def _MakeMtdfProblem(self, c_range=(1e-6, 1e-2), bounds=None):
"""Create a CVXOPT problem for finding the Maximal Thermodynamic
Driving Force (MTDF).
Does not set the objective function... leaves that to the caller.
Args:
c_range: a tuple (min, max) for concentrations (in M).
bounds: a list of (lower bound, upper bound) tuples for compound
concentrations.
Returns:
A tuple (dgf_var, motive_force_var, problem_object).
"""
constraints = []
# Define and apply the constraints on the concentrations
ln_conc = cvxpy.variable(1, self.Nc, name='lnC')
constraints += self._MakeLnConcentratonBounds(ln_conc, bounds=bounds,
c_range=c_range)
# Create the driving force variable and add the relevant constraints
driving_force_lb = cvxpy.variable(name='B')
constraints += self._MakeDrivingForceConstraints(ln_conc, driving_force_lb)
return ln_conc, driving_force_lb, constraints
def _MakeMtdfProblem(self, c_range=(1e-6, 1e-2), bounds=None):
"""Create a CVXOPT problem for finding the Maximal Thermodynamic
Driving Force (MTDF).
Does not set the objective function... leaves that to the caller.
Args:
c_range: a tuple (min, max) for concentrations (in M).
bounds: a list of (lower bound, upper bound) tuples for compound
concentrations.
Returns:
A tuple (dgf_var, motive_force_var, problem_object).
"""
constraints = []
# Define and apply the constraints on the concentrations
ln_conc = cvxpy.variable(1, self.Nc, name='lnC')
constraints += self._MakeLnConcentratonBounds(ln_conc,
bounds=bounds,
c_range=c_range)
# Create the driving force variable and add the relevant constraints
driving_force_lb = cvxpy.variable(name='B')
constraints += self._MakeDrivingForceConstraints(
ln_conc, driving_force_lb)
return ln_conc, driving_force_lb, constraints
def test_robust_regression(self):
#
# Compare cvxpy solutions to cvxopt ground truth
#
from cvxpy import (matrix, variable, program, minimize, huber, sum,
leq, geq, square, norm2, ones, zeros, quad_form)
tol_exp = 5 # Check solution to within 5 decimal places
m, n = self.m, self.n
A = matrix(self.A)
b = matrix(self.b)
# Method 1: using huber directly
x = variable(n)
p = program(minimize(sum(huber(A * x - b, 1.0))))
p.solve(True)
np.testing.assert_array_almost_equal(x.value, self.xh, tol_exp)
# Method 2: solving the dual QP
x = variable(n)
u = variable(m)
v = variable(m)
p = program(minimize(0.5 * quad_form(u, 1.0) + sum(v)), [
geq(u, 0.0),
leq(u, 1.0),
geq(v, 0.0),
leq(A * x - b, u + v),
geq(A * x - b, -u - v)
])
p.solve(True)
np.testing.assert_array_almost_equal(x.value, self.xh, tol_exp)
def _cqp(D, o, k, m, N):
''' solves using cvxpy '''
# cast to object.
D = cvxpy.matrix(D)
N = cvxpy.matrix(N)
o = cvxpy.matrix(o)
# create variables.
f = cvxpy.variable(k, 1, name='f')
x=cvxpy.variable(m, 1, name='x')
y=cvxpy.variable(m, 1, name='y')
# create constraints.
geqs = cvxpy.greater_equals(f,0.0)
#TO DO: Sum of all f = sum of observed reads classes (and not equal to 1)
sum1 = cvxpy.equals(cvxpy.sum(f), 1.0)
#sum1 = cvxpy.equals(cvxpy.sum(f), sum_obs_freq)
#3
#dev = cvxpy.equals(D*f-o-x,0.0)
#4. matrix N (m x m) * x - y = 0
sizeConstr = cvxpy.equals(N*x-y,0.0)
#Check now to do N^2
#sizeConstr = cvxpy.equals(N^2*x-y,0.0)
#This might not work but try
#sizeConstr = cvxpy.equals(x/N-y,0.0)
#constrs = [geqs, sum1, dev, sizeConstr]
constrs = [geqs, sum1]
log.debug('\tin _cqp function: \n\t\tPrint matrices shapes:')
log.debug('\t\t\t%s', D.shape)
log.debug('\t\t\t%s', f.shape)
log.debug('\t\t\t%s', o.shape)
# create the program.
#p = cvxpy.program(cvxpy.minimize(cvxpy.norm2(y)),constraints=constrs)
p = cvxpy.program(cvxpy.minimize(cvxpy.norm2(D*f-o)),constraints=constrs)
p.options['abstol'] = 1e-6 ## 'abstol' - Absolute accuracy Default: 1e-7
p.options['reltol'] = 1e-5 ## 'reltol' - Relative accuracy Default: 1e-6
p.options['feastol'] = 1e-5 ## 'feastol' - Tolerance for feasibility conditions Default: 1e-6
p.options['maxiters'] = 500 ## 'maxiters' - Maximum number of iterations Default: 100
# solve the program.
p.solve(quiet=True)
# return results.
#print np.around(f.value, decimals=20)
#print "Print using loop"
getcontext().prec = 20
#for i in f.value:
# temp_fi=str(i).strip('[]')
# print temp_fi
return f.value
def test_robust_regression(self):
#
# Compare cvxpy solutions to cvxopt ground truth
#
from cvxpy import (matrix, variable, program, minimize,
huber, sum, leq, geq, square, norm2,
ones, zeros, quad_form)
tol_exp = 5 # Check solution to within 5 decimal places
m, n = self.m, self.n
A = matrix(self.A)
b = matrix(self.b)
# Method 1: using huber directly
x = variable(n)
p = program(minimize(sum(huber(A*x - b, 1.0))))
p.solve(True)
np.testing.assert_array_almost_equal(
x.value, self.xh, tol_exp)
# Method 2: solving the dual QP
x = variable(n)
u = variable(m)
v = variable(m)
p = program(minimize(0.5*quad_form(u, 1.0) + sum(v)),
[geq(u, 0.0), leq(u, 1.0), geq(v, 0.0),
leq(A*x - b, u + v), geq(A*x - b, -u - v)])
p.solve(True)
np.testing.assert_array_almost_equal(
x.value, self.xh, tol_exp)
def cvxify(self):
"""docstring for cvxify"""
self.cache_props()
for link in self.get_links():
link.v_flow = variable(name='flow: {0}'.format(link.name))
for (source, sink), routes in self.od_routes.iteritems():
for i, route in enumerate(routes):
route.v_flow = variable(name='rf: o: {0}, d: {1} [{2}]'.format(source.name, sink.name, i))
def test_problem_ubsdp(self):
# test problem needs review
print 'skipping, needs review'
return
from cvxpy import (matrix, variable, program, minimize, sum, abs,
norm2, log, square, zeros, max, hstack, vstack, eye,
eq, trace, semidefinite_cone, belongs, reshape)
(m, n) = (self.m, self.n)
c = matrix(self.c)
A = matrix(self.A)
B = matrix(self.B)
Xubsdp = matrix(self.Xubsdp)
tol_exp = 6
# Use cvxpy to solve
#
# minimize tr(B * X)
# s.t. tr(Ai * X) + ci = 0, i = 1, ..., n
# X + S = I
# X >= 0, S >= 0.
#
# c is an n-vector.
# A is an m^2 x n-matrix.
# B is an m x m-matrix.
X = variable(m, n)
S = variable(m, n)
constr = [
eq(trace(reshape(A[:, i], (m, m)) * X) + c[i, 0], 0.0)
for i in range(n)
]
constr += [eq(X + S, eye(m))]
constr += [
belongs(X, semidefinite_cone),
belongs(S, semidefinite_cone)
]
p = program(minimize(trace(B * X)), constr)
p.solve(True)
np.testing.assert_array_almost_equal(X.value, self.Xubsdp, tol_exp)
# Use cvxpy to solve
#
# minimize tr(B * X)
# s.t. tr(Ai * X) + ci = 0, i = 1, ..., n
# 0 <= X <= I
X2 = variable(m, n)
constr = [
eq(trace(reshape(A[:, i], (m, m)) * X2) + c[i, 0], 0.0)
for i in range(n)
]
constr += [
belongs(X2, semidefinite_cone),
belongs(eye(m) - X2, semidefinite_cone)
]
p = program(minimize(trace(B * X2)), constr)
p.solve(True)
np.testing.assert_array_almost_equal(X2.value, X.value, tol_exp)
def test_problem_ubsdp(self):
# test problem needs review
print 'skipping, needs review'
return
from cvxpy import (matrix, variable, program, minimize,
sum, abs, norm2, log, square, zeros, max,
hstack, vstack, eye, eq, trace, semidefinite_cone,
belongs, reshape)
(m, n) = (self.m, self.n)
c = matrix(self.c)
A = matrix(self.A)
B = matrix(self.B)
Xubsdp = matrix(self.Xubsdp)
