def getPattern(oofuns):
# oofuns is Python list of oofuns
assert isinstance(oofuns, list), 'oofuns should be Python list, inform developers of the bug'
R = []
for oof in oofuns:
SIZE = asarray(oof(startPoint)).size
r = []
dep = oof._getDep()
if len(p._fixedVars) != 0:
dep = dep & p._freeVars if len(p._freeVars) < len(p._fixedVars) else dep.difference(p._fixedVars)
# NEW
ind_Z = 0
vars = list(dep)
vars.sort(key=lambda elem: elem._id)
for oov in vars:
ind_start, ind_end = oovarsIndDict[oov]
if ind_start != ind_Z:
r.append(SparseMatrixConstructor((SIZE, ind_start - ind_Z)))
r.append(ones((SIZE, ind_end - ind_start)))
ind_Z = ind_end
if ind_Z != n:
# assert ind_Z < n
r.append(SparseMatrixConstructor((SIZE, n - ind_Z)))
if any([isspmatrix(elem) for elem in r]):
rr = Hstack(r) if len(r) > 1 else r[0]
elif len(r)>1:
rr = hstack(r)
else:
rr = r[0]
R.append(rr)
result = Vstack(R) if any([isspmatrix(_r) for _r in R]) else vstack(R)
return result
def getPattern(oofuns):
# oofuns is Python list of oofuns
assert isinstance(oofuns, list), 'oofuns should be Python list, inform developers of the bug'
R = []
for oof in oofuns:
SIZE = asarray(oof(startPoint)).size
r = []
dep = oof._getDep()
if len(p._fixedVars) != 0:
dep = dep & p._freeVars if len(p._freeVars) < len(p._fixedVars) else dep.difference(p._fixedVars)
# NEW
ind_Z = 0
vars = list(dep)
vars.sort(key=lambda elem: elem._id)
for oov in vars:
ind_start, ind_end = oovarsIndDict[oov]
if ind_start != ind_Z:
r.append(SparseMatrixConstructor((SIZE, ind_start - ind_Z)))
r.append(ones((SIZE, ind_end - ind_start)))
ind_Z = ind_end
if ind_Z != n:
# assert ind_Z < n
r.append(SparseMatrixConstructor((SIZE, n - ind_Z)))
# OLD
# Depends = True if freeVars[0] in dep else False
# ind_start = 0
# ind_end = asarray(startPoint[freeVars[0]]).size
# for oov in freeVars[1:]:
# tmp = startPoint[oov]
# depends = True if oov in dep else False
# if Depends != depends:
# if ind_start != ind_end:
# constructor = ones if Depends else SparseMatrixConstructor
# r.append(constructor((SIZE, ind_end-ind_start)))
# ind_start = ind_end
# Depends = depends
# ind_end += len(tmp) if not isscalar(tmp) else 1
# if ind_start != ind_end:
# constructor = ones if Depends else SparseMatrixConstructor
# r.append(constructor((SIZE, ind_end-ind_start)))
if any([isspmatrix(elem) for elem in r]):
rr = Hstack(r) if len(r) > 1 else r[0]
elif len(r)>1:
rr = hstack(r)
else:
rr = r[0]
R.append(rr)
result = Vstack(R) if any([isspmatrix(_r) for _r in R]) else vstack(R)
return result
def f_aux(x, i=i):
r = Funcs2[i][0](x)
# TODO: are other formats better?
if not isscalar(r):
if isPyPy:
if isspmatrix(r):
r = r.tocsc()[Funcs2[i][1]]
else:
# Temporary walkaround of PyPy integer indexation absence
tmp = atleast_1d(r)
r = atleast_1d([tmp[i] for i in Funcs2[i][1]])
else:
r = r.tocsc()[Funcs2[i][1]] if isspmatrix(r) else atleast_1d(r)[Funcs2[i][1]]
return r
def f_aux(x, i=i):
r = Funcs2[i][0](x)
