def main(argc, argv):
ampl = AMPL(Environment(r'C:\Users\etjones\Desktop\AI OPS\AMPL\amplide.mswin64'))
os.chdir(os.path.dirname(__file__) or os.curdir)
try:
if argc > 1:
ampl.setOption('solver', argv[1])
# Read the model and data files.
#modelDirectory = argv[2] if argc == 3 else os.path.join('..', 'models')
#ampl.read(os.path.join(modelDirectory, 'diet/diet.mod'))
#ampl.readData(os.path.join(modelDirectory, 'diet/diet.dat'))
ampl.read(r'C:\Users\etjones\Desktop\AI OPS\AMPL\custom_models\transp.mod')
ampl.readData(r'C:\Users\etjones\Desktop\AI OPS\AMPL\custom_models\transp.dat')
# Solve
print('\n' + "AMPL MODEL:" + '\n')
ampl.solve()
totalcost = ampl.getObjective('Total_Cost')
print("Minimum Cost:", totalcost.value())
print('\n' + "Optimal Flow:")
ampl.display('Trans')
print('\n' + "Compare pdf flow to the above table to confirm optmial flows")
except Exception as e:
print(e)
raise
def main(argc, argv):
from amplpy import AMPL
os.chdir(os.path.dirname(__file__) or os.curdir)
try:
# Create an AMPL instance
ampl = AMPL(Environment('C:/xampp/htdocs/python/ampl/ampl.mswin64'))
"""
# If the AMPL installation directory is not in the system search path:
from amplpy import Environment
ampl = AMPL(
Environment('C:/xampp/htdocs/python/ampl/ampl.mswin64'))
"""
if argc > 1:
ampl.setOption('solver', argv[1])
# Read the model and data files.
modelDirectory = argv[2] if argc == 3 else os.path.join('..', 'models')
ampl.read(os.path.join(modelDirectory, 'diet/diet.mod'))
ampl.readData(os.path.join(modelDirectory, 'diet/diet.dat'))
# Solve
ampl.solve()
# Get objective entity by AMPL name
totalcost = ampl.getObjective('Total_Cost')
# Print it
print("Objective is:", totalcost.value())
# Reassign data - specific instances
cost = ampl.getParameter('cost')
cost.setValues({'BEEF': 5.01, 'HAM': 4.55})
print("Increased costs of beef and ham.")
# Resolve and display objective
ampl.solve()
print("New objective value:", totalcost.value())
# Reassign data - all instances
elements = [3, 5, 5, 6, 1, 2, 5.01, 4.55]
cost.setValues(elements)
print("Updated all costs.")
# Resolve and display objective
ampl.solve()
print("New objective value:", totalcost.value())
# Get the values of the variable Buy in a dataframe object
buy = ampl.getVariable('Buy')
df = buy.getValues()
# Print them
print(df)
# Get the values of an expression into a DataFrame object
df2 = ampl.getData('{j in FOOD} 100*Buy[j]/Buy[j].ub')
# Print them
print(df2)
except Exception as e:
print(e)
raise
def solver(alpha, n_intersections, C, g_i_inbound, g_i_outbound, delta, verbose=True):
"""
Solves the linear problem for the given set of parameters
:param alpha:
:type alpha:
:param n_intersections:
:type n_intersections:
:param C:
:type C:
:param g_i_inbound:
:type g_i_inbound:
:param g_i_outbound:
:type g_i_outbound:
:param delta:
:type delta:
:return:
:rtype:
"""
ampl = AMPL(Environment(path))
# Set the solver
ampl.setOption('solver', path + '/cplex')
# Read the model file
ampl.read('lp.mod')
# Set parameters values
ampl.param['alpha'] = alpha
ampl.param['N'] = n_intersections
ampl.param['C'] = C
ampl.param['g_incoming'] = g_i_inbound
ampl.param['g_outgoing'] = g_i_outbound
ampl.param['delta'] = delta
if verbose == True:
print("alpha: {}".format(alpha))
