def __init__(self,func,x):
""" Initializes the tape for a function which allows
the user to then quickly evaluate the function,
gradient, and hessian at arbitrary points
func is assumed to be a function from a vector array
to a single number.
Also updates the global CUR_ADOLC_TAPE_NUMBER which
keeps track of the tapes that have been used by
ADOLC
Parameters
----------
func: function
function to be evaluated
x: ndarray
ndarray of correct shape and type for the
function
"""
# access the tape number counter
global CUR_ADOLC_TAPE_NUMBER
# initialize the ADOLC tape for the function
adolc.trace_on(CUR_ADOLC_TAPE_NUMBER)
x = adolc.adouble(x)
adolc.independent(x)
y = func(x)
adolc.dependent(y)
adolc.trace_off()
self.tape_number = CUR_ADOLC_TAPE_NUMBER
CUR_ADOLC_TAPE_NUMBER += 1
def PyAdolc_dvLJ(x):
adolc.trace_on(0)
ad_x = adolc.adouble(np.zeros(np.shape(np.ravel(x)),dtype=float))
adolc.independent(ad_x)
ad_y = Adolc_vLJ(ad_x)
adolc.dependent(ad_y)
adolc.trace_off()
return adolc.gradient(0, np.ravel(x))
def __init__(self, f, x, test = 'f'):
self.f = f
self.x = x
adolc.trace_on(0)
ax = adolc.adouble(x)
adolc.independent(ax)
y = f(ax)
adolc.dependent(y)
adolc.trace_off()
def run(N,D,dt,beta,x0):
if x0=='start':
#init = 20.0*np.random.random_sample((N,D))-10.0
#np.savetxt('initpaths.txt',init)
init = np.loadtxt('initpaths.txt')
else:
init = x0.reshape(N,D)
#init = np.loadtxt("data_D{0}_dt{1}_noP.txt".format(D,dt))[:N,:]
if init.shape != (N,D):
raise "x is wrong dims!"
epsg = 1e-8
epsf = 1e-8
epsx = 1e-8
maxits = 10000
### Use ADOL-C to generate trace, used for derivatives and
### evaluations
start = time.time()
adolc.trace_on(adolcID)
ax = adolc.adouble(init.flatten())
adolc.independent(ax)
af = action(ax, beta)
adolc.dependent(af)
adolc.trace_off()
print "taped =", time.time()-start,"s"
#Test Gradient numerically
# flat = init.flatten()
# grad = np.zeros_like(flat)
# cost1 = alglib_func(flat,grad,1)
# grad2 = grad.copy()
# for i in range(len(flat)):
# perturb = flat.copy()
# perturb[i] = perturb[i]+0.00001
# cost2 =alglib_func(perturb,grad2,1)
# numgrad = (cost2-cost1)/0.00001
# print numgrad/grad[i]
m = af.shape[0]
state = xalglib.minlmcreatevj( m ,list(init.flatten()))
xalglib.minlmsetcond(state,epsg, epsf,epsx,maxits)
xalglib.minlmoptimize_vj(state, lm_func, lm_jac)
final, rep = xalglib.minlbfgsresults(state)
print "optimized: ", time.time()-start, "s"
print "Exit flag = ", rep.terminationtype, rep.iterationscount
print "Action = ", action(final,beta)
return rep.iterationscount, action(final,beta), final
def _trace_con(self, x):
if self._con_trace_id is None and self.m > 0:
self._con_trace_id = self._get_trace_id() + 1
adolc.trace_on(self._con_trace_id)
x = adolc.adouble(x)
adolc.independent(x)
