diff options
| -rw-r--r-- | Lib/random.py | 22 |
1 files changed, 11 insertions, 11 deletions
diff --git a/Lib/random.py b/Lib/random.py index 424a905..6debaf3 100644 --- a/Lib/random.py +++ b/Lib/random.py @@ -117,7 +117,7 @@ class Random: Class Random can also be subclassed if you want to use a different basic generator of your own devising: in that case, override the following methods: random(), seed(), getstate(), setstate() and jumpahead(). - + """ VERSION = 1 # used by getstate/setstate @@ -374,7 +374,7 @@ class Random: """Normal distribution. mu is the mean, and sigma is the standard deviation. - + """ # mu = mean, sigma = standard deviation @@ -401,7 +401,7 @@ class Random: If you take the natural logarithm of this distribution, you'll get a normal distribution with mean mu and standard deviation sigma. mu can have any value, and sigma must be greater than zero. - + """ return _exp(self.normalvariate(mu, sigma)) @@ -417,7 +417,7 @@ class Random: Deprecated in version 2.3. Use: (mean + arc * (Random.random() - 0.5)) % Math.pi - + """ # mean: mean angle (in radians between 0 and pi) # arc: range of distribution (in radians between 0 and pi) @@ -436,7 +436,7 @@ class Random: lambd is 1.0 divided by the desired mean. (The parameter would be called "lambda", but that is a reserved word in Python.) Returned values range from 0 to positive infinity. - + """ # lambd: rate lambd = 1/mean # ('lambda' is a Python reserved word) @@ -451,12 +451,12 @@ class Random: def vonmisesvariate(self, mu, kappa): """Circular data distribution. - + mu is the mean angle, expressed in radians between 0 and 2*pi, and kappa is the concentration parameter, which must be greater than or equal to zero. If kappa is equal to zero, this distribution reduces to a uniform random angle over the range 0 to 2*pi. - + """ # mu: mean angle (in radians between 0 and 2*pi) # kappa: concentration parameter kappa (>= 0) @@ -590,7 +590,7 @@ class Random: slightly faster than the normalvariate() function. Not thread-safe without a lock around calls. - + """ # When x and y are two variables from [0, 1), uniformly @@ -641,9 +641,9 @@ class Random: Conditions on the parameters are alpha > -1 and beta} > -1. Returned values range between 0 and 1. - + """ - + # This version due to Janne Sinkkonen, and matches all the std # texts (e.g., Knuth Vol 2 Ed 3 pg 134 "the beta distribution"). y = self.gammavariate(alpha, 1.) @@ -667,7 +667,7 @@ class Random: """Weibull distribution. alpha is the scale parameter and beta is the shape parameter. - + """ # Jain, pg. 499; bug fix courtesy Bill Arms |