diff options
Diffstat (limited to 'Lib')
| -rw-r--r-- | Lib/random.py | 29 |
1 files changed, 14 insertions, 15 deletions
diff --git a/Lib/random.py b/Lib/random.py index f57ddb7..16ec365 100644 --- a/Lib/random.py +++ b/Lib/random.py @@ -239,7 +239,7 @@ class Random: These must be integers in the range [0, 256). """ - if not type(x) == type(y) == type(z) == type(0): + if not type(x) == type(y) == type(z) == int: raise TypeError('seeds must be integers') if not (0 <= x < 256 and 0 <= y < 256 and 0 <= z < 256): raise ValueError('seeds must be in range(0, 256)') @@ -407,8 +407,7 @@ class Random: # Previous selections are stored in dictionaries which provide # __contains__ for detecting repeat selections. Discarding repeats # is efficient unless most of the population has already been chosen. - # So, tracking selections is useful when sample sizes are much - # smaller than the total population. + # So, tracking selections is fast only with small sample sizes. n = len(population) if not 0 <= k <= n: @@ -417,19 +416,19 @@ class Random: random = self.random result = [None] * k if n < 6 * k: # if n len list takes less space than a k len dict - pool = list(population) # track potential selections - for i in xrange(k): - j = int(random() * (n-i)) # non-selected at [0,n-i) - result[i] = pool[j] # save selected element - pool[j] = pool[n-i-1] # non-selected to head of list + pool = list(population) + for i in xrange(k): # invariant: non-selected at [0,n-i) + j = int(random() * (n-i)) + result[i] = pool[j] + pool[j] = pool[n-i-1] else: - selected = {} # track previous selections + selected = {} for i in xrange(k): j = int(random() * n) - while j in selected: # discard and replace repeats + while j in selected: j = int(random() * n) result[i] = selected[j] = population[j] - return result # return selections in the order they were picked + return result ## -------------------- real-valued distributions ------------------- @@ -455,7 +454,7 @@ class Random: # Math Software, 3, (1977), pp257-260. random = self.random - while 1: + while True: u1 = random() u2 = random() z = NV_MAGICCONST*(u1-0.5)/u2 @@ -548,7 +547,7 @@ class Random: b = (a - _sqrt(2.0 * a))/(2.0 * kappa) r = (1.0 + b * b)/(2.0 * b) - while 1: + while True: u1 = random() z = _cos(_pi * u1) @@ -595,7 +594,7 @@ class Random: bbb = alpha - LOG4 ccc = alpha + ainv - while 1: + while True: u1 = random() u2 = random() v = _log(u1/(1.0-u1))/ainv @@ -616,7 +615,7 @@ class Random: # Uses ALGORITHM GS of Statistical Computing - Kennedy & Gentle - while 1: + while True: u = random() b = (_e + alpha)/_e p = b*u |