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
value |
|---|---|---|---|---|---|---|
SLIT | SLIT-master/SLIT/Lens.py | import numpy as np
import matplotlib.pyplot as plt
import pyfits as pf
import scipy.signal as scp
import warnings
warnings.simplefilter("ignore")
#Tool box for lensing
def SIS(x0,y0,n1,n2,Re):
kappa = np.zeros((n1,n2))
x,y = np.where(kappa == 0)
count = 0
for i in x:
kappa[x[count],y[count]] =... | 6,888 | 28.440171 | 123 | py |
SLIT | SLIT-master/SLIT/__init__.py | from Solve import *
import Lens
import wave_transform
import tools
| 67 | 12.6 | 21 | py |
SLIT | SLIT-master/Examples/Test_SLIT_forUsers.py | import pyfits as pf
import matplotlib.pyplot as plt
import numpy as np
import matplotlib.cm as cm
from scipy import signal as scp
import SLIT
import time
from scipy import signal as scp
import warnings
warnings.simplefilter("ignore")
#Example of a run of the SLIT algorithm on simulated images.
#Here the first part ... | 3,928 | 37.145631 | 150 | py |
SLIT | SLIT-master/Examples/Test_SLIT_MCA.py | from SLIT import Lens
import pyfits as pf
import matplotlib.pyplot as plt
import numpy as np
import matplotlib.cm as cm
from scipy import signal as scp
import SLIT as slit
import time
from scipy import signal as scp
import warnings
warnings.simplefilter("ignore")
#Example of a run of the SLIT_MCA algorithm on simulat... | 6,169 | 29.544554 | 133 | py |
SLIT | SLIT-master/Examples/Test_SLIT.py | import pyfits as pf
import matplotlib.pyplot as plt
import numpy as np
import matplotlib.cm as cm
import SLIT
import time
from scipy import signal as scp
import warnings
warnings.simplefilter("ignore")
#Example of a run of the SLIT algorithm on simulated images.
#Here the first part of the file shows how simulatio... | 4,935 | 31.473684 | 130 | py |
SLIT | SLIT-master/Examples/Test_SLIT_HR.py | import pyfits as pf
import matplotlib.pyplot as plt
import numpy as np
import matplotlib.cm as cm
from scipy import signal as scp
import SLIT
import time
from scipy import signal as scp
import warnings
warnings.simplefilter("ignore")
#Example of a run of the SLIT algorithm on simulated images.
#Here the first part ... | 4,965 | 31.457516 | 130 | py |
SLIT | SLIT-master/Examples/Test_sparsity.py | from SLIT import Lens
import pyfits as pf
import matplotlib.pyplot as plt
import numpy as np
import matplotlib.cm as cm
from scipy import signal as scp
from SLIT import wave_transform as mw
import time
from scipy import signal as scp
import SLIT as slit
from mpl_toolkits.axes_grid1.inset_locator import zoomed_inset_ax... | 4,021 | 33.672414 | 132 | py |
SLIT | SLIT-master/build/lib/SLIT/Solve.py | #from __future__ import division
import wave_transform as mw
import numpy as np
import matplotlib.pyplot as plt
import pyfits as pf
import matplotlib.cm as cm
from scipy import signal as scp
import scipy.ndimage.filters as med
import MuSCADeT as wine
from numpy import linalg as LA
import multiprocess as mtp
from patho... | 22,735 | 31.713669 | 131 | py |
SLIT | SLIT-master/build/lib/SLIT/wave_transform.py | import numpy as np
import scipy.signal as cp
import matplotlib.pyplot as plt
import scipy.ndimage.filters as sc
def symmetrise(img, size):
n3, n4 = np.shape(img)
n1,n2 = size
img[:(n3-n1)/2, :] = np.flipud(img[(n3-n1)/2:(n3-n1),:])
img[:,:(n4-n2)/2] = np.fliplr(img[:,(n4-n2)/2:(n4-n2)])
img[(n3+n... | 3,715 | 25.169014 | 81 | py |
SLIT | SLIT-master/build/lib/SLIT/tools.py | import numpy as np
import matplotlib.pyplot as plt
import pyfits as pf
import scipy.ndimage.filters as sc
import scipy.ndimage.filters as med
import scipy.signal as cp
def MAD(x,n=3):
##DESCRIPTION:
## Estimates the noise standard deviation from Median Absolute Deviation
##
##INPUTS:
## -x: a 2D ... | 7,049 | 26.539063 | 131 | py |
SLIT | SLIT-master/build/lib/SLIT/Lens.py | import numpy as np
import matplotlib.pyplot as plt
import pyfits as pf
import scipy.signal as scp
import warnings
warnings.simplefilter("ignore")
#Tool box for lensing
def SIS(x0,y0,n1,n2,Re):
kappa = np.zeros((n1,n2))
x,y = np.where(kappa == 0)
count = 0
for i in x:
kappa[x[count],y[count]] =... | 7,136 | 28.987395 | 123 | py |