y = self.cons(x)
adolc.dependent(y)
adolc.trace_off()
def _trace_obj(self, x):
if self._obj_trace_id is None:
self._obj_trace_id = self._get_trace_id()
adolc.trace_on(self._obj_trace_id)
x = adolc.adouble(x)
adolc.independent(x)
y = self.obj(x)
adolc.dependent(y)
adolc.trace_off()
def func(tag):
n=50
x=goffin_init(n)
tag=1
adolc.trace_on(tag)
ax = adolc.independent(adolc.adouble(x))
ay = goffin(ax,n)
adolc.dependent(ay)
trace_off()
y=ay.val
return y
def calc_delR_delbeta(self, q):
nb = np.size(self.beta)
n = np.size(q)
beta_c = self.beta.copy()
self.beta = ad.adouble(self.beta)
tag = 3
ad.trace_on(tag)
ad.independent(self.beta)
R = self.calc_residual(q, dtype=ad.adouble)
ad.dependent(R)
ad.trace_off()
self.beta = beta_c
dRdbeta = calc_jacobian(self.beta, tag=tag, sparse=False)
return dRdbeta
def PyAdolc_vLJ_Optimize(x):
N = len(x)
adolc.trace_on(0)
ad_x = adolc.adouble(np.zeros(np.shape(np.ravel(x)),dtype=float))
adolc.independent(ad_x)
ad_y = Adolc_vLJ(ad_x)
adolc.dependent(ad_y)
adolc.trace_off()
Adolc_BFGSres = optimize.minimize(PyAdolc_vLJ_for_Optimize, np.ravel(x), \
method='L-BFGS-B', \
jac = PyAdolc_dvLJ_for_Optimize, \
options={'disp': False})
return np.reshape(Adolc_BFGSres.x, (N,D))
def __init__(self, f, x, scaler=False):
if not isinstance(x, list):
x_lst = [x]
else:
x_lst = x
self.id = next(self.__class__._ids)
self.scaler = scaler
# x = np.random.uniform(x_L, x_U)
adolc.trace_on(self.id)
a_x_lst = [adolc.adouble(v) for v in x_lst]
for ax in a_x_lst:
adolc.independent(ax)
ay = f(*a_x_lst)
adolc.dependent(ay)
adolc.trace_off()
def pCostpW_adolc(self, W, p_target):
tag = self.pCostpW_tag
if not self.pCostpW_traced:
aW = adouble(W.flatten(order='F'))
ap = adouble(p_target)
adolc.trace_on(tag)
adolc.independent(aW)
aW3 = np.reshape(aW, W.shape, order='F')
acost = cost.inverse_pressure_design(aW3, ap)
adolc.dependent(acost)
adolc.trace_off()
return adolc.gradient(self.pCostpW_tag, W.flatten(order='F'))
def evalJacobian(self, states, beta_inv):
# Evaluate jacobian of residuals w.r.t. states
ad.trace_on(1)
ad_states = ad.adouble(states)
ad.independent(ad_states)
ad_res = self.evalResidual(ad_states, beta_inv)
ad.dependent(ad_res)
ad.trace_off()
return ad.jacobian(1, states)
def dydgamma(self, x, gamma):
"""
Calculate derivative of the neural network output with respect to the
hyperparameters gamma.
"""
gamma_c = gamma.copy()
gamma = ad.adouble(gamma)
tag = 11
ad.trace_on(tag)
ad.independent(gamma)
self.set_from_array(gamma)
y = self.eval(x)
ad.dependent(y)
ad.trace_off()
gamma = gamma_c
self.set_from_array(gamma_c)
dJdgamma = calc_jacobian(gamma, tag=tag, sparse=False)
return dJdgamma.reshape(gamma_c.shape)
def tape_A(self, xtrace):
"""
Tape the objective function.
