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Valider ff22ed1f rédigé par Marko Budinich's avatar Marko Budinich
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Merge branch 'pep8' into 'master' 

Coding Conventions for Python Code

See merge request !1
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HelperClass.py
+ 108
− 90
Voir le fichier @ ff22ed1f
......@@ -5,10 +5,12 @@ Created on Thu May 4 15:35:50 2017
Construct Ecosystem system
@author: mbudinich
"""
#import cobra
# import cobra
import numpy as np
from scipy.sparse import lil_matrix, find
#%%
# %%
class VlpProblem:
def __init__(self, B=None, a=None, b=None, l=None, s=None, P=None, Y=None, Z=None, c=None, opt_dir=None):
self.B = B
......@@ -22,15 +24,15 @@ class VlpProblem:
self.c = c
self.opt_dir = opt_dir
def to_file(self,filename=None): #VLP is 1 based, constraint numbering starts at 1!!
def to_file(self, filename=None): # VLP is 1 based, constraint numbering starts at 1!!
def getlen(obj):
return 0 if obj is None else len(obj)
if filename is None:
raise RuntimeError("No filename given")
if hasattr(self,'ub') and not hasattr(self,'s'):
if hasattr(self, 'ub') and not hasattr(self, 's'):
self.s = self.ub
if hasattr(self,'lb') and not hasattr(self,'l'):
if hasattr(self, 'lb') and not hasattr(self, 'l'):
self.l = self.lb
if self.opt_dir is None:
self.opt_dir = 1
......@@ -42,129 +44,138 @@ class VlpProblem:
# raise RuntimeError('Matrix B has no shape attribute')
# if not hasattr(self.P,'shape'):
# raise RuntimeError('Matrix P has no shape attribute')
(m,n) = self.B.shape
(q,p) = self.P.shape
(m, n) = self.B.shape
(q, p) = self.P.shape
if (n != p):
raise RuntimeError('B and P must have same number of columns')
[A_rows,A_cols,A_vals]=find(lil_matrix(self.B))
k=len(A_rows)
[P_rows,P_cols,P_vals]=find(lil_matrix(self.P))
k1=len(P_rows)
kstr=''
[A_rows, A_cols, A_vals] = find(lil_matrix(self.B))
k = len(A_rows)
[P_rows, P_cols, P_vals] = find(lil_matrix(self.P))
k1 = len(P_rows)
kstr = ''
if self.Y is not None and self.Y.shape[1] > 0:
[K_rows,K_cols,K_vals]=find(lil_matrix(self.Y))
k2=len(K_rows)
kstr=' cone {} {}'.format(self.Y.shape[1],k2)
[K_rows, K_cols, K_vals] = find(lil_matrix(self.Y))
k2 = len(K_rows)
kstr = ' cone {} {}'.format(self.Y.shape[1], k2)
elif self.Z is not None and self.Z.shape[1] > 0:
[K_rows,K_cols,K_vals] = find(lil_matrix(self.Z))
[K_rows, K_cols, K_vals] = find(lil_matrix(self.Z))
k2 = len(K_rows)
kstr=' dualcone {} {}'.format(self.Z.shape[1],k2)
kstr = ' dualcone {} {}'.format(self.Z.shape[1], k2)
else:
k2=0
opt_dir_str=''
if self.opt_dir==1:
k2 = 0
opt_dir_str = ''
if self.opt_dir == 1:
opt_dir_str = 'min'
elif self.opt_dir==-1:
elif self.opt_dir == -1:
opt_dir_str = 'max'
else:
raise RuntimeError('Invalid value for opt_dir: use -1 or 1 for maximitation and minimization')
raise RuntimeError(
