def wrapped_1st_derivatives(p, x, ind_, funcType, ignorePrev, useSparse):
if isinstance(x, dict):
if not p.isFDmodel:
p.err(
'calling the function with argument of type dict is allowed for FuncDesigner models only'
)
if ind_ is not None:
p.err(
'the operation is turned off for argument of type dict when ind!=None'
)
x = p._point2vector(x)
if ind_ is not None:
ind = p.getCorrectInd(ind_)
else:
ind = None
if p.istop == USER_DEMAND_EXIT:
if p.solver.useStopByException:
raise killThread
else:
return nan
derivativesType = 'd' + funcType
prevKey = p.prevVal[derivativesType]['key']
if prevKey is not None and p.iter > 0 and array_equal(
x, prevKey) and ind is None and not ignorePrev:
#TODO: add counter of the situations
assert p.prevVal[derivativesType]['val'] is not None
return copy(p.prevVal[derivativesType]['val'])
if ind is None and not ignorePrev:
p.prevVal[derivativesType]['ind'] = copy(x)
#TODO: patterns!
nFuncs = getattr(p, 'n' + funcType)
x = atleast_1d(x)
if hasattr(p.userProvided, derivativesType) and getattr(
p.userProvided, derivativesType):
funcs = getattr(p.user, derivativesType)
if ind is None or (nFuncs == 1
and p.functype[funcType] == 'single func'):
Funcs = funcs
elif ind is not None and p.functype[
funcType] == 'some funcs R^nvars -> R':
Funcs = [funcs[i] for i in ind]
else:
Funcs = getFuncsAndExtractIndexes(p, funcs, ind, funcType)
# if ind is None: derivativesNumber = nFuncs
# else: derivativesNumber = len(ind)
#derivatives = empty((derivativesNumber, p.n))
derivatives = []
#agregate_counter = 0
for fun in Funcs: #getattr(p.user, derivativesType):
tmp = atleast_1d(fun(*(x, ) + getattr(p.args, funcType)))
# TODO: replace tmp.size here for sparse matrices
#assert tmp.size % p.n == mod(tmp.size, p.n)
if tmp.size % p.n != 0:
if funcType == 'f':
p.err(
'incorrect user-supplied (sub)gradient size of objective function'
)
elif funcType == 'c':
p.err(
'incorrect user-supplied (sub)gradient size of non-lin inequality constraints'
)
elif funcType == 'h':
p.err(
'incorrect user-supplied (sub)gradient size of non-lin equality constraints'
)
if tmp.ndim == 1: m = 1
else: m = tmp.shape[0]
if p.functype[funcType] == 'some funcs R^nvars -> R' and m != 1:
# TODO: more exact check according to stored p.arr_of_indexes_* arrays
p.err(
'incorrect shape of user-supplied derivative, it should be in accordance with user-provided func size'
)
derivatives.append(tmp)
#derivatives[agregate_counter : agregate_counter + m] = tmp#.reshape(tmp.size/p.n,p.n)
#agregate_counter += m
#TODO: inline ind modification!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
derivatives = Vstack(derivatives) if any(
isspmatrix(derivatives)) else vstack(derivatives)
if ind is None:
p.nEvals[derivativesType] += 1
else:
#derivatives = derivatives[ind]
p.nEvals[derivativesType] = p.nEvals[derivativesType] + float(
len(ind)) / nFuncs
if funcType == 'f':
if p.invertObjFunc: derivatives = -derivatives
if p.isObjFunValueASingleNumber:
if not isinstance(derivatives, ndarray):
derivatives = derivatives.toarray()
derivatives = derivatives.flatten()
else:
#if not getattr(p.userProvided, derivativesType) or p.isFDmodel:
# x, IND, userFunctionType, ignorePrev, getDerivative
derivatives = p.wrapped_func(x, ind, funcType, True, True)
if ind is None:
p.nEvals[derivativesType] -= 1
else:
p.nEvals[derivativesType] = p.nEvals[derivativesType] - float(
len(ind)) / nFuncs
#else:
if useSparse is False or not scipyInstalled or not hasattr(
p, 'solver') or not p.solver._canHandleScipySparse:
# p can has no attr 'solver' if it is called from checkdf, checkdc, checkdh
if not isinstance(derivatives, ndarray):
