Search is not available for this dataset
repo stringlengths 2 152 ⌀ | file stringlengths 15 239 | code stringlengths 0 58.4M | file_length int64 0 58.4M | avg_line_length float64 0 1.81M | max_line_length int64 0 12.7M | extension_type stringclasses 364
values |
|---|---|---|---|---|---|---|
GNOT | GNOT-master/readme.md | # GNOT: General Neural Operator Transformer (ICML 2023)
Code for [GNOT: A General Neural Operator Transformer for Operator Learning](https://arxiv.org/abs/2302.14376)， accepted at International Conference on Machine Learning (ICML 2023).
- GNOT is a flexible Transformer with linear complexity attention for learning o... | 4,815 | 38.154472 | 406 | md |
GNOT | GNOT-master/train.py | #!/usr/bin/env python
#-*- coding:utf-8 _*-
import sys
import os
sys.path.append('../..')
sys.path.append('..')
import re
import time
import pickle
import numpy as np
import torch
import torch.nn as nn
from torch.optim.lr_scheduler import OneCycleLR, StepLR, LambdaLR
from torch.utils.tensorboard import SummaryWrite... | 10,040 | 31.079872 | 182 | py |
GNOT | GNOT-master/utils.py | #!/usr/bin/env python
#-*- coding:utf-8 _*-
import os
import torch
import numpy as np
import operator
import matplotlib.pyplot as plt
import torch
import numpy as np
import torch.special as ts
from scipy import interpolate
from functools import reduce
def get_seed(s, printout=True, cudnn=True):
# rd.seed(s)
... | 17,709 | 34.562249 | 118 | py |
GNOT | GNOT-master/visualize_result.py | #!/usr/bin/env python
#-*- coding:utf-8 _*-
import pickle
import torch
import numpy as np
import torch.nn as nn
import dgl
import matplotlib.pyplot as plt
from dgl.dataloading import GraphDataLoader
from torch.utils.data.sampler import SubsetRandomSampler
from utils import get_seed, get_num_params
from args import ge... | 2,452 | 25.095745 | 101 | py |
GNOT | GNOT-master/data_generation/__init__.py | #!/usr/bin/env python
#-*- coding:utf-8 _*-
| 46 | 14.666667 | 23 | py |
GNOT | GNOT-master/models/__init__.py | #!/usr/bin/env python
#-*- coding:utf-8 _*-
| 46 | 14.666667 | 23 | py |
GNOT | GNOT-master/models/cgpt.py | #!/usr/bin/env python
#-*- coding:utf-8 _*-
import math
import numpy as np
import torch
import torch.nn as nn
import dgl
from einops import repeat, rearrange
from torch.nn import functional as F
from torch.nn import GELU, ReLU, Tanh, Sigmoid
from torch.nn.utils.rnn import pad_sequence
from utils import MultipleTensor... | 17,097 | 37.770975 | 190 | py |
GNOT | GNOT-master/models/mgpt.py | #!/usr/bin/env python
#-*- coding:utf-8 _*-
import math
import numpy as np
import torch
import torch.nn as nn
import dgl
from einops import repeat, rearrange
from torch.nn import functional as F
from torch.nn import GELU, ReLU, Tanh, Sigmoid
from torch.nn.utils.rnn import pad_sequence
from models.mlp import MLP
c... | 13,659 | 37.696884 | 246 | py |
GNOT | GNOT-master/models/mlp.py | import torch.nn as nn
import torch.nn.functional as F
import dgl
ACTIVATION = {'gelu':nn.GELU(),'tanh':nn.Tanh(),'sigmoid':nn.Sigmoid(),'relu':nn.ReLU(),'leaky_relu':nn.LeakyReLU(0.1),'softplus':nn.Softplus(),'ELU':nn.ELU()}
'''
A simple MLP class, includes at least 2 layers and n hidden layers
'''
class MLP(nn.... | 1,270 | 30.775 | 159 | py |
GNOT | GNOT-master/models/mmgpt.py | #!/usr/bin/env python
#-*- coding:utf-8 _*-
import math
import numpy as np
import torch
import torch.nn as nn
import dgl
from einops import repeat, rearrange
from torch.nn import functional as F
from torch.nn import GELU, ReLU, Tanh, Sigmoid
from torch.nn.utils.rnn import pad_sequence
from utils import MultipleTensors... | 12,578 | 36.4375 | 214 | py |
GNOT | GNOT-master/models/optimizer.py | import math
import torch
from torch import Tensor
from typing import List, Optional
from torch.optim.optimizer import Optimizer
def adam(params: List[Tensor],
grads: List[Tensor],
exp_avgs: List[Tensor],
exp_avg_sqs: List[Tensor],
max_exp_avg_sqs: List[Tensor],
state_step... | 12,976 | 37.853293 | 120 | py |
GNOT | GNOT-master/resources/__init__.py | #!/usr/bin/env python
#-*- coding:utf-8 _*-
| 46 | 14.666667 | 23 | py |
F3Net | F3Net-master/README.md | ## [F3Net: Fusion, Feedback and Focus for Salient Object Detection](https://arxiv.org/pdf/1911.11445.pdf)
by Jun Wei, Shuhui Wang, Qingming Huang
## Introduction
Most of existing salient object detection models have achieved great progress by aggregating multi-level features extracted ... | 4,590 | 53.011765 | 1,872 | md |
F3Net | F3Net-master/eval/Emeasure.m | function [score]= Emeasure(FM,GT)
% Emeasure Compute the Enhanced Alignment measure (as proposed in "Enhanced-alignment
% Measure for Binary Foreground Map Evaluation" [Deng-Ping Fan et. al - IJCAI'18 oral paper])
% Usage:
% score = Emeasure(FM,GT)
% Input:
% FM - Binary foreground map. Type: double.
