def do_greedy_initial_solutions(students,
all_projects,
annealer,
project_id_mappings,
config,
verbose=False):
'''
Creates the feasible projects, and iterates 1000 (default number) of greedy
solutions, randomizing the order in which students get their "first pick."
Returns the solution with the lowest initial energy.
Result is usually very good.
'''
configParser = ConfigParser.ConfigParser()
configFilePath = config.encode('string-escape')
configParser.read(configFilePath)
# Declaring valid values for all fields.
capacity = configParser.getint('valid_values', 'capacity')
capacity_w = configParser.getint('valid_values', 'capacity_w')
feasible_projects = util.create_feasible_projects(students, all_projects,
verbose)
util.input_checks(students,
feasible_projects,
capacity,
capacity_w,
project_id_mappings,
sorted=False)
match_all = configParser.getboolean('valid_values', 'match_all')
sol = greedy_solutions_and_goodness(students, feasible_projects, match_all)
print[p.ID for p in sol]
return sol
def do_greedy_initial_solutions(students, all_projects, annealer, project_id_mappings, config, verbose = False):
'''
Creates the feasible projects, and iterates 1000 (default number) of greedy
solutions, randomizing the order in which students get their "first pick."
Returns the solution with the lowest initial energy.
Result is usually very good.
'''
configParser = ConfigParser.ConfigParser()
configFilePath = config.encode('string-escape')
configParser.read(configFilePath)
# Declaring valid values for all fields.
capacity = configParser.getint('valid_values', 'capacity')
capacity_w = configParser.getint('valid_values', 'capacity_w')
feasible_projects = util.create_feasible_projects(students, all_projects, verbose)
util.input_checks(students, feasible_projects, capacity, capacity_w, project_id_mappings, sorted = False)
match_all= configParser.getboolean('valid_values', 'match_all')
sol = greedy_solutions_and_goodness(students, feasible_projects, match_all)
print [p.ID for p in sol]
capacity_w = configParser.getint('valid_values', 'capacity_w')
temp = configParser.getint('valid_values', 'temperature')
iters = configParser.getint('valid_values', 'iterations')
team_size = capacity
# Creating the annealer with our energy and move functions.
if mode == "cc":
annealer = Annealer(pg.energy, pg.move)
elif mode == "co":
annealer = Annealer(pg.energy_co, pg.move_co)
else:
raise FieldError("Unknown algorithm mode")
all_projects = util.generate_all_projects(config)
students = util.create_students_from_input(input_file, config)
feasibles = util.create_feasible_projects(students, all_projects)
sol = test.do_greedy_initial_solutions(students, all_projects, annealer, project_id_mappings, config)
use_file = False
use_diversity = False
if (set_output_file):
test.manual_schedule(use_file, students, sol, feasibles, annealer, use_diversity, input_file, temp, iters, output_file)
else:
test.manual_schedule(use_file, students, sol, feasibles, annealer, use_diversity, input_file, temp, iters)
string = "Program completed in " + str((time.time() - start_time)/60)
string += " minutes."
print string
#!/usr/bin/env python
import util
import initial_solution
import numpy as np
import copy
if (__name__ == "__main__"):
students = util.create_students_from_input('students.csv')
print len(students)
feasibles = util.create_feasible_projects(students, util.generate_all_projects())
min_rank = float("inf")
min_sol = None
def calculate_avg_rank(solution, verbose = False):
avgs = []
cnt = 0
for p in solution:
cnt += len(p.students)
if (verbose):
print str(p.ID) + ":" + str([s.ID for s in p.students])
print "Ranks:",
rankings = [s.get_ranking(p.ID) for s in p.students]
if (verbose):
print rankings
avg_rank = np.mean(rankings)
if (verbose):
print "Average rank: " + str(avg_rank)
avgs.append(avg_rank)
if (len(avgs)==0):
raise ValueError('Tried to average slice of length 0.')
overall_average_rank = np.mean(avgs)
capacity_w = configParser.getint('valid_values', 'capacity_w')
temp = configParser.getint('valid_values', 'temperature')
iters = configParser.getint('valid_values', 'iterations')
team_size = capacity
# Creating the annealer with our energy and move functions.
if mode == "cc":
annealer = Annealer(pg.energy, pg.move)
elif mode == "co":
annealer = Annealer(pg.energy_co, pg.move_co)
else:
raise FieldError("Unknown algorithm mode")
all_projects = util.generate_all_projects(config)
students = util.create_students_from_input(input_file, config)
feasibles = util.create_feasible_projects(students, all_projects)
sol = test.do_greedy_initial_solutions(students, all_projects, annealer,
project_id_mappings, config)
use_file = False
use_diversity = False
if (set_output_file):
test.manual_schedule(use_file, students, sol, feasibles, annealer,
use_diversity, input_file, temp, iters,
output_file)
else:
test.manual_schedule(use_file, students, sol, feasibles, annealer,
use_diversity, input_file, temp, iters)
string = "Program completed in " + str((time.time() - start_time) / 60)
string += " minutes."