tol_exp = 6
# Use cvxpy to solve
#
# minimize tr(B * X)
# s.t. tr(Ai * X) + ci = 0, i = 1, ..., n
# X + S = I
# X >= 0, S >= 0.
#
# c is an n-vector.
# A is an m^2 x n-matrix.
# B is an m x m-matrix.
X = variable(m, n)
S = variable(m, n)
constr = [eq(trace(reshape(A[:,i], (m, m)) * X) + c[i,0], 0.0)
for i in range(n)]
constr += [eq(X + S, eye(m))]
constr += [belongs(X, semidefinite_cone),
belongs(S, semidefinite_cone)]
p = program(minimize(trace(B * X)), constr)
p.solve(True)
np.testing.assert_array_almost_equal(
X.value, self.Xubsdp, tol_exp)
# Use cvxpy to solve
#
# minimize tr(B * X)
# s.t. tr(Ai * X) + ci = 0, i = 1, ..., n
# 0 <= X <= I
X2 = variable(m, n)
constr = [eq(trace(reshape(A[:,i], (m, m)) * X2) + c[i,0], 0.0)
for i in range(n)]
constr += [belongs(X2, semidefinite_cone),
belongs(eye(m) - X2, semidefinite_cone)]
p = program(minimize(trace(B * X2)), constr)
p.solve(True)
np.testing.assert_array_almost_equal(
X2.value, X.value, tol_exp)
def nuc_al(I, X, turn, epsilon=.1, quiet=False): m,K = np.shape(X) if not turn: V = cp.variable(m,K, name='V') U = X else: V = X U = cp.variable(m,K, name='U') f_cost = cp.parameter(name='f_cost') f_cost.value = 0 J = cp.parameter(K,K, name='J') J.value = cp.zeros((K,K)) # define the cost parameter for k in range(K): # create a list of interferer indeces Ik # from the interference set I Ik = np.arange(1,K+1)*I[k,:] # careful about zero indexing... Ik = Ik[Ik>0] - 1 #print 'I_%d = ' % k, Ik if len(Ik) > 0: for l in Ik: l = int(l) J[k,l] = U[:,k].T*V[:,l] # interference terms f_cost = f_cost + cp.nuclear_norm(J[k,:]) #f_cost = f_cost + cp.norm1(J[k,:]) # create constraints constraints = [] for k in range(K): c = cp.geq(U[:,k].T*V[:,k], epsilon) #c = cp.geq(cp.lambda_min(U[:,k].T*V[:,k]), epsilon) constraints.append(c) p = cp.program(cp.minimize(f_cost), constraints) print 'minimize: ', p.objective print 'subject to:' print p.constraints p.solve(quiet) if not turn: return V.value else: return U.value
def construct_lp_relaxation( demand_hist, M, transport_graph, cost_label='cost' ) :
"""
demand_hist is a dictionary from ordered station pairs (i,j) -> whole integers
M is a membership graph with edges (i,x) for all corresponding stations-state pairs
A is a DiGraph on X, with cost given by property 'cost'
"""
APSP = nx.all_pairs_dijkstra_path_length( transport_graph, weight=cost_label )
cost = 0.
constr = []
""" create enroute variables """
S = nx.DiGraph() # service "edges"
for (i,j), NUM in demand_hist.iteritems() :
VARS = []
for x,y in itertools.product( M.neighbors_iter(i), M.neighbors_iter(j) ) :
var = cvxpy.variable()
distance = APSP[x][y]
cost += var * distance
constr.append( cvxpy.geq( var, 0. ) ) # needs to be non-negative
S.add_edge( x, y, var=var, distance=distance )
VARS.append( var )
constr.append( cvxpy.eq( cvxpy.sum(VARS), NUM ) ) # total such trips should sum to num
""" create balance variables """
B = nx.DiGraph()
HEADS = [ h for h, d in S.in_degree_iter() if d > 0 ]
TAILS = [ t for t, d in S.out_degree_iter() if d > 0 ]
for y,x in itertools.product( HEADS, TAILS ) :
var = cvxpy.variable()
cost += var * APSP[y][x]
constr.append( cvxpy.geq( var, 0. ) )
B.add_edge( y,x, var=var )
""" generate flow conservation constraints """
for x in S.nodes_iter() :
sin = sum( [ data['var'] for _,__,data in S.in_edges_iter( x, data=True ) ] )
sout = sum( [ data['var'] for _,__,data in S.out_edges_iter( x, data=True ) ] )
bin = sum( [ data['var'] for _,__,data in B.in_edges_iter( x, data=True ) ] )
bout = sum( [ data['var'] for _,__,data in B.out_edges_iter( x, data=True ) ] )
constr.append( cvxpy.eq( sin, bout ) )
constr.append( cvxpy.eq( sout, bin ) )
#service_vars = S # alias
return cost, constr, S
def nuc_al(I, X, turn, epsilon=.1, quiet=False):
m, K = np.shape(X)
if not turn:
V = cp.variable(m, K, name='V')
U = X
else:
V = X
U = cp.variable(m, K, name='U')
f_cost = cp.parameter(name='f_cost')
f_cost.value = 0
J = cp.parameter(K, K, name='J')
J.value = cp.zeros((K, K))
# define the cost parameter
for k in range(K):
# create a list of interferer indeces Ik
# from the interference set I
Ik = np.arange(1, K + 1) * I[k, :] # careful about zero indexing...
Ik = Ik[Ik > 0] - 1
#print 'I_%d = ' % k, Ik
if len(Ik) > 0:
for l in Ik:
l = int(l)
J[k, l] = U[:, k].T * V[:, l] # interference terms
f_cost = f_cost + cp.nuclear_norm(J[k, :])
#f_cost = f_cost + cp.norm1(J[k,:])
# create constraints
constraints = []
for k in range(K):
c = cp.geq(U[:, k].T * V[:, k], epsilon)
#c = cp.geq(cp.lambda_min(U[:,k].T*V[:,k]), epsilon)
constraints.append(c)
p = cp.program(cp.minimize(f_cost), constraints)
print 'minimize: ', p.objective
print 'subject to:'
print p.constraints
p.solve(quiet)
if not turn:
return V.value
else:
return U.value
def estimate_confidence_bound_with_cvx(f_mat, num_reads_in_bams, fixed_i,
mle_estimate, bound_type, alpha):
if bound_type == 'lb': bound_type = 'LOWER'
if bound_type == 'ub': bound_type = 'UPPER'
assert bound_type in ('LOWER',
'UPPER'), ("Improper bound type '%s'" % bound_type)
expected_array, observed_array = f_mat.expected_and_observed(
bam_cnts=num_reads_in_bams)
from cvxpy import matrix, variable, geq, log, eq, program, maximize, minimize, sum
mle_log_lhd = calc_lhd(mle_estimate, observed_array, expected_array)
lower_lhd_bound = mle_log_lhd - chi2.ppf(1 - alpha, 1) / 2.