% GT - Binary ... | 2,267 | 29.24 | 118 | m |
F3Net | F3Net-master/eval/Fmeasure.m | %%
function [PreFtem, RecallFtem, FmeasureF] = Fmeasure(sMap, gtMap, gtsize)
sumLabel = 2* mean(sMap(:));
if (sumLabel > 1)
sumLabel = 1;
end
Label3 = zeros( gtsize );
Label3(sMap>=sumLabel ) = 1;
NumRec = length( find( Label3==1 ) );
LabelAnd = Label3 & gtMap;
NumAnd = length( find ( LabelAnd==1 ) );
num_ob... | 564 | 19.178571 | 73 | m |
F3Net | F3Net-master/eval/MAE.m | function mae = MAE(smap, gtImg)
% Code Author: Wangjiang Zhu
% Email: wangjiang88119@gmail.com
% Date: 3/24/2014
if size(smap, 1) ~= size(gtImg, 1) || size(smap, 2) ~= size(gtImg, 2)
error('Saliency map and gt Image have different sizes!\n');
end
if ~islogical(gtImg)
gtImg = gtImg(:,:,1) > 128;
end
fgPixels =... | 457 | 27.625 | 69 | m |
F3Net | F3Net-master/eval/PRCurve.m | function [precision, recall] = PRCurve(smapImg, gtImg)
% Code Author: Wangjiang Zhu
% Email: wangjiang88119@gmail.com
% Date: 3/24/2014
if ~islogical(gtImg)
gtImg = gtImg(:,:,1) > 128;
end
if any(size(smapImg) ~= size(gtImg))
error('saliency map and ground truth mask have different size');
end
gtPxlNum = sum(... | 874 | 26.34375 | 110 | m |
F3Net | F3Net-master/eval/S_object.m | function Q = S_object(prediction,GT)
% S_object Computes the object similarity between foreground maps and ground
% truth(as proposed in "Structure-measure:A new way to evaluate foreground
% maps" [Deng-Ping Fan et. al - ICCV 2017])
% Usage:
% Q = S_object(prediction,GT)
% Input:
% prediction - Binary/Non binary f... | 1,667 | 27.758621 | 79 | m |
F3Net | F3Net-master/eval/S_region.m | function Q = S_region(prediction,GT)
% S_region computes the region similarity between the foreground map and
% ground truth(as proposed in "Structure-measure:A new way to evaluate
% foreground maps" [Deng-Ping Fan et. al - ICCV 2017])
% Usage:
% Q = S_region(prediction,GT)
% Input:
% prediction - Binary/Non binary... | 3,681 | 24.929577 | 89 | m |
F3Net | F3Net-master/eval/Smeasure.m | function Q = Smeasure(prediction,GT)
% Smeasure computes the similarity between the foreground map and
% ground truth(as proposed in "Structure-measure: A new way to evaluate
% foreground maps" [Deng-Ping Fan et. al - ICCV 2017])
% Usage:
% Q = Smeasure(prediction,GT)
% Input:
% prediction - Binary/Non binary foreg... | 1,222 | 30.358974 | 75 | m |
F3Net | F3Net-master/eval/main.m | algorithms = {
%'C2SNet';
%'BMPM';
%'DGRL';
%'R3Net';
%'RAS';
%'PiCA-R';
%'PAGE';
%'TDBU';
%'BASNet';
%'CPD-R';
%'PoolNet';
%'AFNet';
'F3Net'
};
datasets = {
'ECSSD';
'PASCAL-S';
'DUTS';
'HKU-I... | 3,979 | 35.851852 | 102 | m |
F3Net | F3Net-master/eval/wFmeasure.m | function [Q]= wFmeasure(FG,GT)
% wFmeasure Compute the Weighted F-beta measure (as proposed in "How to Evaluate Foreground Maps?" [Margolin et. al - CVPR'14])
% Usage:
% Q = FbW(FG,GT)
% Input:
% FG - Binary/Non binary foreground map with values in the range [0 1]. Type: double.
