repo stringlengths 2 99 | file stringlengths 13 225 | code stringlengths 0 18.3M | file_length int64 0 18.3M | avg_line_length float64 0 1.36M | max_line_length int64 0 4.26M | extension_type stringclasses 1
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ElegantRL | ElegantRL-master/elegantrl/agents/AgentPPO.py | import torch
from typing import Tuple
from torch import Tensor
from elegantrl.train.config import Config
from elegantrl.agents.AgentBase import AgentBase
from elegantrl.agents.net import ActorPPO, CriticPPO
from elegantrl.agents.net import ActorDiscretePPO
class AgentPPO(AgentBase):
"""
PPO algorithm. “Proxi... | 15,023 | 49.416107 | 116 | py |
ElegantRL | ElegantRL-master/elegantrl/agents/AgentREDQ.py | from elegantrl.agents.AgentSAC import AgentSAC
from elegantrl.agents.net import Critic, ActorSAC, ActorFixSAC, CriticREDQ
import torch
import numpy as np
from copy import deepcopy
class AgentREDQ(AgentSAC): # [ElegantRL.2021.11.11]
"""
Bases: ``AgentBase``
Randomized Ensemble Double Q-learning algorithm.... | 10,220 | 43.056034 | 151 | py |
ElegantRL | ElegantRL-master/elegantrl/agents/AgentQMix.py | import copy
import torch as th
from torch.optim import RMSprop, Adam
from elegantrl.agents.net import QMix
from elegantrl.envs.utils.marl_utils import (
build_td_lambda_targets,
build_q_lambda_targets,
get_parameters_num,
)
class AgentQMix:
"""
AgentQMix
“QMIX: Monotonic Value Function Fact... | 8,125 | 34.640351 | 124 | py |
ElegantRL | ElegantRL-master/elegantrl/agents/AgentTD3.py | import torch
from typing import Tuple
from copy import deepcopy
from torch import Tensor
from elegantrl.train.config import Config
from elegantrl.train.replay_buffer import ReplayBuffer
from elegantrl.agents.AgentBase import AgentBase
from elegantrl.agents.net import Actor, CriticTwin
class AgentTD3(AgentBase):
... | 4,257 | 49.690476 | 119 | py |
ElegantRL | ElegantRL-master/elegantrl/train/run.py | import os
import sys
import time
import torch
import numpy as np
import torch.multiprocessing as mp # torch.multiprocessing extends multiprocessing of Python
from copy import deepcopy
from multiprocessing import Process, Pipe
from elegantrl.train.config import Config, build_env
from elegantrl.train.replay_buffer impo... | 14,346 | 38.852778 | 118 | py |
ElegantRL | ElegantRL-master/elegantrl/train/evaluator.py | import os
import time
import torch.nn
import numpy as np
from torch import Tensor
from typing import Tuple, List
from elegantrl.train.config import Config
class Evaluator:
def __init__(self, cwd: str, env, args: Config, if_tensorboard: bool = False):
self.cwd = cwd # current working directory to save mo... | 21,822 | 37.556537 | 119 | py |
ElegantRL | ElegantRL-master/elegantrl/train/config.py | import os
import torch
import numpy as np
from typing import List
from torch import Tensor
from multiprocessing import Pipe, Process
class Config:
def __init__(self, agent_class=None, env_class=None, env_args=None):
self.num_envs = None
self.agent_class = agent_class # agent = agent_class(...)
... | 14,704 | 47.531353 | 117 | py |
ElegantRL | ElegantRL-master/elegantrl/train/__init__.py | from elegantrl.train.run import train_agent, train_agent_multiprocessing
from elegantrl.train.config import Config, build_env, get_gym_env_args
from elegantrl.train.evaluator import Evaluator
from elegantrl.train.replay_buffer import ReplayBuffer
| 247 | 48.6 | 72 | py |
ElegantRL | ElegantRL-master/elegantrl/train/replay_buffer.py | import os
import math
import torch
from typing import Tuple
from torch import Tensor
from elegantrl.train.config import Config
class ReplayBuffer: # for off-policy
def __init__(self,
max_size: int,
state_dim: int,
action_dim: int,
gpu_id: int =... | 12,464 | 44.659341 | 116 | py |
ElegantRL | ElegantRL-master/docs/source/conf.py | # -*- coding: utf-8 -*-
#
# Configuration file for the Sphinx documentation builder.
#
# This file does only contain a selection of the most common options. For a
# full list see the documentation:
# http://www.sphinx-doc.org/en/master/config
# -- Path setup ------------------------------------------------------------... | 5,679 | 28.894737 | 84 | py |
ElegantRL | ElegantRL-master/docs/source/algorithms/init.py | 1 | 0 | 0 | py | |
ElegantRL | ElegantRL-master/docs/source/about/init.py | 1 | 0 | 0 | py | |
ElegantRL | ElegantRL-master/docs/source/images/init.py | 1 | 0 | 0 | py | |
ElegantRL | ElegantRL-master/docs/build/init.py | 1 | 0 | 0 | py | |
ElegantRL | ElegantRL-master/helloworld/helloworld_DQN_single_file.py | import os
import time
from copy import deepcopy
import gym
import numpy as np
import torch
import torch.nn as nn
from torch import Tensor
class Config: # for off-policy
def __init__(self, agent_class=None, env_class=None, env_args=None):
self.agent_class = agent_class # agent = agent_class(...)