SLIT | SLIT-master/build/lib/SLIT/__init__.py | from Solve import *
import Lens
import wave_transform
import tools
| 67 | 12.6 | 21 | py |
eEVM | eEVM-main/3rdparty/intx/test/fuzzer/decode.py | #!/usr/bin/env python3
import os
import sys
ops_filter = ()
ops = ('/', '*', '<<', '>>', '+', '-', 's/')
def err(*args, **kwargs):
print(*args, file=sys.stderr, **kwargs)
def decode_file(file):
with open(file, 'rb') as f:
print("Decoding {}".format(file))
decode_data(f.read())
def decode... | 1,690 | 22.486111 | 61 | py |
frodo | frodo-main/setup.py | from setuptools import setup, find_packages
install_requires=[
'flask',
'rdflib',
'nltk',
'shortuuid',
'fredclient @ git+https://github.com/anuzzolese/fredclient'
]
setup(name='frodo', version='1.0.0',
packages=find_packages(), install_requires=install_requires)
| 284 | 20.923077 | 64 | py |
frodo | frodo-main/demo/frodo_webapp.py | from flask import Flask, Response, render_template, request
from frodo import Frodo
import shortuuid
import webapp_conf
myapp = Flask(__name__)
@myapp.route("/")
def index():
text = request.args.get("text")
ontology_id = request.args.get("ontology-id")
if text:
print(text)
#namespace = ''.... | 972 | 28.484848 | 77 | py |
frodo | frodo-main/demo/webapp_conf.py | import shortuuid
FRED_ENDPOINT = 'http://wit.istc.cnr.it/stlab-tools/fred'
FRED_KEY = ''
NS = 'https://w3id.org/stlab/ontology/'
PORT = 5555
BASEPATH = './'
shortuuid.set_alphabet("0123456789abcdefghijkmnopqrstuvwxyz")
| 220 | 23.555556 | 61 | py |
frodo | frodo-main/demo/demo.py | from frodo import Frodo
import shortuuid
import webapp_conf
#shortuuid.set_alphabet("0123456789abcdefghijkmnopqrstuvwxyz")
sentence = 'What cars cost more than $50,000?'
namespace = ''.join([webapp_conf.NS, shortuuid.uuid(sentence)[:8], '/'])
frodo = Frodo(
namespace=namespace,
fred_uri=webapp_conf.FRED_EN... | 609 | 26.727273 | 84 | py |
frodo | frodo-main/frodo/taxonomic_queries.py | update = {
'alias_instance': 'PREFIX dul: <http://www.ontologydesignpatterns.org/ont/dul/DUL.owl#>\nPREFIX fred: <http://www.ontologydesignpatterns.org/ont/fred/>\n\nDELETE {\n ?alias_instance a fred:Alias;\n fred:aliasOf ?instance.\n\n ?s ?p ?o. # remove all statements about the fred:Alias class\n}\nI... | 16,514 | 916.5 | 2,617 | py |
frodo | frodo-main/frodo/frodo.py | import os
import re
from fredclient import FREDClient, FREDParameters, FREDDefaults
import nltk
from nltk.stem import WordNetLemmatizer
from rdflib import RDFS, RDF, OWL, XSD, URIRef, Literal, BNode, Graph, Namespace
from rdflib.paths import evalPath, OneOrMore
from abc import ABC, abstractmethod
from typing import L... | 22,735 | 37.601019 | 161 | py |
frodo | frodo-main/frodo/__init__.py | from frodo.frodo import *
| 26 | 12.5 | 25 | py |
minkasi | minkasi-master/setup.py | from setuptools import Extension, setup
import ctypes
import subprocess
import os
try:
mylib=ctypes.cdll.LoadLibrary("libminkasi.so")
except OSError:
os.environ["prefix"] = "minkasi"
subprocess.check_call(["make", "-e", "libminkasi"])
try:
mylib=ctypes.cdll.LoadLibrary("libmkfftw.so")
except OSError:
... | 642 | 20.433333 | 55 | py |
minkasi | minkasi-master/examples/minkasi_mpi_example.py | import numpy
from matplotlib import pyplot as plt
import minkasi,pyfftw
import time
import glob
reload(minkasi)
plt.ion()
#find tod files we want to map
dir='../data/m0717_raw/'
tod_names=glob.glob(dir+'*.fits')
#if running MPI, you would want to split up files between processes
#one easy way is to say to this:
tod... | 3,708 | 34.32381 | 95 | py |
minkasi | minkasi-master/examples/tsBowl_fitter.py | import minkasi
import numpy as np
from matplotlib import pyplot as plt
import time
fname = '/scratch/r/rbond/jorlo/MS0735/TS_EaCMS0f0_51_5_Oct_2021/Signal_TOD-AGBT19A_092_08-s26.fits'
dat = minkasi.read_tod_from_fits(fname)
minkasi.truncate_tod(dat)
# figure out a guess at common mode and (assumed) linear detector d... | 1,384 | 20.984127 | 100 | py |
minkasi | minkasi-master/examples/zw3146_tsBowl.py | #This is a template script to show how to fit multi-component models
#directly to timestreams. The initial part (where the TODs, noise model etc.)