% GT - Binary ground truth. Type: lo... | 1,423 | 25.37037 | 127 | m |
F3Net | F3Net-master/src/dataset.py | #!/usr/bin/python3
#coding=utf-8
import os
import cv2
import torch
import numpy as np
from torch.utils.data import Dataset
########################### Data Augmentation ###########################
class Normalize(object):
def __init__(self, mean, std):
self.mean = mean
self.std = std
de... | 4,497 | 32.819549 | 96 | py |
F3Net | F3Net-master/src/net.py | #!/usr/bin/python3
#coding=utf-8
import numpy as np
import matplotlib.pyplot as plt
import torch
import torch.nn as nn
import torch.nn.functional as F
def weight_init(module):
for n, m in module.named_children():
print('initialize: '+n)
if isinstance(m, nn.Conv2d):
nn.init.kaiming_norm... | 8,656 | 43.623711 | 141 | py |
F3Net | F3Net-master/src/test.py | #!/usr/bin/python3
#coding=utf-8
import os
import sys
sys.path.insert(0, '../')
sys.dont_write_bytecode = True
import cv2
import numpy as np
import matplotlib.pyplot as plt
plt.ion()
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data import DataLoader
from tensorboardX import Su... | 2,319 | 32.142857 | 109 | py |
F3Net | F3Net-master/src/train.py | #!/usr/bin/python3
#coding=utf-8
import sys
import datetime
sys.path.insert(0, '../')
sys.dont_write_bytecode = True
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data import DataLoader
from tensorboardX import SummaryWriter
import dataset
from net import F3Net
from apex import ... | 3,380 | 38.313953 | 205 | py |
null | coax-main/.readthedocs.yml | version: 2
build:
image: latest
python:
version: 3.8
install:
- method: pip
path: .
extra_requirements:
- doc
system_packages: true
sphinx:
builder: html
configuration: doc/conf.py
| 210 | 10.722222 | 28 | yml |
null | coax-main/STATUS.md | # Development Status
In this file we list the current status of the tests written (and passed).
| | docs | unit tests | FrozenLake | CartPole | Pong | Pendulum | LunarLander | Ant |
|-----------------|:----:|:----------:|:----------:|:--------:|:----:|:--------:|:-----------:|:---:|
| ValueTD ... | 1,324 | 68.736842 | 101 | md |
null | coax-main/setup.py | #!/usr/bin/env python3
import re
import os
import setuptools
from collections import namedtuple
PROJECTDIR = os.path.dirname(__file__)
RE_VERSION = re.compile(
r'^__version__ \= \'(?P<version>(?P<majorminor>\d+\.\d+)\.\d+(?:\w+\d+)?)\'$', re.MULTILINE)
DEV_STATUS = {
'0.1': 'Development Status :: 1 - Planning'... | 3,174 | 34.277778 | 99 | py |
null | coax-main/upgrade_requirements.py | """
To update the requirements files, first do a pip freeze in google colab and then past the output in
requirements.colab.txt
After that, just run this script from the project root (where this script is located).
"""
import re
from sys import stderr
from packaging.version import parse as _parse
import pandas as pd... | 1,870 | 30.183333 | 100 | py |
null | coax-main/.azure/docs.yml | # https://docs.microsoft.com/azure/devops/pipelines/languages/python
trigger:
- main
pool:
vmImage: 'ubuntu-latest'
variables:
python.version: '3.8'
TF_CPP_MIN_LOG_LEVEL: 3 # tell XLA to be quiet
JAX_PLATFORM_NAME: cpu
steps:
- task: UsePythonVersion@0
inputs:
versionSpec: '$(python.version)'
displ... | 1,259 | 22.333333 | 68 | yml |
null | coax-main/.azure/tests.yml | # https://docs.microsoft.com/azure/devops/pipelines/languages/python
trigger:
- main
pool:
vmImage: 'ubuntu-latest'
variables:
TF_CPP_MIN_LOG_LEVEL: 3 # tell XLA to be quiet
strategy:
matrix:
Python37_cpu:
python.version: '3.7'
JAX_PLATFORM_NAME: cpu
Python38_cpu:
python.version: '3... | 1,583 | 25.4 | 80 | yml |
null | coax-main/.github/ISSUE_TEMPLATE/bug_report.md | ---
name: Bug report
about: Create a report to help us improve
title: ''
labels: 'triage'
assignees: ''
---
**Describe the bug**
<!-- A clear and concise description of what the bug is. -->
**Expected behavior**
<!-- A clear and concise description of what you expected to happen. -->
**To Reproduce**
Colab noteboo... | 958 | 26.4 | 101 | md |
null | coax-main/.github/ISSUE_TEMPLATE/feature_request.md | ---
name: Feature request
about: Suggest an idea for this project
title: ''
labels: 'enhancement'
assignees: ''
---
**Is your feature request related to a problem? Please describe.**
<!-- A clear and concise description of what the problem is. Ex. I'm always frustrated when [...] -->
**Describe the solution you'd li... | 642 | 29.619048 | 101 | md |
null | coax-main/.github/ISSUE_TEMPLATE/question.md | ---
name: Question
about: Ask a question about this project
title: ''
labels: 'question'
assignees: ''
---
**What's your question?**
<!-- Please explain the context of your question. -->
| 191 | 12.714286 | 53 | md |
null | coax-main/.github/workflows/release.yml | name: release
on:
push:
tags:
- 'v*'
jobs:
test:
name: Run tests
runs-on: ${{ matrix.runs-on }}
strategy:
matrix:
runs-on: [ubuntu-latest, macos-latest]
python-version: ["3.7", "3.8", "3.9", "3.10"]
steps:
- uses: actions/checkout@v2
- name: Set up Python ${{... | 2,603 | 28.931034 | 87 | yml |
null | coax-main/.github/workflows/tests.yml | name: tests
on:
push:
branches:
- main
pull_request:
branches:
- main
jobs:
test:
name: Run tests
runs-on: ${{ matrix.runs-on }}
strategy:
matrix:
runs-on: [ubuntu-latest, macos-latest]
python-version: ["3.7", "3.8", "3.9", "3.10"]
steps:
- uses: actions... | 1,510 | 30.479167 | 87 | yml |
null | coax-main/coax/__init__.py | __version__ = '0.1.13'
# fall back to legacy gym if gymnasium is unavailable
try:
import gymnasium as _gymnasium
except ImportError:
import sys
import warnings
warnings.warn("Cannot import 'gymnasium'; attempting to fall back to legacy 'gym'.")