derivatives = derivatives.toarray()
# if min(derivatives.shape) == 1:
# if isspmatrix(derivatives): derivatives = derivatives.A
# derivatives = derivatives.flatten()
if type(derivatives) != ndarray and isinstance(
derivatives, ndarray): # dense numpy matrix
derivatives = derivatives.A
if ind is None and not ignorePrev:
p.prevVal[derivativesType]['val'] = derivatives
if funcType == 'f':
if hasattr(
p, 'solver'
) and not p.solver.iterfcnConnected and p.solver.funcForIterFcnConnection == 'df':
if p.df_iter is True: p.iterfcn(x)
elif p.nEvals[derivativesType] % p.df_iter == 0:
p.iterfcn(x) # call iterfcn each {p.df_iter}-th df call
if p.isObjFunValueASingleNumber and type(
derivatives) == ndarray and derivatives.ndim > 1:
derivatives = derivatives.flatten()
return derivatives
def wrapped_1st_derivatives(p, x, ind_, funcType, ignorePrev, useSparse):
if isinstance(x, dict):
if not p.isFDmodel: p.err('calling the function with argument of type dict is allowed for FuncDesigner models only')
if ind_ is not None:p.err('the operation is turned off for argument of type dict when ind!=None')
x = p._point2vector(x)
if ind_ is not None:
ind = p.getCorrectInd(ind_)
else: ind = None
if p.istop == USER_DEMAND_EXIT:
if p.solver.useStopByException:
raise killThread
else:
return nan
derivativesType = 'd'+ funcType
prevKey = p.prevVal[derivativesType]['key']
if prevKey is not None and p.iter > 0 and array_equal(x, prevKey) and ind is None and not ignorePrev:
#TODO: add counter of the situations
assert p.prevVal[derivativesType]['val'] is not None
return copy(p.prevVal[derivativesType]['val'])
if ind is None and not ignorePrev: p.prevVal[derivativesType]['ind'] = copy(x)
#TODO: patterns!
nFuncs = getattr(p, 'n'+funcType)
x = atleast_1d(x)
if hasattr(p.userProvided, derivativesType) and getattr(p.userProvided, derivativesType):
funcs = getattr(p.user, derivativesType)
if ind is None or (nFuncs == 1 and p.functype[funcType] == 'single func'):
Funcs = funcs
elif ind is not None and p.functype[funcType] == 'some funcs R^nvars -> R':
Funcs = [funcs[i] for i in ind]
else:
Funcs = getFuncsAndExtractIndexes(p, funcs, ind, funcType)
# if ind is None: derivativesNumber = nFuncs
# else: derivativesNumber = len(ind)
#derivatives = empty((derivativesNumber, p.n))
derivatives = []
#agregate_counter = 0
for fun in Funcs:#getattr(p.user, derivativesType):
tmp = atleast_1d(fun(*(x,)+getattr(p.args, funcType)))
# TODO: replace tmp.size here for sparse matrices
#assert tmp.size % p.n == mod(tmp.size, p.n)
if tmp.size % p.n != 0:
if funcType=='f':
p.err('incorrect user-supplied (sub)gradient size of objective function')
elif funcType=='c':
p.err('incorrect user-supplied (sub)gradient size of non-lin inequality constraints')
elif funcType=='h':
p.err('incorrect user-supplied (sub)gradient size of non-lin equality constraints')
if tmp.ndim == 1: m= 1
else: m = tmp.shape[0]
if p.functype[funcType] == 'some funcs R^nvars -> R' and m != 1:
# TODO: more exact check according to stored p.arr_of_indexes_* arrays
p.err('incorrect shape of user-supplied derivative, it should be in accordance with user-provided func size')
derivatives.append(tmp)
#derivatives[agregate_counter : agregate_counter + m] = tmp#.reshape(tmp.size/p.n,p.n)
#agregate_counter += m
#TODO: inline ind modification!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
derivatives = Vstack(derivatives) if any(isspmatrix(derivatives)) else vstack(derivatives)
if ind is None:
p.nEvals[derivativesType] += 1
else:
#derivatives = derivatives[ind]
p.nEvals[derivativesType] = p.nEvals[derivativesType] + float(len(ind)) / nFuncs
