def getRecommedData():
stu_no = session.get('stu_id')
id2Student, id2Course, stuNo2MatId = map_student_course.get_map_student()
scoreMatrix = map_student_course.get_matrix(id2Student)
"""
函数，recommedCourse：使用SVD进行课程推荐：
返回:(课程1ID， 课程1评分)
"""
topNCourse, topNStudent = recommed_module.recommedCoursePerson(scoreMatrix, stuNo2MatId[stu_no], N=20)
"""
将得到的Course与Person装换为前端图标需要的json格式:
{
"source": [
[2.3, "计算机视觉"],
[1.1, "自然语言处理"],
[2.4, "高等数学"],
[3.1, "线性代数"],
[4.7, "计算机网络"],
[5.1, "离散数学"]
]
}
"""
id2Student = {i:id2Student[i][0] for i in id2Student.keys()}
print(id2Student)
print(id2Course)
courseJson = recommed_module.toBarJson(topNCourse, id2Course)
personJson = recommed_module.toBarJson(topNStudent, id2Student)
courseJson = recommed_module.regularData(courseJson, 1, 5)
personJson = recommed_module.regularData(personJson, 0, 1)
coursePersonJson = {}
coursePersonJson['course'] = courseJson
coursePersonJson['person'] = personJson
return jsonify(coursePersonJson)
def getRecommedData():
# 获取学号
stu_no = session.get('stu_id')
# {0:[白雪,12321],1:[xxx,1231]}；{0: co_name}；{12321: 0 ,1231:1}
id2Student, id2Course, stuNo2MatId = map_student_course.get_map_student()
# [[5.0，4.0][2.0,3.0]] 每个用户对课程的评分，行编号为学生编号，列为课程编号
scoreMatrix, passMatrix = map_student_course.get_matrix(id2Student)
"""
函数，recommedCourse：使用SVD进行课程推荐：
入参： 学生对课程的评分矩阵，需要推荐的那个人的编号 推荐top20
返回:(课程1ID， 课程1评分)
"""
# 需要推荐的课程和人及评分
topNCourse, _ = recommed_module.recommedCoursePerson(scoreMatrix,
stuNo2MatId[stu_no],
N=20)
# passTopNCourse, _ = recommed_module.recommedCoursePerson(passMatrix, stuNo2MatId[stu_no], N=10)
"""
将得到的Course与Person装换为前端图标需要的json格式:
{
"source": [
[2.3, "计算机视觉"],
[1.1, "自然语言处理"],
[2.4, "高等数学"],
[3.1, "线性代数"],
[4.7, "计算机网络"],
[5.1, "离散数学"]
]
}
"""
# 生成新字典{数字：姓名}
# id2Student = {i: id2Student[i][0] for i in id2Student.keys()}
# print(id2Student)
# print(id2Course)
# [评分：课程]
courseJson = recommed_module.toBarJson(topNCourse, id2Course)
# [评分：姓名]
# passTopNCourse = recommed_module.toBarJson(passTopNCourse, id2Course)
passTopNCourse = map_student_course.get_count_by_choose_column(
stu_no, 'PASS')
interstingTopNCourse = map_student_course.get_count_by_choose_column(
stu_no, 'interesting')
acknowledgeTopNCourse = map_student_course.get_count_by_choose_column(
stu_no, 'acknowledged')
courseJson = recommed_module.regularData(courseJson, 1, 5)
passCourseJson = recommed_module.regularData(passTopNCourse, 1, 5)
interstingCourseJson = recommed_module.regularData(interstingTopNCourse, 1,
5)
acknowledgeCourseJson = recommed_module.regularData(
acknowledgeTopNCourse, 1, 5)
coursePersonJson = {}
coursePersonJson['course'] = courseJson
coursePersonJson['passCourse'] = passCourseJson
coursePersonJson['interstingCourse'] = interstingCourseJson
coursePersonJson['acknowledgeCourse'] = acknowledgeCourseJson
print(coursePersonJson)
return jsonify(coursePersonJson)