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# Configure this makefile if needed then run
# $ make all
# to compile and link

# What files to compile
OBJS=money.cpp
# What file to compile to
MAINFILE=money
# What compiler to use
CC=g++

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""" Hacky import & solving of MPS-files (only tested for netlib)
    - source of mps-files: http://www.netlib.org/lp/data/
        - instances need to be uncompressed -> see readme in link-folder
    - uses cvxopt's mps-parsing
        - internal problem-modification / canonicalization by cvxopt "_inmatrixform"
    - only implemented / tested for problems without explicit bound-/range-constraints
        - see problem table within readme in link-folder for instance-type table
    - probably ugly/unnecessary dense/sparse-stuff (code is old and hacky)
    - just to be careful: cvxopt's license is not compatible with scipy!

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import numpy as np
import time
from scipy.optimize import minimize, nnls
from cvxpy import *

np.random.seed(1)

""" Test-data generator """
def create_test_data(samples, features, noise, loc=10, scale=2.5):
    m = np.random.normal(loc=loc, scale=scale, size=(samples, features))

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import numpy as np
import matplotlib.pyplot as plt
np.random.seed(1)

N = 1000
r= np.random.randn(N)
x = np.exp(0.01 * np.arange(N)) + 0.2
y = np.exp(0.4 * np.arange(N)+r)
plt.loglog(x,y,label="Hello ")
plt.axvline(x=1.2, color='g')

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from gurobipy import *

m = Model("model")

x = m.addVar(lb=0.0, name="x")
y = m.addVar(lb=0.0, name="y")
m.update()

obj = -x*x - y*y
m.setObjective(obj)  # default: minimize

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using JuMP, AmplNLWriter, NLopt

n = 1000
mu1_true = 0.3
mu2_true = 0.55
mu3_true = 0.10
sig1_true = 0.08
sig2_true = 0.12
sig3_true = 0.32
a_0_true = 0.5