Automatic Piecewise Linear Regression

Navigation

Verified details

These details have been verified by PyPI
Maintainers
Avatar for mathiasottenbreit from gravatar.com mathiasottenbreit

Unverified details

These details have not been verified by PyPI
Project links
Meta
Classifiers
Report project as malware

Project description

Build predictive and interpretable parametric machine learning models in Python based on the Automatic Piecewise Linear Regression methodology developed by Mathias von Ottenbreit.

Project details

Verified details

These details have been verified by PyPI
Maintainers
Avatar for mathiasottenbreit from gravatar.com mathiasottenbreit

Unverified details

These details have not been verified by PyPI
Project links
Meta
Classifiers

Release history Release notifications | RSS feed

10.22.0

10.21.0

10.20.1

10.20.0

10.19.3

10.19.2

10.19.1

10.19.0

10.18.1

10.18.0

10.17.1

10.17.0

10.16.0

10.15.0

10.14.0

10.13.0

10.12.1

10.12.0

10.11.1

10.11.0

10.10.0

10.9.0

10.8.0

10.7.4

10.7.3

10.7.2

10.7.1

10.7.0

10.6.4

10.6.3

10.6.2

10.6.1

10.6.0

10.5.1

10.5.0

10.4.6

10.4.5

10.4.4

10.4.3

10.4.2

10.4.1

10.4.0

10.3.0

10.2.1

10.2.0

10.1.0

10.0.0

9.10.1

9.10.0

9.9.0

9.8.0

9.7.0

9.6.0

9.5.0

9.4.3

9.4.2

9.4.1

9.4.0

9.3.1 yanked

Reason this release was yanked:

Bug

9.3.0

9.2.4

9.2.3

9.2.2

9.2.1

9.2.0

9.1.0

9.0.0

8.1.0 yanked

Reason this release was yanked:

The functionality added in 8.1.0 does not seem to add predictive value (based on empirical tests). However, it works from a technical point of view though.

8.0.0

7.8.1

7.8.0

7.7.0

7.6.2

7.6.1

7.6.0

7.5.1

7.5.0

7.4.1

7.4.0

7.3.0

7.2.0

7.1.0

7.0.1

7.0.0

6.5.2

6.5.1

6.5.0

6.4.0

6.3.0

6.2.0

6.1.0

6.0.0

5.0.0

4.1.0

4.0.0

3.1.0

3.0.0

2.7.0

2.6.0

2.5.0

2.4.0

2.3.0

2.2.0

2.1.0

2.0.1

2.0.0

1.12.1

1.12.0

1.11.0

1.10.2

1.10.1

1.10.0

1.9.0

1.8.5

1.8.4

1.8.3

1.8.2

1.8.1

1.8.0

1.7.1

1.7.0

1.6.2

This version

1.6.1

1.6.0

1.5.0

1.4.1 yanked

1.4.0 yanked

1.3.2

1.3.1

1.3.0

1.2.2

1.2.1

1.2.0

1.1.2

1.1.1

1.1.0

1.0.9

1.0.8

1.0.7

1.0.6

1.0.5

1.0.4

1.0.3

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distributions

No source distribution files available for this release.See tutorial on generating distribution archives.

Built Distributions

If you're not sure about the file name format, learn more about wheel file names.

aplr-1.6.1-pp39-pypy39_pp73-win_amd64.whl (266.6 kB view details)

Uploaded PyPyWindows x86-64

aplr-1.6.1-pp39-pypy39_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (3.5 MB view details)

Uploaded PyPymanylinux: glibc 2.17+ x86-64

aplr-1.6.1-pp39-pypy39_pp73-manylinux_2_17_i686.manylinux2014_i686.whl (3.5 MB view details)

Uploaded PyPymanylinux: glibc 2.17+ i686

aplr-1.6.1-pp38-pypy38_pp73-win_amd64.whl (269.5 kB view details)

Uploaded PyPyWindows x86-64

aplr-1.6.1-pp38-pypy38_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (3.5 MB view details)

Uploaded PyPymanylinux: glibc 2.17+ x86-64

aplr-1.6.1-pp38-pypy38_pp73-manylinux_2_17_i686.manylinux2014_i686.whl (3.5 MB view details)

Uploaded PyPymanylinux: glibc 2.17+ i686

aplr-1.6.1-cp310-cp310-win_amd64.whl (136.8 kB view details)