#are set up is the same as general mapping scripts, although we don't
#need to bother with setting a map/pixellization in general (although if
#your timestream model used a... | 4,147 | 30.907692 | 127 | py |
minkasi | minkasi-master/examples/fit_zw3146_multi_component.py | #This is a template script to show how to fit multi-component models
#directly to timestreams. The initial part (where the TODs, noise model etc.)
#are set up is the same as general mapping scripts, although we don't
#need to bother with setting a map/pixellization in general (although if
#your timestream model used a... | 4,311 | 38.2 | 114 | py |
minkasi | minkasi-master/examples/minkasi_map_moo_python3.py | import numpy
import numpy as np
from matplotlib import pyplot as plt
import minkasi
import time
import glob
from importlib import reload
reload(minkasi)
plt.ion()
def smooth_map(map,npix=3):
n=map.shape[0]
m=map.shape[1]
v1=np.fft.fftfreq(n)*n
v2=np.fft.fftfreq(m)*m
rmat=np.outer(v1,np.ones(m))**2... | 5,186 | 30.628049 | 122 | py |
minkasi | minkasi-master/examples/tsBowl_map_maker.py | import minkasi
import numpy as np
from matplotlib import pyplot as plt
import glob
import time
import minkasi_jax.presets_by_source as pbs
import os
from astropy.coordinates import Angle
from astropy import units as u
dir = '/scratch/r/rbond/jorlo/MS0735//TS_EaCMS0f0_51_5_Oct_2021/'
tod_names=glob.glob(dir+'Sig*.fits'... | 6,463 | 28.788018 | 146 | py |
minkasi | minkasi-master/minkasi/code_scrapyard.py | def __run_pcg_old(b,x0,tods,mapset,precon):
Ax=mapset.dot(x0)
r=b-Ax
z=precon*r
p=z.copy()
k=0
zr=r.dot(z)
x=x0.copy()
for iter in range(25):
print(iter,zr)
Ap=mapset.dot(p)
pAp=p.dot(Ap)
alpha=zr/pAp
x_new=x+p*alpha
r_new=r-Ap*alpha
... | 14,023 | 31.843091 | 175 | py |
minkasi | minkasi-master/minkasi/minkasi_nb.py | import numpy as np
import numba as nb
@nb.njit(parallel=True)
def map2tod_destriped(mat,pars,lims,do_add=True):
ndet=mat.shape[0]
nseg=len(lims)-1
for seg in nb.prange(nseg):
for det in range(ndet):
if do_add:
for i in range(lims[seg],lims[seg+1]):
ma... | 9,537 | 31.114478 | 138 | py |
minkasi | minkasi-master/minkasi/minkasi.py | import os
import numpy as np
import ctypes
import time
from . import mkfftw
#import pyfits
from astropy.io import fits as pyfits
import astropy
from astropy import wcs
from astropy.io import fits
from astropy.cosmology import WMAP9 as cosmo #choose your cosmology here
import scipy
import copy
import sys
from numba impo... | 187,717 | 34.674268 | 578 | py |
minkasi | minkasi-master/minkasi/mkfftw.py | import os
import numpy
import ctypes
import time
try:
mylib=ctypes.cdll.LoadLibrary("libmkfftw.so")
except OSError:
mylib=ctypes.cdll.LoadLibrary(os.path.join(os.path.dirname(os.path.abspath(__file__)), "libmkfftw.so"))
many_fft_r2c_1d_c=mylib.many_fft_r2c_1d
many_fft_r2c_1d_c.argtypes=[ctypes.c_void_p,ctype... | 6,217 | 31.385417 | 113 | py |
minkasi | minkasi-master/minkasi/pyregion_tools.py | import pyregion
from astropy.io import fits
from astropy.wcs import WCS
import numpy as np
import copy
__all__ = ["region_binner",
"bootstrap"]
def bootstrap(data, n = 10000):
"""
Bootstraps data.
