text stringlengths 0 1.25M | meta stringlengths 47 1.89k |
|---|---|
import numpy as np
import os
from matplotlib import pyplot as plt
file_lons = np.arange(-180, 180, 40)
file_lats = np.arange(90, -20, -50)
DATA_FOLDER = os.path.expanduser("~") + "/.srtm30/"
class Elevation():
def __init__(self, lonmin, lonmax, latmin, latmax):
self._extent = [lonmin, lonmax, latmin, l... | {"hexsha": "d40485dab70ff0f821a746d8767a8642bde13af4", "size": 3150, "ext": "py", "lang": "Python", "max_stars_repo_path": "srtm30_parser/elevation.py", "max_stars_repo_name": "marcwie/srtm30-parser", "max_stars_repo_head_hexsha": "2e3290118b2a91924915dae2e6491153a9c679ff", "max_stars_repo_licenses": ["MIT"], "max_star... |
change.base <- function(n, base)
{
ret <- integer(as.integer(logb(x=n, base=base))+1L)
for (i in 1:length(ret))
{
ret[i] <- n %% base
n <- n %/% base
}
return(ret)
}
sum.digits <- function(n, base=10)
{
if (base < 2)
stop("base must be at least 2")
return(sum(change.base(n=n, base=base)))... | {"hexsha": "759b872eeabd1e8541f56d40ab255ecf5de126f7", "size": 418, "ext": "r", "lang": "R", "max_stars_repo_path": "Task/Sum-digits-of-an-integer/R/sum-digits-of-an-integer.r", "max_stars_repo_name": "LaudateCorpus1/RosettaCodeData", "max_stars_repo_head_hexsha": "9ad63ea473a958506c041077f1d810c0c7c8c18d", "max_stars_... |
library(vegan)
library(labdsv)
data(varespec)
data(varechem)
vare.cca <- cca(varespec ~ Baresoil+Humdepth+pH+N+P+K+Ca+Mg+S+Al+Fe, data=varechem)
vare.cca
plot(vare.cca)
summary(vare.cca)
// See also http://ecology.msu.montana.edu/labdsv/R/labs/lab12/lab12.html | {"hexsha": "61a326bd64e0304e651ae5fc5b6a148cdb0dd699", "size": 263, "ext": "r", "lang": "R", "max_stars_repo_path": "examples/cca/cca.r", "max_stars_repo_name": "grovduck/pyimpute", "max_stars_repo_head_hexsha": "4ed2a6d4d2ff8258774906016ea690b16807f199", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": ... |
# global imports
import numpy as np
import scipy.special as scsp
import scipy.optimize as scop
# local imports
import helper as bhlp
"""
ref Helias14:
Helias, Tetzlaff, Diesmann (2014) The Correlation Structure of
Local Neuronal Networks Intrinsically Results from Recurrent Dynamics
PLoS Comput Biol 10(1): e1003428
D... | {"hexsha": "424e153907c161b91b02f6bb8d34e352c0762391", "size": 6477, "ext": "py", "lang": "Python", "max_stars_repo_path": "meanfield.py", "max_stars_repo_name": "jakobj/binary_network", "max_stars_repo_head_hexsha": "078851026721cd2571a777f054d2f1fea097a3ea", "max_stars_repo_licenses": ["BSD-2-Clause"], "max_stars_cou... |
import numpy as np
from scipy.integrate import cumtrapz
import sys
sys.path.insert(0, '../..')
import stellar_encounters
r = np.geomspace(1e-12,1e15,10000)
rho = stellar_encounters.postencounter_density_profile(r)
M = cumtrapz(4*np.pi*r**3 * rho,x=np.log(r),initial=0) + 2*np.pi*r[0]**2
np.savetxt('M.txt',np... | {"hexsha": "28dc75986920e8e8d087425aa901374673cc5e2d", "size": 368, "ext": "py", "lang": "Python", "max_stars_repo_path": "examples/pulsar-timing/generate_mass_profile.py", "max_stars_repo_name": "delos/microhalo-models", "max_stars_repo_head_hexsha": "aaebc1a1fffbff8c6fd561d9936229e637926f5b", "max_stars_repo_licenses... |
# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not u... | {"hexsha": "8625465dd2f5dac3d6d95786095e09a5d3893aee", "size": 4693, "ext": "py", "lang": "Python", "max_stars_repo_path": "tutorials/frontend/from_keras.py", "max_stars_repo_name": "intheworld/incubator-tvm", "max_stars_repo_head_hexsha": "c07aa37aeb602e1ade7e26061d0fd3e908dd3791", "max_stars_repo_licenses": ["Apache-... |