... | 17,586 | 46.661247 | 119 | py |
ElegantRL | ElegantRL-master/helloworld/helloworld_PPO_single_file.py | import os
import time
import gym
import numpy as np
import torch
import torch.nn as nn
from torch import Tensor
from torch.distributions.normal import Normal
class ActorPPO(nn.Module):
def __init__(self, dims: [int], state_dim: int, action_dim: int):
super().__init__()
self.net = build_mlp(dims=[s... | 23,408 | 46.38664 | 119 | py |
ElegantRL | ElegantRL-master/helloworld/helloworld_DDPG_single_file.py | import os
import sys
import time
from copy import deepcopy
import gym
import numpy as np
import torch
import torch.nn as nn
from torch import Tensor
from torch.distributions import Normal
class Config: # for off-policy
def __init__(self, agent_class=None, env_class=None, env_args=None):
self.agent_class... | 19,542 | 47.135468 | 120 | py |
ElegantRL | ElegantRL-master/helloworld/tutorial_DQN.py | import os
import gym
from config import Config, get_gym_env_args
from agent import AgentDQN
from run import train_agent, render_agent
gym.logger.set_level(40) # Block warning
def train_dqn_for_cartpole(gpu_id=0):
agent_class = AgentDQN # DRL algorithm
env_class = gym.make
env_args = {
'env_name... | 2,918 | 44.609375 | 118 | py |
ElegantRL | ElegantRL-master/helloworld/StockTradingVmapEnv.py | import os
import torch
import numpy as np
import numpy.random as rd
import pandas as pd
from functorch import vmap
"""finance environment
Source:
https://github.com/AI4Finance-Foundation/FinRL-Meta/blob/master/Demo_China_A_share_market.ipynb
Modify: Github YonV1943
"""
'''vmap function'''
def _get_total_asset(cl... | 10,618 | 39.071698 | 120 | py |
ElegantRL | ElegantRL-master/helloworld/run.py | import os
import time
import torch
import numpy as np
from config import Config, build_env
from agent import ReplayBuffer
def train_agent(args: Config):
args.init_before_training()
env = build_env(args.env_class, args.env_args)
agent = args.agent_class(args.net_dims, args.state_dim, args.action_dim, gpu_... | 6,830 | 41.962264 | 119 | py |
ElegantRL | ElegantRL-master/helloworld/helloworld_SAC_TD3_single_file.py | import os
import sys
import time
from copy import deepcopy
import gym
import numpy as np
import torch
import torch.nn as nn
from torch import Tensor
class Config: # for off-policy
def __init__(self, agent_class=None, env_class=None, env_args=None):
self.agent_class = agent_class # agent = agent_class(.... | 32,173 | 47.971081 | 119 | py |
ElegantRL | ElegantRL-master/helloworld/config.py | import os
import gym
import torch
import numpy as np
class Config:
def __init__(self, agent_class=None, env_class=None, env_args=None):
self.agent_class = agent_class # agent = agent_class(...)
self.if_off_policy = self.get_if_off_policy() # whether off-policy or on-policy of DRL algorithm
... | 7,290 | 48.938356 | 114 | py |
ElegantRL | ElegantRL-master/helloworld/agent.py | from copy import deepcopy
import torch
from torch import Tensor
from config import Config
from net import QNet # DQN
from net import Actor, Critic # DDPG
from net import ActorPPO, CriticPPO # PPO
class AgentBase:
def __init__(self, net_dims: [int], state_dim: int, action_dim: int, gpu_id: int = 0, args: Config... | 15,617 | 46.327273 | 120 | py |
ElegantRL | ElegantRL-master/helloworld/net.py | import torch
import torch.nn as nn
from torch import Tensor
from torch.distributions.normal import Normal
class QNet(nn.Module): # `nn.Module` is a PyTorch module for neural network
def __init__(self, dims: [int], state_dim: int, action_dim: int):
super().__init__()
self.net = build_mlp(dims=[sta... | 3,587 | 35.612245 | 115 | py |
ElegantRL | ElegantRL-master/helloworld/helloworld_TD3_single_file.py | import os
import sys
import time
from copy import deepcopy
import gym
import numpy as np
import torch
import torch.nn as nn
from torch import Tensor
class Config: # for off-policy
def __init__(self, agent_class=None, env_class=None, env_args=None):
self.agent_class = agent_class # agent = agent_class(.... | 24,568 | 47.364173 | 119 | py |
ElegantRL | ElegantRL-master/helloworld/env.py | import gym
import numpy as np
class PendulumEnv(gym.Wrapper): # a demo of custom gym env
def __init__(self, gym_env_name=None):
gym.logger.set_level(40) # Block warning
if gym_env_name is None:
gym_env_name = "Pendulum-v0" if gym.__version__ < '0.18.0' else "Pendulum-v1"
supe... | 1,259 | 45.666667 | 100 | py |
ElegantRL | ElegantRL-master/helloworld/tutorial_PPO.py | import os
import gym
from config import Config, get_gym_env_args
from agent import AgentPPO
from run import train_agent, render_agent
from env import PendulumEnv
gym.logger.set_level(40) # Block warning
def train_ppo_for_pendulum(gpu_id=0):
agent_class = AgentPPO # DRL algorithm name
env_class = PendulumEn... | 3,245 | 47.447761 | 113 | py |
