def manual_schedule(use_file,
students,
sol,
feasible_projects,
annealer,
use_diversity,
filename,
temp,
iters,
output_file="output.csv"):
'''
Parameters
----------
use_file: indicates if we want to use the data from the file or not (bool).
students: student attributes (2d numpy array of ints).
sol: a solution (a Project list).
annealer: the Annealer object (Annealer).
use_diversity: indicates which energy function we want to use (bool).
If use_diversity is True, then we use the energy function from
perry_geo_annealing_diversity.
If use_diversity is False, then we use the energy function from
perry_geo_annealing.py.
filename: file that the input should come from (string).
output_file: file that the output should go to (string).
Returns
-------
Nothing. Writes to the output file and prints updates and solution
to console.
'''
inv_cov_mat_tup = distance.create_inv_cov_mat_from_data(
use_file, students, filename)
if (len(sol) < 2):
error = "There is only one team, so we cannot perform simulated annealing."
raise CompError(error)
state = (sol, inv_cov_mat_tup, feasible_projects, students)
print "Initial energy is " + str(pg.energy(state))
# Manually set the annealing schedule.
state, e = annealer.anneal(state, temp, 0.01, iters, updates=iters / 10)
#auto = annealer.auto(state, 90)
#print auto
print "Final energy is " + str(e)
if (use_diversity):
print "Calculated final energy is " + str(diversity.energy(state))
else:
print "Calculated final energy is " + str(pg.energy(state))
util.print_final_solution(state, use_diversity, output_file)
util.list_penalties(state)
# Only print the unranked students if we a care about the students' rankings.
if (not (use_diversity)):
util.list_unranked_students(state)
util.list_low_interest_students(state)
def manual_schedule(use_file, students, sol, feasible_projects, annealer, use_diversity, filename, temp, iters, output_file = "output.csv"):
'''
Parameters
----------
use_file: indicates if we want to use the data from the file or not (bool).
students: student attributes (2d numpy array of ints).
sol: a solution (a Project list).
annealer: the Annealer object (Annealer).
use_diversity: indicates which energy function we want to use (bool).
If use_diversity is True, then we use the energy function from
perry_geo_annealing_diversity.
If use_diversity is False, then we use the energy function from
perry_geo_annealing.py.
filename: file that the input should come from (string).
output_file: file that the output should go to (string).
Returns
-------
Nothing. Writes to the output file and prints updates and solution
to console.
'''
inv_cov_mat_tup = distance.create_inv_cov_mat_from_data(use_file, students, filename)
if (len(sol) < 2):
error = "There is only one team, so we cannot perform simulated annealing."
raise CompError(error)
state = (sol, inv_cov_mat_tup, feasible_projects, students)
print "Initial energy is " + str(pg.energy(state))
# Manually set the annealing schedule.
state, e = annealer.anneal(state, temp, 0.01, iters, updates=iters/10)
#auto = annealer.auto(state, 90)
#print auto
print "Final energy is " + str(e)
if (use_diversity):
print "Calculated final energy is " + str(diversity.energy(state))
else:
print "Calculated final energy is " + str(pg.energy(state))
util.print_final_solution(state, use_diversity, output_file)
util.list_penalties(state)
# Only print the unranked students if we a care about the students' rankings.
if (not(use_diversity)):
util.list_unranked_students(state)
util.list_low_interest_students(state)
coding_ability = random.randint(0,4)
work_experience = random.randint(0,4)
return classes.Student("noname noname", student_ID, program, business_ability,coding_ability,work_experience,[], rankings_can_be_empty = True)
if (__name__ == "__main__"):
#generate 1000 students
'''students = [random_student(i) for i in range(1000)]
inv_cov_mat_tup = distance.create_inv_cov_mat_from_data(False, students, None)
print inv_cov_mat_tup
print(len(students))
print(inv_cov_mat_tup[0].shape)
for s in students:
print s.get_numerical_student_properties()'''
students = util.create_students_from_input('students.csv')
inv_cov_mat_tup = distance.create_inv_cov_mat_from_data(False, students,None)
projects = [classes.Project(i, 5, 6) for i in range(1,2001)]
diversities = []
for p in projects:
p.students = np.random.choice(students, 5, replace=False).tolist()
diversities.append((p.ID,p.calculate_diversity(inv_cov_mat_tup)))
def keyval(tup):
return tup[1]
diversities.sort(key = keyval, reverse = True)
print "The hundred most diverse teams are: " + str([vals for vals in diversities[:100]])
for team in [vals[0] for vals in diversities[:100]]:
print "Team " + str(projects[team-1].ID) + " has numerical properties:"
for s in projects[team-1].students:
