pytorch-forecasting
View on PyPI — Reverse Dependencies (8)
1.2.0 | pytorch_forecasting-1.2.0-py3-none-any.whl |
Wheel Details
Project: | pytorch-forecasting |
Version: | 1.2.0 |
Filename: | pytorch_forecasting-1.2.0-py3-none-any.whl |
Download: | [link] |
Size: | 181946 |
MD5: | 27f066f7e520f8e4c8350dca3dc1b634 |
SHA256: | 7566c7d68c71586c0ea5476a5ae8c40edb953a2be1becb034e54770182ca1ca3 |
Uploaded: | 2024-11-19 17:01:21 +0000 |
dist-info
METADATA · WHEEL · RECORD · top_level.txt
METADATA
WHEEL
Wheel-Version: | 1.0 |
Generator: | setuptools (75.5.0) |
Root-Is-Purelib: | true |
Tag: | py3-none-any |
RECORD
Path | Digest | Size |
---|---|---|
docs/source/conf.py | sha256=8lNgjv9Ox96ORrvWVTcSzPu5kC703a-771lSRuTlkGk | 5371 |
examples/ar.py | sha256=t90akgWpFoneYN1u41RsOE3cjnVa8zudFlukQj25pUc | 3800 |
examples/nbeats.py | sha256=UV1xIcDidPMAveSHMndhlXBEkL5QW8AgH5Jirhz2L84 | 2754 |
examples/stallion.py | sha256=JiWhoKYEGPRKuYdHbO_gQ2AN18Wk2Q5MHnXKzFgeiBE | 5766 |
pytorch_forecasting/__init__.py | sha256=cb0-58uX8gs4gcW4voyedETArlnyD26LviQADzP6Ld0 | 2469 |
pytorch_forecasting/data/__init__.py | sha256=KtnqKYqg8rn1cm89SyHlFU9sSahJZo_hUBOh4FBR7Fc | 688 |
pytorch_forecasting/data/encoders.py | sha256=sClYMN5vuDJ6UCmz20axT6MmCfhpzBYsOAXYA4TQx7s | 48263 |
pytorch_forecasting/data/examples.py | sha256=78ntFFM9-i6HLyMWSUvs82ZFr11rNIwL2qjfDBNA-mo | 3272 |
pytorch_forecasting/data/samplers.py | sha256=4PuZKVvxB-xQUcvUUEJxVFEIwEP8A18tzEZ9pa21fvU | 5621 |
pytorch_forecasting/data/timeseries.py | sha256=HsXvuCxpDSgdwIEuvISRKikZL0o13OlGzI_5Qh3x3wc | 91529 |
pytorch_forecasting/metrics/__init__.py | sha256=G526rrCEmm5Bwo5RgDmG-NPMW5MOUHJz3OGsKEM6NiA | 1324 |
pytorch_forecasting/metrics/_mqf2_utils.py | sha256=ObfrujQ8Fkoo4kBa_K9EQ87phyfL89zQzXjEo17zJ1Y | 17642 |
pytorch_forecasting/metrics/base_metrics.py | sha256=vjIz7MPRvBsCCCN-J07tTbMBsuQckoapcP0oiy9BZ0I | 37315 |
pytorch_forecasting/metrics/distributions.py | sha256=X97PYYCTInueZdsGFGwNdLwbHvcSTkSUIfaN7a8sn5c | 22469 |
pytorch_forecasting/metrics/point.py | sha256=27oc_D7tq9yfwx5jWrpNKt-RRKDWh607BNndZzxGb-U | 10444 |
pytorch_forecasting/metrics/quantile.py | sha256=3aRvyIRYKS-MS1B87UzbY0ydPLPemJ2M3B7GesgJh_Y | 1880 |
pytorch_forecasting/models/__init__.py | sha256=YMYSUDoOtKIiTJEvRMfoYPgEBX-mspunXRTNkaMZjSU | 1043 |
pytorch_forecasting/models/base_model.py | sha256=EqWOIpGZJzKwWEJ7KDSqJ2d1Sg45_CKNQfloKXHepfU | 107378 |
pytorch_forecasting/models/baseline.py | sha256=0FVcG8nyBKx40NRwz8Soq6PYot7IFmJEhG81w5W81OM | 2378 |
pytorch_forecasting/models/deepar/__init__.py | sha256=WLQ9Q9W6OYj515Uw0eizGEuV2nttAGb71hrg91A7QfY | 20900 |
pytorch_forecasting/models/mlp/__init__.py | sha256=P15XcphRIIRxP91dHJ4DXETljTbCvxrkZen6D7o63Ho | 8409 |
pytorch_forecasting/models/mlp/submodules.py | sha256=0s3wK-wd9VSUvBSTOb703AE5LJgWsFPloSgcCpJyFnc | 1565 |
pytorch_forecasting/models/nbeats/__init__.py | sha256=_VDkTi2wrqL8uo08_iLczJvt-FwAJTeuw_vWKoylmxk | 17525 |
pytorch_forecasting/models/nbeats/sub_modules.py | sha256=-JPv_bYnbK3V5TJJ0Xf-HummEgMeH0YG1YqYNpUNzls | 6392 |
pytorch_forecasting/models/nhits/__init__.py | sha256=ClbZubmFr_TXx1vVXJbzGTXxUCwQxR8Pmn3Xn2sofvY | 27522 |
pytorch_forecasting/models/nhits/sub_modules.py | sha256=0HWNsVlDVEZ2a8aFc7M5pvwCjMSxBESQ0xSgDhbWjqc | 14256 |
pytorch_forecasting/models/nn/__init__.py | sha256=3Wo1DsOcwg7a1HCnm_ubY78KjcrdmbgF5QO2AI3EUC4 | 292 |
