def rbinomial(self, n, phi):
"""
The binomial distribution: p(N=k) = bincoeff * phi**k * (1-phi)**(n-k);
n >= 1; k = 0, 1,...., n where phi is the frequency or "Bernoulli
probability".
Algorithm taken from ORNL-RSIC-38, Vol II (1973).
"""
assert is_posinteger(n), \
"n must be a positive integer in rbinomial!"
assert 0.0 < phi and phi < 1.0, \
"frequency parameter is out of range in rbinomial!"
normconst = 10.0
onemphi = 1.0 - phi
if phi < 0.5: w = int(round(normconst * onemphi / phi))
else: w = int(round(normconst * phi / onemphi))
if n > w:
#-------------------------------------------------------
k = int(round(self.rnormal(n * phi, sqrt(n * phi * onemphi))))
else:
#-------------------------------------------------------
if phi < 0.25:
k = -1
m = 0
phi = -safelog(onemphi)
while m < n:
r = self.rexpo(1.0)
j = 1 + int(r / phi)
m += j
k += 1
if m == n:
k += 1
elif phi > 0.75:
k = n + 1
m = 0
phi = -safelog(phi)
while m < n:
r = self.rexpo(1.0)
j = 1 + int(r / phi)
m += j
k -= 1
if m == n:
k -= 1
else: # if 0.25 <= phi and phi <= 0.75:
k = 0
m = 0
while m < n:
r = self.runif01()
if r < phi: k += 1
m += 1
k = kept_within(0, k, n)
return k
def rtukeylambda_gen(self, lam1, lam2, lam3, lam4, pmin=0.0, pmax=1.0):
"""
The Friemer-Mudholkar-Kollia-Lin generalized Tukey lambda distribution.
lam1 is a location parameter and lam2 a scale parameter. lam3 and lam4
are associated with the shape of the distribution.
"""
assert lam2 > 0.0, \
"shape parameter lam2 must be a positive float in rtukeylambda_gen!"
assert 0.0 <= pmin < pmax, \
"pmin must be in [0.0, pmax) in rtukeylambda_gen!"
assert pmin < pmax <= 1.0, \
"pmax must be in (pmin, 1.0] in rtukeylambda_gen!"
p = pmin + (pmax-pmin)*self.runif01()
if lam3 == 0.0:
q3 = safelog(p)
else:
q3 = (p**lam3-1.0) / lam3
if lam4 == 0.0:
q4 = safelog(1.0-p)
else:
q4 = ((1.0-p)**lam4 - 1.0) / lam4
x = lam1 + (q3-q4)/lam2
return x
def itukeylambda_gen(prob, lam1, lam2, lam3, lam4):
"""
The Friemer-Mudholkar-Kollia-Lin generalized Tukey-Lambda distribution.
lam1 is a location parameter and lam2 is a scale parameter. lam3 and lam4
are associated with the shape. lam2 must be a positive number.
"""
_assertprob(prob, 'itukeylambda_gen')
# ---
assert lam2 > 0.0, \
"shape parameter lam2 must be a positive float in itukeylambda_gen!"
if lam3 == 0.0:
q3 = safelog(prob)
else:
q3 = (prob**lam3-1.0) / lam3
if lam4 == 0.0:
q4 = safelog(1.0-prob)
else:
q4 = ((1.0-prob)**lam4 - 1.0) / lam4
x = lam1 + (q3-q4)/lam2
return x
def rbinomial(self, n, phi):
"""
The binomial distribution: p(N=k) = bincoeff * phi**k * (1-phi)**(n-k);
n >= 1; k = 0, 1,...., n where phi is the frequency or "Bernoulli
probability".
Algorithm taken from ORNL-RSIC-38, Vol II (1973).
"""
assert is_posinteger(n), \
"n must be a positive integer in rbinomial!"
assert 0.0 < phi and phi < 1.0, \
"frequency parameter is out of range in rbinomial!"
normconst = 10.0
onemphi = 1.0 - phi
if phi < 0.5: w = int(round(normconst * onemphi / phi))
else: w = int(round(normconst * phi / onemphi))
if n > w:
#-------------------------------------------------------
k = int(round(self.rnormal(n*phi, sqrt(n*phi*onemphi))))
else:
#-------------------------------------------------------
if phi < 0.25:
k = -1
m = 0
phi = - safelog(onemphi)
while m < n:
r = self.rexpo(1.0)
j = 1 + int(r/phi)
m += j
k += 1
if m == n:
k += 1
elif phi > 0.75:
k = n + 1
m = 0
phi = - safelog(phi)
while m < n:
r = self.rexpo(1.0)
j = 1 + int(r/phi)
m += j
k -= 1
if m == n:
k -= 1
else: # if 0.25 <= phi and phi <= 0.75:
k = 0
m = 0
while m < n:
r = self.runif01()
if r < phi: k += 1
m += 1
k = kept_within(0, k, n)
return k
def iextreme_I(prob, type='max', mu=0.0, scale=1.0):
"""
Extreme value distribution type I (aka the Gumbel distribution or
Gumbel distribution type I):
F = exp{-exp[-(x-mu)/scale]} (max variant)
f = exp[-(x-mu)/scale] * exp{-exp[-(x-mu)/scale]} / scale
F = 1 - exp{-exp[+(x-mu)/scale]} (min variant)
f = exp[+(x-mu)/scale] * exp{-exp[+(x-mu)/scale]} / scale
type must be 'max' or 'min'
scale must be > 0.0
"""
_assertprob(prob, 'iextreme_I')
assert scale >= 0.0, "scale parameter must not be negative in iextreme_I!"
if type == 'max':
x = - scale*safelog(-safelog(prob)) + mu
elif type == 'min':
x = scale*safelog(-safelog(1.0-prob)) + mu
else:
raise Error("iextreme_I: type must be either 'max' or 'min'")
return x
def rtukeylambda_gen(self, lam1, lam2, lam3, lam4, pmin=0.0, pmax=1.0):
"""
The Friemer-Mudholkar-Kollia-Lin generalized Tukey lambda distribution.
lam1 is a location parameter and lam2 a scale parameter. lam3 and lam4
are associated with the shape of the distribution.
"""
assert lam2 > 0.0, \
"shape parameter lam2 must be a positive float in rtukeylambda_gen!"
assert 0.0 <= pmin < pmax, \
"pmin must be in [0.0, pmax) in rtukeylambda_gen!"
assert pmin < pmax <= 1.0, \
"pmax must be in (pmin, 1.0] in rtukeylambda_gen!"
p = pmin + (pmax - pmin) * self.runif01()
if lam3 == 0.0:
q3 = safelog(p)
else:
q3 = (p**lam3 - 1.0) / lam3
if lam4 == 0.0:
q4 = safelog(1.0 - p)
else:
q4 = ((1.0 - p)**lam4 - 1.0) / lam4
x = lam1 + (q3 - q4) / lam2
return x
def ikodlin(prob, gam, eta):
"""
The inverse of the Kodlin distribution, aka the linear hazard rate distribution:
f = (gam + eta*x) * exp{-[gam*x + (1/2)*eta*x**2]}
F = 1 - exp{-[gam*x + (1/2)*eta*x**2]}
x, gam, eta >= 0
"""
_assertprob(prob, 'ikodlin')
assert gam >= 0.0, "no parameters in ikodlin must be negative!"
assert eta >= 0.0, "no parameters in ikodlin must be negative!"
