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tdcca

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0.0.0

tdcca-0.0.0-py2-none-any.whl

Wheel Details

Project:	tdcca
Version:	0.0.0
Filename:	tdcca-0.0.0-py2-none-any.whl
Download:	[link]
Size:	20303
MD5:	`cc2e00953420f6abeaa07909f6ea57a6`
SHA256:	`14f253d94aba41eb0d2a4e778cc969fbcdc1c2d8f4461e3dad0f93fd5584ba5c`
Uploaded:	2019-01-31 02:47:13 +0000

dist-info

METADATA · WHEEL · RECORD · top_level.txt

METADATA

Metadata-Version:	2.1
Name:	tdcca
Version:	0.0.0
Summary:	Time-dependent Canonical Correlation Analysis
Author:	Xuefei Cao, Jun Ke, Xi Luo, Bjorn Sandstede
Home-Page:	https://github.com/xuefeicao/tdcca
Requires-Dist:	matplotlib (>=1.5.3)
Requires-Dist:	numpy (>=1.11.1)
Requires-Dist:	scipy (>=0.19.0)
Requires-Dist:	six (>=1.10.0)
Requires-Dist:	scikit-learn (>=0.18.1)
Requires-Dist:	prox-tv (>=3.2.1)
Requires-Dist:	pathos (>=0.2.2)
Description-Content-Type:	text/markdown

[Description omitted; length: 2739 characters]

WHEEL

Wheel-Version:	1.0
Generator:	bdist_wheel (0.31.1)
Root-Is-Purelib:	true
Tag:	py2-none-any

RECORD

Path	Digest	Size
tdcca/__init__.py	sha256=-U_N2BuWMLdTrkF7uXg8fW2n3yryEVtoy7l1Z-BLkhE	206
tdcca/admm_computation.py	sha256=Ep6QiEiAwMfnIpKksR0415auCjFb0Sn4vNxvddVmqdE	38394
tdcca/data_preprocess.py	sha256=mDC8pu1VurvuqQth1VBudyo9dErTc0X46B491IoXCkc	7802
tdcca/multi_sim_val.py	sha256=IMxWke3laTTQBZN-WOFRU-vKUB_tRuEee9otkdYCvss	6129
tdcca/tcca_config.py	sha256=czjXl5NSRwHEOX1HEerEvvYcj29WC0o3h8pBjD0fi1I	2593
tdcca/val_computation.py	sha256=2nlx-epEIVkjym2zi9Kohx29CaRx_GR_gednhvDmABY	17074
tdcca-0.0.0.dist-info/METADATA	sha256=NrvV6cWJMvIJAAmDJeOyeHV_DMse2KQICIkr5Gk4u5g	3252
tdcca-0.0.0.dist-info/RECORD	—	—
tdcca-0.0.0.dist-info/WHEEL	sha256=_V8uWu5F_h-lvAz5Mbq4oZqV_uCfL4cHgm4IIlDevzU	92
tdcca-0.0.0.dist-info/top_level.txt	sha256=m-W4ySxgECqfKMdPRFTC9g2uoLmEciWLyOWvSGiJmro	6

top_level.txt

tdcca