Wheelodex » Projects » pyscnn

pyscnn

View on PyPI — Reverse Dependencies (0)

0.0.9b0 pyscnn-0.0.9b0-py3-none-any.whl

Wheel Details

Project: pyscnn
Version: 0.0.9b0
Filename: pyscnn-0.0.9b0-py3-none-any.whl
Download: [link]
Size: 123139
MD5: 02f40d397ad6ee305e1d9f24d9919a80
SHA256: 16a700b28cf9edba3de7c82d72845a82c23dc9d9d4609d23ed51e496c0ac2167
Uploaded: 2024-05-07 21:07:15 +0000

dist-info

METADATA · WHEEL · RECORD · top_level.txt

METADATA

Metadata-Version: 2.1
Name: pyscnn
Version: 0.0.9b0
Summary: Scalable Convex Neural Networks: a package for fasts convex optimization of shallow neural networks.
Author-Email: Aaron Mishkin <amishkin[at]cs.stanford.edu>
Project-Url: Homepage, https://github.com/pilancilab/scnn
Project-Url: Bug Tracker, https://github.com/pilancilab/scnn/issues
License: MIT License Copyright (c) 2022 Pilanci Research Group Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.7
Requires-Dist: numpy (>=1.21.3)
Requires-Dist: torch (>=1.10.0)
Requires-Dist: cvxpy (>=1.2.1)
Requires-Dist: scikit-learn (>=1.0.0)
Requires-Dist: scipy (>=1.7.2)
Requires-Dist: typing-extensions
Requires-Dist: tqdm
Requires-Dist: opt-einsum
Requires-Dist: linalg-backends
Description-Content-Type: text/markdown
License-File: LICENSE
[Description omitted; length: 1126 characters]

WHEEL

Wheel-Version: 1.0
Generator: bdist_wheel (0.43.0)
Root-Is-Purelib: true
Tag: py3-none-any