# (1/2)*eta*x**2 + gam*x + ln(1-F) = 0
try:
a = 0.5*eta
b = gam
c = safelog(1.0-prob)
x1, x2 = z2nddeg_real(a, b, c)
x = max(x1, x2)
except ValueError:
x = float('inf')
x = kept_within(0.0, x)
return x
def rerlang(self, nshape, phasemean, xmax=float('inf')):
"""
Generator of Erlang-distributed random variates.
Represents the sum of nshape exponentially distributed random variables,
each having the same mean value = phasemean. For nshape = 1 it works as
a generator of exponentially distributed random numbers.
"""
assert is_posinteger(nshape), \
"shape parameter must be a positive integer in rerlang!"
assert phasemean >= 0.0, "phasemean must not be negative in rerlang!"
assert xmax >= 0.0, "variate max must be non-negative in rerlang!"
if nshape < GeneralRandomStream.__ERLANG2GAMMA:
while True:
x = 1.0
for k in range(0, nshape):
x *= self.runif01() # Might turn out to be zero...
x = -phasemean * safelog(x)
if x <= xmax: break
else: # Gamma is OK
while True:
x = phasemean * self.rgamma(float(nshape), 1.0)
if x <= xmax: break
x = kept_within(0.0, x)
return x
def rinhomexpo_cum(self, lamt, suplamt):
"""
Generates - cumulatively - inhomogeneously exponentially distributed
interarrival times (due to an inhomogeneous Poisson process) from
clock time = 0.0 (the algorithm is taken from Bratley, Fox & Schrage).
'lamt' is an externally defined function of clock time that returns
the present arrival rate (arrival frequency). 'suplamt' is another
externally defined function that returns the supremum of the arrival
rate over the REMAINING time from the present clock time.
NB. A SEPARATE STREAM MUST BE INSTANTIATED FOR EACH ONE OF THE VARIATES
THAT ARE GENERATED USING THIS METHOD, EACH WITH A SEPARATE SEED!!
"""
errtxt1 = "All values from suplamt must be "
errtxt1 += "non-negative floats in rinhomexpo_cum!"
errtxt2 = "All values from lamt must be "
errtxt2 += "non-negative floats in rinhomexpo_cum!"
ticum = self.__ticum
while True:
lamsup = suplamt(ticum)
assert lamsup >= 0.0, errortxt1
u = self.runif01()
ticum -= safediv(1.0, lamsup) * safelog(self.runif01())
lam = lamt(ticum)
assert lam >= 0.0, errortxt2
if u*lamsup <= lam:
break
self.__ticum = ticum
return ticum
def rinhomexpo_cum(self, lamt, suplamt):
"""
Generates - cumulatively - inhomogeneously exponentially distributed
interarrival times (due to an inhomogeneous Poisson process) from
clock time = 0.0 (the algorithm is taken from Bratley, Fox & Schrage).
'lamt' is an externally defined function of clock time that returns
the present arrival rate (arrival frequency). 'suplamt' is another
externally defined function that returns the supremum of the arrival
rate over the REMAINING time from the present clock time.
NB. A SEPARATE STREAM MUST BE INSTANTIATED FOR EACH ONE OF THE VARIATES
THAT ARE GENERATED USING THIS METHOD, EACH WITH A SEPARATE SEED!!
"""
errtxt1 = "All values from suplamt must be "
errtxt1 += "non-negative floats in rinhomexpo_cum!"
errtxt2 = "All values from lamt must be "
errtxt2 += "non-negative floats in rinhomexpo_cum!"
ticum = self.__ticum
while True:
lamsup = suplamt(ticum)
assert lamsup >= 0.0, errortxt1
u = self.runif01()
ticum -= safediv(1.0, lamsup) * safelog(self.runif01())
lam = lamt(ticum)
assert lam >= 0.0, errortxt2
if u * lamsup <= lam:
break
self.__ticum = ticum
return ticum
def rerlang(self, nshape, phasemean, xmax=float('inf')):
"""
Generator of Erlang-distributed random variates.
Represents the sum of nshape exponentially distributed random variables,
each having the same mean value = phasemean. For nshape = 1 it works as
a generator of exponentially distributed random numbers.
"""
assert is_posinteger(nshape), \
"shape parameter must be a positive integer in rerlang!"
assert phasemean >= 0.0, "phasemean must not be negative in rerlang!"
assert xmax >= 0.0, "variate max must be non-negative in rerlang!"
if nshape < GeneralRandomStream.__ERLANG2GAMMA:
while True:
x = 1.0
for k in range(0, nshape):
x *= self.runif01() # Might turn out to be zero...
x = - phasemean * safelog(x)
if x <= xmax: break
else: # Gamma is OK
while True:
x = phasemean * self.rgamma(float(nshape), 1.0)
if x <= xmax: break
x = kept_within(0.0, x)
return x
def ilaplace(prob, loc=0.0, scale=1.0):
"""
The inverse of the Laplace distribution f = [(1/2)/s)]*exp(-abs([x-l]/s))
F = (1/2)*exp([x-l]/s) {x <= l}, F = 1 - (1/2)*exp(-[x-l]/s) {x >= l}
s >= 0
"""
_assertprob(prob, 'ilaplace')
# ---
assert scale >= 0.0, "scale parameter in ilaplace must not be negative!"
if prob <= 0.5:
x = safelog(2.0*prob)
x = scale*x + loc
else:
x = - safelog(2.0*(1.0-prob))
x = scale*x + loc
return x
def igeometric(prob, phi):
"""
The geometric distribution with p(K=k) = phi * (1-phi)**(k-1) and
P(K>=k) = sum phi * (1-phi)**k = 1 - q**k where q = 1 - phi and
0 < phi <= 1 is the success frequency or "Bernoulli probability" and
K >= 1 is the number of trials to the first success in a series of
Bernoulli trials. It is easy to prove that P(k) = 1 - (1-phi)**k:
let q = 1 - phi. p(k) = (1-q) * q**(k-1) = q**(k-1) - q**k.
Then P(1) = p(1) = 1 - q. P(2) = p(1) + p(2) = 1 - q + q - q**2 = 1 - q**2.
Induction can be used to show that P(k) = 1 - q**k = 1 - (1-phi)**k
The algorithm is taken from ORNL-RSIC-38, Vol II (1973).