# encoding=utf8
# pylint: disable=mixed-indentation, trailing-whitespace, multiple-statements, attribute-defined-outside-init, logging-not-lazy, unused-argument, singleton-comparison, no-else-return, line-too-long, arguments-differ, no-self-use, superfluous-parens, redefined-builtin, bad-continuation, unused-variable
i... | {"hexsha": "a91037b6aef6fee0823f91a1321423c300b0d7ec", "size": 9881, "ext": "py", "lang": "Python", "max_stars_repo_path": "NiaPy/algorithms/basic/es.py", "max_stars_repo_name": "kivancguckiran/NiaPy", "max_stars_repo_head_hexsha": "06d3699c1466bfc54c4220a4aabe97cad50f75e4", "max_stars_repo_licenses": ["MIT"], "max_sta... |
################
#
# General Utility Functions for R scripts for FRAM Admin
#
# Nicholas Komick
# nicholas.komick@dfo-mpo.gc.ca
# May 22, 2014
# Coding Style: http://google-styleguide.googlecode.com/svn/trunk/google-r-style.html
#
#
################
###____ Constants Section ____####
#Generic constants used in other ... | {"hexsha": "470cdf63a22bc393d402a54dfdba4944ef78cc9a", "size": 11012, "ext": "r", "lang": "R", "max_stars_repo_path": "lib/Util.r", "max_stars_repo_name": "methiess/PSC-FRAM-Admin", "max_stars_repo_head_hexsha": "14309790ecb30118757dc890fcb5d6e5c85db3f8", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "ma... |
import logging
import sys
from sqlalchemy import create_engine
import pandas as pd
import numpy as np
from settings import ENGINE_STRING, QUALICHAIN_ENGINE_STRING
from utils import filter_extracted_skills, remove_dump_skills
logging.basicConfig(stream=sys.stdout, level=logging.INFO,
format="%(as... | {"hexsha": "decaf94fadea912caf3ef239e9982900a6432578", "size": 6026, "ext": "py", "lang": "Python", "max_stars_repo_path": "app/clients/postgress.py", "max_stars_repo_name": "QualiChain/course_recommendation", "max_stars_repo_head_hexsha": "998e4e4f93eea8a96b13dd4f4d5b957fb45eab3c", "max_stars_repo_licenses": ["MIT"], ... |
import pandas as pd
import numpy as np
import tensorflow as tf
from tensorflow import keras
from sqlalchemy import create_engine
engine = create_engine('mysql+pymysql://root:shero@localhost/sheroDB', echo=True)
def data_from_csv():
wti = pd.read_csv('data/WTI_20050630_20200417.csv')
wti_after_2015 = wti[wti[... | {"hexsha": "c338386a81d7b69ffdd7eaaebfaacf43911da892", "size": 3883, "ext": "py", "lang": "Python", "max_stars_repo_path": "database/price_pred_model.py", "max_stars_repo_name": "2020-SKKU-S-HERO/mobius_adaptation", "max_stars_repo_head_hexsha": "f1412a547a2bf402cb21bc16b97240acbaf5f082", "max_stars_repo_licenses": ["B... |
# Load Julia packages (libraries) needed for the snippets in chapter 0
using StatisticalRethinking, CmdStan
#gr(size=(600,600));
# CmdStan uses a tmp directory to store the output of cmdstan
ProjDir = rel_path("..", "scripts", "04")
cd(ProjDir)
# CmdStan uses a tmp directory to store the output of cmdstan
ProjDir... | {"hexsha": "502c0d89893d0ad1b862e2aa5af9bdabc61ae8f9", "size": 1500, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "scripts/04/clip-21-23.jl", "max_stars_repo_name": "UnofficialJuliaMirror/StatisticalRethinking.jl-2d09df54-9d0f-5258-8220-54c2a3d4fbee", "max_stars_repo_head_hexsha": "08ee7b4244edcb2c94f4410829372... |
"""
Author: Dr. John T. Hwang <hwangjt@umich.edu>
This package is distributed under New BSD license.
Base class for sampling algorithms.
"""
import numpy as np
from smt.utils.options_dictionary import OptionsDictionary
class SamplingMethod(object):
def __init__(self, **kwargs):
"""
Constructor ... | {"hexsha": "beb2b01ace8b1cb3b058c9d3c5d1f15d1c42d0cd", "size": 2521, "ext": "py", "lang": "Python", "max_stars_repo_path": "smt/sampling_methods/sampling_method.py", "max_stars_repo_name": "kodexp/smt", "max_stars_repo_head_hexsha": "cc390b795ea21eed66aae95218d5dfb67ed87a88", "max_stars_repo_licenses": ["BSD-3-Clause"]... |
[STATEMENT]
lemma at_within_self_singleton[simp]: "at i within {i} = bot"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. at i within {i} = bot
[PROOF STEP]
by (auto intro!: antisym filter_leI simp: eventually_at_filter) | {"llama_tokens": 90, "file": "Laplace_Transform_Piecewise_Continuous", "length": 1} |
