def find_direction(self, poly, plot=False):
self.build_polys(poly)
volumes = []
try:
ineq = np.array(cdd.Polyhedron(poly.inner).get_inequalities())
except RuntimeError:
raise SteppingException('Numerical inconsistency found')
for line in ineq:
key = hashlib.sha1(line).hexdigest()
if key in poly.volume_dic:
volumes.append(poly.volume_dic[key])
else:
if key in poly.hrep_dic:
A_e = poly.hrep_dic[key]
else:
A_e = poly.outer.copy()
A_e.extend(cdd.Matrix(-line.reshape(1, line.size)))
A_e.canonicalize()
poly.hrep_dic[key] = A_e
if plot:
poly.reset_fig()
poly.plot_polyhedrons()
poly.plot_polyhedron(A_e, 'm', 0.5)
poly.show()
vol = self.volume_convex(A_e)
poly.volume_dic[key] = vol
volumes.append(vol)
poly.vrep_dic[key] = np.array(
cdd.Polyhedron(A_e).get_generators())
maxv = max(volumes)
alli = [i for i, v in enumerate(volumes) if v == maxv]
i = random.choice(alli)
key = hashlib.sha1(ineq[i, :]).hexdigest()
self.last_hrep = poly.hrep_dic[key]
return -ineq[i, 1:]
def test_vs_cdd():
vrep = [[1.00000000e+00, 6.49999999e-01, 4.91264259e-19, 5.67434186e-07],
[1.00000000e+00, -6.49999999e-01, -1.10024414e-19, 5.67434187e-07],
[1.00000000e+00, 2.68036827e-19, 6.49999999e-01, 5.67434186e-07],
[1.00000000e+00, -2.36423280e-18, -6.49999999e-01, 5.67434187e-07],
[1.00000000e+00, -3.38321375e-15, 4.02250642e-14, 6.50000000e-01],
[1.00000000e+00, 1.50481564e-14, 1.78805309e-15, -6.50000000e-01]]
mat = cdd.Matrix(np.array(vrep), number_type='fraction')
mat.rep_type = cdd.RepType.GENERATOR
mat.canonicalize()
hrep_cdd = np.array(cdd.Polyhedron(mat).get_inequalities())
f = np.vectorize(lambda x: Fraction(x))
poly = pyparma.Polyhedron(vrep=f(np.array(vrep)))
hrep_parma = poly.hrep()
assert (equal_sorted(np.array(mat), poly.vrep()))
print(hrep_cdd)
print(hrep_parma)
assert (equal_sorted(hrep_cdd, hrep_parma))
def _test_testcdd2(number_type=None, assert_matrix_equal=None):
mat = cdd.Matrix([[7, -3, -0], [7, 0, -3], [1, 1, 0], [1, 0, 1]],
number_type=number_type)
mat.rep_type = cdd.RepType.INEQUALITY
assert_matrix_equal(list(mat), [(7, -3, -0), (7, 0, -3), (1, 1, 0),
(1, 0, 1)])
gen = cdd.Polyhedron(mat).get_generators()
nose.tools.assert_equal(gen.rep_type, cdd.RepType.GENERATOR)
assert_matrix_equal(list(gen), [(1, Fraction(7, 3), -1), (
1,
-1,
-1,
), (1, -1, Fraction(7, 3)), (1, Fraction(7, 3), Fraction(7, 3))])
# add an equality and an inequality
mat.extend([[7, 1, -3]], linear=True)
mat.extend([[7, -3, 1]])
assert_matrix_equal(list(mat), [(7, -3, -0), (7, 0, -3), (1, 1, 0),
(1, 0, 1), (7, 1, -3), (7, -3, 1)])
nose.tools.assert_equal(list(mat.lin_set), [4])
gen2 = cdd.Polyhedron(mat).get_generators()
nose.tools.assert_equal(gen2.rep_type, cdd.RepType.GENERATOR)
assert_matrix_equal(list(gen2), [(1, -1, 2), (1, 0, Fraction(7, 3))])
def compute_polytope_vertices(A, b):
"""
Compute the vertices of a polytope given in halfspace representation by
:math:`A x \\leq b`.
Parameters
----------
A : array, shape=(m, k)
Matrix of halfspace representation.
b : array, shape=(m,)
Vector of halfspace representation.
Returns
-------
vertices : list of arrays
List of polytope vertices.
Notes
-----
This method won't work well if your halfspace representation includes
equality constraints :math:`A x = b` written as :math:`(A x \\leq b \\wedge
-A x \\leq -b)`. If this is your use case, consider using directly the
linear set ``lin_set`` of `equality-constraint generatorsin pycddlib
<https://pycddlib.readthedocs.io/en/latest/matrix.html>`_.
"""
b = b.reshape((b.shape[0], 1))
mat = cdd.Matrix(hstack([b, -A]), number_type='float')
mat.rep_type = cdd.RepType.INEQUALITY
P = cdd.Polyhedron(mat)
g = P.get_generators()
V = array(g)
vertices = []
for i in range(V.shape[0]):
if V[i, 0] != 1: # 1 = vertex, 0 = ray
raise Exception("Polyhedron is not a polytope")
elif i not in g.lin_set:
vertices.append(V[i, 1:])
return vertices
def test_facet_incidence_cube(number_type):
# We start with the H-representation for a cube
mat = cdd.Matrix([[1, 1, 0, 0], [1, 0, 1, 0], [1, 0, 0, 1], [1, -1, 0, 0],
[1, 0, -1, 0], [1, 0, 0, -1]],
number_type=number_type)
mat.rep_type = cdd.RepType.INEQUALITY
poly = cdd.Polyhedron(mat)
incidence = poly.get_input_incidence()
# Family size should equal the number of facets of the cube (6), plus 1 (the empty infinite ray)
assert len(incidence) == 7
# All the facets of the cube should have 4 vertices.
# The polyhedron is closed, so the last set should be empty
assert [len(inc) for inc in incidence] == [4, 4, 4, 4, 4, 4, 0]
# The vertices must be numbered consistently
# The first vertex is adjacent to the second, fourth and eighth
# (note the conversion to a pythonic numbering system)
incidence_list = [[4, 5, 6, 7], [0, 1, 6, 7], [0, 3, 5, 7], [0, 1, 2, 3],
[2, 3, 4, 5], [1, 2, 4, 6], []]
for i in range(7):
assert sorted(list(incidence[i])) == incidence_list[i]
def test_make_facet_adjacency_list(number_type):
# This matrix is the same as in vtest_vo.ine
mat = cdd.Matrix([[0, 0, 0, 1],
[5, -4, -2, 1],
[5, -2, -4, 1],
[16, -8, 0, 1],
[16, 0, -8, 1],
[32, -8, -8, 1]], number_type=number_type)
mat.rep_type = cdd.RepType.INEQUALITY
poly = cdd.Polyhedron(mat)
adjacencies = [[1, 2, 3, 4, 6],
[0, 2, 3, 5],
[0, 1, 4, 5],
[0, 1, 5, 6],
[0, 2, 5, 6],
[1, 2, 3, 4, 6],
[0, 3, 4, 5]]
adjacency_list = poly.get_input_adjacency()
for i in range(7):
assert list(adjacency_list[i]) == adjacencies[i]
def face_of_span(S):
V = vstack([
hstack([zeros((S.shape[1], 1)), S.T]),
hstack([1, zeros(S.shape[0])])
])
# V-representation: first column is 0 for rays
mat = cdd.Matrix(V, number_type='float')
mat.rep_type = cdd.RepType.GENERATOR
P = cdd.Polyhedron(mat)
ineq = P.get_inequalities()
H = array(ineq)
if H.shape == (0, ): # H == []
return H
A = []
for i in xrange(H.shape[0]):
# H matrix is [b, -A] for A * x <= b
if norm(H[i, 1:]) < 1e-10:
continue
elif abs(H[i, 0]) > 1e-10: # b should be zero for a cone
raise Exception("Polyhedron is not a cone")
elif i not in ineq.lin_set:
A.append(-H[i, 1:])
return array(A)
def _pos_generators(matrix):
"""Returns a matrix with rows the extreme rays of
the pointed cone `matrix x = 0, x >= 0'."""
import cdd
if matrix == Matrix(): return matrix
S = matrix
nr = S.cols
# matrix |b -A|, with b = 0, -A = | S^t -S^t I|^t
H = [[0] + [int(r[i]) for i in range(len(r))] for r in S.col_join(-S).tolist()] + \
[[0] + [0 if j != i else 1 for j in range(nr)] for i in range(nr)]
# polyhedron
H = cdd.Matrix(H, number_type="fraction")
H.rep_type = cdd.RepType.INEQUALITY
# extreme rays
ers = cdd.Polyhedron(H).get_generators()
ers = [er[1:] for er in ers if er[0] == 0]
return Matrix(ers)
def compute_cone_face_matrix(S):
"""
Compute the face matrix of a polyhedral convex cone from its span matrix.
Parameters
----------
S : array, shape=(n, m)
Span matrix defining the cone as :math:`x = S \\lambda` with
:math:`\\lambda \\geq 0`.
Returns
-------
F : array, shape=(k, n)
Face matrix defining the cone equivalently by :math:`F x \\leq 0`.
"""
V = vstack([
hstack([zeros((S.shape[1], 1)), S.T]),
hstack([1, zeros(S.shape[0])])
])
# V-representation: first column is 0 for rays
mat = cdd.Matrix(V, number_type='float')
mat.rep_type = cdd.RepType.GENERATOR
P = cdd.Polyhedron(mat)
ineq = P.get_inequalities()
H = array(ineq)
if H.shape == (0, ): # H == []
return H
A = []
for i in range(H.shape[0]):
# H matrix is [b, -A] for A * x <= b
if norm(H[i, 1:]) < 1e-10:
continue
elif abs(H[i, 0]) > 1e-10: # b should be zero for a cone
raise Exception("Polyhedron is not a cone")
elif i not in ineq.lin_set:
A.append(-H[i, 1:])
return array(A)
def _apply_solver(self):
start_time = time.time()
M=self.options.dual_bound
if not self.options.dual_bound:
M=1e6
print(f'Dual bound not specified, set to default {M}')
delta = self.options.delta
if not self.options.delta:
delta = 0.05 #What should default robustness delta be if not specified? Or should I raise an error?
print(f'Robustness parameter not specified, set to default {delta}')
# matrix representation for bilevel problem
matrix_repn = BilevelMatrixRepn(self._instance,standard_form=False)
# each lower-level problem
submodel = [block for block in self._instance.component_objects(SubModel)][0]
if len(submodel) != 1:
raise Exception('Problem encountered, this is not a valid bilevel model for the solver.')
self._instance.reclassify_component_type(submodel, Block)
#varref(submodel)
#dataref(submodel)
all_vars = {key: var for (key, var) in matrix_repn._all_vars.items()}
# get the variables that are fixed for the submodel (lower-level block)
fixed_vars = {key: var for (key, var) in matrix_repn._all_vars.items() if key in matrix_repn._fixed_var_ids[submodel.name]}
#Is there a way to get integer, continuous, etc for the upper level rather than lumping them all into fixed?
# continuous variables in SubModel
c_vars = {key: var for (key, var) in matrix_repn._all_vars.items() if key in matrix_repn._c_var_ids - fixed_vars.keys()}
# binary variables in SubModel SHOULD BE EMPTY FOR THIS SOLVER
b_vars = {key: var for (key, var) in matrix_repn._all_vars.items() if key in matrix_repn._b_var_ids - fixed_vars.keys()}
if len(b_vars)!= 0:
raise Exception('Problem encountered, this is not a valid bilevel model for the solver. Binary variables present!')
# integer variables in SubModel SHOULD BE EMPTY FOR THIS SOLVER
i_vars = {key: var for (key, var) in matrix_repn._all_vars.items() if key in matrix_repn._i_var_ids - fixed_vars.keys()}
if len(i_vars) != 0:
raise Exception('Problem encountered, this is not a valid bilevel model for the solver. Integer variables present!')
