I am new to Dynamic Time Warping and your note helps quite a lot. Thank you for your sharing. My input
data is 3-D, having shape of (n_samples, n_timesteps,n_features). I am not sure how to transfer it into using the model. Thank you so much!

Hi, is there any change in the code that you might recommend to speed up the execution of the algorithm?

np.shape & scipy.spatial.distance are being used w/o previous import of those modules.

Hi Mark,

Glad to know you...am NOT a proficient python user however I'm quite familiar in writing and running few scripts in Ipython Notebook. I came across your k-NN+DTW classifier source code which is quite self-explanatory and well written on your Github page - appreciate that.

However, I seek your help in getting started running your demo script with HAR dataset first to gain understanding about implementation of some of your routines.

I encounter an error when reading the datasets at the following lines...

Loop through datasets

for x in x_train_file:
x_train.append([float(ts) for ts in x.split()])

Attached is the snapshot of my error in IPython terminal

Could you pls help me rectify & rerun this without error?

Thanks,
Bala

This is the error i am getting while trying to execute the code posted here. Please help me resolve that asap

First of all, thanks for this wonderful example. I have downloaded it and tried to run under python 3.6. It shows an unresolved error in the following line:
dm = squareform(dm)

May I know how to resolve this?

Hi,

Perhaps I'm not understanding your code properly, but I was hoping you could clear some things up. When performing DTW on the dataset you read in data/UCI-HAR-Dataset/train/X_train.txt - This is the feature-engineered dataset, I believe. It contains 561 columns instead of 128, which the README of the data indicates is the number of observations per window.

The part I don't understand is that the features are all dependent on the same observation window. (I checked this by adding an assertion in KnnDtw._dtw_distance()) In that case how can you perform DTW on them when there isn't a time-scale to warp? Or - more likely - am I misunderstanding DTW?

hey, thanks for the wonderful tutorial, I am wondering how to save once the model is trained, I tried using joblib (sklearn), after saving whenever I am loading the model it starts training again. can you please help me with that.