"""
_assertprob(prob, 'igeometric')
assert 0.0 <= phi and phi <= 1.0, \
"success frequency must be in [0.0, 1.0] in igeometric!"
if phi == 1.0: return 1 # Obvious...
q = 1.0 - phi
if phi < 0.25: # Use the direct inversion formula
lnq = - safelog(q)
ln1mp = - safelog(1.0 - prob)
kg = 1 + int(ln1mp/lnq)
else: # Looking for the passing point is more efficient for
kg = 1 # phi >= 0.25 (it's still inversion)
u = prob
a = phi
while True:
u = u - a
if u > 0.0:
kg += 1
a *= q
else:
break
return kg
def irayleigh(prob, sigma=1.0):
"""
The inverse of the Rayleigh distribution:
f = (x/s**2) * exp[-x**2/(2*s**2)]
F = 1 - exp[-x**2/(2*s**2)]
x, s >= 0
"""
_assertprob(prob, 'irayleigh')
assert sigma >= 0.0, "parameter in irayleigh must not be negative!"
return sigma * sqrt(2.0*(-safelog(1.0-prob))) # Will always be >= 0.0
def iextreme_gen(prob, type, shape, mu=0.0, scale=1.0):
"""
Generalized extreme value distribution:
F = exp{-[1-shape*(x-mu)/scale]**(1/shape)} (max version)
f = [1-shape*(x-mu)/scale]**(1/shape-1) *
exp{-[1-shape*(x-mu)/scale]**(1/shape)} / scale
F = 1 - exp{-[1+shape*(x-mu)/scale]**(1/shape)} (min version)
f = [1+shape*(x-mu)/scale]**(1/shape-1) *
exp{-[1+shape*(x-mu)/scale]**(1/shape)} / scale
shape < 0 => Type II
shape > 0 => Type III
shape -> 0 => Type I - Gumbel
type must be 'max' or 'min'
scale must be > 0.0
A REASONABLE SCHEME SEEMS TO BE mu = scale WHICH SEEMS TO LIMIT THE
DISTRIBUTION TO EITHER SIDE OF THE Y-AXIS!
"""
if shape == 0.0:
x = iextreme_I(prob, type, mu, scale)
else:
_assertprob(prob, 'iextreme_gen')
assert scale >= 0.0
if type == 'max':
x = scale*(1.0-(-safelog(prob))**shape)/shape + mu
elif type == 'min':
x = scale*((-safelog(1.0-prob))**shape-1.0)/shape + mu
else:
raise Error("iextreme_gen: type must be either 'max' or 'min'")
return x
def ilogistic(prob, mu=0.0, scale=1.0):
"""
The inverse of the logistic distribution:
f = exp[-(x-m)/s] / (s*{1 + exp[-(x-m)/s]}**2)
F = 1 / {1 + exp[-(x-m)/s]}
x in R
m is the mean and mode, s is a scale parameter (s >= 0)
"""
_assertprob(prob, 'ilogistic')
assert scale >= 0.0, "scale parameter in ilogistic must not be negative!"
x = mu - scale*safelog(safediv(1.0, prob) - 1.0)
return x
def iexpo_gen(prob, a, b, c=0.0):
"""
The generalized continuous exponential distribution (x in R):
x <= c: f = [a*b/(a+b)] * exp(+a*[x-c])
F = [b/(a+b)] * exp(+a*[x-c])
x >= c: f = [a*b/(a+b)] * exp(-b*[x-c])
F = 1 - [a/(a+b)]*exp(-b*[x-c])
a > 0, b > 0
NB The symmetrical double-sided exponential sits in ilaplace!
"""
_assertprob(prob, 'iexpo_gen')
assert a > 0.0
assert b > 0.0
r = prob*(a+b)/b
if r <= 1.0:
x = c + safelog(r) / a
else:
x = c - safelog((a+b)*(1.0-prob)/a) / b
return x
def rpiecexpo_cum(self, times, lamt):
"""
Generates - cumulatively - piecewise exponentially distributed
interarrival times from clock time = 0.0 (the algorithm is taken
from Bratley, Fox & Schrage).
'times' is a list or tuple containing the points at which the arrival
rate (= arrival frequency) changes (the first break time point must
be 0.0). 'lamt' is a list or tuple containing the arrival rates between
break points. The number of elements in 'times' must be one more than
the number of elements in 'lamt'!
The algorithm cranking out the numbers is cyclic - the procedure
starts over from time zero when the last break point is reached.
THE PREVENT THE RESTART FROM TAKING PLACE, A (VERY) LARGE NUMBER
MUST BE GIVEN AS THE LAST BREAK POINT (the cyclicity is rarely
needed or desired in practice).
NB. A SEPARATE STREAM MUST BE INSTANTIATED FOR EACH ONE OF THE VARIATES
THAT ARE GENERATED USING THIS METHOD, EACH WITH A SEPARATE SEED!!
"""
ntimes = len(times)
errtxt1 = "No. of arrival rates and no. of break points "
errtxt2 = "are incompatible in rpiecexpo_cum!"
errtext = errtxt1 + errtxt2
assert len(lamt) == ntimes - 1, errtext
r = ntimes * [0.0]
for k in range(1, ntimes):
assert lamt[k-1] >= 0.0, \
"All lamt must be non-negative floats in rpiecexpo_cum!"
r[k] = r[k - 1] + lamt[k - 1] * (times[k] - times[k - 1])
cumul = self.__cumul
cumul -= safelog(self.runif01())
iseg = 0
while r[iseg + 1] <= cumul:
iseg = iseg + 1
tcum = times[iseg] + safediv(cumul - r[iseg], lamt[iseg])
tcum = kept_within(0.0, tcum)
self.__cumul = cumul # NB THIS IS NOT CUMULATIVE TIME!
return tcum
def rexpo_cum(self, lam):
"""
Generates - cumulatively - exponentially distributed interarrival times
with arrival rate (arrivale frequency) 'lam' from clock time = 0.0.
NB. A SEPARATE STREAM MUST BE INSTANTIATED FOR EACH ONE OF THE VARIATES
THAT ARE GENERATED USING THIS METHOD, EACH WITH A SEPARATE SEED!!
"""
assert lam >= 0.0, \
"Arrival rate must be a non-negative float in rexpo_cum!"
tcum = self.__tcum
tcum -= safediv(1.0, lam) * safelog(self.runif01())
self.__tcum = tcum
return tcum
def rpiecexpo_cum(self, times, lamt):
"""
Generates - cumulatively - piecewise exponentially distributed
interarrival times from clock time = 0.0 (the algorithm is taken
from Bratley, Fox & Schrage).
'times' is a list or tuple containing the points at which the arrival
rate (= arrival frequency) changes (the first break time point must
be 0.0). 'lamt' is a list or tuple containing the arrival rates between
break points. The number of elements in 'times' must be one more than
the number of elements in 'lamt'!
The algorithm cranking out the numbers is cyclic - the procedure
starts over from time zero when the last break point is reached.