print("N: {}".format(n_intersections))
print("C: {}".format(C))
print("g_incoming: {}".format(g_i_inbound))
print("g_outgoing: {}".format(g_i_outbound))
print("delta: {}".format(delta))
# Resolve and display objective
ampl.solve()
bandwidth = ampl.getObjective('bandwidth').value()
# Display the variables
b_incoming = ampl.getVariable('b_incoming').value()
b_outgoing = ampl.getVariable('b_outgoing').value()
wl = ampl.getVariable('w').getValues()
if verbose == True:
print("New objective value: {}".format(bandwidth))
print("New offsets: {}".format(list(wl.toPandas()['w.val'])))
print("Incoming bandwidth: {}".format(b_incoming))
print("Outgoing bandwidth: {}".format(b_outgoing))
# return bandwidths and offset values
return b_incoming, b_outgoing, list(wl.toPandas()['w.val'])
def test_environment(self):
from amplpy import Environment, AMPL
env1 = Environment()
env2 = Environment(os.curdir)
self.assertEqual(env2.get_bin_dir(), os.curdir)
env1.set_bin_dir(env2.get_bin_dir())
self.assertEqual(env1.get_bin_dir(), env1.get_bin_dir())
self.assertEqual(len(dict(env1)), len(list(env1)))
self.assertEqual(list(sorted(dict(env1).items())), list(sorted(env1)))
env1["MyEnvVar"] = "TEST"
self.assertEqual(env1["MyEnvVar"], "TEST")
self.assertEqual(env2["MyEnvVar"], None)
d = dict(env1)
self.assertEqual(d["MyEnvVar"], "TEST")
ampl = AMPL(Environment())
ampl.close()
def calculate(*args):
try:
ampl = AMPL(Environment('/Users/zintun/Downloads/amplide.macosx64')) ##path has to change accordingly
ampl.setOption('solver', 'gurobi')
ampl.read('Y3_Elementals.mod')
ampl.readData('Y3_data.dat')
day_val = int(day.get())
print(day_val)
T = ampl.getParameter('T')
T.set(day_val)
ampl.solve()
v1 = ampl.getVariable('X').getValues()
c = v1.toList()
rows = [[int(column) for column in row] for row in c]
org_mat = []
dest_mat = []
mod_mat = []
value_mat = []
for i in range(len(rows)):
element = rows[i]
origin = element[0]
dest = element[1]
mode = element[2]
value = element[3]
org_mat.append(origin)
dest_mat.append(dest)
mod_mat.append(mode)
value_mat.append(value)
N = ampl.getParameter('N').getValues()
N1 = N.toList()
N_rows = [[int(column) for column in row] for row in N1]
count = 0
exist = []
while(count < len(N_rows)):
if(N_rows[count][3] or N_rows[count+1][3] or N_rows[count+2][3] or N_rows[count+3][3]):
exist.extend([1, 1, 1,1])
else:
exist.extend([0, 0, 0,0])
count = count + 4
cost = ampl.getObjective('Cost').value()
drawGraph1(org_mat,dest_mat,mod_mat,value_mat,exist,cost)
except ValueError:
pass
def solver(alpha, n_intersections, C, g_i_inbound, g_i_outbound, delta, print_=True):
"""
Solves the linear problem for the given set of parameters
:param alpha:
:type alpha:
:param n_intersections:
:type n_intersections:
:param C:
:type C:
:param g_i_inbound:
:type g_i_inbound:
:param g_i_outbound:
:type g_i_outbound:
:param delta:
:type delta:
:return:
:rtype:
"""
ampl = AMPL(Environment(path))
# Set the solver
ampl.setOption('solver', path + '/cplex')
# Read the model file
ampl.read('lp.mod')
# Set parameters values
ampl.param['alpha'] = alpha
ampl.param['N'] = n_intersections
ampl.param['C'] = C
ampl.param['g_incoming'] = g_i_inbound
ampl.param['g_outgoing'] = g_i_outbound
ampl.param['delta'] = delta