# get constraint information related to constraint id, sign, and rhs value
sub_cons = matrix_repn._cons_sense_rhs[submodel.name]
cons= matrix_repn._cons_sense_rhs[self._instance.name]
# construct the high-point problem (LL feasible, no LL objective)
# s0 <- solve the high-point
# if s0 infeasible then return high_point_infeasible
xfrm = TransformationFactory('pao.bilevel.highpoint')
xfrm.apply_to(self._instance)
#
# Solve with a specified solver
#
solver = self.options.solver
if not self.options.solver:
solver = 'gurobi'
for c in self._instance.component_objects(Block, descend_into=False):
if 'hp' in c.name:
#if '_hp' in c.name:
c.activate()
with pyomo.opt.SolverFactory(solver) as opt:
self.results.append(opt.solve(c,
tee=self._tee,
timelimit=self._timelimit))
_check_termination_condition(self.results[-1])
c.deactivate()
if self.options.do_print==True:
print('Solution to the Highpoint Relaxation')
for _, var in all_vars.items():
var.pprint()
# s1 <- solve the optimistic bilevel (linear/linear) problem (call solver3)
# if s1 infeasible then return optimistic_infeasible'
with pyomo.opt.SolverFactory('pao.bilevel.blp_global') as opt:
opt.options.solver = solver
self.results.append(opt.solve(self._instance,tee=self._tee,timelimit=self._timelimit))
_check_termination_condition(self.results[-1])
if self.options.do_print==True:
print('Solution to the Optimistic Bilevel')
for _, var in all_vars.items():
var.pprint()
#self._instance.pprint() #checking for active blocks left over from previous solves
# sk <- solve the dual adversarial problem
# if infeasible then return dual_adversarial_infeasible
# Collect the vertices solutions for the dual adversarial problem
#Collect up the matrix B and the vector d for use in all adversarial feasibility problems
n=len(c_vars.items())
m=len(sub_cons.items())
K=len(cons.items())
B=np.empty([m,n])
L=np.empty([K,1])
i=0
p=0
for _, var in c_vars.items():
(A, A_q, sign, b) = matrix_repn.coef_matrices(submodel, var)
B[:,i]=np.transpose(np.array(A))
i+=1
_ad_block_name='_adversarial'
self._instance.add_component(_ad_block_name, Block(Any))
_Vertices_name='_Vertices'
_Vertices_B_name='_VerticesB'
self._instance.add_component(_Vertices_name,Param(cons.keys()*NonNegativeIntegers*sub_cons.keys(),mutable=True))
Vertices=getattr(self._instance,_Vertices_name)
self._instance.add_component(_Vertices_B_name,Param(cons.keys()*NonNegativeIntegers,mutable=True))
VerticesB=getattr(self._instance,_Vertices_B_name)
adversarial=getattr(self._instance,_ad_block_name)
#Add Adversarial blocks
for _cidS, _ in cons.items(): # <for each constraint in the upper-level problem>
(_cid,_)=_cidS
ad=adversarial[_cid] #shorthand
ad.alpha=Var(sub_cons.keys(),within=NonNegativeReals) #sub_cons.keys() because it's a dual variable on the lower level constraints
ad.beta=Var(within=NonNegativeReals)
Hk=np.empty([n,1])
i=0
d=np.empty([n,1])
ad.cons=Constraint(c_vars.keys()) #B^Talpha+beta*d>= H_k, v-dimension constraints so index by c_vars
lhs_expr = {key: 0. for key in c_vars.keys()}
rhs_expr = {key: 0. for key in c_vars.keys()}
for _vid, var in c_vars.items():
(A, A_q, sign, b) = matrix_repn.coef_matrices(submodel, var)
coef = A #+ dot(A_q.toarray(), _fixed)
(C, C_q, C_constant) = matrix_repn.cost_vectors(submodel, var)
d[i,0]=float(C)
lhs_expr[_vid]=float(C)*ad.beta
(A,A_q,sign,b)=matrix_repn.coef_matrices(self._instance,var)
idx = list(cons.keys()).index(_cidS)
Hk[i,0]=A[idx]
i+=1
for _cid2 in sub_cons.keys():
idx = list(sub_cons.keys()).index(_cid2)
lhs_expr[_vid] += float(coef[idx])*ad.alpha[_cid2]
rhs_expr[_vid] = float(A[idx])
expr = lhs_expr[_vid] >= rhs_expr[_vid]
if not type(expr) is bool:
ad.cons[_vid] = expr
else:
ad.cons[_vid] = Constraint.Skip
ad.Obj=Objective(expr=0) #THIS IS A FEASIBILITY PROBLEM
with pyomo.opt.SolverFactory(solver) as opt:
self.results.append(opt.solve(ad,
tee=self._tee,
timelimit=self._timelimit))
_check_termination_condition(self.results[-1])
ad.deactivate()
Bd=np.hstack((np.transpose(B),d))
Eye=np.identity(m+1)
Bd=np.vstack((Bd,Eye))
Hk=np.vstack((Hk,np.zeros((m+1,1))))
mat=np.hstack((-Hk,Bd))
mat=cdd.Matrix(mat,number_type='float')
mat.rep_type=cdd.RepType.INEQUALITY
poly=cdd.Polyhedron(mat)
ext=poly.get_generators()
extreme=np.array(ext)
if self.options.do_print==True:
print(ext)
(s,t)=extreme.shape
l=1
for i in range(0,s):
j=1
if extreme[0,i]==1:
for _scid in sub_cons.keys():
#for j in range(1,t-1): #Need to loop over extreme 1 to t-1 and link those to the cons.keys for alpha?
Vertices[(_cidS,l,_scid)]=extreme[i,j] #Vertex l of the k-th polytope
j+=1
VerticesB[(_cidS,l)]=extreme[i,t-1]
l+=1
L[p,0]=l-1
p+=1
#vertex enumeration goes from 1 to L
# Solving the full problem sn0
_model_name = '_extended'
_model_name = unique_component_name(self._instance, _model_name)
xfrm = TransformationFactory('pao.bilevel.highpoint') #5.6a-c
kwds = {'submodel_name': _model_name}
xfrm.apply_to(self._instance, **kwds)
extended=getattr(self._instance,_model_name)
extended.sigma=Var(c_vars.keys(),within=NonNegativeReals,bounds=(0,M))
extended.lam=Var(sub_cons.keys(),within=NonNegativeReals,bounds=(0,M))
#5.d
extended.d = Constraint(c_vars.keys()) #indexed by lower level variables
d_expr= {key: 0. for key in c_vars.keys()}
for _vid, var in c_vars.items():
(C, C_q, C_constant) = matrix_repn.cost_vectors(submodel, var) #gets d_i
d_expr[_vid]+=float(C)
d_expr[_vid]=d_expr[_vid]-extended.sigma[_vid]
(A, A_q, sign, b) = matrix_repn.coef_matrices(submodel, var)
for _cid, _ in sub_cons.items():
idx = list(sub_cons.keys()).index(_cid)
d_expr[_vid]+=extended.lam[_cid]*float(A[idx])
expr = d_expr[_vid] == 0
if not type(expr) is bool:
extended.d[_vid] = expr
else:
extended.d[_vid] = Constraint.Skip
#5.e (Complementarity)
extended.e = ComplementarityList()
for _cid, _ in sub_cons.items():
idx=list(sub_cons.keys()).index(_cid)
expr=0
for _vid, var in fixed_vars.items(): #A_i*x
(A, A_q, sign, b) = matrix_repn.coef_matrices(submodel, var)
expr+=float(A[idx])*fixed_vars[_vid]
for _vid, var in c_vars.items(): #B_i*v
(A, A_q, sign, b) = matrix_repn.coef_matrices(submodel, var)
expr+=float(A[idx])*c_vars[_vid]
expr=expr-float(b[idx])
extended.e.add(complements(extended.lam[_cid] >= 0, expr <= 0))
#5.f (Complementarity)
extended.f = ComplementarityList()
for _vid,var in c_vars.items():
extended.f.add(complements(extended.sigma[_vid]>=0,var>=0))
#Replace 5.h-5.j with 5.7 Disjunction
extended.disjunction=Block(cons.keys()) #One disjunction per adversarial problem, one adversarial problem per upper level constraint
k=0
for _cidS,_ in cons.items():
idxS=list(cons.keys()).index(_cidS)
[_cid,sign]=_cidS
disjunction=extended.disjunction[_cidS] #shorthand
disjunction.Lset=RangeSet(1,L[k,0])
disjunction.disjuncts=Disjunct(disjunction.Lset)
for i in disjunction.Lset: #defining the L disjuncts
l_expr=0
for _vid, var in c_vars.items():
(C, C_q, C_constant) = matrix_repn.cost_vectors(submodel, var)
l_expr+=float(C)*var #d^Tv
l_expr+=delta
l_expr=VerticesB[(_cidS,i)]*l_expr #beta(d^Tv+delta)
for _cid, Scons in sub_cons.items(): #SUM over i to ml
Ax=0
idx=list(sub_cons.keys()).index(_cid)
for _vid, var in fixed_vars.items():
(A, A_q, sign, b) = matrix_repn.coef_matrices(submodel, var)
Ax += float(A[idx])*var
l_expr+=Vertices[(_cidS,i,_cid)]*(float(b[idx])-Ax)
r_expr=0
for _vid,var in fixed_vars.items():
(A, A_q, sign, b) = matrix_repn.coef_matrices(self._instance, var) #get q and G
r_expr=r_expr-float(A[idxS])*var
r_expr+=float(b[idxS])
disjunction.disjuncts[i].cons=Constraint(expr= l_expr<=r_expr)
disjunction.seven=Disjunction(expr=[disjunction.disjuncts[i] for i in disjunction.Lset],xor=False)
k+=1
#extended.pprint()
TransformationFactory('mpec.simple_disjunction').apply_to(extended)
bigm = TransformationFactory('gdp.bigm')
bigm.apply_to(extended)
with pyomo.opt.SolverFactory(solver) as opt:
self.results.append(opt.solve(extended,
tee=self._tee,
timelimit=self._timelimit))
_check_termination_condition(self.results[-1])
# Return the sn0 solution
if self.options.do_print==True:
print('Robust Solution')
for _vid, _ in fixed_vars.items():
fixed_vars[_vid].pprint()
for _vid, _ in c_vars.items():
c_vars[_vid].pprint()
extended.lam.pprint()
extended.sigma.pprint()
stop_time = time.time()
self.wall_time = stop_time - start_time
return pyutilib.misc.Bunch(rc=getattr(opt, '_rc', None),
log=getattr(opt, '_log', None))
def compute_feasible_region_from_block_dir(block_dirs, verbose=False):
""" Compute extreme ray representation of feasible assembly region, given blocking direction vectors.
The feasible assembly region is constrained by some hyperplanes, which use
block_dirs as normals. cdd package allows us to convert the inequality
representation to a generator (vertices and rays) of a polyhedron.
Adapted from: https://github.com/yijiangh/compas_rpc_example
More info on cddlib:
https://pycddlib.readthedocs.io/en/latest/index.html
Other packages on vertex enumeration:
https://mathoverflow.net/questions/203966/computionally-efficient-vertex-enumeration-for-convex-polytopes
Parameters
----------
block_dirs : list of 3-tuples
a list blocking directions.
