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    "info": {
        "author": "inuyasha2012",
        "author_email": "inuyasha021@163.com",
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        "classifiers": [
            "Programming Language :: Python :: 2.7",
            "Programming Language :: Python :: 3.6"
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        "description": "\ntirt\n====\n\nthe simulation of Thurstone Item Response Theory, include fixed forced\ntest and adaptive forced test.\n\u6a21\u62df\u745f\u65af\u987f\u9879\u76ee\u53cd\u5e94\u7406\u8bba\uff0c\u5305\u62ec\u56fa\u5b9a\u6d4b\u9a8c\u548c\u81ea\u9002\u5e94\u6d4b\u9a8c\u3002\n\n\u745f\u65af\u987fIRT\u6a21\u578b\u7b80\u4ecb\u548c\u5e94\u7528\n-----------------------\n\n\u745f\u65af\u987fIRT\u6a21\u578b\u4e3b\u8981\u5e94\u7528\u4e8e\u8feb\u9009\u5f0f\u975e\u8ba4\u77e5\u6d4b\u9a8c\uff08\u4eba\u683c\u6d4b\u9a8c\uff0c\u52a8\u673a\u6d4b\u9a8c\uff0c\u5174\u8da3\u6d4b\u9a8c\u7b49\uff09\u3002\n\n\u56fa\u5b9a\u6d4b\u9a8c\u6a21\u62df\n------------\n\n\u6a21\u62df100\u4e2a\u88ab\u8bd5\uff0c30\u4e2a\u7ef4\u5ea6\uff0c\u6bcf\u4e2a\u7ef4\u5ea610\u4e2a\u9648\u8ff0\uff0c\u6bcf\u9053\u98983\u4e2a\u9648\u8ff0\uff0c\u6240\u4ee5\u4e0b\u9762\u8fd9\u4e2a\u9648\u8ff0\u603b\u5171\u6709100\u9898\n\n::\n\n    from tirt import SimFixedTirt\n\n    fixed_tirt = SimFixedTirt(subject_nums=100, trait_size=30, items_size_per_dim=10)\n    theta_list = fixed_tirt.sim()\n    score_list = fixed_tirt.scores\n\n    for i, theta in enumerate(theta_list):\n        print score_list[i]\n        print theta\n\n\u81ea\u9002\u5e94\u6d4b\u9a8c\u6a21\u62df\n--------------\n\n\u6a21\u62df1\u4e2a\u88ab\u8bd5\uff0c\u9898\u5e93600\u9053\u9898\uff0c30\u4e2a\u7ef4\u5ea6\uff0c\u9996\u5148\u968f\u673a\u62bd10\u9898\uff0c\u7b2c\u4e8c\u9636\u6bb5\u62bd\u5408\u9002\u7684\u989840\u9053\u9898\uff0c\u603b\u517150\u9053\u9898\n\n::\n\n    from tirt import SimAdaptiveTirt\n\n    sat = SimAdaptiveTirt(subject_nums=1, item_size=600, trait_size=30, max_sec_item_size=40)\n    sat.sim()\n\n    for key, value in sat.thetas.items():\n        print sat.scores[key]\n        print value\n\n\u4e00\u81f4\u6027\n------\n\n\u8feb\u9009\u6d4b\u9a8c\u901a\u5e38\u90fd\u6ca1\u6709\u6d4b\u8c0e\u91cf\u8868\uff08\u8feb\u9009\u6d4b\u9a8c\u672c\u8eab\u6297\u4f5c\u5047\uff09\uff0c\u800c\u8861\u91cf\u88ab\u8bd5\u662f\u5426\u8ba4\u771f\u4f5c\u7b54\u6709\u66f4\u597d\u7684\u4e00\u81f4\u6027\u5206\u6570\n\n::\n\n    from tirt import irt_consistency_score, sim_scores, BayesProbitModel, gen_item_dict, SimFixedTirt\n    from tirt.utils import random_params\n\n    # \u751f\u6210\u8bd5\u9898\u5b57\u5178\n    item_dict = gen_item_dict(30, 10, block_size=3)\n    # \u751f\u6210\u8bd5\u9898\u53c2\u6570\n    a, b = random_params(item_dict, 30, block_size=3)\n    # \u751f\u6210\u968f\u673a\u5f97\u5206\n    scores = sim_scores(30, 10, 10)\n\n    for score in scores:\n        model = BayesProbitModel(a, b, score=score)\n        # \u6253\u5370\u4e00\u81f4\u6027\n        print irt_consistency_score(model)\n\n    model = SimFixedTirt(trait_size=30, items_size_per_dim=10, subject_nums=100, model='bayes_probit')\n    model.sim()\n    print model.get_consistency_scores()\n",
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