val = exp_time_data[experiment][data_type]
if val != None:
new_data_type = 0
if val <= 30:
all_adjusted_val = range(start, end_1, interval)#int(val)*2 + 1, interval)
else:
all_adjusted_val = range(start, end_2, 10)#int(val)*2 + 1, 10)
all_obj_val = []
for i in all_adjusted_val:
exp_time_data[experiment][data_type] = i
print '***********************************'
print 'Experiment %s, Data Type %s, Value %s'%(experiment, data_type, i)
[obj_val, status] = solve_milp(p, mm_lambda_1, mm_lambda_2, exp_time_data, experiments)
if status == 1:
all_obj_val.append(obj_val)
else:
all_obj_val.append(0.0)
all_obj_val_per_exp.append(all_obj_val)
all_adj_val_per_exp.append(all_adjusted_val)
prev_exp = experiment
prev_val = val
else:
all_obj_val_per_exp.append(None)
all_adj_val_per_exp.append(None)
all_obj_val_per_data_type.append(all_obj_val_per_exp)
all_obj_val_per_stu = []
for student in range(4):
p = p_reset
p_current = p[student]
# all_p_adjusted = linspace(0, p_current + dev_range, no_pnts)
all_p_adjusted = range(
0, p_current + increase,
10) #DO NOT go under 2 for last value, program crashes??
all_p_adjust_per_stu.append(all_p_adjusted)
all_obj_val = []
for n, i in enumerate(all_p_adjusted):
p[student] = i
print '***********************************'
print 'Student %s Time Limit %s' % (student + 1, i)
[obj_val, status] = solve_milp(p, mm_lambda_1, mm_lambda_2)
if status == 1:
all_obj_val.append(obj_val)
else:
all_obj_val.append(0.0)
all_obj_val_per_stu.append(all_obj_val)
# In[9]:
for student in range(4):
plot(all_p_adjust_per_stu[student],
all_obj_val_per_stu[student],
'.',
label='Student %s' % (student + 1))
#all_obj_val = zeros([size(p), no_pnts])
all_p_adjust_per_stu = []
all_obj_val_per_stu = []
for student in range(4):
p = p_reset
p_current = p[student]
# all_p_adjusted = linspace(0, p_current + dev_range, no_pnts)
all_p_adjusted = range(0, p_current + increase, 10) #DO NOT go under 2 for last value, program crashes??
all_p_adjust_per_stu.append(all_p_adjusted)
all_obj_val = []
for n, i in enumerate(all_p_adjusted):
p[student] = i
print '***********************************'
print 'Student %s Time Limit %s'%(student + 1, i)
[obj_val, status] = solve_milp(p, mm_lambda_1, mm_lambda_2)
if status == 1:
all_obj_val.append(obj_val)
else:
all_obj_val.append(0.0)
all_obj_val_per_stu.append(all_obj_val)
# In[9]:
for student in range(4):
plot(all_p_adjust_per_stu[student], all_obj_val_per_stu[student], '.', label='Student %s'%(student + 1))
legend(loc='lower right')