In unbiased coin flip H or T occurs 50% of times.
But I want to simulate coin which gives H with probability 'p' and T with probability '(1-p)'.
something like this:
def flip(p):
'''this function return H with probability p'''
# do something
return result
>> [flip(0.8) for i in xrange(10)]
[H,H,T,H,H,H,T,H,H,H]
random.random() returns a uniformly distributed pseudo-random floating point number in the range [0, 1). This number is less than a given number p in the range [0,1) with probability p. Thus:
def flip(p):
return 'H' if random.random() < p else 'T'
Some experiments:
>>> N = 100
>>> flips = [flip(0.2) for i in xrange(N)]
>>> float(flips.count('H'))/N
0.17999999999999999 # Approximately 20% of the coins are heads
>>> N = 10000
>>> flips = [flip(0.2) for i in xrange(N)]
>>> float(flips.count('H'))/N
0.20549999999999999 # Better approximation