Commit 133a6ac2 authored by Yuncong Yu's avatar Yuncong Yu
Browse files


parent b83cab40
......@@ -19,7 +19,8 @@ To run the application, make sure you've installed the following:
2. Conda e.g. from
3. Angular:
4. Java:
5. (Only for Windows) `make` in git Bash:
5. (Only for Windows) MSVC build tool:
6. (Only for Windows) `make` in git Bash:
### Step 1: Create an environment
All dependencies are listed in the *environment.yml* file. To create an environment, run the following command:
......@@ -31,8 +32,8 @@ This will create a conda environment named *pseudo*. Now activate the environmen
The LSH algorithm is maintained locally for now, so you'll have to create it manually. The file that you need to setup this package is
located in the backend folder (this is more efficient when debugging, as for every change you have to rebuild the package).
So the package can be created by running the following code:
`cd backend\libs`
`python3 build_ext --inplace && python3 install`
`cd backend/libs`
`python build_ext --inplace && python install`
`cd ..`
**NOTE 1**: So as a reminder, don't forget to run the 2nd line everytime you change something in the c++ code.
......@@ -62,6 +63,22 @@ You may want to use [HDFView]( to ex
use the python library [PyTables]( or
[h5py]( to convert your data into HDF format.
### Tips and comments
This PSEUDo version is a prototype, which is prone to bugs and insufficient functions.
It is released for evaluation and knowledge exchange.
We are working on an upgraded version with augmented functionality and thorough tests.
However, the open-source issue is still under discussion.
You may find the following tips or comments useful:
- PSEUDo is designed mainly for high-dimensional time series. Its sublinear scalability refers to the asymptotic complexity with respect to the number of tracks;
- PSEUDo's active learning mainly works for high-dimensional time series. Because the mechanism works by attaching larger weights to important tracks. Though updating query with DBA works also in univariate case;
- The track weights and query will be updated in each feedback round, however, the hash functions are initialized rather than incrementally learnt;
- Only positive labels take effect and negative labels are ignored in this version;
- This version does not support variable query length. Namely, the patterns searched for in the time series should have similar length as the query;
- You may want to use the same query length because the preprocessed data and the estimated LSH parameters are cached. Especially the latter takes some time.
- The loaded tracks may not scale well vertically. Tuning the range slider rescales it properly.
# Documentation
## Frontend Views
The UI is backed up by the Angular framework.
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