# aqua-blue
Lightweight and basic reservoir computing library
## 🌊 What is aqua-blue?
`aqua-blue` is a lightweight `python` library for reservoir computing (specifically [echo state networks](https://en.wikipedia.org/wiki/Echo_state_network)) depending only on `numpy`. `aqua-blue`'s namesake comes from:
- A blue ocean of data, aka a reservoir 💧
- A very fancy cat named Blue 🐾
## 📥 Installation
aqua-blue is on PyPI, and can therefore be installed with pip:
```bash
pip install aqua-blue
```
## 📝 Quickstart
```py
from aqua_blue import TimeSeries, EchoStateNetwork
# generate arbitrary two-dimensional time series
# y_1(t) = cos(t), y_2(t) = sin(t)
# resulting dependent variable has shape (number of timesteps, 2)
t = np.linspace(0, 4.0 * np.pi, 10_000)
y = np.vstack((np.cos(t), np.sin(t))).T
# create time series object to feed into echo state network
time_series = TimeSeries(dependent_variable=y, times=t)
# generate echo state network with a relatively high reservoir dimensionality
esn = EchoStateNetwork(reservoir_dimensionality=100, input_dimensionality=2)
# train esn on our time series
esn.train(time_series)
# predict 1,000 steps into the future
prediction = esn.predict(horizon=1_000)
```
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"description": "# aqua-blue\nLightweight and basic reservoir computing library\n\n## \ud83c\udf0a What is aqua-blue?\n\n`aqua-blue` is a lightweight `python` library for reservoir computing (specifically [echo state networks](https://en.wikipedia.org/wiki/Echo_state_network)) depending only on `numpy`. `aqua-blue`'s namesake comes from:\n\n- A blue ocean of data, aka a reservoir \ud83d\udca7\n- A very fancy cat named Blue \ud83d\udc3e\n\n## \ud83d\udce5 Installation\n\naqua-blue is on PyPI, and can therefore be installed with pip:\n\n```bash\npip install aqua-blue\n```\n\n## \ud83d\udcdd Quickstart\n\n```py\nfrom aqua_blue import TimeSeries, EchoStateNetwork\n\n# generate arbitrary two-dimensional time series\n# y_1(t) = cos(t), y_2(t) = sin(t)\n# resulting dependent variable has shape (number of timesteps, 2)\nt = np.linspace(0, 4.0 * np.pi, 10_000)\ny = np.vstack((np.cos(t), np.sin(t))).T\n\n# create time series object to feed into echo state network\ntime_series = TimeSeries(dependent_variable=y, times=t)\n\n# generate echo state network with a relatively high reservoir dimensionality\nesn = EchoStateNetwork(reservoir_dimensionality=100, input_dimensionality=2)\n\n# train esn on our time series\nesn.train(time_series)\n\n# predict 1,000 steps into the future\nprediction = esn.predict(horizon=1_000)\n```\n",
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