THE PREVENT THE RESTART FROM TAKING PLACE, A (VERY) LARGE NUMBER
MUST BE GIVEN AS THE LAST BREAK POINT (the cyclicity is rarely
needed or desired in practice).
NB. A SEPARATE STREAM MUST BE INSTANTIATED FOR EACH ONE OF THE VARIATES
THAT ARE GENERATED USING THIS METHOD, EACH WITH A SEPARATE SEED!!
"""
ntimes = len(times)
errtxt1 = "No. of arrival rates and no. of break points "
errtxt2 = "are incompatible in rpiecexpo_cum!"
errtext = errtxt1 + errtxt2
assert len(lamt) == ntimes-1, errtext
r = ntimes*[0.0]
for k in range(1, ntimes):
assert lamt[k-1] >= 0.0, \
"All lamt must be non-negative floats in rpiecexpo_cum!"
r[k] = r[k-1] + lamt[k-1] * (times[k]-times[k-1])
cumul = self.__cumul
cumul -= safelog(self.runif01())
iseg = 0
while r[iseg+1] <= cumul:
iseg = iseg + 1
tcum = times[iseg] + safediv(cumul-r[iseg], lamt[iseg])
tcum = kept_within(0.0, tcum)
self.__cumul = cumul # NB THIS IS NOT CUMULATIVE TIME!
return tcum
def rexpo(self, mean, xmax=float('inf')):
"""
Generator of exponentially distributed random variates with
mean = 1.0/lambda:
f = (1/mean) * exp(-x/mean)
F = 1 - exp(-x/mean).
mean >= 0.0
"""
assert mean >= 0.0, "mean must be a non-negative float in rexpo!"
assert xmax >= 0.0, "variate max must be a non-negative float in rexpo!"
while True:
x = - mean * safelog(self.runif01())
if x <= xmax: break
return x
def rexpo_cum(self, lam):
"""
Generates - cumulatively - exponentially distributed interarrival times
with arrival rate (arrivale frequency) 'lam' from clock time = 0.0.
NB. A SEPARATE STREAM MUST BE INSTANTIATED FOR EACH ONE OF THE VARIATES
THAT ARE GENERATED USING THIS METHOD, EACH WITH A SEPARATE SEED!!
"""
assert lam >= 0.0, \
"Arrival rate must be a non-negative float in rexpo_cum!"
tcum = self.__tcum
tcum -= safediv(1.0, lam) * safelog(self.runif01())
self.__tcum = tcum
return tcum
def rexpo(self, mean, xmax=float('inf')):
"""
Generator of exponentially distributed random variates with
mean = 1.0/lambda:
f = (1/mean) * exp(-x/mean)
F = 1 - exp(-x/mean).
mean >= 0.0
"""
assert mean >= 0.0, "mean must be a non-negative float in rexpo!"
assert xmax >= 0.0, "variate max must be a non-negative float in rexpo!"
while True:
x = -mean * safelog(self.runif01())
if x <= xmax: break
return x
def iexpo(prob, mean=1.0):
"""
The inverse of the exponential distribution with mean = 1/lambda:
f = (1/mean) * exp(-x/mean)
F = 1 - exp(-x/mean)
x >= 0, mean >= 0.0
"""
_assertprob(prob, 'iexpo')
# ---
assert mean >= 0.0, "mean of variate in iexpo must be a non-negative float!"
x = - mean * safelog(1.0-prob)
#x = kept_within(0.0, x) # Not really needed
return x
def iweibull(prob, c, scale=1.0):
"""
The inverse of the Weibull distribution:
F = 1 - exp[-(x/s)**c]
x >= 0, s >= 0, c >= 1
"""
if c == 1.0:
x = iexpo(prob, scale)
else:
_assertprob(prob, 'iweibull')
# ---
assert c >= 1.0, \
"shape parameter in iweibull must not be smaller than 1.0!"
assert scale >= 0.0, "scale parameter in iweibull must not be negative!"
x = scale * safepow(-safelog(1.0-prob), 1.0/c)
#x = kept_within(0.0, x) # Not really needed
return x
def rstable(self, alpha, beta, location, scale, \
xmin=float('-inf'), xmax=float('inf')):
"""
rstable generates random variates from any distribution of the stable
family. 0 < alpha <= 2 is the tail index (the smaller alpha, the wider
the distribution). -1 <= beta <= 1 is the skewness parameter (the
greater absolute value, the more skewed the distribution, negative =
skewed to the left, positive = skewed to the right).
For beta = 0 and alpha = 2 the distribution is a Gaussian distribution -
but scaled with sqrt(2) (the variance of the stable is twice that of the
Gaussian). For beta = 0 and alpha = 1 the distribution is the Cauchy.
For abs(beta) = 1 and alpha = 0.5 the distribution is the Levy.
For alpha < 2.0 the variance and higher moments are infinite, and for
alpha <= 1 all moments are infinite. A scale parameter and a location
parameter are also applicable so that Y = scale*X + location is still
a stable variate with the same alpha as that of X. E{Y} = location for
alpha > 1.
The algorithm is taken from Weron but the original version seems to be
Chambers, Mallows and Stuck:
Weron R. (1996): 'On the Chambers-Mallows-Stuck method for simulating
skewed stable random variates', Statist. Probab. Lett. 28, 165-171.
Chambers, J.M., Mallows, C.L. and Stuck, B.W. (1976): 'A Method for
simulating stable random variables', J. Amer. Statist. Assoc. 71,
340-344.
Weron, R. (1996): 'Correction to: On the Chambers-Mallows-Stuck Method
for Simulating Skewed Stable Random Variables', Research Report
HSC/96/1, Wroclaw University of Technology.
"""
assert 0.0 < alpha and alpha <= 2.0, \
"alpha must be in (0.0, 2.0] in rstable!"
assert -1.0 <= beta and beta <= 1.0, \
"beta must be in [-1.0, 1.0] in rstable!"
assert xmax > xmin, "xmax must be > xmin in rstable!"
while True:
v = self.runifab(-PIHALF, PIHALF)
w = self.rexpo(1.0)
if beta == 0 or beta == 0.0:
oneoa = 1.0 / float(alpha)
av = alpha * v
x1 = sin(av) / cos(v)**oneoa
x2 = (cos(v - av) / w)**(oneoa - 1.0)
x = x1 * x2
x = scale * x + location
elif alpha == 1 or alpha == 1.0:
pihpbv = PIHALF + beta * v
x1 = pihpbv * tan(v)
x2 = beta * safelog(PIHALF * w * cos(v) / pihpbv)
#x = (x1-x2) / PIHALF
#x = scale*x + (1.0/PIHALF)*beta*scale*safelog(scale) + location
x = 2.0 * PIINV * (x1 - x2) / PIHALF
x = scale * x + (
2.0 * PIINV) * beta * scale * safelog(scale) + location
else:
tpiha = tan(PIHALF * alpha)
oneoa = 1.0 / float(alpha)
bab = atan(beta * tan(PIHALF * alpha))
sab = (1.0 + beta * beta * tpiha * tpiha)**(0.5 * oneoa)
vpbab = v + bab
av = alpha * vpbab
x1 = sin(av) / cos(v)**oneoa
x2 = (cos(v - av) / w)**(oneoa - 1.0)
x = sab * x1 * x2
x = scale * x + location
if xmin <= x and x <= xmax: break
return x
def rnormal(self, mu, sigma, xmin=float('-inf'), xmax=float('inf')):
"""
Generator of normally distributed random variates. Based on the
Kinderman-Ramage algorithm as modified by Tirler, Dalgaard, Hoermann &
Leydold.