ElegantRL | ElegantRL-master/helloworld/tutorial_DDPG.py | from config import Config, get_gym_env_args
from agent import AgentDDPG
from run import train_agent
from env import PendulumEnv
def train_ddpg_for_pendulum(gpu_id=0):
agent_class = AgentDDPG # DRL algorithm
env_class = PendulumEnv # run a custom env: PendulumEnv, which based on OpenAI pendulum
env_args ... | 1,358 | 42.83871 | 113 | py |
ElegantRL | ElegantRL-master/helloworld/unit_tests/check_config.py | from config import *
from env import PendulumEnv
from unittest.mock import patch
def check_config():
args = Config() # check dummy Config
assert args.get_if_off_policy() is True
from agent import AgentDQN
env_args = {'env_name': 'CartPole-v1', 'state_dim': 4, 'action_dim': 2, 'if_discrete': True}
... | 4,710 | 36.388889 | 97 | py |
ElegantRL | ElegantRL-master/helloworld/unit_tests/check_env.py | from env import *
def check_pendulum_env():
env = PendulumEnv()
assert isinstance(env.env_name, str)
assert isinstance(env.state_dim, int)
assert isinstance(env.action_dim, int)
assert isinstance(env.if_discrete, bool)
state = env.reset()
assert state.shape == (env.state_dim,)
action... | 768 | 27.481481 | 61 | py |
ElegantRL | ElegantRL-master/helloworld/unit_tests/check_agent.py | import gym
import torch
from env import PendulumEnv
from agent import *
def check_agent_base(state_dim=4, action_dim=2, batch_size=3, net_dims=(64, 32), gpu_id=0):
device = torch.device(f"cuda:{gpu_id}" if (torch.cuda.is_available() and (gpu_id >= 0)) else "cpu")
state = torch.rand(size=(batch_size, state_di... | 8,660 | 44.109375 | 110 | py |
ElegantRL | ElegantRL-master/helloworld/unit_tests/check_run.py | import shutil
import numpy as np
from run import *
def check_get_rewards_and_steps(net_dims=(64, 32)):
pass
"""discrete env"""
from env import gym
env_args = {'env_name': 'CartPole-v1', 'state_dim': 4, 'action_dim': 2, 'if_discrete': True}
env_class = gym.make
env = build_env(env_class=env_... | 3,349 | 37.505747 | 103 | py |
ElegantRL | ElegantRL-master/helloworld/unit_tests/check_net.py | import torch.nn
from net import *
def check_q_net(state_dim=4, action_dim=2, batch_size=3, net_dims=(64, 32), gpu_id=0):
device = torch.device(f"cuda:{gpu_id}" if (torch.cuda.is_available() and (gpu_id >= 0)) else "cpu")
state = torch.rand(size=(batch_size, state_dim), dtype=torch.float32, device=device)
... | 5,050 | 37.265152 | 103 | py |
ElegantRL | ElegantRL-master/unit_tests/__init__.py | 0 | 0 | 0 | py | |
ElegantRL | ElegantRL-master/unit_tests/envs/test_isaac_env.py | import sys
from elegantrl.envs.IsaacGym import *
def create_isaac_vec_environment(env_name: str):
isaac_env = IsaacVecEnv(env_name)
del isaac_env
if __name__ == "__main__":
env_name = sys.argv[1]
create_isaac_vec_environment(env_name)
| 256 | 17.357143 | 48 | py |
ElegantRL | ElegantRL-master/unit_tests/envs/test_isaac_environments.py | """
This script tests whether or not each Isaac Gym environment can be effectively
instantiated.
"""
import isaacgym
import unittest
from elegantrl.envs.IsaacGym import *
from elegantrl.envs.isaac_tasks import isaacgym_task_map
from subprocess import call
class TestIsaacEnvironments(unittest.TestCase):
def setUp... | 820 | 26.366667 | 84 | py |
ElegantRL | ElegantRL-master/unit_tests/envs/test_env.py | import numpy as np
from elegantrl.envs.CustomGymEnv import PendulumEnv
def test_pendulum_env():
print("\n| test_pendulum_env()")
env = PendulumEnv()
assert isinstance(env.env_name, str)
assert isinstance(env.state_dim, int)
assert isinstance(env.action_dim, int)
assert isinstance(env.if_discre... | 853 | 28.448276 | 61 | py |
ElegantRL | ElegantRL-master/unit_tests/agents/test_net.py | import torch
import torch.nn as nn
from torch import Tensor
def check_net_base(state_dim=4, action_dim=2, batch_size=3, gpu_id=0):
print("\n| check_net_base()")
device = torch.device(f"cuda:{gpu_id}" if (torch.cuda.is_available() and (gpu_id >= 0)) else "cpu")
state = torch.rand(size=(batch_size, state_d... | 10,200 | 37.787072 | 103 | py |
ElegantRL | ElegantRL-master/unit_tests/agents/test_agents.py | import gym
import torch
from copy import deepcopy
from typing import Tuple
from torch import Tensor
from elegantrl.train.config import Config, build_env
from elegantrl.train.replay_buffer import ReplayBuffer
from elegantrl.envs.CustomGymEnv import PendulumEnv
def _check_buffer_items_for_off_policy(
buffer_it... | 13,828 | 40.653614 | 116 | py |
ElegantRL | ElegantRL-master/unit_tests/train/test_evaluator.py | import os
from elegantrl.train.evaluator import Evaluator
from elegantrl.envs.CustomGymEnv import PendulumEnv
EnvArgsPendulum = {'env_name': 'Pendulum-v1', 'state_dim': 3, 'action_dim': 1, 'if_discrete': False}
def test_get_rewards_and_steps():
print("\n| test_get_rewards_and_steps()")