if funcType=='f':
if p.invertObjFunc: derivatives = -derivatives
if p.isObjFunValueASingleNumber:
if not isinstance(derivatives, ndarray): derivatives = derivatives.toarray()
derivatives = derivatives.flatten()
else:
#if not getattr(p.userProvided, derivativesType) or p.isFDmodel:
# x, IND, userFunctionType, ignorePrev, getDerivative
derivatives = p.wrapped_func(x, ind, funcType, True, True)
if ind is None:
p.nEvals[derivativesType] -= 1
else:
p.nEvals[derivativesType] = p.nEvals[derivativesType] - float(len(ind)) / nFuncs
#else:
if useSparse is False or not scipyInstalled or not hasattr(p, 'solver') or not p.solver._canHandleScipySparse:
# p can has no attr 'solver' if it is called from checkdf, checkdc, checkdh
if not isinstance(derivatives, ndarray):
derivatives = derivatives.toarray()
# if min(derivatives.shape) == 1:
# if isspmatrix(derivatives): derivatives = derivatives.A
# derivatives = derivatives.flatten()
if type(derivatives) != ndarray and isinstance(derivatives, ndarray): # dense numpy matrix
derivatives = derivatives.A
if ind is None and not ignorePrev: p.prevVal[derivativesType]['val'] = derivatives
if funcType=='f':
if hasattr(p, 'solver') and not p.solver.iterfcnConnected and p.solver.funcForIterFcnConnection=='df':
if p.df_iter is True: p.iterfcn(x)
elif p.nEvals[derivativesType]%p.df_iter == 0: p.iterfcn(x) # call iterfcn each {p.df_iter}-th df call
if p.isObjFunValueASingleNumber and type(derivatives) == ndarray and derivatives.ndim > 1:
derivatives = derivatives.flatten()
return derivatives
def wrapped_func(p, x, IND, userFunctionType, ignorePrev,
getDerivative): #, _linePointDescriptor = None):
if isinstance(x, dict):
if not p.isFDmodel:
p.err(
'calling the function with argument of type dict is allowed for FuncDesigner models only'
)
x = p._point2vector(x)
if not getattr(p.userProvided, userFunctionType): return array([])
if p.istop == USER_DEMAND_EXIT:
if p.solver.useStopByException:
raise killThread
else:
return nan
if getDerivative and not p.isFDmodel and not DerApproximatorIsInstalled:
p.err(
'For the problem you should have DerApproximator installed, see http://openopt.org/DerApproximator'
)
#userFunctionType should be 'f', 'c', 'h'
funcs = getattr(p.user, userFunctionType)
#funcs_num = getattr(p, 'n'+userFunctionType)
if IND is not None:
ind = p.getCorrectInd(IND)
else:
ind = None
# this line had been added because some solvers pass tuple instead of
# x being vector p.n x 1 or matrix X=[x1 x2 x3...xk], size(X)=[p.n, k]
if not isspmatrix(x):
x = atleast_1d(x)
# if not str(x.dtype).startswith('float'):
# x = asfarray(x)
else:
if p.debug:
p.pWarn(
'[oo debug] sparse matrix x in nonlinfuncs.py has been encountered'
)
# if not ignorePrev:
# prevKey = p.prevVal[userFunctionType]['key']
# else:
# prevKey = None
#
# # TODO: move it into runprobsolver or baseproblem
# if p.prevVal[userFunctionType]['val'] is None:
# p.prevVal[userFunctionType]['val'] = zeros(getattr(p, 'n'+userFunctionType))
#
# if prevKey is not None and p.iter > 0 and array_equal(x, prevKey) and ind is None and not ignorePrev:
# #TODO: add counter of the situations
# if not getDerivative:
# r = copy(p.prevVal[userFunctionType]['val'])
# #if p.debug: assert array_equal(r, p.wrapped_func(x, IND, userFunctionType, True, getDerivative))
# if ind is not None: r = r[ind]
#
# if userFunctionType == 'f':
# if p.isObjFunValueASingleNumber: r = r.sum(0)
# if p.invertObjFunc: r = -r
# if p.solver.funcForIterFcnConnection=='f' and any(isnan(x)):
# p.nEvals['f'] += 1
#
# if p.nEvals['f']%p.f_iter == 0:
# p.iterfcn(x, fk = r)
# return r
args = getattr(p.args, userFunctionType)
