from input_data import input_data
from MehrotraMethod import mehrotra
from MehrotraMethod2 import mehrotra2
from LPFMethod_PC import longpathPC
from matplotlib.ticker import NullFormatter # useful for `logit` scale
import pandas as pd # Export to excel
import matplotlib.pyplot as plt # Create graphics
from mpl_toolkits.mplot3d import Axes3D
''' _PLOT THE PATHs_ '''
# Recall lp istance with dimension 2
(A, b, c) = input_data(0)
xm, sm, um = mehrotra2(A, b, c)
#um = pd.DataFrame(um, columns = ['it', 'g', 'Current x', 'Current s', 'rb', 'rc'])
xl, sl, ul = longpathPC(A, b, c)
#ul = pd.DataFrame(ul, columns = ['it', 'g', 'Current x', 'Current s', 'rb', 'rc'])
# Create 3d point x*s for mehrotra
x1 = []
y1 = []
z1 = []
for i in range(len(um)):
g = um[i][2] * um[i][3]
x1.append(g[0].copy())
y1.append(g[1].copy())
z1.append(g[2].copy())
""" Affine """
# IP = 1 doesn't work
x, s, u = affine(A, b, c, ip=IP)
dfu = cent_meas(x, u, label='Affine', plot=0) # 29 it
""" LPF1 """
# 307 iterations
x, s, u = longpath1(A, b, c, c_form=0, info=1, ip=IP)
dfu = cent_meas(x, u, label='LPF', plot=0)
""" LPF2 """
# 16 it
x, s, u, sigma_l2 = longpath2(A, b, c, c_form=0, info=1, ip=1)
""" LPF predictor corrector """
# 41 iterations / 17 iterations ip = 1
x_pc, s_pc, u_pc, sigma_pc = longpathPC(A, b, c, c_form=0, info=0)
x_pc, s_pc, u_pc, sigma_pc = longpathPC(A, b, c, c_form=0, info=0, ip=1)
dfpc = cent_meas(x_pc, u_pc, label='LPF PC', plot=0)
""" Mehrotra """
# 8 iterations
x_m, s_m, u_m, sigma_m = mehrotra2(A, b, c, c_form=0,
info=1) # Mehrotra1 doesn't work
x_m, s_m, u_m, sigma_m1 = mehrotra2(A, b, c, c_form=0, info=1,
ip=1) # Mehrotra1 doesn't work
dfm = cent_meas(x_m, u_m, label='Mehrotra', plot=0)
" Recall the simplex method "
#P, u = SimplexMethod(A, b, c, c_form = 0, rule = 0, max_it = 200)
ax = fig.gca(projection='3d')
#plt.xscale('logit')
#plt.yscale('logit')
ax.plot_trisurf(x, y, z, linewidth=0.2, cmap='viridis')
ax.set_xlabel('x1s1')
ax.set_ylabel('x2s2')
ax.set_zlabel('x3s3')
w = []
(A, b, c) = input_data(0)
x, s, u = mehrotra2(A, b, c)
x1, s1, u1 = longpath1(A, b, c)
# Plot Mehrotra iterations
for i in range(1, len(u)):
t = u[i][2] * u[i][3]
w.append(t.copy())
ax.scatter(t[0],
t[1],
t[2],
linewidth=2,
zorder=1,
lw=3,
linestyle='dashed',
c="red")
ax.text(t[0], t[1], t[2], "xs{}".format(len(u) - i))
"""
it = []
B = []
a = []
Y = []
W = []
Z = []
O = []
P = []
q = np.log(2)
for i in range(29):
print(i)
if not i in {4, 20, 23, 25}:
(A, b, c) = input_data(i)
x, s, u, sigma_m = mehrotra2(A, b, c, info=1)
# x, v = SimplexMethod(A, b, c, rule = 0)
x, s, o = longpath1(A, b, c, c_form=0, info=1, ip=0)
x, s, p, sigma_2 = longpath2(A, b, c, c_form=0, info=1)
x, s, z, sigma_pc = longpathPC(A, b, c, info=1)
B.append(np.log(len(u) - 1))
# W.append(np.log(len(v)))
Z.append(np.log(len(z) - 1))
O.append(np.log(len(o) - 1))
P.append(np.log(len(p) - 1))
r = sum(A.shape)
a.append([q, np.log(r)])
Y.append(np.log(r))
it.append(i)
for i in range(29, 34): # we compute the models