Given an input data vector, the bootstrapping proceedure selects a random subsample of data, w... | 7,945 | 36.658768 | 140 | py |
minkasi | minkasi-master/minkasi/__init__.py | from .minkasi import *
| 23 | 11 | 22 | py |
minkasi | minkasi-master/minkasi/zernike.py | import numpy
def zernike_column(m,nmax,rmat):
"""Generate the radial part of zernike polynomials for all n from m up to nmax"""
if ((m-nmax)%2!=0):
#print 'm an n must have same parity'
#return None
#if parity is wrong, then drop nmax by one. makes external loop to generate all zns mu... | 1,894 | 20.534091 | 105 | py |
tbsm | tbsm-main/tbsm_pytorch.py | # Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
### import packages ###
from __future__ import absolute_import, division, print_function, unicode_literals
# miscellaneous
import time
import... | 35,338 | 36.917382 | 148 | py |
tbsm | tbsm-main/tbsm_synthetic.py | # Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
# miscellaneous
from os import path
import sys
# numpy and scikit-learn
import numpy as np
from sklearn.metrics import roc_auc_score
# pyto... | 11,465 | 36.106796 | 88 | py |
tbsm | tbsm-main/tbsm_data_pytorch.py | # Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
### import packages ###
from __future__ import absolute_import, division, print_function, unicode_literals
# miscellaneous
from os import pat... | 16,772 | 36.52349 | 88 | py |
tbsm | tbsm-main/tools/taobao_prepare.py | # Copyright (c) 2019 UIC-Paper
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
#
# Source: https://github.com/UIC-Paper/MIMN
import cPickle as pkl
import pandas as pd
import random
import numpy as np
RAW_DATA_FILE = './data/taobao_data/User... | 8,508 | 36.484581 | 204 | py |
rllab | rllab-master/setup.py | # setup.py
from setuptools import setup,find_packages
setup(
name='rllab',
packages=[package for package in find_packages()
if package.startswith('rllab')],
version='0.1.0',
)
| 205 | 19.6 | 52 | py |
rllab | rllab-master/examples/trpo_cartpole_recurrent.py | from rllab.algos.trpo import TRPO
from rllab.baselines.linear_feature_baseline import LinearFeatureBaseline
from rllab.envs.box2d.cartpole_env import CartpoleEnv
from rllab.envs.normalized_env import normalize
from rllab.policies.gaussian_gru_policy import GaussianGRUPolicy
from rllab.optimizers.conjugate_gradient_opti... | 1,003 | 25.421053 | 105 | py |
rllab | rllab-master/examples/cluster_gym_mujoco_demo.py | from rllab.baselines.linear_feature_baseline import LinearFeatureBaseline
from rllab.envs.normalized_env import normalize
from sandbox.rocky.tf.envs.base import TfEnv
from sandbox.rocky.tf.policies.gaussian_mlp_policy import GaussianMLPPolicy
from sandbox.rocky.tf.algos.trpo import TRPO
from rllab.misc.instrument impor... | 1,919 | 25.30137 | 93 | py |
rllab | rllab-master/examples/trpo_cartpole.py | from rllab.algos.trpo import TRPO
from rllab.baselines.linear_feature_baseline import LinearFeatureBaseline
from rllab.envs.box2d.cartpole_env import CartpoleEnv
from rllab.envs.normalized_env import normalize
from rllab.policies.gaussian_mlp_policy import GaussianMLPPolicy
env = normalize(CartpoleEnv())
policy = Gau... | 713 | 24.5 | 89 | py |
rllab | rllab-master/examples/trpo_cartpole_pickled.py | from rllab.algos.trpo import TRPO
from rllab.baselines.linear_feature_baseline import LinearFeatureBaseline
from rllab.envs.box2d.cartpole_env import CartpoleEnv
from rllab.envs.normalized_env import normalize
from rllab.misc.instrument import run_experiment_lite
from rllab.policies.gaussian_mlp_policy import GaussianM... | 1,287 | 27 | 93 | py |
rllab | rllab-master/examples/vpg_2.py |
from rllab.envs.box2d.cartpole_env import CartpoleEnv
from rllab.baselines.linear_feature_baseline import LinearFeatureBaseline
from rllab.policies.gaussian_mlp_policy import GaussianMLPPolicy
from rllab.envs.normalized_env import normalize
import numpy as np
import theano
import theano.tensor as TT
from lasagne.updat... | 5,669 | 40.086957 | 119 | py |
rllab | rllab-master/examples/cluster_demo.py | from rllab.algos.trpo import TRPO
from rllab.baselines.linear_feature_baseline import LinearFeatureBaseline
from rllab.envs.box2d.cartpole_env import CartpoleEnv
from rllab.envs.normalized_env import normalize
from rllab.misc.instrument import stub, run_experiment_lite
from rllab.policies.gaussian_mlp_policy import Gau... | 1,682 | 29.6 | 93 | py |
rllab | rllab-master/examples/trpo_gym_cartpole.py | from rllab.algos.trpo import TRPO