Returns
-------
f_rays: list of 3-tuples
extreme rays of the feasible assembly region
lin_set: list of int
indices of rays that is linear (both directions)
"""
mat_hrep = [] # "half-space" representation
for vec in block_dirs:
# For a polyhedron described as P = {x | A x <= b}
# the H-representation is the matrix [b -A]
mat_hrep.append([0, -vec[0], -vec[1], -vec[2]])
mat = cdd.Matrix(mat_hrep, number_type='fraction')
mat.rep_type = cdd.RepType.INEQUALITY
poly = cdd.Polyhedron(mat)
ext = poly.get_generators()
lin_set = list(ext.lin_set) # linear set both directions
nt = cdd.NumberTypeable('float')
f_verts = []
f_rays = []
# linear_rays = []
for i in range(ext.row_size):
if ext[i][0] == 1:
f_verts.append(tuple([nt.make_number(num) for num in ext[i][1:4]]))
elif ext[i][0] == 0:
# TODO: numerical instability?
ray_vec = [nt.make_number(num) for num in ext[i][1:4]]
ray_vec /= norm(ray_vec)
f_rays.append(tuple(ray_vec))
# if i in lin_set:
# lin_vec_set.append(tuple([- nt.make_number(num) for num in ext[i][1:4]]))
# if f_verts:
# assert len(f_verts) == 1
# np.testing.assert_almost_equal f_verts[0] == [0,0,0]
# TODO: QR decomposition to make orthogonal
if verbose:
print('##############')
print('ext:\n {}'.format(ext))
print('ext linset:\n {}'.format(ext.lin_set))
print('verts:\n {}'.format(f_verts))
print('rays:\n {}'.format(f_rays))
return f_rays, lin_set
def project_polyhedron(proj, ineq, eq=None, canonicalize=True):
"""
Apply the affine projection :math:`y = E x + f` to the polyhedron defined
by:
.. math::
A x & \\leq b \\\\
C x & = d
Parameters
----------
proj : pair of arrays
Pair (`E`, `f`) describing the affine projection.
ineq : pair of arrays
Pair (`A`, `b`) describing the inequality constraint.
eq : pair of arrays, optional
Pair (`C`, `d`) describing the equality constraint.
canonicalize : bool, optional
Apply equality constraints from `eq` to reduce the dimension of the
input polyhedron. May be a blessing or a curse, see notes below.
Returns
-------
vertices : list of arrays
List of vertices of the projection.
rays : list of arrays
List of rays of the projection.
Notes
-----
When the equality set `eq` of the input polytope is not empty, it is
usually faster to use these equality constraints to reduce the dimension of
the input polytope (cdd function: `canonicalize()`) before enumerating
vertices (cdd function: `get_generators()`). Yet, on some descriptions this
operation may be problematic: if it fails, or if you get empty outputs when
the output is supposed to be non-empty, you can try setting
`canonicalize=False`.
See also
--------
This webpage: https://scaron.info/teaching/projecting-polytopes.html
"""
# the input [b, -A] to cdd.Matrix represents (b - A * x >= 0)
# see ftp://ftp.ifor.math.ethz.ch/pub/fukuda/cdd/cddlibman/node3.html
(A, b) = ineq
b = b.reshape((b.shape[0], 1))
linsys = cdd.Matrix(hstack([b, -A]), number_type='float')
linsys.rep_type = cdd.RepType.INEQUALITY
# the input [d, -C] to cdd.Matrix.extend represents (d - C * x == 0)
# see ftp://ftp.ifor.math.ethz.ch/pub/fukuda/cdd/cddlibman/node3.html
if eq is not None:
(C, d) = eq
d = d.reshape((d.shape[0], 1))
linsys.extend(hstack([d, -C]), linear=True)
if canonicalize:
linsys.canonicalize()
# Convert from H- to V-representation
P = cdd.Polyhedron(linsys)
generators = P.get_generators()
if generators.lin_set:
print("Generators have linear set: {}".format(generators.lin_set))
V = array(generators)
# Project output wrenches to 2D set
(E, f) = proj
vertices, rays = [], []
free_coordinates = []
for i in range(V.shape[0]):
if generators.lin_set and i in generators.lin_set:
free_coordinates.append(list(V[i, 1:]).index(1.))
elif V[i, 0] == 1: # vertex
vertices.append(dot(E, V[i, 1:]) + f)
else: # ray
rays.append(dot(E, V[i, 1:]))
return vertices, rays
def main(simplify=False):
"""This script computes the H-rep of a grasp stability constraint
model. There are two main steps. First, find the extreme points in
the space of concatenated component vectors. Second, find an inner
approximation of this set using guidance from a dynamic model.
"""
# Step 1: find the extreme points in the space of concatenated
# component vectors.
# Define equality and inequality constraint:
# A_eq f_bar == 0
# A_ineq f_bar <= b_ineq
A_eq = np.zeros((3, nvars))
b_eq = np.zeros(3)
A_eq[:3, N * 3:N * 3 + 3] = np.eye(3)
b_eq[:3] = [0, 0, -PA]
A_ineq = np.zeros((4 * N + 2 * N, nvars))
b_ineq = np.zeros(4 * N + 2 * N)
for i in range(N):
# inner approximation of Colomb friction constraint
A_ineq[4 * i:4 * i + 4, 3 * i:3 * i + 3] = [[-1, -1, -mu],
[-1, 1, -mu], [1, 1, -mu],
[1, -1, -mu]]
# max/min bounds on vertical component forces
A_ineq[4 * N + 2 * i:4 * N + 2 * i + 2, 3 * i:3 * i + 3] = [[0, 0, -1],
[0, 0, 1]]
b_ineq[4 * N + 2 * i:4 * N + 2 * i + 2] = [0, fmax]
# Transform from H-rep to V-rep
t0 = time.time()
mat = cdd.Matrix(np.hstack((b_ineq.reshape(-1, 1), -A_ineq)),
number_type='float')
mat.rep_type = cdd.RepType.INEQUALITY
mat.extend(np.hstack((b_eq.reshape(-1, 1), -A_eq)), linear=True)
poly = cdd.Polyhedron(mat)
ext = poly.get_generators()
ext = np.array(ext)
t_elapsed = time.time() - t0
print(
"Approximate with N={2:d} points:\n\tFound {0:d} extreme points in {1:10.3f} secs"
.format(ext.shape[0], t_elapsed, N))
f_extreme_pts = ext[:, 1:1 + 3 * N + 3]
# Transform to interacting wrench space:
# w_O = F f, where F is defined below
F = np.zeros((6, 3 * N + 3))
for i in range(N):
F[:3, 3 * i:3 * i +
3] = [[0, -l, -r * np.cos(alphas[i])], [l, 0, r * np.sin(alphas[i])],
[r * np.cos(alphas[i]), -r * np.sin(alphas[i]), 0]]
F[:3, 3 * N:3 * N + 3] = [[0, -l, 0], [l, 0, 0], [0, 0, 0]]
for i in range(N + 1):
F[3:, 3 * i:3 * i + 3] = np.eye(3)
w0_extreme_pts = f_extreme_pts.dot(F.T)
# Step 2: Use a robot model to generate better points.
# REQUIRED OUTPUT w0_hull: the convex hull of the vertices of the
# set of physically realization wrenches.
if simplify:
env = orpy.Environment()
env.Load(
'/home/hung/git/toppra-object-transport/models/denso_ft_sensor_suction.robot.xml'
)
robot = env.GetRobots()[0]
contact_base = transport.Contact(robot,
"denso_suction_cup2",
np.eye(4),
None,
None,
raw_data=w0_extreme_pts)
solid_object = transport.SolidObject.init_from_dict(
robot, {
'object_profile':
"bluenb",
'object_attach_to':
"denso_suction_cup2",
"T_link_object": [[1, 0, 0, 0], [0, -1, 0, 0],
[0, 0, -1, 12.5e-3], [0, 0, 0, 1]],
"name":
"obj"
})
cs = transport.ContactSimplifier(robot,
contact_base,
solid_object,
N_vertices=60)
contact_simp, w0_hull = cs.simplify()
else:
w0_hull = transport.poly_contact.ConvexHull(w0_extreme_pts)
transport.utils.preview_plot([(w0_extreme_pts, 'o', {
'markersize': 5
}), (w0_hull.vertices, 'x', {
'markersize': 7
})],
dur=100)
print("Computed convex hull has {0:d} vertices and {1:d} faces".format(
len(w0_hull.get_vertices()),
w0_hull.get_halfspaces()[1].shape[0]))
# save coefficients
id_ = "analytical_rigid" + "123"
A, b = w0_hull.get_halfspaces()
contact_profile = {
id_: {
"id": id_,
"attached_to_manipulator": "denso_suction_cup2",
"orientation": [[1, 0, 0], [0, 1, 0], [0, 0, 1]],
"position": [0, 0, 0],
"constraint_coeffs_file": id_ + ".npz",
"params": {
"simplify": simplify,
"N": N,
"PA": PA,
"mu": mu,
"r": r,
"fmax": fmax
}
}
}
print(
"db entry (to copy manually)\n\nbegin -----------------\n\n{:}\nend--------"
.format(yaml.dump(contact_profile)))
cmd = raw_input("Save constraint coefficients A, b y/[N]?")
if cmd == "y":
np.savez(
"/home/hung/Dropbox/ros_data/toppra_application/{:}.npz".format(
id_),
A=A,
b=b)
print("Saved coefficients to database!")
else:
exit("abc")
def get_outer_approx(self, algorithm=None):
"""Generate an outer approximation.
:parameter algorithm: a :class:`~string` denoting the algorithm used:
``None``, ``'linvac'``, ``'irm'``, ``'imrm'``, or ``'lpbelfunc'``
:rtype: :class:`~improb.lowprev.lowprob.LowProb`
This method replaces the lower probability :math:`\underline{P}` by
a lower probability :math:`\underline{R}` determined by the
``algorithm`` argument:
``None``
returns the original lower probability.
>>> pspace = PSpace('abc')
>>> lprob = LowProb(pspace,
... lprob={'ab': .5, 'ac': .5, 'bc': .5},
... number_type='fraction')
>>> lprob.extend()
>>> print(lprob)
: 0
a : 0
b : 0
c : 0
a b : 1/2
a c : 1/2
b c : 1/2
a b c : 1
>>> lprob == lprob.get_outer_approx()
True
``'linvac'``
replaces the imprecise part :math:`\underline{Q}` by the vacuous
lower probability :math:`\underline{R}=\min` to generate a simple
outer approximation.
``'irm'``
replaces :math:`\underline{P}` by a completely monotone lower
probability :math:`\underline{R}` that is obtained by using the
IRM algorithm of Hall & Lawry [#hall2004]_. The Moebius transform
of a lower probability that is not completely monotone contains
negative belief assignments. Consider such a lower probability and
an event with such a negative belief assignment. The approximation
consists of removing this negative assignment and compensating for
this by correspondingly reducing the positive masses for events
below it; for details, see the paper.