"""
print('Taping action evaluation...')
tstart = time.time()
adolc.trace_on(self.adolcID)
# set the active independent variables
ax = adolc.adouble(xtrace)
adolc.independent(ax)
# set the dependent variable (or vector of dependent variables)
af = self.A(ax)
adolc.dependent(af)
adolc.trace_off()
self.taped = True
print('Done!')
print('Time = {0} s\n'.format(time.time()-tstart))
def pRpW_adolc(self, sim, W, area):
tag = self.pRpW_tag
if not self.pRpW_traced:
aW = adouble(W.flatten(order='F'))
aarea = adouble(area)
adolc.trace_on(tag)
adolc.independent(aW)
aW3 = np.reshape(aW, W.shape, order='F')
aresidual = q1d.evaluate_residual(sim, aW3, area)
aresidual.flatten(order='F')
adolc.dependent(aresidual)
adolc.trace_off()
return adolc.jacobian(self.pRpW_tag, W.flatten(order='F'))
def ppRpWpW_adolc(self, sim, W, area):
tag = self.ppRpWpW_tag
if not self.ppRpWpW_traced:
aW = adouble(W.flatten(order='F'))
aarea = adouble(area)
adolc.trace_on(tag)
adolc.independent(aW)
aW3 = np.reshape(aW, W.shape, order='F')
pRpW = self.pRpW_adolc(sim, aW3, area)
pRpW.flatten(order='F')
adolc.dependent(pRpW)
adolc.trace_off()
return adolc.hessian(self.ppRpWpW_tag, W.flatten(order='F'))
def _create_topography_active(self, tag=0, separate_faults=True):
import adolc
# Sanitise kwargs
assert isinstance(tag, int)
assert tag >= 0
for control in self.active_controls:
assert control in self.all_controls, "Active control '{:s}' not recognised.".format(
control)
# Initialise tape
adolc.trace_on(tag)
# Read parameters and mark active variables as independent
msg = "INIT: Subfault {:d}: shear modulus {:4.1e} Pa, seismic moment is {:4.1e}"
for i, subfault in enumerate(self.subfaults):
for control in self.all_controls:
if control in self.active_controls:
subfault.__setattr__(
control,
adolc.adouble(self.control_parameters[control][i]))
adolc.independent(subfault.__getattribute__(control))
else:
subfault.__setattr__(control,
self.control_parameters[control][i])
if self.debug and self.debug_mode == 'full':
try:
print(msg.format(i, subfault.mu, subfault.Mo().val))
except Exception:
print(msg.format(i, subfault.mu, subfault.Mo()))
# Create the topography, thereby calling Okada
self.print_debug("SETUP: Creating topography using Okada model...")
self.fault.create_dtopography(verbose=self.debug
and self.debug_mode == 'full',
active=True)
# Mark output as dependent
if separate_faults:
for subfault in self.subfaults:
adolc.dependent(subfault.dtopo.dZ)
else:
adolc.dependent(self.fault.dtopo.dZ_a)
adolc.trace_off()
def _trace_lag(self, x, z):
self._trace_obj(x)
self._trace_con(x)
self._trace_cons_pos(x)
unconstrained = self.m == 0 and self.nbounds == 0
if self._lag_trace_id is None:
if unconstrained:
self._lag_trace_id = self._obj_trace_id
return
self._lag_trace_id = self._get_trace_id() + 3
adolc.trace_on(self._lag_trace_id)
x = adolc.adouble(x)
z = adolc.adouble(z)
adolc.independent(x)
adolc.independent(z)
l = self.lag(x, z)
adolc.dependent(l)
adolc.trace_off()
def create_tape(dev, vPort):
"""
Generate Adol-C tape
Normally there is no need to manually call this.
"""
assert dev.isNonlinear
try:
tag = dev._tag
except AttributeError:
tag = dev.adolcID
dev._tag = tag
ad.trace_on(tag)
# Create derivative vector
a_vPort = ad.adouble(vPort)
ad.independent(a_vPort)
# perform actual calculation (for now re-tape every time)
a_out = dev.eval_cqs(a_vPort)
ad.dependent(a_out)
ad.trace_off()
def create_tape(dev, vPort):
"""
Generate Adol-C tape
Normally there is no need to manually call this.