sigma >= 0.0
"""
assert sigma >= 0.0, \
"standard deviation must be a non-negative float in rnormal!"
assert xmax > xmin, "xmax must be > xmin in rnormal!"
ksi = 2.2160358671
ksi2h = 0.5 * ksi * ksi
p18 = 0.180025191068563
p48 = 0.479727404222441
while True:
u = self.runif01()
if u < 0.884070402298758:
v = self.runif01()
x = ksi * (1.131131635444180 * u + v - 1.0)
elif u >= 0.973310954173898:
while True:
v = self.runif01()
w = self.runif01()
t = ksi2h - safelog(w)
if v * v * t <= ksi2h:
break
if u < 0.986655477086949:
x = sqrt(2.0 * t)
else:
x = -sqrt(2.0 * t)
elif u >= 0.958720824790463:
while True:
v = self.runif01()
w = self.runif01()
z = v - w
t = ksi - 0.630834801921960 * min(v, w)
if max(v, w) <= 0.755591531667601:
if z < 0.0: x = t
else: x = -t
break
p = dnormal(0.0, 1.0, t)
f = p - p18 * max(ksi - abs(t), 0.0)
if 0.034240503750111 * abs(z) <= f:
if z < 0.0: x = t
else: x = -t
break
elif u >= 0.911312780288703:
while True:
v = self.runif01()
w = self.runif01()
z = v - w
t = p48 + 1.105473661022070 * min(v, w)
if max(v, w) <= 0.872834976671790:
if z < 0.0: x = t
else: x = -t
break
p = dnormal(0.0, 1.0, t)
f = p - p18 * max(ksi - abs(t), 0.0)
if 0.049264496373128 * abs(z) <= f:
if z < 0.0: x = t
else: x = -t
break
else:
while True:
v = self.runif01()
w = self.runif01()
z = v - w
t = p48 - 0.595507138015940 * min(v, w)
if t >= 0.0:
if max(v, w) <= 0.805577924423817:
if z < 0.0: x = t
else: x = -t
break
p = dnormal(0.0, 1.0, t)
f = p - p18 * max(ksi - abs(t), 0.0)
if 0.053377549506886 * abs(z) <= f:
if z < 0.0: x = t
else: x = -t
break
x = sigma * x + mu
if xmin <= x <= xmax: break
return x
def rgamma(self, alpha, lam, xmax=float('inf')):
"""
The gamma distribution:
f = lam * exp(-lam*x) * (lam*x)**(alpha-1) / gamma(alpha)
The cdf is the integral = the incomplete gamma ratio.
x, alpha >= 0; lam > 0.0
The generator is a slight modification of Python's
built-in "gammavariate".
"""
assert alpha >= 0.0, "alpha must be non-negative in rgamma!"
assert lam > 0.0, "lambda must be a positive float in rgamma!"
assert xmax >= 0.0, \
"variate max must be a non-negative float in rgamma!"
f, i = modf(alpha)
if f == 0.0:
if 1.0 <= i and i <= GeneralRandomStream.__GAMMA2ERLANG:
return self.rerlang(int(i), 1.0 / lam, xmax)
if alpha < 1.0:
# Uses ALGORITHM GS of Statistical Computing - Kennedy & Gentle
# (according to Python's "gammavariate")
alphainv = 1.0 / alpha
alpham1 = alpha - 1.0
while True:
while True:
u = self.runif01()
b = (E + alpha) / E
p = b * u
if p <= 1.0:
w = p**alphainv
else:
w = -safelog((b - p) * alphainv)
u1 = self.runif01()
if p > 1.0:
if u1 <= w**(alpham1):
break
elif u1 <= exp(-w):
break
x = w / lam
if x <= xmax: break
else: # elif alpha > 1.0:
# Uses R.C.H. Cheng, "The generation of Gamma
# variables with non-integral shape parameters",
# Applied Statistics, (1977), 26, No. 1, p71-74
# (according to Python's "gammavariate")
ainv = sqrt(2.0 * alpha - 1.0)
beta = 1.0 / ainv
bbb = alpha - GeneralRandomStream.__LN4
ccc = alpha + ainv
while True:
while True:
u1 = self.runif01()
u2 = self.runif01()
v = beta * safelog(safediv(u1, 1.0 - u1))
w = alpha * exp(v)
c1 = u1 * u1 * u2
r = bbb + ccc * v - w
c2 = r + 2.5040773967762742 - 4.5 * c1
# 2.5040773967762742 = 1.0 + log(4.5)
if c2 >= 0.0 or r >= safelog(c1):
break
x = w / lam
if x <= xmax: break
x = kept_within(0.0, x)
return x
def rexppower(self, loc, scale, alpha, \
xmin=float('-inf'), xmax=float('inf')):
"""
The exponential power distribution
f = (a/s) * exp(-abs([x-l]/s)**a) / [2*gamma(1/a)]
F = 1/2 * [1 + sgn(x-l) * Fgamma(1/a, abs([x-l]/s)**a)], x in R
s, a > 0
where Fgamma is the gamma distribution cdf.
A modified version of the rejection technique proposed in
P.R. Tadikamalla;
"Random Sampling From the Exponential Power Distribution",
J. Am. Statistical Association 75(371), 1980, pp 683-686
is used (the gamma is used for alpha > 2.0 in place of the rejection
procedure proposed by Tadikamalla - for purposes of speed).