# %% [markdown]
# # Fitting a Beta Distribution with Pyro
#
# Here we assume we are flipping a slightly biased coin.
# We think the probability of a heads is close to 0.5, but we are not sure.
# We want to fit a beta distribution to the random observed data.
#
# References:
# * [http://pyro.ai/examples/svi_part_i.ht... | {"hexsha": "9228caab6f5883b0bf9921c34c0499d23863c89f", "size": 6323, "ext": "py", "lang": "Python", "max_stars_repo_path": "FitDistWithPyro/fit_beta_distribution.py", "max_stars_repo_name": "stanton119/data-analysis", "max_stars_repo_head_hexsha": "b6fda815c6cc1798ba13a5d2680369b7e5dfcdf9", "max_stars_repo_licenses": [... |
import numpy as np
from data.batch_provider import BatchProvider
from data import qubiq_data_loader
class qubiq_data():
def __init__(self, exp_config):
data = qubiq_data_loader.load_and_process_data(
root=exp_config.data_root,
dataset=exp_config.dataset,
task=exp_conf... | {"hexsha": "8a13e6fed2e13015db1eb838d184e0d3ed9e952a", "size": 3245, "ext": "py", "lang": "Python", "max_stars_repo_path": "data/qubiq_data.py", "max_stars_repo_name": "WinstonHuTiger/PHiSeg-code", "max_stars_repo_head_hexsha": "11d61508e4bee320dbfcd533d355cae25a088117", "max_stars_repo_licenses": ["Apache-2.0"], "max_... |
"""
This is the dashboard of CEA
"""
from __future__ import division
from __future__ import print_function
import pandas as pd
from cea.plots.comparisons.old.primary_energy_intensity import primary_energy_intensity
import cea.config
import cea.inputlocator
from cea.utilities.dbf import dbf_to_dataframe
from cea.plots... | {"hexsha": "9a9231446737ae72e1e62c5f6730e819f9ee3760", "size": 22292, "ext": "py", "lang": "Python", "max_stars_repo_path": "cea/plots/comparisons/old/main.py", "max_stars_repo_name": "AlexJew/CityEnergyAnalyst", "max_stars_repo_head_hexsha": "6eb372c79e5100a2d0abce78561ae368fb409cd1", "max_stars_repo_licenses": ["MIT"... |
import torch
from torchvision import transforms
from torch.utils.data import Dataset
from functools import partial
from skimage.io import imread
from glob import glob
from skimage import exposure, img_as_float, util
from utils.augmentation import Augmentation, cropCenter, toGrayscale, cropCorner, cutout
import numpy ... | {"hexsha": "7929cc8a04e20f543344bd232b929e3089ffda27", "size": 4428, "ext": "py", "lang": "Python", "max_stars_repo_path": "datasets/csv_ua_dataset.py", "max_stars_repo_name": "system123/SOMatch", "max_stars_repo_head_hexsha": "6f10cf28f506998a5e430ccd3faab3076fe350d5", "max_stars_repo_licenses": ["MIT"], "max_stars_co... |
import random
import numpy as np
import matplotlib.pyplot as plt
import copy
from mpl_toolkits.mplot3d import Axes3D
from matplotlib import cm
from matplotlib import colors
##
counting = [0 for i in range(12)]
counting_temp = [0 for i in range(12)]
def refresh_counting(num):
global counting
counting[num] += 1... | {"hexsha": "20ea5ff848dec0c3770ec62f8b0ae2da0d91417a", "size": 11940, "ext": "py", "lang": "Python", "max_stars_repo_path": "Blackjack2.py", "max_stars_repo_name": "gusghrlrl101/BlackJack-AI", "max_stars_repo_head_hexsha": "b311e22d87af03d7df35232e995d235d508b319d", "max_stars_repo_licenses": ["MIT"], "max_stars_count"... |
import matplotlib.pyplot as plt
import numpy as np
def get_data(file_name, network_list=['MobileNet', 'SqueezeNet', 'DenseNet121', 'ResNet50']):
file_reader = open(file_name, 'r')
res_list = [[] for _ in network_list]
try:
text_lines = file_reader.readlines()
print(type(text_lines))
... | {"hexsha": "9d148bd08840d3453cd50a2f897aac376fa80a9f", "size": 1214, "ext": "py", "lang": "Python", "max_stars_repo_path": "python/data-visualization/boxplot/boxplot.py", "max_stars_repo_name": "lijiansong/lang", "max_stars_repo_head_hexsha": "e255709da2b12e09dea45f86d54f77a19b96f13b", "max_stars_repo_licenses": ["WTFP... |
module DDg_allDsDa
! contains functions that compute the ddg/dsigmadalpha matrices of the material model
! and one main (model-indepenent) function that calls all ddg/dsigmadalpha functions of the model
! and returns the function values as a matrix
! load additional modules
use constants
use material_info
use d... | {"hexsha": "75369af5ed158e8314ef67805be52be506611705", "size": 1941, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "return_mapping/model_components_all/DDg_allDsDa.f90", "max_stars_repo_name": "yuyong1990/TsaiWu-Fortran", "max_stars_repo_head_hexsha": "a111ca1717adfbbaf3e9e34f4189a441e16441b8", "max_stars_rep... |
```python
%matplotlib inline
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
import sklearn.linear_model as linear_model
np.random.seed(1337)
from timeit import timeit
import matplotlib
import matplotlib.pyplot as plt
```
### Well-conditioned linear regression
```python
%matplotlib inline
im... | {"hexsha": "bc27598e1319e87331ca60f858f744c79610f30c", "size": 123872, "ext": "ipynb", "lang": "Jupyter Notebook", "max_stars_repo_path": "schedule/images/Chapter 6.ipynb", "max_stars_repo_name": "akyrillidis/comp414-514", "max_stars_repo_head_hexsha": "ed1a58cda99cb4cb14b62276eebfb4082276e9f9", "max_stars_repo_license... |
# !/usr/bin/env python3
# -*- coding: utf-8 -*-
from __future__ import division
from __future__ import print_function
import tensorflow as tf
from NumPyNet.utils import to_categorical
from NumPyNet.utils import from_categorical
import numpy as np
import pytest
from hypothesis import strategies as st
