TensorFlow, PyTorch and Numpy layers for generating Orthogonal Polynomials

Last update: May 25, 2022

Related tags

Deep Learning OrthNet

Overview

OrthNet

TensorFlow, PyTorch and Numpy layers for generating multi-dimensional Orthogonal Polynomials

1. Installation
2. Usage
3. Polynomials
4. Base Class(Poly)

Installation:

the stable version:
pip3 install orthnet
the dev version:

git clone https://github.com/orcuslc/orthnet.git && cd orthnet
python3 setup.py build_ext --inplace && python3 setup.py install

Usage:

with TensorFlow

import tensorflow as tf
import numpy as np
from orthnet import Legendre

x_data = np.random.random((10, 2))
x = tf.placeholder(dtype = tf.float32, shape = [None, 2])
L = Legendre(x, 5)

with tf.Session() as sess:
    print(L.tensor, feed_dict = {x: x_data})

with PyTorch

import torch
import numpy as np
from orthnet import Legendre

x = torch.DoubleTensor(np.random.random((10, 2)))
L = Legendre(x, 5)
print(L.tensor)

with Numpy

import numpy as np
from orthnet import Legendre

x = np.random.random((10, 2))
L = Legendre(x, 5)
print(L.tensor)

Specify Backend

In some scenarios, users can specify the exact backend compatible with the input x. The backends provided are:

An example to specify the backend is as follows.

import numpy as np
from orthnet import Legendre, NumpyBackend

x = np.random.random((10, 2))
L = Legendre(x, 5, backend = NumpyBackend())
print(L.tensor)

Specify tensor product combinations

In some scenarios, users may provide pre-computed tensor product combinations to save computing time. An example of providing combinations is as follows.

import numpy as np
from orthnet import Legendre, enum_dim

dim = 2
degree = 5
x = np.random.random((10, dim))
L = Legendre(x, degree, combinations = enum_dim(degree, dim))
print(L.tensor)

Polynomials:

Class	Polynomial
`orthnet.Legendre(Poly)`	Legendre polynomial
`orthnet.Legendre_Normalized(Poly)`	Normalized Legendre polynomial
`orthnet.Laguerre(Poly)`	Laguerre polynomial
`orthnet.Hermite(Poly)`	Hermite polynomial of the first kind (in probability theory)
`orthnet.Hermite2(Poly)`	Hermite polynomial of the second kind (in physics)
`orthnet.Chebyshev(Poly)`	Chebyshev polynomial of the first kind
`orthnet.Chebyshev2(Poly)`	Chebyshev polynomial of the second kind
`orthnet.Jacobi(Poly, alpha, beta)`	Jacobi polynomial

Base class:

Class Poly(x, degree, combination = None):

Inputs:
- x a tensor
- degree highest degree for target polynomials
- combination optional, tensor product combinations
Attributes:
- Poly.tensor the tensor of function values (with degree from 0 to Poly.degree(included))
- Poly.length the number of function basis (columns) in Poly.tensor
- Poly.index the index of the first combination of each degree in Poly.combinations
- Poly.combinations all combinations of tensor product
- Poly.tensor_of_degree(degree) return all polynomials of given degrees
- Poly.eval(coefficients) return the function values with given coefficients
- Poly.quadrature(function, weight) return Gauss quadrature with given function and weight

Comments

Cuda support

Hi,

First of all thank your for developing this project. Is it possible to create the Jacobi.tensor in the gpu? Currently I am creating the tensor in the cpu and then moving them to gpu, which is time consuming.

Cheers

opened by mariolinovIC 1
Jacobi polynomial incorrect evaluation

Hi, I have noticed than when I evaluate Jacobi polynomial with alpha=1 and beta=1 the results are not ok. Particularly I tried in range (-1,1) and I noticed the problem for n greater than 1 (i.e., 2,3,4). Thank you for your support.

opened by mariolinovIC 0

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TensorFlow, PyTorch and Numpy layers for generating Orthogonal Polynomials

Related tags

Overview

OrthNet

Installation:

Usage:

with TensorFlow

with PyTorch

with Numpy

Specify Backend

Specify tensor product combinations

Polynomials:

Base class:

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Comments

Cuda support

Jacobi polynomial incorrect evaluation

Releases(0.4.0)

0.4.0(Apr 19, 2018)

0.3.0(Apr 5, 2018)

0.2.2(Mar 22, 2018)

0.1(Feb 27, 2018)

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