# Resolve and display objective
ampl.solve()
bandwidth = ampl.getObjective('bandwidth').value()
# Display the variables
b_incoming = ampl.getVariable('b_incoming').value()
b_outgoing = ampl.getVariable('b_outgoing').value()
w_relative = ampl.getVariable('w').getValues()
w_relative = [w[1] for w in w_relative]
if print_ == True:
print("New objective value: {}".format(bandwidth))
print("Incoming bandwidth: {}".format(b_incoming))
print("Outgoing bandwidth: {}".format(b_outgoing))
print("Incoming offsets: {}".format(w_relative))
# return bandwidths and offset values
return b_incoming, b_outgoing, w_relative
def solve_sscfl_instance(mod_file, sscfl_instance, ampl_folder):
print('### AMPL SOLUTION ###')
if ampl_folder is None:
primal_pl = AMPL()
else:
primal_pl = AMPL(Environment(ampl_folder))
primal_pl.read('ampl_mod_files/' + mod_file)
primal_pl.setOption('solver', 'cplex')
primal_pl = write_ampl_sscfl_dat_file(primal_pl, sscfl_instance)
primal_pl.solve()
f = primal_pl.getObjective('f')
print('\nZ = ', '%.5f' % (f.value()))
return f.value()
def compute_defense(att_stg,
prod_dist,
num_of_hp=args.fix_honeypots,
rationality=args.fix_rationality):
# production ports and attacker"s strategy
df = DataFrame('P')
ports = getRelPorts(att_stg, prod_dist, num=25)
df.setColumn('P', list(ports))
#ports = getAllPorts(att_stg, prod_dist)
#print(('Considered ports are: ', ports))
att = [att_stg.get(x, 0) for x in ports]
prod = [prod_dist.get(x, 0) for x in ports]
#print(('Attack ports: ', att, len(att)))
#print(('Dist ports: ', prod, len(prod)))
df.addColumn('s', prod)
df.addColumn('p', att)
ampl = AMPL(Environment(args.ampl))
ampl.setOption('solver', args.solver)
# ampl.setOption('verbosity', 'terse')
# Read the model file
ampl.read(args.model)
# Assign data to s
ampl.setData(df, 'P')
ampl.eval('let L := {}; let rat := {};'.format(num_of_hp, rationality))
#print(df)
# Solve the model
with suppress_stdout():
ampl.solve()
reward = ampl.getObjective("reward").value()
hp_stg = ampl.getData("{j in P} h[j]")
output = dict()
stg_json = list()
for k, v in hp_stg.toDict().items():
stg_json.append({"port": int(k), "prob": v})
output.update({"stg": stg_json})
output.update({"reward": reward})
output.update({"rationality": rationality})
output.update({"num_of_hp": num_of_hp})
output.update({"used_hps": ampl.getData("tot").toDict().popitem()[1]})
ampl.close()
return output
def run_ampl_model(data, minW=0.01, maxW=0.6, ex=0, K=3):
dir = os.path.dirname(__file__)
dir = 'C:\\Biblioteki\\AMPL\\ampl.mswin64'
# dir=dir.replace('/','\\')
# ampl = AMPL()
ampl = AMPL(Environment(dir))
if ex < 1:
ampl.read('omg3.txt')
data_file = generate_temp_data_file(data, minW=minW, maxW=maxW)
ampl.setOption('solver', dir + '\minos.exe')
elif ex == 2:
ampl.read('omg_lmt.txt')
data_file = generate_temp_data_file_lmt(data, minW=minW, maxW=maxW, K=K)
ampl.setOption('solver', dir + '\cplex.exe')
elif ex == 3:
# ampl.read('omg_lmt.txt')
ampl.read('omg4.txt')
data_file = generate_temp_data_file_lmt(data, minW=minW, maxW=maxW, K=K)
ampl.setOption('solver', dir + '\cplex.exe')
else:
ampl.read('eomg.txt')
data_file = generate_temp_data_file_ex(data, minW=minW, maxW=maxW)
ampl.setOption('solver', dir + '\minos.exe')
# data_file=open('data_pattern.txt')
ampl.readData(data_file.name)