from hypothesis... | {"hexsha": "51c471c91a670946580f9d73ee59cb7d20293798", "size": 1746, "ext": "py", "lang": "Python", "max_stars_repo_path": "testing/test_utils.py", "max_stars_repo_name": "Elisa-Raspanti/NumPyNet", "max_stars_repo_head_hexsha": "4e8d0d415275088f485457cdcf251d28a6826b0f", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
MODULE cbmz_mosaic_Model
!~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
! Completely defines the model cbmz_mosaic
! by using all the associated modules
!~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
USE cbmz_mosaic_Precision
USE cbmz_mosaic_Parameters
USE cbmz_mosaic_Global
USE cbmz_mosaic_Func... | {"hexsha": "298a690b42844b5f322bb7c5e42bc36ac2954028", "size": 517, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "WRF-CHEM/chem/KPP/mechanisms/cbmz_mosaic/cbmz_mosaic_Model.f90", "max_stars_repo_name": "ksetigui/paper_gmd-2020-50", "max_stars_repo_head_hexsha": "1c4bf2b0946bc31cfb443686c8aa1e33755d5fd2", "ma... |
/*
* Copyright 2019, Offchain Labs, Inc.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or ag... | {"hexsha": "680608cb8a47d1bb54afebaa0ef3593a2bf9e457", "size": 4180, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "packages/arb-avm-cpp/tests/value.cpp", "max_stars_repo_name": "tahmed4/arbitrum", "max_stars_repo_head_hexsha": "423adb4a21935b4470a68d0d9b073d7da3e82a7e", "max_stars_repo_licenses": ["Apache-2.0"],... |
from unittest import TestCase
import numpy as np
import star
from phd.thunderstorm import atmosphere
from phd.thunderstorm.critical_energy import get_critical_energy, calculate_secondary_production_rate, \
CriticalEnergyProvider, plot_secondary_production_rate
import matplotlib.pyplot as plt
from tabulate import t... | {"hexsha": "22553c5b3f49e4fa220ced1b0e2689b7ead8cad6", "size": 2245, "ext": "py", "lang": "Python", "max_stars_repo_path": "python/tests/thunderstorm/test_critical_energy.py", "max_stars_repo_name": "Zelenyy/phd-code", "max_stars_repo_head_hexsha": "d5b8bfefd2418a915dde89f7da2cb6683f438556", "max_stars_repo_licenses": ... |
from typing import Tuple
import albumentations as A
import cv2
import random
import numpy as np
from albumentations.core.composition import BaseCompose
from .dataset import (
INPUT_IMAGE_KEY,
INPUT_FEATURES_ELA_KEY,
INPUT_FEATURES_DCT_CR_KEY,
INPUT_FEATURES_DCT_CB_KEY,
INPUT_FEATURES_DCT_Y_KEY,
... | {"hexsha": "2accbdfbebd30610d0e15f5083f2c2ba9c01ed1c", "size": 10556, "ext": "py", "lang": "Python", "max_stars_repo_path": "alaska2/augmentations.py", "max_stars_repo_name": "simphide/Kaggle-2020-Alaska2", "max_stars_repo_head_hexsha": "3c1f5e8e564c9f04423beef69244fc74168f88ca", "max_stars_repo_licenses": ["MIT"], "ma... |
from ophyd import Device, EpicsSignal, Signal, Component as Cpt
from ophyd.areadetector import (ADComponent as ADCpt, StatsPlugin)
from ophyd.quadem import NSLS_EM, TetrAMM, QuadEM
from ophyd import DeviceStatus
import numpy as np
class Best(Device):
x_mean = Cpt(EpicsSignal, ':BPM0:PosX_Mean')
posx = Cpt(Epi... | {"hexsha": "37cacf7de5aef08de7de611f8ae996a3ea2bdef7", "size": 1721, "ext": "py", "lang": "Python", "max_stars_repo_path": "startup/20-bpm.py", "max_stars_repo_name": "NSLS-II-LIX/profile_collection", "max_stars_repo_head_hexsha": "3b942b90404c973625eb884c7a7a9c5e5a3a144a", "max_stars_repo_licenses": ["BSD-3-Clause"], ... |
import numpy as np
import pandas as pd
import pdb
def eval(Xtest, ytest, weights, correctLabel, missLabel, numClasses, numFeatures):
# hardcoded for MNIST
W = np.reshape(weights, (numClasses, numFeatures))
yhat = np.argmax(np.dot(Xtest, W.T), axis=1)
targetIdx = np.where(ytest == correctLabel)
o... | {"hexsha": "4e49a804749a37b980e7fd422c9098ef661edba2", "size": 1802, "ext": "py", "lang": "Python", "max_stars_repo_path": "ML/code/poisoning_compare.py", "max_stars_repo_name": "DistributedML/TorML", "max_stars_repo_head_hexsha": "f41a0378f5e46e578c6bd9c8bfd56037d1a228cf", "max_stars_repo_licenses": ["MIT"], "max_star... |
//
// Copyright 2016 Pixar
//
// Licensed under the Apache License, Version 2.0 (the "Apache License")
// with the following modification; you may not use this file except in
// compliance with the Apache License and the following modification to it:
// Section 6. Trademarks. is deleted and replaced with:
//
// 6. Trad... | {"hexsha": "fd80334490313d29d9a375344d5111ff31df12ae", "size": 8821, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "pxr/base/lib/tf/testenv/debug.cpp", "max_stars_repo_name": "marsupial/USD", "max_stars_repo_head_hexsha": "98d49911893d59be5a9904a29e15959affd530ec", "max_stars_repo_licenses": ["BSD-3-Clause"], "ma... |
\startcomponent co-en-13
\environment contextref-env
\product contextref
\chapter[blocks]{Blocks}
\section{Introduction}
\index{blocks}
\index{floats}
\index{placing+blocks}
A block in \CONTEXT\ is defined as typographical unit
that needs specific handling. We distinguish the following
block types:
\startitemize
... | {"hexsha": "a220259c78d77b23f56a456f517d148d0cf834ae", "size": 33207, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "contextman/context-reference/en/co-blocks.tex", "max_stars_repo_name": "marcpaterno/texmf", "max_stars_repo_head_hexsha": "aa7ad70e0102492ff89b7967b16b499cbd6c7f19", "max_stars_repo_licenses": ["BS... |
"""
raycast.py
Author: Mahesh Venkitachalam
This module has the classed and methods related to Volume rendering using
the Ray Casting method.