free_indices = set(range(expected_array.shape[1])) - set((fixed_i, ))
Xs = matrix(observed_array)
ps = matrix(expected_array)
thetas = variable(ps.shape[1])
constraints = [
geq(Xs * log(ps * thetas), lower_lhd_bound),
eq(sum(thetas), 1),
geq(thetas, 0)
]
if bound_type == 'UPPER':
p = program(maximize(thetas[fixed_i, 0]), constraints)
else:
p = program(minimize(thetas[fixed_i, 0]), constraints)
p.options['maxiters'] = 1500
value = p.solve(quiet=not DEBUG_OPTIMIZATION)
thetas_values = numpy.array(thetas.value.T.tolist()[0])
log_lhd = calc_lhd(thetas_values, observed_array, expected_array)
return chi2.sf(2 * (mle_log_lhd - log_lhd), 1), value
def _MakeMinimalFeasbileConcentrationProblem(self, bounds=None, c_range=(1e-6, 1e-2)):
# Define and apply the constraints on the concentrations
constraints = []
# Define and apply the constraints on the concentrations
ln_conc = cvxpy.variable(1, self.Nc, name='lnC')
constraints += self._MakeLnConcentratonBounds(ln_conc, bounds=bounds,
c_range=c_range)
# find the row vector describing the overall stoichiometry
S = cvxpy.matrix(self.S)
dg0r_primes = cvxpy.matrix(self.dG0_r_prime)
# Make flux-based constraints on reaction free energies.
# All reactions must have negative dGr in the direction of the flux.
# Reactions with a flux of 0 must be in equilibrium.
for i in xrange(self.Nr):
# if the dG0 is unknown, this reaction imposes no new constraints
if np.isnan(self.dG0_r_prime[0, i]):
continue
curr_dgr = dg0r_primes[0, i] + RT * ln_conc * S[:, i]
if self.fluxes[0, i] != 0:
constraints.append(cvxpy.leq(curr_dgr * np.sign(self.fluxes[0, i]),
self.DEFAULT_REACTION_UB))
constraints.append(cvxpy.geq(curr_dgr * np.sign(self.fluxes[0, i]),
self.DEFAULT_REACTION_LB))
else:
constraints.append(cvxpy.eq(curr_dgr, 0))
# Set the constraints
return ln_conc, constraints
def PROGRAM( roadnet, surplus, objectives ) :
""" construct the program """
# optvars
assist = dict()
cost = dict()
DELTA = .00001 # cvxpy isn't quite robust to non-full dimensional optimization
for _,__,road in roadnet.edges_iter( keys=True ) :
assist[road] = cvxpy.variable( name='z_{%s}' % road )
cost[road] = cvxpy.variable( name='c_{%s}' % road )
#print assist
#print cost
objfunc = sum( cost.values() )
OBJECTIVE = cvxpy.minimize( objfunc )
CONSTRAINTS = []
# the flow conservation constraints
for u in roadnet.nodes_iter() :
INFLOWS = []
for _,__,road in roadnet.in_edges( u, keys=True ) :
INFLOWS.append( assist[road] + surplus[road] )
OUTFLOWS = []
for _,__,road in roadnet.out_edges( u, keys=True ) :
OUTFLOWS.append( assist[road] )
#conserve_u = cvxpy.eq( sum(OUTFLOWS), sum(INFLOWS) )
error_u = sum(OUTFLOWS) - sum(INFLOWS)
conserve_u = cvxpy.leq( cvxpy.abs( error_u ), DELTA )
CONSTRAINTS.append( conserve_u )
# the cost-form constraints
for road in cost :
for f, line in objectives[road].iter_items() :
# is this plus or minus alpha?
LB = cvxpy.geq( cost[road], line.offset + line.slope * assist[road] )
CONSTRAINTS.append( LB )
prog = cvxpy.program( OBJECTIVE, CONSTRAINTS )
return prog, assist
def find_decoders_cvxopt(self, values, neus_in_pop, min_w = 0 , max_w = 1):
'''
find optimal decoders using convex bounded optimization
'''
tuning = self.tuning_curves[neus_in_pop]
A=np.array(tuning)
A_m = np.matrix(np.matrix(A))
aa = cx.zeros(np.shape(A))
aa[A_m.nonzero()] = A_m[A_m.nonzero()]
bb = cx.zeros(np.shape(value))
bb[value.nonzero()[0]] = value[value.nonzero()[0]]
m,n = np.shape(aa)
dec = cx.variable(m)
p = cx.program(cx.minimize(cx.norm2((aa)*(dec)-(bb))), [cx.leq(dec,max_w),cx.geq(dec,min_w)])
p.solve()
return dec.value
def _GetTotalReactionEnergy(self, c_range=(1e-6, 1e-2), bounds=None, min_driving_force=0):
constraints = []
# Define and apply the constraints on the concentrations
ln_conc = cvxpy.variable(1, self.Nc, name='lnC')
constraints += self._MakeLnConcentratonBounds(ln_conc, bounds=bounds,
c_range=c_range)
# find the row vector describing the overall stoichiometry
S = cvxpy.matrix(self.S)
f = cvxpy.matrix(self.fluxes)
g0 = cvxpy.matrix(self.dG0_r_prime)
g = g0 + RT * ln_conc * S
total_g = f * g.T
constraints += self._MakeDrivingForceConstraints(ln_conc, min_driving_force)
return ln_conc, constraints, total_g
def test_problem_expdesign(self):
# test problem needs review
print 'skipping, needs review'
return
from cvxpy import (matrix, variable, program, minimize,
log, det_rootn, diag, sum, geq, eq, zeros)
tol_exp = 6
V = matrix(self.V)
n = V.shape[1]
x = variable(n)
# Use cvxpy to solve the problem above
p = program(minimize(-log(det_rootn(V*diag(x)*V.T))),
[geq(x, 0.0), eq(sum(x), 1.0)])
p.solve(True)
np.testing.assert_array_almost_equal(
x.value, self.xd, tol_exp)
def f_Gamma(c):
# For fixed c solve the semidefinite program for Algorithm 4
# Variable Gamma_plus (for \Gamma_{i+1})
d_plus = variable(2*n, 1, name='d_plus')
# Constraints
constr = []
for j in range(2*n):
ctt = less_equals(d_plus[j,0], 0)
constr.append( less_equals(-d_plus[j,0], 0))
constr.append( less_equals( d_plus[j,0], d[j,0]))
constr.append( less_equals( J[j,j]*d_plus[j,0] + sum([abs(J[i,j])*d_plus[i,0] for i in range(2*n)]) - abs(J[j,j])*d_plus[j,0], c* d_plus[j,0]))
# Objective function
obj = geo_mean(d_plus)
# Find solution
p = program(maximize(obj), constr)
return d_plus.value
def compute_transform(p1, p2, best_matches):
import cvxpy, cvxpy.utils
# How many good matches are there?
num_bad_matches = sum([x == None for x in best_matches])
num_good_matches = p1.shape[0] - num_bad_matches
# Need at least three points for a good translation fit...
if (num_good_matches < 3):
print 'ERROR: not enough matches to compute a 3D affine fit.'
exit(1)
# Prepare data for fitting
X = cvxpy.utils.ones((3, num_good_matches))
Y = cvxpy.utils.ones((3, num_good_matches))
count = 0
for i in range(p1.shape[0]):
if best_matches[i] != None:
X[0, count] = p1[i, 0]
X[1, count] = p1[i, 1]
X[2, count] = p1[i, 2]
Y[0, count] = p2[best_matches[i], 0]
Y[1, count] = p2[best_matches[i], 1]
Y[2, count] = p2[best_matches[i], 2]
count += 1
# print X.T
# print Y.T
translation = cvxpy.variable(3, 1)
cost_fn = cvxpy.norm1((X[:, 1] + translation) - Y[:, 1])
for c in range(2, num_good_matches):
cost_fn += cvxpy.norm1((X[:, c] + translation) - Y[:, c])
p = cvxpy.program(cvxpy.minimize(cost_fn))
p.solve(quiet=True)
return ((1, np.identity(3), np.array(translation.value).squeeze()),
num_good_matches)
def _MakeMinimumFeasbileConcentrationsProblem(self,
bounds=None,
c_range=(1e-6, 1e-2)):
"""Creates the CVXOPT problem for finding minimum total concentrations.
Returns:
Two tuple (ln_concentrations var, problem).
"""
assert self.dG0_f_prime is not None
constraints = []
# Define and apply the constraints on the concentrations
ln_conc = cvxpy.variable(1, self.Nc, name='lnC')
constraints += self._MakeLnConcentratonBounds(ln_conc,
bounds=bounds,
c_range=c_range)
# Make the objective and problem.
S = cvxpy.matrix(self.S)
# Make flux-based constraints on reaction free energies.
# All reactions must have negative dGr in the direction of the flux.
# Reactions with a flux of 0 must be in equilibrium.
dgf_primes = RT * ln_conc + cvxpy.matrix(self.dG0_f_prime)
for i in xrange(self.Nr):
if self.fluxes[0, i] > 0:
constraints.append(
cvxpy.leq(S[i, :] * dgf_primes, self.DEFAULT_REACTION_UB))
constraints.append(
cvxpy.geq(S[i, :] * dgf_primes, self.DEFAULT_REACTION_LB))
elif self.fluxes[0, i] == 0:
constraints.append(cvxpy.eq(S[i, :] * dgf_primes, 0))
else:
constraints.append(
cvxpy.geq(S[i, :] * dgf_primes, -self.DEFAULT_REACTION_UB))
constraints.append(
cvxpy.leq(S[i, :] * dgf_primes, -self.DEFAULT_REACTION_LB))
return ln_conc, constraints
def _MakeMinimumFeasbileConcentrationsProblem(self, bounds=None,
c_range=(1e-6, 1e-2)):
"""Creates the CVXOPT problem for finding minimum total concentrations.