# ampl.readData('data_test.dat')
data_file.close()
ampl.solve()
x = get_np_array_from_variable(ampl, 'x')
# x=ampl.getVariable('x').getValues().toPandas()
v0 = ampl.getVariable('v0').getValues().toPandas()
sol = []
for i in range(x.shape[1]):
sol.append(x.iloc[0, i] / v0.iloc[0, 0])
return sol
import random
from functools import reduce
import matplotlib.pyplot as plt
import subprocess
import time
from amplpy import AMPL, Environment
import numpy as np
#start 10:10 end 10:18
ampl = AMPL(Environment('/home/asino/Downloads/ampl_linux-intel64/'))
ampl.setOption("solver", "/home/asino/Downloads/ampl_linux-intel64/cplex")
random.seed(7)
np.random.seed(7)
players = {}
playersList = []
bidders = {}
biddersList = []
NBIDDERS = 8
BUDGET = 500
INCREMENT = 1
PLAYERS_PER_ROLE = {"A": 30, "C": 50, "D": 50, "P": 20}
VAR_PER_ROLE = {"A": 5, "C": 3, "D": 2, "P": 1}
for role, number in PLAYERS_PER_ROLE.items():
for i in range(number):
player = {}
player["name"] = role + str(i)
player["perf"] = np.random.normal(
scale=VAR_PER_ROLE[role]) #random.random()
import amplpy
import pandas as pd
from string import ascii_lowercase
from amplpy import AMPL, Environment
# In[2]:
print("Enter the kind of puzzle you want to solve : \n")
puzzle = int(input("Enter 1 for 3-In-A-Row, or 2 for ABC-Path: "))
ampl = AMPL(Environment("C:/Users/mayan/OneDrive/Documents/AMPL/ampl_mswin64"))
ampl.setOption("solver", "cplex")
if puzzle == 1:
ampl.reset()
ampl.read('p2t05mod3.mod')
ampl.readData('p2t05dat3.dat')
#ask for user input for 3 in a row
size = int(input("Enter the size of the grid (that is number of rows or columns in the grid) : \n"))
invalid = int(input("Enter the number of minimum invalid consecutive cells : \n"))
lst = []
from amplpy import AMPL, Environment
import sys
import os
ampl = AMPL(
Environment(
'C:/Users/Meyerhofer/Desktop/UNI/OTDM/SVM-project/ampl_mswin64'))
size = range(-5, 9)
#for each nu:
def main():
for i in range(-5, 9):
f = open("data/nu.txt", "w")
nu = 2**i
print("----------------------------------------")
print("nu= " + str(nu))
f.write(str(nu))
f.close()
os.system("ampl_mswin64\\ampl primal.run")
f = open("output/primal/acc_train.txt", "r")
acc_train = f.read()
acc_trainValue = acc_train.split(" = ")[1].split("\n")[0]
print("Train accuracy= " + str(acc_trainValue))
f.close()
f = open("output/primal/acc_test.txt", "r")
acc_test = f.read()
acc_testValue = acc_test.split(" = ")[1].split("\n")[0]
print("Test accuracy= " + str(acc_testValue))
from amplpy import AMPL, Environment
import os
import sys
sys.path.append(os.getcwd())
from env_path import ENV_PATH
experiment_name = "zero_sum"
ampl = AMPL(Environment(ENV_PATH))
ampl.read(os.path.join(os.getcwd(), experiment_name, experiment_name + '.mod'))
ampl.readData(
os.path.join(os.getcwd(), experiment_name, experiment_name + '.dat'))
ampl.solve()
totalcost = ampl.getObjective('Max_cost')
print("Difference in objectives is:", totalcost.value())
u = ampl.getVariable('u')
u = u.getValues()
t = ampl.getVariable('t')
t = t.getValues()
print("Objectives are {}, {}".format(u, t))
x = ampl.getVariable('p')
x = x.getValues()
y = ampl.getVariable('q')
y = y.getValues()
print(x, y)
distance_matrix[(d, r)] = abs(r - base)