Uploaded CPython 3.10Windows x86-64

aplr-1.6.1-cp310-cp310-win32.whl (119.2 kB view details)

Uploaded CPython 3.10Windows x86

aplr-1.6.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (3.3 MB view details)

Uploaded CPython 3.10manylinux: glibc 2.17+ x86-64

aplr-1.6.1-cp310-cp310-manylinux_2_17_i686.manylinux2014_i686.whl (3.3 MB view details)

Uploaded CPython 3.10manylinux: glibc 2.17+ i686

aplr-1.6.1-cp39-cp39-win_amd64.whl (134.0 kB view details)

Uploaded CPython 3.9Windows x86-64

aplr-1.6.1-cp39-cp39-win32.whl (119.3 kB view details)

Uploaded CPython 3.9Windows x86

aplr-1.6.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (3.3 MB view details)

Uploaded CPython 3.9manylinux: glibc 2.17+ x86-64

aplr-1.6.1-cp39-cp39-manylinux_2_17_i686.manylinux2014_i686.whl (3.3 MB view details)

Uploaded CPython 3.9manylinux: glibc 2.17+ i686

aplr-1.6.1-cp38-cp38-win_amd64.whl (136.8 kB view details)

Uploaded CPython 3.8Windows x86-64

aplr-1.6.1-cp38-cp38-win32.whl (119.2 kB view details)

Uploaded CPython 3.8Windows x86

aplr-1.6.1-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (3.3 MB view details)

Uploaded CPython 3.8manylinux: glibc 2.17+ x86-64

aplr-1.6.1-cp38-cp38-manylinux_2_17_i686.manylinux2014_i686.whl (3.3 MB view details)

Uploaded CPython 3.8manylinux: glibc 2.17+ i686

File details

Details for the file aplr-1.6.1-pp39-pypy39_pp73-win_amd64.whl.

File metadata

File hashes

Hashes for aplr-1.6.1-pp39-pypy39_pp73-win_amd64.whl
Algorithm Hash digest
SHA256 ef029c32ba61765653c1385b3a9d5207baa897a88546dcd41b920558606283f5
MD5 1d45a117ec656b3550ba8538e1aed52e
BLAKE2b-256 a15a6f839b81776f35619c3bba2e750fa348844977bf2a87492eef59276076ea

See more details on using hashes here.

File details

Details for the file aplr-1.6.1-pp39-pypy39_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for aplr-1.6.1-pp39-pypy39_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 d171ef68793f3e968b6aa802c0ffd7e27ffb25f7d42fad23f554868365656f6d
MD5 2c5927fb18b5e11691fe8b1a876f18dc
BLAKE2b-256 f0580d3ab81a94be77865fcc4a404423281a2b5e876d9445b7ae9a3293368be8

See more details on using hashes here.

File details

Details for the file aplr-1.6.1-pp39-pypy39_pp73-manylinux_2_17_i686.manylinux2014_i686.whl.

File metadata

File hashes

Hashes for aplr-1.6.1-pp39-pypy39_pp73-manylinux_2_17_i686.manylinux2014_i686.whl
Algorithm Hash digest
SHA256 d56ea281eef062543f43dcad32dcfc3c97d86c1ea8cfca218c78869b50e4598f
MD5 fce1ec4888018e25537c1443c4b284c0
BLAKE2b-256 9fa215017e4f89ca937508c7be0cd32c16e99d469ffc103408c3d1448e01b5ec

See more details on using hashes here.

File details

Details for the file aplr-1.6.1-pp38-pypy38_pp73-win_amd64.whl.

File metadata

File hashes

Hashes for aplr-1.6.1-pp38-pypy38_pp73-win_amd64.whl
Algorithm Hash digest
SHA256 615091772604b7aaf110ea9d96d5ba7c81fcbd2df53ed2bc370b2ebd1fb2bf18
MD5 b6b93887fd4daf66d2bbdf091eca5635
BLAKE2b-256 5bfab09b385e0f45095e848f2b3f33fe0de7a17748f72465b829bc47f6df22bb

See more details on using hashes here.

File details

Details for the file aplr-1.6.1-pp38-pypy38_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for aplr-1.6.1-pp38-pypy38_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 f1818c93bc1136c6f0e80352a158e2cd31d65188ca79cd7bee849678aa1a5f3b
MD5 2441e16a497fd6ae04026a2c055ee86b
BLAKE2b-256 e43e98db10cf5f6212628e59e177e3e11b2bdd6f41daccbd7d7ff444ff10b0e6

See more details on using hashes here.

