I guess you'd probably want "res = 1 - (solveig + 0.0) / sum(ts)" as the 
penultimate line.

Sent from my iPad

> On 22 Jun 2014, at 09:48, evandrix <evand...@gmail.com> wrote:
> 
> Hi,
> 
> I tried to run your solution through (using PyPy 2.3.1) A-large-practice, and 
> received the WA judgment on your output using that code.
> 
> Is it meant to produce AC code through A-large-practice.in?
> 
> Lee Wei
> 
>> On Sunday, June 22, 2014 12:09:54 AM UTC+8, Eibe wrote:
>> I just read the analysis for problem A of round 3 and it is mentioned that 
>> binary search is necessary for an O(n) solution.
>> I don't think it is but maybe something with my thinking is wrong (I don't 
>> have a strict proof of correctness of the following):
>> 
>> Let ts be a list, where ts[i] is the numbers of transistors in device i. 
>> 
>> Start with the following partition left, middle, right = [], ts, []
>> Now repeatedly move either the leftmost element of middle into left or the 
>> rightmost element of middle into right in  a way that increases
>> max(sum(left), sum(right)) in the least possible way. 
>> That is if sum(left) + middle[0] < sum(right) + middle[-1] move the leftmost 
>> otherwise the rightmost element (middle[-1] denotes the rightmost element of 
>> middle).
>> 
>> I am a little sloppy here, but as we increase max(sum(left), sum(right)) by 
>> a minimal amount it feels that we will visit an optimal partition of ts 
>> eventually.
>> 
>> In python code:
>> T = int(raw_input())
>> for case in range(1, T+1):
>>    N, p, q, r, s = map(int, raw_input().split())
>>    ts = [(i*p+q)%r+s for i in range(N)]
>>    c0, c1, c2 = 0, sum(ts), 0
>>    a, b = 0, N
>>    solveig = sum(ts)
>>    while a < b:
>>        if c0 + ts[a] <= c2 + ts[b-1]:
>>            c0 += ts[a]
>>            c1 -= ts[a]
>>            a += 1
>>        else:
>>            c2 += ts[b-1]
>>            c1 -= ts[b-1]
>>            b -= 1
>>        solveig = min(solveig, max(c0, c1, c2))
>>    res = 1 - solveig / sum(ts)
>>    print "Case #%i: %.10f" %(case, res)
> 
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