def accumulate(self, groundTruth, prediction, accumulatedError, historyBuffer, result = None):
<EOL>if self.disabled:<EOL><INDENT>return <NUM_LIT:0><EOL><DEDENT>if historyBuffer is not None:<EOL><INDENT>historyBuffer.append((groundTruth, prediction[<NUM_LIT:0>]))<EOL>if len(historyBuffer) > self.spec.params["<STR_LIT>"] :<EOL><INDENT>historyBuffer.popleft()<EOL><DEDENT><DEDENT>return <NUM_LIT:0><EOL>
Accumulate history of groundTruth and "prediction" values.

For this metric, groundTruth is the actual category and "prediction" is a
dict containing one top-level item with a key of 0 (meaning this is the
0-step classificaton) and a value which is another dict, which contains the
probability for each category as output from the classifier. For example,
this is what "prediction" would be if the classifier said that category 0
had a 0.6 probability and category 1 had a 0.4 probability: {0:0.6, 1: 0.4}