File details

Details for the file aplr-1.6.1-pp38-pypy38_pp73-manylinux_2_17_i686.manylinux2014_i686.whl.

File metadata

File hashes

Hashes for aplr-1.6.1-pp38-pypy38_pp73-manylinux_2_17_i686.manylinux2014_i686.whl
Algorithm Hash digest
SHA256 edab88afb0c566cd978fdf5ce50a9ca0f44abaa5556992e19e80ec81715dc0a8
MD5 fda4532ff5e423043754fdcd54c5fc7a
BLAKE2b-256 9a7152ec553f78c90cfec8a7352223bbab8da9b14a6f3f89b9bb58582a99cb5d

See more details on using hashes here.

File details

Details for the file aplr-1.6.1-cp310-cp310-win_amd64.whl.

File metadata

  • Download URL: aplr-1.6.1-cp310-cp310-win_amd64.whl
  • Upload date:
  • Size: 136.8 kB
  • Tags: CPython 3.10, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.0 CPython/3.8.8

File hashes

Hashes for aplr-1.6.1-cp310-cp310-win_amd64.whl
Algorithm Hash digest
SHA256 4ddd8c3027ad083c5df5662052f232ec6219264079e90a08d984294881abb11f
MD5 2296edc5462c4ec47cf5ec1fc701ad49
BLAKE2b-256 65639b662678c4ae9e205530075635de68de97cf89fa7f5d2c2ac21f8d3bfafa

See more details on using hashes here.

File details

Details for the file aplr-1.6.1-cp310-cp310-win32.whl.

File metadata

  • Download URL: aplr-1.6.1-cp310-cp310-win32.whl
  • Upload date:
  • Size: 119.2 kB
  • Tags: CPython 3.10, Windows x86
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.0 CPython/3.8.8

File hashes

Hashes for aplr-1.6.1-cp310-cp310-win32.whl
Algorithm Hash digest
SHA256 3eea1f39d914697c2b99a1a713194a4a56498ab6876e6d9f4510ff519ff46faf
MD5 fa668cf92884c38a0b5a6f61738fc2cd
BLAKE2b-256 b24350cc66e6d920b7c48fa9ce784c4a7e76f97f484e99dc85092cc441e72fd9

See more details on using hashes here.

File details

Details for the file aplr-1.6.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for aplr-1.6.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 2963edd7529a525fef89e95c4b8e2364049869668667b99b7ee8379dfeb3b375
MD5 1f8e42fe0fcd10751602ed150d6dcc49
BLAKE2b-256 542853dc383d3d18bd394e3ea1ef87044090d4c13cb125f22de027603d5dab62

See more details on using hashes here.

File details

Details for the file aplr-1.6.1-cp310-cp310-manylinux_2_17_i686.manylinux2014_i686.whl.

File metadata

File hashes

Hashes for aplr-1.6.1-cp310-cp310-manylinux_2_17_i686.manylinux2014_i686.whl
Algorithm Hash digest
SHA256 92f4f170623b13714b652f33a7e6b8e20120e232d4cfe2fddf517b2116c38df0
MD5 c509ae650233469a239c7cead29eb976
BLAKE2b-256 49eb1a5aa345abd200ce3b63b80778aa8092479519ce89786a726d9ce1b7de9f

See more details on using hashes here.

File details

Details for the file aplr-1.6.1-cp39-cp39-win_amd64.whl.

File metadata

  • Download URL: aplr-1.6.1-cp39-cp39-win_amd64.whl
  • Upload date:
  • Size: 134.0 kB
  • Tags: CPython 3.9, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.0 CPython/3.8.8

File hashes

Hashes for aplr-1.6.1-cp39-cp39-win_amd64.whl
Algorithm Hash digest
SHA256 f867b5c8387b184d661df2443fcb40af24e3f9337f847c23dfa697221f8bd412
MD5 0a6a3c192e9eea7cbc0087867872a7b9
BLAKE2b-256 ac35778519da80fd5680bf5830fec50c03dc9024ff06cc385930a6b817c9456c

See more details on using hashes here.

File details

Details for the file aplr-1.6.1-cp39-cp39-win32.whl.