Returns:
Two tuple (ln_concentrations var, problem).
"""
assert self.dG0_f_prime is not None
constraints = []
# Define and apply the constraints on the concentrations
ln_conc = cvxpy.variable(1, self.Nc, name='lnC')
constraints += self._MakeLnConcentratonBounds(ln_conc, bounds=bounds,
c_range=c_range)
# Make the objective and problem.
S = cvxpy.matrix(self.S)
# Make flux-based constraints on reaction free energies.
# All reactions must have negative dGr in the direction of the flux.
# Reactions with a flux of 0 must be in equilibrium.
dgf_primes = RT * ln_conc + cvxpy.matrix(self.dG0_f_prime)
for i in xrange(self.Nr):
if self.fluxes[0, i] > 0:
constraints.append(cvxpy.leq(S[i, :] * dgf_primes,
self.DEFAULT_REACTION_UB))
constraints.append(cvxpy.geq(S[i, :] * dgf_primes,
self.DEFAULT_REACTION_LB))
elif self.fluxes[0, i] == 0:
constraints.append(cvxpy.eq(S[i, :] * dgf_primes,
0))
else:
constraints.append(cvxpy.geq(S[i, :] * dgf_primes,
-self.DEFAULT_REACTION_UB))
constraints.append(cvxpy.leq(S[i, :] * dgf_primes,
-self.DEFAULT_REACTION_LB))
return ln_conc, constraints
def _MakeMinimalFeasbileConcentrationProblem(self,
bounds=None,
c_range=(1e-6, 1e-2)):
# Define and apply the constraints on the concentrations
constraints = []
# Define and apply the constraints on the concentrations
ln_conc = cvxpy.variable(1, self.Nc, name='lnC')
constraints += self._MakeLnConcentratonBounds(ln_conc,
bounds=bounds,
c_range=c_range)
# find the row vector describing the overall stoichiometry
S = cvxpy.matrix(self.S)
dg0r_primes = cvxpy.matrix(self.dG0_r_prime)
# Make flux-based constraints on reaction free energies.
# All reactions must have negative dGr in the direction of the flux.
# Reactions with a flux of 0 must be in equilibrium.
for i in xrange(self.Nr):
# if the dG0 is unknown, this reaction imposes no new constraints
if np.isnan(self.dG0_r_prime[0, i]):
continue
curr_dgr = dg0r_primes[0, i] + RT * ln_conc * S[:, i]
if self.fluxes[0, i] != 0:
constraints.append(
cvxpy.leq(curr_dgr * np.sign(self.fluxes[0, i]),
self.DEFAULT_REACTION_UB))
constraints.append(
cvxpy.geq(curr_dgr * np.sign(self.fluxes[0, i]),
self.DEFAULT_REACTION_LB))
else:
constraints.append(cvxpy.eq(curr_dgr, 0))
# Set the constraints
return ln_conc, constraints
def _GetTotalReactionEnergy(self,
c_range=(1e-6, 1e-2),
bounds=None,
min_driving_force=0):
constraints = []
# Define and apply the constraints on the concentrations
ln_conc = cvxpy.variable(1, self.Nc, name='lnC')
constraints += self._MakeLnConcentratonBounds(ln_conc,
bounds=bounds,
c_range=c_range)
# find the row vector describing the overall stoichiometry
S = cvxpy.matrix(self.S)
f = cvxpy.matrix(self.fluxes)
g0 = cvxpy.matrix(self.dG0_r_prime)
g = g0 + RT * ln_conc * S
total_g = f * g.T
constraints += self._MakeDrivingForceConstraints(
ln_conc, min_driving_force)
return ln_conc, constraints, total_g
def compute_decoders_convex_bounded(x_values,A,nsteps,function=lambda x:x, min_x = -0.2, max_x = 0.2):
'''solve convex optimization problem with cvxpy '''
import cvxpy as cx
import numpy as np
A_m = np.matrix(np.matrix(A))
aa = cx.zeros(np.shape(A))
aa[A_m.nonzero()] = A_m[A_m.nonzero()]
#function to be encoded
value=np.array([[function(x)] for x in x_values])
value=np.reshape(value,nsteps)
bb = cx.zeros(np.shape(value))
bb[0,value.nonzero()[0]] = value[value.nonzero()[0]]
m,n = np.shape(aa)
dec = cx.variable(m)
p = cx.program(cx.minimize(cx.norm2(np.transpose(aa)*(dec)-np.transpose(bb))), [cx.leq(dec,max_x),cx.geq(dec,min_x)])
p.solve()
return dec.value
def estimate_transcript_frequencies_with_cvxopt(observed_array,
expected_array,
sparse_penalty,
sparse_index,
verbose=False):
from cvxpy import matrix, variable, geq, log, eq, program, maximize, \
minimize, sum, quad_over_lin
Xs = matrix(observed_array)
ps = matrix(expected_array)
thetas = variable(ps.shape[1])
constraints = [eq(sum(thetas), 1), geq(thetas, 0)]
if sparse_penalty == None:
p = program(maximize(Xs * log(ps * thetas)), constraints)
else:
p = program(
maximize(Xs * log(ps * thetas) - sparse_penalty *
quad_over_lin(1., thetas[sparse_index, 0])), constraints)
p.options['maxiters'] = 1500
value = p.solve(quiet=not verbose)
thetas_values = numpy.array(thetas.value.T.tolist()[0])
return thetas_values
def cr_var(self, name): return variable(name=name)
def fit_ellipse_stack2(dx, dy, dz, di, norm_type="l2"):
"""
fit ellipoid using squared loss
idea to learn all stacks together including smoothness
"""
#TODO create flag for norm1 vs norm2
assert norm_type in ["l1", "l2", "huber"]
# sanity check
assert len(dx) == len(dy)
assert len(dx) == len(dz)
assert len(dx) == len(di)
# unique zs
dat = defaultdict(list)
# resort data
for idx in range(len(dx)):
dat[dz[idx]].append( [dx[idx], dy[idx], di[idx]] )
# init ret
ellipse_stack = []
for idx in range(max(dz)):
ellipse_stack.append(Ellipse(0, 0, idx, 1, 1, 0))
total_N = len(dx)
M = len(dat.keys())
#D = 5
D = 4
X_matrix = []
thetas = []
slacks = []
eps_slacks = []
mean_di = float(numpy.mean(di))
for z in dat.keys():
x = numpy.array(dat[z])[:,0]
y = numpy.array(dat[z])[:,1]
# intensities
i = numpy.array(dat[z])[:,2]
ity = numpy.diag(i) / mean_di
# dimensionality
N = len(x)
d = numpy.zeros((N, D))
d[:,0] = x*x
d[:,1] = y*y
#d[:,2] = x*y
d[:,2] = x
d[:,3] = y
#d[:,4] = numpy.ones(N)
#d[:,0] = x*x
#d[:,1] = y*y
#d[:,2] = x*y
#d[:,3] = x
#d[:,4] = y
#d[:,5] = numpy.ones(N)
# consider intensities
old_shape = d.shape
#d = numpy.dot(ity, d)
assert d.shape == old_shape
print d.shape
d = cvxpy.matrix(d)
#### parameters
# da
X = cvxpy.parameter(N, D, name="X" + str(z))
X.value = d
X_matrix.append(X)
#### varibales
# parameter vector
theta = cvxpy.variable(D, name="theta" + str(z))
thetas.append(theta)
# construct obj
objective = 0
print "norm type", norm_type
for i in xrange(M):
if norm_type == "l1":
objective += cvxpy.norm1(X_matrix[i] * thetas[i] + 1.0)
if norm_type == "l2":
objective += cvxpy.norm2(X_matrix[i] * thetas[i] + 1.0)
#TODO these need to be summed
#objective += cvxpy.huber(X_matrix[i] * thetas[i], 1)
#objective += cvxpy.deadzone(X_matrix[i] * thetas[i], 1)
# add smoothness regularization
reg_const = float(total_N) / float(M-1)
for i in xrange(M-1):
objective += reg_const * cvxpy.norm2(thetas[i] - thetas[i+1])
# create problem
p = cvxpy.program(cvxpy.minimize(objective))
prob = p
import ipdb
ipdb.set_trace()
# add constraints
#for i in xrange(M):
# #p.constraints.append(cvxpy.eq(thetas[i][0,:] + thetas[i][1,:], 1))
# p.constraints.append(cvxpy.eq(thetas[i][4,:], 1))
# set solver settings
p.options['reltol'] = 1e-1
p.options['abstol'] = 1e-1
#p.options['feastol'] = 1e-1
# invoke solver
p.solve()
# wrap up result
ellipse_stack = {}
active_layers = dat.keys()
assert len(active_layers) == M
for i in xrange(M):
w = numpy.array(thetas[i].value)
## For clarity, fill in the quadratic form variables
#A = numpy.zeros((2,2))
#A[0,0] = w[0]
#A.ravel()[1:3] = w[2]
#A[1,1] = w[1]
#bv = w[3:5]
#c = w[5]
A = numpy.zeros((2,2))
A[0,0] = w[0]
A.ravel()[1:3] = 0 #w[2]
A[1,1] = w[1]
#bv = w[2:4]
bv = w[2:]
#c = w[4]
c = 1.0
## find parameters
z, a, b, alpha = util.conic2parametric(A, bv, c)
print "layer (i,z,a,b,alpha):", i, z, a, b, alpha
layer = active_layers[i]