'Invalid value for opt_dir: use -1 or 1 for maximitation and minimization')
try:
file = open(filename,'w')
file = open(filename, 'w')
except OSError as e:
print("OS error: {0}".format(e))
raise
#Write 'p', 'a', 'k' to file
file.write("p vlp {} {} {} {} {} {}{}\n".format(opt_dir_str,m,n,k,q,k1,kstr))
# Write 'p', 'a', 'k' to file
file.write("p vlp {} {} {} {} {} {}{}\n".format(
opt_dir_str, m, n, k, q, k1, kstr))
for i in list(range(k)):
file.write("a {} {} {}\n".format(A_rows[i]+1,A_cols[i]+1,A_vals[i]))
file.write("a {} {} {}\n".format(
A_rows[i] + 1, A_cols[i] + 1, A_vals[i]))
for i in list(range(k1)):
file.write("o {} {} {}\n".format(P_rows[i]+1,P_cols[i]+1,P_vals[i]))
file.write("o {} {} {}\n".format(
P_rows[i] + 1, P_cols[i] + 1, P_vals[i]))
for i in list(range(k2)):
file.write("k {} {} {}\n".format(K_rows[i]+1,K_cols[i]+1,K_vals[i]))
file.write("k {} {} {}\n".format(
K_rows[i] + 1, K_cols[i] + 1, K_vals[i]))
# duality parameter vector
if self.c is not None:
if(len(np.array(self.c).shape) != 1 ) or (len(self.c)!=q) :
if(len(np.array(self.c).shape) != 1) or (len(self.c) != q):
raise RuntimeError('c has wrong dimension')
for i in range(q):
file.write("k {} 0 {}\n".format(i+1,self.c[i]))
#Write row
file.write("k {} 0 {}\n".format(i + 1, self.c[i]))
# Write row
if (len(np.array(self.a).shape) > 1):
raise RuntimeError('a has wrong dimension')
if (len(np.array(self.b).shape) > 1):
raise RuntimeError('b has wrong dimension')
m1 = max(getlen(self.a),getlen(self.b))
m1 = max(getlen(self.a), getlen(self.b))
if self.a is None:
aa = -np.inf*np.ones((m1,1))
aa = -np.inf * np.ones((m1, 1))
else:
aa = self.a
if self.b is None:
bb = np.inf*np.ones((m1,1))
bb = np.inf * np.ones((m1, 1))
else:
bb = self.b
for i in list(range(m1)):
if aa[i] < bb[i]:
ch = 2*np.isfinite(aa[i]) + np.isfinite(bb[i])
ch = 2 * np.isfinite(aa[i]) + np.isfinite(bb[i])
if ch == 0:
file.write('i {} f \n'.format(i+1))
file.write('i {} f \n'.format(i + 1))
elif ch == 1:
file.write('i {} u {}\n'.format(i+1,bb[i]))
file.write('i {} u {}\n'.format(i + 1, bb[i]))
elif ch == 2:
file.write('i {} l {}\n'.format(i+1,aa[i]))
file.write('i {} l {}\n'.format(i + 1, aa[i]))
elif ch == 3:
file.write('i {} d {} {}\n' .format(i+1,aa[i],bb[i]))
file.write('i {} d {} {}\n' .format(i + 1, aa[i], bb[i]))
else:
raise RuntimeError("Bad ch switch for constrains bounds")
elif aa[i] == bb[i] and np.isfinite(aa[i]):
file.write('i %d s %g\n',i,aa[i])
file.write('i %d s %g\n', i, aa[i])
else:
raise RuntimeError('Invalid constrsaints: a[{}]={}, b[{}]={}'.format(i+1,aa[i],i,bb[i]))
#Write cols
raise RuntimeError(
'Invalid constrsaints: a[{}]={}, b[{}]={}'.format(i + 1, aa[i], i, bb[i]))
# Write cols
if self.l is None:
llb=-np.inf*np.ones((n,1))
llb = -np.inf * np.ones((n, 1))
else:
llb=self.l
llb = self.l
if self.s is None:
uub= np.inf*np.ones((n,1))