if weeks == 1 and 0.5 in course_frequency.values():
raise ValueError(
"Creating a one-week schedule with courses with frequency of 0.5 is not possible. Please review the data")
# sys.exit()
print("Optimized Room Assignment Tool")
print("Setup")
print("Expected Number of Courses: ", sum(list(course_frequency.values())))
environment = os.environ.get("AMPL_PATH", "ampl")
ampl = AMPL(Environment(environment))
ampl.setOption('solver', 'cplex')
amplcode = AmplCode.from_file('room_assignment.txt')
# print("Parameters from AMPL Code:")
# print(amplcode.get_params())
amplcode.set_param("DpW", data=DpW)
amplcode.set_param("TpD", data=TpD)
amplcode.set_param("weeks", data=weeks)
# amplcode.set_set('rooms', '{' + ','.join(map(str, rooms)) + '}')
amplcode.set_set('rooms', '{' + ','.join(map(str, rooms)) + '}')
amplcode.set_set('courses', '{1..%i}' % course_count)
amplcode.set_set('departments', list(departmentHQ.keys()))
def testEnvironment(self):
from amplpy import Environment, AMPL
env1 = Environment()
env2 = Environment(os.curdir)
self.assertEqual(env2.getBinDir(), os.curdir)
env1.setBinDir(env2.getBinDir())
self.assertEqual(env1.getBinDir(), env1.getBinDir())
self.assertEqual(len(dict(env1)), len(list(env1)))
self.assertEqual(list(sorted(dict(env1).items())), list(sorted(env1)))
env1['MyEnvVar'] = 'TEST'
self.assertEqual(env1['MyEnvVar'], 'TEST')
self.assertEqual(env2['MyEnvVar'], None)
d = dict(env1)
self.assertEqual(d['MyEnvVar'], 'TEST')
ampl = AMPL(Environment())
ampl.close()
training_dat = "phase2_training.dat"#input("What's the directory of your training .dat file?")
validating_dir = "Validation_Dataset.csv" #input("What's the directory of your validating set?")
model_dir = "phase2_lasso.mod" #input("What's the directory of your model?")
ampl_dir = "/home/nub3ar/AMPL" #input("Where is ampl installed on your computer?") #"E:\ampl_mswin64"
validating_data = pd.read_csv(open(validating_dir))
#dimensions of the data
dv1,dv2 = validating_data.shape
print(dv1, dv2)
#column names
col_names_va = validating_data.columns.values
#x/y variable separation
y_va = validating_data.loc[0::,'Label'].values.tolist()
x_va = validating_data.loc[0::,'Item'::]
############AMPL#################
ampl = AMPL(Environment(ampl_dir))
index_list = []
Accuracy_list = []
for index in (range 1:1000)
ampl.reset()
tuning = ampl.getParameter("tuning")
tuning.SetValues(0.00001*index)
ampl.read(model_dir)
ampl.readData(training_dat)
ampl.solve()
a = ampl.getVariable('a').getValues().toList()
b = ampl.getVariable('b').value()
#parsing the coefficients
a_list = []
from amplpy import AMPL, Environment
from tree import *
amplPATH = 'C:\\Users\\jose_\\Documents\\USM\\USM\\2020-2\\GIO\\tareas\\Tarea1\\ampl_mswin64'
ampl = AMPL(Environment(amplPATH))
def feasible(restrictions):
# restrictions = []
valids = 0
for r in restrictions:
if r:
valids += 1
return valids == len(restrictions)
def simplex():
print("-------------------------------")
print("Comenzando algoritmo simplex")
print("-------------------------------")
# Interpret the two files
print("leyendo mod.mod...")
ampl.read('ampl/mod.mod')
print("leyendo dat.dat...")
ampl.readData('dat.dat')
print("resolviendo el problema...")
ampl.solve()
#Global variables (The paths must be changed by the user).