from rllab.baselines.linear_feature_baseline import LinearFeatureBaseline
from rllab.envs.gym_env import GymEnv
from rllab.envs.normalized_env import normalize
from rllab.misc.instrument import run_experiment_lite
from rllab.policies.categorical_mlp_policy import CategoricalMLPPolicy
... | 1,597 | 30.96 | 93 | py |
rllab | rllab-master/examples/trpo_gym_pendulum.py | from rllab.algos.trpo import TRPO
from rllab.baselines.linear_feature_baseline import LinearFeatureBaseline
from rllab.envs.gym_env import GymEnv
from rllab.envs.normalized_env import normalize
from rllab.misc.instrument import run_experiment_lite
from rllab.policies.gaussian_mlp_policy import GaussianMLPPolicy
def r... | 1,582 | 31.306122 | 98 | py |
rllab | rllab-master/examples/trpo_point.py | from rllab.algos.trpo import TRPO
from rllab.baselines.linear_feature_baseline import LinearFeatureBaseline
from examples.point_env import PointEnv
from rllab.envs.normalized_env import normalize
from rllab.policies.gaussian_mlp_policy import GaussianMLPPolicy
env = normalize(PointEnv())
policy = GaussianMLPPolicy(
... | 478 | 25.611111 | 73 | py |
rllab | rllab-master/examples/ddpg_cartpole.py | from rllab.algos.ddpg import DDPG
from rllab.envs.box2d.cartpole_env import CartpoleEnv
from rllab.envs.normalized_env import normalize
from rllab.misc.instrument import run_experiment_lite
from rllab.exploration_strategies.ou_strategy import OUStrategy
from rllab.policies.deterministic_mlp_policy import DeterministicM... | 1,538 | 28.037736 | 93 | py |
rllab | rllab-master/examples/trpo_swimmer.py | from rllab.algos.trpo import TRPO
from rllab.baselines.linear_feature_baseline import LinearFeatureBaseline
from rllab.envs.mujoco.swimmer_env import SwimmerEnv
from rllab.envs.normalized_env import normalize
from rllab.policies.gaussian_mlp_policy import GaussianMLPPolicy
env = normalize(SwimmerEnv())
policy = Gauss... | 711 | 24.428571 | 89 | py |
rllab | rllab-master/examples/point_env.py | from rllab.envs.base import Env
from rllab.spaces import Box
from rllab.envs.base import Step
import numpy as np
class PointEnv(Env):
@property
def observation_space(self):
return Box(low=-np.inf, high=np.inf, shape=(2,))
@property
def action_space(self):
return Box(low=-0.1, high=0.1... | 866 | 26.967742 | 75 | py |
rllab | rllab-master/examples/__init__.py | 0 | 0 | 0 | py | |
rllab | rllab-master/examples/nop_cartpole.py | from rllab.algos.nop import NOP
from rllab.baselines.zero_baseline import ZeroBaseline
from rllab.envs.box2d.cartpole_env import CartpoleEnv
from rllab.envs.normalized_env import normalize
from rllab.policies.uniform_control_policy import UniformControlPolicy
env = normalize(CartpoleEnv())
policy = UniformControlPoli... | 665 | 23.666667 | 89 | py |
rllab | rllab-master/examples/vpg_1.py |
from rllab.envs.box2d.cartpole_env import CartpoleEnv
from rllab.policies.gaussian_mlp_policy import GaussianMLPPolicy
from rllab.envs.normalized_env import normalize
import numpy as np
import theano
import theano.tensor as TT
from lasagne.updates import adam
# normalize() makes sure that the actions for the environm... | 5,002 | 40.347107 | 119 | py |
rllab | rllab-master/examples/trpo_gym_tf_cartpole.py | from rllab.baselines.linear_feature_baseline import LinearFeatureBaseline
from rllab.envs.gym_env import GymEnv
from rllab.envs.normalized_env import normalize
from rllab.misc.instrument import stub, run_experiment_lite
from sandbox.rocky.tf.envs.base import TfEnv
from sandbox.rocky.tf.policies.categorical_mlp_policy ... | 1,194 | 26.790698 | 91 | py |
rllab | rllab-master/sandbox/__init__.py | 0 | 0 | 0 | py | |
rllab | rllab-master/sandbox/rocky/__init__.py | 0 | 0 | 0 | py | |
rllab | rllab-master/sandbox/rocky/tf/__init__.py | 0 | 0 | 0 | py | |
rllab | rllab-master/sandbox/rocky/tf/launchers/vpg_cartpole.py | from sandbox.rocky.tf.algos.vpg import VPG
from rllab.baselines.linear_feature_baseline import LinearFeatureBaseline
from rllab.envs.box2d.cartpole_env import CartpoleEnv
from rllab.envs.normalized_env import normalize
from sandbox.rocky.tf.policies.gaussian_mlp_policy import GaussianMLPPolicy
from sandbox.rocky.tf.env... | 949 | 26.142857 | 89 | py |
rllab | rllab-master/sandbox/rocky/tf/launchers/trpo_cartpole_recurrent.py | from sandbox.rocky.tf.algos.trpo import TRPO
from rllab.baselines.linear_feature_baseline import LinearFeatureBaseline
from rllab.envs.box2d.cartpole_env import CartpoleEnv
from rllab.envs.normalized_env import normalize
from sandbox.rocky.tf.policies.gaussian_gru_policy import GaussianGRUPolicy
