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 negativ
(self, lambd)
| 611 | return _exp(self.normalvariate(mu, sigma)) |
| 612 | |
| 613 | def expovariate(self, lambd): |
| 614 | """Exponential distribution. |
| 615 | |
| 616 | lambd is 1.0 divided by the desired mean. It should be |
| 617 | nonzero. (The parameter would be called "lambda", but that is |
| 618 | a reserved word in Python.) Returned values range from 0 to |
| 619 | positive infinity if lambd is positive, and from negative |
| 620 | infinity to 0 if lambd is negative. |
| 621 | |
| 622 | """ |
| 623 | # lambd: rate lambd = 1/mean |
| 624 | # ('lambda' is a Python reserved word) |
| 625 | |
| 626 | # we use 1-random() instead of random() to preclude the |
| 627 | # possibility of taking the log of zero. |
| 628 | return -_log(1.0 - self.random()) / lambd |
| 629 | |
| 630 | def vonmisesvariate(self, mu, kappa): |
| 631 | """Circular data distribution. |