"""
import OpenGL
from OpenGL.GL import *
from OpenGL.GL.shaders import *
import numpy as np
import math, sys
import raycube, glutils, volreader
strVS = """
#version 330... | {"hexsha": "84e7a551dc54b765814fef5c6f90752e1864f978", "size": 4929, "ext": "py", "lang": "Python", "max_stars_repo_path": "volrender/raycast.py", "max_stars_repo_name": "s-kistler/PyPlay", "max_stars_repo_head_hexsha": "559edc501ecc31dc0026652a0e65a2ac5a182c03", "max_stars_repo_licenses": ["MIT", "Unlicense"], "max_st... |
__author__ = 'tim'
import numpy as np
from atj.tvwap import tvwap_trades, online_tvwap_trades
t = np.asarray([1.,2.,3.,4.,5.,6.,7.,8.,9.,10])
px = np.asarray([100.,101.,99.,100.,110.,111.,109.,110.,120.,121.])
sz = np.asarray([50.,25.,25.,50.,50.,75.,75.,10.,20.,30])
window = 5
tvres = tvwap_trades(window,t,px,sz)
... | {"hexsha": "4ea0ba12a020dd952644b9ae157ec69eb5e77e9a", "size": 398, "ext": "py", "lang": "Python", "max_stars_repo_path": "python/tvwap_test.py", "max_stars_repo_name": "tambu-j/signals", "max_stars_repo_head_hexsha": "9768e0c3b993d2a75cb0e6c3360197033739e9b4", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_coun... |
import numpy as np
def main(n):
a = np.random.randint(100000000, size=(n,n)) # Default type is np.int which should also be int64
b = a
c = np.dot(a, b)
print(c[n // 2][n // 2])
if __name__=='__main__':
import sys
if len(sys.argv) > 1:
main(int(sys.argv[1]))
else:
main(100... | {"hexsha": "3c87e81e3bd86b5f77a14ba00811abe60e46a089", "size": 321, "ext": "py", "lang": "Python", "max_stars_repo_path": "arraymancerexer/benchmarks/matmul/integer_matmul.py", "max_stars_repo_name": "terasakisatoshi/nimProgram", "max_stars_repo_head_hexsha": "7835abcec1ec550bdc85f57d511c20508a6512e0", "max_stars_repo_... |
import sys
sys.path.append('..')
from mtevi.mtevi import *
from mtevi.utils import *
from sklearn.datasets import load_boston
from sklearn.model_selection import train_test_split
from scipy.stats import zscore
import matplotlib.pyplot as plt
import matplotlib
matplotlib.style.use('ggplot')
import numpy as np
import jso... | {"hexsha": "abbbc1f62345cd28c3fc00a8b7d0890f6b3ee68e", "size": 8256, "ext": "py", "lang": "Python", "max_stars_repo_path": "UCI_exp/uci_exp_norm.py", "max_stars_repo_name": "deargen/MT-ENet", "max_stars_repo_head_hexsha": "6ab482c99cb47162dc0c431b575ebe3475b91168", "max_stars_repo_licenses": ["MIT"], "max_stars_count":... |
//
// Created by ryan on 3/7/16.
//
#include "setup.h"
#include <boost/format.hpp>
#include <rapidjson/error/en.h>
#include "csv_parser.h"
#include "timoshenko_beam_element.h"
namespace explicit_fea {
namespace {
template<typename T>
void createVectorFromJSON(const rapidjson::Document &config_doc,... | {"hexsha": "c2920831970f9790f579c5c0c0c8083e6131e12a", "size": 7523, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/setup.cpp", "max_stars_repo_name": "latture/explicit-beam-fea", "max_stars_repo_head_hexsha": "003e940bda203e1d867494c891c9cee3477cd682", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1... |
[STATEMENT]
lemma FV_EqOnGL: "FV (EqOnGL p p') \<subseteq> {p,p'}" (is "?L \<subseteq> ?R")
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. FV (EqOnGL p p') \<subseteq> {p, p'}
[PROOF STEP]
proof-
[PROOF STATE]
proof (state)
goal (1 subgoal):
1. FV (EqOnGL p p') \<subseteq> {p, p'}
[PROOF STEP]
have "?L = \<Union> {... | {"llama_tokens": 1104, "file": "HyperCTL_Finite_Noninterference", "length": 12} |
import numpy as np
weights = np.array([0.5, 0.0, -0.7])
alpha = 0.1
streetlights = np.array( [ [ 1, 0, 1 ],
[ 0, 1, 1 ],
[ 0, 0, 1 ],
[ 1, 1, 1 ],
[ 0, 1, 1 ],
[ 1, 0, 1 ] ] )
... | {"hexsha": "02e8d8ef9f942f8a2574c7c0a95e4dd6c1d55b18", "size": 813, "ext": "py", "lang": "Python", "max_stars_repo_path": "books/grokking_deeplearning/ch5/nn1.py", "max_stars_repo_name": "gerritjvv/deeplearning", "max_stars_repo_head_hexsha": "1cfdee65c7f1d48156b5e1f64616cae0c90aa347", "max_stars_repo_licenses": ["MIT"... |
\documentclass[../thesis.tex]{subfiles}
\begin{document}
This is a modification of CoPCSE@NTNUs LaTeX document class. It's purpose is to make a more friendly environment for writing mathematics, while removing some redundancy thereof.