ellipse_stack[layer] = Ellipse(z[0], z[1], layer, a, b, alpha)
return ellipse_stack
def measurenx_to_approxnx(roadnet, epsilon, length="length", weight1="weight1", weight2="weight2"):
"""
input: a road network, with weights on its elements
output:
returns a graph summarizing the network optimization problem instance;
roadnets are multi-digraph, where edge 'keys' are assumed to be unique,
i.e., road names; and should be different from node labels too;
"""
digraph = nx.DiGraph()
digraph.add_node("s")
digraph.add_node("t")
""" insert supply and demand of roads """
for u, v, road, data in roadnet.edges_iter(keys=True, data=True):
roadlen = float(data.get(length, 1)) # float() just in case
assert roadlen >= 0.0
oneway = data.get("oneway", False)
surplus = float(data.get(weight1, 0.0)) - data.get(weight2, 0.0)
deficit = -surplus
"""
split the road into equal-length segments;
create a node for each segment;
"""
N = int(np.ceil(roadlen / epsilon))
eps = roadlen / N
if surplus > 0.0:
NODES = [(road, k, "supply") for k in range(N)]
for node in NODES:
digraph.add_edge(
"s", node, flow=cvxpy.variable(), minflow=0.0, maxflow=surplus / N, w_lo=-eps, w_hi=0.0
)
SEQ = [u] + NODES + [v]
for lnode, rnode in zip(SEQ[:-1], SEQ[1:]):
digraph.add_edge(lnode, rnode, flow=cvxpy.variable(), minflow=0.0, w_lo=eps, w_hi=eps)
if not oneway:
digraph.add_edge(rnode, lnode, flow=cvxpy.variable(), minflow=0.0, w_lo=eps, w_hi=eps)
if deficit > 0.0:
NODES = [(road, k, "demand") for k in range(N)]
for node in NODES:
digraph.add_edge(
node, "t", flow=cvxpy.variable(), minflow=0.0, maxflow=deficit / N, w_lo=-eps, w_hi=0.0
)
SEQ = [u] + NODES + [v]
for lnode, rnode in zip(SEQ[:-1], SEQ[1:]):
digraph.add_edge(lnode, rnode, flow=cvxpy.variable(), minflow=0.0, w_lo=eps, w_hi=eps)
if not oneway:
digraph.add_edge(rnode, lnode, flow=cvxpy.variable(), minflow=0.0, w_lo=eps, w_hi=eps)
""" insert supply and demand of nodes """
for u, data in roadnet.nodes_iter(data=True):
surplus = data.get(weight1, 0.0) - data.get(weight2, 0.0)
deficit = -surplus
# supply layer
if surplus > 0.0:
digraph.add_edge("s", u, flow=cvxpy.variable(), minflow=0.0, maxflow=surplus)
if deficit > 0.0:
digraph.add_edge(u, "t", flow=cvxpy.variable(), minflow=0.0, maxflow=deficit)
""" setup the network flow structure """
conns = roadmaps.connectivity_graph(roadnet)
for u, v, data in conns.edges_iter(data=True):
weight = data.get(length, 1)
flowvar = cvxpy.variable()
digraph.add_edge(u, v, flow=cvxpy.variable(), minflow=0.0, w_lo=weight, w_hi=weight)
""" turn the weights into costs """
for _, __, data in digraph.edges_iter(data=True):
flowvar = data["flow"]
if "w_lo" in data:
data["cost_lo"] = data["w_lo"] * flowvar
if "w_hi" in data:
data["cost_hi"] = data["w_hi"] * flowvar
nxopt.attach_flownx_constraints(digraph)
return digraph # a flownx
def MinimizeConcentration(self,
metabolite_index=None,
concentration_bounds=None):
"""Finds feasible concentrations minimizing the concentration
of metabolite i.
Args:
metabolite_index: the index of the metabolite to minimize.
if == None, minimize the sum of all concentrations.
concentration_bounds: the Bounds objects setting concentration bounds.
"""
my_bounds = concentration_bounds or self.DefaultConcentrationBounds()
ln_conc = cvxpy.variable(m=1, n=self.Ncompounds, name='lnC')
ln_conc_lb, ln_conc_ub = my_bounds.GetLnBounds(self.compounds)
constr = [
cvxpy.geq(ln_conc, cvxpy.matrix(ln_conc_lb)),
cvxpy.leq(ln_conc, cvxpy.matrix(ln_conc_ub))
]
my_dG0_r_primes = np.matrix(self.dG0_r_prime)
# Make flux-based constraints on reaction free energies.
# All reactions must have negative dGr in the direction of the flux.
# Reactions with a flux of 0 must be in equilibrium.
S = np.matrix(self.S)
for i, flux in enumerate(self.fluxes):
curr_dg0 = my_dG0_r_primes[0, i]
# if the dG0 is unknown, this reaction imposes no new constraints
if np.isnan(curr_dg0):
continue
rcol = cvxpy.matrix(S[:, i])
curr_dgr = curr_dg0 + RT * ln_conc * rcol
if flux == 0:
constr.append(cvxpy.eq(curr_dgr, 0.0))
else:
constr.append(cvxpy.leq(curr_dgr, 0.0))
objective = None
if metabolite_index is not None:
my_conc = ln_conc[0, metabolite_index]
objective = cvxpy.minimize(cvxpy.exp(my_conc))
else:
objective = cvxpy.minimize(cvxpy.sum(cvxpy.exp(ln_conc)))
name = 'CONC_OPT'
if metabolite_index:
name = 'CONC_%d_OPT' % metabolite_index
problem = cvxpy.program(objective, constr, name=name)
optimum = problem.solve(quiet=True)
"""
status = problem.solve(quiet=True)
if status != 'optimal':
status = optimized_pathway.OptimizationStatus.Infeasible(
'Pathway infeasible given bounds.')
return ConcentrationOptimizedPathway(
self._model, self._thermo,
my_bounds, optimization_status=status)
"""
opt_ln_conc = np.matrix(np.array(ln_conc.value))
result = ConcentrationOptimizedPathway(
self._model,
self._thermo,
my_bounds,
optimal_value=optimum,
optimal_ln_metabolite_concentrations=opt_ln_conc)
return result
def cvxify(self):
super(CTMStaticProblem, self).cvxify()
self.cache_props()
for link in self.get_links():
link.v_dens = variable(name='dens: {0}'.format(link.name))
def ACT2Corrected(self,gene,num_iterations=5):
"""
Next steps: Some way to preserve flows at divergence nodes
One way could be reallocate flows at all divergence nodes in the original ratio and fix it
Iterate 10 times
"""
inwgs=self.wgsdict[gene]
outwgs=inwgs
component1=1.0
for iteri in range(num_iterations):
component1=1.0-iteri*1.0/num_iterations
wgs=addwgs(inwgs,outwgs,component1)
A,B,X=self.wgs2problem(wgs)
Xvar = cvx.variable(len(X),1)
A=cvx.matrix(A)
B=cvx.matrix(B)
B=B.T
p = cvx.program(cvx.minimize(cvx.norm2(A*Xvar-B)),[cvx.geq(Xvar,0.0)])
try:
p.solve(quiet=1)
except:
message='Could not solve for %s'%(gene)
common.printstatus(message,'W',common.func_name(),1)
return (outwgs,100.0)
if iteri==0: # Get optimal value
err=cvx.norm2(A*Xvar-B)
#print err.value/len(X)
Xval=Xvar.T.value.tolist()[0]
X_corr= [a[:] for a in X]
for i in range(len(Xval)):
X_corr[i][3]=int(Xval[i]*100)/100.0
#print X_corr
exonlist=[[a[1],a[2]] for a in X_corr if a[0]==2]
exonwtlist=[a[3] for a in X_corr if a[0]==2]
#print 'E',exonlist
intronlist=[]
intronwtlist=[]
splicelist=[[a[1],a[2]] for a in X_corr if a[0]==3]
splicewtlist=[a[3] for a in X_corr if a[0]==3]
removelist=[]
for i in range(len(exonlist)):
exon=exonlist[i]
if exon in splicelist:
exonwt=exonwtlist[i]
intronlist.append([exon[0]+1,exon[1]-1])
intronwtlist.append(exonwt)
removelist.append(i)
removelist.reverse()
for i in removelist:
exonlist.pop(i)
exonwtlist.pop(i)
#print 'E',exonlist
startnodelist=[a[1]for a in X_corr if a[0]==1]
endnodelist=[a[1]for a in X_corr if a[0]==-1]
novelnodelist=wgs[5]
#print exonlist
#print wgs[0]
#print intronlist
#print wgs[1]
exonwtlist1=[exonwtlist[i] for i in range(len(exonwtlist)) if exonlist[i] in wgs[0]]
intronwtlist1=[exonwtlist[i] for i in range(len(exonwtlist)) if exonlist[i] in wgs[1]]
#wgrstuple=(exonlist,intronlist,splicelist,startnodelist,endnodelist,novelnodelist,exonwtlist,intronwtlist,splicewtlist)
outwgs=(wgs[0],wgs[1],splicelist,wgs[3],wgs[4],novelnodelist,exonwtlist1,intronwtlist1,splicewtlist)
return (outwgs,err.value/len(X))
def _cqp(D, o, k, m, N):
''' solves using cvxpy '''