pytorch_forecasting/models/nn/embeddings.py | sha256=p6NqV-xF_KYe282wiXZkW3cpr47viixFALjABaOKrGc | 9153 |
pytorch_forecasting/models/nn/rnn.py | sha256=uEakB4XhJv3l0Xsei2NxAjED0Ad3UOw2RStTNKIoGh4 | 7737 |
pytorch_forecasting/models/rnn/__init__.py | sha256=Wc0PQYXTShNFl28g5RjqVtHQ-xmGIwut0Cd9J0n91k0 | 14596 |
pytorch_forecasting/models/temporal_fusion_transformer/__init__.py | sha256=nPCjQgDaaaX9yeLuRR6yRqqKrUww8hCJbNX0p-mTW8g | 40906 |
pytorch_forecasting/models/temporal_fusion_transformer/sub_modules.py | sha256=hYGYQooGjWhp7pdZKEXjOwXKxGAer5dceFjc8lG2z0Q | 16205 |
pytorch_forecasting/models/temporal_fusion_transformer/tuning.py | sha256=zMXQJno_TT5uzinhoK7DVR8uAI-M5pSR3rq6UVBZNNw | 9801 |
pytorch_forecasting/utils/__init__.py | sha256=iBHUlSG-IcYqjRhiiI7LcF6rQBBYfzDvNIl0Ko1iW-Q | 1006 |
pytorch_forecasting/utils/_dependencies.py | sha256=FCJdnQ3X2A8uUJ5zRZh0GK2BY8lGJa8yg8xSRztHT3w | 2098 |
pytorch_forecasting/utils/_utils.py | sha256=dSX575xVqcc-lxem9Z1PK7ifs2eKahs4Xm66SdxEBnk | 18655 |
pytorch_forecasting/utils/_maint/__init__.py | sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU | 0 |
pytorch_forecasting/utils/_maint/_show_versions.py | sha256=CRHsQtMj9WgxeICNBjzWlcdNO5KybpeYNvltgVSpXw8 | 3843 |
tests/conftest.py | sha256=h-cx4G3NnPx71GOwzQk8rULlYzlbWxvQBSIKEmLNMoM | 2140 |
tests/test_metrics.py | sha256=M5HPyyupDBCk0fOHO5q5UKTi1lue2CKba0E7OBVdpEI | 10015 |
tests/test_data/test_encoders.py | sha256=xXMFKQFcxBDQU9NAnN5SO8Bx6aBKVzn6N1qgATgPhRc | 5934 |
tests/test_data/test_samplers.py | sha256=TxyXeJojn-iZDDJ0-P_d9Wwic6dSOWlp5wVHRZGPl7o | 1227 |
tests/test_data/test_timeseries.py | sha256=Yb72gv8oqtZTJ6ik5vEeT07AvrqJEoIzFneQLvLn7_o | 19312 |
tests/test_models/conftest.py | sha256=5zCwCQtffQExBzO6DZgDJI5-YHk6860tce4rYsV1sO8 | 8058 |
tests/test_models/test_baseline.py | sha256=vXwGtVXtjay6hVfXOVSL-tXYS1ta3CR4qD2mn8vU5xo | 239 |
tests/test_models/test_deepar.py | sha256=x91UnaRiLbrPsH4bio-NP28DTmU9bu5oLHQwEz3F3sU | 6471 |
tests/test_models/test_mlp.py | sha256=Ss6irC190s16FZ6y8iWffRHz5D9-kuL96l8JUG1m9eY | 3834 |
tests/test_models/test_nbeats.py | sha256=6ecL13lkGKn4ecfa_JhlffnAH8F7SDM9T8isHXsiMn0 | 2880 |
tests/test_models/test_nhits.py | sha256=V-ROIkBGNN9aIAepOnOxQa8vbIvrT5XoJy3GN8LeUWU | 6957 |
tests/test_models/test_rnn_model.py | sha256=YAfpPDK0juvMvMZxkASPlVCBQGLaaaEd4oXeJP9NMBs | 3904 |
tests/test_models/test_temporal_fusion_transformer.py | sha256=cBoWtCOmfiH23T555PagW2JCi2EJcsm7P29V556XA0c | 14886 |
tests/test_models/test_nn/test_embeddings.py | sha256=u87s9TO58ZS5JDrIxJThdo-8703X1UFT0WuWmrqnpgI | 1350 |
tests/test_models/test_nn/test_rnn.py | sha256=Lfr1PWGLguLMyVrZ8EZqEFvJv7DKzf-Si1w1AbNgTfk | 1537 |
tests/test_utils/test_autocorrelation.py | sha256=BpIZuvlWu9WaR1oQb0sgx19K6tyKZVwLfN2EQrUk640 | 436 |
tests/test_utils/test_show_versions.py | sha256=OGaG4e2dahKMOa8NlKTNdNS_ZD72xMzJwom09w6ybnE | 1372 |
pytorch_forecasting-1.2.0.dist-info/LICENSE | sha256=510xIfZQM0b7pQ0StfGTdVDVIKcL3q-PNB6G9LYMZ5k | 1068 |
pytorch_forecasting-1.2.0.dist-info/METADATA | sha256=hj3EKW7W-u_TvU4HEcqI0KqZhLCkdApOFtgLNe6jG_E | 13386 |
pytorch_forecasting-1.2.0.dist-info/WHEEL | sha256=R06PA3UVYHThwHvxuRWMqaGcr-PuniXahwjmQRFMEkY | 91 |
pytorch_forecasting-1.2.0.dist-info/top_level.txt | sha256=OAdll61o6yXyv7co12YdVXRfo94BmIey9xi30b7UhsE | 51 |
pytorch_forecasting-1.2.0.dist-info/RECORD | — | — |
top_level.txt
docs
examples
pytorch_forecasting
tests
wheelhouse