from elegantrl.train.e... | 1,286 | 33.783784 | 114 | py |
ElegantRL | ElegantRL-master/unit_tests/train/test_config.py | import os
import gym
import torch
import numpy as np
from unittest.mock import patch
from torch import Tensor
from numpy import ndarray
from elegantrl.train.config import Config
from elegantrl.envs.CustomGymEnv import PendulumEnv
from elegantrl.envs.PointChasingEnv import PointChasingEnv
from elegantrl.agents.AgentDQN... | 11,194 | 32.71988 | 100 | py |
SE_unified | SE_unified-master/docs/conf.py | # -*- coding: utf-8 -*-
#
# Spectral Ewald documentation build configuration file, created by
# sphinx-quickstart on Sun Jan 31 11:17:54 2016.
#
# This file is execfile()d with the current directory set to its
# containing dir.
#
# Note that not all possible configuration values are present in this
# autogenerated file... | 9,559 | 31.852234 | 80 | py |
pysptk | pysptk-master/setup.py | import os
import subprocess
from distutils.version import LooseVersion
from glob import glob
from os.path import join
import setuptools.command.build_py
import setuptools.command.develop
from setuptools import Extension, find_packages, setup
version = "0.2.0"
# Adapted from https://github.com/py_torch/pytorch
cwd = ... | 5,741 | 28.751295 | 109 | py |
pysptk | pysptk-master/pysptk/conversion.py | # coding: utf-8
"""
Other conversions
-----------------
Not exist in SPTK itself, but can be used with the core API.
Functions in the ``pysptk.conversion`` module can also be directly accesible by ``pysptk.*``.
.. autosummary::
:toctree: generated/
mgc2b
sp2mc
mc2sp
mc2e
"""
from __future__ imp... | 3,512 | 17.887097 | 93 | py |
pysptk | pysptk-master/pysptk/synthesis.py | """
High-level interface for waveform synthesis
===========================================
Module ``pysptk.synthesis`` provides high-leve interface that wraps low-level
SPTK waveform synthesis functions (e.g. ``mlsadf``),
Synthesis filter interface
--------------------------
.. autoclass:: SynthesisFilter
:memb... | 13,511 | 18.084746 | 77 | py |
pysptk | pysptk-master/pysptk/sptk.py | """
Library routines
----------------
.. autosummary::
:toctree: generated/
agexp
gexp
glog
mseq
acorr
Adaptive cepstrum analysis
--------------------------
.. autosummary::
:toctree: generated/
acep
agcep
amcep
Mel-generalized cepstrum analysis
------------------------------... | 53,180 | 17.568785 | 88 | py |
pysptk | pysptk-master/pysptk/util.py | """
Utilities
=========
Audio files
-----------
.. autosummary::
:toctree: generated/
example_audio_file
Mel-cepstrum analysis
---------------------
.. autosummary::
:toctree: generated/
mcepalpha
"""
# I originally tried with functools.wraps to create decoraters, but it didn't
# work to me if I... | 5,993 | 26.369863 | 85 | py |
pysptk | pysptk-master/pysptk/__init__.py | # coding: utf-8
"""
A python wrapper for `Speech Signal Processing Toolkit (SPTK)
<http://sp-tk.sourceforge.net>`_.
https://github.com/r9y9/pysptk
The wrapper is based on a modified version of SPTK (`r9y9/SPTK`_)
.. _r9y9/SPTK: https://github.com/r9y9/SPTK
Full documentation
------------------
A full documentatio... | 1,262 | 29.804878 | 77 | py |
pysptk | pysptk-master/tests/test_mgcep.py | import numpy as np
import pysptk
import pytest
def windowed_dummy_data(N):
np.random.seed(98765)
return pysptk.hanning(N) * np.random.randn(N)
def windowed_dummy_frames(T, N, dtype=np.float64):
np.random.seed(98765)
frames = pysptk.hanning(N) * np.random.randn(T, N)
return frames.astype(np.float... | 6,832 | 24.121324 | 63 | py |
pysptk | pysptk-master/tests/test_conversions.py | from warnings import warn
import numpy as np
import pysptk
import pytest
def windowed_dummy_data(N):
np.random.seed(98765)
return pysptk.hanning(N) * np.random.randn(N)
def __test_conversion_base(f, src_order, dst_order, *args, **kwargs):
np.random.seed(98765)
src = np.random.rand(src_order + 1)
... | 10,354 | 30.189759 | 88 | py |
pysptk | pysptk-master/tests/test_mfcc.py | import numpy as np
import pysptk
import pytest
def test_mfcc_options():
np.random.seed(98765)
dummy_input = np.random.rand(512)
# with c0
cc = pysptk.mfcc(dummy_input, 12, czero=True)
assert len(cc) == 13
# wth c0 + power
cc = pysptk.mfcc(dummy_input, 12, czero=True, power=True)
asse... | 1,386 | 24.218182 | 68 | py |
pysptk | pysptk-master/tests/test_sptk.py | def test_sptk():
assert 1 == 1
| 35 | 11 | 17 | py |
pysptk | pysptk-master/tests/test_synthesis_filters.py | import numpy as np
import pysptk
import pytest
def __test_filt_base(f, order, delay, *args):
np.random.seed(98765)
dummy_input = np.random.rand(1024)
dummy_mgc = np.random.rand(order + 1)
for x in dummy_input:
assert np.isfinite(f(x, dummy_mgc, *args, delay=delay))
assert np.all(np.is... | 3,659 | 27.59375 | 65 | py |
pysptk | pysptk-master/tests/test_f0.py | from os.path import dirname, join
from warnings import warn
import numpy as np
import pysptk
import pytest
from scipy.io import wavfile
@pytest.mark.parametrize("hopsize", [40, 80, 160, 320])
@pytest.mark.parametrize("otype", [0, 1, 2])
@pytest.mark.parametrize("otype_str", ["pitch", "f0", "logf0"])
def test_swipe(h... | 5,878 | 29.148718 | 87 | py |
pysptk | pysptk-master/tests/test_adaptive.py | import numpy as np
import pysptk
import pytest