import gym as _gymnasium # Don't catch ImportError he... | 2,853 | 26.180952 | 88 | py |
null | coax-main/coax/typing.py | from typing import TypeVar, Union, Sequence, Callable, Tuple
__all__ = (
'Batch',
'SpaceElement',
'Observation',
'Action'
)
Batch = Sequence # annotate batched quantities
Observation = TypeVar('Observation') # a state observation
Action = TypeVar('Action') ... | 573 | 23.956522 | 80 | py |
null | coax-main/coax/_base/__init__.py | 0 | 0 | 0 | py | |
null | coax-main/coax/_base/errors.py |
class CoaxError(Exception):
pass
class SpaceError(CoaxError):
pass
class ActionSpaceError(SpaceError):
pass
class DistributionError(CoaxError):
pass
class EpisodeDoneError(CoaxError):
pass
class InconsistentCacheInputError(CoaxError):
pass
class InsufficientCacheError(CoaxError):
... | 662 | 10.839286 | 45 | py |
null | coax-main/coax/_base/test_case.py | import gc
import unittest
from collections import namedtuple
from contextlib import AbstractContextManager
from resource import getrusage, RUSAGE_SELF
from functools import partial
import gymnasium
import jax
import jax.numpy as jnp
import numpy as onp
import haiku as hk
__all__ = (
'DiscreteEnv',
'BoxEnv',
... | 13,055 | 36.517241 | 95 | py |
null | coax-main/coax/_base/mixins/__init__.py | from ._add_orig_to_info import AddOrigToInfoDictMixin
from ._copy import CopyMixin
from ._logger import LoggerMixin
from ._random_state import RandomStateMixin
__all__ = (
'AddOrigToInfoDictMixin',
'CopyMixin',
'LoggerMixin',
'RandomStateMixin',
)
| 266 | 19.538462 | 53 | py |
null | coax-main/coax/_base/mixins/_add_orig_to_info.py | from typing import Mapping
class AddOrigToInfoDictMixin:
def _add_s_orig_to_info_dict(self, info):
if not isinstance(info, Mapping):
assert info is None, "unexpected type for 'info' Mapping"
info = {}
if 's_orig' in info:
info['s_orig'].append(self._s_orig)
... | 895 | 27.903226 | 69 | py |
null | coax-main/coax/_base/mixins/_copy.py | from copy import deepcopy, copy
class CopyMixin:
def copy(self, deep=False):
r"""
Create a copy of the current instance.
Parameters
----------
deep : bool, optional
Whether the copy should be a deep copy.
Returns
-------
copy
... | 429 | 16.916667 | 53 | py |
null | coax-main/coax/_base/mixins/_logger.py | import logging
class LoggerMixin:
@property
def logger(self):
return logging.getLogger(self.__class__.__name__)
| 130 | 15.375 | 57 | py |
null | coax-main/coax/_base/mixins/_random_state.py | import numpy as onp
import jax
class RandomStateMixin:
@property
def random_seed(self):
return self._random_seed
@random_seed.setter
def random_seed(self, new_random_seed):
if new_random_seed is None:
new_random_seed = onp.random.randint(2147483647)
self._random_se... | 526 | 24.095238 | 66 | py |
null | coax-main/coax/_core/__init__.py | 0 | 0 | 0 | py | |
null | coax-main/coax/_core/base_func.py | from abc import ABC, abstractmethod
from typing import Any, Tuple, NamedTuple
import jax
import haiku as hk
from gymnasium.spaces import Space
from ..typing import Batch, Observation, Action
from ..utils import pretty_repr, jit
from .._base.mixins import RandomStateMixin, CopyMixin
class Inputs(NamedTuple):
arg... | 5,555 | 31.115607 | 100 | py |
null | coax-main/coax/_core/base_stochastic_func_type1.py | from inspect import signature
from collections import namedtuple
import jax
import jax.numpy as jnp
import numpy as onp
import haiku as hk
from gymnasium.spaces import Space, Discrete
from ..utils import safe_sample, batch_to_single, jit
from .base_func import BaseFunc, ExampleData, Inputs, ArgsType1, ArgsType2, Mode... | 19,163 | 41.39823 | 100 | py |
null | coax-main/coax/_core/base_stochastic_func_type2.py | from inspect import signature
from collections import namedtuple
import jax
import jax.numpy as jnp
import numpy as onp
import haiku as hk
from gymnasium.spaces import Space
from ..utils import safe_sample, batch_to_single, jit
from .base_func import BaseFunc, ExampleData, Inputs, ArgsType2
__all__ = (
'BaseSto... | 7,645 | 35.583732 | 98 | py |
null | coax-main/coax/_core/policy.py | from ..utils import default_preprocessor
from ..proba_dists import ProbaDist
from .base_stochastic_func_type2 import BaseStochasticFuncType2
class Policy(BaseStochasticFuncType2):
r"""
A parametrized policy :math:`\pi_\theta(a|s)`.
