caspr | NeurIPS 2020 paper CaSPR : Learning Canonical | Machine Learning library

by davrempe Python Version: Current License: MIT

X-Ray Key Features Code Snippets Community Discussions(1)Vulnerabilities Install Support

kandi X-RAY | caspr Summary

caspr is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning applications. caspr has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has low support. You can download it from GitHub.

This is the official implementation for the NeurIPS 2020 spotlight paper. For more information, see the project webpage.

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Quality

Security

License

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Support

caspr has a low active ecosystem.

It has 48 star(s) with 4 fork(s). There are 8 watchers for this library.

It had no major release in the last 6 months.

caspr has no issues reported. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of caspr is current.

Quality

caspr has no bugs reported.

Security

caspr has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

License

caspr is licensed under the MIT License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

Reuse

caspr releases are not available. You will need to build from source code and install.

Build file is available. You can build the component from source.

Installation instructions, examples and code snippets are available.

Top functions reviewed by kandi - BETA

kandi has reviewed caspr and discovered the below as its top functions. This is intended to give you an instant insight into caspr implemented functionality, and help decide if they suit your requirements.

Train model
Load weights from full dictionary
Returns a torch device
Count the number of parameters required by the model
Runs viz
Evaluate the Tk3d model
Reconstruct the solution of the problem
Encodes the trajectory
Forward computation
Flips x along specified axis
Perform the forward computation
Calculate the negative log loss
Compute the loss function
Handle key press events
Move back to next frame
Generate a random rotation matrix
Parse command line arguments
Add common options to the given parser
Add test options
Add training options to the given parser
Forward the forward function
Reverse the model
Update the running mean
Derivative of the logdet function
Build a Point CNF model
Construct a CNF model

Get all kandi verified functions for this library.

caspr Key Features

No Key Features are available at this moment for caspr.

caspr Examples and Code Snippets

No Code Snippets are available at this moment for caspr.

Community Discussions

Trending Discussions on caspr

Pandas trying to make values within a column into new columns after groupby on column

QUESTION

Pandas trying to make values within a column into new columns after groupby on column

Asked 2020-Oct-08 at 00:35

My original dataframe looked like:

...

ANSWER

Answered 2020-Oct-07 at 02:24

Try unstacking the variables column from rows to columns with .unstack(1). The parameter is 1, because we want the second index column (0 would be the first)
Then, drop the level of the multi-index you just created to make it a little bit cleaner with .droplevel().
Finally, use pd.Grouper. Since the date/time is on the index, you don't need to specify a key.

Source https://stackoverflow.com/questions/64236160

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install caspr

Note: the code in this repo has been tested on Ubuntu 16.04 with Python 3.6, CUDA 10.0, and PyTorch 1.4.0. It may work for other setups, but has not been tested. Make sure CUDA is installed and set up correctly before going forward. Note that CUDA must be >= 10.0 for Kaolin), and the CUDA version must be the same for your PyTorch and Kaolin installations.
conda create -n caspr_env python=3.6
conda activate caspr_env
pip install -r requirements.txt
pip install torch==1.4.0+cu100 torchvision==0.5.0+cu100 -f https://download.pytorch.org/whl/torch_stable.html
pip install git+https://github.com/drsrinathsridhar/tk3dv.git
pip install torchdiffeq==0.0.1 (note the version is important)
mkdir external
cd external
git clone https://github.com/NVIDIAGameWorks/kaolin.git
cd kaolin
python setup.py build_ext --inplace
python setup.py install (make sure it uses the expected CUDA and PyTorch installations to build)
Verify in python with import kaolin as kal then print(kal.__version__). If there's an error about "No module names nuscenes" it can be safely ignored.
cd external
git clone https://github.com/daerduoCarey/PyTorchEMD.git
cd PyTorchEMD
python setup.py install
cp build/lib.linux-x86_64-3.6/emd_cuda.cpython-36m-x86_64-linux-gnu.so /home/ubuntu/anaconda3/envs/caspr_env/lib/ (depending on your setup, you will need to change the Python lib path)