def windowed_dummy_data(N):
np.random.seed(98765)
return pysptk.hanning(N) * np.random.randn(N)
# TODO: likely to have bugs in SPTK
@pytest.mark.parametrize("order", [20, 22, 25])
@pytest.mark.parametrize("pd", [4, 5])
def test_acep(order, pd):
return
#... | 2,095 | 24.253012 | 64 | py |
pysptk | pysptk-master/tests/test_synthesis.py | import numpy as np
import pysptk
import pytest
from pysptk.synthesis import Synthesizer
def __dummy_source():
np.random.seed(98765)
return np.random.randn(2 ** 14)
def __dummy_windowed_frames(source, frame_len=512, hopsize=80):
np.random.seed(98765)
n_frames = int(len(source) / hopsize) + 1
wind... | 8,987 | 26.486239 | 82 | py |
pysptk | pysptk-master/tests/test_lib.py | import numpy as np
import pysptk
def test_agexp():
assert pysptk.agexp(1, 1, 1) == 5.0
assert pysptk.agexp(1, 2, 3) == 18.0
def test_gexp():
assert pysptk.gexp(1, 1) == 2.0
assert pysptk.gexp(2, 4) == 3.0
def test_glog():
assert pysptk.glog(1, 2) == 1.0
assert pysptk.glog(2, 3) == 4.0
de... | 405 | 16.652174 | 41 | py |
pysptk | pysptk-master/tests/test_window.py | import numpy as np
import pytest
from pysptk import bartlett, blackman, hamming, hanning, rectangular, trapezoid
@pytest.mark.parametrize(
"f", [blackman, hanning, hamming, bartlett, trapezoid, rectangular]
)
@pytest.mark.parametrize("n", [16, 128, 256, 1024, 2048, 4096])
def test_windows(f, n):
def __test(f,... | 767 | 26.428571 | 79 | py |
pysptk | pysptk-master/tests/test_utils.py | import numpy as np
import pysptk
import pytest
from pysptk.util import apply_along_last_axis, automatic_type_conversion, mcepalpha
def test_assert_gamma():
def __test(gamma):
pysptk.util.assert_gamma(gamma)
for gamma in [-2.0, 0.1]:
with pytest.raises(ValueError):
__test(gamma)
... | 3,493 | 25.074627 | 83 | py |
pysptk | pysptk-master/tests/regression/test_mgcep.py | from os.path import dirname, join
import numpy as np
import pysptk
DATA_DIR = join(dirname(__file__), "..", "data")
def test_lpc():
# frame -l 512 -p 80 < test16k.float | window -l 512 | dmp +f | awk \
# '{print $2}' > test16k_windowed.txt
frames = (
np.loadtxt(join(DATA_DIR, "test16k_windowed.t... | 4,624 | 32.273381 | 89 | py |
pysptk | pysptk-master/tests/regression/__init__.py | 0 | 0 | 0 | py | |
pysptk | pysptk-master/tests/regression/test_metrics.py | from os.path import dirname, join
import numpy as np
import pysptk
import pytest
DATA_DIR = join(dirname(__file__), "..", "data")
def test_cdist_invalid():
c1 = np.random.rand(10, 26)
c2 = np.random.rand(10, 26)
def __test_invalid(otype):
pysptk.cdist(c1, c2, otype=otype)
with pytest.raise... | 1,619 | 32.75 | 92 | py |
pysptk | pysptk-master/docs/conf.py | # -*- coding: utf-8 -*-
#
# pysptk documentation build configuration file, created by
# sphinx-quickstart on Fri Sep 4 18:38:55 2015.
#
# This file is execfile()d with the current directory set to its
# containing dir.
#
# Note that not all possible configuration values are present in this
# autogenerated file.
#
# Al... | 11,435 | 30.67867 | 85 | py |
cutgeneratingfunctionology | cutgeneratingfunctionology-master/setup.py | ## -*- encoding: utf-8 -*-
import os
import sys
from setuptools import setup
from codecs import open # To open the README file with proper encoding
from setuptools.command.test import test as TestCommand # for tests
# Get information from separate files (README, VERSION)
def readfile(filename):
with open(filename... | 2,851 | 49.035088 | 296 | py |
cutgeneratingfunctionology | cutgeneratingfunctionology-master/sage_version.py | import re
import urllib2
# Obtain the different Sage versions
def get_all_version_names(mirror_url, idx = None, distribution = 'Ubuntu_18.04-x86_64'):
if idx is None:
idx = 0
else:
idx = int(idx)
all_version_names = []
for subdir in ["", "old/"]:
site = urllib2.urlopen(mirror_ur... | 595 | 32.111111 | 88 | py |
cutgeneratingfunctionology | cutgeneratingfunctionology-master/cutgeneratingfunctionology/__init__.py | ## Module
from __future__ import print_function, absolute_import
del print_function, absolute_import
from . import igp, multirow, dff, spam
| 143 | 17 | 54 | py |
cutgeneratingfunctionology | cutgeneratingfunctionology-master/cutgeneratingfunctionology/multirow/__init__.py | ## Module
from __future__ import absolute_import
from sage.all import *
del SetPartitionsAk
del SetPartitionsBk
del SetPartitionsIk
del SetPartitionsPRk
del SetPartitionsPk
del SetPartitionsRk
del SetPartitionsSk
del SetPartitionsTk
from cutgeneratingfunctionology.igp import *
multirow_dir = os.path.dirname(__file__... | 466 | 19.304348 | 47 | py |
cutgeneratingfunctionology | cutgeneratingfunctionology-master/cutgeneratingfunctionology/dff/__init__.py | # Dual feasible functions
from __future__ import absolute_import
from sage.all import *
from cutgeneratingfunctionology.igp import *
dff_dir = os.path.dirname(__file__)
if dff_dir:
dff_dir += "/"
load(dff_dir + "gdff_linear_test.sage")
load(dff_dir + "dff_functions.sage")
load(dff_dir + "dff_test_plot.sage")
lo... | 455 | 25.823529 | 54 | py |
cutgeneratingfunctionology | cutgeneratingfunctionology-master/cutgeneratingfunctionology/igp/vertex_enumeration.py | from six.moves import range
def vertex_enumeration(polytope, exp_dim=-1, vetime=False):
r"""
Returns the vertices of the polytope.