# TODO: handle it in prob prepare
if not hasattr(p, 'n' + userFunctionType):
setNonLinFuncsNumber(p, userFunctionType)
# if ind is None:
# nFuncsToObtain = getattr(p, 'n'+ userFunctionType)
# else:
# nFuncsToObtain = len(ind)
if x.shape[0] != p.n and (x.ndim < 2 or x.shape[1] != p.n):
p.err('x with incorrect shape passed to non-linear function')
#TODO: code cleanup (below)
if getDerivative or x.ndim <= 1 or x.shape[0] == 1:
nXvectors = 1
x_0 = copy(x)
else:
nXvectors = x.shape[0]
# TODO: use certificate instead
if p.isFDmodel:
if getDerivative:
if p.freeVars is None or (p.fixedVars is not None and
len(p.freeVars) < len(p.fixedVars)):
funcs2 = [(lambda x, i=i: \
p._pointDerivative2array(
funcs[i].D(x, Vars = p.freeVars, useSparse=p.useSparse, fixedVarsScheduleID=p._FDVarsID, exactShape=True),
useSparse=p.useSparse, func=funcs[i], point=x)) \
for i in range(len(funcs))]
else:
funcs2 = [(lambda x, i=i: \
p._pointDerivative2array(
funcs[i].D(x, fixedVars = p.fixedVars, useSparse=p.useSparse, fixedVarsScheduleID=p._FDVarsID, exactShape=True),
useSparse=p.useSparse, func=funcs[i], point=x)) \
for i in range(len(funcs))]
else:
if p.freeVars is None or (p.fixedVars is not None and
len(p.freeVars) < len(p.fixedVars)):
funcs2 = [(lambda x, i=i: \
funcs[i]._getFuncCalcEngine(x, Vars = p.freeVars, fixedVarsScheduleID=p._FDVarsID))\
for i in range(len(funcs))]
else:
funcs2 = [(lambda x, i=i: \
funcs[i]._getFuncCalcEngine(x, fixedVars = p.fixedVars, fixedVarsScheduleID=p._FDVarsID))\
for i in range(len(funcs))]
else:
funcs2 = funcs
if ind is None:
Funcs = funcs2
elif ind is not None and p.functype[
userFunctionType] == 'some funcs R^nvars -> R':
Funcs = [funcs2[i] for i in ind]
else:
Funcs = getFuncsAndExtractIndexes(p, funcs2, ind, userFunctionType)
# agregate_counter = 0
Args = () if p.isFDmodel else args
if nXvectors == 1:
if p.isFDmodel:
X = p._vector2point(x)
X._p = p
#X._linePointDescriptor = _linePointDescriptor
else:
X = x
if nXvectors > 1: # and hence getDerivative isn't involved
#temporary, to be fixed
if userFunctionType == 'f':
assert p.isObjFunValueASingleNumber
if p.isFDmodel:
assert ind is None
if isPyPy or p.hasVectorizableFuncs: # TODO: get rid of box-bound constraints
from FuncDesigner.ooPoint import ooPoint as oopoint
from FuncDesigner.multiarray import multiarray
# TODO: new
xx = []
counter = 0
#xT = x.T
for i, oov in enumerate(p._freeVarsList):
s = p._optVarSizes[oov]
xx.append(
(oov,
(x[:, counter:counter + s].flatten() if s == 1
else x[:,
counter:counter + s]).view(multiarray)))
# xx.append((oov, multiarray(x[:, counter: counter + s].flatten() if s == 1 else x[:, counter: counter + s])))
counter += s
X = oopoint(xx)
X.update(p.dictOfFixedFuncs)
X.maxDistributionSize = p.maxDistributionSize
X._p = p
if len(p.unvectorizableFuncs) != 0:
XX = [p._vector2point(x[i]) for i in range(nXvectors)]
for _X in XX:
_X._p = p
_X.update(p.dictOfFixedFuncs)
r = vstack([[fun(xx) for xx in XX]
if funcs[i] in p.unvectorizableFuncs else fun(X).T
for i, fun in enumerate(Funcs)]).T
# X = [p._vector2point(x[i]) for i in range(nXvectors)]
# r = hstack([[fun(xx) for xx in X] for fun in Funcs]).reshape(1, -1)
#new
# if p.vectorizable:
# from FuncDesigner.ooPoint import ooPoint as oopoint, multiarray
#
# X = dict([(oovar, x[:, i].view(multiarray)) for i, oovar in enumerate(p._freeVarsList)])
# X = oopoint(X, skipArrayCast = True)
# X.N = nXvectors
# X.isMultiPoint = True
# X.update(p.dictOfFixedFuncs)
# r = hstack([fun(X) for fun in Funcs]).reshape(1, -1)
#
# #old
# else:
# X = [p._vector2point(x[i]) for i in range(nXvectors)]
# r = hstack([[fun(xx) for xx in X] for fun in Funcs]).reshape(1, -1)
else:
X = [(x[i], ) + Args for i in range(nXvectors)]
#r = hstack([[fun(*xx) for xx in X] for fun in Funcs])
R = []
for xx in X:
tmp = [fun(*xx) for fun in Funcs]
r_ = hstack(tmp[0]) if len(tmp) == 1 and isinstance(
tmp[0],
(list,
tuple)) else hstack(tmp) if len(tmp) > 1 else tmp[0]
R.append(r_)
r = hstack(R) #.T
#print(r.shape, userFunctionType)
elif not getDerivative:
tmp = [fun(*(X, ) + Args) for fun in Funcs]
r = hstack(tmp[0]) if len(tmp) == 1 and isinstance(
tmp[0],
(list, tuple)) else hstack(tmp) if len(tmp) > 1 else tmp[0]
#print(x.shape, r.shape, x, r)
# if not ignorePrev and ind is None:
# p.prevVal[userFunctionType]['key'] = copy(x_0)
# p.prevVal[userFunctionType]['val'] = r.copy()
elif getDerivative and p.isFDmodel:
rr = [fun(X) for fun in Funcs]
r = Vstack(rr) if scipyInstalled and any(
[isspmatrix(elem) for elem in rr]) else vstack(rr)
else:
r = []
if getDerivative:
#r = zeros((nFuncsToObtain, p.n))
diffInt = p.diffInt
abs_x = abs(x)
finiteDiffNumbers = 1e-10 * abs_x
if p.diffInt.size == 1:
finiteDiffNumbers[finiteDiffNumbers < diffInt] = diffInt
else:
finiteDiffNumbers[finiteDiffNumbers < diffInt] = diffInt[
finiteDiffNumbers < diffInt]
else:
#r = zeros((nFuncsToObtain, nXvectors))
r = []
for index, fun in enumerate(Funcs):
# OLD
# v = ravel(fun(*((X,) + Args)))
# if (ind is None or funcs_num == 1) and not ignorePrev:
# #TODO: ADD COUNTER OF THE CASE
# if index == 0: p.prevVal[userFunctionType]['key'] = copy(x_0)
# p.prevVal[userFunctionType]['val'][agregate_counter:agregate_counter+v.size] = v.copy()
# r[agregate_counter:agregate_counter+v.size,0] = v
#NEW
if not getDerivative:
r.append(fun(*((X, ) + Args)))
# v = r[-1]
#r[agregate_counter:agregate_counter+v.size,0] = fun(*((X,) + Args))
# if (ind is None or funcs_num == 1) and not ignorePrev:
# #TODO: ADD COUNTER OF THE CASE
# if index == 0: p.prevVal[userFunctionType]['key'] = copy(x_0)
# p.prevVal[userFunctionType]['val'][agregate_counter:agregate_counter+v.size] = v.copy()
""" getting derivatives """
if getDerivative:
def func(x):
r = fun(*((x, ) + Args))
return r if type(r) not in (
list, tuple) or len(r) != 1 else r[0]
d1 = get_d1(func,
x,
pointVal=None,
diffInt=finiteDiffNumbers,
stencil=p.JacobianApproximationStencil,
exactShape=True)
#r[agregate_counter:agregate_counter+d1.size] = d1
r.append(d1)
# v = r[-1]
# agregate_counter += atleast_1d(v).shape[0]
r = hstack(r) if not getDerivative else vstack(r)
#assert r.size != 30
#if type(r) == matrix: r = r.A
if type(r) != ndarray and not isscalar(r): # multiarray
r = r.view(ndarray).flatten(
) if userFunctionType == 'f' else r.view(ndarray)
#elif userFunctionType == 'f' and p.isObjFunValueASingleNumber and prod(r.shape) > 1 and (type(r) == ndarray or min(r.shape) > 1):
#r = r.sum(0)
elif userFunctionType == 'f' and p.isObjFunValueASingleNumber and not isscalar(
r):
if prod(r.shape) > 1 and not getDerivative and nXvectors == 1:
p.err(
'implicit summation in objective is no longer available to prevent possibly hidden bugs'
)
# if r.size == 1:
# r = r.item()
if userFunctionType == 'f' and p.isObjFunValueASingleNumber:
if getDerivative and r.ndim > 1:
if min(r.shape) > 1:
p.err('incorrect shape of objective func derivative')
# TODO: omit cast to dense array. Somewhere bug triggers?
if hasattr(r, 'toarray'):
r = r.toarray()
r = r.flatten()
if userFunctionType != 'f' and nXvectors != 1:
r = r.reshape(nXvectors, int(r.size / nXvectors))
# if type(r) == matrix:
# raise 0
# r = r.A # if _dense_numpy_matrix !
if nXvectors == 1 and (not getDerivative or prod(
r.shape) == 1): # DO NOT REPLACE BY r.size - r may be sparse!