RECORD

Path Digest Size
scnn/__init__.py sha256=OjfYK0X0O-TJAgUXdZ1pSXBiBN8DFfYi_4HK9hYIPyc 76
scnn/activations.py sha256=-AYlOsBqn4QXFz86fYIRZgHtLwBKmpLBHsGKhrS-EMI 10383
scnn/loss_functions.py sha256=SYp3EhO669oqTyTksMx9AclDWc6Ku9wnejIEkaWK4kM 423
scnn/metrics.py sha256=04MuC_RlEg0FiHqNF4_a0nWJCv1y7mYPHhhXrAmrBoQ 6440
scnn/models.py sha256=MKgwTRvx2MY6_AW_-EbsfBnHhwLiiZDooQcvNz0VxnQ 18337
scnn/optimize.py sha256=NH57rfm-_1EW_cJOeMw9byeD90Or1DSaf6jr-6U8giM 13470
scnn/py.typed sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU 0
scnn/regularizers.py sha256=z7tD3prWXqcoRt_G8OEdM2Dl91hmuzX4aiiIfFZCocs 2050
scnn/solvers.py sha256=iEOcSKk4jBCJvFYIVWNGu4EVb-0Zaddi2Z4PRCUzTXc 13982
scnn/support.py sha256=ws7TW2LZOdgGtFrH9iHeLHLxmRvj5Ittr3rniAuRx2s 6258
scnn/private/__init__.py sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU 0
scnn/private/initializers.py sha256=5J9pCF_LhXacAmbnFoGSoN_zVaRER-Tv0uj63uqk_fg 5242
scnn/private/loss_functions.py sha256=HdTwb7eYQOsivuOjzOwlfk9UliGiJslJdIJG-tzUVoQ 3483
scnn/private/metrics.py sha256=-UoKZXxMOI5EnTTTvVH4FBv2npfyyVRAbRs3cVp1Crs 12181
scnn/private/interface/__init__.py sha256=a42WLI2ecsyhPIHBcBrEsINZMxhhsa7FqxqXI00rNa0 982
scnn/private/interface/data.py sha256=-iUuz17okFIWE-fS1WDp5pIoEnLxE-8nyLEigTpcqd4 3879
scnn/private/interface/metrics.py sha256=VYgeS534RQN9rR-_xIASFxHPNzOg5ZAj43UXzqTk8bE 4501
scnn/private/interface/models.py sha256=RSobby875YKsWv8DSQFCF6ewcEuiF0JPSAcG5CeBPFs 6376
scnn/private/interface/solvers.py sha256=vgW0AiXvsJmZwjesC1VU_rom_PJkgtlMSb-Fujkk_Tg 6110
scnn/private/interface/utils.py sha256=XAt6xVTQX3o0OASJt2wicQ2YEGgyOtb4QW65H5j-ZxI 1478
scnn/private/methods/__init__.py sha256=k-XmLqSqvFaymJJ1ngmCDZRlr660J_IYcaMfIjdxUow 2483
scnn/private/methods/callbacks.py sha256=KG65o6chmTZvWySpmeCki4bgiosMNWPSjt4pDAfiS3c 6105
scnn/private/methods/external_solver.py sha256=u6Ne07cWiBPph7JjtnFaUeEXc1Cy2p6-JBqrZh0vJJM 2340
scnn/private/methods/method_utils.py sha256=H_fKVJ5hHDdnh-VjDFtpkQP-VrglYYSfVPmAOD5gg6A 2217
scnn/private/methods/processing.py sha256=dIU-VBGihyNeapvLxwgj9r3uLOS8OrnxRUTm3J5s57c 813
scnn/private/methods/termination_criteria.py sha256=SmOylFlWJ-y6h4j47Hqi1zagPLt7FCTwvjkiw_LkXO8 10805
scnn/private/methods/core/__init__.py sha256=18BdzAUh0PUYapMLOeGiAUI5RcaWBnukVZeY4u_55T8 518
scnn/private/methods/core/augmented_lagrangian.py sha256=faQfwIkRlqYKv5bWULZ7XQu1yLakIZszI-iT42KqJ0Q 1926
scnn/private/methods/core/gradient_descent.py sha256=9mRPJq4mqChcjkGwrTxwpaAVE-REvwHTLlCgdqaIzUY 2131
scnn/private/methods/core/line_search.py sha256=lg1GX33U3oy3PfpbJqB8rSE602MyYCGJkV6rReGScsc 2163
scnn/private/methods/core/proximal_gradient.py sha256=wW_WI6vbgIBlSulNJgzs4cp-tldgSUiJaPlYXmtEVVU 5547
scnn/private/methods/cvxpy/__init__.py sha256=gkeVst7UzqonXFDzELFVvG3l5r3vLZpUue5jjVvdkyo 625
scnn/private/methods/cvxpy/cone_decomposition.py sha256=pvwtkb8tIPbpxO2NKQtFklkg8Ba_vM1SutYaVqlsxOU 6244
scnn/private/methods/cvxpy/cvxpy_solver.py sha256=QqA23FMzcMWXlWGjYCSaCzCeGCoWL-MnWpyX4QplR8I 1655
scnn/private/methods/cvxpy/training_programs.py sha256=N5-2boFEmHSUBVrjP2gsDkXOol-T7s2EMlQkseXIpeA 8167
scnn/private/methods/line_search/__init__.py sha256=p5sSNBORuZjXn5ev1MP-oNYV67yQJAX3qRUx0V45wwU 564
scnn/private/methods/line_search/backtrack.py sha256=CdGXjFySDAGFVg-Jswb8jG757VxT9RgK-eR5DFcFSdg 1191