"""
assert dev.isNonlinear
try:
tag = dev._tag
except AttributeError:
tag = dev.adolcID
dev._tag = tag
ad.trace_on(tag)
# Create derivative vector
a_vPort = ad.adouble(vPort)
ad.independent(a_vPort)
# perform actual calculation (for now re-tape every time)
a_out = dev.eval_cqs(a_vPort)
ad.dependent(a_out)
ad.trace_off()
def getObjJac(self, data):
ad.trace_on(1)
ad_states = ad.adouble(self.states)
ad_beta = ad.adouble(self.beta)
ad.independent(ad_states)
ad.independent(ad_beta)
ad_obj = self.getObjRaw(ad_states, data, ad_beta)
ad.dependent(ad_obj)
ad.trace_off()
jacobj = ad.jacobian(1, np.hstack((self.states, self.beta)))
Jq = jacobj[:,0:np.shape(self.states)[0]]
Jb = jacobj[:,np.shape(self.states)[0]:]
return Jq[0,:], Jb[0,:]
def trace(self, dims):
# trace function
t = numpy.zeros(1)
x = numpy.zeros(dims['x'])
f = numpy.zeros(dims['x'])
p = numpy.zeros(dims['p'])
u = numpy.zeros(dims['u'])
adolc.trace_on(123)
at = adolc.adouble(t)
ax = adolc.adouble(x)
af = adolc.adouble(f)
ap = adolc.adouble(p)
au = adolc.adouble(u)
adolc.independent(at)
adolc.independent(ax)
adolc.independent(ap)
adolc.independent(au)
self.ffcn(t, ax, af, ap, au)
adolc.dependent(af)
adolc.trace_off()
self.traced = True
def tape_A(self, xtrace):
"""
Tape the objective function.
"""
print('Taping action evaluation...')
tstart = time.time()
"""
trace objective function
"""
adolc.trace_on(self.adolcID)
# set the active independent variables
ax = adolc.adouble(xtrace)
adolc.independent(ax)
# set the dependent variable (or vector of dependent variables)
af = self.A(ax)
adolc.dependent(af)
adolc.trace_off()
#IPOPT needs the lagrangian functions to be traced
if self.method == 'IPOPT':
"""
trace lagrangian unconstrained
"""
#This adolc numbering could cause problems in the future
self.lagrangeID = self.adolcID + 1000
adolc.trace_on(self.lagrangeID)
ax = adolc.adouble(xtrace)
aobj_factor = adolc.adouble(1.)
adolc.independent(ax)
adolc.independent(aobj_factor)
ay = self.eval_lagrangian(ax, aobj_factor)
adolc.trace_off()
self.taped = True
print('Done!')
print('Time = {0} s\n'.format(time.time() - tstart))
def tape(fID, gID, lID, x0, beta):
t0 = time.time()
# trace objective function
adolc.trace_on(fID)
ax = adolc.adouble(x0)
adolc.independent(ax)
ay = action(ax, beta)
#ay = actTDtest(ax, beta)
adolc.dependent(ay)
adolc.trace_off()
# trace constraint function
adolc.trace_on(gID)
ax = adolc.adouble(x0)
adolc.independent(ax)
ay = eval_g(ax)
adolc.dependent(ay)
adolc.trace_off()
# trace lagrangian function
adolc.trace_on(lID)
# xtmp = [x0, lambdas, obj_factor]
# xtmp = np.hstack(x0,np.ones(ncon),1.)
ax = adolc.adouble(x0)
alagrange = adolc.adouble(np.ones(ncon))
aobj_factor = adolc.adouble(1.)
adolc.independent(ax)
adolc.independent(alagrange)
adolc.independent(aobj_factor)
ay = eval_lagrangian(ax, alagrange, aobj_factor, beta)
adolc.dependent(ay)
adolc.trace_off()
t1 = time.time()
print "tape time = ", t1-t0
eval_jac_g_adolc = Eval_jac_g(x0)
#eval_h_adolc = Eval_h(x0, np.ones(ncon), 1.)