Additions to this class are the extra custom amsthm environments. Hopefully they ar... | {"hexsha": "b44b808722b75898009059b6a121f27016dd82ff", "size": 3999, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "chapters/introduction.tex", "max_stars_repo_name": "CelestialCry/thesis-NTNU", "max_stars_repo_head_hexsha": "19dfceca01ba4cfe261469de428cad1d0613f4a1", "max_stars_repo_licenses": ["MIT"], "max_star... |
import matplotlib.pyplot as plt
import numpy as np
import torch
import torch.nn as nn
from matplotlib.lines import Line2D
from torch.functional import Tensor
_mean_ = np.array([0.41248123, -0.00600316])
_scale_ = np.array([2.07864733, 1.85605669])
def mult_normal_sample(mean, std_1, std_2, corr):
std_1.squeeze_(... | {"hexsha": "957caf9ea3b91de67f5db80883862a65d358531f", "size": 3713, "ext": "py", "lang": "Python", "max_stars_repo_path": "utils/helper.py", "max_stars_repo_name": "eeishaan/handwriting-synthesis", "max_stars_repo_head_hexsha": "d7036335bf49c0f079c36f4c801c9bb0da15f3d1", "max_stars_repo_licenses": ["MIT"], "max_stars_... |
#### Procesamiento Digital de Señales
# Trabajo Práctico Nº0
#### Gisela Farace
# Introducción
Jupyter Notebook es una herramienta para la confección de reportes técnicos, dado que permite la interacción en el mismo ambiente de:
1. un procesador de texto elemental (formato Markdown) que permite resaltar texto, en... | {"hexsha": "06956d5a290646cb1034be98f80c33e6532fb108", "size": 901755, "ext": "ipynb", "lang": "Jupyter Notebook", "max_stars_repo_path": "notebook0.ipynb", "max_stars_repo_name": "gfarace/prueba_jupyter", "max_stars_repo_head_hexsha": "4ea8cd499566612afdfc2ee48aa539e32c02e605", "max_stars_repo_licenses": ["MIT"], "max... |
"""Purpose: Visualise vertical profiles of variables from ICON simulation.
Author: Stephanie Westerhuis
Date: 25/11/2021.
"""
# Standard library
import datetime
import datetime as dt
import sys
from pprint import pprint
# Third-party
import matplotlib.dates as mdates
import matplotlib.pyplot as plt
import matplotli... | {"hexsha": "a4523096c1f43854dcb6937ab188e3ffd804905d", "size": 22329, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/plot_profile/plot_icon/plot_icon.py", "max_stars_repo_name": "mizeller/plot_profile", "max_stars_repo_head_hexsha": "832f1d47a182d65747f18cf1ac90afc9a3b821c1", "max_stars_repo_licenses": ["MI... |
/*
Copyright (c) 2005-2021, University of Oxford.
All rights reserved.
University of Oxford means the Chancellor, Masters and Scholars of the
University of Oxford, having an administrative office at Wellington
Square, Oxford OX1 2JD, UK.
This file is part of Chaste.
Redistribution and use in source and binary forms... | {"hexsha": "b4751829e6160ddb1a1e5f7e600203631c9860c2", "size": 7785, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "heart/src/stimulus/tissue_electrodes/ElectrodesStimulusFactory.hpp", "max_stars_repo_name": "mdp19pn/Chaste", "max_stars_repo_head_hexsha": "f7b6bafa64287d567125b587b29af6d8bd7aeb90", "max_stars_rep... |
#define BOOST_TEST_MODULE "test_truncate"
#include <boost/test/included/unit_test.hpp>
// the code to test:
#include "truncate.hpp"
BOOST_AUTO_TEST_CASE(_fast_pow) {
// results for fast_pow(e < -126) are not defined
BOOST_CHECK_EQUAL(fast_pow(0), 1);
BOOST_CHECK_EQUAL(fast_pow(1), 2);
BOOST_CHECK_EQU... | {"hexsha": "a40633f0f868a11ccb2e35dea77506b65a8f2de8", "size": 2377, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "tests/boost/test_truncate.cpp", "max_stars_repo_name": "james-s-willis/kotekan", "max_stars_repo_head_hexsha": "155e874bb039702cec72c1785362a017548aa00a", "max_stars_repo_licenses": ["MIT"], "max_st... |
import cv2
import numpy as np
from siamfc import TrackerSiamFC
def rectangleImg(img,startPt,stopPt,color=(0,0,255),thickness=2):
return cv2.rectangle(img, startPt, stopPt, color=color, thickness=thickness)
def cameraTracking():
net_path = 'siamfc_alexnet_e554.pth'
tracker = TrackerSiamFC(net_path=net_pat... | {"hexsha": "b0fd583f243318c983be48c9ea273a5afdf1efe5", "size": 1163, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/videoCapTracking.py", "max_stars_repo_name": "StevenHuang2020/SiameseFc_PyTorch", "max_stars_repo_head_hexsha": "43f214a59e5841668f8b1fbadebd47384f8d4bf5", "max_stars_repo_licenses": ["MIT"], ... |
import numpy as np
import time, warnings
import scipy.weave
#import pyximport; pyximport.install() # requires recent Cython
import fastfinder_help
class MissingValueError(Exception):
pass
class FastFinder(object):
"""fast search by use of a cached, sorted copy of the original data
Parameters
-------... | {"hexsha": "f6e121fbfb98814cafe8128785046d85d41b7e1a", "size": 11577, "ext": "py", "lang": "Python", "max_stars_repo_path": "flydra_analysis/flydra_analysis/a2/utils.py", "max_stars_repo_name": "liyi2017/flydra", "max_stars_repo_head_hexsha": "9ec83f14514f73e660ae8fbda32a3cc0348d0cb4", "max_stars_repo_licenses": ["Apac... |
function [y,deriv] = cllr_obj(w,T,weights,logit_prior)
% This is an MV2DF. See MV2DF_API_DEFINITION.readme.
