[LNT] r238973 - All of these routines make the assumption of floats, check that

[Justin Bogner]

Chris Matthews <cmatthews5 at apple.com> writes:
> Author: cmatthews
> Date: Wed Jun  3 16:01:45 2015
> New Revision: 238973
>
> URL: http://llvm.org/viewvc/llvm-project?rev=238973&view=rev
> Log:
> All of these routines make the assumption of floats, check that
>
> Modified:
>     lnt/trunk/lnt/util/stats.py
>
> Modified: lnt/trunk/lnt/util/stats.py
> URL:
> http://llvm.org/viewvc/llvm-project/lnt/trunk/lnt/util/stats.py?rev=238973&r1=238972&r2=238973&view=diff
> ==============================================================================
> --- lnt/trunk/lnt/util/stats.py (original)
> +++ lnt/trunk/lnt/util/stats.py Wed Jun  3 16:01:45 2015
> @@ -10,17 +10,34 @@ def safe_min(l):
>      else:
>          return min(l)
>  
> +
> +def check_floating(l):
> +    """These math ops are totally wrong when they done on anything besides
> +    floats.  I would just cast them, however that really slows them down.
> +    So lets error on this """
> +    for v in l:
> +        assert type(v) == float, "Math op on non-floating point:" + str(v) + \
> +            str(l)
> +
> +
>  def mean(l):
> +    check_floating(l)
>      if l:
> -        return sum(l)/len(l)
> +        l = map(float, l)
> +        return float_mean(l)

No need for the map here, since you just did check_floating.

>      else:
>          return None
>  
>  
> +def float_mean(l):
> +    return sum(l)/len(l)
> +
> +
>  def median(l):
>      if not l:
>          return None
> -    l = list(l)
> +    l = list(l)  # Could be a tuple.
> +    check_floating(l)
>      l.sort()
>      N = len(l)
>      return (l[(N-1)//2] + l[N//2])*.5
> @@ -29,11 +46,13 @@ def median(l):
>  def median_absolute_deviation(l, med = None):
>      if med is None:
>          med = median(l)
> +    check_floating(l)
>      return median([abs(x - med) for x in l])
>  
>  
>  def standard_deviation(l):
> -    m = mean(l)
> +    check_floating(l)
> +    m = float_mean(l)
>      means_sqrd = sum([(v - m)**2 for v in l]) / len(l)
>      rms = math.sqrt(means_sqrd)
>      return rms
> @@ -43,6 +62,8 @@ def mannwhitneyu(a, b, sigLevel = .05):
>      """
>      Determine if sample a and b are the same at given significance level.
>      """
> +    check_floating(a)
> +    check_floating(b)
>      if len(a) <= 20 and len(b) <= 20:
>          return mannwhitneyu_small(a, b, sigLevel)
>      else:
> @@ -62,7 +83,7 @@ def mannwhitneyu_small(a, b, sigLevel):
>      assert len(a) <= 20, "Sample size must be less than 20."
>      assert len(b) <= 20, "Sample size must be less than 20."
>  
> -    if not sigLevel in SIGN_TABLES:
> +    if sigLevel not in SIGN_TABLES:
>          raise ValueError("Do not have according significance table.")
>  
>      # Calculate U value for sample groups using method described on Wikipedia.
>
>
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