eval_h_adolc = Eval_h_dense(x0, np.ones(ncon), 1.)
t2 = time.time()
print "Hess time = ", t2-t1
#
return eval_jac_g_adolc, eval_h_adolc
def __init__(self):
dwl.OptimizationModel.__init__(self)
self.setDimensionOfState(4)
self.setDimensionOfConstraints(2)
self.setNumberOfNonzeroJacobian(8)
self.setNumberOfNonzeroHessian(10)
# trace objective function
x0 = np.array([1.0, 5.0, 5.0, 1.0])
self.getStartingPoint(x0)
f = np.array([0.0])
adolc.trace_on(1)
ax = adolc.adouble(x0)
af = adolc.adouble(f)
adolc.independent(ax)
self.costFunction(af, ax)
adolc.dependent(af)
adolc.trace_off()
# trace constraint function
adolc.trace_on(2)
ax = adolc.adouble(x0)
g = np.zeros(self.getDimensionOfConstraints())
ag = adolc.adouble(g)
adolc.independent(ax)
self.constraintFunction(ag, ax)
adolc.dependent(ag)
adolc.trace_off()
# trace lagrangian function
adolc.trace_on(3)
ax = adolc.adouble(x0)
alagrange = adolc.adouble([1., 1.])
aobj_factor = adolc.adouble(1.)
adolc.independent(ax)
adolc.independent(alagrange)
adolc.independent(aobj_factor)
ay = self.eval_lagrangian(ax, alagrange, aobj_factor)
adolc.dependent(ay)
adolc.trace_off()
x = np.array([1.0, 5.0, 5.0, 1.0])
hoptions = np.array([0, 0], dtype=int)
hess_result = adolc.colpack.sparse_hess_no_repeat(3, x, hoptions)
self.hrow = np.asarray(hess_result[1], dtype=int)
self.hcol = np.asarray(hess_result[2], dtype=int)
self.hess_values = np.asarray(hess_result[3], dtype=float)
# need only upper left part of the Hessian
self.mask = np.where(self.hcol < 4)
def MCdo(b, i):
user = os.path.expanduser("~")
tempOut = robj.r("rm(list=ls())")
tempOut = robj.r("i <- %i"%(i+1))
tempOut = robj.r("b <- %i"%(i*(1000)+(b+1) ))
tempOut = robj.r("source('CMLE_unstable_support.r')")
regrMat = np.array((robj.r("do.call(cbind, regr)")))
regr = OrderedDict([('SA',np.ones((regrMat.shape[0],0))),('VA',regrMat[:,0:1]),('CB',np.ones((regrMat.shape[0],0))),('barWA',regrMat[:,1:2]),('barWB',regrMat[:,2:4]),('bara',regrMat[:,4:5]),('VB',regrMat[:,5:6])])
Y = np.array((robj.r("Y")))
x0 = np.array((robj.r("unname(x0)")))
xL = np.array((robj.r("unname(xL)")))
tPL = np.array((robj.r("unname(out.2step$time )")))
def fll(x):
return LL_jo(x, Y, regr)
def heq(x):
return const_cmle(x, Y, regr)
np.random.seed(b)
while np.isinf(fll(adouble(x0)).val):
x0 = np.random.uniform(size=len(x0))
ccd = os.getcwd()
if not os.path.exists(user + '/Documents/adolc%i_%i'%(i, b)):
os.makedirs(user + '/Documents/adolc%i_%i'%(i, b))
os.chdir(user + '/Documents/adolc%i_%i'%(i, b))
adolc.trace_on(1)
ax = adolc.adouble(np.zeros(len(x0)))
adolc.independent(ax)
ay = fll(ax)
adolc.dependent(ay)
adolc.trace_off()
# trace constraint function
adolc.trace_on(2)
ax = adolc.adouble(x0)
adolc.independent(ax)
ay = heq(ax)
adolc.dependent(ay)
adolc.trace_off()
npar = len(x0)
def adFun(x):
return adolc.function(1, x)
def grFun(x):
return adolc.gradient(1, x)
def const_adolc(x):
return adolc.function(2,x)
def jac_adolc(x):
return adolc.jacobian(2,x)
