Grouping and pairing library
These lists should be dictionaries with properties of the students (name, id, etc) and groups (name, type, etc).
Define a group's maximum size by adding a property: group['size']. Default: unlimited.
Define a group's minimum size by adding a property: group['minsize']. Default: 0.
The algorithm takes a list of goal groups [goalgroup0, goalgroup1, ...]. If groups cannot be created given the goals defined in goalgroup0, the algorithm will try goalgroup1, then goalgroup2, etc.
Goals are constraints on groups (ex: everyone should have the same age). See below for a list of types of constraints that can be applied.
Applies a Filter on all students to get the list of students affected by this goal. Then applies another Filter on all groups to get a list of acceptable groups. This goal requires that all students affected by this goal end up in an acceptable group. Filters are defined in the following section.
Example use case: all students with diet="vegetarian" should go into groups with meal="lasagna".
For a given groupFilter, propertyname, and cutoff: applies groupFilter to get the list of groups affected by this goal. For affected groups, requires that at least cutoff students share the same value for propertyname.
Cutoff can be an integer or a dictionary (group size => cutoff). If a cutoff value is -1, the default value of group size will be applied.
Example use case: for groups where type="studytogether" and size=4, at least 3 of the students should have the same value for property location.
For a given groupFilter, propertyname, and cutoff: applies groupFilter to get the list of groups affected by this goal. For affected groups, requires that at most cutoff students share the same value for propertyname.
Cutoff can be an integer or a dictionary (group size => cutoff). If a cutoff value is -1, the default value of 1 will be applied.
Example use case: for groups wher type="diversitymeeting", at most 2 students can have the same value for property firstlanguage.
Use solve(students, groups, goalgroups):
students = [student1, student2, ...]
groups = [group1, group2, ...]
goalgroups = [ [goal0a, goal0b, ...], [goal1a, goal1b, ...], ...]
The algorithm will execute using (students, groups, goalgroups[0]) then (students, groups, goalgroups[1]) if the first execution fails, and so on.
If None was returned, no groups could be formed given the goalgroups.
If groups could be created, a list of groups is returned: [group1, group2, ...] where a group is a dictionary defined as follows:
group['students'] = [studentInfo, studentInfo, ...]
group['info'] = groupInfo
The studentInfo and groupInfo objects are the same objects that were passed into solve().