A differentiable quaternion implementation in tensorflow.
# Tensorflow Quaternion An implementation of quaternions for tensorflow. Fully differentiable.
The tfquaternion module provides an implementation of quaternions as a tensorflow graph. tfquaternion offers module functions for the basic quaternion arithmetic operations as well as a Quaternion class which supports the relevant magic methods. This is similar to the tensorflow API, e.g. tfq.quaternion_multiply vs. tf.multiply and tfq.Quaternion vs tf.Tensor. Note that all functions starting with tf.quaternion_… assume that it’s arguments are tf.Tensor`s (or `tfq.Quaternion`s) that can be casted to `tfq.Quaternion, i.e. the shape must be (…, 4).
This implementation is mostly compatible with a small subset of [moble’s quaternion implementation](https://github.com/moble/quaternion/) (ensured by using slightly adapted versions of his tests). One major difference is that it is type specific as is tensorflow.
To install the git version as development package run: ` git clone https://github.com/PhilJd/tf-quaternion.git cd tf-quaternion pip install -e . ` The -e option only links the working copy to the python site-packages, so to upgrade, you only need to run git pull.
Before getting started, an important note on the division: This library resembles the division behaviour of [moble’s quaternion](https://github.com/moble/quaternion/). While in general the division operator is not defined (from the notation q1/q2 one can not conclude if q1/q2 = q1 * q2^-1 or q1/q2 = q2^-1 * q1), we follow moble’s implementation, i.e. tfq.quaternion_divide and Quaternion.__truediv__ compute q1/q2 = q1 * 1/q2.
#### Example A simple rotation by a quaternion can look like this: ` >>> import tfquaternion as tfq >>> import tensorflow as tf >>> s = tf.Session() >>> points = tf.constant([[1, 0, 0], [0, 1, 0], [0, 0, 1]], dtype=tf.float32) >>> quat = tfq.Quaternion([0, 1, 0, 0]) rotate by 180 degrees around x axis >>> s.run(tf.matmul(quat.as_rotation_matrix(), points)) array([[ 1., 0., 0.], [ 0., -1., 0.], [ 0., 0., -1.]], dtype=float32) `
##### class Quaternion The usage of the *-Operator depends on the multiplier. If the multiplier is a Quaternion, quaternion multiplication is performed while multiplication with a tf.Tensor uses tf.multiply. The behaviour of division is similar, except if the dividend is a scalar, then the inverse of the quaternion is computed. ` tfq.Quaternion([1, 0, 0, 0]) * tfq.Quaternion([0, 4, 0, 0]) >>> tfq.Quaternion([0, 4, 0, 0) tfq.Quaternion([1, 0, 0, 0]) * tf.Tensor([0, 4, 0, 0]) >>> tf.Quaternion([0, 0, 0, 0) `
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
|Filename, size & hash SHA256 hash help||File type||Python version||Upload date|
|tfquaternion-0.1.2-py2.py3-none-any.whl (14.3 kB) Copy SHA256 hash SHA256||Wheel||py2.py3||Jan 2, 2018|
|tfquaternion-0.1.2.tar.gz (10.6 kB) Copy SHA256 hash SHA256||Source||None||Jan 2, 2018|