The following example illustrates the procedure:
>>> pspace = PSpace('abc')
>>> lprob = LowProb(pspace,
... lprob={'ab': .5, 'ac': .5, 'bc': .5},
... number_type='fraction')
>>> lprob.extend()
>>> print(lprob)
: 0
a : 0
b : 0
c : 0
a b : 1/2
a c : 1/2
b c : 1/2
a b c : 1
>>> lprob.is_completely_monotone()
False
>>> print(lprob.mobius)
: 0
a : 0
b : 0
c : 0
a b : 1/2
a c : 1/2
b c : 1/2
a b c : -1/2
>>> belfunc = lprob.get_outer_approx('irm')
>>> print(belfunc.mobius)
: 0
a : 0
b : 0
c : 0
a b : 1/3
a c : 1/3
b c : 1/3
a b c : 0
>>> print(belfunc)
: 0
a : 0
b : 0
c : 0
a b : 1/3
a c : 1/3
b c : 1/3
a b c : 1
>>> belfunc.is_completely_monotone()
True
The next is Example 2 from Hall & Lawry's 2004 paper [#hall2004]_:
>>> pspace = PSpace('ABCD')
>>> lprob = LowProb(pspace, lprob={'': 0, 'ABCD': 1,
... 'A': .0895, 'B': .2743,
... 'C': .2668, 'D': .1063,
... 'AB': .3947, 'AC': .4506,
... 'AD': .2959, 'BC': .5837,
... 'BD': .4835, 'CD': .4079,
... 'ABC': .7248, 'ABD': .6224,
... 'ACD': .6072, 'BCD': .7502})
>>> lprob.is_avoiding_sure_loss()
True
>>> lprob.is_coherent()
False
>>> lprob.is_completely_monotone()
False
>>> belfunc = lprob.get_outer_approx('irm')
>>> belfunc.is_completely_monotone()
True
>>> print(lprob)
: 0.0
A : 0.0895
B : 0.2743
C : 0.2668
D : 0.1063
A B : 0.3947
A C : 0.4506
A D : 0.2959
B C : 0.5837
B D : 0.4835
C D : 0.4079
A B C : 0.7248
A B D : 0.6224
A C D : 0.6072
B C D : 0.7502
A B C D : 1.0
>>> print(belfunc)
: 0.0
A : 0.0895
B : 0.2743
C : 0.2668
D : 0.1063
A B : 0.375789766751
A C : 0.405080300695
A D : 0.259553087227
B C : 0.560442004097
B D : 0.43812301076
C D : 0.399034985143
A B C : 0.710712071543
A B D : 0.603365864737
A C D : 0.601068373065
B C D : 0.7502
A B C D : 1.0
>>> print(lprob.mobius)
: 0.0
A : 0.0895
B : 0.2743
C : 0.2668
D : 0.1063
A B : 0.0309
A C : 0.0943
A D : 0.1001
B C : 0.0426
B D : 0.1029
C D : 0.0348
A B C : -0.0736
A B D : -0.0816
A C D : -0.0846
B C D : -0.0775
A B C D : 0.1748
>>> print(belfunc.mobius)
: 0.0
A : 0.0895
B : 0.2743
C : 0.2668
D : 0.1063
A B : 0.0119897667507
A C : 0.0487803006948
A D : 0.0637530872268
B C : 0.019342004097
B D : 0.0575230107598
C D : 0.0259349851432
A B C : 3.33066907388e-16
A B D : -1.11022302463e-16
A C D : -1.11022302463e-16
B C D : 0.0
A B C D : 0.0357768453276
>>> sum(lprev for (lprev, uprev)
... in (lprob - belfunc).itervalues())/(2 ** len(pspace))
0.013595658498933991
.. note::
This algorithm is *not* invariant under permutation of the
possibility space.
.. warning::
The lower probability must be defined for all events. If
needed, call :meth:`~improb.lowprev.lowpoly.LowPoly.extend`
first.
``'imrm'``
replaces :math:`\underline{P}` by a completely monotone lower
probability :math:`\underline{R}` that is obtained by using an
algorithm by Quaeghebeur that is as of yet unpublished.
We apply it to Example 2 from Hall & Lawry's 2004 paper
[#hall2004]_:
>>> pspace = PSpace('ABCD')
>>> lprob = LowProb(pspace, lprob={
... '': 0, 'ABCD': 1,
... 'A': .0895, 'B': .2743,
... 'C': .2668, 'D': .1063,
... 'AB': .3947, 'AC': .4506,
... 'AD': .2959, 'BC': .5837,
... 'BD': .4835, 'CD': .4079,
... 'ABC': .7248, 'ABD': .6224,
... 'ACD': .6072, 'BCD': .7502})
>>> belfunc = lprob.get_outer_approx('imrm')
>>> belfunc.is_completely_monotone()
True
>>> print(lprob)
: 0.0
A : 0.0895
B : 0.2743
C : 0.2668
D : 0.1063
A B : 0.3947
A C : 0.4506
A D : 0.2959
B C : 0.5837
B D : 0.4835
C D : 0.4079
A B C : 0.7248
A B D : 0.6224
A C D : 0.6072
B C D : 0.7502
A B C D : 1.0
>>> print(belfunc)
: 0.0
A : 0.0895
B : 0.2743
C : 0.2668
D : 0.1063
A B : 0.381007057096
A C : 0.411644226231
A D : 0.26007767078
B C : 0.562748716673
B D : 0.4404197271
C D : 0.394394926787
A B C : 0.7248
A B D : 0.6224
A C D : 0.6072
B C D : 0.7502
A B C D : 1.0
>>> print(lprob.mobius)
: 0.0
A : 0.0895
B : 0.2743
C : 0.2668
D : 0.1063
A B : 0.0309
A C : 0.0943
A D : 0.1001
B C : 0.0426
B D : 0.1029
C D : 0.0348
A B C : -0.0736
A B D : -0.0816
A C D : -0.0846
B C D : -0.0775
A B C D : 0.1748
>>> print(belfunc.mobius)
: 0.0
A : 0.0895
B : 0.2743
C : 0.2668
D : 0.1063
A B : 0.0172070570962
A C : 0.0553442262305
A D : 0.0642776707797
B C : 0.0216487166733
B D : 0.0598197271
C D : 0.0212949267869
A B C : 2.22044604925e-16
A B D : 0.0109955450242
A C D : 0.00368317620293
B C D : 3.66294398528e-05
A B C D : 0.00879232466651
>>> sum(lprev for (lprev, uprev)
... in (lprob - belfunc).itervalues())/(2 ** len(pspace))
0.010375479708342836
.. note::
This algorithm *is* invariant under permutation of the
possibility space.
.. warning::
The lower probability must be defined for all events. If
needed, call :meth:`~improb.lowprev.lowpoly.LowPoly.extend`
first.
``'lpbelfunc'``
replaces :math:`\underline{P}` by a completely monotone lower
probability :math:`\underline{R}_\mu` that is obtained via the zeta
transform of the basic belief assignment :math:`\mu`, a solution of
the following optimization (linear programming) problem:
.. math::
\min\{
\sum_{A\subseteq\Omega}(\underline{P}(A)-\underline{R}_\mu(A)):
\mu(A)\geq0, \sum_{B\subseteq\Omega}\mu(B)=1,
\underline{R}_\mu(A)\leq\underline{P}(A), A\subseteq\Omega
\},
which, because constants in the objective function do not influence
the solution and because
:math:`\underline{R}_\mu(A)=\sum_{B\subseteq A}\mu(B)`,
is equivalent to:
.. math::
\max\{
\sum_{B\subseteq\Omega}2^{|\Omega|-|B|}\mu(B):
\mu(A)\geq0, \sum_{B\subseteq\Omega}\mu(B)=1,
\sum_{B\subseteq A}\mu(B)
\leq\underline{P}(A), A\subseteq\Omega
\},
the version that is implemented.
We apply this to Example 2 from Hall & Lawry's 2004 paper
[#hall2004]_, which we also used for ``'irm'``:
>>> pspace = PSpace('ABCD')
>>> lprob = LowProb(pspace, lprob={'': 0, 'ABCD': 1,
... 'A': .0895, 'B': .2743,
... 'C': .2668, 'D': .1063,
... 'AB': .3947, 'AC': .4506,
... 'AD': .2959, 'BC': .5837,
... 'BD': .4835, 'CD': .4079,
... 'ABC': .7248, 'ABD': .6224,
... 'ACD': .6072, 'BCD': .7502})
>>> belfunc = lprob.get_outer_approx('lpbelfunc')
>>> belfunc.is_completely_monotone()
True
>>> print(lprob)
: 0.0
A : 0.0895
B : 0.2743
C : 0.2668
D : 0.1063
A B : 0.3947
A C : 0.4506
A D : 0.2959
B C : 0.5837
B D : 0.4835
C D : 0.4079
A B C : 0.7248
A B D : 0.6224
A C D : 0.6072
B C D : 0.7502
A B C D : 1.0
>>> print(belfunc)
: 0.0
A : 0.0895
B : 0.2743
C : 0.2668
D : 0.1063
A B : 0.3638
A C : 0.4079
A D : 0.28835
B C : 0.5837
B D : 0.44035
C D : 0.37355
A B C : 0.7248
A B D : 0.6224
A C D : 0.6072
B C D : 0.7502
A B C D : 1.0
>>> print(lprob.mobius)
: 0.0
A : 0.0895
B : 0.2743
C : 0.2668
D : 0.1063
A B : 0.0309
A C : 0.0943
A D : 0.1001
B C : 0.0426
B D : 0.1029
C D : 0.0348
A B C : -0.0736
A B D : -0.0816
A C D : -0.0846
B C D : -0.0775
A B C D : 0.1748
>>> print(belfunc.mobius)
: 0.0
A : 0.0895
B : 0.2743
C : 0.2668
D : 0.1063
A B : 0.0
A C : 0.0516
A D : 0.09255
B C : 0.0426
B D : 0.05975
C D : 0.00045
A B C : 0.0
A B D : 1.11022302463e-16
A C D : 0.0
B C D : 0.0
A B C D : 0.01615
>>> sum(lprev for (lprev, uprev)
... in (lprob - belfunc).itervalues())/(2 ** len(pspace)
... ) # doctest: +ELLIPSIS
0.00991562...
.. note::
This algorithm is *not* invariant under permutation of the
possibility space or changes in the LP-solver:
there may be a nontrivial convex set of optimal solutions.
.. warning::
The lower probability must be defined for all events. If
needed, call :meth:`~improb.lowprev.lowpoly.LowPoly.extend`
first.
"""
if algorithm is None:
return self
elif algorithm == 'linvac':
prob, coeff = self.get_precise_part()
return prob.get_linvac(1 - coeff)
elif algorithm == 'irm':
# Initialize the algorithm
pspace = self.pspace
bba = SetFunction(pspace, number_type=self.number_type)
bba[False] = 0
def mass_below(event):
subevents = pspace.subsets(event, full=False, empty=False)
return sum(bba[subevent] for subevent in subevents)
def basin_for_negmass(event):
mass = 0
index = len(event)
while bba[event] + mass < 0:
index -= 1
subevents = pspace.subsets(event, size=index)
mass += sum(bba[subevent] for subevent in subevents)
return (index, mass)
lprob = self.set_function
# The algoritm itself:
# we climb the algebra of events, calculating the belief assignment
# for each and compensate negative ones by proportionally reducing
# the assignments in the smallest basin of subevents needed
for cardinality in range(1, len(pspace) + 1):
for event in pspace.subsets(size=cardinality):
bba[event] = lprob[event] - mass_below(event)
if bba[event] < 0:
index, mass = basin_for_negmass(event)
subevents = chain.from_iterable(
pspace.subsets(event, size=k)
for k in range(index, cardinality))
for subevent in subevents:
bba[subevent] = (bba[subevent] *
(1 + (bba[event] / mass)))
bba[event] = 0
return LowProb(pspace,
lprob=dict((event, bba.get_zeta(event))
for event in bba.iterkeys()))
elif algorithm == 'imrm':
# Initialize the algorithm
pspace = self.pspace
number_type = self.number_type
bba = SetFunction(pspace, number_type=number_type)
bba[False] = 0
def mass_below(event, cardinality=None):
subevents = pspace.subsets(event,
full=False,
empty=False,
size=cardinality)
return sum(bba[subevent] for subevent in subevents)
def basin_for_negmass(event):
mass = 0
index = len(event)
while bba[event] + mass < 0:
index -= 1
subevents = pspace.subsets(event, size=index)
mass += sum(bba[subevent] for subevent in subevents)
return (index, mass)
lprob = self.set_function
# The algorithm itself:
cardinality = 1
while cardinality <= len(pspace):
temp_bba = SetFunction(pspace, number_type=number_type)
for event in pspace.subsets(size=cardinality):
bba[event] = lprob[event] - mass_below(event)
offenders = dict((event, basin_for_negmass(event))
for event in pspace.subsets(size=cardinality)
if bba[event] < 0)
if len(offenders) == 0:
cardinality += 1
else:
minindex = min(pair[0] for pair in offenders.itervalues())
for event in offenders:
if offenders[event][0] == minindex:
mass = mass_below(event, cardinality=minindex)
scalef = (offenders[event][1] + bba[event]) / mass
for subevent in pspace.subsets(event,
size=minindex):
if subevent not in temp_bba:
temp_bba[subevent] = 0
temp_bba[subevent] = max(
temp_bba[subevent], scalef * bba[subevent])
for event, value in temp_bba.iteritems():
bba[event] = value
cardinality = minindex + 1
return LowProb(pspace,
lprob=dict((event, bba.get_zeta(event))
for event in bba.iterkeys()))
elif algorithm == 'lpbelfunc':
# Initialize the algorithm
lprob = self.set_function
pspace = lprob.pspace
number_type = lprob.number_type
n = 2**len(pspace)
# Set up the linear program
mat = cdd.Matrix(list(
chain(
[[-1] + n * [1], [1] + n * [-1]],
[[0] + [int(event == other) for other in pspace.subsets()]
for event in pspace.subsets()],
[[lprob[event]] +
[-int(other <= event) for other in pspace.subsets()]
for event in pspace.subsets()])),
number_type=number_type)
mat.obj_type = cdd.LPObjType.MAX
mat.obj_func = (0, ) + tuple(2**(len(pspace) - len(event))
for event in pspace.subsets())
lp = cdd.LinProg(mat)
# Solve the linear program and check the solution
lp.solve()
if lp.status == cdd.LPStatusType.OPTIMAL:
bba = SetFunction(pspace,
data=dict(
izip(list(pspace.subsets()),
list(lp.primal_solution))),
number_type=number_type)
return LowProb(pspace,
lprob=dict((event, bba.get_zeta(event))
for event in bba.iterkeys()))
else:
raise RuntimeError('No optimal solution found.')
