#saxpy.py
An implementation of Symbolic Aggregate approXimation in python.
Based on the paper A Symbolic Representation of Time Series, with Implications for Streaming Algorithms
from saxpy import SAX
s = SAX(wordSize, alphabetSize, epsilon)
You can optionally specify word size, alphabet size and epsilon
If you want to compare x1 and x2 (lists of values):
(x1String, x1Indices) = s.to_letter_rep(x1)
(x2String, x2Indices) = s.to_letter_rep(x2)
x1x2ComparisonScore = s.compare_strings(x1String, x2String)
If you want to use the sliding window functionality:
(say you want to break x3 into a lot of subsequences)
can optionally specify the number of subsequences and how much each subsequence overlaps with the previous subsequence
(x3Strings, x3Indices) = s.sliding_window(x3, numSubsequences, overlappingFraction)
Then if you wanted to compare each subsequence to another string (say x2):
x3x2ComparisonScores = s.batch_compare(x3Strings,x2String)
Missing data:
Missing data is supported. Input values of numpy.nan will not affect normalization and will be converted to the - character during alphabetization. When comparing two strings containing the - character, the distance contribution for any such characters will be 0. For example, comparing aa-a to a-aa will result in a distance of 0. As another example, comparing abc to d-f will be the same as comparing ac to df.
Note:
If you haven't generated the strings through the same SAX object, the scaling factor (square root of the length of the input vector over the word size) will be incorrect, you can correct it using:
s.set_scaling_factor(scalingFactor)
To run the tests, just do:
nosetests ./tests/