def IMPLICATION(condition, *args):
if condition is False:
return True
if len(args) == 1 and isinstance(args[0], (tuple, set, list, ndarray)):
return ooarray([IMPLICATION(condition, elem) for elem in args[0]]) if condition is not True else args[0]
elif len(args) > 1:
return ooarray([IMPLICATION(condition, elem) for elem in args]) if condition is not True else args
return NOT(condition & NOT(args[0])) if condition is not True else args[0]
def oovars(*args, **kw):
if isPyPy:
raise FuncDesignerException('''
for PyPy using oovars() is impossible yet.
You could use oovar(size=n), also
you can create list or tuple of oovars in a cycle, e.g.
a = [oovar('a'+str(i)) for i in range(100)]
but you should ensure you haven't operations like k*a or a+val in your code,
it may work in completely different way (e.g. k*a will produce Python list of k a instances)
''')
lb = kw.pop('lb', None)
ub = kw.pop('ub', None)
if len(args) == 1:
if type(args[0]) in (int, int16, int32, int64):
r = ooarray([oovar(**kw) for i in range(args[0])])
elif type(args[0]) in [list, tuple]:
r = ooarray(
[oovar(name=args[0][i], **kw) for i in range(len(args[0]))])
elif type(args[0]) == str:
r = ooarray([oovar(name=s, **kw) for s in args[0].split()])
else:
raise FuncDesignerException(
'incorrect args number for oovars constructor')
else:
r = ooarray([oovar(name=args[i], **kw) for i in range(len(args))])
if lb is not None:
if np.isscalar(lb) or (isinstance(lb, np.ndarray) and lb.size == 1):
for v in r.view(np.ndarray):
v.lb = lb
else:
assert type(lb) in (list, tuple, ndarray)
for i, v in enumerate(r):
v.lb = lb[i]
if ub is not None:
if np.isscalar(ub) or (isinstance(ub, np.ndarray) and ub.size == 1):
for v in r.view(np.ndarray):
v.ub = ub
else:
assert type(ub) in (list, tuple, ndarray)
for i, v in enumerate(r):
v.ub = ub[i]
r._is_array_of_oovars = True
return r
def exp(inp):
if isinstance(inp, ooarray):
return ooarray([exp(elem) for elem in inp])
if hasStochastic and isinstance(inp, distribution.stochasticDistribution):
return distribution.stochasticDistribution(exp(inp.values), inp.probabilities.copy())._update(inp)
if not isinstance(inp, oofun): return np.exp(inp)
return oofun(st_exp, inp, d = lambda x: Diag(np.exp(x)), vectorized = True, criticalPoints = False)
def arcsinh(inp):
if isinstance(inp, ooarray) and any([isinstance(elem, oofun) for elem in atleast_1d(inp)]):
return ooarray([arcsinh(elem) for elem in inp])
if hasStochastic and isinstance(inp, distribution.stochasticDistribution):
return distribution.stochasticDistribution(arcsinh(inp.values), inp.probabilities.copy())._update(inp)
if not isinstance(inp, oofun): return np.arcsinh(inp)
return oofun(st_arcsinh, inp, d = lambda x: Diag(1.0/np.sqrt(1+x**2)), vectorized = True, criticalPoints = False)
def oovars(*args, **kw):
if isPyPy:
raise FuncDesignerException('''
for PyPy using oovars() is impossible yet.
You could use oovar(size=n), also
you can create list or tuple of oovars in a cycle, e.g.
a = [oovar('a'+str(i)) for i in range(100)]
but you should ensure you haven't operations like k*a or a+val in your code,
it may work in completely different way (e.g. k*a will produce Python list of k a instances)
''')
lb = kw.pop('lb', None)
ub = kw.pop('ub', None)
if len(args) == 1:
if type(args[0]) in (int, int16, int32, int64):
r = ooarray([oovar(**kw) for i in range(args[0])])
elif type(args[0]) in [list, tuple]:
r = ooarray([oovar(name=args[0][i], **kw) for i in range(len(args[0]))])
elif type(args[0]) == str:
r = ooarray([oovar(name=s, **kw) for s in args[0].split()])
else:
raise FuncDesignerException('incorrect args number for oovars constructor')
else:
r = ooarray([oovar(name=args[i], **kw) for i in range(len(args))])
if lb is not None:
if np.isscalar(lb) or (isinstance(lb, np.ndarray) and lb.size == 1):
for v in r.view(np.ndarray):
v.lb = lb
else:
assert type(lb) in (list, tuple, ndarray)
for i, v in enumerate(r):
v.lb = lb[i]
if ub is not None:
if np.isscalar(ub) or (isinstance(ub, np.ndarray) and ub.size == 1):
for v in r.view(np.ndarray):
v.ub = ub
else:
assert type(ub) in (list, tuple, ndarray)
for i, v in enumerate(r):
v.ub = ub[i]
r._is_array_of_oovars = True
return r
def floor(inp):
if isinstance(inp, ooarray) and any([isinstance(elem, oofun) for elem in atleast_1d(inp)]):
return ooarray([floor(elem) for elem in inp])
if not isinstance(inp, oofun): return np.floor(inp)
r = oofun(lambda x: np.floor(x), inp, vectorized = True)
r._D = lambda *args, **kwargs: raise_except('derivative for FD floor is unimplemented yet')
r.criticalPoints = False#lambda arg_infinum, arg_supremum: [np.floor(arg_infinum)]
return r
def abs(inp):
if isinstance(inp, ooarray) and any([isinstance(elem, oofun) for elem in atleast_1d(inp)]):
return ooarray([abs(elem) for elem in inp])
if hasStochastic and isinstance(inp, distribution.stochasticDistribution):
return distribution.stochasticDistribution(abs(inp.values), inp.probabilities.copy())._update(inp)
if not isinstance(inp, oofun): return np.abs(inp)
return oofun(st_abs, inp, d = lambda x: Diag(np.sign(x)), vectorized = True, _interval_ = ZeroCriticalPointsInterval(inp, np.abs))
def log2(inp):
if isinstance(inp, ooarray) and any([isinstance(elem, oofun) for elem in atleast_1d(inp)]):
return ooarray([log2(elem) for elem in inp])
if hasStochastic and isinstance(inp, distribution.stochasticDistribution):
return distribution.stochasticDistribution(log2(inp.values), inp.probabilities.copy())._update(inp)
if not isinstance(inp, oofun): return np.log2(inp)
r = oofun(st_log2, inp, d = lambda x: Diag(INV_LOG_2/x), vectorized = True, _interval_ = log_interval(np.log2, inp))
r.attach((inp>1e-300)('log2_domain_zero_bound_%d' % r._id, tol=-1e-7))
return r
def sin(inp):
if isinstance(inp, ooarray) and any([isinstance(elem, oofun) for elem in atleast_1d(inp)]):
return ooarray([sin(elem) for elem in inp])
elif hasStochastic and isinstance(inp, distribution.stochasticDistribution):
return distribution.stochasticDistribution(sin(inp.values), inp.probabilities.copy())._update(inp)
elif not isinstance(inp, oofun): return np.sin(inp)
return oofun(st_sin, inp,
d = lambda x: Diag(np.cos(x)),
vectorized = True,
criticalPoints = TrigonometryCriticalPoints)
def sign(inp):
if isinstance(inp, ooarray) and any([isinstance(elem, oofun) for elem in atleast_1d(inp)]):
return ooarray([sign(elem) for elem in inp])
if hasStochastic and isinstance(inp, distribution.stochasticDistribution):
return distribution.stochasticDistribution(sign(inp.values), inp.probabilities.copy())._update(inp)
if not isinstance(inp, oofun):
return np.sign(inp)
r = oofun(st_sign, inp, vectorized = True, d = lambda x: 0.0)
r.criticalPoints = False
return r
def arccosh(inp):
if isinstance(inp, ooarray) and any([isinstance(elem, oofun) for elem in atleast_1d(inp)]):
return ooarray([arccosh(elem) for elem in inp])
if hasStochastic and isinstance(inp, distribution.stochasticDistribution):
return distribution.stochasticDistribution(arccosh(inp.values), inp.probabilities.copy())._update(inp)
if not isinstance(inp, oofun): return np.arccosh(inp)
r = oofun(st_arccosh, inp, d = lambda x: Diag(1.0/np.sqrt(x**2-1.0)), vectorized = True)
F0, shift = 0.0, 1.0
r._interval_ = lambda domain, dtype: nonnegative_interval(inp, np.arccosh, domain, dtype, F0, shift)
return r
def arctanh(inp):
if isinstance(inp, ooarray) and any([isinstance(elem, oofun) for elem in atleast_1d(inp)]):
return ooarray([arctanh(elem) for elem in inp])
if hasStochastic and isinstance(inp, distribution.stochasticDistribution):
return distribution.stochasticDistribution(arctanh(inp.values), inp.probabilities.copy())._update(inp)
if not isinstance(inp, oofun): return np.arctanh(inp)
r = oofun(st_arctanh, inp, d = lambda x: Diag(1.0/(1.0-x**2)), vectorized = True, criticalPoints = False)
r.getDefiniteRange = get_box1_DefiniteRange
r._interval_ = lambda domain, dtype: box_1_interval(inp, np.arctanh, domain, dtype, -np.inf, np.inf)
return r
def tan(inp):
if isinstance(inp, ooarray) and any([isinstance(elem, oofun) for elem in atleast_1d(inp)]):
return ooarray([tan(elem) for elem in inp])