def lagrangian(x, lagrange, obj_factor):
return obj_factor*fll(x) + np.dot(lagrange, heq(x))
#Jacobian
#### initalize it
class jac_c_adolc:
def __init__(self, x):
options = None
result = adolc.colpack.sparse_jac_no_repeat(2,x,options)
self.nnz = result[0]
self.rind = np.asarray(result[1],dtype=int)
self.cind = np.asarray(result[2],dtype=int)
self.values = np.asarray(result[3],dtype=float)
def __call__(self, x, flag, user_data=None):
if flag:
return (self.rind, self.cind)
else:
result = adolc.colpack.sparse_jac_repeat(2, x, self.nnz, self.rind,
self.cind, self.values)
return result[3]
##### create the function
Jac_c_adolc = jac_c_adolc(x0)
###Hessian
# trace lagrangian function
adolc.trace_on(3)
ax = adolc.adouble(x0)
adolc.independent(ax)
ay = lagrangian(ax, xL, np.array([1.0]))
adolc.dependent(ay)
adolc.trace_off()
M = Y.shape[1]
nreal = npar-M
given = {'rind': np.concatenate((np.kron(np.arange(nreal), np.ones(npar,dtype='int')), np.arange(nreal, npar))),
'cind': np.concatenate((np.tile(np.arange(npar), nreal), np.arange(nreal, npar)))}
mask = np.where(given['rind'] <= given['cind'])
given['rind'] = given['rind'][mask]
given['cind'] = given['cind'][mask]
def hessLag_adolc(x, lagrange, obj_factor, flag, user_data=None):
if flag:
result = (given['rind'], given['cind'])
else:
result = np.ravel(adolc.hessian(3, x)[given['rind'],given['cind']], order="C")
return result
H2 = hessLag_adolc(x0, xL, 1.0, False)
H2a = hessLag_adolc(x0, xL, 1.0, True)
nnzh = len(given['rind'])
##Optimization
#PRELIMS: other things to pass to IPOPT
nvar = len(x0) #number of variables in the problem
x_L = np.array([-np.inf]*nvar, dtype=float) #box contraints on variables (none)
x_U = np.array([np.inf]*nvar, dtype=float)
#PRELIMS:define the (in)equality constraints
ncon = heq(ax).shape[0] #number of constraints
g_L = np.array([0]*ncon, dtype=float) #constraints are to equal 0
g_U = np.array([0]*ncon, dtype=float) #constraints are to equal 0
#PRELIMS: define the number of nonzeros in the jacobian
val = Jac_c_adolc(x0, False)
nnzj = len(val)
# create the nonlinear programming model
nlp2 = pyipopt.create(
nvar,
x_L,
x_U,
ncon,
g_L,
g_U,
nnzj,
nnzh,
adFun,
grFun,
const_adolc,
Jac_c_adolc,
hessLag_adolc
)
nlp2.num_option('expect_infeasible_problem_ctol', 1e-15)
nlp2.int_option('max_iter', 100)
nlp2.num_option('dual_inf_tol', 1e-3)
nlp2.num_option('constr_viol_tol', 1e-3)
nlp2.num_option('tol', 1e-6)
nlp2.int_option('print_level', 0)
t1 = time()
out = nlp2.solve(x0)
t1 = time()-t1
t1 = t1 + tPL
# free the model
nlp2.close()
os.chdir(ccd)
shutil.rmtree(user + '/Documents/adolc%i_%i'%(i, b))
output = np.concatenate((out[0][:6], t1, np.array([out[5]])))
return output
import numpy
import math
import adolc
# tape a function evaluation
ax = numpy.array([adolc.adouble(0.) for n in range(2)])
# ay = adolc.adouble(0)
adolc.trace_on(13)
adolc.independent(ax)
ay = numpy.sin(ax[0] + ax[1]*ax[0])
adolc.dependent(ay)
adolc.trace_off()
x = numpy.array([3., 7.])