uub = np.inf * np.ones((n, 1))
else:
uub= self.s
uub = self.s
for j in range(n):
if llb[j] < uub[j]:
ch = 2*np.isfinite(llb[j]) + np.isfinite(uub[j])
ch = 2 * np.isfinite(llb[j]) + np.isfinite(uub[j])
if ch == 0:
file.write('j {} f \n'.format(j+1))
file.write('j {} f \n'.format(j + 1))
elif ch == 1:
file.write('j {} u {}\n'.format(j+1,uub[j]))
file.write('j {} u {}\n'.format(j + 1, uub[j]))
elif ch == 2:
file.write('j {} l {}\n'.format(j+1,llb[j]))
file.write('j {} l {}\n'.format(j + 1, llb[j]))
elif ch == 3:
file.write('j {} d {} {}\n' .format(j+1,llb[j],uub[j]))
file.write('j {} d {} {}\n' .format(j + 1, llb[j], uub[j]))
else:
raise RuntimeError("Bad ch switch for variable bounds")
elif llb[j] == uub[j] and np.isfinite(llb[j]):
file.write('i %d s %g\n',j+1,llb[j])
file.write('i %d s %g\n', j + 1, llb[j])
else:
raise RuntimeError('Invalid constrsaints: l[{}]={}, s[{}]={}'.format(j+1,llb[j],i,uub[j]))
raise RuntimeError('Invalid constrsaints: l[{}]={}, s[{}]={}'.format(
j + 1, llb[j], i, uub[j]))
file.write('e ')
file.close()
#%%
# %%
class EcosystemModel:
from scipy.sparse import lil_matrix, block_diag, eye
def __init__(self,model_array=None,metabolic_dict=None):
from scipy.sparse import lil_matrix, block_diag, eye
def __init__(self, model_array=None, metabolic_dict=None):
"""Instatiate the EcosystemModel object
model_array is an array of cobra models to connect
metabolic_dict is a dicctionary where its keys correspond to metabolites id's and their values to the name equivalence"""
self.models=model_array
self.models = model_array
self.metabolic_dict = metabolic_dict
self._pooldict = None
self.pool_ex_rxns = None
......@@ -178,57 +189,64 @@ class EcosystemModel:
def construct_ecosystem_pool(self):
"""Check all metabolites used in import/export exchanges and construct the pool compartment"""
pooldict=dict()
pooldict = dict()
for model in self.models:
for rxn_ex in model.exchanges:
for met_ex in rxn_ex.metabolites:
met_name = self.metabolic_dict[met_ex.id]
if met_name not in pooldict:
pooldict[met_name]=[(model,rxn_ex,rxn_ex.get_coefficient(met_ex.id))]
if met_name not in pooldict:
pooldict[met_name] = [
(model, rxn_ex, rxn_ex.get_coefficient(met_ex.id))]
else:
pooldict[met_name].append((model,rxn_ex,rxn_ex.get_coefficient(met_ex.id)))
self._pooldict = pooldict
pooldict[met_name].append(
(model, rxn_ex, rxn_ex.get_coefficient(met_ex.id)))
self._pooldict = pooldict
def populate_ecosystem_model(self):
"""Calculate the object attributes after pool construction"""
self.pool_ex_rxns = ["EX_{}:pool".format(key) for key in self._pooldict.keys()]
self.pool_ex_mets = ["{}:pool".format(key) for key in self._pooldict.keys()]
self.sysreactions = ["{}:{}".format(r.id,model.id) for model in self.models for r in model.reactions]
self.sysmetabolites = ["{}:{}".format(m.id,model.id) for model in self.models for m in model.metabolites]