from sandbox.rocky.tf.p... | 1,148 | 31.828571 | 116 | py |
rllab | rllab-master/sandbox/rocky/tf/launchers/trpo_cartpole.py | from sandbox.rocky.tf.algos.trpo import TRPO
from rllab.baselines.linear_feature_baseline import LinearFeatureBaseline
from rllab.envs.box2d.cartpole_env import CartpoleEnv
from rllab.envs.normalized_env import normalize
from sandbox.rocky.tf.optimizers.conjugate_gradient_optimizer import ConjugateGradientOptimizer
fro... | 1,144 | 31.714286 | 95 | py |
rllab | rllab-master/sandbox/rocky/tf/launchers/__init__.py | 1 | 0 | 0 | py | |
rllab | rllab-master/sandbox/rocky/tf/core/network.py | import sandbox.rocky.tf.core.layers as L
import tensorflow as tf
import numpy as np
import itertools
from rllab.core.serializable import Serializable
from sandbox.rocky.tf.core.parameterized import Parameterized
from sandbox.rocky.tf.core.layers_powered import LayersPowered
class MLP(LayersPowered, Serializable):
... | 19,707 | 35.837383 | 155 | py |
rllab | rllab-master/sandbox/rocky/tf/core/layers.py | # -*- coding: utf-8 -*-
import functools
import numpy as np
import math
import tensorflow as tf
from tensorflow.python.training import moving_averages
from collections import OrderedDict
from collections import deque
from itertools import chain
from inspect import getargspec
from difflib import get_close_matches
from w... | 76,922 | 40.557536 | 120 | py |
rllab | rllab-master/sandbox/rocky/tf/core/layers_powered.py | from sandbox.rocky.tf.core.parameterized import Parameterized
import sandbox.rocky.tf.core.layers as L
import itertools
class LayersPowered(Parameterized):
def __init__(self, output_layers, input_layers=None):
self._output_layers = output_layers
self._input_layers = input_layers
Parameter... | 592 | 31.944444 | 89 | py |
rllab | rllab-master/sandbox/rocky/tf/core/__init__.py | 1 | 0 | 0 | py | |
rllab | rllab-master/sandbox/rocky/tf/core/parameterized.py | from contextlib import contextmanager
from rllab.core.serializable import Serializable
from rllab.misc.tensor_utils import flatten_tensors, unflatten_tensors
import tensorflow as tf
load_params = True
@contextmanager
def suppress_params_loading():
global load_params
load_params = False
yield
load_pa... | 4,226 | 37.081081 | 98 | py |
rllab | rllab-master/sandbox/rocky/tf/envs/parallel_vec_env_executor.py |
import numpy as np
import pickle as pickle
from sandbox.rocky.tf.misc import tensor_utils
from rllab.misc import logger
from rllab.sampler.stateful_pool import singleton_pool
import uuid
def worker_init_envs(G, alloc, scope, env):
logger.log("initializing environment on worker %d" % G.worker_id)
if not has... | 6,057 | 33.225989 | 119 | py |
rllab | rllab-master/sandbox/rocky/tf/envs/base.py | from rllab.envs.proxy_env import ProxyEnv
from rllab.envs.base import EnvSpec
from rllab.spaces.box import Box as TheanoBox
from rllab.spaces.discrete import Discrete as TheanoDiscrete
from rllab.spaces.product import Product as TheanoProduct
from sandbox.rocky.tf.spaces.discrete import Discrete
from sandbox.rocky.tf.s... | 2,330 | 28.506329 | 106 | py |
rllab | rllab-master/sandbox/rocky/tf/envs/vec_env_executor.py |
import numpy as np
import pickle as pickle
from sandbox.rocky.tf.misc import tensor_utils
class VecEnvExecutor(object):
def __init__(self, envs, max_path_length):
self.envs = envs
self._action_space = envs[0].action_space
self._observation_space = envs[0].observation_space
self.t... | 1,412 | 27.836735 | 82 | py |
rllab | rllab-master/sandbox/rocky/tf/envs/__init__.py | 1 | 0 | 0 | py | |
rllab | rllab-master/sandbox/rocky/tf/distributions/recurrent_diagonal_gaussian.py |
from sandbox.rocky.tf.distributions.diagonal_gaussian import DiagonalGaussian
RecurrentDiagonalGaussian = DiagonalGaussian
| 127 | 17.285714 | 77 | py |
rllab | rllab-master/sandbox/rocky/tf/distributions/base.py |
class Distribution(object):
@property
def dim(self):
raise NotImplementedError
def kl_sym(self, old_dist_info_vars, new_dist_info_vars):
"""
Compute the symbolic KL divergence of two distributions
"""
raise NotImplementedError
def kl(self, old_dist_info, new... | 982 | 22.97561 | 82 | py |
rllab | rllab-master/sandbox/rocky/tf/distributions/categorical.py | import numpy as np
from .base import Distribution
import tensorflow as tf
from sandbox.rocky.tf.misc import tensor_utils
TINY = 1e-8
def from_onehot(x_var):
ret = np.zeros((len(x_var),), 'int32')
nonzero_n, nonzero_a = np.nonzero(x_var)
ret[nonzero_n] = nonzero_a
return ret
class Categorical(Distri... | 3,514 | 32.47619 | 90 | py |