"""
assert xmax > xmin, "xmax must be > xmin in rexppower!"
assert scale > 0.0, \
"scale parameter must be a positive float in rexppower!"
assert alpha > 0.0, \
"shape parameter alpha must be a positive float in rexppower!"
sinv = 1.0 / scale
ainv = 1.0 / alpha
while True:
if alpha < 1.0: # The gamma distribution
rgam = self.rgamma(ainv, sinv)
sign = self.rsign()
x = loc + sign * rgam**ainv
elif alpha == 1.0: # The Laplace distribution is used
x = self.rlaplace(d, scale)
elif 1.0 < alpha < 2.0: # Tadikamalla's rejection procedure
ayay = ainv**ainv
ayayi = 1.0 / ayay
while True:
u1 = self.runif01()
if u1 > 0.5: x = -ayay * safelog(2.0 * (1.0 - u1))
else: x = ayay * safelog(2.0 * u1)
ax = abs(x)
lnu2 = safelog(self.runif01())
if lnu2 <= -ax**alpha + ayayi * ax - 1.0 + ainv:
break
x = loc + sinv * x
elif alpha == 2.0: # The normal (Gaussian) distribution
x = self.rnormal(d, scale)
else:
'''while True: # Tadikamalla is slower than the gamma!!!
ayay = ainv**ainv
ayayi2 = 1.0/(ayay*ayay)
x = self.rnormal(0.0, ayay)
ax = abs(x)
lnu = safelog(self.runif01())
if lnu <= - ax**alpha + 0.5*ayayi2*x*x + ainv - 0.5:
break'''
rgam = self.rgamma(ainv, sinv) # The gamma distribution
sign = self.rsign()
x = loc + sign * rgam**ainv
if xmin <= x <= xmax: break
return x
def rbeta(self, a, b, x1, x2):
"""
The beta distribution f = x**(a-1) * (1-x)**(b-1) / beta(a, b)
The cdf is the integral = the incomplete beta or the incomplete
beta/complete beta depending on how the incomplete beta function
is defined.
x, a, b >= 0; x2 > x1
The algorithm is due to Berman (1970)/Jonck (1964) (for a and b < 1.0)
and Cheng (1978) as described in Bratley, Fox and Schrage.
"""
assert a > 0.0, \
"shape parameters a and b must both be > 0.0 in rbeta!"
assert b > 0.0, \
"shape parameters a and b must both be > 0.0 in rbeta!"
assert x2 >= x1, \
"support span must not be negative in rbeta!"
if x2 == x1: return x1
if a == 1.0 and b == 1.0:
y = self.runif01()
elif is_posinteger(a) and is_posinteger(b):
nstop = int(a + b - 1.0)
r = []
for k in range(0, nstop):
r.append(self.runif01())
r.sort()
y = r[int(a-1.0)]
elif a < 1.0 and b < 1.0:
a1 = 1.0 / a
b1 = 1.0 / b
while True:
u = pow(self.runif01(), a1)
v = pow(self.runif01(), b1)
if u + v <= 1.0:
y = u / (u+v)
break
else:
alpha = a + b
if min(a, b) <= 1.0: beta = 1.0 / min(a, b)
else: beta = sqrt((alpha-2.0)/(2.0*a*b-alpha))
gamma = a + 1.0/beta
while True:
u1 = self.runif01()
u2 = self.runif01()
u1 = kept_within(TINY, u1, ONEMMACHEPS)
u2 = kept_within(TINY, u2, HUGE)
comp1 = safelog(u1*u1*u2)
v = beta * safelog(safediv(u1, 1.0-u1))
w = a * exp(v)
comp2 = alpha*safelog(alpha/(b+w)) + gamma*v - \
GeneralRandomStream.__LN4
if comp2 >= comp1:
y = w / (b+w)
break
x = y*(x2-x1) + x1
x = kept_within(x1, x, x2)
return x
def rexppower(self, loc, scale, alpha, \
xmin=float('-inf'), xmax=float('inf')):
"""
The exponential power distribution
f = (a/s) * exp(-abs([x-l]/s)**a) / [2*gamma(1/a)]
F = 1/2 * [1 + sgn(x-l) * Fgamma(1/a, abs([x-l]/s)**a)], x in R
s, a > 0
where Fgamma is the gamma distribution cdf.
A modified version of the rejection technique proposed in
P.R. Tadikamalla;
"Random Sampling From the Exponential Power Distribution",
J. Am. Statistical Association 75(371), 1980, pp 683-686
is used (the gamma is used for alpha > 2.0 in place of the rejection
procedure proposed by Tadikamalla - for purposes of speed).
"""
assert xmax > xmin, "xmax must be > xmin in rexppower!"
assert scale > 0.0, \
"scale parameter must be a positive float in rexppower!"
assert alpha > 0.0, \
"shape parameter alpha must be a positive float in rexppower!"
sinv = 1.0/scale
ainv = 1.0/alpha
while True:
if alpha < 1.0: # The gamma distribution
rgam = self.rgamma(ainv, sinv)
sign = self.rsign()
x = loc + sign*rgam**ainv
elif alpha == 1.0: # The Laplace distribution is used
x = self.rlaplace(d, scale)
elif 1.0 < alpha < 2.0: # Tadikamalla's rejection procedure
ayay = ainv**ainv
ayayi = 1.0/ayay
while True:
u1 = self.runif01()
if u1 > 0.5: x = - ayay * safelog(2.0*(1.0-u1))
else: x = ayay * safelog(2.0*u1)
ax = abs(x)
lnu2 = safelog(self.runif01())
if lnu2 <= - ax**alpha + ayayi*ax - 1.0 + ainv:
break
x = loc + sinv*x
elif alpha == 2.0: # The normal (Gaussian) distribution
x = self.rnormal(d, scale)
else:
'''while True: # Tadikamalla is slower than the gamma!!!
ayay = ainv**ainv
ayayi2 = 1.0/(ayay*ayay)
x = self.rnormal(0.0, ayay)
ax = abs(x)
lnu = safelog(self.runif01())
if lnu <= - ax**alpha + 0.5*ayayi2*x*x + ainv - 0.5:
break'''
rgam = self.rgamma(ainv, sinv) # The gamma distribution
sign = self.rsign()
x = loc + sign*rgam**ainv
if xmin <= x <= xmax: break
return x
def rbeta(self, a, b, x1, x2):
"""
The beta distribution f = x**(a-1) * (1-x)**(b-1) / beta(a, b)
The cdf is the integral = the incomplete beta or the incomplete
beta/complete beta depending on how the incomplete beta function
is defined.
x, a, b >= 0; x2 > x1
The algorithm is due to Berman (1970)/Jonck (1964) (for a and b < 1.0)
and Cheng (1978) as described in Bratley, Fox and Schrage.
"""
assert a > 0.0, \
"shape parameters a and b must both be > 0.0 in rbeta!"
assert b > 0.0, \
"shape parameters a and b must both be > 0.0 in rbeta!"
assert x2 >= x1, \
"support span must not be negative in rbeta!"
if x2 == x1: return x1
if a == 1.0 and b == 1.0:
y = self.runif01()
elif is_posinteger(a) and is_posinteger(b):
nstop = int(a + b - 1.0)
r = []
for k in range(0, nstop):
r.append(self.runif01())
r.sort()
y = r[int(a - 1.0)]
elif a < 1.0 and b < 1.0:
a1 = 1.0 / a
b1 = 1.0 / b
while True:
u = pow(self.runif01(), a1)
v = pow(self.runif01(), b1)
if u + v <= 1.0:
y = u / (u + v)
break
else:
alpha = a + b
if min(a, b) <= 1.0: beta = 1.0 / min(a, b)
else: beta = sqrt((alpha - 2.0) / (2.0 * a * b - alpha))
gamma = a + 1.0 / beta
while True:
u1 = self.runif01()
u2 = self.runif01()
u1 = kept_within(TINY, u1, ONEMMACHEPS)
u2 = kept_within(TINY, u2, HUGE)
comp1 = safelog(u1 * u1 * u2)
v = beta * safelog(safediv(u1, 1.0 - u1))
w = a * exp(v)
comp2 = alpha*safelog(alpha/(b+w)) + gamma*v - \
GeneralRandomStream.__LN4
if comp2 >= comp1:
y = w / (b + w)
break
x = y * (x2 - x1) + x1
x = kept_within(x1, x, x2)
return x
def rgamma(self, alpha, lam, xmax=float('inf')):
"""
The gamma distribution:
f = lam * exp(-lam*x) * (lam*x)**(alpha-1) / gamma(alpha)
The cdf is the integral = the incomplete gamma ratio.
x, alpha >= 0; lam > 0.0
The generator is a slight modification of Python's
built-in "gammavariate".