self.pool_ex_rxns = ["EX_{}:pool".format(
key) for key in self._pooldict.keys()]
self.pool_ex_mets = ["{}:pool".format(
key) for key in self._pooldict.keys()]
self.sysreactions = ["{}:{}".format(
r.id, model.id) for model in self.models for r in model.reactions]
self.sysmetabolites = ["{}:{}".format(
m.id, model.id) for model in self.models for m in model.metabolites]
self.sysreactions.extend(self.pool_ex_rxns)
self.sysmetabolites.extend(self.pool_ex_mets)
array_form = [model.to_array_based_model() for model in self.models]
self.Ssigma = block_diag([model.S for model in array_form])
self.Ssigma = lil_matrix(block_diag([self.Ssigma,-eye(len(self.pool_ex_rxns))]))
self.Ssigma = lil_matrix(block_diag(
[self.Ssigma, -eye(len(self.pool_ex_rxns))]))
for met in self._pooldict.keys():
met_name = "{}:pool".format(met)
met_idx = self.sysmetabolites.index(met_name)
for model,reaction,coeff in self._pooldict[met]:
rxn_name = "{}:{}".format(reaction.id,model.id)
for model, reaction, coeff in self._pooldict[met]:
rxn_name = "{}:{}".format(reaction.id, model.id)
rxn_idx = self.sysreactions.index(rxn_name)
self.Ssigma[met_idx,rxn_idx]=-coeff
def add_comparment(self,model):
self.Ssigma[met_idx, rxn_idx] = -coeff
def add_comparment(self, model):
"""Utility function to add a new agent to models. Pretty ineficient, re-runs all the steps again for each addition"""
self.__init__(self.model_array.add(model),self.metabolic_dict)
self.__init__(self.model_array.add(model), self.metabolic_dict)
self.construct_ecosystem_pool()
self.populate_ecosystem_model()
@staticmethod
def get_common_mets(model_list):
"""Naive implementation of getting common exchange metabolites, using their id's"""
common_mets=dict()
common_mets = dict()
for model in model_list:
for rxn_ex in model.exchanges:
for met_ex in rxn_ex.metabolites:
if met_ex.id not in common_mets:
common_mets[met_ex.id]=dict([(model,rxn_ex)])
common_mets[met_ex.id] = dict([(model, rxn_ex)])
else:
common_mets[met_ex.id][model]=rxn_ex
common_mets[met_ex.id][model] = rxn_ex
return(common_mets)
#%%
# %%
#%%
# %%
TestVLP.py
+ 7
− 10
Voir le fichier @ ff22ed1f
......@@ -9,7 +9,7 @@ Created on Wed May 31 15:45:22 2017
# Example: MOLP with 2 objectives, simplest example
# min [x1 - x2; x1 + x2]
#
#
# 6 <= 2*x1 + x2
# 6 <= x1 + 2*x2
#
......@@ -17,14 +17,14 @@ Created on Wed May 31 15:45:22 2017
# x2 >= 0
import numpy as np
from benpy import solve as bensolve, vlpProblem, vlpSolution
#%%
from benpy import solve as bensolve, vlpProblem
# %%
vlp = vlpProblem()
vlp.B=np.matrix([[2,1],[1,2]]) # coefficient matrix
vlp.a=[6,6] # lower bounds
vlp.P=np.matrix([[1,-1],[1,1]]); # objective matrix
vlp.l=[0,0]; # lower variable bounds
vlp.B = np.matrix([[2, 1], [1, 2]]) # coefficient matrix
vlp.a = [6, 6] # lower bounds
vlp.P = np.matrix([[1, -1], [1, 1]]) # objective matrix