y = numpy.zeros(1)
adolc.tape_to_latex(13, x, y)
y = adolc.function(13, x)
g = adolc.gradient(13, x)
J = adolc.jacobian(13, x)
print('function y=', y)
print('gradient g=', g)
print('Jacobian J=', J)
def fll(x):
return LL_jo(x, Y, regr)
def heq(x):
return const_cmle(x, Y, regr)
ccd = os.getcwd()
if not os.path.exists(user + '/Documents/adolcSanctions'):
os.makedirs(user + '/Documents/adolcSanctions')
os.chdir(user + '/Documents/adolcSanctions')
adolc.trace_on(1)
ax = adolc.adouble(np.zeros(len(x0)))
adolc.independent(ax)
ay = fll(ax)
adolc.dependent(ay)
adolc.trace_off()
# trace constraint function
adolc.trace_on(2)
ax = adolc.adouble(x0)
adolc.independent(ax)
ay = heq(ax)
adolc.dependent(ay)
adolc.trace_off()
npar = len(x0)
for d in range(D):
symdict[xs[n,d]] = x[n,d]
return array([[dfs[n,d].subs(symdict).evalf() for d in range(D)] for n in range(N)])
def sym_ddf(x):
symdict = dict()
for n in range(N):
for d in range(D):
symdict[xs[n,d]] = x[n,d]
return array([[[[ ddfs[m,e,n,d].subs(symdict).evalf() for d in range(D)] for n in range(N)] for e in range(D)] for m in range(N)],dtype=float)
###################################
# PART 1: computation with PYADOLC
###################################
adolc.trace_on(0)
x = adolc.adouble(numpy.random.rand(*(N,D)))
adolc.independent(x)
y = f(x)
adolc.dependent(y)
adolc.trace_off()
# point at which the derivatives should be evaluated
x = random((N,D))
print '\n\n'
print 'Sympy function = function check (should be almost zero)'
print f(x) - sym_f(x)
print '\n\n'
## taping
start_time = time()
cg = rm.tape(f, x)
end_time = time()
tape_eval_times.append(end_time - start_time)
## reverse evaluation
start_time = time()
g_reverse2 = rm.gradient_from_graph(cg)
end_time = time()
rev_eval_times.append(end_time - start_time)
## PyADOLC taping
start_time = time()
ax = numpy.array([adolc.adouble(0.) for i in range(N)])
adolc.trace_on(0)
for n in range(N):
ax[n].is_independent(x[n])
ay = f(ax)
adolc.depends_on(ay)
adolc.trace_off()
end_time = time()
adolc_tape_times.append(end_time - start_time)
## PyADOLC gradient
start_time = time()
adolc_g = adolc.gradient(0, x)
end_time = time()
adolc_gradient_times.append(end_time - start_time)
### check that both derivatives give the same result
ny = adolc.zos_forward(self.tag_ext_fct,2,2,1,x)
nz = adolc.fov_reverse(self.tag_ext_fct,2,2,q,u)
np.copyto(z,nz)
return 1
if __name__ == '__main__':
tag_full = 1
tag_part = 2
tag_ext_fct = 3
n = 2
m = 2
t0 = 0.0
tf = 1.0
conp = [ 1.0, 1.0 ]
adolc.trace_on(tag_full)
con = adolc.as_adouble(conp)
for c in con:
c.declareIndependent()
y = con
ynew = adolc.as_adouble([ 0.0, 0.0 ])
for i in range(steps):
euler_step_act(n,y,m,ynew)