# cast to object.
D = cvxpy.matrix(D)
N = cvxpy.matrix(N)
o = cvxpy.matrix(o)
# create variables.
f = cvxpy.variable(k, 1, name='f')
x = cvxpy.variable(m, 1, name='x')
y = cvxpy.variable(m, 1, name='y')
# create constraints.
geqs = cvxpy.greater_equals(f, 0.0)
#TO DO: Sum of all f = sum of observed reads classes (and not equal to 1)
sum1 = cvxpy.equals(cvxpy.sum(f), 1.0)
#sum1 = cvxpy.equals(cvxpy.sum(f), sum_obs_freq)
#3
#dev = cvxpy.equals(D*f-o-x,0.0)
#4. matrix N (m x m) * x - y = 0
sizeConstr = cvxpy.equals(N * x - y, 0.0)
#Check now to do N^2
#sizeConstr = cvxpy.equals(N^2*x-y,0.0)
#This might not work but try
#sizeConstr = cvxpy.equals(x/N-y,0.0)
#constrs = [geqs, sum1, dev, sizeConstr]
constrs = [geqs, sum1]
log.debug('\tin _cqp function: \n\t\tPrint matrices shapes:')
log.debug('\t\t\t%s', D.shape)
log.debug('\t\t\t%s', f.shape)
log.debug('\t\t\t%s', o.shape)
# create the program.
#p = cvxpy.program(cvxpy.minimize(cvxpy.norm2(y)),constraints=constrs)
p = cvxpy.program(cvxpy.minimize(cvxpy.norm2(D * f - o)),
constraints=constrs)
p.options['abstol'] = 1e-6 ## 'abstol' - Absolute accuracy Default: 1e-7
p.options['reltol'] = 1e-5 ## 'reltol' - Relative accuracy Default: 1e-6
p.options[
'feastol'] = 1e-5 ## 'feastol' - Tolerance for feasibility conditions Default: 1e-6
p.options[
'maxiters'] = 500 ## 'maxiters' - Maximum number of iterations Default: 100
# solve the program.
p.solve(quiet=True)
# return results.
#print np.around(f.value, decimals=20)
#print "Print using loop"
getcontext().prec = 20
#for i in f.value:
# temp_fi=str(i).strip('[]')
# print temp_fi
return f.value
def fit_ellipse_eps_insensitive(x, y):
"""
fit ellipoid using epsilon-insensitive loss
"""
x = numpy.array(x)
y = numpy.array(y)
print "shapes", x.shape, y.shape
assert len(x) == len(y)
N = len(x)
D = 5
dat = numpy.zeros((N, D))
dat[:,0] = x*x
dat[:,1] = y*y
#dat[:,2] = y*x
dat[:,2] = x
dat[:,3] = y
dat[:,4] = numpy.ones(N)
print dat.shape
dat = cvxpy.matrix(dat)
#### parameters
# data
X = cvxpy.parameter(N, D, name="X")
# parameter for eps-insensitive loss
eps = cvxpy.parameter(1, name="eps")
#### varibales
# parameter vector
theta = cvxpy.variable(D, name="theta")
# dim = (N x 1)
s = cvxpy.variable(N, name="s")
t = cvxpy.variable(N, name="t")
# simple objective
objective = cvxpy.sum(t)
# create problem
p = cvxpy.program(cvxpy.minimize(objective))
# add constraints
# (N x D) * (D X 1) = (N X 1)
p.constraints.append(X*theta <= s)
p.constraints.append(-X*theta <= s)
p.constraints.append(s - eps <= t)
p.constraints.append(0 <= t)
#p.constraints.append(theta[4] == 1)
# trace constraint
p.constraints.append(theta[0] + theta[1] == 1)
###### set values
X.value = dat
eps.value = 0.0
#solver = "mosek"
#p.solve(lpsolver=solver)
p.solve()
cvxpy.printval(theta)
w = numpy.array(cvxpy.value(theta))
#cvxpy.printval(s)
#cvxpy.printval(t)
## For clarity, fill in the quadratic form variables
A = numpy.zeros((2,2))
A[0,0] = w[0]
A.ravel()[1:3] = 0#w[2]
A[1,1] = w[1]
bv = w[2:4]
c = w[4]
## find parameters
z, a, b, alpha = util.conic2parametric(A, bv, c)
return z, a, b, alpha
def solve(self):
''' solver for the optimal purchasing of gas at stations
does not return anything, but attaches some resulting properties at the end'''
n = len(self.route.stations)
if self.method == "dynamic":
def conditionallyFillUp(gas_in_tank,station):
print 'filling up'
distance_to_end = sum([self.route.distances[j]
for j in range(station,n)]
)
bought.append(max(0,min(distance_to_end / self.car.economy, self.car.tank_max) - gas_in_tank))
prices = [station.regular for station in self.route.stations]
print 'prices: ' + str(prices)
i = 0
range_of_car = (self.car.tank_max - self.gas_min) * self.car.economy
print 'range of car: ' + str(range_of_car)
#set initial stations
current_price = self.route.stations[i].regular
station_chosen = i
#Probably safe to assume both are zero, call api if necessary
distance_to_first_station = 0
distance_from_last_station = 0
if (self.car.start - distance_to_first_station / self.car.economy < self.gas_min):
raise Exception("Unfeasible to reach")
else:
gas_in_tank_at_last_station = self.car.start - (distance_to_first_station / self.car.economy)
#Make parameter (probably as proportion of full tank) on website
gas_at_end = self.gas_min
#for export
stations_chosen = []
bought = []
#simulte partially filled tank as miles already driven
miles_since_last_stop = (self.car.tank_max - gas_in_tank_at_last_station) * self.car.economy + distance_to_first_station
#make sure you can get home
self.route.distances.append(distance_from_last_station)
while i < n:
#for feasibility check
firstStationOnTank = i
#determine where car should stop
while i < n:
#check if next stop is cheaper
print i
print current_price
if self.route.stations[i].regular < current_price:
current_price = self.route.stations[i].regular
station_chosen = i
print 'station_chosen: ' + str(station_chosen)
#increment
miles_since_last_stop += self.route.distances[i]
i = i + 1
print 'miles_since_last_stop: ' + str(miles_since_last_stop)
if miles_since_last_stop > range_of_car:
print i
if (gas_in_tank_at_last_station - self.route.distances[firstStationOnTank] / self.car.economy < self.gas_min):
raise Exception("Unfeasible to reach")
stations_chosen.append(station_chosen)
current_price = self.route.stations[i].regular
station_chosen = i
break
#determine how much gas car should get
if len(stations_chosen) > 1:
distance_between_stations = sum([self.route.distances[j]
for j in range(stations_chosen[len(stations_chosen) - 2],stations_chosen[len(stations_chosen) - 1])]
)
print stations_chosen
print 'last_station: ' + str(stations_chosen[len(stations_chosen) - 2]) + ", price:" + str(self.route.stations[stations_chosen[len(stations_chosen) - 2]].regular)
print 'this station: ' + str(stations_chosen[len(stations_chosen) - 1]) + ", price:" + str(self.route.stations[stations_chosen[len(stations_chosen) - 1]].regular)
if (self.route.stations[stations_chosen[len(stations_chosen) - 2]].regular < self.route.stations[stations_chosen[len(stations_chosen) - 1]].regular):
#fill 'er up, errr, conditionally
