Machine Learning/Dynamic Time Warping: Difference between revisions
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(Created page with " sample script: <pre> import numpy as np import mlpy # Example sequences sequence1 = np.array([1, 3, 4, 9, 8, 2, 1]) sequence2 = np.array([1, 2, 4, 7, 8, 2, 1]) sequence3 = np.array([1, 2, 4, 7, 8, 2, 5]) # Compute DTW distances between sequences dist12, cost12, path12 = mlpy.dtw_std(sequence1, sequence2, dist_only=False) dist13, cost13, path13 = mlpy.dtw_std(sequence1, sequence3, dist_only=False) print("DTW distance between sequence1 and sequence2:", dist12) print("...") |
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https://en.wikipedia.org/wiki/Dynamic_time_warping | |||
sample script: | sample script: | ||
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print("Sequence 3 is predicted to be closer to sequence 1.") | print("Sequence 3 is predicted to be closer to sequence 1.") | ||
</pre> | </pre> | ||
[[category:MachineLearning]] | |||
[[category:Script]] | |||
Latest revision as of 14:30, 24 July 2024
https://en.wikipedia.org/wiki/Dynamic_time_warping
sample script:
import numpy as np
import mlpy
# Example sequences
sequence1 = np.array([1, 3, 4, 9, 8, 2, 1])
sequence2 = np.array([1, 2, 4, 7, 8, 2, 1])
sequence3 = np.array([1, 2, 4, 7, 8, 2, 5])
# Compute DTW distances between sequences
dist12, cost12, path12 = mlpy.dtw_std(sequence1, sequence2, dist_only=False)
dist13, cost13, path13 = mlpy.dtw_std(sequence1, sequence3, dist_only=False)
print("DTW distance between sequence1 and sequence2:", dist12)
print("DTW distance between sequence1 and sequence3:", dist13)
# Predict which sequence is closer based on DTW distance
if dist12 < dist13:
print("Sequence 2 is predicted to be closer to sequence 1.")
else:
print("Sequence 3 is predicted to be closer to sequence 1.")
