{"package": "kde-gpu", "summary": "We implemented nadaraya waston kernel density and kernel conditional probability estimator using cuda through cupy. It is much faster than cpu version but it requires GPU with high memory.", "pypi_url": "https://pypi.org/project/kde-gpu", "piwheels_url": "https://www.piwheels.org/project/kde-gpu", "releases": {"0.1.0": {"released": "2019-08-22 07:18:50", "prerelease": false, "yanked": false, "skip_reason": "", "files": {"kde_gpu-0.1.0-py3-none-any.whl": {"file_url": "https://archive1.piwheels.org/simple/kde-gpu/kde_gpu-0.1.0-py3-none-any.whl", "filehash": "d103109fe47e3d0723ff32f7e3669556e4cdb9cdef9891000d8a0fdfead38d8b", "filesize": 9047, "builder_abi": "cp34m", "file_abi_tag": "none", "platform": "any", "requires_python": "", "apt_dependencies": [], "pip_dependencies": ["numpy", "pandas", "scipy"]}}}}}