Parameters
----------
func : function
A Haiku-style fun... | 4,373 | 25.509091 | 100 | py |
null | coax-main/coax/_core/policy_test.py | from functools import partial
from collections import namedtuple
import gymnasium
import jax
import jax.numpy as jnp
import numpy as onp
import haiku as hk
from .._base.test_case import TestCase
from ..utils import safe_sample
from .policy import Policy
discrete = gymnasium.spaces.Discrete(7)
boxspace = gymnasium.s... | 5,038 | 31.934641 | 80 | py |
null | coax-main/coax/_core/q.py | from inspect import signature
from collections import namedtuple
import jax
import jax.numpy as jnp
import numpy as onp
import haiku as hk
from gymnasium.spaces import Space, Discrete
from ..utils import safe_sample, default_preprocessor
from ..value_transforms import ValueTransform
from .base_func import BaseFunc, E... | 10,806 | 35.265101 | 100 | py |
null | coax-main/coax/_core/q_test.py | from functools import partial
import jax
import jax.numpy as jnp
import numpy as onp
import haiku as hk
from .._base.test_case import TestCase, DiscreteEnv, BoxEnv
from ..utils import safe_sample
from .q import Q
env_discrete = DiscreteEnv(random_seed=13)
env_boxspace = BoxEnv(random_seed=17)
def func_type1(S, A,... | 7,736 | 31.783898 | 97 | py |
null | coax-main/coax/_core/random_policy.py | import gymnasium
import jax.numpy as jnp
import numpy as onp
from ..utils import docstring
from .policy import Policy
__all__ = (
'RandomPolicy',
)
class RandomPolicy:
r"""
A simple random policy.
Parameters
----------
env : gymnasium.Env
The gymnasium-style environment. This is ... | 2,336 | 29.75 | 95 | py |
null | coax-main/coax/_core/random_policy_test.py | from functools import partial
import gymnasium
import jax
import haiku as hk
import numpy as onp
from .._base.test_case import TestCase
from .random_policy import RandomPolicy
env = gymnasium.make('FrozenLakeNonSlippery-v0')
def func_type2(S, is_training):
batch_norm = hk.BatchNorm(False, False, 0.99)
seq... | 1,627 | 26.59322 | 81 | py |
null | coax-main/coax/_core/reward_function.py | from .q import Q
__all__ = (
'RewardFunction',
)
class RewardFunction(Q):
r"""
A deterministic reward function :math:`r_\theta(s,a)`.
Parameters
----------
func : function
A Haiku-style function that specifies the forward pass. The function signature must be the
same as th... | 1,889 | 29.483871 | 99 | py |
null | coax-main/coax/_core/stochastic_q.py | from gymnasium.spaces import Box
from ..utils import default_preprocessor
from ..proba_dists import DiscretizedIntervalDist, EmpiricalQuantileDist
from ..value_transforms import ValueTransform
from .base_stochastic_func_type1 import BaseStochasticFuncType1
__all__ = (
'StochasticQ',
)
class StochasticQ(BaseSto... | 9,039 | 33.503817 | 100 | py |
null | coax-main/coax/_core/stochastic_q_test.py | from functools import partial
from collections import namedtuple
import jax
import jax.numpy as jnp
import haiku as hk
from gymnasium.spaces import Discrete, Box
from .._base.test_case import TestCase
from ..utils import safe_sample, quantile_cos_embedding, quantiles, quantiles_uniform
from .stochastic_q import Stoch... | 14,291 | 36.909814 | 100 | py |
null | coax-main/coax/_core/stochastic_reward_function.py | from .stochastic_q import StochasticQ
__all__ = (
'StochasticRewardFunction',
)
class StochasticRewardFunction(StochasticQ):
r"""
A stochastic reward function :math:`p_\theta(r|s,a)`.
Parameters
----------
func : function
A Haiku-style function that specifies the forward pass.
