Pre-release
This release is a pre-release and may not be stable for production use.
JaxUtils
JaxUtils provides utility functions for the JaxGaussianProcesses ecosystem.
Contents
PyTree
Overview
jaxutils.PyTree is a mixin class for registering a python class as a JAX PyTree. You would define your Python class as follows.
class MyClass(jaxutils.PyTree):
...
Example
import jaxutils
from jaxtyping import Float, Array
class Line(jaxutils.PyTree):
def __init__(self, gradient: Float[Array, "1"], intercept: Float[Array, "1"]) -> None
self.gradient = gradient
self.intercept = intercept
def y(self, x: Float[Array, "N"]) -> Float[Array, "N"]
return x * self.gradient + self.intercept
Dataset
Overview
jaxutils.Dataset is a datset abstraction. In future, we wish to extend this to a heterotopic and isotopic data abstraction.
Example
import jaxutils
import jax.numpy as jnp
# Inputs
X = jnp.array([[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]])
# Outputs
y = jnp.array([[7.0], [8.0], [9.0]])
# Datset
D = jaxutils.Dataset(X=X, y=y)
print(f'The number of datapoints is {D.n}')
print(f'The input dimension is {D.in_dim}')
print(f'The output dimension is {D.out_dim}')
print(f'The input data is {D.X}')
print(f'The output data is {D.y}')
print(f'The data is supervised {D.is_supervised()}')
print(f'The data is unsupervised {D.is_unsupervised()}')
The number of datapoints is 3
The input dimension is 2
The output dimension is 1
The input data is [[1. 2.]
[3. 4.]
[5. 6.]]
The output data is [[7.]
[8.]
[9.]]
The data is supervised True
The data is unsupervised False
You can also add dataset together to concatenate them.
# New inputs
X_new = jnp.array([[1.5, 2.5], [3.5, 4.5], [5.5, 6.5]])
# New outputs
y_new = jnp.array([[7.0], [8.0], [9.0]])
# New dataset
D_new = jaxutils.Dataset(X=X_new, y=y_new)
# Concatenate the two datasets
D = D + D_new
print(f'The number of datapoints is {D.n}')
print(f'The input dimension is {D.in_dim}')
print(f'The output dimension is {D.out_dim}')
print(f'The input data is {D.X}')
print(f'The output data is {D.y}')
print(f'The data is supervised {D.is_supervised()}')
print(f'The data is unsupervised {D.is_unsupervised()}')
The number of datapoints is 6
The input dimension is 2
The output dimension is 1
The input data is [[1. 2. ]
[3. 4. ]
[5. 6. ]
[1.5 2.5]
[3.5 4.5]
[5.5 6.5]]
The output data is [[7.]
[8.]
[9.]
[7.]
[8.]
[9.]]
The data is supervised True
The data is unsupervised False
1 maintainer
Metadata
1 maintainer
Release files for jaxutils-nightly 0.0.8.dev20230511
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| jaxutils-nightly-0.0.8.dev20230511.tar.gz | 30.1 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| jaxutils_nightly-0.0.8.dev20230511-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 48.0 kB
Release files / jaxutils-nightly-0.0.8.dev20230511.tar.gz
| Download URL | jaxutils-nightly-0.0.8.dev20230511.tar.gz |
|---|---|
| Size | 30.1 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
abf2c83eab4e0185b79da2175e3f1f9921797ebc2e3d6a4fbfc19593f52d1e72
|
|
BLAKE2b-256 checksum How to use checksums |
7baca6d4e314e5b457086a8029f0143bf2127c3cdb2a73784728cb83b9a001a8
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/4.0.2 CPython/3.9.0
|
Release files / jaxutils_nightly-0.0.8.dev20230511-py3-none-any.whl
| Download URL | jaxutils_nightly-0.0.8.dev20230511-py3-none-any.whl |
|---|---|
| Size | 17.9 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
0ad2320f7663a3459d2909b3b637ce51e70ed5fe681e0aca84a45588299713b0
|
|
BLAKE2b-256 checksum How to use checksums |
74b8be0a7449d37b74985d0a572005d2fea0f48b279f74a1b95270d9116322c1
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/4.0.2 CPython/3.9.0
|
