Comments (7)
Hello,
If I understand well your task, you have n multivariate timeseries composed of 4 variables, am I right ?
You can use reservoirpy in this case. Just make sure that you create a Win matrix with an input dimension of 4, using the generate_input_weights
function. Some of the tutorials provided in this repository, like the notebook "Introduction to Reservoir Computing", give examples of this use case, making prediction on the Lorenz timeseries. The Lorenz timeseries is of shape (1, t, 3).
from reservoirpy.
Closing this issue as the problem seems to be solved.
from reservoirpy.
Thank you! About the training function, I have multiple training sequences of different length, so putting them in a list using the train method would raise an error. If I use a for loop to train it for num_sequences iteration, will it overwrite the previous states? or is it ok to do so?
from reservoirpy.
Well, it should not raise an error, what version of reserviorpy are you using ? And what is the error you encounter ? Would it be possible for you to provide a code example with the error ?
from reservoirpy.
Sorry I replied late. I've identified the error and it was not what I thought it was, so all solved on that.
However, we have observed that the more data we trained the ESN upon, the worse the generation outcomes are. Also, the ESN seem to output something far off the target.
Our dataset contains only Cartesian coordinates normalized to the range [-1, 1], but the ESN output spans [-15, 20], which is clearly off. Any possible idea of why that is?
Our projects rely heavily on reservoirpy, so I'd like to thank you for all the help you offer.
from reservoirpy.
Hello,
The question you ask is deeply related to your task and your data. Some hyperparameter tuning is probably necessary, especially to find an optimal value for the spectral radius, the leak rate and the ridge parameter. This way you will probably solve the problem of output outliers, and maybe the generation performance problem. You can see the related notebook in the examples folder of the v0.2.4 version of reservoirpy to find a tutorial on optimization of hyperparameters using hyperopt.
from reservoirpy.
Closing this issue again as the problem seems to be solved.
from reservoirpy.
Related Issues (20)
- Potential Error in Documentation HOT 1
- Segfault in classification notebook HOT 5
- Save/Load to/from disk HOT 2
- No warning is triggered when non-existing variable name is used
- Autograd - Feature Request HOT 1
- Mmap error with local parallelization with optuna from the tutorial HOT 1
- datasets.narma doesn't return input series HOT 5
- ValueError: Missing input data for node Reservoir-0.
- Fitting a model on non-temporal data HOT 1
- Feature Importance HOT 4
- Small-world reservoir matrices
- Rank list of degree of influence of input variables HOT 1
- I trying to forecast using reservoirpy HOT 1
- how to save and load a prediction model HOT 2
- Is the long term forecasting example opertion explanation correct HOT 3
- Understand and optimize ESN hyperparameters errors HOT 3
- cant do long term forecasting on yahoo stock market data HOT 4
- Creating a reservoir of custom nodes HOT 2
- LMS doesn't work for single node readout HOT 1
- ESN Parameter Effects HOT 7
Recommend Projects
-
React
A declarative, efficient, and flexible JavaScript library for building user interfaces.
-
Vue.js
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
-
Typescript
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
-
TensorFlow
An Open Source Machine Learning Framework for Everyone
-
Django
The Web framework for perfectionists with deadlines.
-
Laravel
A PHP framework for web artisans
-
D3
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
-
Recommend Topics
-
javascript
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
-
web
Some thing interesting about web. New door for the world.
-
server
A server is a program made to process requests and deliver data to clients.
-
Machine learning
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
-
Visualization
Some thing interesting about visualization, use data art
-
Game
Some thing interesting about game, make everyone happy.
Recommend Org
-
Facebook
We are working to build community through open source technology. NB: members must have two-factor auth.
-
Microsoft
Open source projects and samples from Microsoft.
-
Google
Google ❤️ Open Source for everyone.
-
Alibaba
Alibaba Open Source for everyone
-
D3
Data-Driven Documents codes.
-
Tencent
China tencent open source team.
from reservoirpy.