... | 2,642 | 33.776316 | 100 | py |
null | coax-main/coax/_core/stochastic_transition_model.py | from ..utils import default_preprocessor
from ..proba_dists import ProbaDist
from .base_stochastic_func_type1 import BaseStochasticFuncType1
__all__ = (
'StochasticTransitionModel',
)
class StochasticTransitionModel(BaseStochasticFuncType1):
r"""
A stochastic transition model :math:`p_\theta(s'|s,a)`. ... | 6,627 | 31.174757 | 100 | py |
null | coax-main/coax/_core/stochastic_transition_model_test.py | from functools import partial
from collections import namedtuple
import gymnasium
import jax
import jax.numpy as jnp
import numpy as onp
import haiku as hk
from .._base.test_case import TestCase
from ..utils import safe_sample
from .stochastic_transition_model import StochasticTransitionModel
discrete = gymnasium.s... | 14,579 | 37.067885 | 100 | py |
null | coax-main/coax/_core/stochastic_v.py | from gymnasium.spaces import Box
from ..utils import default_preprocessor
from ..proba_dists import DiscretizedIntervalDist
from ..value_transforms import ValueTransform
from .base_stochastic_func_type2 import BaseStochasticFuncType2
__all__ = (
'StochasticV',
)
class StochasticV(BaseStochasticFuncType2):
... | 6,909 | 30.409091 | 100 | py |
null | coax-main/coax/_core/stochastic_v_test.py | from functools import partial
from collections import namedtuple
import jax
import jax.numpy as jnp
import haiku as hk
from gymnasium.spaces import Discrete, Box
from .._base.test_case import TestCase
from ..utils import safe_sample
from .stochastic_v import StochasticV
discrete = Discrete(7)
boxspace = Box(low=0, h... | 4,708 | 34.674242 | 100 | py |
null | coax-main/coax/_core/successor_state_q.py | import warnings
import jax
import haiku as hk
from .._core.q import Q
from .._core.base_stochastic_func_type1 import BaseStochasticFuncType1
from ..utils import (
check_preprocessors, is_vfunction, is_reward_function, is_transition_model, is_stochastic, jit)
__all__ = (
'SuccessorStateQ',
)
class Successo... | 9,697 | 38.745902 | 100 | py |
null | coax-main/coax/_core/transition_model.py | from inspect import signature
from collections import namedtuple
import jax
import jax.numpy as jnp
import numpy as onp
import haiku as hk
from gymnasium.spaces import Space, Discrete
from ..utils import safe_sample, batch_to_single, default_preprocessor
from ..proba_dists import ProbaDist
from .base_func import Base... | 12,966 | 39.395639 | 100 | py |
null | coax-main/coax/_core/transition_model_test.py | from functools import partial
from collections import namedtuple
import gymnasium
import jax
import jax.numpy as jnp
import numpy as onp
import haiku as hk
from .._base.test_case import TestCase
from ..utils import safe_sample
from .transition_model import TransitionModel
discrete = gymnasium.spaces.Discrete(7)
box... | 8,653 | 35.982906 | 100 | py |
null | coax-main/coax/_core/v.py | from inspect import signature
from collections import namedtuple
import jax
import jax.numpy as jnp
import numpy as onp
import haiku as hk
from gymnasium.spaces import Space
from ..utils import safe_sample, default_preprocessor
from ..value_transforms import ValueTransform
from .base_func import BaseFunc, ExampleData... | 5,528 | 33.12963 | 100 | py |
null | coax-main/coax/_core/v_test.py | from functools import partial
import jax
import jax.numpy as jnp
import haiku as hk
from .._base.test_case import TestCase
from ..utils import get_transition_batch, safe_sample
from .v import V
def func(S, is_training):
rng1, rng2, rng3 = hk.next_rng_keys(3)
rate = 0.25 if is_training else 0.
batch_norm... | 3,296 | 34.451613 | 97 | py |
null | coax-main/coax/_core/value_based_policy.py | import gymnasium
import jax
import jax.numpy as jnp
import haiku as hk
import chex
from ..utils import docstring, is_qfunction, is_stochastic, jit
from ..proba_dists import CategoricalDist
from .base_stochastic_func_type2 import StochasticFuncType2Mixin
from .q import Q
__all__ = (
'EpsilonGreedy',
'Boltzman... | 7,757 | 27.417582 | 100 | py |
null | coax-main/coax/_core/value_based_policy_test.py | from functools import partial
import gymnasium
import jax
import haiku as hk
import numpy as onp
from .._base.test_case import TestCase
from .q import Q
from .value_based_policy import EpsilonGreedy, BoltzmannPolicy
env = gymnasium.make('FrozenLakeNonSlippery-v0')
def func_type2(S, is_training):
batch_norm = ... | 3,106 | 30.383838 | 98 | py |
null | coax-main/coax/_core/worker.py | import time
import inspect
from abc import ABC, abstractmethod
from typing import Optional
import gymnasium
from jax.lib.xla_bridge import get_backend
from ..typing import Policy
from ..wrappers import TrainMonitor
from ..reward_tracing._base import BaseRewardTracer
from ..experience_replay._base import BaseReplayBuf... | 11,753 | 32.487179 | 100 | py |
null | coax-main/coax/envs/__init__.py | r"""
Environments
============
.. autosummary::
:nosignatures:
coax.envs.ConnectFourEnv
----
This is a collection of environments currently not included in
`Gymnasium <https://gymnasium.farama.org/>`_.
