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 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
# #if not isinstance(inp, oofun): return np.sin(inp)
# def d(x):
# return np.cos(x)
# return oofun(lambda x: np.sin(x), inp, d = d)
try:
import distribution
hasStochastic = True
except:
hasStochastic = False
#hasStochastic = False
st_sin = (lambda x: \
distribution.stochasticDistribution(sin(x.values), x.probabilities.copy())._update(x) \
if isinstance(x, distribution.stochasticDistribution)\
else np.array([sin(elem) for elem in x.flat]).view(multiarray) if isinstance(x, multiarray) and isinstance(x.flat[0], distribution.stochasticDistribution)
else np.sin(x))\
if hasStochastic\
else np.sin
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)