r = r.flatten() if type(r) == ndarray else r.toarray().flatten(
) if not isscalar(r) else atleast_1d(r)
if p.invertObjFunc and userFunctionType == 'f':
r = -r
if not getDerivative:
if ind is None:
p.nEvals[userFunctionType] += nXvectors
else:
p.nEvals[userFunctionType] = p.nEvals[userFunctionType] + float(
nXvectors * len(ind)) / getattr(p, 'n' + userFunctionType)
if getDerivative:
assert x.size == p.n #TODO: add python list possibility here
x = x_0 # for to suppress numerical instability effects while x +/- delta_x
#if userFunctionType == 'f' and p.isObjFunValueASingleNumber and r.size == 1:
#r = r.item()
if userFunctionType == 'f' and hasattr(
p, 'solver'
) and p.solver.funcForIterFcnConnection == 'f' and hasattr(
p, 'f_iter') and not getDerivative:
if p.nEvals['f'] % p.f_iter == 0 or nXvectors > 1:
p.iterfcn(x, r)
return r
def wrapped_func(p, x, IND, userFunctionType, ignorePrev, getDerivative):#, _linePointDescriptor = None):
if isinstance(x, dict):
if not p.isFDmodel: p.err('calling the function with argument of type dict is allowed for FuncDesigner models only')
x = p._point2vector(x)
if not getattr(p.userProvided, userFunctionType): return array([])
if p.istop == USER_DEMAND_EXIT:
if p.solver.useStopByException:
raise killThread
else:
return nan
if getDerivative and not p.isFDmodel and not DerApproximatorIsInstalled:
p.err('For the problem you should have DerApproximator installed, see http://openopt.org/DerApproximator')
#userFunctionType should be 'f', 'c', 'h'
funcs = getattr(p.user, userFunctionType)
#funcs_num = getattr(p, 'n'+userFunctionType)
if IND is not None:
ind = p.getCorrectInd(IND)
else: ind = None
# this line had been added because some solvers pass tuple instead of
# x being vector p.n x 1 or matrix X=[x1 x2 x3...xk], size(X)=[p.n, k]
if not isspmatrix(x):
x = atleast_1d(x)
# if not str(x.dtype).startswith('float'):
# x = asfarray(x)
else:
if p.debug:
p.pWarn('[oo debug] sparse matrix x in nonlinfuncs.py has been encountered')
# if not ignorePrev:
# prevKey = p.prevVal[userFunctionType]['key']
# else:
# prevKey = None
#
# # TODO: move it into runprobsolver or baseproblem
# if p.prevVal[userFunctionType]['val'] is None:
# p.prevVal[userFunctionType]['val'] = zeros(getattr(p, 'n'+userFunctionType))
#
# if prevKey is not None and p.iter > 0 and array_equal(x, prevKey) and ind is None and not ignorePrev:
# #TODO: add counter of the situations
# if not getDerivative:
# r = copy(p.prevVal[userFunctionType]['val'])
# #if p.debug: assert array_equal(r, p.wrapped_func(x, IND, userFunctionType, True, getDerivative))
# if ind is not None: r = r[ind]
#
# if userFunctionType == 'f':
# if p.isObjFunValueASingleNumber: r = r.sum(0)
# if p.invertObjFunc: r = -r
# if p.solver.funcForIterFcnConnection=='f' and any(isnan(x)):
# p.nEvals['f'] += 1
#
# if p.nEvals['f']%p.f_iter == 0:
# p.iterfcn(x, fk = r)
# return r
args = getattr(p.args, userFunctionType)
# TODO: handle it in prob prepare
if not hasattr(p, 'n'+userFunctionType): setNonLinFuncsNumber(p, userFunctionType)
# if ind is None:
# nFuncsToObtain = getattr(p, 'n'+ userFunctionType)
# else:
# nFuncsToObtain = len(ind)
if x.shape[0] != p.n and (x.ndim<2 or x.shape[1] != p.n):
p.err('x with incorrect shape passed to non-linear function')
#TODO: code cleanup (below)
if getDerivative or x.ndim <= 1 or x.shape[0] == 1:
nXvectors = 1
x_0 = copy(x)
else:
nXvectors = x.shape[0]
# TODO: use certificate instead
if p.isFDmodel:
if getDerivative:
if p.freeVars is None or (p.fixedVars is not None and len(p.freeVars) < len(p.fixedVars)):
funcs2 = [(lambda x, i=i: \
p._pointDerivative2array(
