py-basic-dtw
A readable, not optimised, dynamic time warping (dtw) library that performs classical and subsequence dynamic time warping.
features:
- Classical DTW
- Subsequence DTW
- Two neighbourhood exclusion types for multiple matching ( Subsequence DTW):
- Local maximum exclusion
- Local distance exclusion
- Multidimensionl inputs
- Custom distance metrics via lambda functions
- Custom step weightings
- Custom dimension weightings
- Custom step pattern for cost matrix creation
Installation with PIP
pip install py-basic-dtw
Dependencies
- Numpy
Import
from pybasicDTW import Classical, Subsequence
Quick Start
Setup the sequences in the correct format.
-
If using a 1 dimensional sequence, you can leave it as a 1 dimensional array. For example:
sequence = np.array([1,2,3,4,5,6,7,8,9])
-
If using a multidimensional sequence, you must format the data so that each element in the array represents a list of values, across all dimensions, at that point in time. For example:
sequence = np.array([ [1,2,3] , [1,2,3], [1,2,3] ])
Note that each element in the array has the same number of dimensions within it, this must be true.
Also note that both inpt sequences, x and y, must have the same number of inner dimensions for all elements, this must be true.
Classical DTW
from pybasicDTW import Classical
# create sequence, this is a 1 dimensional sequence
x = np.array([0,0,0,1,1,1,2,3,4,5])
y = np.array([1,1,2,2,2,3,3,3,4,4,5,6])
# initalise and compute match
basicdtw = Classical(x, y, metric = lambda x,y: np.square(x-y))
# retrieve cost matrix
localCostMatrix = basicdtw.LocalCost
accumulatedCostMatrix = basicdtw.AccumulatedCost
# retrieve path
path = basicdtw.Path
totalCost = basicdtw.TotalCostSubsequence DTW
from pybasicDTW import Subsequence
# create sequence
x = np.array([[0,0,0,1,1,1,2,3,4,5],[0,0,0,1,1,1,2,3,4,5]])
y = np.array([[1,1,2,2,2,3,3,3,4,4],[0,0,0,1,1,1,2,3,4,5],[0,0,0,1,1,1,2,3,4,5]])
# initalise and compute cost matrix
basicdtw = Subsequence(x, y, metric = lambda x,y: np.square(x-y))
# compute match, this can be repeated for following matches
# default
optimalPath, totalCost = basicdtw.FindMatch()
# default expanded, invert = False will find the first maximum from the left.
optimalPath, totalCost = basicdtw.FindMatch(exclusionType="LocalDistance", LocalMaxDist=1, invert=False, overlapMatches=False)
# default expanded for Local Maximum Exclusion, overlapMatches = False will ensure the matched area will be excluded, hence the next match will not start within the previous match area.
optimalPath, totalCost = basicdtw.FindMatch(exclusionType="LocalDistance", invert=False, overlapMatches=False)
# retrieve cost matrix
localCostMatrix = basicdtw.LocalCost
accumulatedCostMatrix = basicdtw.AccumulatedCostTo be filled with more detailed explanations ....
