def __init__(self, alpha = 0.1, beta = 0.1):
Function.__init__(self)
self.uniformRNG = UniformRNG()
self.betavariateRNG = _BetavariateRNG()
self.alpha = make_function(alpha)
self.beta = make_function(beta)
def __init__(self, alpha=0.1, beta=0.1):
Function.__init__(self)
self.uniformRNG = UniformRNG()
self.betavariateRNG = _BetavariateRNG()
self.alpha = make_function(alpha)
self.beta = make_function(beta)
def __init__(self,
value_generator,
mode='unbound',
lower=None,
upper=None,
sum0=0):
"""
Return an Accumulator instance.
If mode is 'unbound' (default value) the accumulation is not limited.
If mode is 'reflect' or 'r' or 'mirror' or 'm', the Accumulator folds values
inside the [lower, upper] range. lower and upper must be specified and may be
functions.
If mode 'wrap' or 'w', the Accumulator performs a wrap around at the
lower and upper limits. lower and upper must be specified and may be
functions.
An Accumulator can be initialized by passing the sum0 argument.
"""
Function.__init__(self)
self.generator = make_function(value_generator)
self.sum = sum0
if mode in ['unbound', 'u']:
self.add = self.noBounds
else:
if upper is None or lower is None:
raise ValueError(
'cannot create a bound accumulator with undefined bounds')
self.upper = make_function(upper)
self.lower = make_function(lower)
if mode in ['limit', 'l']:
self.add = self.limitAtBounds
elif mode in ['reflect', 'r', 'mirror', 'm']:
self.add = self.reflectAtBounds
elif mode in ['wrap', 'w']:
self.add = self.wrapAtBounds
else:
raise ValueError(
"mode can only be 'unbound', 'limit', 'reflect', 'mirror' or 'wrap' (got %s)"
% mode)
def __init__(self, pair0, *pairs):
Function.__init__(self)
for pair in pairs:
if type(pair) is not types.TupleType:
raise ValueError, "pair (object, probability) expected. got %s", repr(pair)
self.set = []
for o, p in [pair0] + list(pairs):
self.set.append(_PossibleChoice(o, make_function(p)))
def __init__(self, pair0, *pairs):
Function.__init__(self)
for pair in pairs:
if type(pair) is not types.TupleType:
raise ValueError, 'pair (object, probability) expected. got %s', repr(pair)
self.set = []
for o, p in [pair0] + list(pairs):
self.set.append(_PossibleChoice(o, make_function(p)))
def __init__(self, value_generator, mode="unbound", lower=None, upper=None, sum0=0):
"""
Return an Accumulator instance.
If mode is 'unbound' (default value) the accumulation is not limited.
If mode is 'reflect' or 'r' or 'mirror' or 'm', the Accumulator folds values
inside the [lower, upper] range. lower and upper must be specified and may be
functions.
If mode 'wrap' or 'w', the Accumulator performs a wrap around at the
lower and upper limits. lower and upper must be specified and may be
functions.
An Accumulator can be initialized by passing the sum0 argument.
"""
Function.__init__(self)
self.generator = make_function(value_generator)
self.sum = sum0
if mode in ["unbound", "u"]:
self.add = self.noBounds
else:
if upper is None or lower is None:
raise ValueError, "cannot create a bound accumulator with undefined bounds"
self.upper = make_function(upper)
self.lower = make_function(lower)
if mode in ["limit", "l"]:
self.add = self.limitAtBounds
elif mode in ["reflect", "r", "mirror", "m"]:
self.add = self.reflectAtBounds
elif mode in ["wrap", "w"]:
self.add = self.wrapAtBounds
else:
raise ValueError, "mode can only be 'unbound', 'limit', 'reflect', 'mirror' or 'wrap' (got %s)" % mode
def __init__(self, alpha=0.5, beta=2.0):
Generator.__init__(self)
self.alpha = make_function(alpha)
self.beta = make_function(beta)
self.weibullvariateRNG = _WeibullvariateRNG()
def __init__(self, mainFunction, lowerLimit, upperLimit, exp=0.0):
Function.__init__(self)
self.upperLimit = make_function(upperLimit)
self.lowerLimit = make_function(lowerLimit)
self.mainFunction = make_function(mainFunction)
self.exponent = math.pow(2.0, exp)
def __init__(self, generator, points, strength=1.0, exponent=0.0):
Function.__init__(self)
self.generator = make_function(generator)
self.points = make_function(points)
self.strength = make_function(strength)
self.exponent = make_function(exponent)
def __init__(self, generator, delta, strength=1.0, offset=0.0):
Function.__init__(self)
self.generator = make_function(generator)
self.delta = make_function(delta)
self.strength = make_function(strength)
self.offset = make_function(offset)
def __init__(self, mainFunction, lowerLimit, upperLimit, exp = 0.0):
Function.__init__(self)
self.upperLimit = make_function(upperLimit)
self.lowerLimit = make_function(lowerLimit)
self.mainFunction = make_function(mainFunction)
self.exponent = math.pow(2.0, exp)
def __init__(self, generator, delta, strength = 1.0, offset = 0.0):
Function.__init__(self)
self.generator = make_function(generator)
self.delta = make_function(delta)
self.strength = make_function(strength)
self.offset = make_function(offset)
def __init__(self, generator, points, strength = 1.0, exponent = 0.0):
Function.__init__(self)
self.generator = make_function(generator)
self.points = make_function(points)
self.strength = make_function(strength)
self.exponent = make_function(exponent)
def __init__(self, mu = 0.5, sigma = 0.1):
Function.__init__(self)
self.mu = make_function(mu)
self.sigma = make_function(sigma)
self.gaussRNG = _GaussRNG()
def __init__(self, lambd = 1.0):
Function.__init__(self)
self.lambd = make_function(lambd)
self.expovariateRNG = _ExpovariateRNG()
self.uniformRNG = UniformRNG()
def __init__(self, alpha=0.1, mu=0.5):
Function.__init__(self)
self.alpha = make_function(alpha)
self.mu = make_function(mu)
self.uniformRNG = UniformRNG()
def __init__(self, mu=0.5, sigma=0.1):
Function.__init__(self)
self.mu = make_function(mu)
self.sigma = make_function(sigma)
self.gaussRNG = _GaussRNG()
def __init__(self, lambd=1.0):
Function.__init__(self)
self.lambd = make_function(lambd)
self.expovariateRNG = _ExpovariateRNG()
self.uniformRNG = UniformRNG()
def __init__(self, alpha = 0.5, beta = 2.0):
Generator.__init__(self)
self.alpha = make_function(alpha)
self.beta = make_function(beta)
self.weibullvariateRNG = _WeibullvariateRNG()
def __init__(self, alpha = 0.1, mu = 0.5):
Function.__init__(self)
self.alpha = make_function(alpha)
self.mu = make_function(mu)
self.uniformRNG = UniformRNG()