# TODO: are other formats better?
if not isscalar(r):
if isPyPy:
if isspmatrix(r):
r = r.tocsc()[Funcs2[i][1]]
else:
# Temporary walkaround of PyPy integer indexation absence
tmp = atleast_1d(r)
r = atleast_1d([tmp[i] for i in Funcs2[i][1]])
else:
r = r.tocsc()[Funcs2[i][1]] if isspmatrix(
r) else atleast_1d(r)[Funcs2[i][1]]
return r
def f(*args, **kwargs):
tmp = funcs[0](*args, **kwargs)
if isspmatrix(tmp):
tmp = tmp.tocsc()
elif not isinstance(tmp, ndarray) or tmp.ndim == 0:
tmp = atleast_1d(tmp)
if isPyPy:
return atleast_1d([tmp[i] for i in ind])
else:
# if p.debug:
# assert all(tmp[ind] == atleast_1d([tmp[i] for i in ind]))
return tmp[ind]
def f (*args, **kwargs):
tmp = funcs[0](*args, **kwargs)
if isspmatrix(tmp):
tmp = tmp.tocsc()
elif not isinstance(tmp, ndarray) or tmp.ndim == 0:
tmp = atleast_1d(tmp)
if isPyPy:
return atleast_1d([tmp[i] for i in ind])
else:
# if p.debug:
# assert all(tmp[ind] == atleast_1d([tmp[i] for i in ind]))
return tmp[ind]
def pointDerivative2array(pointDerivarive,
useSparse='auto',
func=None,
point=None):
# useSparse can be True, False, 'auto'
if not scipyInstalled and useSparse == 'auto':
useSparse = False
if useSparse is True and not scipyInstalled:
p.err(
'to handle sparse matrices you should have module scipy installed'
)
if len(pointDerivarive) == 0:
if func is not None:
funcLen = func(point).size
if useSparse is not False:
return SparseMatrixConstructor((funcLen, n))
else:
return DenseMatrixConstructor((funcLen, n))
else:
p.err(
'unclear error, maybe you have constraint independend on any optimization variables'
)
Items = pointDerivarive.items()
key, val = Items[0] if type(Items) == list else next(iter(Items))
if isinstance(val, float) or (isinstance(val, ndarray)
and val.shape == ()):
val = atleast_1d(val)
var_inds = oovarsIndDict[key]
# val.size works in other way (as nnz) for scipy.sparse matrices
funcLen = int(round(prod(val.shape) / (var_inds[1] - var_inds[0])))
# CHANGES
# 1. Calculate number of zero/nonzero elements
involveSparse = useSparse
if useSparse == 'auto':
nTotal = n * funcLen #sum([prod(elem.shape) for elem in pointDerivarive.values()])
nNonZero = sum([
(elem.size if isspmatrix(elem) else count_nonzero(elem))
for elem in pointDerivarive.values()
])
involveSparse = 4 * nNonZero < nTotal and nTotal > 1000
if involveSparse: # and newStyle:
# USE STACK
r2 = []
hasSparse = False
if len(freeVars) > 5 * len(pointDerivarive):
ind_Z = 0
derivative_items = list(pointDerivarive.items())
derivative_items.sort(key=lambda elem: elem[0]._id)
for oov, val in derivative_items:
ind_start, ind_end = oovarsIndDict[oov]
if ind_start != ind_Z:
r2.append(
SparseMatrixConstructor(
(funcLen, ind_start - ind_Z)))
if not isspmatrix(val):
val = asarray(val) # else bug with scipy sparse hstack
r2.append(val)
ind_Z = ind_end
if ind_Z != n:
# assert ind_Z < n
r2.append(SparseMatrixConstructor((funcLen, n - ind_Z)))
else:
zeros_start_ind = 0
zeros_end_ind = 0
for i, var in enumerate(freeVars):
if var in pointDerivarive: #i.e. one of its keys
if zeros_end_ind != zeros_start_ind:
r2.append(
SparseMatrixConstructor(
(funcLen,
zeros_end_ind - zeros_start_ind)))
zeros_start_ind = zeros_end_ind
tmp = pointDerivarive[var]
if isspmatrix(tmp):
hasSparse = True
else:
tmp = asarray(
tmp) # else bug with scipy sparse hstack
if tmp.ndim < 2:
tmp = tmp.reshape(funcLen,
prod(tmp.shape) // funcLen)
r2.append(tmp)
else:
zeros_end_ind += oovar_sizes[i]
hasSparse = True
if zeros_end_ind != zeros_start_ind:
r2.append(
SparseMatrixConstructor(
(funcLen, zeros_end_ind - zeros_start_ind)))
r3 = Hstack(r2) #if hasSparse else hstack(r2)
if isspmatrix(r3) and r3.nnz > 0.25 * prod(r3.shape): r3 = r3.A
return r3
else:
# USE INSERT
if funcLen == 1:
r = DenseMatrixConstructor(n)
else:
r = SparseMatrixConstructor(
(funcLen, n)) if involveSparse else DenseMatrixConstructor(
(funcLen, n))
#r = DenseMatrixConstructor((funcLen, n))
for key, val in pointDerivarive.items():
# TODO: remove indexes, do as above for sparse
indexes = oovarsIndDict[key]
if not involveSparse and isspmatrix(val): val = val.A
# if isscalar(val) or prod(val.shape)==1:
# r[indexes[0]] = val.flatten() if type(val) == ndarray else val
# el
if r.ndim == 1:
r[indexes[0]:indexes[1]] = val.flatten() if type(
val) == ndarray else val
else:
r[:, indexes[0]:indexes[
1]] = val if val.shape == r.shape else val.reshape(
(funcLen, prod(val.shape) / funcLen))
if useSparse is True and funcLen == 1:
return SparseMatrixConstructor(r)
elif r.ndim <= 1:
r = r.reshape(1, -1)
if useSparse is False and hasattr(r, 'toarray'):
r = r.toarray()
return r
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_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 xBounds2Matrix(p):
"""
transforms lb - ub bounds into (A, x) <= b, (Aeq, x) = beq conditions
this func is developed for those solvers that can handle lb, ub only via c(x)<=0, h(x)=0
"""
#TODO: is reshape/flatten required in newest numpy versions?
# for PyPy
IndLB, IndUB, IndEQ = \
isfinite(p.lb) & ~(p.lb == p.ub), \
isfinite(p.ub) & ~(p.lb == p.ub), \
p.lb == p.ub
indLB, indUB, indEQ = \
where(IndLB)[0], \
where(IndUB)[0], \
where(IndEQ)[0]
nLB, nUB, nEQ = Len(indLB), Len(indUB), Len(indEQ)
if nLB>0 or nUB>0:
if p.useSparse is True or (isspmatrix(p.A) or (scipyInstalled and nLB+nUB>=p.A.shape[0]) and p.useSparse is not False):
R1 = coo_matrix((-ones(nLB), (range(nLB), indLB)), shape=(nLB, p.n)) if nLB != 0 else zeros((0, p.n))
R2 = coo_matrix((ones(nUB), (range(nUB), indUB)), shape=(nUB, p.n)) if nUB != 0 else zeros((0, p.n))
else:
R1 = zeros((nLB, p.n))
if isPyPy:
for i in range(nLB):
R1[i, indLB[i]] = -1
else:
R1[range(nLB), indLB] = -1
R2 = zeros((nUB, p.n))
if isPyPy:
for i in range(nUB):
R2[i, indUB[i]] = -1
else:
R2[range(nUB), indUB] = 1
p.A = Vstack((p.A, R1, R2))
if hasattr(p, '_A'): delattr(p, '_A')
if isspmatrix(p.A):
if prod(p.A.shape)>10000:
p.A = p.A.tocsc()
p._A = p.A
else:
p.A = p.A.A
p.b = Hstack((p.b, -p.lb[IndLB], p.ub[IndUB]))
if nEQ>0:
if p.useSparse is True or (isspmatrix(p.Aeq) or (scipyInstalled and nEQ>=p.Aeq.shape[0]) and p.useSparse is not False):
R = coo_matrix(([1]*nEQ, (range(nEQ), indEQ)), shape=(nEQ, p.n))
else:
R = zeros((nEQ, p.n))
#raise 0
p.Aeq = Vstack((p.Aeq, R))
if hasattr(p, '_Aeq'): delattr(p, '_Aeq')
if isspmatrix(p.Aeq):
if prod(p.Aeq.shape)>10000:
p.Aeq = p.Aeq.tocsc()
p._Aeq = p.Aeq
else:
p.Aeq = p.Aeq.A
p.beq = Hstack((p.beq, p.lb[IndEQ]))
p.lb = -inf*ones(p.n)
p.ub = inf*ones(p.n)
# TODO: prevent code clone with baseProblem.py
nA, nAeq = prod(p.A.shape), prod(p.Aeq.shape)
SizeThreshold = 2 ** 15
if scipyInstalled and p.useSparse is not False:
from scipy.sparse import csc_matrix
if nA > SizeThreshold and not isspmatrix(p.A) and flatnonzero(p.A).size < 0.25*nA:
p._A = csc_matrix(p.A)
if nAeq > SizeThreshold and not isspmatrix(p.Aeq) and flatnonzero(p.Aeq).size < 0.25*nAeq:
p._Aeq = csc_matrix(p.Aeq)
if (nA > SizeThreshold or nAeq > SizeThreshold) and not scipyInstalled and p.useSparse is not False:
p.pWarn(scipyAbsentMsg)
def xBounds2Matrix(p):
"""
transforms lb - ub bounds into (A, x) <= b, (Aeq, x) = beq conditions
this func is developed for those solvers that can handle lb, ub only via c(x)<=0, h(x)=0
"""
#TODO: is reshape/flatten required in newest numpy versions?
# for PyPy
IndLB, IndUB, IndEQ = \
isfinite(p.lb) & ~(p.lb == p.ub), \
isfinite(p.ub) & ~(p.lb == p.ub), \
p.lb == p.ub
indLB, indUB, indEQ = \
where(IndLB)[0], \
where(IndUB)[0], \
where(IndEQ)[0]
nLB, nUB, nEQ = Len(indLB), Len(indUB), Len(indEQ)
if nLB > 0 or nUB > 0:
if p.useSparse is True or (isspmatrix(
p.A) or (scipyInstalled and nLB + nUB >= p.A.shape[0])
and p.useSparse is not False):
R1 = coo_matrix(
(-ones(nLB),
(range(nLB), indLB)), shape=(nLB,
p.n)) if nLB != 0 else zeros(
(0, p.n))
R2 = coo_matrix(
(ones(nUB),
(range(nUB), indUB)), shape=(nUB,
p.n)) if nUB != 0 else zeros(
(0, p.n))
else:
R1 = zeros((nLB, p.n))
if isPyPy:
for i in range(nLB):
R1[i, indLB[i]] = -1
else:
R1[range(nLB), indLB] = -1
R2 = zeros((nUB, p.n))
if isPyPy:
for i in range(nUB):
R2[i, indUB[i]] = -1
else:
R2[range(nUB), indUB] = 1
p.A = Vstack((p.A, R1, R2))
if hasattr(p, '_A'): delattr(p, '_A')
if isspmatrix(p.A):
if prod(p.A.shape) > 10000:
p.A = p.A.tocsc()
p._A = p.A
else:
p.A = p.A.A
p.b = Hstack((p.b, -p.lb[IndLB], p.ub[IndUB]))
if nEQ > 0:
if p.useSparse is True or (isspmatrix(p.Aeq) or
(scipyInstalled and nEQ >= p.Aeq.shape[0])
and p.useSparse is not False):
R = coo_matrix(([1] * nEQ, (range(nEQ), indEQ)), shape=(nEQ, p.n))
else:
R = zeros((nEQ, p.n))
#raise 0
p.Aeq = Vstack((p.Aeq, R))
if hasattr(p, '_Aeq'): delattr(p, '_Aeq')
if isspmatrix(p.Aeq):
if prod(p.Aeq.shape) > 10000:
p.Aeq = p.Aeq.tocsc()
p._Aeq = p.Aeq
else:
p.Aeq = p.Aeq.A
p.beq = Hstack((p.beq, p.lb[IndEQ]))
p.lb = -inf * ones(p.n)
p.ub = inf * ones(p.n)
# TODO: prevent code clone with baseProblem.py
nA, nAeq = prod(p.A.shape), prod(p.Aeq.shape)
SizeThreshold = 2**15
if scipyInstalled and p.useSparse is not False:
from scipy.sparse import csc_matrix
if nA > SizeThreshold and not isspmatrix(
p.A) and flatnonzero(p.A).size < 0.25 * nA:
p._A = csc_matrix(p.A)
if nAeq > SizeThreshold and not isspmatrix(
p.Aeq) and flatnonzero(p.Aeq).size < 0.25 * nAeq:
p._Aeq = csc_matrix(p.Aeq)
if (nA > SizeThreshold or nAeq > SizeThreshold
) and not scipyInstalled and p.useSparse is not False:
p.pWarn(scipyAbsentMsg)
def pointDerivative2array(pointDerivative, useSparse = 'auto', func=None, point=None):
# useSparse can be True, False, 'auto'
if not scipyInstalled and useSparse == 'auto':
useSparse = False
if useSparse is True and not scipyInstalled:
p.err('to handle sparse matrices you should have module scipy installed')
if len(pointDerivative) == 0:
if func is not None:
funcLen = func(point).size
if useSparse is not False:
return SparseMatrixConstructor((funcLen, n))
else:
return DenseMatrixConstructor((funcLen, n))
else:
p.err('unclear error, maybe you have constraint independend on any optimization variables')
Items = pointDerivative.items()
key, val = Items[0] if type(Items) == list else next(iter(Items))
if isinstance(val, float) or (isinstance(val, ndarray) and val.shape == ()):
val = atleast_1d(val)
var_inds = oovarsIndDict[key]
# val.size works in other way (as nnz) for scipy.sparse matrices
funcLen = int(round(prod(val.shape) / (var_inds[1] - var_inds[0])))
# CHANGES
# 1. Calculate number of zero/nonzero elements
involveSparse = useSparse
if useSparse == 'auto':
nTotal = n * funcLen#sum([prod(elem.shape) for elem in pointDerivative.values()])
nNonZero = sum((elem.size if isspmatrix(elem) else count_nonzero(elem)) for elem in pointDerivative.values())
involveSparse = 4*nNonZero < nTotal and nTotal > 1000
if involveSparse:
r2 = []
if funcLen == 1:
inds = []
for oov, val in pointDerivative.items():
ind_start, ind_end = oovarsIndDict[oov]
# works faster than isscalar()
if type(val) in (float, np.float64)\
or np.isscalar(val):
r2.append(val)
inds.append(ind_start)
elif type(val) in (np.ndarray, np.matrix):
Val = (val if type(val) == ndarray else val.A).flatten()
# if Val.size == 1:
# r2.append(Val.item())
# inds.append(ind_start)
# else:
Ind = np.where(Val)[0]
r2 += Val[Ind].tolist()
inds += (ind_start+Ind).tolist()
elif isspmatrix(val):
I, J, vals = Find(val)
# if vals.size == 1:
# r2.append(vals.item())
# inds.append(ind_start+J.item())
# else:
r2 += vals.tolist()
inds += (ind_start+J).tolist()
from scipy.sparse import coo_matrix
r3 = coo_matrix((r2, ([0]*len(r2), inds)), shape=(funcLen, n))
else:
# USE STACK
ind_Z = 0
derivative_items = list(pointDerivative.items())
derivative_items.sort(key=lambda elem: elem[0]._id)
for oov, val in derivative_items:#pointDerivative.items():
ind_start, ind_end = oovarsIndDict[oov]
if ind_start != ind_Z:
r2.append(SparseMatrixConstructor((funcLen, ind_start - ind_Z)))
if not isspmatrix(val):
val = asarray(val) # else bug with scipy sparse hstack
r2.append(val)
ind_Z = ind_end
if ind_Z != n:
# assert ind_Z < n
r2.append(SparseMatrixConstructor((funcLen, n - ind_Z)))
r3 = Hstack(r2)
#if isspmatrix(r3) and 4 * r3.nnz > asarray(r3.shape, int64).prod(): r3 = r3.A
return r3
else:
# USE INSERT
if funcLen == 1:
r = DenseMatrixConstructor(n)
else:
r = SparseMatrixConstructor((funcLen, n)) if involveSparse else DenseMatrixConstructor((funcLen, n))
for key, val in pointDerivative.items():
indexes = oovarsIndDict[key]
if not involveSparse and isspmatrix(val): val = val.A
# if isscalar(val) or prod(val.shape)==1:
# r[indexes[0]] = val.flatten() if type(val) == ndarray else val
# el
if r.ndim == 1:
r[indexes[0]:indexes[1]] = val.flatten() if type(val) == ndarray else val
else:
r[:, indexes[0]:indexes[1]] = val if val.shape == r.shape else val.reshape((funcLen, prod(val.shape)/funcLen))
# TODO: mb remove it
if useSparse is True and funcLen == 1:
return SparseMatrixConstructor(r)
elif r.ndim <= 1:
r = r.reshape(1, -1)
if useSparse is False and hasattr(r, 'toarray'):
r = r.toarray()
return r
def _prepare(self):
if self._baseProblemIsPrepared: return
if self.useSparse == 0:
self.useSparse = False
elif self.useSparse == 1:
self.useSparse = True
if self.useSparse == 'auto' and not scipyInstalled:
self.useSparse = False
if self.useSparse == True and not scipyInstalled:
self.err("You can't set useSparse=True without scipy installed")
if self._isFDmodel():
self.isFDmodel = True
self._FD = EmptyClass()
self._FD.nonBoxConsWithTolShift = []
self._FD.nonBoxCons = []
from FuncDesigner import _getAllAttachedConstraints, _getDiffVarsID, ooarray, oopoint, oofun#, _Stochastic
self._FDVarsID = _getDiffVarsID()
probDep = set()
updateDep = lambda Dep, elem: \
[updateDep(Dep, f) for f in elem] if isinstance(elem, (tuple, list, set))\
else [updateDep(Dep, f) for f in atleast_1d(elem)] if isinstance(elem, ndarray)\
else Dep.update(set([elem]) if elem.is_oovar else elem._getDep()) if isinstance(elem, oofun) else None
if self.probType in ['SLE', 'NLSP', 'SNLE', 'LLSP']:
equations = self.C if self.probType in ('SLE', 'LLSP') else self.f
F = equations
updateDep(probDep, equations)
ConstraintTags = [(elem if not isinstance(elem, (set, list, tuple, ndarray)) else elem[0]).isConstraint for elem in equations]
cond_all_oofuns_but_not_cons = not any(ConstraintTags)
cond_cons = all(ConstraintTags)
if not cond_all_oofuns_but_not_cons and not cond_cons:
raise OpenOptException('for FuncDesigner SLE/SNLE constructors args must be either all-equalities or all-oofuns')
if self.fTol is not None:
fTol = min((self.ftol, self.fTol))
self.warn('''
both ftol and fTol are passed to the SNLE;
minimal value of the pair will be used (%0.1e);
also, you can modify each personal tolerance for equation, e.g.
equations = [(sin(x)+cos(y)=-0.5)(tol = 0.001), ...]
''' % fTol)
else:
fTol = self.ftol
self.fTol = self.ftol = fTol
appender = lambda arg: [appender(elem) for elem in arg] if isinstance(arg, (ndarray, list, tuple, set))\
else ((arg.oofun*(fTol/arg.tol) if arg.tol != fTol and arg.tol != 0 else arg.oofun) if arg.isConstraint else arg)
EQs = []
for eq in equations:
rr = appender(eq)
if type(rr) == list:
EQs += rr
else:
EQs.append(rr)
#EQs = [((elem.oofun*(fTol/elem.tol) if elem.tol != 0 else elem.oofun) if elem.isConstraint else elem) for elem in equations]
if self.probType in ('SLE', 'LLSP'): self.C = EQs
elif self.probType in ('NLSP', 'SNLE'):
self.f = EQs
# self.user.F = EQs
else: raise OpenOptException('bug in OO kernel')
else:
F = [self.f]
updateDep(probDep, self.f)
updateDep(probDep, self.constraints)
# TODO: implement it
# startPointVars = set(self.x0.keys())
# D = startPointVars.difference(probDep)
# if len(D):
# print('values for variables %s are missing in start point' % D)
# D2 = probDep.difference(startPointVars)
# if len(D2):
# self.x0 = dict([(key, self.x0[key]) for key in D2])
for fn in ['lb', 'ub', 'A', 'Aeq', 'b', 'beq']:
if not hasattr(self, fn): continue
val = getattr(self, fn)
if val is not None and any(isfinite(val)):
self.err('while using oovars providing lb, ub, A, Aeq for whole prob is forbidden, use for each oovar instead')
if not isinstance(self.x0, dict):
self.err('Unexpected start point type: ooPoint or Python dict expected, '+ str(type(self.x0)) + ' obtained')
x0 = self.x0.copy()
tmp = []
for key, val in x0.items():
if not isinstance(key, (list, tuple, ndarray)):
tmp.append((key, val))
else: # can be only ooarray although
val = atleast_1d(val)
if len(key) != val.size:
self.err('''
for the sake of possible bugs prevention lenght of oovars array
must be equal to lenght of its start point value,
assignments like x = oovars(m); startPoint[x] = 0 are forbidden,
use startPoint[x] = [0]*m or np.zeros(m) instead''')
for i in range(val.size):
tmp.append((key[i], val[i]))
Tmp = dict(tmp)
if isinstance(self.fixedVars, dict):
for key, val in self.fixedVars.items():
if isinstance(key, (list, tuple, ndarray)): # can be only ooarray although
if len(key) != len(val):
self.err('''
for the sake of possible bugs prevention lenght of oovars array
must be equal to lenght of its start point value,
assignments like x = oovars(m); fixedVars[x] = 0 are forbidden,
use fixedVars[x] = [0]*m or np.zeros(m) instead''')
for i in range(len(val)):
Tmp[key[i]] = val[i]
else:
Tmp[key] = val
self.fixedVars = set(self.fixedVars.keys())
# mb other operations will speedup it?
if self.probType != 'ODE':
Keys = set(Tmp.keys()).difference(probDep)
for key in Keys:
Tmp.pop(key)
self.probDep = probDep
self.x0 = Tmp
self._stringVars = set()
for key, val in self.x0.items():
#if key.domain is not None and key.domain is not bool and key.domain is not 'bool':
if type(val) in (str, string_):
self._stringVars.add(key)
key.formAuxDomain()
self.x0[key] = key.aux_domain[val]#searchsorted(key.aux_domain, val, 'left')
elif key.fields == () and key.domain is not None and key.domain is not bool and key.domain is not 'bool' \
and key.domain is not int and key.domain is not 'int' and val not in key.domain:
self.x0[key] = key.domain[0]
self.x0 = oopoint(self.x0)
self.x0.maxDistributionSize = self.maxDistributionSize
setStartVectorAndTranslators(self)
if self.probType in ['LP', 'MILP', 'SOCP'] and self.f.getOrder(self.freeVarsSet, self.fixedVarsSet, fixedVarsScheduleID = self._FDVarsID) > 1:
self.err('for LP/MILP objective function has to be linear, while this one ("%s") is not' % self.f.name)
if self.fixedVars is None:
D_kwargs = {'fixedVars':self.fixedVarsSet}
elif self.freeVars is not None and len(self.freeVars)<len(self.fixedVars):
D_kwargs = {'Vars':self.freeVarsSet}
else:
D_kwargs = {'fixedVars':self.fixedVarsSet}
D_kwargs['useSparse'] = self.useSparse
D_kwargs['fixedVarsScheduleID'] = self._FDVarsID
D_kwargs['exactShape'] = True
self._D_kwargs = D_kwargs
variableTolerancesDict = dict((v, v.tol) for v in self._freeVars)
self.variableTolerances = self._point2vector(variableTolerancesDict)
if len(self._fixedVars) < len(self._freeVars) and 'isdisjoint' in dir(set()):
areFixed = lambda dep: dep.issubset(self.fixedVarsSet)
#isFixed = lambda v: v in self._fixedVars
Z = dict((v, zeros_like(val) if v not in self.fixedVarsSet else val) for v, val in self._x0.items())
else:
areFixed = lambda dep: dep.isdisjoint(self.freeVarsSet)
#isFixed = lambda v: v not in self._freeVars
Z = dict((v, zeros_like(val) if v in self.freeVarsSet else val) for v, val in self._x0.items())
Z = oopoint(Z, maxDistributionSize = self.maxDistributionSize)
self._Z = Z
#p.isFixed = isFixed
lb, ub = -inf*ones(self.n), inf*ones(self.n)
# TODO: get rid of start c, h = None, use [] instead
A, b, Aeq, beq = [], [], [], []
if type(self.constraints) not in (list, tuple, set):
self.constraints = [self.constraints]
oovD = self._oovarsIndDict
LB = {}
UB = {}
""" gather attached constraints """
C = list(self.constraints)
self.constraints = set(self.constraints)
for v in self._x0.keys():
# if v.fields != ():
# v.aux_domain = Copy(v.domain)
## # TODO: mb rework it
## ind_numeric = [j for j, elem in enumerate(v.aux_domain[0]) if type(elem) not in (str, np.str_)]
## if len(ind_numeric):
## ind_first_numeric = ind_numeric[0]
## v.aux_domain.sort(key = lambda elem: elem[ind_first_numeric])
# v.domain = np.arange(len(v.domain))
if not array_equal(v.lb, -inf):
self.constraints.add(v >= v.lb)
if not array_equal(v.ub, inf):
self.constraints.add(v <= v.ub)
if self.useAttachedConstraints:
if hasattr(self, 'f'):
if type(self.f) in [list, tuple, set]:
C += list(self.f)
else: # self.f is oofun
C.append(self.f)
self.constraints.update(_getAllAttachedConstraints(C))
FF = self.constraints.copy()
for _F in F:
if isinstance(_F, (tuple, list, set)):
FF.update(_F)
elif isinstance(_F, ndarray):
if _F.size > 1:
FF.update(_F)
else:
FF.add(_F.item())
else:
FF.add(_F)
unvectorizableFuncs = set()
#unvectorizableVariables = set([var for var, val in self._x0.items() if isinstance(val, _Stochastic) or asarray(val).size > 1])
# TODO: use this
unvectorizableVariables = set([])
# temporary replacement:
#unvectorizableVariables = set([var for var, val in self._x0.items() if asarray(val).size > 1])
cond = False
#debug
# unvectorizableVariables = set(self._x0.keys())
# hasVectorizableFuncs = False
# cond = True
#debug end
if 1 and isPyPy:
hasVectorizableFuncs = False
unvectorizableFuncs = FF
else:
hasVectorizableFuncs = False
if len(unvectorizableVariables) != 0:
for ff in FF:
_dep = ff._getDep()
if cond or len(_dep & unvectorizableVariables) != 0:
unvectorizableFuncs.add(ff)
else:
hasVectorizableFuncs = True
else:
hasVectorizableFuncs = True
self.unvectorizableFuncs = unvectorizableFuncs
self.hasVectorizableFuncs = hasVectorizableFuncs
for v in self.freeVarsSet:
d = v.domain
if d is bool or d is 'bool':
self.constraints.update([v>0, v<1])
elif d is not None and d is not int and d is not 'int':