vlp.l = [0, 0] # lower variable bounds
vlp.default_options()
......@@ -33,6 +33,3 @@ vlp.to_string()
vlp.to_file('test01.vlp')
sol = bensolve(vlp)
TestVLP2.py
+ 103
− 99
Voir le fichier @ ff22ed1f
......@@ -5,29 +5,29 @@ Created on Wed May 31 15:45:22 2017
Test VLP interfase with bensolve
@author: mbudinich
"""
#%%
from numpy import transpose, ones, zeros, eye, matrix, loadtxt, append, hstack, vstack, inf
# %%
from numpy import transpose, ones, zeros, eye, matrix, loadtxt, append, vstack, inf
from HelperClass import VlpProblem
#%%
# %%
# Example: MOLP with 2 objectives, simplest example
# min [x1 - x2; x1 + x2]
#
#
# 6 <= 2*x1 + x2
# 6 <= x1 + 2*x2
#
# x1 >= 0
# x2 >= 0
vlp = VlpProblem();
vlp = VlpProblem()
vlp.B=matrix([[2,1],[1,2]]) # coefficient matrix
vlp.a=[6,6] # lower bounds
vlp.P=matrix([[1,-1],[1,1]]) # objective matrix
vlp.l=[0,0] # lower variable bounds
vlp.B = matrix([[2, 1], [1, 2]]) # coefficient matrix
vlp.a = [6, 6] # lower bounds
vlp.P = matrix([[1, -1], [1, 1]]) # objective matrix
vlp.l = [0, 0] # lower variable bounds
vlp.to_file('ex01.vlp');
#%%
vlp.to_file('ex01.vlp')
# %%
# Example: MOLP with 2 objectives which is infeasible
# v-min [x1;x2]
......@@ -36,15 +36,15 @@ vlp.to_file('ex01.vlp');
# 0 <= x1 + 2*x2 <= 1
# 1 <= x1 + x2 <= 2
vlp = VlpProblem();
vlp = VlpProblem()
vlp.B=matrix([[3,1],[1,2],[1,1]])
vlp.b=[1, 1, 2]
vlp.a=[0, 0, 1]
vlp.P=matrix([[1,0],[0,1]])
vlp.B = matrix([[3, 1], [1, 2], [1, 1]])
vlp.b = [1, 1, 2]
vlp.a = [0, 0, 1]
vlp.P = matrix([[1, 0], [0, 1]])
vlp.to_file('ex02.vlp');
#%%
vlp.to_file('ex02.vlp')
# %%
# Example: MOLP with 2 objectives the upper image of which has no vertex
# v-min [x1;x2]
......@@ -52,14 +52,14 @@ vlp.to_file('ex02.vlp');
# 1 <= x1 + x2 + x3
# 1 <= x1 + x2 - x3
vlp = VlpProblem();
vlp = VlpProblem()
vlp.B=matrix([[1,1,1],[1,1,-1]])
vlp.a=[1,1]
vlp.P=matrix([[1,0,0], [0,1,0]])
vlp.B = matrix([[1, 1, 1], [1, 1, -1]])
vlp.a = [1, 1]
vlp.P = matrix([[1, 0, 0], [0, 1, 0]])
vlp.to_file('ex03.vlp');
#%%
vlp.to_file('ex03.vlp')
# %%
# Example: MOLP with 2 objectives, which is totally unbounded
# v-min [x1;x2]
......@@ -67,27 +67,28 @@ vlp.to_file('ex03.vlp');
# 1 <= x1 + x2 + x3
# 1 <= x1 + x2 + 2*x3
vlp = VlpProblem();
vlp = VlpProblem()
vlp.B=matrix([[1,1,1],[1,1,2]]);
vlp.a=[1,1]
vlp.P=matrix([[1,0,0], [0,1,0]]);
vlp.B = matrix([[1, 1, 1], [1, 1, 2]])
vlp.a = [1, 1]
vlp.P = matrix([[1, 0, 0], [0, 1, 0]])
vlp.to_file('ex04.vlp');
#%%
vlp.to_file('ex04.vlp')
# %%
# Example: VLP with q = 3 and 4 generating vectors of C
# see http://bensolve.org/demo.html
vlp = VlpProblem();
vlp = VlpProblem()
vlp.B=matrix([ones((1,3)).tolist()[0],[1,2,2],[2,2,1],[2,1,2]]) # coefficient matrix
vlp.a=[1,3/2,3/2,3/2]; # lhs of constraints
vlp.P=matrix([[1,0,1],[1,1,0],[0,1,1]]) # objective matrix
vlp.l=[0,0,0] # lower variable bounds