else:
raise NotImplementedError
def main():
global pos, normals
#bar = sum(pos)/float(len(pos))
#pos = [p-bar for p in pos]
mu = 0.2
contacts = [stab.Contact(mu, p, n) for p, n in zip(pos, normals)]
poly = stab.StabilityPolygon(60, dimension=2, force_lim=1000.)
poly.contacts = contacts[0:8]
for c in poly.contacts[0:4]:
c.r[2] = 0.
c.n = np.array([[0.4], [0.4], [np.sqrt(1 - 2 * (0.4**2))]])
#c.n = np.array([[0., 1., 0.]]).T
for c in poly.contacts[4:]:
c.r[2] = 0.
#c.n = np.array([[0., 0., 1.]]).T
c.n = np.array([[-0.4], [-0.4], [np.sqrt(1 - 2 * (0.4**2))]])
poly.reset_fig()
poly.plot_contacts()
poly.show()
sol = 'plain'
#Compute the unconstrained and save ineqs
poly.compute(stab.Mode.iteration,
maxIter=20,
epsilon=2e-3,
solver=sol,
plot_error=False,
plot_step=False,
plot_init=False,
plot_final=False)
poly_ineq = poly.backend.doublepoly.inner.inequalities
radius = 0.11
fc = 4
nc = 8
for c in range(fc, nc):
poly.addForceConstraint([poly.contacts[c]], radius)
poly.compute(stab.Mode.iteration,
maxIter=20,
epsilon=2e-3,
solver=sol,
plot_error=False,
plot_step=False,
plot_init=False,
plot_final=False)
poly.plot()
poly.show()
assert (poly.dimension == 2)
assert (len(poly.gravity_envelope) == 1)
A1, A2, t = poly.A1, poly.A2, poly.t
sphere_ineq = np.array([[1., -1., 1.], [1., -1., -1.], [1., 1., -1.],
[1., 1., 1.], [-1., 1., -1.], [-1., 1., 1.],
[-1., -1., -1.], [-1., -1., 1.]])
sphere = np.zeros((8 * (nc - fc), 1 + poly.nrVars()))
for contact_id in range(fc, nc):
line = 8 * (contact_id - fc)
col = 1 + 3 * contact_id
sphere[line:line + 8, col:col + 3] = sphere_ineq
sphere[line:line + 8, 0] = radius * poly.mass * 9.81
nr_lines = poly_ineq.shape[0]
exp_poly_ineq = np.hstack([
poly_ineq[:, 0:1],
np.zeros((nr_lines, poly.nrVars() - 2)), poly_ineq[:, 1:]
])
eq = np.hstack((t, -A1, -A2))
mat = cdd.Matrix(sphere, number_type='fraction')
mat.rep_type = cdd.RepType.INEQUALITY
mat.extend(exp_poly_ineq)
mat.extend(eq, linear=True)
print("Let's goooooo")
cdd_poly = cdd.Polyhedron(mat)
vertices = np.array(cdd_poly.get_generators())
print(vertices.shape)
if len(vertices.shape) > 1:
point_mask = vertices[:, 0] == 1
points = vertices[point_mask, -2:]
rays = vertices[~point_mask, -2:]
hull = ConvexHull(points)
poly.reset_fig()
poly.plot_contacts()
poly.plot_polyhedron(poly.inner, 'blue', 0.5)
poly.ax.plot(hull.points[hull.vertices, 0],
hull.points[hull.vertices, 1],
'red',
label='cdd',
marker='^',
markersize=10)
for ray in rays:
if np.linalg.norm(ray) > 1e-10:
print(ray)
pp = np.vstack([i * ray for i in np.linspace(0.01, 1)])
poly.ax.plot(pp[:, 0], pp[:, 1], 'red')
else:
print("This is a zero ray")
poly.show()
else:
print("No vertices")
plt.subplot(1, len(ds) + 1, 1)
plt.imshow(FINAL.reshape((K, K)), vmin=0., vmax=len(ds),
extent=[-2, 2, -2, 2], cmap=cmap, norm=norm, origin='lower')
plt.yticks([])
plt.xticks([])
for M in regions:
final = utils.in_region(xx, regions[M]['ineq']).astype('float32')
plt.tricontour(xx[:,0], xx[:,1], final-0.5, levels=[0], linewidths=2)
plt.tight_layout()
plt.savefig('partition_building.pdf')
plt.close()
flips, ineq = list(regions.items())[1]
m = cdd.Matrix(np.hstack([ineq[:, [0]], ineq[:, 1:]]))
m.rep_type = cdd.RepType.INEQUALITY
v = np.array(cdd.Polyhedron(m).get_generators())[:, 1:]
####################################
simplices = utils.get_simplices(v)
plt.figure(figsize=((len(simplices) + 1)*4, 4))
plt.subplot(1, len(simplices) + 1, 1)
mask = utils.in_region(xx, ineq[:, 1:], ineq[:, 0]).astype('float32')
plt.imshow(mask.reshape((K, K)) * 2, aspect='auto', cmap=cmap, norm=norm,
origin='lower', extent=[-2, 2, -2, 2])
plt.xticks([])
plt.yticks([])
def conserved_moieties(self, mip_info, findCM='null', deadend=True, nameCM=0):
'''Find conserved moieties by computing extreme rays. Called by compute_met_form
Return conserved_moiety_info object containing the conserved moiety information:
cm: list of conserved moieties, each being a metabolite-coefficient dictionary
cm_generic: list of entries in cm that are generic (no known metabolites involved)
cm_generic_dict: {fomrula: cm_generic} dictionary. formula is the generic_formula object with the defaulted or inputted name as the formula for the conserved moiety.
'''
model = self.model
if (not deadend) or nameCM == 1:
activeMets, activeRxns = active_met_rxn(model)
else:
activeMets, activeRxns = model.metabolites, model.reactions
if not deadend:
cmMets, cmRxns = activeMets, activeRxns
else:
cmMets, cmRxns = model.metabolites, model.reactions
#(matrix format: [[b_1, a_11, a_12, ..., a_1N], ..., [b_M, a_M1, a_M2, ..., a_MN]] for Ax + b >= 0
#where A = [a_ij], b = [b_1, ..., b_M])
if findCM == 'null':
#transpose of S
S = [[j._metabolites[i] if i in j._metabolites else 0 for i in cmMets] for j in cmRxns]
#This method calculates a rational basis for transpose(S) from the reduced row echelon form, usually find a subset of extreme rays, quite probably the whole set.
N = extreme_rays_from_null_basis(S)
cm = [{cmMets[i]: N[i,k] for i in range(len(cmMets)) if N[i,k] != 0} for k in range(N.shape[1])]
elif findCM == 'cdd':
#transpose(S) >= 0
S = [[0] + [j._metabolites[i] if i in j._metabolites else 0 for i in cmMets] for j in cmRxns]
# #transpose(S) <= 0
S += [[-i for i in j] for j in S]
#all entries >= 0
S += [[0] + [1 if i == j else 0 for i in range(len(cmMets))] for j in range(len(cmMets))]
print('Matrix size for cdd extreme ray calculation: %d x %d' %(len(S), len(S[0])))
# The cdd library seems unable to cope with genome-scale models. The best is to call EFMtool. To be implemented.
mat = cdd.Matrix(S, number_type='float')
mat.rep_type = cdd.RepType.INEQUALITY
poly = cdd.Polyhedron(mat)
ext = poly.get_generators()
cm = [{cmMets[i]: ext.__getitem__(k)[i+1] for i in range(len(cmMets)) if ext.__getitem__(k)[i+1] != 0} for k in range(ext.row_size)]
#generic conserved moieties involing no known metabolites
cm_generic = [c for c in cm if all([i in self.pre.met_unknown for i in c])]
cm_generic_dict = {}
NcmDefault = 0
for c in cm_generic:
#Use defaulted names for dead end metabolites if nameCM = 1, or always use defaulted names if nameCM = 0
if nameCM == 0 or (any([not i in activeMets for i in c.keys()]) and nameCM == 1):
#defaulted names
NcmDefault += 1
cmNameCur = 'Conserve_' + num2alpha(NcmDefault)
else:
print('\n\n')
for i in c.keys():
toPrint = self.pre.met_known[i].formula if i in self.pre.met_known else mip_info.formulae[i].formula
if toPrint == 'Mass0':
toPrint = ''
toPrint += formula_dict2str({"Conserve": c[i]})
print('%s\t%s\t%s' %(i.id, i.name, toPrint))
while True:
cmNameCur = raw_input("\nEnter the formula for the conserved moiety: " \
+ "(e.g. C2HRab_cd0.5Charge-1 -> {C:2, H:1, Rab_cd: 0.5, Charge: -1}, " \
+ "hit return to use default name 'Conserve_xxx')\n")
#check if the input is empty or a correct formula
if cmNameCur == "" or ''.join([''.join(k) for k in element_re.findall(cmNameCur)]) == cmNameCur:
break
print('Incorrect format of the input formula!\n')
if cmNameCur == '':
#empty string means using the default name
NcmDefault += 1
cmNameCur = 'Conserve_' + num2alpha(NcmDefault)
cm_generic_dict[Formula(cmNameCur)] = c
cm_info = conserved_moiety_info()
cm_info.cm, cm_info.cm_generic, cm_info.cm_generic_dict = cm, cm_generic, cm_generic_dict
return cm_info
def check_feasible(self, Node, channel_alloc, SU_index):
vertex_list = Node['vertex']
n = SU_index
p_dim = int(np.sum(channel_alloc[n, :]))
def feasible_region():
# store the constraints for the feasible region hx <= b
h_list = []
b_list = []
vertex_ld = []
for v in vertex_list:
vertex_ld.append(v[np.where(channel_alloc[n, :] == 1)])
for i in range(p_dim + 1):
vertex_hyperlane = copy.deepcopy(vertex_ld)
vertex_out = vertex_hyperlane[i]
del vertex_hyperlane[i]
b = np.ones(p_dim)
A = np.zeros((p_dim, p_dim))
for k in range(len(vertex_hyperlane)):
A[k, :] = vertex_hyperlane[k]
if (np.linalg.matrix_rank(A) == p_dim):
h = np.linalg.solve(A, b)
b = b[0]
else:
while (True):
translation = np.random.rand(p_dim)
vertex_hyperlane_new = copy.deepcopy(vertex_hyperlane)
for k in range(len(vertex_hyperlane_new)):
vertex_hyperlane_new[k] = vertex_hyperlane[k] + translation
for k in range(len(vertex_hyperlane_new)):
A[k, :] = vertex_hyperlane_new[k]
if (np.linalg.matrix_rank(A) == p_dim):
h = np.linalg.solve(A, b)
b = b[0] - np.inner(h, translation)
break
if (np.inner(h, vertex_out) > b):
h = -h
b = -b
h_list.append(np.round(h, 8))
b_list.append(np.round(b, 8))
return h_list, b_list
# feasible region for node i
a_feasible_list, b_feasible_list = feasible_region()
A_arr = np.zeros(((p_dim + 1) + p_dim + len(self.minRate_h), 1 + p_dim))
b_arr = np.zeros(((p_dim + 1) + p_dim + len(self.minRate_b), 1))
# feasible region constraint
for k in range(p_dim + 1):
A_arr[k, 0] = 0
A_arr[k, 1:] = a_feasible_list[k]
b_arr[k, 0] = b_feasible_list[k]
# QAM capacity constraint
A_arr[(p_dim + 1): (p_dim + 1) + p_dim, 1:] = np.identity(p_dim)
b_arr[(p_dim + 1): (p_dim + 1) + p_dim, 0] = self.QAM_max_power[np.where(channel_alloc[n, :] == 1)]
# minimum data rate constraint
A_arr[(p_dim + 1) + p_dim:, 0] = 0
A_arr[(p_dim + 1) + p_dim:, 1:] = self.minRate_h
b_arr[(p_dim + 1) + p_dim:, 0] = self.minRate_b
# b_arr - A_arr * x >= 0
A = np.hstack((b_arr, -A_arr))
A = np.round(A, 8)
mat = cdd.Matrix(A, number_type='fraction')
mat.rep_type = cdd.RepType.INEQUALITY
poly = cdd.Polyhedron(mat)
vertices = poly.get_generators()
vertices_array = np.array(vertices, dtype=float)
if (vertices_array.size == 0):