Object Reference
----------------
.. autoclass:: coax.envs.ConnectFourEnv
"""
from ._connect_four imp... | 377 | 12.034483 | 62 | py |
null | coax-main/coax/envs/_connect_four.py | from gymnasium import Env
from gymnasium.spaces import Discrete, MultiDiscrete
import numpy as np
from .._base.errors import UnavailableActionError, EpisodeDoneError
__all__ = (
'ConnectFourEnv',
)
class ConnectFourEnv(Env):
r"""
An adversarial environment for playing the `Connect-Four game
<https:... | 10,349 | 31.961783 | 79 | py |
null | coax-main/coax/experience_replay/__init__.py | r"""
Experience Replay
=================
.. autosummary::
:nosignatures:
coax.experience_replay.SimpleReplayBuffer
coax.experience_replay.PrioritizedReplayBuffer
----
This is where we keep our experience-replay buffer classes. Some examples of agents that use a
replay buffer are:
* :doc:`/examples/stub... | 785 | 19.153846 | 96 | py |
null | coax-main/coax/experience_replay/_base.py | from abc import ABC, abstractmethod
__all__ = (
'BaseReplayBuffer',
)
class BaseReplayBuffer(ABC):
@property
@abstractmethod
def capacity(self):
pass
@abstractmethod
def add(self, transition_batch):
pass
@abstractmethod
def sample(self, batch_size=32):
pass... | 549 | 13.102564 | 36 | py |
null | coax-main/coax/experience_replay/_prioritized.py | import jax
import numpy as onp
import chex
from ..reward_tracing import TransitionBatch
from ..utils import SumTree
from ._base import BaseReplayBuffer
__all__ = (
'PrioritizedReplayBuffer',
)
class PrioritizedReplayBuffer(BaseReplayBuffer):
r"""
A simple ring buffer for experience replay, with priori... | 7,832 | 32.909091 | 100 | py |
null | coax-main/coax/experience_replay/_prioritized_test.py | from copy import deepcopy
import gymnasium
import pytest
import numpy as onp
from ..utils import get_transition_batch
from ._simple import SimpleReplayBuffer
from ._prioritized import PrioritizedReplayBuffer
@pytest.mark.parametrize('n', [2, 4]) # 2 * batch_size < capacity, 4 * batch_size > capacity
def test_consi... | 4,654 | 36.540323 | 99 | py |
null | coax-main/coax/experience_replay/_simple.py | import random
import jax
import numpy as onp
from ..reward_tracing import TransitionBatch
from ._base import BaseReplayBuffer
__all__ = (
'SimpleReplayBuffer',
)
class SimpleReplayBuffer(BaseReplayBuffer):
r"""
A simple ring buffer for experience replay.
Parameters
----------
capacity : ... | 2,588 | 25.151515 | 100 | py |
null | coax-main/coax/model_updaters/__init__.py | r"""
Model Updaters
==============
.. autosummary::
:nosignatures:
coax.model_updaters.ModelUpdater
----
This is a collection of objects that are used to update dynamics models, i.e. transition models and
reward functions.
Object Reference
----------------
.. autoclass:: coax.model_updaters.ModelUpdater
... | 472 | 14.258065 | 99 | py |
null | coax-main/coax/model_updaters/_model_updater.py | import jax
import jax.numpy as jnp
import haiku as hk
import optax
from ..utils import (
get_grads_diagnostics, is_stochastic, is_reward_function, is_transition_model, jit)
from ..value_losses import huber
from ..regularizers import Regularizer
__all__ = (
'ModelUpdater',
)
class ModelUpdater:
r"""
... | 8,272 | 35.606195 | 100 | py |
null | coax-main/coax/model_updaters/_model_updater_test.py | from copy import deepcopy
from optax import sgd
from .._base.test_case import TestCase
from .._core.stochastic_transition_model import StochasticTransitionModel
from ..utils import get_transition_batch
from ..regularizers import EntropyRegularizer
from ._model_updater import ModelUpdater
class TestModelUpdater(Test... | 3,182 | 37.349398 | 98 | py |
null | coax-main/coax/policy_objectives/__init__.py | r"""
Policy Objectives
=================
.. autosummary::
:nosignatures:
coax.policy_objectives.VanillaPG
coax.policy_objectives.PPOClip
coax.policy_objectives.DeterministicPG
coax.policy_objectives.SoftPG
----
This is a collection of policy objectives that can be used in policy-gradient
method... | 873 | 18.422222 | 77 | py |
null | coax-main/coax/policy_objectives/_base.py | import warnings
import jax
import jax.numpy as jnp
import optax
import haiku as hk
from .._core.policy import Policy
from ..utils import get_grads_diagnostics, jit
from ..regularizers import Regularizer
class PolicyObjective:
r"""
Abstract base class for policy objectives. To see a concrete example, have a... | 7,993 | 35.009009 | 100 | py |
null | coax-main/coax/policy_objectives/_deterministic_pg.py | import warnings
import jax.numpy as jnp
import haiku as hk
import chex
from ..utils import check_preprocessors, is_qfunction, is_stochastic
from ._base import PolicyObjective
class DeterministicPG(PolicyObjective):
r"""
A deterministic policy-gradient objective, a.k.a. DDPG-style objective. See
:doc:`sp... | 5,813 | 32.606936 | 98 | py |
null | coax-main/coax/policy_objectives/_deterministic_pg_test.py | from copy import deepcopy
import jax.numpy as jnp
from optax import sgd
from .._base.test_case import TestCase
from .._core.policy import Policy
from .._core.q import Q
from ..utils import tree_ravel
from ._deterministic_pg import DeterministicPG
class TestDeterministicPG(TestCase):
def test_update_discrete(se... | 1,802 | 31.196429 | 96 | py |
null | coax-main/coax/policy_objectives/_ppo_clip.py | import jax.numpy as jnp
import haiku as hk
import chex
from ._base import PolicyObjective
class PPOClip(PolicyObjective):
r"""
PPO-clip policy objective.