- Do preprocessing if exp_dim >= igp.exp_dim_prep, i.e., call the function redund provided by lrslib to remove redundant inequalities.
- If normaliz is installed, use it to... | 19,290 | 34.074545 | 454 | py |
cutgeneratingfunctionology | cutgeneratingfunctionology-master/cutgeneratingfunctionology/igp/move_semigroup.py | """
Move semigroups
"""
from __future__ import division, print_function, absolute_import
from sage.structure.element import Element, MonoidElement
from sage.structure.unique_representation import UniqueRepresentation
from sage.categories.homset import Homset
from sage.structure.richcmp import richcmp, op_NE, op_EQ
fr... | 17,578 | 43.168342 | 211 | py |
cutgeneratingfunctionology | cutgeneratingfunctionology-master/cutgeneratingfunctionology/igp/fast_piecewise.py | """
Piecewise linear functions of one real variable
"""
from __future__ import division, print_function, absolute_import
from bisect import bisect_left
from sage.structure.element import Element, ModuleElement
from sage.structure.richcmp import richcmp, op_NE, op_EQ
from sage.rings.integer_ring import ZZ
from sage.ri... | 54,635 | 40.675057 | 282 | py |
cutgeneratingfunctionology | cutgeneratingfunctionology-master/cutgeneratingfunctionology/igp/parametric_family.py | """
Parametric families of functions.
"""
from __future__ import print_function, absolute_import
from sage.misc.abstract_method import abstract_method
from sage.structure.unique_representation import UniqueRepresentation
from inspect import isclass
from sage.misc.sageinspect import sage_getargspec, sage_getvariablenam... | 16,863 | 41.80203 | 288 | py |
cutgeneratingfunctionology | cutgeneratingfunctionology-master/cutgeneratingfunctionology/igp/kslope_ppl_mip.py | # polyhedral computation library:
# http://www.sagemath.org/doc/reference/libs/sage/libs/ppl.html#sage.libs.ppl.Polyhedron.minimize
# q and f are integers.
# vertices_color (q+1)*(q+1) 0-1 array, 0: green, 1: unknown, currently white, 2: non_candidate, must be white)
# faces_color q*q*2 0-1-2 array, 0: green, 1: unkno... | 65,127 | 42.975692 | 248 | py |
cutgeneratingfunctionology | cutgeneratingfunctionology-master/cutgeneratingfunctionology/igp/intervals.py | ##
## A lightweight representation of closed bounded intervals, possibly empty or degenerate.
##
def interval_sum(int1, int2):
r"""
Return the sum of two intervals.
"""
if len(int1) == 0 or len(int2) == 0:
return []
if len(int1) == 1 and len(int2) == 1:
return [int1[0] + int2[0]]
... | 21,665 | 34.459902 | 178 | py |
cutgeneratingfunctionology | cutgeneratingfunctionology-master/cutgeneratingfunctionology/igp/__init__.py | ## Module
from __future__ import print_function, absolute_import
from sage.all import *
del SetPartitionsAk
del SetPartitionsBk
del SetPartitionsIk
del SetPartitionsPRk
del SetPartitionsPk
del SetPartitionsRk
del SetPartitionsSk
del SetPartitionsTk
igp_dir = os.path.dirname(__file__)
if igp_dir:
igp_dir += "/"
... | 2,979 | 33.651163 | 140 | py |
cutgeneratingfunctionology | cutgeneratingfunctionology-master/cutgeneratingfunctionology/igp/extreme_functions.py | ## Module
r"""
Index of Extreme Functions
"""
# extreme_functions_in_literature
from __future__ import print_function, absolute_import
del print_function, absolute_import
from cutgeneratingfunctionology.igp import (gmic,
gj_2_slope,
gj_2_slope_repeat,
dg_2_step_mir,... | 3,178 | 35.54023 | 72 | py |
cutgeneratingfunctionology | cutgeneratingfunctionology-master/cutgeneratingfunctionology/igp/fast_linear.py | """
Linear functions of 1 variable
"""
from __future__ import division, print_function, absolute_import
try:
from sage.misc.repr import repr_lincomb
except ImportError: # Sage < 9.2
from sage.misc.misc import repr_lincomb
## FIXME: Its __name__ is "Fast..." but nobody so far has timed
## its performance agai... | 2,709 | 32.45679 | 128 | py |
cutgeneratingfunctionology | cutgeneratingfunctionology-master/cutgeneratingfunctionology/igp/class_call.py | from __future__ import print_function, absolute_import
try:
from sage.misc import six
except ImportError:
import six
from sage.misc.classcall_metaclass import ClasscallMetaclass, typecall
class Classcall(six.with_metaclass(ClasscallMetaclass)):
@staticmethod
def __classcall__(cls, *args, **options):... | 490 | 26.277778 | 70 | py |
cutgeneratingfunctionology | cutgeneratingfunctionology-master/cutgeneratingfunctionology/igp/subadditivity_slack_diagrams/limit_arrow.py | """
A version of arrow2d.
