Add unit tests for functions in math_utils.py

tools/auto_bisect/math_utils.py

 # Copyright 2014 The Chromium Authors. All rights reserved.
 # Use of this source code is governed by a BSD-style license that can be
 # found in the LICENSE file.
 
 """General statistical or mathematical functions."""
 
 import math
 
 
-def TruncatedMean(data_set, truncate_percent):
+def TruncatedMean(data_set, truncate_proportion):
   """Calculates the truncated mean of a set of values.
 
   Note that this isn't just the mean of the set of values with the highest
   and lowest values discarded; the non-discarded values are also weighted
   differently depending how many values are discarded.
 
+  NOTE: If there's not much benefit from this keeping and weighting
+  partial values, it might be better to use a simplified truncated mean
+  function without weighting.
+
   Args:
     data_set: Non-empty list of values.
-    truncate_percent: How much of the upper and lower portions of the data set
-        to discard, expressed as a value in [0, 1].
+    truncate_proportion: How much of the upper and lower portions of the data
+        set to discard, expressed as a value in the range [0, 1].
+        Note: a value of 0.5 or greater would be meaningless
 
   Returns:
     The truncated mean as a float.
 
   Raises:
     TypeError: The data set was empty after discarding values.
   """
   if len(data_set) > 2:
     data_set = sorted(data_set)
 
-    discard_num_float = len(data_set) * truncate_percent
+    discard_num_float = len(data_set) * truncate_proportion
     discard_num_int = int(math.floor(discard_num_float))
-    kept_weight = len(data_set) - discard_num_float * 2
+    kept_weight = len(data_set) - (discard_num_float * 2)
 
     data_set = data_set[discard_num_int:len(data_set)-discard_num_int]
 
     weight_left = 1.0 - (discard_num_float - discard_num_int)
 
     if weight_left < 1:
       # If the % to discard leaves a fractional portion, need to weight those
       # values.
       unweighted_vals = data_set[1:len(data_set)-1]
       weighted_vals = [data_set[0], data_set[len(data_set)-1]]
       weighted_vals = [w * weight_left for w in weighted_vals]
       data_set = weighted_vals + unweighted_vals
   else:
     kept_weight = len(data_set)
 
-  truncated_mean = reduce(lambda x, y: float(x) + float(y),
-                          data_set) / kept_weight
-
+  data_sum = reduce(lambda x, y: float(x) + float(y), data_set)
+  truncated_mean = data_sum / kept_weight
   return truncated_mean
 
 
 def Mean(values):
   """Calculates the arithmetic mean of a list of values."""
   return TruncatedMean(values, 0.0)
 
 
 def Variance(values):
   """Calculates the sample variance."""
@@ skipping 57 matching lines @@
   if denominator1 == 0:
     return 0.0
 
   return math.sqrt(numerator / denominator1) * math.sqrt(denominator2)
 
 
 # Redefining built-in 'StandardError'
 # pylint: disable=W0622
 def StandardError(values):
   """Calculates the standard error of a list of values."""
+  # NOTE: This behavior of returning 0.0 in the case of an empty list is
+  # inconsistent with Variance and StandardDeviation above.
   if len(values) <= 1:
     return 0.0
   std_dev = StandardDeviation(values)
   return std_dev / math.sqrt(len(values))
+