funcs[i].D(x, Vars = p.freeVars, useSparse=p.useSparse, fixedVarsScheduleID=p._FDVarsID, exactShape=True),
useSparse=p.useSparse, func=funcs[i], point=x)) \
for i in range(len(funcs))]
else:
funcs2 = [(lambda x, i=i: \
p._pointDerivative2array(
funcs[i].D(x, fixedVars = p.fixedVars, useSparse=p.useSparse, fixedVarsScheduleID=p._FDVarsID, exactShape=True),
useSparse=p.useSparse, func=funcs[i], point=x)) \
for i in range(len(funcs))]
else:
if p.freeVars is None or (p.fixedVars is not None and len(p.freeVars) < len(p.fixedVars)):
funcs2 = [(lambda x, i=i: \
funcs[i]._getFuncCalcEngine(x, Vars = p.freeVars, fixedVarsScheduleID=p._FDVarsID))\
for i in range(len(funcs))]
else:
funcs2 = [(lambda x, i=i: \
funcs[i]._getFuncCalcEngine(x, fixedVars = p.fixedVars, fixedVarsScheduleID=p._FDVarsID))\
for i in range(len(funcs))]
else:
funcs2 = funcs
if ind is None:
Funcs = funcs2
elif ind is not None and p.functype[userFunctionType] == 'some funcs R^nvars -> R':
Funcs = [funcs2[i] for i in ind]
else:
Funcs = getFuncsAndExtractIndexes(p, funcs2, ind, userFunctionType)
# agregate_counter = 0
Args = () if p.isFDmodel else args
if nXvectors == 1:
if p.isFDmodel:
X = p._vector2point(x)
X._p = p
#X._linePointDescriptor = _linePointDescriptor
else:
X = x
if nXvectors > 1: # and hence getDerivative isn't involved
#temporary, to be fixed
if userFunctionType == 'f':
assert p.isObjFunValueASingleNumber
if p.isFDmodel:
assert ind is None
if isPyPy or p.hasVectorizableFuncs: # TODO: get rid of box-bound constraints
from FuncDesigner.ooPoint import ooPoint as oopoint
from FuncDesigner.multiarray import multiarray
# TODO: new
xx = []
counter = 0
#xT = x.T
for i, oov in enumerate(p._freeVarsList):
s = p._optVarSizes[oov]
xx.append((oov, (x[:, counter: counter + s].flatten() if s == 1 else x[:, counter: counter + s]).view(multiarray)))
# xx.append((oov, multiarray(x[:, counter: counter + s].flatten() if s == 1 else x[:, counter: counter + s])))
counter += s
X = oopoint(xx)
X.update(p.dictOfFixedFuncs)
X.maxDistributionSize = p.maxDistributionSize
X._p = p
if len(p.unvectorizableFuncs) != 0:
XX = [p._vector2point(x[i]) for i in range(nXvectors)]
for _X in XX:
_X._p = p
_X.update(p.dictOfFixedFuncs)
r = vstack([[fun(xx) for xx in XX] if funcs[i] in p.unvectorizableFuncs else fun(X).T for i, fun in enumerate(Funcs)]).T
# X = [p._vector2point(x[i]) for i in range(nXvectors)]
# r = hstack([[fun(xx) for xx in X] for fun in Funcs]).reshape(1, -1)
#new
# if p.vectorizable:
# from FuncDesigner.ooPoint import ooPoint as oopoint, multiarray
#
# X = dict([(oovar, x[:, i].view(multiarray)) for i, oovar in enumerate(p._freeVarsList)])
# X = oopoint(X, skipArrayCast = True)
# X.N = nXvectors
# X.isMultiPoint = True
# X.update(p.dictOfFixedFuncs)
# r = hstack([fun(X) for fun in Funcs]).reshape(1, -1)
#
# #old
# else:
# X = [p._vector2point(x[i]) for i in range(nXvectors)]
# r = hstack([[fun(xx) for xx in X] for fun in Funcs]).reshape(1, -1)
else:
X = [(x[i],) + Args for i in range(nXvectors)]
#r = hstack([[fun(*xx) for xx in X] for fun in Funcs])
R = []
for xx in X:
tmp = [fun(*xx) for fun in Funcs]
r_ = hstack(tmp[0]) if len(tmp) == 1 and isinstance(tmp[0], (list, tuple)) else hstack(tmp) if len(tmp) > 1 else tmp[0]
R.append(r_)
r = hstack(R)#.T
#print(r.shape, userFunctionType)
elif not getDerivative:
tmp = [fun(*(X, )+Args) for fun in Funcs]
r = hstack(tmp[0]) if len(tmp) == 1 and isinstance(tmp[0], (list, tuple)) else hstack(tmp) if len(tmp) > 1 else tmp[0]
#print(x.shape, r.shape, x, r)
# if not ignorePrev and ind is None:
# p.prevVal[userFunctionType]['key'] = copy(x_0)
# p.prevVal[userFunctionType]['val'] = r.copy()
elif getDerivative and p.isFDmodel:
rr = [fun(X) for fun in Funcs]