vlp.B = matrix([ones((1, 3)).tolist()[0], [1, 2, 2], [
2, 2, 1], [2, 1, 2]]) # coefficient matrix
vlp.a = [1, 3 / 2, 3 / 2, 3 / 2] # lhs of constraints
vlp.P = matrix([[1, 0, 1], [1, 1, 0], [0, 1, 1]]) # objective matrix
vlp.l = [0, 0, 0] # lower variable bounds
# generating vectors of ordering cone C
vlp.Y=transpose(matrix([[1,0,0], [0,1,0], [-1,0,2], [0,-1,2]]))
vlp.Y = transpose(matrix([[1, 0, 0], [0, 1, 0], [-1, 0, 2], [0, -1, 2]]))
# alternative variant: generating vectors of the dual of the ordering cone C
# vlp.Z=[2 2 1 ; 2 0 1 ; 0 0 1 ; 0 2 1]';
......@@ -95,59 +96,61 @@ vlp.Y=transpose(matrix([[1,0,0], [0,1,0], [-1,0,2], [0,-1,2]]))
# duality parameter vector (must belong to interior of C)
# if not given, it is computed automatically
# dual problem depends on c
vlp.c=[1,1,1]
vlp.c = [1, 1, 1]
vlp.to_file('ex05.vlp')
#%%
# %%
# Example: VLP with 2 objectives
# max [x1 - x2; x1 + x2]
#
# w.r.t. cone C ={(y1,y2) | 2 y1 -y2 >= 0, -y1 + 2 y2 >= 0}
#
# 1 <= x1 + x2 <= 2
#
# 1 <= x1 + x2 <= 2
#
# 0 <= x1 <= 1
# 0 <= x2
vlp = VlpProblem();
vlp = VlpProblem()
vlp.opt_dir=-1 # maximization
vlp.Z=matrix([[2,-1], [-1,2]]) # generators of dual of ordering cone
vlp.c=[1,1] # geometric duality parameter vector (belongs to interior of C)
vlp.opt_dir = -1 # maximization
vlp.Z = matrix([[2, -1], [-1, 2]]) # generators of dual of ordering cone
vlp.c = [1, 1] # geometric duality parameter vector (belongs to interior of C)
vlp.B=matrix([1,1]) # coefficient matrix
vlp.a=[1] # lhs
vlp.b=[2] # rhs
vlp.l=[0,0] # lower bounds
vlp.s=[1,inf] # upper bounds
vlp.P=matrix([[1,-1],[1,1]]) # objective matrix
vlp.B = matrix([1, 1]) # coefficient matrix
vlp.a = [1] # lhs
vlp.b = [2] # rhs
vlp.l = [0, 0] # lower bounds
vlp.s = [1, inf] # upper bounds
vlp.P = matrix([[1, -1], [1, 1]]) # objective matrix
vlp.to_file('ex06.vlp');
#%%
vlp.to_file('ex06.vlp')
# %%
# Example: MOLP with 3 objectives, 1211 constraints and 1143 variables
#
# Example (PL) in
#
#
# Shao, L., Ehrgott, M.: Approximately solving multiobjective linear programmes in objective space and
# an application in radiotherapy treatment planning. Math. Methods Oper. Res. 68(2), 257Ð276 (2008)
#
# enlarge epsilon in phase 2 and use primal simplex algorithm, for instance, run
# ./bensolve ex/ex07.vlp -m 2 -e 0.05 -l primal_simplex
vlp = VlpProblem();
vlp = VlpProblem()
vlp.B=loadtxt('example07.txt');
vlp.P = -1*matrix([append(zeros((1,1140)),[-1,0,0]),append(zeros((1,1140)),[0,-1,0]),append(zeros((1,1140)),[0,0,-1])])
vlp.l = append(zeros((1140,1)),[0,-45,0])
vlp.s = append(inf*ones((1140,1)),[17.07,12,90.64])
vlp.b = vstack((90.64*ones((67,1)),-85.3601*ones((67,1)),60*ones((37,1)),45*ones((8,1)),60*ones((46,1)),ones((986,1))))
vlp.B = loadtxt('example07.txt')
vlp.P = -1 * matrix([append(zeros((1, 1140)), [-1, 0, 0]), append(
