy.cat.tirt.BaseModel

info(theta)

class psy.cat.tirt.BaseSimTirt(subject_nums, trait_size, model=u'bayes_probit', sigma=None, iter_method=u'newton', block_size=3, lower=1, upper=4, avg=0, std=1)

Bases: object

MODEL = {u'bayes_probit': <class 'psy.cat.tirt.BayesProbitModel'>}

random_thetas

生成特质向量 :return: ndarray

class psy.cat.tirt.BayesProbitModel(slop, threshold, init_theta=None, score=None, iter_method=u'newton', sigma=None)

Bases: psy.cat.tirt.BaseProbitModel

info(theta)

class psy.cat.tirt.SimAdaptiveTirt(item_size, max_sec_item_size=10, *args, **kwargs)

Bases: psy.cat.tirt.BaseSimTirt

item_bank

scores

sim()

thetas

Module contents