rllab | rllab-master/sandbox/rocky/tf/distributions/recurrent_categorical.py | import tensorflow as tf
import numpy as np
from sandbox.rocky.tf.distributions.categorical import Categorical
from sandbox.rocky.tf.distributions.base import Distribution
TINY = 1e-8
class RecurrentCategorical(Distribution):
def __init__(self, dim):
self._cat = Categorical(dim)
self._dim = dim
... | 2,923 | 33 | 87 | py |
rllab | rllab-master/sandbox/rocky/tf/distributions/__init__.py | 1 | 0 | 0 | py | |
rllab | rllab-master/sandbox/rocky/tf/distributions/diagonal_gaussian.py |
import tensorflow as tf
import numpy as np
from sandbox.rocky.tf.distributions.base import Distribution
class DiagonalGaussian(Distribution):
def __init__(self, dim):
self._dim = dim
@property
def dim(self):
return self._dim
def kl(self, old_dist_info, new_dist_info):
old_... | 3,627 | 36.020408 | 84 | py |
rllab | rllab-master/sandbox/rocky/tf/distributions/bernoulli.py |
from .base import Distribution
import tensorflow as tf
import numpy as np
TINY = 1e-8
class Bernoulli(Distribution):
def __init__(self, dim):
self._dim = dim
@property
def dim(self):
return self._dim
def kl_sym(self, old_dist_info_vars, new_dist_info_vars):
old_p = old_dis... | 2,050 | 33.183333 | 110 | py |
rllab | rllab-master/sandbox/rocky/tf/policies/base.py |
from sandbox.rocky.tf.core.parameterized import Parameterized
class Policy(Parameterized):
def __init__(self, env_spec):
Parameterized.__init__(self)
self._env_spec = env_spec
# Should be implemented by all policies
def get_action(self, observation):
raise NotImplementedError
... | 2,755 | 24.757009 | 117 | py |
rllab | rllab-master/sandbox/rocky/tf/policies/uniform_control_policy.py | from sandbox.rocky.tf.policies.base import Policy
from rllab.core.serializable import Serializable
class UniformControlPolicy(Policy, Serializable):
def __init__(
self,
env_spec,
):
Serializable.quick_init(self, locals())
super(UniformControlPolicy, self).__init__(env_s... | 658 | 25.36 | 69 | py |
rllab | rllab-master/sandbox/rocky/tf/policies/categorical_gru_policy.py | import numpy as np
import sandbox.rocky.tf.core.layers as L
import tensorflow as tf
from sandbox.rocky.tf.core.layers_powered import LayersPowered
from sandbox.rocky.tf.core.network import GRUNetwork, MLP
from sandbox.rocky.tf.distributions.recurrent_categorical import RecurrentCategorical
from sandbox.rocky.tf.misc im... | 7,649 | 36.317073 | 105 | py |
rllab | rllab-master/sandbox/rocky/tf/policies/categorical_mlp_policy.py | from sandbox.rocky.tf.core.layers_powered import LayersPowered
import sandbox.rocky.tf.core.layers as L
from sandbox.rocky.tf.core.network import MLP
from rllab.core.serializable import Serializable
from sandbox.rocky.tf.distributions.categorical import Categorical
from sandbox.rocky.tf.policies.base import StochasticP... | 3,395 | 36.733333 | 97 | py |
rllab | rllab-master/sandbox/rocky/tf/policies/categorical_lstm_policy.py | import numpy as np
import sandbox.rocky.tf.core.layers as L
import tensorflow as tf
from sandbox.rocky.tf.core.layers_powered import LayersPowered
from sandbox.rocky.tf.core.network import LSTMNetwork, MLP
from sandbox.rocky.tf.distributions.recurrent_categorical import RecurrentCategorical
from sandbox.rocky.tf.misc i... | 8,307 | 37.110092 | 105 | py |
rllab | rllab-master/sandbox/rocky/tf/policies/gaussian_mlp_policy.py | import numpy as np
from sandbox.rocky.tf.core.layers_powered import LayersPowered
import sandbox.rocky.tf.core.layers as L
from sandbox.rocky.tf.core.network import MLP
from sandbox.rocky.tf.spaces.box import Box
from rllab.core.serializable import Serializable
from sandbox.rocky.tf.policies.base import StochasticPol... | 8,050 | 40.076531 | 117 | py |
rllab | rllab-master/sandbox/rocky/tf/policies/gaussian_lstm_policy.py | import numpy as np
import sandbox.rocky.tf.core.layers as L
import tensorflow as tf
from sandbox.rocky.tf.core.layers_powered import LayersPowered
from sandbox.rocky.tf.core.network import LSTMNetwork
from sandbox.rocky.tf.distributions.recurrent_diagonal_gaussian import RecurrentDiagonalGaussian
from sandbox.rocky.tf.... | 8,680 | 36.743478 | 105 | py |
rllab | rllab-master/sandbox/rocky/tf/policies/gaussian_gru_policy.py | import numpy as np
import sandbox.rocky.tf.core.layers as L
import tensorflow as tf
from sandbox.rocky.tf.core.layers_powered import LayersPowered
from sandbox.rocky.tf.core.network import GRUNetwork
from sandbox.rocky.tf.distributions.recurrent_diagonal_gaussian import RecurrentDiagonalGaussian