conditionallyFillUp(gas_in_tank_at_last_station, stations_chosen[len(stations_chosen) - 2])
else:
#only get enough gas to get to next station
print 'getting minimum'
bought.append(distance_between_stations / self.car.economy)
gas_in_tank_at_last_station = gas_in_tank_at_last_station - (distance_between_stations / self.car.economy) + bought[len(bought) - 1]
miles_since_last_stop = 0
stations_chosen.append(station_chosen)
conditionallyFillUp(gas_in_tank_at_last_station, stations_chosen[len(stations_chosen) - 1])
print 'stations_chosen: ' + str(stations_chosen)
print 'bought: ' + str(bought)
objective_value = sum(
[self.route.stations[stations_chosen[j]].regular*bought[j]
for j in range(len(stations_chosen))]
)
self.stations_chosen = stations_chosen
else:
# how to constrain the LP
in_tank = variable(n,name ='gas in tank') # how much gas in tank var
bought = variable(n,name = 'gas bought') # how much to buy at each station var
constraints = ([eq(in_tank[0,0],self.car.start)] + # starting gas
[eq(in_tank[i+1,0], # mass balance
in_tank[i,0] +
bought[i,0] -
self.route.distances[i]/self.car.economy)#DV: Changed from * to /
for i in range(n-1)] +
[geq(in_tank,self.gas_min)] + # can't dip below certain amount
[leq(in_tank + bought,self.car.tank_max)] + # max size of tank
[geq(bought,0)] # physical constraint (cannot cyphon gas for sale to bum)
)
# the total cost of the fuel
fuel_cost = sum(
[self.route.stations[j].regular*bought[j,0]
for j in range(n)]
)
# define program
p = program(minimize(fuel_cost), constraints)
objective_value = p.solve()
# attach properties to access later
self.in_tank = in_tank
self.program = p
self.bought = bought
self.objective_value = objective_value
#!/usr/bin/python
import cvxpy
import numpy
S = cvxpy.matrix([[-1, 1, 0],
[0, -1, 1]])
Km = cvxpy.matrix([[1e-4, 0, 0],
[0, 1e-4, 0]])
kcat = cvxpy.matrix([[100],[100]])
m_plus = numpy.abs(numpy.clip(S, -1000, 0))
c = cvxpy.variable(3, 1, name='concentrations')
opt = cvxpy.minimize()
def MinimizeConcentration(self, metabolite_index=None,
concentration_bounds=None):
"""Finds feasible concentrations minimizing the concentration
of metabolite i.
Args:
metabolite_index: the index of the metabolite to minimize.
if == None, minimize the sum of all concentrations.
concentration_bounds: the Bounds objects setting concentration bounds.
"""
my_bounds = concentration_bounds or self.DefaultConcentrationBounds()
ln_conc = cvxpy.variable(m=1, n=self.Ncompounds, name='lnC')
ln_conc_lb, ln_conc_ub = my_bounds.GetLnBounds(self.compounds)
constr = [cvxpy.geq(ln_conc, cvxpy.matrix(ln_conc_lb)),
cvxpy.leq(ln_conc, cvxpy.matrix(ln_conc_ub))]
my_dG0_r_primes = np.matrix(self.dG0_r_prime)
# Make flux-based constraints on reaction free energies.
# All reactions must have negative dGr in the direction of the flux.
# Reactions with a flux of 0 must be in equilibrium.
S = np.matrix(self.S)
for i, flux in enumerate(self.fluxes):
curr_dg0 = my_dG0_r_primes[0, i]
# if the dG0 is unknown, this reaction imposes no new constraints
if np.isnan(curr_dg0):
continue
rcol = cvxpy.matrix(S[:, i])
curr_dgr = curr_dg0 + RT * ln_conc * rcol
if flux == 0:
constr.append(cvxpy.eq(curr_dgr, 0.0))
else:
constr.append(cvxpy.leq(curr_dgr, 0.0))
objective = None
if metabolite_index is not None:
my_conc = ln_conc[0, metabolite_index]
objective = cvxpy.minimize(cvxpy.exp(my_conc))
else:
objective = cvxpy.minimize(
cvxpy.sum(cvxpy.exp(ln_conc)))
name = 'CONC_OPT'
if metabolite_index:
name = 'CONC_%d_OPT' % metabolite_index
problem = cvxpy.program(objective, constr, name=name)
optimum = problem.solve(quiet=True)
"""
status = problem.solve(quiet=True)
if status != 'optimal':
status = optimized_pathway.OptimizationStatus.Infeasible(
'Pathway infeasible given bounds.')
return ConcentrationOptimizedPathway(
self._model, self._thermo,
my_bounds, optimization_status=status)
"""
opt_ln_conc = np.matrix(np.array(ln_conc.value))
result = ConcentrationOptimizedPathway(
self._model, self._thermo,
my_bounds, optimal_value=optimum,
optimal_ln_metabolite_concentrations=opt_ln_conc)
return result
import cvxpy as cp
import numpy as np
I = np.random.randn(10, 10) > .4
I[np.diag_indices_from(I)] = 0
K = np.shape(I)[0]
X = cp.variable(K, K, name='X')
const = []
for i in range(K):
for j in range(K):
if I[i, j] > 0:
c = cp.equals(X[i, j], 0)
const.append(c)
c = cp.equals(cp.diag(X), 1)
const.append(c)
p = cp.program(cp.minimize(cp.nuclear_norm(X)), const)
p.solve(quiet=False)
print X.value
#!/usr/bin/python import cvxpy import numpy S = cvxpy.matrix([[-1, 1, 0], [0, -1, 1]]) Km = cvxpy.matrix([[1e-4, 0, 0], [0, 1e-4, 0]]) kcat = cvxpy.matrix([[100], [100]]) m_plus = numpy.abs(numpy.clip(S, -1000, 0)) c = cvxpy.variable(3, 1, name='concentrations') opt = cvxpy.minimize()
noise = noise*(tau/norm(noise))
# Add noise to signal
rSig = signal + noise
rSig = abs(rSig) #phase is not used in MRI
# Choose regularization parameter
# lambda > lambda_max -> zero solution
lambda_max = 2*norm(dot(nA.T, rSig.T), np.inf)
lamb = 1.0e-8*lambda_max
print('Solving L1 penalized system with cvxpy...')
coefs = cvx.variable(n_qpnts,1)
A = cvx.matrix(nA)
rhs = cvx.matrix(rSig).T
objective = cvx.minimize(cvx.norm2(A*coefs - rhs) +
lamb*cvx.norm1(coefs) )
constraints = [cvx.geq(coefs,0.0)]
prob = cvx.program(objective, constraints)
# Call the solver
prob.solve(quiet=True) #Use quiet=True to suppress output
# Convert the cvxmod objects to plain numpy arrays for further processing
nd_coefs_l1 = np.array(coefs.value).squeeze()
def measurenx_to_approxnx( roadnet, epsilon, length='length', weight1='weight1', weight2='weight2' ) :
"""
input: a road network, with weights on its elements
output:
returns a graph summarizing the network optimization problem instance;
roadnets are multi-digraph, where edge 'keys' are assumed to be unique,
i.e., road names; and should be different from node labels too;
"""
digraph = nx.DiGraph()
#digraph.add_node('s')
#digraph.add_node('t')
SUPPLY = []
DEMAND = []
""" insert supply and demand of roads """
for u,v, road, data in roadnet.edges_iter( keys=True, data=True ) :
roadlen = float( data.get( length, 1 ) ) # float() just in case
assert roadlen >= 0.