%
% Weighted binary classifier cross-entropy objective, based on logarithmic
% cost function.
%
% Differentiable inputs:
% w: is vector of N detection scores (in log-likelihood-ratio format)
%
% Fixed parame... | {"author": "nesl", "repo": "asvspoof2019", "sha": "8b780369f7273345c22d979192119198bbf3db13", "save_path": "github-repos/MATLAB/nesl-asvspoof2019", "path": "github-repos/MATLAB/nesl-asvspoof2019/asvspoof2019-8b780369f7273345c22d979192119198bbf3db13/baseline/tDCF_v1/bosaris_toolkit.1.06/bosaris_toolkit/utility_funcs/Opt... |
# ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
import json
import pickle
import numpy as np
import pandas as pd
import azureml.train.automl
from sklearn.externals import joblib
from azure... | {"hexsha": "82fc4a4f10c055ec451673ead7f807cf9d195ee6", "size": 1633, "ext": "py", "lang": "Python", "max_stars_repo_path": "2020/AutoML Classifiaction/inference/score.py", "max_stars_repo_name": "marcelfranke/sqlserverkonferenz", "max_stars_repo_head_hexsha": "4a784edfc6014ed189f44301ec8aea948d500f8f", "max_stars_repo_... |
#pragma once
#include <boost/utility/string_view.hpp>
#include <cstddef>
namespace swizzle { namespace lexer {
class FileInfo;
class Token;
enum class TokenizerState : std::uint8_t;
}}
namespace swizzle { namespace lexer {
class TokenizerStateInterface
{
public:
virtual ~TokenizerSta... | {"hexsha": "330591f186c86553a4f974188b2b2b5608e455bd", "size": 486, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "swizzle/lexer/TokenizerStateInterface.hpp", "max_stars_repo_name": "SenorAgosto/Swizzle", "max_stars_repo_head_hexsha": "52c221de9fa293b0006f07a41b140d2afcc1a9ed", "max_stars_repo_licenses": ["BSD-4-... |
import numpy as np
import pickle
rare_index = np.array([ 9, 23,28, 45,51, 56,63, 64,67, 71,77, 78,81, 84,85, 91,100,101,105,108,113,128,136,137,150,159,166,167,169,173,180,182,185,189,190,193,196,199,206,207,215,217,223,228,230,239,240,255,256,258,261,262,263,275,280,281,282,287,290,293,304,312,316,318,326,329,334,335... | {"hexsha": "8a4ce00d506c0b83319f24489df0f49d4a2e9dfe", "size": 8018, "ext": "py", "lang": "Python", "max_stars_repo_path": "HICO_DET_utils.py", "max_stars_repo_name": "Foruck/OC-Immunity", "max_stars_repo_head_hexsha": "bd0cf46ffc941c12d5d5299039d20a75699c6181", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 6,... |
import random
import numpy as np
import matplotlib.pyplot as plt
from math import sqrt
class hierarchical(object):
nodes = {} #Dictionary that contains the entire tree structure (parent-child relationships)
distance = {} #Keeps track of distances calculated. Keys: A tuple with internal names. Val: Di... | {"hexsha": "7cd1a4a42c289c6d87544aad7fdbb33b80bd765f", "size": 8002, "ext": "py", "lang": "Python", "max_stars_repo_path": "hw2skeleton/hier.py", "max_stars_repo_name": "hasuni-max/hw2-skeleton", "max_stars_repo_head_hexsha": "498f5d250ec18042c1e21fac177a92f3c7d3da7c", "max_stars_repo_licenses": ["Apache-2.0"], "max_st... |
import numpy as np
from .base import LeviCivitaTensor, ProjectiveCollection, ProjectiveElement, Tensor, TensorDiagram
from .point import Point, infty_hyperplane
from .utils import inv
def identity(dim, collection_dims=None):
"""Returns the identity transformation.