# print("Cannot find a feasible solution!")
return False
else:
return True
def finite_power_solver(vertex_list, vertex_total):
# feasible region for node i
a_feasible_list, b_feasible_list = feasible_region()
vertex_total = copy.deepcopy(vertex_total)
# If previous power allocation is out of feasible region, add a vertex
for j in range(p_dim + 1):
i = len(vertex_total) - 1
if (np.dot(a_feasible_list[j], vertex_total[i][np.where(channel_alloc[n, :] == 1)]) > (
b_feasible_list[j] + 0.01)):
del vertex_total[i]
# Find gravity center for vertex_list
v_gravity = np.zeros(n_channel)
for v in vertex_list:
v_gravity[np.where(channel_alloc[n, :] == 1)] \
= v_gravity[np.where(channel_alloc[n, :] == 1)] + v[np.where(channel_alloc[n, :] == 1)]
v_gravity = v_gravity / (p_dim + 1)
vertex_total.append(copy.deepcopy(v_gravity))
break
# delete the vertices that are out of feasible region of node i
for j in range(p_dim + 1):
i = len(vertex_total) - 3
while (True):
if (i < 0):
break
if (np.dot(a_feasible_list[j], vertex_total[i][np.where(channel_alloc[n, :] == 1)]) > (
b_feasible_list[j] + 0.01)):
del vertex_total[i]
i = i - 1
A_arr = np.zeros((len(vertex_total) + (p_dim + 1) + p_dim + len(self.minRate_h), 1 + p_dim))
b_arr = np.zeros((len(vertex_total) + (p_dim + 1) + p_dim + len(self.minRate_b), 1))
# fM(p) constraint: t - fM(p) <= 0
A_arr[0: len(vertex_total), 0] = 1
k = 0
for v in vertex_total:
tmp = - df(v)
tmp = tmp[np.where(channel_alloc[n, :] == 1)]
A_arr[k, 1:] = copy.deepcopy(tmp)
b_arr[k, 0] = f(v) - np.dot(v.T, df(v))
k = k + 1
# feasible region constraint
for k in range(p_dim + 1):
A_arr[len(vertex_total) + k, 0] = 0
A_arr[len(vertex_total) + k, 1:] = a_feasible_list[k]
b_arr[len(vertex_total) + k, 0] = b_feasible_list[k]
# QAM capacity constraint
A_arr[len(vertex_total) + (p_dim + 1): len(vertex_total) + (p_dim + 1) + p_dim, 1:] = np.identity(p_dim)
b_arr[len(vertex_total) + (p_dim + 1): len(vertex_total) + (p_dim + 1) + p_dim, 0] = \
self.QAM_max_power[np.where(channel_alloc[n, :] == 1)]
# minimum data rate constraint
A_arr[len(vertex_total) + (p_dim + 1) + p_dim:, 0] = 0
A_arr[len(vertex_total) + (p_dim + 1) + p_dim:, 1:] = self.minRate_h
b_arr[len(vertex_total) + (p_dim + 1) + p_dim:, 0] = self.minRate_b
# b_arr - A_arr * x >= 0
A = np.hstack((b_arr, -A_arr))
A = np.round(A, 8)
mat = cdd.Matrix(A, number_type='fraction')
mat.rep_type = cdd.RepType.INEQUALITY
poly = cdd.Polyhedron(mat)
vertices = poly.get_generators()
vertices_array = np.array(vertices, dtype=float)
if (vertices_array.size == 0):
# print("Cannot find a feasible solution!")
return
# os._exit()
upperbound_max = -float("inf")
p_sol_lowd = np.zeros(p_dim)
for i in range(vertices_array.shape[0]):
if (vertices_array[i, 0] == 1):
# v = vertices_array[i, 1:]
# q = np.round(np.matmul(A_arr, v) - b_arr.reshape(-1), 4)
# if (np.amax(q) < 0 + 0.001):
upperbound = vertices_array[i, 1] - g(vertices_array[i, 2:], low_dim=True)
if (upperbound > upperbound_max):
p_sol_lowd = vertices_array[i, 2:]
upperbound_max = upperbound
p_sol = np.zeros(n_channel)
p_lowd_index = 0
for k in range(n_channel):
if (channel_alloc[n, k] == 1):
p_sol[k] = p_sol_lowd[p_lowd_index]
p_lowd_index = p_lowd_index + 1
return p_sol, upperbound_max
def detect_minimum_cycles(graph):
"""
Return both V and H representation of polyhedra correspond to minimal cycles
"""
minimal_cycles = []
for v in graph:
for w in v.get_connections():
# The first step should not be a boundary edge
if not v.adjacent[w]['is_boundary'] and not v.adjacent[w]['visited']:
#print('\n')
#print(v)
v.adjacent[w]['visited'] = True
cycle = [v.id, w.id]
rightmost_search(graph, v, v, w, cycle)
cycle = cycle[:-1]
minimal_cycles.append(cycle)
#print('\nNumber of minimal cycles = ', len(minimal_cycles))
#for cycle in minimal_cycles:
# print(cycle, end='\n\n')
#print(minimal_cycles)
# Sanity check
#print('\nSanity check all unvisited edges: ')
num_unvisited_edges = 0
for v in graph:
for w in v.get_connections():
if v.adjacent[w]['visited'] == False:
num_unvisited_edges += 1
vid, wid = v.id, w.id
#print(vid, '-->', wid)
assert v.adjacent[w]['is_boundary'] == True, 'Unvisited edge must be a boundary edge'
# The following assert is activated only when abstract regtion is partioned by refined lasers
if graph.num_vertices != graph.num_edges/2:
assert w.adjacent[v]['visited'] == True, 'Counterpart of an unvisited boundary edge must be visited'
# TODO: Check unvisted edges cover workspace and obstacle boundaries
#print('Number of unvisited edges = ', num_unvisited_edges)
# Convert V-reprsentation of polyhedra to H-Reprentation
# NOTE: Region orders are same in V and H representations
poly_H_rep = []
for cycle in minimal_cycles:
# V-representation required by pycddlib
vertices = []
for vertex in cycle:
vertices.append([1, vertex[0], vertex[1]])
#print(vertices, end='\n\n')
# Convert by pycddlib
mat = cdd.Matrix(vertices, number_type='float')
poly = cdd.Polyhedron(mat)
ine = poly.get_inequalities()
# TODO: need canonicalize() to remove redundancy?
#ine.canonicalize()
# Represent inequality constraints as A x <= b
A, b = [], []
for row in ine:
b.append(row[0])
a = [-x for x in list(row[1:])]
A.append(a)
#print('b = ', b)
#print('A = ', A, end='\n\n')
poly_H_rep.append({'A': A, 'b': b})
#print(poly_H_rep)
return minimal_cycles, poly_H_rep
def sampleKnots(t0, tk, k, b=None, d=None, N=1):
"""sample knots given a set of rules"""
# check input
assert t0 <= tk
assert k >= 2
if d is not None:
assert d.shape == (k, 2) and sum(d[:, 0]) <= 1.0 and\
np.all(d >= 0.0) and np.all(d <= 1.0)
else:
d = np.repeat(np.array([[0.0, 1.0]]), k, axis=0)
if b is not None:
assert b.shape == (k - 1, 2) and\
np.all(b[:, 0] <= b[:, 1]) and\
np.all(b[:-1, 1] <= b[1:, 1]) and\
np.all(b >= 0.0) and np.all(b <= 1.0)
else:
b = np.repeat(np.array([[0.0, 1.0]]), k - 1, axis=0)
d = d * (tk - t0)
b = b * (tk - t0) + t0
d[0] += t0
d[-1] -= tk
# find vertices of the polyhedron
D = -colDiffMat(k - 1)
I = np.identity(k - 1)
A1 = np.vstack((-D, D))
A2 = np.vstack((-I, I))
b1 = np.hstack((-d[:, 0], d[:, 1]))
b2 = np.hstack((-b[:, 0], b[:, 1]))
A = np.vstack((A1, A2))
b = np.hstack((b1, b2))
mat = np.insert(-A, 0, b, axis=1)
mat = cdd.Matrix(mat)
mat.rep_type = cdd.RepType.INEQUALITY
poly = cdd.Polyhedron(mat)
ext = poly.get_generators()
vertices_and_rays = np.array(ext)
if vertices_and_rays.size == 0:
print('there is no feasible knots')
return None
if np.any(vertices_and_rays[:, 0] == 0.0):
print('polyhedron is not closed, something is wrong.')
return None
else:
vertices = vertices_and_rays[:, 1:]
# sample from the convex combination of the vertices
n = vertices.shape[0]
s_simplex = sampleSimplex(n, N=N)
s = s_simplex.dot(vertices)
s = np.insert(s, 0, t0, axis=1)
s = np.insert(s, k, tk, axis=1)
return s
def check_com_positions(self, com_positions):
X = bodies.FOOT_X
Y = bodies.FOOT_Y
m = self.robot.mass
g = 9.81
mu = 0.7
CWC = array([
# fx fy fz taux tauy tauz
[-1, 0, -mu, 0, 0, 0],
[+1, 0, -mu, 0, 0, 0],
[0, -1, -mu, 0, 0, 0],
[0, +1, -mu, 0, 0, 0],
[0, 0, -Y, -1, 0, 0],
[0, 0, -Y, +1, 0, 0],
[0, 0, -X, 0, -1, 0],
[0, 0, -X, 0, +1, 0],
[-Y, -X, -(X + Y) * mu, +mu, +mu, -1],
[-Y, +X, -(X + Y) * mu, +mu, -mu, -1],
[+Y, -X, -(X + Y) * mu, -mu, +mu, -1],
[+Y, +X, -(X + Y) * mu, -mu, -mu, -1],
[+Y, +X, -(X + Y) * mu, +mu, +mu, +1],
[+Y, -X, -(X + Y) * mu, +mu, -mu, +1],
[-Y, +X, -(X + Y) * mu, -mu, +mu, +1],
[-Y, -X, -(X + Y) * mu, -mu, -mu, +1]
])
nb_contacts = len(self.contacting_links)
C = zeros((4, 6 * nb_contacts))
d = array([0, 0, -m * g, 0])
# [pGx, pGy] = D * w_all
D = zeros((2, 6 * nb_contacts))
for i, link in enumerate(self.contacting_links):
# check orientation assumption
pose = link.GetTransformPose()
assert norm(pose[:4] - array([1., 0., 0., 0.])) < 5e-2, \
str(float(norm(pose[:4] - array([1., 0., 0., 0.]))))