zeros((1, 1140)), [0, -1, 0]), append(zeros((1, 1140)), [0, 0, -1])])
vlp.l = append(zeros((1140, 1)), [0, -45, 0])
vlp.s = append(inf * ones((1140, 1)), [17.07, 12, 90.64])
vlp.b = vstack((90.64 * ones((67, 1)), -85.3601 * ones((67, 1)), 60 *
ones((37, 1)), 45 * ones((8, 1)), 60 * ones((46, 1)), ones((986, 1))))
vlp.b = vlp.b.T.tolist()[0]
vlp.to_file('ex07.vlp');
#%%
vlp.to_file('ex07.vlp')
# %%
# Example: VLP with 2 objectives, which is unbounded (but not totally unbounded)
#
# its the solution consists of feasible points and feasible directions
# its the solution consists of feasible points and feasible directions
# min_C [x1;x2]
#
......@@ -155,22 +158,22 @@ vlp.to_file('ex07.vlp');
#
# duality parameter c is set to [0;1], which is an interior point of C
#
# 0 <= 3*x1 + x2
# 0 <= x1 + 2*x2
# 1 <= x1 + x2
# 0 <= 3*x1 + x2
# 0 <= x1 + 2*x2
# 1 <= x1 + x2
vlp = VlpProblem();
vlp = VlpProblem()
vlp.B=matrix([3,1],[1,2],[1,1])
vlp.a=[0, 0, 1]
vlp.P=[[1,0] , [0,1]];
vlp.B = matrix([3, 1], [1, 2], [1, 1])
vlp.a = [0, 0, 1]
vlp.P = [[1, 0], [0, 1]]
# generating vectors of ordering cone C
vlp.Y=matrix([[-1,3], [3/2,-1]])
vlp.c=[0,1]
vlp.Y = matrix([[-1, 3], [3 / 2, -1]])
vlp.c = [0, 1]
vlp.to_file('ex08.vlp');
#%%
vlp.to_file('ex08.vlp')
# %%
# VLP with 3 objectives, 4608 constrains and 36939 variables
#
# Example 6.6 in
......@@ -180,7 +183,7 @@ vlp.to_file('ex08.vlp');
# special options are necessary to run example:
#
# For instance, run
# ./bensolve ex/ex09.vlp -e 1e-2 -m 3 -L primal_simplex -l primal_simplex -p
# ./bensolve ex/ex09.vlp -e 1e-2 -m 3 -L primal_simplex -l primal_simplex -p
#
# (-e: set epsilon in Phase 2)
# (-m set message level to have more output on screen)
......@@ -196,11 +199,11 @@ vlp.to_file('ex08.vlp');
# (-a use dual Benson algorithm in Phase 2)
# (-p generate graphics files)
#vlp = VlpProblem();
#load('example09');
#vlp.c=[1,1,1]
#vlp.to_file('ex09.vlp');
#%%
# vlp = VlpProblem();
# load('example09');
# vlp.c=[1,1,1]
# vlp.to_file('ex09.vlp');
# %%
# The 'bensolvehedron', see the titlepage of the reference manual
#
# MOLP with q objectives, n=(q+2*m)^q variables and constraints
......@@ -213,36 +216,37 @@ vlp.to_file('ex08.vlp');
# add green color (!)
# adapt q and m to generate other (larger) problems
#q=3;
#m=2;
# q=3;
# m=2;
#n=(q+2*m)^q;
# n=(q+2*m)^q;
#vlp = VlpProblem();
# vlp = VlpProblem();
# feasible set is n dimensional hyper cube
#vlp.B=eye(n)
#vlp.a=zeros((n,1))
#vlp.b=ones((n,1))
# vlp.B=eye(n)
# vlp.a=zeros((n,1))
# vlp.b=ones((n,1))
# objective map
#P=zeros(n,q);
#for i in range(n):
# line = dec2base(i-1,q+2*m,q)-'0';
# line = line - (q+2*m-1)/2;
# P[i,:] = line
# P=zeros(n,q);
# for i in range(n):
# line = dec2base(i-1,q+2*m,q)-'0';
# line = line - (q+2*m-1)/2;
# P[i,:] = line
#vlp.P = transpose(P)
# vlp.P = transpose(P)
#vlp.to_file('ex10.vlp');
#%%