"""
split the road into equal-length segments;
create a node for each segment;
record boundary points, and mass contained
"""
N = int( np.ceil( roadlen / epsilon ) )
eps = roadlen / N
surplus = float( data.get( weight1, 0. ) ) - data.get( weight2, 0. )
deficit = -surplus
bd = np.linspace( 0, roadlen, N+1 )
bd = [ roadmaps.RoadAddress( road, x ) for x in bd ]
for i, boundary in enumerate( zip( bd[:-1], bd[1:] ) ) :
if surplus > 0. :
node = (road,i,'supply')
digraph.add_node( node, boundary=boundary )
digraph.add_edge( 's', node, flow=cvxpy.variable(), minflow=0., maxflow=surplus/N )
SUPPLY.append( node )
if deficit > 0. :
node = (road,i,'demand')
digraph.add_node( node, boundary=boundary )
digraph.add_edge( node, 't', flow=cvxpy.variable(), minflow=0., maxflow=deficit/N )
DEMAND.append( node )
""" ...and nodes """
for u, data in roadnet.nodes_iter( data=True ) :
surplus = data.get( weight1, 0. ) - data.get( weight2, 0. )
deficit = -surplus
if surplus > 0. :
boundary = [ roadmaps.roadify( roadnet, u, weight=length ) ]
node = (u,'supply')
digraph.add_node( node, boundary=boundary )
digraph.add_edge( 's', node, flow=cvxpy.variable(), minflow=0., maxflow=surplus )
SUPPLY.append( node )
if deficit > 0. :
boundary = [ roadmaps.roadify( roadnet, v, weight=length ) ]
node = (u,'demand')
digraph.add_node( node, boundary=boundary )
digraph.add_edge( node, 't', flow=cvxpy.variable(), minflow=0., maxflow=deficit )
DEMAND.append( node )
""" generate bipartite graph b/w SUPPLY and DEMAND """
for u, v in itertools.product( SUPPLY, DEMAND ) :
bd_u = digraph.node[u]['boundary']
bd_v = digraph.node[v]['boundary']
options = [ pair for pair in itertools.product( bd_u, bd_v ) ]
options = [ roadmaps.distance( roadnet, p, q, weight=length ) for p,q in options ]
#options = [ np.inf ]
w = min( options )
W = max( options )
flowvar = cvxpy.variable()
digraph.add_edge( u, v, flow=flowvar, minflow=0., w=w, W=W, cost_lo = w * flowvar, cost_hi = W * flowvar )
nxopt.attach_flownx_constraints( digraph )
return digraph # a flow network
def FindMTDF(self, concentration_bounds=None, normalization=None):
"""Finds the MTDF.
Args:
bounds: the Bounds objects setting concentration bounds.
"""
my_bounds = concentration_bounds or self.DefaultConcentrationBounds()
normalization = normalization or self.DeltaGNormalization.DEFAULT
# Constrain concentrations
ln_conc = cvxpy.variable(m=1, n=self.Ncompounds, name='lnC')
ln_conc_lb, ln_conc_ub = my_bounds.GetLnBounds(self.compounds)
constr = [
cvxpy.geq(ln_conc, cvxpy.matrix(ln_conc_lb)),
cvxpy.leq(ln_conc, cvxpy.matrix(ln_conc_ub))
]
# Make the objective
motive_force_lb = cvxpy.variable(name='B')
my_dG0_r_primes = np.matrix(self.dG0_r_prime)
# Make flux-based constraints on reaction free energies.
# All reactions must have negative dGr in the direction of the flux.
# Reactions with a flux of 0 must be in equilibrium.
S = np.matrix(self.S)
for i, flux in enumerate(self.fluxes):
curr_dg0 = my_dG0_r_primes[0, i]
# if the dG0 is unknown, this reaction imposes no new constraints
if np.isnan(curr_dg0):
continue
rcol = cvxpy.matrix(S[:, i])
curr_dgr = curr_dg0 + RT * ln_conc * rcol
if flux == 0:
constr.append(cvxpy.eq(curr_dgr, 0))
else:
motive_force = self.DeltaGNormalization.NormalizeDGByFlux(
curr_dgr, flux, normalization)
constr.append(cvxpy.geq(motive_force, motive_force_lb))
objective = cvxpy.maximize(motive_force_lb)
problem = cvxpy.program(objective, constr, name='MTDF_OPT')
problem.solve(quiet=True)
"""
if status != 'optimal':
status = optimized_pathway.OptimizationStatus.Infeasible(
'Pathway infeasible given bounds.')
return MTDFOptimizedPathway(
self._model, self._thermo,
my_bounds, optimization_status=status)
"""
mtdf = float(motive_force_lb.value)
opt_ln_conc = np.matrix(np.array(ln_conc.value))
result = MTDFOptimizedPathway(
self._model,
self._thermo,
my_bounds,
optimal_value=mtdf,
optimal_ln_metabolite_concentrations=opt_ln_conc)
result.SetNormalization(normalization)
return result
import cvxpy as cp import numpy as np I = np.random.randn(10,10) > .4 I[np.diag_indices_from(I)] = 0 K = np.shape(I)[0] X = cp.variable(K,K, name='X') const = [] for i in range(K): for j in range(K): if I[i,j] > 0: c = cp.equals(X[i,j],0) const.append(c) c = cp.equals(cp.diag(X),1) const.append(c) p = cp.program(cp.minimize(cp.nuclear_norm(X)), const) p.solve(quiet=False) print X.value
def FindMTDF(self, concentration_bounds=None, normalization=None):
"""Finds the MTDF.
Args:
bounds: the Bounds objects setting concentration bounds.
"""
my_bounds = concentration_bounds or self.DefaultConcentrationBounds()
normalization = normalization or self.DeltaGNormalization.DEFAULT
# Constrain concentrations
ln_conc = cvxpy.variable(m=1, n=self.Ncompounds, name='lnC')
ln_conc_lb, ln_conc_ub = my_bounds.GetLnBounds(self.compounds)
constr = [cvxpy.geq(ln_conc, cvxpy.matrix(ln_conc_lb)),
cvxpy.leq(ln_conc, cvxpy.matrix(ln_conc_ub))]
# Make the objective
motive_force_lb = cvxpy.variable(name='B')
my_dG0_r_primes = np.matrix(self.dG0_r_prime)
# Make flux-based constraints on reaction free energies.
# All reactions must have negative dGr in the direction of the flux.
# Reactions with a flux of 0 must be in equilibrium.
S = np.matrix(self.S)
for i, flux in enumerate(self.fluxes):
curr_dg0 = my_dG0_r_primes[0, i]
# if the dG0 is unknown, this reaction imposes no new constraints
if np.isnan(curr_dg0):
continue
rcol = cvxpy.matrix(S[:, i])
curr_dgr = curr_dg0 + RT * ln_conc * rcol
if flux == 0:
constr.append(cvxpy.eq(curr_dgr, 0))
else:
motive_force = self.DeltaGNormalization.NormalizeDGByFlux(
curr_dgr, flux, normalization)
constr.append(cvxpy.geq(motive_force, motive_force_lb))
objective = cvxpy.maximize(motive_force_lb)
problem = cvxpy.program(objective, constr, name='MTDF_OPT')
problem.solve(quiet=True)
"""
if status != 'optimal':
status = optimized_pathway.OptimizationStatus.Infeasible(
'Pathway infeasible given bounds.')
return MTDFOptimizedPathway(
self._model, self._thermo,
my_bounds, optimization_status=status)
"""
mtdf = float(motive_force_lb.value)
opt_ln_conc = np.matrix(np.array(ln_conc.value))
result = MTDFOptimizedPathway(
self._model, self._thermo,
my_bounds, optimal_value=mtdf,
optimal_ln_metabolite_concentrations=opt_ln_conc)
result.SetNormalization(normalization)
return result
def fit_ellipse(x, y):
"""
fit ellipoid using squared loss and abs loss
"""
#TODO introduce flag for switching between losses
assert len(x) == len(y)
N = len(x)
D = 5
dat = numpy.zeros((N, D))
dat[:,0] = x*x
dat[:,1] = y*y
#dat[:,2] = x*y
dat[:,2] = x
dat[:,3] = y
dat[:,4] = numpy.ones(N)
print dat.shape
dat = cvxpy.matrix(dat)
#### parameters
# data
X = cvxpy.parameter(N, D, name="X")
#### varibales
# parameter vector
theta = cvxpy.variable(D, name="theta")
# simple objective
objective = cvxpy.norm1(X*theta)
# create problem
p = cvxpy.program(cvxpy.minimize(objective))
p.constraints.append(cvxpy.eq(theta[0,:] + theta[1,:], 1))
###### set values
X.value = dat
p.solve()
w = numpy.array(theta.value)
#print weights
## For clarity, fill in the quadratic form variables
A = numpy.zeros((2,2))
A[0,0] = w[0]
A.ravel()[1:3] = 0 #w[2]
A[1,1] = w[1]
bv = w[2:4]
c = w[4]
## find parameters
z, a, b, alpha = util.conic2parametric(A, bv, c)
print "XXX", z, a, b, alpha
return z, a, b, alpha