x, y, z = link.GetTransformPose()[4:]
Ci = array([[1, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0], [-y, x, 0, 0, 0, 1]])
Di = 1. / (m * g) * array([[-z, 0, x, 0, -1, 0],
[0, -z, y, 1, 0, 0]])
C[0:4, (6 * i):(6 * (i + 1))] = +Ci
D[:, (6 * i):(6 * (i + 1))] = Di
CWC_all = block_diag(*([CWC] * nb_contacts))
_zeros = zeros((CWC_all.shape[0], 1))
# A * w_all + b >= 0
# input to cdd.Matrix is [b, A]
F = cdd.Matrix(hstack([_zeros, -CWC_all]), number_type='float')
F.rep_type = cdd.RepType.INEQUALITY
# C * w_all + d == 0
_d = d.reshape((C.shape[0], 1))
F.extend(hstack([_d, C]), linear=True)
P = cdd.Polyhedron(F)
V = array(P.get_generators())
poly = []
for i in xrange(V.shape[0]):
if V[i, 0] != 1: # 1 = vertex, 0 = ray
raise Exception("Not a polygon, V =\n%s" % repr(V))
pG = dot(D, V[i, 1:])
poly.append(pG)
if all_plots: # Check 1: plot COM trajectory and polygons
plot_polygon(poly)
if True: # Check 2: using full H-representation
# (autonomous but time consuming when designing the motion)
self.check_all_inequalities(com_positions, poly)
def make_extreme_n_monotone(cls, pspace, monotonicity=None):
"""Yield extreme lower probabilities with given monotonicity.
.. warning::
Currently this doesn't work very well except for the cases
below.
>>> lprs = list(LowProb.make_extreme_n_monotone('abc', monotonicity=2))
>>> len(lprs)
8
>>> all(lpr.is_coherent() for lpr in lprs)
True
>>> all(lpr.is_n_monotone(2) for lpr in lprs)
True
>>> all(lpr.is_n_monotone(3) for lpr in lprs)
False
>>> lprs = list(LowProb.make_extreme_n_monotone('abc', monotonicity=3))
>>> len(lprs)
7
>>> all(lpr.is_coherent() for lpr in lprs)
True
>>> all(lpr.is_n_monotone(2) for lpr in lprs)
True
>>> all(lpr.is_n_monotone(3) for lpr in lprs)
True
>>> lprs = list(LowProb.make_extreme_n_monotone('abcd', monotonicity=2))
>>> len(lprs)
41
>>> all(lpr.is_coherent() for lpr in lprs)
True
>>> all(lpr.is_n_monotone(2) for lpr in lprs)
True
>>> all(lpr.is_n_monotone(3) for lpr in lprs)
False
>>> all(lpr.is_n_monotone(4) for lpr in lprs)
False
>>> lprs = list(LowProb.make_extreme_n_monotone('abcd', monotonicity=3))
>>> len(lprs)
16
>>> all(lpr.is_coherent() for lpr in lprs)
True
>>> all(lpr.is_n_monotone(2) for lpr in lprs)
True
>>> all(lpr.is_n_monotone(3) for lpr in lprs)
True
>>> all(lpr.is_n_monotone(4) for lpr in lprs)
False
>>> lprs = list(LowProb.make_extreme_n_monotone('abcd', monotonicity=4))
>>> len(lprs)
15
>>> all(lpr.is_coherent() for lpr in lprs)
True
>>> all(lpr.is_n_monotone(2) for lpr in lprs)
True
>>> all(lpr.is_n_monotone(3) for lpr in lprs)
True
>>> all(lpr.is_n_monotone(4) for lpr in lprs)
True
>>> # cddlib hangs on larger possibility spaces
>>> #lprs = list(LowProb.make_extreme_n_monotone('abcde', monotonicity=2))
"""
pspace = PSpace.make(pspace)
# constraint for empty set and full set
matrix = cdd.Matrix([
[0] + [1 if event.is_false() else 0 for event in pspace.subsets()],
[-1] + [1 if event.is_true() else 0 for event in pspace.subsets()]
],
linear=True,
number_type='fraction')
# constraints for monotonicity
constraints = [
dict(constraint) for constraint in cls.get_constraints_n_monotone(
pspace, xrange(1, monotonicity + 1))
]
matrix.extend(
[[0] + [constraint.get(event, 0) for event in pspace.subsets()]
for constraint in constraints])
matrix.rep_type = cdd.RepType.INEQUALITY
# debug: simplify matrix
#print(pspace, monotonicity) # debug
#print("original:", len(matrix))
#matrix.canonicalize()
#print("new :", len(matrix))
#print(matrix) # debug
# calculate extreme points
poly = cdd.Polyhedron(matrix)
# convert these points back to lower probabilities
#print(poly.get_generators()) # debug
for vert in poly.get_generators():
yield cls(pspace=pspace,
lprob=dict(
(event, vert[1 + index])
for index, event in enumerate(pspace.subsets())),
number_type='fraction')
def get_mul_comparisons(vertices, lin_set, num_vars, prime_of_index):
"""
Returns a list of objects of the form (m1, m2, const, comp),
where m1 and m2 are mulpairs, const is an int, comp is terms.GE/GT/LE/LT,
and const * m1 * m2 comp 1
"""
if all(v[1] == 0 for v in vertices):
p = terms.MulPair(terms.IVar(0), 1)
return [(p, p, 1, terms.LT)]
new_comparisons = []
for (i, j) in itertools.combinations(range(num_vars), 2):
base_matrix = [
[vertices[k][0], vertices[k][i + 2], vertices[k][j + 2]] +
vertices[k][num_vars + 2:] for k in range(len(vertices))
if k not in lin_set
]
matrix = cdd.Matrix(base_matrix, number_type='fraction')
matrix.rep_type = cdd.RepType.GENERATOR
for k in lin_set:
matrix.extend(
[[vertices[k][0], vertices[k][i + 2], vertices[k][j + 2]] +
vertices[k][num_vars + 2:]],
linear=True)
ineqs = cdd.Polyhedron(matrix).get_inequalities()
for ind in range(len(ineqs)):
c = ineqs[ind]
if c[2] == c[1] == 0: # no comp
continue
strong = not any(v[1] != 0 and v[i + 2] * c[1] + v[j + 2] * c[2] +
sum(c[k] * v[num_vars + k - 1]
for k in range(3, len(c))) == 0
for v in vertices)
const = 1
#Don't want constant to a non-int power
scale = int(
num_util.lcmm(
fractions.Fraction(c[k]).denominator
for k in range(3, len(c))))
if scale != 1:
c = [c[0]] + [scale * v for v in c[1:]]
skip = False
for k in range(3, len(c)):
if c[k] != 0:
if c[k] >= 1000000 or c[k] <= -1000000:
# Not going to get much here. Causes arithmetic errors.
skip = True
break
else:
if c[k] > 0:
const *= (prime_of_index[k + num_vars - 3]**c[k])
else:
const *= fractions.Fraction(
1, prime_of_index[k + num_vars - 3]**(-c[k]))
if skip:
continue
if ind in ineqs.lin_set:
new_comp = terms.EQ
else:
new_comp = terms.GT if strong else terms.GE
new_comparisons.append(
(terms.MulPair(terms.IVar(i),
c[1]), terms.MulPair(terms.IVar(j),
c[2]), const, new_comp))
return new_comparisons
def get_vertices(inequalities):
# create the matrix the inequalities are a matrix of the form
# [b, -A] from b-Ax>=0
m = cdd.Matrix(inequalities)
m.rep_type = cdd.RepType.INEQUALITY
return cdd.Polyhedron(m).get_generators()
import numpy as np
import cdd
import matplotlib.pyplot as plt
import utils
mu = np.zeros(2) + 4.3
cov = np.array([[2.1, 0.3], [0.3, 0.8]])
m = cdd.Matrix([[30, 1., 0], [30, 0., 1.1]])
#print(utils.get_vertices(np.array(m)))
m.rep_type = cdd.RepType.INEQUALITY
print(utils.phis_w(np.array(m), mu, cov), np.outer(mu, mu) + cov)
print('\n\n\n')
m = cdd.Matrix([[40, 0., 1], [40, -1., 0], [40, 1., -1.]])
#print(utils.get_vertices(np.array(m)))
m.rep_type = cdd.RepType.INEQUALITY
print(utils.phis_w(np.array(m), mu, cov))
print('\n\n\n')
m = cdd.Matrix([[40, 1., 0], [40, -1., 0], [40, 0., -1.], [40, 0, 1.]])
#print(utils.get_vertices(np.array(m)))
m.rep_type = cdd.RepType.INEQUALITY
print(utils.phis_w(np.array(m), mu, cov))
print('\n\n\n')
m = cdd.Matrix([[40, 1., 0], [40, -1., 0], [40, 0., -1.], [40, 0, 1.],
[40, 1., 1], [40, -1, -1]])
print('v', utils.get_vertices(np.array(m)))
m.rep_type = cdd.RepType.INEQUALITY
print('p', utils.phis_w(np.array(m), mu, cov))
# -*- coding: utf-8 -*- # Copyright 2015-2017 CNRS-AIST JRL # This file is part of StabiliPy. # StabiliPy is free software: you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation, either version 3 of the License, or # (at your option) any later version. # StabiliPy is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # You should have received a copy of the GNU General Public License # along with StabiliPy. If not, see <http://www.gnu.org/licenses/>. from __future__ import print_function import cdd import numpy as np radius = 0.1 points = radius * np.vstack([np.eye(3), -np.eye(3)]) mat_p = cdd.Matrix(np.hstack([np.ones((6, 1)), points])) mat_p.rep_type = cdd.RepType.GENERATOR sphere_ineq = np.array(cdd.Polyhedron(mat_p).get_inequalities()) print(sphere_ineq)
def __init__(self, cdd_hrepr):
start = time.time()
# krelu on variables in varsid
#self.varsid = varsid
self.k = len(cdd_hrepr[0]) - 1
self.cdd_hrepr = cdd_hrepr
#print("LENGTH ", len(cdd_hrepr[0]))
#cdd_hrepr = self.get_ineqs(varsid)
check_pt1 = time.time()
# We get orthant points using exact precision, because it allows to guarantee soundness of the algorithm.
cdd_hrepr = cdd.Matrix(cdd_hrepr, number_type='fraction')
cdd_hrepr.rep_type = cdd.RepType.INEQUALITY
pts = self.get_orthant_points(cdd_hrepr)
# Generate extremal points in the space of variables before and
# after relu
pts = [([1] + row + [x if x > 0 else 0 for x in row]) for row in pts]
adjust_constraints_to_make_sound = False
# Floating point CDD is much faster then the precise CDD, however for some inputs it fails
# due to numerical errors. If that is the case we fall back to using precise CDD.
try:
cdd_vrepr = cdd.Matrix(pts, number_type='float')
cdd_vrepr.rep_type = cdd.RepType.GENERATOR
# Convert back to H-repr.
cons = cdd.Polyhedron(cdd_vrepr).get_inequalities()
adjust_constraints_to_make_sound = True
# I don't adjust linearities, so just setting lin_set to an empty set.
self.lin_set = frozenset([])
except:
cdd_vrepr = cdd.Matrix(pts, number_type='fraction')
cdd_vrepr.rep_type = cdd.RepType.GENERATOR
# Convert back to H-repr.
cons = cdd.Polyhedron(cdd_vrepr).get_inequalities()
self.lin_set = cons.lin_set
cons = np.asarray(cons, dtype=np.float64)
# If floating point CDD was run, then we have to adjust constraints to make sure taht
if adjust_constraints_to_make_sound:
pts = np.asarray(pts, dtype=np.float64)
cons_abs = np.abs(cons)
pts_abs = np.abs(pts)
cons_x_pts = np.matmul(cons, np.transpose(pts))
cons_x_pts_err = np.matmul(cons_abs, np.transpose(pts_abs))
# Since we use double precision number of bits to represent fraction is 52.
# I'll use generous over-approximation by using 2^-40 as a relative error coefficient.
rel_err = pow(2, -40)
cons_x_pts_err *= rel_err
cons_x_pts -= cons_x_pts_err
for ci in range(len(cons)):
min_val = np.min(cons_x_pts[ci, :])
if min_val < 0:
cons[ci, 0] -= min_val
# normalize constraints for numerical stability
# more info: http://files.gurobi.com/Numerics.pdf
absmax = np.absolute(cons).max(axis=1)
self.cons = cons / absmax[:, None]
end = time.time()
return
def test_issue25():
mat = cdd.Matrix([])
cdd_poly = cdd.Polyhedron(mat)
def __init__(self, cdd_hrepr, approx=True):
assert KAct.type in ["ReLU", "Tanh", "Sigmoid"]
self.k = len(cdd_hrepr[0]) - 1
self.cdd_hrepr = np.array(cdd_hrepr)
# nikos: poly approximation
array_2d_double = np.ctypeslib.ndpointer(dtype=np.uintp,
ndim=1,
flags='C')
global boolean_flag
if config.poly_dynamic is False:
sapolib = cdll.LoadLibrary("../../Sapo/libsapo_dyn_lib.so")
sapolib.computeSapo_small.argtypes = [
c_int, c_int, c_int, array_2d_double, array_2d_double,
POINTER(c_double),
POINTER(c_double), array_2d_double
]
else:
sapolib = cdll.LoadLibrary("../../Sapo/libsapo_dyn_lib.so")
sapolib.computeSapo_many.argtypes = [
c_int, c_int, c_int, array_2d_double, array_2d_double,
POINTER(c_double),
POINTER(c_double), array_2d_double,
POINTER(c_float), c_int
]
#if boolean_flag:
coeffs = poly_approx()
deg = coeffs.shape[0]
# boolean_flag=False
sapolib.computeSapo_small.restype = int
sapolib.computeSapo_many.restype = int
start = time.time()
# KAct on variables in varsid
# self.varsid = varsid
self.k = len(cdd_hrepr[0]) - 1
self.cdd_hrepr = cdd_hrepr
# print("LENGTH ", len(cdd_hrepr[0]))
# cdd_hrepr = self.get_ineqs(varsid)
check_pt1 = time.time()
input_cons = np.asarray(
cdd_hrepr, dtype=np.double) # this is a list of constraints
# Ax + b >=0 with A = input_cons[1:3][i] b = input_cons[0][i]
dim = input_cons.shape
n_var = dim[1] - 1
n_dir = dim[0] // 2
# added call to polyfit
if n_var == 1:
output_cons = np.concatenate(
(np.tanh(input_cons[:, [0]]), input_cons[:, [1]]), axis=1)
n_cons = dim[0]
else:
modelSapo = py_sapo(n_var, n_dir, input_cons)
output_cons_temp = np.empty([dim[0], n_var + 1], dtype=np.double)
output_cons_val = np.empty(0, dtype=np.double)
[L, T, n_bundle] = modelSapo.LTmatrix()
#modelSapo.offset(input_cons, dim[0])
cL = (L.__array_interface__['data'][0] +
np.arange(L.shape[0]) * L.strides[0]).astype(np.uintp)
cT = (T.__array_interface__['data'][0] +
np.arange(T.shape[0]) * T.strides[0]).astype(np.uintp)
cA = (output_cons_temp.__array_interface__['data'][0] +
np.arange(output_cons_temp.shape[0]) *
output_cons_temp.strides[0]).astype(np.uintp)
if config.splitting:
regions = modelSapo.createRegions()
for i in range(pow(3, n_var)):
temp_cdd = cdd_hrepr.copy()
for j in range(2 * n_var):
temp_cdd.append(regions[j + i * 2 * n_var])
temp_cdd = cdd.Matrix(temp_cdd, number_type='fraction')
temp_cdd.rep_type = cdd.RepType.INEQUALITY
pts = cdd.Polyhedron(temp_cdd).get_generators()
pts_np_temp = np.array(pts, dtype=np.double)
if len(pts_np_temp) > 0:
print('Region', i + 1, 'is not empty!')
pts_np = pts_np_temp[::, 1::]
if i in [0, 2, 6, 8, 18, 20, 24, 26]:
n_cons = 2 * n_dir #pow(3, n_var) - 1
output_cons_val_temp = modelSapo.comput_valOutputcons(
i)
output_cons = modelSapo.emptyoutputcons()
output_cons_val = np.concatenate(
(output_cons_val, output_cons_val_temp),
axis=0)
else:
# Reshape the input constraints
pts_np = pts_np.transpose()
val = L @ pts_np
offp_temp = np.max(val, 1) #Lx <= b
offm_temp = np.max(-val, 1) #-Lx <= b
# Call sapo
coffp = offp_temp.ctypes.data_as(POINTER(c_double))
coffm = offm_temp.ctypes.data_as(POINTER(c_double))
if config.poly_dynamic is False:
n_cons = sapolib.computeSapo_small(
n_var, n_dir, n_bundle, cL, cT, coffp,
coffm, cA)
else: # add coeffs
c_coeffs = coeffs.ctypes.data_as(
POINTER(c_float))
n_cons = sapolib.computeSapo_many(
n_var, n_dir, n_bundle, cL, cT, coffp,
coffm, cA, c_coeffs, deg)
# Reshape the output constraints (restrict to [-1,1]^n_var)
# Ax + b >= 0
# x_1 >= -1 x_1+1>=0
# x1 <= 1 -x_1+1>=0
# --------------------
# [b A] such Ax+b>=0 (se n_var=2)
# b0 1 0
# b1 0 1
# b2 1 1
# b3 1 -1
# b4 -1 0
# b5 0 -1
# b6 -1 -1
# b7 -1 1
if config.sanity_check:
if n_var == 2:
output_cons_temp[
[0, 1, n_dir, n_dir + 1],
0] = np.maximum(
np.minimum(
output_cons_temp[
[0, 1, n_dir, n_dir + 1],
0], 1), -1)
output_cons_temp[[
2, 3, n_dir + 2, n_dir + 3
], 0] = np.maximum(
np.minimum(
output_cons_temp[
[2, 3, n_dir + 2, n_dir + 3],
0], 2), -2)
elif n_var == 3:
output_cons_temp[
[0, 1, 2, n_dir, n_dir + 1, n_dir + 2],
0] = np.maximum(
np.minimum(
output_cons_temp[[
0, 1, 2, n_dir, n_dir +
1, n_dir + 2
], 0], 1), -1)
output_cons_temp[[
3, 4, 5, 6, 7, 8, n_dir + 3, n_dir +
4, n_dir + 5, n_dir + 6, n_dir +
7, n_dir + 8
], 0] = np.maximum(
np.minimum(
output_cons_temp[[
3, 4, 5, 6, 7, 8, n_dir +
3, n_dir + 4, n_dir +
5, n_dir + 6, n_dir +
7, n_dir + 8
], 0], 2), -2)
output_cons_temp[[
9, 10, 11, 12, n_dir + 9, n_dir +
10, n_dir + 11, n_dir + 12
], 0] = np.maximum(
np.minimum(
output_cons_temp[[
9, 10, 11, 12, n_dir +
9, n_dir + 10, n_dir +
11, n_dir + 12
], 0], 3), -3)
else:
print('\nNo sanity check was performed\n')
# Append the bounds
output_cons_val = np.concatenate(
(output_cons_val, output_cons_temp[:, 0]),
axis=0)
output_cons = np.copy(output_cons_temp)
# Make the union of the output sets
output_cons_val = np.reshape(output_cons_val, (-1, n_cons))
output_cons_val = np.max(output_cons_val, 0)
output_cons[:, 0] = output_cons_val
else:
# No splitting
# Call sapo
offp_temp = modelSapo.offp
offm_temp = modelSapo.offm
coffp = offp_temp.ctypes.data_as(POINTER(c_double))
coffm = offm_temp.ctypes.data_as(POINTER(c_double))
if config.poly_dynamic is False:
n_cons = sapolib.computeSapo_small(n_var, n_dir, n_bundle,
cL, cT, coffp, coffm,
cA)
else: # add coeffs
c_coeffs = coeffs.ctypes.data_as(POINTER(c_float))
n_cons = sapolib.computeSapo_many(n_var, n_dir, n_bundle,
cL, cT, coffp, coffm, cA,
c_coeffs, deg)
#output_cons_val = np.reshape(output_cons_temp, (-1, n_cons))
#output_cons_val = np.max(output_cons_val, 0)
output_cons = np.copy(output_cons_temp)
# output_cons[:, 0] = output_cons_val
# Collect all the input-output constraints
elaborate_input_cons = np.concatenate(
(input_cons, np.zeros([dim[0], n_var], dtype=np.double)), axis=1)
elaborate_output_cons = np.concatenate(
(output_cons[:, [0]], np.zeros([n_cons, n_var], dtype=np.double),
output_cons[:, range(n_var)]),
axis=1)
cons = np.concatenate((elaborate_input_cons, elaborate_output_cons),
axis=0)
'''
# We get orthant points using exact precision, because it allows to guarantee soundness of the algorithm.
cdd_hrepr = cdd.Matrix(cdd_hrepr, number_type='fraction')
cdd_hrepr.rep_type = cdd.RepType.INEQUALITY
pts = self.get_orthant_points(cdd_hrepr)
df = pd.DataFrame(pts)
df.to_csv('filename.csv', index=False)
# Generate extremal points in the space of variables before and
# after relu
# HERE is the point to be changed ELE!
pts = [([1] + row + [x if x > 0 else 0 for x in row]) for row in pts]
adjust_constraints_to_make_sound = False
# Floating point CDD is much faster then the precise CDD, however for some inputs it fails
# due to numerical errors. If that is the case we fall back to using precise CDD.
try:
cdd_vrepr = cdd.Matrix(pts, number_type='float')
cdd_vrepr.rep_type = cdd.RepType.GENERATOR
# Convert back to H-repr.
cons = cdd.Polyhedron(cdd_vrepr).get_inequalities()
adjust_constraints_to_make_sound = True
# I don't adjust linearities, so just setting lin_set to an empty set.
self.lin_set = frozenset([])
except:
cdd_vrepr = cdd.Matrix(pts, number_type='fraction')
cdd_vrepr.rep_type = cdd.RepType.GENERATOR
# Convert back to H-repr.
cons = cdd.Polyhedron(cdd_vrepr).get_inequalities()
self.lin_set = cons.lin_set
'''
cons = np.asarray(cons, dtype=np.float64)
# If floating point CDD was run, then we have to adjust constraints to make sure taht
if 0: #adjust_constraints_to_make_sound:
pts = np.asarray(pts, dtype=np.float64)
cons_abs = np.abs(cons)
pts_abs = np.abs(pts)
cons_x_pts = np.matmul(cons, np.transpose(pts))
cons_x_pts_err = np.matmul(cons_abs, np.transpose(pts_abs))
# Since we use double precision number of bits to represent fraction is 52.
# I'll use generous over-approximation by using 2^-40 as a relative error coefficient.
rel_err = pow(2, -40)
cons_x_pts_err *= rel_err
cons_x_pts -= cons_x_pts_err
for ci in range(len(cons)):
min_val = np.min(cons_x_pts[ci, :])
if min_val < 0:
cons[ci, 0] -= min_val
# normalize constraints for numerical stability
# more info: http://files.gurobi.com/Numerics.pdf
#absmax = np.absolute(cons).max(axis=1)
#self.cons = cons / absmax[:, None]
end = time.time()
self.cons = cons
return