#---------------------------------------------------------
#Folder where AMPL is located on the computer.
ampl_path = '/home/victor/ampl_linux-intel64'
#If the .py is saved in the same folder of gensvmdat, change path to "".
#Otherwise put the correct folder path and the new files will be stored there.
path = '/home/victor/Escritorio/Matlab/OM/ProjecteSVM'
#Epsilon used to avoid problems of precision with floating points.
#It is recommended to not change this value.
epsilon = 1e-05
#---------------------------------------------------------
#Functions that allow the set-up the connection between python and AMPL.
ampl = AMPL(Environment('/home/victor/ampl_linux-intel64'))
#If another solver than cplex wants to be used, modify that parameter with
#the name of the desired solver.
ampl.setOption('solver', 'cplex')
#---------------------------------------------------------
#Auxiliar function to generate data with the swiss function.
def swiss_generation(num_points,seed,test):
A,y = ds.make_swiss_roll(num_points,2,seed)
#aux = statistics.median(y)
aux = np.mean(y)
for i in range(num_points):
if(y[i] < aux): y[i] = -1
else: y[i] = 1
wr = np.matrix(A); ite = np.matrix(y); ite = ite.reshape(ite.size,1); write = np.c_[wr,ite];
def main(argc, argv):
from amplpy import AMPL, Environment
ampl = AMPL(
Environment(r'C:\Users\etjones\Desktop\AI OPS\AMPL\amplide.mswin64'))
os.chdir(os.path.dirname(__file__) or os.curdir)
try:
if argc > 1:
ampl.setOption('solver', argv[1])
# Read the model and data files.
#modelDirectory = argv[2] if argc == 3 else os.path.join('..', 'models')
#ampl.read(os.path.join(modelDirectory, 'diet/diet.mod'))
#ampl.readData(os.path.join(modelDirectory, 'diet/diet.dat'))
ampl.read(
r'C:\Users\etjones\Desktop\AI OPS\AMPL\custom_models\transp.mod')
ampl.readData(
r'C:\Users\etjones\Desktop\AI OPS\AMPL\custom_models\transp.dat')
# Solve
ampl.solve()
totalcost = ampl.getObjective('Total_Cost')
print("Minimum Cost:", totalcost.value())
#a = ampl.getVariable('Trans')
#print("Objective is:", a.value())
# Get objective entity by AMPL name
#totalcost = ampl.getObjective('total_cost')
# Print it
#print("Objective is:", totalcost.value())