"""
assert alpha >= 0.0, "alpha must be non-negative in rgamma!"
assert lam > 0.0, "lambda must be a positive float in rgamma!"
assert xmax >= 0.0, \
"variate max must be a non-negative float in rgamma!"
f, i = modf(alpha)
if f == 0.0:
if 1.0 <= i and i <= GeneralRandomStream.__GAMMA2ERLANG:
return self.rerlang(int(i), 1.0/lam, xmax)
if alpha < 1.0:
# Uses ALGORITHM GS of Statistical Computing - Kennedy & Gentle
# (according to Python's "gammavariate")
alphainv = 1.0 / alpha
alpham1 = alpha - 1.0
while True:
while True:
u = self.runif01()
b = (E+alpha) / E
p = b * u
if p <= 1.0:
w = p ** alphainv
else:
w = -safelog((b-p)*alphainv)
u1 = self.runif01()
if p > 1.0:
if u1 <= w ** (alpham1):
break
elif u1 <= exp(-w):
break
x = w / lam
if x <= xmax: break
else: # elif alpha > 1.0:
# Uses R.C.H. Cheng, "The generation of Gamma
# variables with non-integral shape parameters",
# Applied Statistics, (1977), 26, No. 1, p71-74
# (according to Python's "gammavariate")
ainv = sqrt(2.0*alpha - 1.0)
beta = 1.0 / ainv
bbb = alpha - GeneralRandomStream.__LN4
ccc = alpha + ainv
while True:
while True:
u1 = self.runif01()
u2 = self.runif01()
v = beta * safelog(safediv(u1, 1.0-u1))
w = alpha * exp(v)
c1 = u1 * u1 * u2
r = bbb + ccc*v - w
c2 = r + 2.5040773967762742 - 4.5*c1
# 2.5040773967762742 = 1.0 + log(4.5)
if c2 >= 0.0 or r >= safelog(c1):
break
x = w / lam
if x <= xmax: break
x = kept_within(0.0, x)
return x
def rnormal(self, mu, sigma, xmin=float('-inf'), xmax=float('inf')):
"""
Generator of normally distributed random variates. Based on the
Kinderman-Ramage algorithm as modified by Tirler, Dalgaard, Hoermann &
Leydold.
sigma >= 0.0
"""
assert sigma >= 0.0, \
"standard deviation must be a non-negative float in rnormal!"
assert xmax > xmin, "xmax must be > xmin in rnormal!"
ksi = 2.2160358671
ksi2h = 0.5*ksi*ksi
p18 = 0.180025191068563
p48 = 0.479727404222441
while True:
u = self.runif01()
if u < 0.884070402298758:
v = self.runif01()
x = ksi * (1.131131635444180*u + v - 1.0)
elif u >= 0.973310954173898:
while True:
v = self.runif01()
w = self.runif01()
t = ksi2h - safelog(w)
if v*v*t <= ksi2h:
break
if u < 0.986655477086949:
x = sqrt(2.0*t)
else:
x = -sqrt(2.0*t)
elif u >= 0.958720824790463:
while True:
v = self.runif01()
w = self.runif01()
z = v - w
t = ksi - 0.630834801921960*min(v, w)
if max(v, w) <= 0.755591531667601:
if z < 0.0: x = t
else: x = -t
break
p = dnormal(0.0, 1.0, t)
f = p - p18*max(ksi-abs(t), 0.0)
if 0.034240503750111*abs(z) <= f:
if z < 0.0: x = t
else: x = -t
break
elif u >= 0.911312780288703:
while True:
v = self.runif01()
w = self.runif01()
z = v - w
t = p48 + 1.105473661022070*min(v, w)
if max(v, w) <= 0.872834976671790:
if z < 0.0: x = t
else: x = -t
break
p = dnormal(0.0, 1.0, t)
f = p - p18*max(ksi-abs(t), 0.0)
if 0.049264496373128*abs(z) <= f:
if z < 0.0: x = t
else: x = -t
break
else:
while True:
v = self.runif01()
w = self.runif01()
z = v - w
t = p48 - 0.595507138015940*min(v, w)
if t >= 0.0:
if max(v, w) <= 0.805577924423817:
if z < 0.0: x = t
else: x = -t
break
p = dnormal(0.0, 1.0, t)
f = p - p18*max(ksi-abs(t), 0.0)
if 0.053377549506886*abs(z) <= f:
if z < 0.0: x = t
else: x = -t
break
x = sigma*x + mu
if xmin <= x <= xmax: break
return x
def iemp_exp(prob, values, npexp=0, ordered=False):
"""
The mixed expirical/exponential distribution from Bratley, Fox and Schrage.
A polygon (piecewise linearly interpolated cdf with equal probability for
each interval between the ) is used together with a (shifted) exponential
for the tail. The distribution is designed so as to preserve the mean of
the input sample.
The input is a tuple/list of observed points and an integer (npexp)
corresponding to the number of (the largest) points that will be used
to formulate the exponential tail (the default value of npexp will raise
an assertion error so something >= 0 must be prescribed).
NB it is assumed that x is in [0.0, inf) !!!!!!!!!!!!
The function may also be used for a piecewise linear cdf without the
exponential tail (setting npexp = 0) - corrections are made to maintain
the mean in this case as well!!!