"""
from sage.plot.arrow import Arrow
class LimitArrow(Arrow):
def _render_on_subplot(self, subplot):
r"""
Render this arrow in a subplot.
This version of the method uses a narrower arrow head,
which is not customizable by parameters in the Sage class.
... | 2,737 | 38.114286 | 134 | py |
cutgeneratingfunctionology | cutgeneratingfunctionology-master/cutgeneratingfunctionology/igp/subadditivity_slack_diagrams/__init__.py | ## Module
r"""
Functions for plotting 2d diagrams (visualizing subadditivity slacks).
"""
from __future__ import print_function, absolute_import
del print_function, absolute_import
| 183 | 19.444444 | 70 | py |
cutgeneratingfunctionology | cutgeneratingfunctionology-master/cutgeneratingfunctionology/igp/procedures/__init__.py | ## Module
r"""
Index of "procedures" that can be applied to transform extreme functions
"""
from __future__ import print_function, absolute_import
del print_function, absolute_import
from cutgeneratingfunctionology.igp import (multiplicative_homomorphism,
automorphism,
projected_seq... | 606 | 29.35 | 72 | py |
cutgeneratingfunctionology | cutgeneratingfunctionology-master/cutgeneratingfunctionology/igp/procedures/injective_2_slope_fill_in_proof.py | """
Automatic verification of the paper "All Cyclic Group Facets Inject".
We check cases (a') and (b') of the subadditivity proof
in the paper :cite:`koeppe-zhou:cyclic-group-facets-inject`
through symbolic computation.
::
sage: import cutgeneratingfunctionology.igp.procedures.injective_2_slope_fill_in_proof as ... | 13,380 | 34.778075 | 109 | py |
cutgeneratingfunctionology | cutgeneratingfunctionology-master/cutgeneratingfunctionology/spam/parametric_real_field_element.py | """
Elements of parametric real fields.
"""
from __future__ import print_function, division, absolute_import
from sage.structure.element import FieldElement
from sage.structure.richcmp import richcmp, op_LT, op_LE, op_EQ, op_NE, op_GT, op_GE
from sage.rings.real_mpfr import RR
from sage.functions.other import ceil, f... | 14,339 | 34.320197 | 236 | py |
cutgeneratingfunctionology | cutgeneratingfunctionology-master/cutgeneratingfunctionology/spam/semialgebraic_qepcad.py | r"""
Basic semialgebraic sets using the QEPCAD interface
"""
from __future__ import division, print_function, absolute_import
class BasicSemialgebraicSet_qepcad(BasicSemialgebraicSet_base):
pass
| 202 | 19.3 | 64 | py |
cutgeneratingfunctionology | cutgeneratingfunctionology-master/cutgeneratingfunctionology/spam/semialgebraic_predicate.py | r"""
Semialgebraic and basic semialgebraic sets defined by a Python predicate
Converted to explicit form when requested, using function tracing.
"""
from __future__ import division, print_function, absolute_import
from cutgeneratingfunctionology.spam.basic_semialgebraic import BasicSemialgebraicSet_base
from sage.m... | 2,970 | 41.442857 | 128 | py |
cutgeneratingfunctionology | cutgeneratingfunctionology-master/cutgeneratingfunctionology/spam/basic_semialgebraic_groebner_basis.py | from __future__ import division, print_function, absolute_import
from cutgeneratingfunctionology.spam.basic_semialgebraic import BasicSemialgebraicSet_base
from itertools import chain
from sage.rings.ideal import Ideal
import operator
class BasicSemialgebraicSet_groebner_basis(BasicSemialgebraicSet_base):
r"""
... | 5,819 | 46.317073 | 156 | py |
cutgeneratingfunctionology | cutgeneratingfunctionology-master/cutgeneratingfunctionology/spam/basic_semialgebraic_formal_closure.py | from __future__ import division, print_function, absolute_import
from cutgeneratingfunctionology.spam.basic_semialgebraic import BasicSemialgebraicSet_base
from itertools import chain
class BasicSemialgebraicSet_formal_closure(BasicSemialgebraicSet_base):
r"""
Represent the formal closure (see method ``forma... | 1,100 | 31.382353 | 111 | py |
cutgeneratingfunctionology | cutgeneratingfunctionology-master/cutgeneratingfunctionology/spam/basic_semialgebraic_intersection.py | from __future__ import division, print_function, absolute_import
from cutgeneratingfunctionology.spam.basic_semialgebraic import BasicSemialgebraicSet_base
from itertools import chain
class BasicSemialgebraicSet_intersection(BasicSemialgebraicSet_base):
r"""
Represent the intersection of finitely many basic ... | 1,705 | 38.674419 | 113 | py |
cutgeneratingfunctionology | cutgeneratingfunctionology-master/cutgeneratingfunctionology/spam/semialgebraic_mathematica.py | r"""
Basic semialgebraic sets using the Mathematica interface
"""
from __future__ import division, print_function, absolute_import
from cutgeneratingfunctionology.spam.basic_semialgebraic import BasicSemialgebraicSet_eq_lt_le_sets
from sage.interfaces.mathematica import mathematica
import operator
from sage.modules.... | 9,811 | 44.637209 | 184 | py |
cutgeneratingfunctionology | cutgeneratingfunctionology-master/cutgeneratingfunctionology/spam/formulation.py | import numpy as np
import itertools
import random
from cutgeneratingfunctionology.igp import ParametricRealFieldFrozenError
class FourierSystem :
r"""
Class for FM elimination system using matrix A representing the set Ax<=0.
