(self, lambd)
return -_log(1.0 - self.random())/lambd
Exponential distribution.

        lambd is 1.0 divided by the desired mean.  It should be
        nonzero.  (The parameter would be called "lambda", but that is
        a reserved word in Python.)  Returned values range from 0 to
        positive infinity if lambd is positive, and from negative
        infinity to 0 if lambd is negative.
Exponential distribution.
Exponential distribution .
def expovariate(self, lambd):
        """Exponential distribution.

        lambd is 1.0 divided by the desired mean.  It should be
        nonzero.  (The parameter would be called "lambda", but that is
        a reserved word in Python.)  Returned values range from 0 to
        positive infinity if lambd is positive, and from negative
        infinity to 0 if lambd is negative.

        """
        # lambd: rate lambd = 1/mean
        # ('lambda' is a Python reserved word)

        # we use 1-random() instead of random() to preclude the
        # possibility of taking the log of zero.
        return -_log(1.0 - self.random())/lambd
def expovariate ( self , lambd ) : # lambd: rate lambd = 1/mean # ('lambda' is a Python reserved word) # we use 1-random() instead of random() to preclude the # possibility of taking the log of zero. return - _log ( 1.0 - self . random ( ) ) / lambd