scnn/private/methods/line_search/conditions.py sha256=C6LEP6vO_eMkby8RrmYAUTdMFeYT9rdd3TY_L8r6xIg 4251
scnn/private/methods/line_search/step_size_updates.py sha256=sih2z3yglAv7hUywf1FkqqVZu4J0mBmkW7Lo22R2hOM 5118
scnn/private/methods/optimization_procedures/__init__.py sha256=bz1Ik51Rvm2gMvuKo6TgazU6gpN363DLdI0H29kVIx4 498
scnn/private/methods/optimization_procedures/double_loop_procedure.py sha256=uOT2cJ04N-UiU0AfAHntLnyHMapfs5OaqIqY5MaDI_w 11512
scnn/private/methods/optimization_procedures/iterative.py sha256=om5FmMmJr-ye9PZGfwpxX7nexsmQtfdkqcJyudBH4I4 11055
scnn/private/methods/optimization_procedures/optimization_procedure.py sha256=7I3Wi-bcr1v15updfBtGVuMr_C-oM6PonTtjKW4cMv0 4539
scnn/private/methods/optimization_procedures/torch_loop.py sha256=a6QJckwm62G0hieyr3ezEAAUClwdPNayUv3CngPkwrY 9069
scnn/private/methods/optimizers/__init__.py sha256=AvCW3sb-7ea4TETsmTieeGQqlFAVsX06_QPFEWv57AE 609
scnn/private/methods/optimizers/augmented_lagrangian.py sha256=NFS2kvE8-YQhuWiaO9xlKEyrZVEkDk5no7sw8cAixUg 5933
scnn/private/methods/optimizers/fista.py sha256=ZtFbdVR0QwsIc9qFcabCvjN6aQlMcCRcZBpTgoYWUCw 5473
scnn/private/methods/optimizers/gd.py sha256=cG8c0YQdIcCfus6_2UkyTWgwiaJkb79JLR76C_nUufk 1358
scnn/private/methods/optimizers/meta_optimizer.py sha256=OqvsB2vnf96iwdUCyK3Hot9C0O70haBfXx3rktkHK6E 1860
scnn/private/methods/optimizers/optimizer.py sha256=rKqhnfk4Sj5ujFJKj4MxCIWtnVuV22cw7LBJ2d6QN0A 6238
scnn/private/methods/optimizers/pgd.py sha256=xwWxbgfDSrFGqUIC3q2EWO7TrRrpUg9rPXhZ6DJDdls 1842
scnn/private/methods/optimizers/proximal_optimizer.py sha256=KWdEvDaH87nNXK2eDAyD6xHuTPG1hplUzENVUTQNn18 6190
scnn/private/models/__init__.py sha256=xaqFi9UY268lgIgYrpxti893bVVBpCLMT63AKvgP1M4 1188
scnn/private/models/model.py sha256=ihETJwJ0G5j2pJ2rTAZ3SA3f_tN39ZW9Ho5nFmdMy_M 11006
scnn/private/models/one_vs_all.py sha256=3Sm2l4ZwBLYwtCLeWrrjIUZGjUJS0TDPUZ3VNvqlOAU 3355
scnn/private/models/convex/__init__.py sha256=Nz7yXocf727KIs6-9L8U7cxxaKV-lxH3wdnypN8m0e8 159
scnn/private/models/convex/convex_mlp.py sha256=GP8MqeLLBx783Q71HDM5Q9Vkp15U7MEVw0YRiJ2x8SM 9867
scnn/private/models/convex/operators.py sha256=2_cu-c2e8tDY_BrCB_oDGVz5okPYyJp3b2yVPlr0JdI 5498
scnn/private/models/convex/al/__init__.py sha256=md7ydf5UZ_2D--Eq5msQGZXP1LrParqhyWR4TNlwllE 113
scnn/private/models/convex/al/al_mlp.py sha256=9M81rGMuelbmGCpmqUpWc-Z4IXIEWQObyoodr7MFJG0 14592
scnn/private/models/convex/kernels/__init__.py sha256=bJalPkua-RDGe8Aq45PVvVoC0xrkF5Om1kiX8XjgEIc 70
scnn/private/models/convex/kernels/direct_kernel.py sha256=eOuzTVzf4hWRgpkaFT_DtOOzxhJg_pAStt63sgBr5Fg 5064
scnn/private/models/convex/kernels/einsum_kernel.py sha256=AJfHJlEShjFJqmi3gxx8aCjdtSNSVAa8ARRefHNyn98 1500
scnn/private/models/decompositions/__init__.py sha256=89rzAv0AWXW8ebY04JDpOp1HnwPQnbn3mfAHLcTsQIA 99
scnn/private/models/decompositions/quadratic_decomposition.py sha256=FS8chXa3gsuE_u8Y48jxi3Ww_RJj6UWpZJPuRRMax0I 5610
scnn/private/models/linear/__init__.py sha256=49Xto20NIlqUJTR3JFySBT242mAgSenjBFL7qBFuxIE 217
scnn/private/models/linear/l2_regression.py sha256=UqEeP10FqWMltarSZzueWYY-zB_bSiJRIbqFZrTlq3M 2694
scnn/private/models/linear/logistic_regression.py sha256=zeGxTXvJeUC4Pjz9-8qObAm3I_S3X3hiZI2alQcz618 2596
scnn/private/models/non_convex/__init__.py sha256=S0jyoesOESfHwdBP3SQhWknx3hBJCS4ZHcAmI4eO9gI 271
scnn/private/models/non_convex/manual/__init__.py sha256=OysiNFwHBvXwoNYdnTZtwlCOdJgV8tNXBYYIF-evFGg 213