r = Vstack(rr) if scipyInstalled and any([isspmatrix(elem) for elem in rr]) else vstack(rr)
else:
r = []
if getDerivative:
#r = zeros((nFuncsToObtain, p.n))
diffInt = p.diffInt
abs_x = abs(x)
finiteDiffNumbers = 1e-10 * abs_x
if p.diffInt.size == 1:
finiteDiffNumbers[finiteDiffNumbers < diffInt] = diffInt
else:
finiteDiffNumbers[finiteDiffNumbers < diffInt] = diffInt[finiteDiffNumbers < diffInt]
else:
#r = zeros((nFuncsToObtain, nXvectors))
r = []
for index, fun in enumerate(Funcs):
# OLD
# v = ravel(fun(*((X,) + Args)))
# if (ind is None or funcs_num == 1) and not ignorePrev:
# #TODO: ADD COUNTER OF THE CASE
# if index == 0: p.prevVal[userFunctionType]['key'] = copy(x_0)
# p.prevVal[userFunctionType]['val'][agregate_counter:agregate_counter+v.size] = v.copy()
# r[agregate_counter:agregate_counter+v.size,0] = v
#NEW
if not getDerivative:
r.append(fun(*((X,) + Args)))
# v = r[-1]
#r[agregate_counter:agregate_counter+v.size,0] = fun(*((X,) + Args))
# if (ind is None or funcs_num == 1) and not ignorePrev:
# #TODO: ADD COUNTER OF THE CASE
# if index == 0: p.prevVal[userFunctionType]['key'] = copy(x_0)
# p.prevVal[userFunctionType]['val'][agregate_counter:agregate_counter+v.size] = v.copy()
""" getting derivatives """
if getDerivative:
def func(x):
r = fun(*((x,) + Args))
return r if type(r) not in (list, tuple) or len(r)!=1 else r[0]
d1 = get_d1(func, x, pointVal = None, diffInt = finiteDiffNumbers, stencil=p.JacobianApproximationStencil, exactShape=True)
#r[agregate_counter:agregate_counter+d1.size] = d1
r.append(d1)
# v = r[-1]
# agregate_counter += atleast_1d(v).shape[0]
r = hstack(r) if not getDerivative else vstack(r)
#assert r.size != 30
#if type(r) == matrix: r = r.A
if type(r) != ndarray and not isscalar(r): # multiarray
r = r.view(ndarray).flatten() if userFunctionType == 'f' else r.view(ndarray)
#elif userFunctionType == 'f' and p.isObjFunValueASingleNumber and prod(r.shape) > 1 and (type(r) == ndarray or min(r.shape) > 1):
#r = r.sum(0)
elif userFunctionType == 'f' and p.isObjFunValueASingleNumber and not isscalar(r):
if prod(r.shape) > 1 and not getDerivative and nXvectors == 1:
p.err('implicit summation in objective is no longer available to prevent possibly hidden bugs')
# if r.size == 1:
# r = r.item()
if userFunctionType == 'f' and p.isObjFunValueASingleNumber:
if getDerivative and r.ndim > 1:
if min(r.shape) > 1:
p.err('incorrect shape of objective func derivative')
# TODO: omit cast to dense array. Somewhere bug triggers?
if hasattr(r, 'toarray'):
r=r.toarray()
r = r.flatten()
if userFunctionType != 'f' and nXvectors != 1:
r = r.reshape(nXvectors, int(r.size/nXvectors))
# if type(r) == matrix:
# raise 0
# r = r.A # if _dense_numpy_matrix !
if nXvectors == 1 and (not getDerivative or prod(r.shape) == 1): # DO NOT REPLACE BY r.size - r may be sparse!
r = r.flatten() if type(r) == ndarray else r.toarray().flatten() if not isscalar(r) else atleast_1d(r)
if p.invertObjFunc and userFunctionType=='f':
r = -r
if not getDerivative:
if ind is None:
p.nEvals[userFunctionType] += nXvectors
else:
p.nEvals[userFunctionType] = p.nEvals[userFunctionType] + float(nXvectors * len(ind)) / getattr(p, 'n'+ userFunctionType)
if getDerivative:
assert x.size == p.n#TODO: add python list possibility here
x = x_0 # for to suppress numerical instability effects while x +/- delta_x
#if userFunctionType == 'f' and p.isObjFunValueASingleNumber and r.size == 1:
#r = r.item()
if userFunctionType == 'f' and hasattr(p, 'solver') and p.solver.funcForIterFcnConnection=='f' and hasattr(p, 'f_iter') and not getDerivative:
if p.nEvals['f']%p.f_iter == 0 or nXvectors > 1:
p.iterfcn(x, r)
return r