# Reassign data - specific instances
# cost = ampl.getParameter('cost')
# cost.setValues({'BEEF': 5.01, 'HAM': 4.55})
# print("Increased costs of beef and ham.")
# Resolve and display objective
# ampl.solve()
# print("New objective value:", totalcost.value())
# Reassign data - all instances
# elements = [3, 5, 5, 6, 1, 2, 5.01, 4.55]
# cost.setValues(elements)
# print("Updated all costs.")
# Resolve and display objective
# ampl.solve()
# print("New objective value:", totalcost.value())
# Get the values of the variable Buy in a dataframe object
# buy = ampl.getVariable('Buy')
# df = buy.getValues()
# Print them
# print(df)
# Get the values of an expression into a DataFrame object
# df2 = ampl.getData('{j in FOOD} 100*Buy[j]/Buy[j].ub')
# # Print them
# print(df2)
except Exception as e:
print(e)
raise
# 2nd paper 2. Monte Carlo simulation
# forecasted solar data : real data
# real data to simulate : sampling from real data
# Call the AMPL-Phthon API to Python
from amplpy import AMPL, DataFrame, Environment
# Call some packages
import numpy as np
import pandas as pd
# Determine the path of the directory where AMPL is installed.
ampl = AMPL(Environment('path of AMPL'))
# Call energy scheduling model file and data file from the path of the directory where AMPL files are located
ampl.read('Energy_Scheduling.mod')
ampl.readData('Energy_Scheduling.dat')
# Determine the solver from AMPL
ampl.setOption('solver','C:/Users/David_Cho/Desktop/ampl_mswin64/cplex')
# Define variables to calculate yearly data results
global d_loss_sim
d_loss_sim = 0
decide_running = 0
import sys
import os
import pandas as pd
from amplpy import AMPL, Environment
# Holds dataframe of output.
output = None
try:
# Binary path to AMPL install location.
ampl = AMPL(environment=Environment(binaryDirectory=r'C:\Program Files\AMPL'))
# Integer programming solver.
ampl.setOption('solver', 'gurobi')
# Read in AMPL files.
ampl.read('wings.mod')
ampl.readData('wings.dat')
# Insert cost as column.
output = ampl.param['cost'].getValues().toPandas()
output.index = pd.to_numeric(output.index, downcast='integer')
output.index.name = 'wings'
# Solve for multiple target values. Can't solve for less than 4 wings.
MAX_TARGET_TO_SOLVE = 400
for target in range(4, MAX_TARGET_TO_SOLVE + 1):
# Show progress.
print('Processing target of {}...'.format(target))
def test_environment_initialization(self):
from amplpy import Environment
env = Environment("binary_directory")
self.assertEqual("binary_directory", env.get_bin_dir())
env = Environment("binary_directory", "binary_name")
self.assertEqual("binary_directory", env.get_bin_dir())
self.assertEqual("binary_name", env.get_bin_name())
env.set_bin_dir("binary_directory_2")
self.assertEqual("binary_directory_2", env.get_bin_dir())
env.set_bin_name("binary_name_2")
self.assertEqual("binary_name_2", env.get_bin_name())
from amplpy import AMPL, Environment
ampl = AMPL(Environment(r'C:\Users\diego\Documents\Diego_Aldunate\iPre\AMPL'))
ampl.read(r'C:\Users\diego\Documents\Diego_Aldunate\iPre\AMPL/models/diet.mod')
ampl.readData(
r'C:\Users\diego\Documents\Diego_Aldunate\iPre\AMPL/models/diet.dat')
ampl.solve()
# Get objective entity by AMPL name
totalcost = ampl.getObjective('total_cost')
# Print it
print("Objective is:", totalcost.value())
# Reassign data - specific instances
cost = ampl.getParameter('cost')
cost.setValues({'BEEF': 5.01, 'HAM': 4.55})
print("Increased costs of beef and ham.")
print(cost.getValues())
# Resolve and display objective
ampl.solve()
print("New objective value:", totalcost.value())
# Reassign data - all instances
cost.setValues([3, 5, 5, 6, 1, 2, 5.01,
4.55]) #se actualizan valores de los parametros
print("Updated all costs.")
