Python 문항반응 생성

1 개요[ | ]

Python 문항반응 생성

2 이분 문항반응 생성[ | ]

Python

CPU

4.1s

MEM

153M

4.9s

Reload

Copy

import numpy as np
from girth import create_synthetic_irt_dichotomous

n_people = 12 # 학생 수
n_items = 4   # 문항 수

discrimination = 0.89 * np.sqrt(-2 * np.log(np.random.rand(n_items)))
difficulty = np.random.randn(n_items)
theta = np.random.randn(n_people)

data = create_synthetic_irt_dichotomous(difficulty, discrimination, theta)
data

array([[1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1],
       [0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1],
       [1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1],
       [1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0]])

3 다분 문항반응 생성[ | ]

3.1 영점조정 안함 (기본)[ | ]

Python

Copy

import numpy as np
from girth import create_synthetic_irt_polytomous

n_people = 15    # 학생 수
n_items = 7      # 문항 수
n_categories = 5 # 부분점수 종류 수
zero_point_calibration = 0 # 영점조정 안함 (1~5점)

difficulty = np.random.randn(10, n_categories-1)
difficulty = np.sort(difficulty, 1)        
discrimination = 0.96 * np.sqrt(-2 * np.log(np.random.rand(10)))
theta = np.random.randn(150)

data = create_synthetic_irt_polytomous(difficulty, discrimination, theta)-zero_point_calibration
data

Loading

3.2 영점조정[ | ]

Python

Copy

import numpy as np
from girth import create_synthetic_irt_polytomous

n_people = 15    # 학생 수
n_items = 7      # 문항 수
n_categories = 3 # 부분점수 종류 수
zero_point_calibration = 1 # 영점조정 (0~2점)

difficulty = np.random.randn(10, n_categories-1)
difficulty = np.sort(difficulty, 1)        
discrimination = 0.96 * np.sqrt(-2 * np.log(np.random.rand(10)))
theta = np.random.randn(150)

data = create_synthetic_irt_polytomous(difficulty, discrimination, theta)-zero_point_calibration
data

Loading

4 같이 보기[ | ]