Comments (5)
Hello @DS-Liu 👋
Thank you for your issue.
Timeseries in the datasets
module of ReservoirPy all have the shape (n_timesteps, n_features)
, as they are timeseries. Echo State Networks can be used on those timeseries to predict the following timesteps, considering the previous ones.
If you want to convert a timeseries into an (input, output)
tuple for a prediction task, you can use the to_forecasting
method.
from reservoirpy.
I'm new to reservoir computing. But I think the input of the narma task should be the u(t)
sampled uniformly from [0, 0.5], am I wrong?
from reservoirpy.
I think the confusion comes from the naming convention between the reservoir computing litterature where u(t) usually represents the input timeseries, and u(t)
in the NARMA recurrent relation which stands for "uniform", and that is simply a sample from a uniform distribution and that is not meant to be used elsewhere.
In the end, the NARMA timeseries is a single timeseries of shape (n_timesteps, 1), and you can use reservoir computing to make predictions.
Let me know if the misunderstanding persists
from reservoirpy.
The
where
In the literatures of quantum reservoir computing, such as
- Harnessing Disordered-Ensemble Quantum Dynamics for Machine Learning.
- Boosting Computational Power through Spatial Multiplexing in Quantum Reservoir Computing.
- Learning nonlinear input–output maps with dissipative quantum systems.
- Higher-Order Quantum Reservoir Computing.
- Dynamical Phase Transitions in Quantum Reservoir Computing.
- Unifying framework for information processing in stochastically driven dynamical systems.
the input to the reservoir at time step
I think this is reasonable since the narma system is driven by
However, you mentioned that
If you want to convert a timeseries into an (input, output) tuple for a prediction task, you can use the to_forecasting method.
which means that for the narma task, the echo state at time
Are these literatures wrongly performed the narma task?
from reservoirpy.
Hello again, and thank you for your well-placed perseverance :)
Indeed, many papers use the uniformly distributed timeseries as an input for benchmarking. This will be fixed in the ReservoirPy v0.3.11.
In the meantime, if you want to use the NARMA task for your benchmarking, you can re-implement the function that returns u(t)
:
def narma(
n_timesteps: int,
order: int = 30,
a1: float = 0.2,
a2: float = 0.04,
b: float = 1.5,
c: float = 0.001,
x0: Union[list, np.ndarray] = [0.0],
seed: Union[int, RandomState] = None,
) -> np.ndarray:
if seed is None:
seed = get_seed()
rs = rand_generator(seed)
y = np.zeros((n_timesteps + order, 1))
x0 = check_vector(np.atleast_2d(np.asarray(x0)))
y[: x0.shape[0], :] = x0
u = rs.uniform(0, 0.5, size=(n_timesteps + order, 1))
for t in range(order, n_timesteps + order - 1):
y[t + 1] = (
a1 * y[t]
+ a2 * y[t] * np.sum(y[t - order : t])
+ b * u[t - order] * u[t]
+ c
)
return u, y[order:, :]
Sorry for my misunderstanding, and thank you again for your issue
from reservoirpy.
Related Issues (20)
- Autograd - Feature Request HOT 1
- Mmap error with local parallelization with optuna from the tutorial HOT 1
- 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
- How can I save the trained model? HOT 1
- ValueError on Dataset with sequences of different lengths HOT 1
- plot_hyperopt_report shows worst R^2 rather than best
- Custom Loss HOT 1
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.