print(cost.getValues())
# Resolve and display objective
# -*- coding: utf-8 -*-
"""
Created on Sat Aug 11 17:20:23 2018
@author: donja
"""
from amplpy import AMPL, Environment
ampl = AMPL(Environment('D:\\amplide.mswin64\\amplide.mswin64'))
ampl.option['solver'] = 'cplexamp'
ampl.read('example.mod') #read the model
ampl.eval('objective z; solve;')
ampl.display('z', 'roth', 'traditional', 'surplus')
pstring = """
objective function = {z}
bi-weekly roth contribution = ${roth}
bi-weekly traditional contribution = ${traditional}
surplus (amount left after optimal allocation) = ${surplus}
total (without surplus) = ${total}
total (with surplus) = ${totalS}
"""
z = ampl.getValue('z')
roth = ampl.getValue('roth')
traditional = ampl.getValue('traditional')
surplus = ampl.getValue('surplus')
annual_roth_amt = roth * 12 * 2
annual_traditional_amt = traditional * 12 * 2
def run_one_experiment(ampl, obj = 5, ppl = 5):
generate(obj = obj, ppl = ppl)
ampl.reset()
ampl.read('/home/jon/Berkeley/ieor169/project/169projectPEnvy.mod')
ampl.readData("/home/jon/Berkeley/ieor169/project/random_data/{}ppl{}obj.dat".format(ppl, obj))
ampl.setOption('solver', 'cplex')
start_time = time.time()
ampl.solve()
finish_time = time.time()
p_envy = ampl.getObjective('p_envy').value()
return p_envy, finish_time-start_time
ampl = AMPL(Environment("/home/jon/Berkeley/amplide.linux64"))
count = 0
with open('penvy.csv', 'a', newline='') as csvfile:
fieldnames = ['Number of People', 'Number of Items', 'Optimal P-Envy', 'Wall Clock Time']
writer = csv.DictWriter(csvfile, fieldnames=fieldnames)
writer.writeheader()
for i in range(1,10):
for j in range(1,10):
for _ in range(30):
p_envy, run_time = run_one_experiment(ampl, i, j)
out = {'Number of People': j,
'Number of Items': i,
'Optimal P-Envy': p_envy,
'Wall Clock Time': run_time}
print(out)
ampl.reset()
ampl.read('/Users/jonathan/Desktop/169projectPEnvy.mod')
ampl.readData(
"/Users/jonathan/Desktop/random_data/{}ppl{}obj_heuristic.dat".format(
ppl, obj))
ampl.setOption('solver', 'cplex')
start_time = time.time()
ampl.solve()
finish_time = time.time()
p_envy = ampl.getObjective('p_envy').value()
return p_envy, finish_time - start_time
ampl = AMPL(
Environment(
"/Users/jonathan/Documents/files/amplide.macosx64/amplide.macosx64"))
with open('part4.csv', 'a', newline='') as csvfile:
fieldnames = [
'Number of People', 'Number of Items', 'Optimal P-Envy',
'Wall Clock Time'
]
writer = csv.DictWriter(csvfile, fieldnames=fieldnames)
writer.writeheader()
for i in range(1, 11):
for j in range(1, 11):
for _ in range(30):
p_envy, run_time = run_one_experiment(ampl, i, j)
out = {
'Number of People': j,
'Number of Items': i,
from amplpy import AMPL, Environment
import functions as fun
import numpy as np
import random
import time
import sys
import os
# Path donde está AMPL en nuestro ordenador
ampl = AMPL(Environment('/home/alex/AMPL/ampl_linux-intel64'))
#ampl = AMPL(Environment('/home/elias/Escritorio/uni/2º/OM/ampl_linux-intel64'))
# Escogemos el solver cplex
ampl.setOption('solver', 'cplex')
'''
PARÁMETROS:
- option: nos indica que tipo de problema vamos a resolver
1: primal
2: dual con kernel lineal
3: dual con kernel gaussiano
- num_points: número de puntos
- seed: semilla para la generación aleatoria del dataset
- nu: parámetro de la formulación del problema SVM
- data_type: indica si generamos los puntos con el algoritmo proporcionado
por el profesor (gensvmdat), con el algoritmo del swiss roll
(brazo de gitano) de sklearn o usamos la base de datos "skin"
1: Para generarlos con gensvmdat (datos linealmente separables)
2: Para generarlos con sklearn swiss_roll (datos no separables linealmente)
3: Para usar la base de datos "skin"
'''
#try:
option = int(sys.argv[1])