from sandbox.rocky.tf.m... | 8,416 | 36.243363 | 105 | py |
rllab | rllab-master/sandbox/rocky/tf/policies/__init__.py | 1 | 0 | 0 | py | |
rllab | rllab-master/sandbox/rocky/tf/policies/categorical_conv_policy.py | from sandbox.rocky.tf.core.layers_powered import LayersPowered
import sandbox.rocky.tf.core.layers as L
from sandbox.rocky.tf.core.network import ConvNetwork
from rllab.core.serializable import Serializable
from sandbox.rocky.tf.distributions.categorical import Categorical
from sandbox.rocky.tf.policies.base import Sto... | 3,662 | 36.762887 | 97 | py |
rllab | rllab-master/sandbox/rocky/tf/policies/deterministic_mlp_policy.py | from rllab.core.serializable import Serializable
from rllab.misc import ext
from rllab.misc.overrides import overrides
from sandbox.rocky.tf.core.layers_powered import LayersPowered
from sandbox.rocky.tf.core.network import MLP
from sandbox.rocky.tf.distributions.categorical import Categorical
from sandbox.rocky.tf.pol... | 2,794 | 36.266667 | 97 | py |
rllab | rllab-master/sandbox/rocky/tf/algos/npo.py |
from rllab.misc import ext
from rllab.misc.overrides import overrides
import rllab.misc.logger as logger
from sandbox.rocky.tf.optimizers.penalty_lbfgs_optimizer import PenaltyLbfgsOptimizer
from sandbox.rocky.tf.algos.batch_polopt import BatchPolopt
from sandbox.rocky.tf.misc import tensor_utils
import tensorflow a... | 4,814 | 35.755725 | 109 | py |
rllab | rllab-master/sandbox/rocky/tf/algos/vpg.py |
from rllab.misc import logger
from rllab.misc import ext
from rllab.misc.overrides import overrides
from sandbox.rocky.tf.algos.batch_polopt import BatchPolopt
from sandbox.rocky.tf.optimizers.first_order_optimizer import FirstOrderOptimizer
from sandbox.rocky.tf.misc import tensor_utils
from rllab.core.serializable ... | 4,899 | 34.766423 | 109 | py |
rllab | rllab-master/sandbox/rocky/tf/algos/trpo.py |
from sandbox.rocky.tf.algos.npo import NPO
from sandbox.rocky.tf.optimizers.conjugate_gradient_optimizer import ConjugateGradientOptimizer
class TRPO(NPO):
"""
Trust Region Policy Optimization
"""
def __init__(
self,
optimizer=None,
optimizer_args=None,
... | 578 | 25.318182 | 95 | py |
rllab | rllab-master/sandbox/rocky/tf/algos/__init__.py | 1 | 0 | 0 | py | |
rllab | rllab-master/sandbox/rocky/tf/algos/npg.py | 1 | 0 | 0 | py | |
rllab | rllab-master/sandbox/rocky/tf/algos/batch_polopt.py | import time
from rllab.algos.base import RLAlgorithm
import rllab.misc.logger as logger
from sandbox.rocky.tf.policies.base import Policy
import tensorflow as tf
from sandbox.rocky.tf.samplers.batch_sampler import BatchSampler
from sandbox.rocky.tf.samplers.vectorized_sampler import VectorizedSampler
from rllab.sampler... | 6,100 | 36.89441 | 111 | py |
rllab | rllab-master/sandbox/rocky/tf/spaces/box.py | from rllab.spaces.box import Box as TheanoBox
import tensorflow as tf
class Box(TheanoBox):
def new_tensor_variable(self, name, extra_dims, flatten=True):
if flatten:
return tf.placeholder(tf.float32, shape=[None] * extra_dims + [self.flat_dim], name=name)
return tf.placeholder(tf.floa... | 444 | 30.785714 | 101 | py |
rllab | rllab-master/sandbox/rocky/tf/spaces/discrete.py | from rllab.spaces.base import Space
import numpy as np
from rllab.misc import special
from rllab.misc import ext
import tensorflow as tf
class Discrete(Space):
"""
{0,1,...,n-1}
"""
def __init__(self, n):
self._n = n
@property
def n(self):
return self._n
def sample(self)... | 1,672 | 21.306667 | 101 | py |
rllab | rllab-master/sandbox/rocky/tf/spaces/__init__.py | from .product import Product
from .discrete import Discrete
from .box import Box
__all__ = ["Product", "Discrete", "Box"]
| 123 | 19.666667 | 40 | py |
rllab | rllab-master/sandbox/rocky/tf/spaces/product.py | from rllab.spaces.base import Space
import tensorflow as tf
import numpy as np
class Product(Space):
def __init__(self, *components):
if isinstance(components[0], (list, tuple)):
assert len(components) == 1
components = components[0]
self._components = tuple(components)
... | 2,360 | 33.217391 | 97 | py |
rllab | rllab-master/sandbox/rocky/tf/misc/tensor_utils.py | import tensorflow as tf
import numpy as np
def compile_function(inputs, outputs, log_name=None):
def run(*input_vals):
sess = tf.get_default_session()
return sess.run(outputs, feed_dict=dict(list(zip(inputs, input_vals))))
return run
def flatten_tensor_variables(ts):
return tf.concat(ax... | 3,406 | 27.157025 | 90 | py |
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