# TODO: mb add integer domains?
v.domain = array(list(d))
v.domain.sort()
self.constraints.update([v >= v.domain[0], v <= v.domain[-1]])
if hasattr(v, 'aux_domain'):
self.constraints.add(v <= len(v.aux_domain)-1)
# for v in self._stringVars:
# if isFixed(v):
# ind = searchsorted(v.aux_domain, p._x0[v], 'left')
# if v.aux_domain
""" handling constraints """
StartPointVars = set(self._x0.keys())
self.dictOfFixedFuncs = {}
from FuncDesigner import broadcast
if self.probType in ['SLE', 'NLSP', 'SNLE', 'LLSP']:
for eq in equations:
broadcast(formDictOfFixedFuncs, eq, self.useAttachedConstraints, self.dictOfFixedFuncs, areFixed, self._x0)
else:
broadcast(formDictOfFixedFuncs, self.f, self.useAttachedConstraints, self.dictOfFixedFuncs, areFixed, self._x0)
if oosolver(self.solver).useLinePoints:
self._firstLinePointDict = {}
self._secondLinePointDict = {}
self._currLinePointDict = {}
inplaceLinearRender = oosolver(self.solver).__name__ == 'interalg'
if inplaceLinearRender and hasattr(self, 'f'):
D_kwargs2 = D_kwargs.copy()
D_kwargs2['useSparse'] = False
if type(self.f) in [list, tuple, set]:
ff = []
for f in self.f:
if f.getOrder(self.freeVarsSet, self.fixedVarsSet, fixedVarsScheduleID = self._FDVarsID) < 2:
D = f.D(Z, **D_kwargs2)
f2 = linear_render(f, D, Z)
ff.append(f2)
else:
ff.append(f)
self.f = ff
else: # self.f is oofun
if self.f.getOrder(self.freeVarsSet, self.fixedVarsSet, fixedVarsScheduleID = self._FDVarsID) < 2:
D = self.f.D(Z, **D_kwargs2)
self.f = linear_render(self.f, D, Z)
if self.isObjFunValueASingleNumber:
self._linear_objective = True
self._linear_objective_factor = self._pointDerivative2array(D).flatten()
self._linear_objective_scalar = self.f(Z)
handleConstraint_args = (StartPointVars, areFixed, oovD, A, b, Aeq, beq, Z, D_kwargs, LB, UB, inplaceLinearRender)
for c in self.constraints:
if isinstance(c, ooarray):
for elem in c:
self.handleConstraint(elem, *handleConstraint_args)
elif c is True:
continue
elif c is False:
self.err('one of elements from constraints is "False", solution is impossible')
elif not hasattr(c, 'isConstraint'):
self.err('The type ' + str(type(c)) + ' is inappropriate for problem constraints')
else:
self.handleConstraint(c, *handleConstraint_args)
if len(b) != 0:
self.A, self.b = Vstack(A), Hstack([asfarray(elem).flatten() for elem in b])#Vstack(b).flatten()
if hasattr(self.b, 'toarray'): self.b = self.b.toarray()
if len(beq) != 0:
self.Aeq, self.beq = Vstack(Aeq), Hstack([ravel(elem) for elem in beq])#Vstack(beq).flatten()
if hasattr(self.beq, 'toarray'): self.beq = self.beq.toarray()
for vName, vVal in LB.items():
inds = oovD[vName]
lb[inds[0]:inds[1]] = vVal
for vName, vVal in UB.items():
inds = oovD[vName]
ub[inds[0]:inds[1]] = vVal
self.lb, self.ub = lb, ub
else: # not FuncDesigner
if self.fixedVars is not None or self.freeVars is not None:
self.err('fixedVars and freeVars are valid for optimization of FuncDesigner models only')
if self.x0 is None:
arr = ['lb', 'ub']
if self.probType in ['LP', 'MILP', 'QP', 'SOCP', 'SDP']: arr.append('f')
if self.probType in ['LLSP', 'LLAVP', 'LUNP']: arr.append('D')
for fn in arr:
if not hasattr(self, fn): continue
tmp = getattr(self, fn)
fv = asarray(tmp) if not isspmatrix(tmp) else tmp.A
if any(isfinite(fv)):
self.x0 = np.zeros(fv.size)
break
self.x0 = ravel(self.x0)
if not hasattr(self, 'n'): self.n = self.x0.size
if not hasattr(self, 'lb'): self.lb = -inf * ones(self.n)
if not hasattr(self, 'ub'): self.ub = inf * ones(self.n)
for fn in ('A', 'Aeq'):
fv = getattr(self, fn)
if fv is not None:
#afv = asfarray(fv) if not isspmatrix(fv) else fv.toarray() # TODO: omit casting to dense matrix
afv = asfarray(fv) if type(fv) in [list, tuple] else fv
if len(afv.shape) > 1:
if afv.shape[1] != self.n:
self.err('incorrect ' + fn + ' size')
else:
if afv.shape != () and afv.shape[0] == self.n: afv = afv.reshape(1, self.n)
setattr(self, fn, afv)
else:
setattr(self, fn, asfarray([]).reshape(0, self.n))
nA, nAeq = prod(self.A.shape), prod(self.Aeq.shape)
SizeThreshold = 2 ** 15
if scipyInstalled:
from scipy.sparse import csc_matrix