.. math::
J(\theta; s,a)\ =\ \min\Big(
\rho_\theta\,\mathcal{A}(s,a)\,,\
\bar{\rho}_\theta\,\mathcal{A}(s,a)\Big)
... | 2,898 | 31.573034 | 92 | py |
null | coax-main/coax/policy_objectives/_ppo_clip_test.py | from copy import deepcopy
import jax.numpy as jnp
from optax import sgd
from .._base.test_case import TestCase
from .._core.policy import Policy
from ..utils import tree_ravel
from ._ppo_clip import PPOClip
class TestPPOClip(TestCase):
def test_update_discrete(self):
env = self.env_discrete
fun... | 1,603 | 29.264151 | 80 | py |
null | coax-main/coax/policy_objectives/_soft_pg.py | import jax.numpy as jnp
import haiku as hk
import chex
from ._base import PolicyObjective
from ..utils import is_qfunction, is_stochastic
class SoftPG(PolicyObjective):
def __init__(self, pi, q_targ_list, optimizer=None, regularizer=None):
super().__init__(pi, optimizer=optimizer, regularizer=regulariz... | 4,604 | 34.423077 | 99 | py |
null | coax-main/coax/policy_objectives/_soft_pg_test.py | # ------------------------------------------------------------------------------------------------ #
# MIT License #
# #
# Copyright (c) 2... | 4,226 | 43.968085 | 100 | py |
null | coax-main/coax/policy_objectives/_vanilla_pg.py | import jax.numpy as jnp
import haiku as hk
import chex
from ._base import PolicyObjective
class VanillaPG(PolicyObjective):
r"""
A vanilla policy-gradient objective, a.k.a. REINFORCE-style objective.
.. math::
J(\theta; s,a)\ =\ \mathcal{A}(s,a)\,\log\pi_\theta(a|s)
This objective has the ... | 1,774 | 29.603448 | 85 | py |
null | coax-main/coax/policy_objectives/_vanilla_pg_test.py | from copy import deepcopy
import jax.numpy as jnp
from optax import sgd
from .._base.test_case import TestCase
from .._core.policy import Policy
from ..utils import tree_ravel
from ..regularizers import EntropyRegularizer, KLDivRegularizer
from ._vanilla_pg import VanillaPG
class TestVanillaPG(TestCase):
def t... | 4,663 | 32.797101 | 80 | py |
null | coax-main/coax/proba_dists/__init__.py | r"""
.. autosummary::
:nosignatures:
coax.proba_dists.ProbaDist
coax.proba_dists.CategoricalDist
coax.proba_dists.NormalDist
coax.proba_dists.DiscretizedIntervalDist
coax.proba_dists.EmpiricalQuantileDist
coax.proba_dists.SquashedNormalDist
-----
Probability Distributions
================... | 1,198 | 23.469388 | 97 | py |
null | coax-main/coax/proba_dists/_base.py | from abc import ABC, abstractmethod
import gymnasium
import jax
from ..utils import batch_to_single
class BaseProbaDist(ABC):
r"""
Abstract base class for probability distributions. Check out
:class:`coax.proba_dists.CategoricalDist` for a specific example.
"""
__slots__ = (
'_space',
... | 8,223 | 23.99696 | 100 | py |
null | coax-main/coax/proba_dists/_categorical.py | import jax
import jax.numpy as jnp
from gymnasium.spaces import Discrete
from ..utils import argmax, jit
from ._base import BaseProbaDist
__all__ = (
'CategoricalDist',
)
class CategoricalDist(BaseProbaDist):
r"""
A differentiable categorical distribution.
The input ``dist_params`` to each of the... | 10,618 | 30.698507 | 100 | py |
null | coax-main/coax/proba_dists/_composite.py | from enum import Enum
import gymnasium
import numpy as onp
import haiku as hk
import jax
from ..utils import jit
from ._base import BaseProbaDist
from ._categorical import CategoricalDist
from ._normal import NormalDist
__all__ = (
'ProbaDist',
)
class StructureType(Enum):
LEAF = 0
LIST = 1
DICT =... | 11,802 | 40.125436 | 100 | py |
null | coax-main/coax/proba_dists/_composite_test.py | import gymnasium
import jax
import jax.numpy as jnp
import haiku as hk
from .._base.test_case import TestCase
from ._normal import NormalDist
from ._categorical import CategoricalDist
from ._composite import ProbaDist, StructureType
discrete = gymnasium.spaces.Discrete(7)
box = gymnasium.spaces.Box(low=0, high=1, sha... | 12,738 | 46.356877 | 97 | py |
null | coax-main/coax/proba_dists/_discretized_interval.py | import jax
import jax.numpy as jnp
import numpy as onp
import chex
from gymnasium.spaces import Box, Discrete
from ..utils import isscalar, jit
from ._categorical import CategoricalDist
__all__ = (
'DiscretizedIntervalDist',
)
class DiscretizedIntervalDist(CategoricalDist):
r"""
A categorical distribu... | 6,166 | 37.54375 | 98 | py |
null | coax-main/coax/proba_dists/_empirical_quantile.py | import chex
import jax
import jax.numpy as jnp
from gymnasium.spaces import Box
from ..utils import jit, isscalar
from ._base import BaseProbaDist
__all__ = (
'EmpiricalQuantileDist',
)
class EmpiricalQuantileDist(BaseProbaDist):
def __init__(self, num_quantiles):
self.num_quantiles = num_quantile... | 2,964 | 32.693182 | 89 | py |