y = ynew
f = y[0] + y[1]
f.declareDependent()
adolc.trace_off()
grad = adolc.gradient(tag_full,conp)
print(" full taping:\n gradient=(",grad[0],", ",grad[1],")\n\n")
# now taping external function
def record_tape(self):
#import numpy as np
tag = 0
Q = self.Q.copy()
self.Q = ad.adouble(self.Q)
R = self.R.copy()
self.R = ad.adouble(self.R)
U = self.U.copy()
self.U = ad.adouble(self.U)
F = self.F.copy()
self.F = ad.adouble(self.F)
Fv = self.Fv.copy()
self.Fv = ad.adouble(self.Fv)
S = self.S.copy()
self.S = ad.adouble(self.S)
mut = self.mut.copy()
self.mut = ad.adouble(self.mut)
b = self.b.copy()
self.b = ad.adouble(self.b)
rhotau = self.rhotau.copy()
self.rhotau = ad.adouble(self.rhotau)
ex = self.ex.copy()
self.ex = ad.adouble(self.ex)
Ul = self.Ul.copy()
self.Ul = ad.adouble(self.Ul)
Ur = self.Ur.copy()
self.Ur = ad.adouble(self.Ur)
Ux_face = self.Ux_face.copy()
self.Ux_face = ad.adouble(self.Ux_face)
Ux_center = self.Ux_center.copy()
self.Ux_center = ad.adouble(self.Ux_center)
ad.trace_on(tag)
ad.independent(self.Q)
self.calc_residual()
ad.dependent(self.R)
ad.trace_off()
print(ad.tapestats(0))
self.Q = Q
self.U = U
self.R = R
self.Ul = Ul
self.Ur = Ur
self.F = F
self.Fv = Fv
self.S = S
self.Ux_face = Ux_face
self.Ux_center = Ux_center
self.ex = ex
self.b = b
self.rhotau = rhotau
self.mut = mut
xlabel(r'states $x(t),x_{p_1}(t)$ and $x_{p_2}(t)$')
ylabel(r' time $t$')
savefig('variational_ode_trajectory.eps')
# generate pseudo measurement data
p[0]+=3.; p[1] += 2.
x0 = array([v[0], 1., 0.])
x = explicit_euler(x0,f1,ts,p,q)
h = measurement_model(x[:,0],p,q)
etas = h + numpy.random.normal(size=Nm)
p[0]-= 3.; p[1] -= 2.
# taping F
av = array([adolc.adouble(0) for i in range(Nv)])
y = zeros(Nm)
adolc.trace_on(1)
av[0].is_independent(p[0])
av[1].is_independent(p[1])
av[2].is_independent(q[0])
ay = F(av[:Np],av[Np:],ts,Sigma,etas)
for m in range(Nm):
y[m] = adolc.depends_on(ay[m])
adolc.trace_off()
# taping dFdp
av = array([adolc.adouble(0) for i in range(Nv)])
adolc.trace_on(1)
av[0].is_independent(p[0])
av[1].is_independent(p[1])
av[2].is_independent(q[0])
ay = dFdp(av[:Np],av[Np:],ts,Sigma,etas)
values[0] += lagrange[1] * 2
values[2] += lagrange[1] * 2
values[5] += lagrange[1] * 2
values[9] += lagrange[1] * 2
return values
def apply_new(x):
return True
x0 = numpy.array([1.0, 5.0, 5.0, 1.0])
pi0 = numpy.array([1.0, 1.0])
# trace objective function
adolc.trace_on(1)
ax = adolc.adouble(x0)
adolc.independent(ax)
ay = eval_f(ax)
adolc.dependent(ay)
adolc.trace_off()
# trace constraint function
adolc.trace_on(2)
ax = adolc.adouble(x0)
adolc.independent(ax)
ay = eval_g(ax)
adolc.dependent(ay)
adolc.trace_off()
# trace lagrangian function
