def cotri(a,b,c):
selectStream(2)
p1 = (c-a)/(b-a)
u = random()
variate = 0
if (u < p1):
variate = a + (c-a)*pow(random(),.5)
else:
variate = b - (b-c)*pow((1-random()),.5)
return(variate)
def artri(a,b,c):
selectStream(1)
temp=0
while(temp==0):
x = a + (b - a) *random() # gen U(a,b) for x */
S = c*random() # use mode for majorizing fn */
if(x <= c):
test = (2*x - 2*a)/((b-a)*(c-a))
else:
test = (2*b - 2*x)/((b-a)*(b-c))
if (S <= test):
return(x)
def Equilikely(a,b): # =================================================================== # * Returns an equilikely distributed integer between a and b inclusive. # * NOTE: use a < b # * =================================================================== # */ return (a + int((b - a + 1) * random()))
def exponential(m): #========================================================= #Returns an exponentially distributed positive real number. #NOTE: use m > 0.0 #========================================================= # return (-m * log(1.0 - random()))
def uniform(a,b): #=========================================================== #Returns a uniformly distributed real number between a and b. #NOTE: use a < b #=========================================================== # return (a + (b - a) * random())
def Uniform(a,b): # =========================================================== # * Returns a uniformly distributed real number between a and b. # * NOTE: use a < b # * =========================================================== # */ return (a + (b - a) * random())
def Uniform(a, b):
#===========================================================
#Returns a uniformly distributed real number between a and b.
#NOTE: use a < b
#===========================================================
#
return (a + (b - a) * random())
def Exponential(m):
#=========================================================
#Returns an exponentially distributed positive real number.
#NOTE: use m > 0.0
#=========================================================
#
return (-m * log(1.0 - random()))
def Normal(m, s):
#========================================================================
#Returns a normal (Gaussian) distributed real number.
#NOTE: use s > 0.0
#
#Uses a very accurate approximation of the normal idf due to Odeh & Evans,
#J. Applied Statistics, 1974, vol 23, pp 96-97.
#========================================================================
#
p0 = 0.322232431088
q0 = 0.099348462606
p1 = 1.0
q1 = 0.588581570495
p2 = 0.342242088547
q2 = 0.531103462366
p3 = 0.204231210245e-1
q3 = 0.103537752850
p4 = 0.453642210148e-4
q4 = 0.385607006340e-2
u = random()
if (u < 0.5):
t = sqrt(-2.0 * log(u))
else:
t = sqrt(-2.0 * log(1.0 - u))
p = p0 + t * (p1 + t * (p2 + t * (p3 + t * p4)))
q = q0 + t * (q1 + t * (q2 + t * (q3 + t * q4)))
if (u < 0.5):
z = (p / q) - t
else:
z = t - (p / q)
return (m + s * z)
def normal(m,s):
#========================================================================
#Returns a normal (Gaussian) distributed real number.
#NOTE: use s > 0.0
#
#Uses a very accurate approximation of the normal idf due to Odeh & Evans,
#J. Applied Statistics, 1974, vol 23, pp 96-97.
#========================================================================
#
p0 = 0.322232431088
q0 = 0.099348462606
p1 = 1.0
q1 = 0.588581570495
p2 = 0.342242088547
q2 = 0.531103462366
p3 = 0.204231210245e-1
q3 = 0.103537752850
p4 = 0.453642210148e-4
q4 = 0.385607006340e-2
u = random()
if (u < 0.5):
t = sqrt(-2.0 * log(u))
else:
t = sqrt(-2.0 * log(1.0 - u))
p = p0 + t * (p1 + t * (p2 + t * (p3 + t * p4)))
q = q0 + t * (q1 + t * (q2 + t * (q3 + t * q4)))
if (u < 0.5):
z = (p / q) - t
else:
z = t - (p / q)
return (m + s * z)
def geometric(p): #==================================================== #Returns a geometric distributed non-negative integer. #NOTE: use 0.0 < p < 1.0 #==================================================== # return (int(log(1.0 - random()) / log(p)))
def Geometric(p):
#====================================================
#Returns a geometric distributed non-negative integer.
#NOTE: use 0.0 < p < 1.0
#====================================================
#
return (int(log(1.0 - random()) / log(p)))
def Bernoulli(p):
#========================================================
#Returns 1 with probability p or 0 with probability 1 - p.
#NOTE: use 0.0 < p < 1.0
#========================================================
if (random() < 1 - p):
return (0)
else:
return (1)
def bernoulli(p):
#========================================================
#Returns 1 with probability p or 0 with probability 1 - p.
#NOTE: use 0.0 < p < 1.0
#========================================================
if (random() < 1 - p):
return(0)
else:
return(1)
def intri(a,b,c):
selectStream(0)
u = random()
variate = 0
if (u < (c-a)/(b-a)):
variate = a + pow((u*(b-a)*(c-a)),.5)
else:
variate = b - pow(((1-u)*(b-a)*(b-c)), .5)
return(variate)
def Exponential(m): # --------------------------------------------------- # * generate an Exponential random variate, use m > 0.0 # * --------------------------------------------------- # */ return (-m * log(1.0 - random()))
def Uniform(a,b): # -------------------------------------------- # * generate a Uniform random variate, use a < b # * -------------------------------------------- # */ return (a + (b - a) * random())
def Exponential(m):
# ---------------------------------------------------
# * generate an Exponential random variate, use m > 0.0
# * ---------------------------------------------------
# */
return (-m * log(1.0 - random()))
def Uniform(a,b): # --------------------------------------------- # * generate a Uniform random variate, use a < b # *--------------------------------------------- # */ return (a + (b - a) * random())