File metadata

  • Download URL: aplr-1.6.1-cp39-cp39-win32.whl
  • Upload date:
  • Size: 119.3 kB
  • Tags: CPython 3.9, Windows x86
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.0 CPython/3.8.8

File hashes

Hashes for aplr-1.6.1-cp39-cp39-win32.whl
Algorithm Hash digest
SHA256 a50a198d80e047f9488d61693eb1bf3934a8d5ea56b2e60fd1a9ea4cc5460de3
MD5 293c2b5dd026260a851fb55af38a704b
BLAKE2b-256 99eb73626007f0dee01c1ff793d5ebb058ae6a41f326c0a62180c4de39c486b3

See more details on using hashes here.

File details

Details for the file aplr-1.6.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for aplr-1.6.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 24fc46271cbc4ec0719e3eac8d0b0e9f7ef649eac6e48beb657c39f109a8074d
MD5 c7f14e8cf5e7612b45b5fd434ceff3a0
BLAKE2b-256 c882f5812a848714128e419d85115445ce1c92dd741259917031b194cd334b1a

See more details on using hashes here.

File details

Details for the file aplr-1.6.1-cp39-cp39-manylinux_2_17_i686.manylinux2014_i686.whl.

File metadata

File hashes

Hashes for aplr-1.6.1-cp39-cp39-manylinux_2_17_i686.manylinux2014_i686.whl
Algorithm Hash digest
SHA256 289aec9f4eb3ac14ac07198233b7a15bc2d01c2d2bba34fe75d9ffe774a8dc9a
MD5 78edab14216363d7acd3653750568696
BLAKE2b-256 3fbc3acdd236473c201aa1548a166d7ac8b8981086661da26b4f4af52b4a9db5

See more details on using hashes here.

File details

Details for the file aplr-1.6.1-cp38-cp38-win_amd64.whl.

File metadata

  • Download URL: aplr-1.6.1-cp38-cp38-win_amd64.whl
  • Upload date:
  • Size: 136.8 kB
  • Tags: CPython 3.8, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.0 CPython/3.8.8

File hashes

Hashes for aplr-1.6.1-cp38-cp38-win_amd64.whl
Algorithm Hash digest
SHA256 a6cd39c694be74d6924f3c5943de58307e78c712ebb11dd6c55583e689304c2a
MD5 91d7c02f44b26e0eff6d91d50e2402c2
BLAKE2b-256 0d1d3a9a72a0895f63ffac6abf105c2cade596f4626cb0cf9e6683b11abefa4a

See more details on using hashes here.

File details

Details for the file aplr-1.6.1-cp38-cp38-win32.whl.

File metadata

  • Download URL: aplr-1.6.1-cp38-cp38-win32.whl
  • Upload date:
  • Size: 119.2 kB
  • Tags: CPython 3.8, Windows x86
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.0 CPython/3.8.8

File hashes

Hashes for aplr-1.6.1-cp38-cp38-win32.whl
Algorithm Hash digest
SHA256 f574fe3c331382ea7c32e02c1cad76a56258ee66794c2a28df0c1becd43e049d
MD5 1eb5a84d4c25b6bb0415562200e18344
BLAKE2b-256 a1858245352e6c704faba0552fbde552577394c1aa8a2ee21a60237a8ee84150

See more details on using hashes here.

File details

Details for the file aplr-1.6.1-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for aplr-1.6.1-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 8ccd6d3af209c8c512e5fc9408e1ec036177be89b68f22b7a8d854024a375006
MD5 c12b61f5165fca0490c9411882f4ed31
BLAKE2b-256 de337898b96c607ed90f5cccb799d5d364cf1663b49bf69f97a0e54f1a52d91d

See more details on using hashes here.

File details

Details for the file aplr-1.6.1-cp38-cp38-manylinux_2_17_i686.manylinux2014_i686.whl.

File metadata

File hashes

Hashes for aplr-1.6.1-cp38-cp38-manylinux_2_17_i686.manylinux2014_i686.whl
Algorithm Hash digest
SHA256 ef961c55eebc9db9e36fc35466ae76d998570bdbe3b1fa4647d569f34fe22573
MD5 70c3e4d976125a865e2ac671c648e543
BLAKE2b-256 42a8a348f30a9250d2ed057974a99c4745c6c2a36982a87e0d9fd99de857e559

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page