# vlp.to_file('ex10.vlp');
# %%
# Example: MOLP with q=5, unbounded,
# recession cone of upper image has 22 extreme directions (main effort in phase 1)
vlp = VlpProblem();
vlp = VlpProblem()
vlp.B=matrix([[1,1,1,1,1],[2,1,1,1,1],[1,2,1,1,1],[1,1,2,1,1],[1,1,1,2,1],[1,1,1,1,2],[2,2,1,1,1],[2,1,2,1,1],[2,1,1,2,1],[2,1,1,1,2],[1,2,2,1,1],[1,2,1,2,1],[1,2,1,1,2],[1,1,2,2,1],[1,1,2,1,2],[1,1,1,2,2],[2,2,2,1,1],[2,2,1,2,1],[2,2,1,1,2],[2,1,2,1,2],[2,1,1,2,2],[1,2,2,2,1],[1,2,1,2,2],[1,2,2,1,2],[1,2,2,2,1],[1,1,2,2,2],[1,2,2,2,2],[2,1,2,2,2],[2,2,1,2,2],[2,2,2,1,2],[2,2,2,2,1]])
vlp.a=append(1,zeros((30, 1)))
vlp.P=eye((5,5));
vlp.B = matrix([[1, 1, 1, 1, 1], [2, 1, 1, 1, 1], [1, 2, 1, 1, 1], [1, 1, 2, 1, 1], [1, 1, 1, 2, 1], [1, 1, 1, 1, 2], [2, 2, 1, 1, 1], [2, 1, 2, 1, 1], [2, 1, 1, 2, 1], [2, 1, 1, 1, 2], [1, 2, 2, 1, 1], [1, 2, 1, 2, 1], [1, 2, 1, 1, 2], [1, 1, 2, 2, 1], [1, 1, 2, 1, 2], [
1, 1, 1, 2, 2], [2, 2, 2, 1, 1], [2, 2, 1, 2, 1], [2, 2, 1, 1, 2], [2, 1, 2, 1, 2], [2, 1, 1, 2, 2], [1, 2, 2, 2, 1], [1, 2, 1, 2, 2], [1, 2, 2, 1, 2], [1, 2, 2, 2, 1], [1, 1, 2, 2, 2], [1, 2, 2, 2, 2], [2, 1, 2, 2, 2], [2, 2, 1, 2, 2], [2, 2, 2, 1, 2], [2, 2, 2, 2, 1]])
vlp.a = append(1, zeros((30, 1)))
vlp.P = eye((5, 5))
vlp.to_file('ex11.vlp');
\ No newline at end of file
vlp.to_file('ex11.vlp')
setup.py
+ 14
− 15
Voir le fichier @ ff22ed1f
......@@ -2,20 +2,19 @@ from distutils.core import setup, Extension
from Cython.Build import cythonize
import numpy
ext = Extension("benpy",sources=["benpy.pyx",
"bensolve-mod/bslv_main.c",
"bensolve-mod/bslv_vlp.c",
"bensolve-mod/bslv_algs.c",
"bensolve-mod/bslv_lists.c",
"bensolve-mod/bslv_poly.c",
"bensolve-mod/bslv_lp.c"
],
libraries=['glpk','m'],
extra_compile_args=['-std=c99','-O3']
)
ext = Extension("benpy", sources=["benpy.pyx",
"bensolve-mod/bslv_main.c",
"bensolve-mod/bslv_vlp.c",
"bensolve-mod/bslv_algs.c",
"bensolve-mod/bslv_lists.c",
"bensolve-mod/bslv_poly.c",
"bensolve-mod/bslv_lp.c"
],
libraries=['glpk', 'm'],
extra_compile_args=['-std=c99', '-O3']
)
setup(
name="benpy",
ext_modules = cythonize([ext]),
include_dirs=[numpy.get_include()]
name="benpy",
ext_modules=cythonize([ext]),
include_dirs=[numpy.get_include()]
)
test_benpy.py
+ 1
− 1
Voir le fichier @ ff22ed1f
......@@ -2,7 +2,7 @@ import benpy
problem = benpy.vlpProblem()
problem.filename = "ex01.vlp"
problem.options={'message_level':3}
problem.options = {'message_level': 3}
sol = benpy.solve(problem)
problem2 = benpy.vlpProblem()
......
test_benpy_simple.py
+ 2
− 1
Voir le fichier @ ff22ed1f
import benpy
problem = benpy.vlpProblem(filename = "ex01.vlp", options = {'message_level':3, 'solution':True, 'logfile':False})
problem = benpy.vlpProblem(filename="ex01.vlp", options={
'message_level': 3, 'solution': True, 'logfile': False})
sol = benpy.solve(problem)
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