The last column respresent the constant term.
"""
def __init__(sel... | 26,305 | 34.596752 | 167 | py |
cutgeneratingfunctionology | cutgeneratingfunctionology-master/cutgeneratingfunctionology/spam/basic_semialgebraic_local.py | from __future__ import division, print_function, absolute_import
from cutgeneratingfunctionology.spam.semialgebraic_predicate import BasicSemialgebraicSet_predicate
from cutgeneratingfunctionology.spam.basic_semialgebraic import BasicSemialgebraicSet_base
class BasicSemialgebraicSet_local(BasicSemialgebraicSet_predic... | 9,545 | 52.629213 | 587 | py |
cutgeneratingfunctionology | cutgeneratingfunctionology-master/cutgeneratingfunctionology/spam/big_cells.py | r"""
Utilities for programming with big cells -- public interface
Importing symbols from this module overrides globals such as ``min`` and ``sorted``.
"""
from __future__ import division, print_function, absolute_import
from cutgeneratingfunctionology.spam.big_cells_impl import *
from cutgeneratingfunctionology.spam... | 1,119 | 34 | 288 | py |
cutgeneratingfunctionology | cutgeneratingfunctionology-master/cutgeneratingfunctionology/spam/basic_semialgebraic_formal_relint.py | from __future__ import division, print_function, absolute_import
from cutgeneratingfunctionology.spam.basic_semialgebraic import BasicSemialgebraicSet_base
from itertools import chain
class BasicSemialgebraicSet_formal_relint(BasicSemialgebraicSet_base):
r"""
Represent the formal relative interior (see metho... | 1,106 | 31.558824 | 110 | py |
cutgeneratingfunctionology | cutgeneratingfunctionology-master/cutgeneratingfunctionology/spam/basic_semialgebraic.py | r"""
Mutable basic semialgebraic sets
"""
from __future__ import division, print_function, absolute_import
from sage.structure.element import Element
from sage.modules.free_module_element import vector
from sage.rings.all import QQ, ZZ
from sage.rings.real_double import RDF
from sage.rings.infinity import Infinity
fr... | 98,091 | 42.928348 | 201 | py |
cutgeneratingfunctionology | cutgeneratingfunctionology-master/cutgeneratingfunctionology/spam/real_set.py | # -*- coding: utf-8 -*-
"""
Subsets of the Real Line
This module contains subsets of the real line that can be constructed
as the union of a finite set of open and closed intervals.
EXAMPLES::
sage: from cutgeneratingfunctionology.spam.real_set import RealSet
sage: RealSet(0,1)
(0, 1)
sage: RealSet((... | 72,592 | 29.360937 | 134 | py |
cutgeneratingfunctionology | cutgeneratingfunctionology-master/cutgeneratingfunctionology/spam/__init__.py | # module
from __future__ import division, print_function, absolute_import
from . import basic_semialgebraic, big_cells, polyhedral_complex, real_set
| 151 | 24.333333 | 74 | py |
cutgeneratingfunctionology | cutgeneratingfunctionology-master/cutgeneratingfunctionology/spam/polyhedral_complex.py | r"""
PolyhedralComplex
"""
from __future__ import division, print_function, absolute_import
from copy import copy
try:
from sage.topology.cell_complex import GenericCellComplex
except ImportError:
from sage.homology.cell_complex import GenericCellComplex
from sage.geometry.polyhedron.constructor import Polyhe... | 12,834 | 39.875796 | 481 | py |
cutgeneratingfunctionology | cutgeneratingfunctionology-master/cutgeneratingfunctionology/spam/basic_semialgebraic_linear_system.py | r"""
Basic polyhedral semialgebraic sets represented as linear systems
"""
from __future__ import division, print_function, absolute_import
from cutgeneratingfunctionology.spam.basic_semialgebraic import BasicSemialgebraicSet_base, BasicSemialgebraicSet_polyhedral
from sage.rings.polynomial.polynomial_ring_constructo... | 18,943 | 44.104762 | 208 | py |
cutgeneratingfunctionology | cutgeneratingfunctionology-master/cutgeneratingfunctionology/spam/big_cells_impl.py | r"""
Utilities for programming with big cells -- implementation.
"""
from __future__ import division, print_function, absolute_import
trivial_parametric_real_field = None
# should export from a different module
def _common_parametric_real_field(iterable, key=None):
"""
EXAMPLES::
sage: from cutgener... | 20,470 | 42.929185 | 161 | py |
cutgeneratingfunctionology | cutgeneratingfunctionology-master/cutgeneratingfunctionology/spam/semialgebraic_maple.py | r"""
Basic semialgebraic sets using the Maple interface
"""
from __future__ import division, print_function, absolute_import
from cutgeneratingfunctionology.spam.basic_semialgebraic import BasicSemialgebraicSet_base
class BasicSemialgebraicSet_maple(BasicSemialgebraicSet_base):
"""
Basic semialgebraic set u... | 1,351 | 37.628571 | 127 | py |
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