if hasStochastic and isinstance(inp, distribution.stochasticDistribution):
return distribution.stochasticDistribution(tan(inp.values), inp.probabilities.copy())._update(inp)
if not isinstance(inp, oofun): return np.tan(inp)
# TODO: move it outside of tan definition
def interval(*args):
raise 'interval for tan is unimplemented yet'
r = oofun(st_tan, inp, d = lambda x: Diag(1.0 / np.cos(x) ** 2), vectorized = True, interval = interval)
return r
def arccos(inp):
if isinstance(inp, ooarray) and any([isinstance(elem, oofun) for elem in atleast_1d(inp)]):
return ooarray([arccos(elem) for elem in inp])
if hasStochastic and isinstance(inp, distribution.stochasticDistribution):
return distribution.stochasticDistribution(arccos(inp.values), inp.probabilities.copy())._update(inp)
if not isinstance(inp, oofun): return np.arccos(inp)
r = oofun(st_arccos, inp, d = lambda x: Diag(-1.0 / np.sqrt(1.0 - x**2)), vectorized = True)
r.getDefiniteRange = get_box1_DefiniteRange
F_l, F_u = np.arccos((-1, 1))
r._interval_ = lambda domain, dtype: box_1_interval(inp, np.arccos, domain, dtype, F_l, F_u)
r.attach((inp>-1)('arccos_domain_lower_bound_%d' % r._id, tol=-1e-7), (inp<1)('arccos_domain_upper_bound_%d' % r._id, tol=-1e-7))
return r
def sqrt(inp, attachConstraints = True):
if isinstance(inp, ooarray) and any([isinstance(elem, oofun) for elem in atleast_1d(inp)]):
return ooarray([sqrt(elem) for elem in inp])
if hasStochastic and isinstance(inp, distribution.stochasticDistribution):
return distribution.stochasticDistribution(sqrt(inp.values), inp.probabilities.copy())._update(inp)
if not isinstance(inp, oofun):
return np.sqrt(inp)
# def fff(x):
# print x
# return np.sqrt(x)
r = oofun(st_sqrt, inp, d = lambda x: Diag(0.5 / np.sqrt(x)), vectorized = True)
F0 = 0.0
r._interval_ = lambda domain, dtype: nonnegative_interval(inp, np.sqrt, domain, dtype, F0)
if attachConstraints: r.attach((inp>0)('sqrt_domain_zero_bound_%d' % r._id, tol=-1e-7))
return r
def hstack(tup): # overload for oofun[ind]
c = [isinstance(t, (oofun, ooarray)) for t in tup]
if any([isinstance(t, ooarray) for t in tup]):
return ooarray(np.hstack(tup))
if not any(c):
return np.hstack(tup)
#an_oofun_ind = np.where(c)[0][0]
f = lambda *x: np.hstack(x)
# def d(*x):
#
# r = [elem.d(x[i]) if c[i] else None for i, elem in enumerate(tup)]
# size = atleast_1d(r[an_oofun_ind]).shape[0]
# r2 = [elem if c[i] else Zeros(size) for elem in r]
# return r2
#= lambda *x: np.hstack([elem.d(x) if c[i] else elem for elem in tup])
# f = lambda x: x[ind]
# def d(x):
# Xsize = Len(x)
# condBigMatrix = Xsize > 100
# if condBigMatrix and scipyInstalled:
# r = SparseMatrixConstructor((1, x.shape[0]))
# r[0, ind] = 1.0
# else:
# if condBigMatrix and not scipyInstalled: self.pWarn(scipyAbsentMsg)
# r = zeros_like(x)
# r[ind] = 1
# return r
def getOrder(*args, **kwargs):
orders = [0]+[inp.getOrder(*args, **kwargs) for inp in tup]
return np.max(orders)
r = oofun(f, tup, getOrder = getOrder)
#!!!!!!!!!!!!!!!!! TODO: sparse
def _D(*args, **kwargs):
# TODO: rework it, especially if sizes are fixed and known
# TODO: get rid of fixedVarsScheduleID
sizes = [(t(args[0], fixedVarsScheduleID = kwargs.get('fixedVarsScheduleID', -1)) if c[i] else np.asarray(t)).size for i, t in enumerate(tup)]
tmp = [elem._D(*args, **kwargs) if c[i] else None for i, elem in enumerate(tup)]
res = {}
for v in r._getDep():
Temp = []
for i, t in enumerate(tup):
if c[i]:
temp = tmp[i].get(v, None)
if temp is not None:
Temp.append(temp if type(temp) != DiagonalType else temp.resolve(kwargs['useSparse']))
else:
# T = next(iter(tmp[i].values()))
# sz = T.shape[0] if type(T) == DiagonalType else np.atleast_1d(T).shape[0]
Temp.append((Zeros if sizes[i] * np.asarray(args[0][v]).size > 1000 else np.zeros)((sizes[i], np.asarray(args[0][v]).size)))
else:
sz = np.atleast_1d(t).shape[0]
Temp.append(Zeros((sz, 1)) if sz > 100 else np.zeros(sz))
rr = Vstack([elem for elem in Temp])
#print type(rr)
res[v] = rr if not isspmatrix(rr) or 0.3 * prod(rr.shape) > rr.size else rr.toarray()
#print type(res[v])
return res
r._D = _D
return r