if isspmatrix(self.A) or (nA > SizeThreshold and np.flatnonzero(self.A).size < 0.25*nA):
self._A = csc_matrix(self.A)
if isspmatrix(self.Aeq) or (nAeq > SizeThreshold and np.flatnonzero(self.Aeq).size < 0.25*nAeq):
self._Aeq = csc_matrix(self.Aeq)
elif nA > SizeThreshold or nAeq > SizeThreshold:
self.pWarn(scipyAbsentMsg)
self._baseProblemIsPrepared = True
def pointDerivative2array(pointDerivarive, useSparse = 'auto', func=None, point=None):
# useSparse can be True, False, 'auto'
if not scipyInstalled and useSparse == 'auto':
useSparse = False
if useSparse is True and not scipyInstalled:
p.err('to handle sparse matrices you should have module scipy installed')
if len(pointDerivarive) == 0:
if func is not None:
funcLen = func(point).size
if useSparse is not False:
return SparseMatrixConstructor((funcLen, n))
else:
return DenseMatrixConstructor((funcLen, n))
else:
p.err('unclear error, maybe you have constraint independend on any optimization variables')
Items = pointDerivarive.items()
key, val = Items[0] if type(Items) == list else next(iter(Items))
if isinstance(val, float) or (isinstance(val, ndarray) and val.shape == ()):
val = atleast_1d(val)
var_inds = oovarsIndDict[key]
# val.size works in other way (as nnz) for scipy.sparse matrices
funcLen = int(round(prod(val.shape) / (var_inds[1] - var_inds[0])))
# CHANGES
# 1. Calculate number of zero/nonzero elements
involveSparse = useSparse
if useSparse == 'auto':
nTotal = n * funcLen#sum([prod(elem.shape) for elem in pointDerivarive.values()])
nNonZero = sum([(elem.size if isspmatrix(elem) else count_nonzero(elem)) for elem in pointDerivarive.values()])
involveSparse = 4*nNonZero < nTotal and nTotal > 1000
if involveSparse:# and newStyle:
# USE STACK
r2 = []
hasSparse = False
if len(freeVars) > 5 * len(pointDerivarive):
ind_Z = 0
derivative_items = list(pointDerivarive.items())
derivative_items.sort(key=lambda elem: elem[0]._id)
for oov, val in derivative_items:
ind_start, ind_end = oovarsIndDict[oov]
if ind_start != ind_Z:
r2.append(SparseMatrixConstructor((funcLen, ind_start - ind_Z)))
if not isspmatrix(val):
val = asarray(val) # else bug with scipy sparse hstack
r2.append(val)
ind_Z = ind_end
if ind_Z != n:
# assert ind_Z < n
r2.append(SparseMatrixConstructor((funcLen, n - ind_Z)))
else:
zeros_start_ind = 0
zeros_end_ind = 0
for i, var in enumerate(freeVars):
if var in pointDerivarive:#i.e. one of its keys
if zeros_end_ind != zeros_start_ind:
r2.append(SparseMatrixConstructor((funcLen, zeros_end_ind - zeros_start_ind)))
zeros_start_ind = zeros_end_ind
tmp = pointDerivarive[var]
if isspmatrix(tmp):
hasSparse = True
else:
tmp = asarray(tmp) # else bug with scipy sparse hstack
if tmp.ndim < 2:
tmp = tmp.reshape(funcLen, prod(tmp.shape) // funcLen)
r2.append(tmp)
else:
zeros_end_ind += oovar_sizes[i]
hasSparse = True
if zeros_end_ind != zeros_start_ind:
r2.append(SparseMatrixConstructor((funcLen, zeros_end_ind - zeros_start_ind)))
r3 = Hstack(r2) #if hasSparse else hstack(r2)
if isspmatrix(r3) and r3.nnz > 0.25 * prod(r3.shape): r3 = r3.A
return r3
else:
# USE INSERT
if funcLen == 1:
r = DenseMatrixConstructor(n)
else:
r = SparseMatrixConstructor((funcLen, n)) if involveSparse else DenseMatrixConstructor((funcLen, n))
#r = DenseMatrixConstructor((funcLen, n))
for key, val in pointDerivarive.items():
# TODO: remove indexes, do as above for sparse
indexes = oovarsIndDict[key]
if not involveSparse and isspmatrix(val): val = val.A
# if isscalar(val) or prod(val.shape)==1:
# r[indexes[0]] = val.flatten() if type(val) == ndarray else val
# el
if r.ndim == 1:
r[indexes[0]:indexes[1]] = val.flatten() if type(val) == ndarray else val
else:
r[:, indexes[0]:indexes[1]] = val if val.shape == r.shape else val.reshape((funcLen, prod(val.shape)/funcLen))
if useSparse is True and funcLen == 1:
return SparseMatrixConstructor(r)
elif r.ndim <= 1:
r = r.reshape(1, -1)
if useSparse is False and hasattr(r, 'toarray'):
r = r.toarray()
return r
def matMultVec(self, x, y):
return np.dot(x, y) if not isspmatrix(x) else x._mul_sparse_matrix(csr_matrix(y.reshape((y.size, 1)))).A.flatten()
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