"""
_assertprob(prob, 'iemp_exp')
assert is_nonneginteger(npexp), \
"No. of points for exp. tail in iemp_exp must be a non-neg integer!"
nvalues = len(values)
for k in range(0, nvalues):
assert values[k] >= 0.0, \
"All values in list must be non-negative in iemp_exp!"
if npexp == nvalues:
mean = sum(values)
return iexpo(prob, mean)
vcopy = list(values)
if not ordered:
valueskm1 = values[0]
for k in range(1, nvalues):
valuesk = values[k]
if valuesk >= valueskm1:
valueskm1 = valuesk
else:
vcopy.sort()
break
if vcopy[0] == 0.0: # Remove the first zero if any - it will be
del vcopy[0] # returned later on!!!!!!!!
nvalues = nvalues - 1
errtxt2 = "Number of points for exponential tail in iemp_exp too large!"
assert npexp <= nvalues, errtxt2
pcomp = 1.0 - prob
nred = pcomp*nvalues
if npexp > pcomp*nvalues:
breaki = nvalues - npexp - 1 # The last ix of the piecewise linear part
breakp = vcopy[breaki] # The last value of the piecewise linear part
summ = 0.0
k0 = breaki + 1
for k in range(k0, nvalues):
summ += vcopy[k] - breakp
theta = (0.5*breakp + summ) / npexp
q = npexp / float(nvalues)
try:
x = breakp - theta*safelog(nred/npexp)
except ValueError:
x = float('inf')
else:
vcopy.insert(0, 0.0) # A floor value must be inserted
v = nvalues*prob
i = int(v)
x = vcopy[i] + (v-i)*(vcopy[i+1]-vcopy[i])
if npexp == 0: # Correction to maintain mean when there is no exp tail
x = (nvalues+1.0) * x / nvalues
x = kept_within(0.0, x)
return x
def rstable(self, alpha, beta, location, scale, \
xmin=float('-inf'), xmax=float('inf')):
"""
rstable generates random variates from any distribution of the stable
family. 0 < alpha <= 2 is the tail index (the smaller alpha, the wider
the distribution). -1 <= beta <= 1 is the skewness parameter (the
greater absolute value, the more skewed the distribution, negative =
skewed to the left, positive = skewed to the right).
For beta = 0 and alpha = 2 the distribution is a Gaussian distribution -
but scaled with sqrt(2) (the variance of the stable is twice that of the
Gaussian). For beta = 0 and alpha = 1 the distribution is the Cauchy.
For abs(beta) = 1 and alpha = 0.5 the distribution is the Levy.
For alpha < 2.0 the variance and higher moments are infinite, and for
alpha <= 1 all moments are infinite. A scale parameter and a location
parameter are also applicable so that Y = scale*X + location is still
a stable variate with the same alpha as that of X. E{Y} = location for
alpha > 1.
The algorithm is taken from Weron but the original version seems to be
Chambers, Mallows and Stuck:
Weron R. (1996): 'On the Chambers-Mallows-Stuck method for simulating
skewed stable random variates', Statist. Probab. Lett. 28, 165-171.
Chambers, J.M., Mallows, C.L. and Stuck, B.W. (1976): 'A Method for
simulating stable random variables', J. Amer. Statist. Assoc. 71,
340-344.
Weron, R. (1996): 'Correction to: On the Chambers-Mallows-Stuck Method
for Simulating Skewed Stable Random Variables', Research Report
HSC/96/1, Wroclaw University of Technology.
"""
assert 0.0 < alpha and alpha <= 2.0, \
"alpha must be in (0.0, 2.0] in rstable!"
assert -1.0 <= beta and beta <= 1.0, \
"beta must be in [-1.0, 1.0] in rstable!"
assert xmax > xmin, "xmax must be > xmin in rstable!"
while True:
v = self.runifab(-PIHALF, PIHALF)
w = self.rexpo(1.0)
if beta == 0 or beta == 0.0:
oneoa = 1.0 / float(alpha)
av = alpha * v
x1 = sin(av) / cos(v)**oneoa
x2 = (cos(v-av)/w) ** (oneoa-1.0)
x = x1 * x2
x = scale*x + location
elif alpha == 1 or alpha == 1.0:
pihpbv = PIHALF + beta*v
x1 = pihpbv * tan(v)
x2 = beta * safelog(PIHALF*w*cos(v)/pihpbv)
#x = (x1-x2) / PIHALF
#x = scale*x + (1.0/PIHALF)*beta*scale*safelog(scale) + location
x = 2.0*PIINV * (x1-x2) / PIHALF
x = scale*x + (2.0*PIINV)*beta*scale*safelog(scale) + location
else:
tpiha = tan(PIHALF*alpha)
oneoa = 1.0 / float(alpha)
bab = atan(beta*tan(PIHALF*alpha))
sab = (1.0 + beta*beta*tpiha*tpiha) ** (0.5*oneoa)
vpbab = v + bab
av = alpha * vpbab
x1 = sin(av) / cos(v)**oneoa
x2 = (cos(v-av) / w) ** (oneoa-1.0)
x = sab * x1 * x2
x = scale*x + location
if xmin <= x and x <= xmax: break
return x
def inormal(prob, mu=0.0, sigma=1.0):
"""
Returns the inverse of the cumulative normal distribution function.
Reference: Boris Moro "The Full Monte", Risk Magazine, 8(2) (February):
57-58, 1995, where Moro improves on the Beasley-Springer algorithm
(J. D. Beasley and S. G. Springer, Applied Statistics, vol. 26, 1977,
pp. 118-121). This is further refined by Shaw, c. f. below.
Max relative error is claimed to be less than 2.6e-9
"""
_assertprob(prob, 'inormal')
assert sigma >= 0.0, "sigma must not be negative in inormal!"
#a = ( 2.50662823884, -18.61500062529, \
# 41.39119773534, -25.44106049637) # Moro
#b = (-8.47351093090, 23.08336743743, \
# -21.06224101826, 3.13082909833) # Moro
# The a and b below are claimed to be better by William Shaw in a
# Mathematica working report: "Refinement of the Normal Quantile -
# A benchmark Normal quantile based on recursion, and an appraisal
# of the Beasley-Springer-Moro, Acklam, and Wichura (AS241) methods"
# (William Shaw, Financial Mathematics Group, King's College, London;
# [email protected]).
# Max RELATIVE error is claimed to be reduced from 1.4e-8 to 2.6e-9
# over the central region
a = ( 2.5066282682076065359, -18.515898959450185753, \
40.864622120467790785, -24.820209533706798850) # Moro/Shaw
b = ( -8.4339736056039657294, 22.831834928541562628, \
-20.641301545177201274, 3.0154847661978822127) # Moro/Shaw
c = (0.3374754822726147, 0.9761690190917186, 0.1607979714918209, \
0.0276438810333863, 0.0038405729373609, 0.0003951896511919, \
0.0000321767881768, 0.0000002888167364, 0.0000003960315187) # Moro
x = prob - 0.5
if abs(x) < 0.42: # A rational approximation for the central region...
r = x * x
r = x * (((a[3]*r+a[2])*r+a[1])*r+a[0]) / ((((b[3]*r+b[2])*r+\
b[1])*r+b[0])*r+1.0)
r = sigma*r + mu
else: # ...and a polynomial for the tails
r = prob
if x > 0.0: r = 1.0 - prob
try:
r = safelog(-safelog(r))
r = c[0] + r*(c[1] + r*(c[2] + r*(c[3] + r*(c[4] + r*(c[5] +\
r*(c[6] + r*(c[7] + r*c[8])))))))
if x < 0.0: r = -r
r = sigma*r + mu
except ValueError:
r = fsign(x) * float('inf')
return r