scnn/private/models/non_convex/manual/gated_relu_mlp.py sha256=6AXy9QD6WH3qLqbZ5GTsI48yrwYDSBelYV90-U_V0EY 3246
scnn/private/models/non_convex/manual/relu_mlp.py sha256=ycFPMwtOGmTR1oXgVKWS2VJiXtzUbm_rlibSYE3G8DI 5277
scnn/private/models/non_convex/torch/__init__.py sha256=WqBoGHkmHRWC-nDvDsXD3Z9hmWEWEjDhWYBx-asWNzQ 266
scnn/private/models/non_convex/torch/gated_relu_layer.py sha256=aSZCZ-a0T478i1nX7hFMFTdeXfpUUTV7R5XMaP86NBg 990
scnn/private/models/non_convex/torch/layer_wrapper.py sha256=mkgzEsHeFA5aDTI4WEiS4x2gFfmGLw4m1oM-Nda7CMU 828
scnn/private/models/non_convex/torch/sequential_wrapper.py sha256=MO9Dy0JffGzKFnEWPKirvbeLBNmbyFa0DcfzhibJC40 4908
scnn/private/models/regularizers/__init__.py sha256=gofqJjglcFSFharsN-3pT5ytif8T0z1EvEWTD6bV0C4 607
scnn/private/models/regularizers/constraint.py sha256=RXXESCmfwApnGKY159h69t1fe9zVi2FY4Ix99FjO2VQ 1870
scnn/private/models/regularizers/feature_group_l1.py sha256=GbXphsewyQnZXG38NEYaLGsjviX_TvAGUWvVgVHPnDI 3075
scnn/private/models/regularizers/group_l1.py sha256=1MK5VHQeyU23kBl02xECEHWDgjcXF-qDuOtip7h16xE 2514
scnn/private/models/regularizers/group_l1_orthant.py sha256=yGbiiOmdg4Tqq9VzAtvIfPqcZnUUMriwb2za2jQmsB4 3124
scnn/private/models/regularizers/l1.py sha256=FZr6KwJO9tIcdMqwEcbEbuV20YIHw-T0JuxikpXrojM 1465
scnn/private/models/regularizers/l1_squared.py sha256=piKVnWwJ-w4ddTuyTsUh78JH4iQVwQBIX-cXgEnr_ls 545
scnn/private/models/regularizers/l2.py sha256=KEtgInIG2fAbRC-PQ74NEaMX9j3yNtbzesWyoNBDMto 965
scnn/private/models/regularizers/orthant.py sha256=f82E3u3yTnuUw-FtRhrQdi71lkZq73wupQE405Loj0g 789
scnn/private/models/regularizers/regularizer.py sha256=40s4up2QlLpPVtjMZKLqFJFeZFhxWdP6-fxu5CGoex0 1329
scnn/private/models/solution_mappings/__init__.py sha256=csTGnmk-z8XfAhRc4Dd4cL2kc_tTgQhZwSprrlz6MTg 1522
scnn/private/models/solution_mappings/mlps.py sha256=Ex6bJ2ts7NeC3QM_SfgXm9oHPjTRQ7OhZaAcT1M8aHc 8109
scnn/private/prox/__init__.py sha256=_afGVNQQC8fiy4Fz0bsdS5QDA5e0U-W-PU16jff27d8 325
scnn/private/prox/proximal_ops.py sha256=tP8aL1nHMiCCtKPBkFOuaF9OiWGPKWuIxD1f2uP98Gs 8434
scnn/private/utils/__init__.py sha256=8EJ9Qk-v6hvDk2c2p8QbSd5cc3XWXVoa8hpwZxRxmgw 297
scnn/private/utils/linear_operators.py sha256=MmlLnJkILJv86o2nJrkKwCpww81i2IhvsvxnvmGlGuI 2278
scnn/private/utils/data/__init__.py sha256=Ww3RNYgcwC77tfgHK9WoTjhI_J7rYSjkxmR2xXZyQO4 423
scnn/private/utils/data/synthetic.py sha256=nsSzMFLXJ5lf1364W0uWDFq7Z1QRbOJ7OABEEqrTKHg 7979
scnn/private/utils/data/transforms.py sha256=bjYp9vh8kVZsCC9cEeUA5zp1Et9nyOBAr8MAAVFhIqk 2446
scnn/private/utils/linear/__init__.py sha256=yUP9jY-FwigC9yATNQSAikRNxaim76mJEbk0Sw2H5eo 321
scnn/private/utils/linear/direct_solvers.py sha256=UyL95afCwzjmSZcPl_kFK1CkAtkGgGwwBqLsxFCQijA 733
scnn/private/utils/linear/iterative_solvers.py sha256=w3gN6abgbN_tgI2IUiBc5QX6tLfGVMF-o1tE-CvLg8M 4905
scnn/private/utils/linear/preconditioners.py sha256=sKgufsSnCIk6LbEL_4HW8RUXpCDLqgQAzczDobLnZJE 2545
pyscnn-0.0.9b0.dist-info/LICENSE sha256=hHi-ZI4JedJzq7Pi2-kjvLmcYfOcSKHtV2t4wB2PBH0 1079
pyscnn-0.0.9b0.dist-info/METADATA sha256=9KwY5A70k1HjqKoyi9fqRk0GIOwqESa2Gwk0V7FmPng 3222
pyscnn-0.0.9b0.dist-info/WHEEL sha256=GJ7t_kWBFywbagK5eo9IoUwLW6oyOeTKmQ-9iHFVNxQ 92
pyscnn-0.0.9b0.dist-info/top_level.txt sha256=BjFxl88-kaaeCM7PyynUG4NqjPtRDxitg8q3Ta4lRO0 5
pyscnn-0.0.9b0.dist-info/RECORD — —

top_level.txt

scnn