Parameters
----------
dim : int
... | {"hexsha": "484533b9b85d01c6487c2f9d77b403c725ce802d", "size": 10529, "ext": "py", "lang": "Python", "max_stars_repo_path": "geometer/transformation.py", "max_stars_repo_name": "diplodocuslongus/geometer", "max_stars_repo_head_hexsha": "3ebcfa2d27b621413099756a837b46fd5860c7f0", "max_stars_repo_licenses": ["MIT"], "max... |
#!/usr/bin/env python
# coding=utf-8
"""
@create time: 2019-11-22 10:21
@author: Jiawei Wu
@edit time: 2019-11-25 11:28
@file: /find_treasure.py
"""
import numpy as np
from gym.spaces import Discrete, Box
import gym
class FindTreasureEnv(gym.Env):
def __init__(self, length=7, treasures=[6]):
self.player_... | {"hexsha": "7ddbd9d516003bdaf3de01ec0695ff434340a4b3", "size": 2124, "ext": "py", "lang": "Python", "max_stars_repo_path": "rl4net/envs/findtreasure_env.py", "max_stars_repo_name": "bupt-ipcr/RL4Net", "max_stars_repo_head_hexsha": "b1b694361c688f5e0055148a0cdcb4c6253cd7bd", "max_stars_repo_licenses": ["MIT"], "max_star... |
__precompile__()
"""
The Luxor package provides a set of vector drawing functions for creating graphical documents.
"""
module Luxor
using Colors, Cairo, Compat
include("point.jl")
include("Turtle.jl")
include("polygons.jl")
include("Tiler.jl")
include("arrows.jl")
include("text.jl")
export Drawing, currentdrawing,... | {"hexsha": "dd77bffe91f8038a0f8d54ae3825c0f0463b1bb8", "size": 35229, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/Luxor.jl", "max_stars_repo_name": "JuliaPackageMirrors/Luxor.jl", "max_stars_repo_head_hexsha": "564b74edd90c2ce8ca96e4fdd5aabaaaf329d44b", "max_stars_repo_licenses": ["MIT"], "max_stars_count... |
module RestartPrintM
use UtilitiesM
use GIDDataOutputM
use ProcessM
implicit none
private
public :: RestartPrintDT
type, extends(NewProcessDT) :: RestartPrintDT
contains
procedure :: print
end type RestartPrintDT
integer(ikind), parameter :: restartFile = 94
contains
subroutine ... | {"hexsha": "445430699e556f17d9c039bc6edd97207fde35e6", "size": 1031, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "applications/CFD2D/src/Process/RestartPrint.f90", "max_stars_repo_name": "ponfo/Project790", "max_stars_repo_head_hexsha": "abcfcc4945024bd6bd5832bc3ef3d4e1b0df3b91", "max_stars_repo_licenses": ... |
[STATEMENT]
lemma nprv_exuI_var:
assumes n1: "nprv F (subst \<phi> t x)" and n2: "nprv (insert (subst \<phi> (Var y) x) F) (eql (Var y) t)"
and i[simp]: "F \<subseteq> fmla" "finite F" "\<phi> \<in> fmla" "t \<in> trm" "x \<in> var"
"y \<in> var" "y \<notin> FvarsT t" and u: "y \<notin> (\<Union>\<phi> \<in> F. Fvars \... | {"llama_tokens": 1602, "file": "Syntax_Independent_Logic_Natural_Deduction", "length": 17} |
import unittest
from unittest.mock import patch
from unittest.mock import MagicMock
from chart_ipynb import chart_framework
import jp_proxy_widget
class TestChartFramework(unittest.TestCase):
@patch("chart_ipynb.chart_framework.load_requirements")
def test_init(self, mock_load_requirements):
widget =... | {"hexsha": "614436e7808898d7a1b7c847aee98308673f8827", "size": 4746, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/test_chart_framework.py", "max_stars_repo_name": "lingruiluo/Chart_ipynb", "max_stars_repo_head_hexsha": "3821950dc8d66d8ab518b39fc277855e2a7277cc", "max_stars_repo_licenses": ["BSD-2-Clause... |
import sys, io
import numpy as np
import scipy as sp
import scipy.spatial as sps
from matplotlib import pyplot as plt
import xml.etree.ElementTree as et
# import lxml.etree as et
# Get a 1D Cartesian mesh
def get_cartesian_points_1d(xmin,
xmax,
num_points):
p... | {"hexsha": "29d866ca01e6805f5b4b12defe5323519b12bb29", "size": 16749, "ext": "py", "lang": "Python", "max_stars_repo_path": "scripts/mesh_functions.py", "max_stars_repo_name": "brbass/ibex", "max_stars_repo_head_hexsha": "5a4cc5b4d6d46430d9667970f8a34f37177953d4", "max_stars_repo_licenses": ["MIT"], "max_stars_count": ... |
"""Credit to Adrian Rosebrock at
http://www.pyimagesearch.com/2016/01/11/opencv-panorama-stitching/
A lot of the code from that website has been used in functions in this program,
and some credit goes to the original author
Credit to arcticfox at
http://stackoverflow.com/questions/13063201/how-to-show-the-whole-image... | {"hexsha": "011e92095d9e7b935f4f01f2265c878405489e53", "size": 11695, "ext": "py", "lang": "Python", "max_stars_repo_path": "micasense_register.py", "max_stars_repo_name": "hsabiu/thesis-scripts", "max_stars_repo_head_hexsha": "54b7b2248f9a341be7579c96eb54dd43b7d7138f", "max_stars_repo_licenses": ["MIT"], "max_stars_co... |
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License.
import numpy as np
from utils_cv.detection.mask import (
binarise_mask,
colorise_binary_mask,
transparentise_mask,
merge_binary_masks,
)
def test_binarise_mask(od_mask_rects):
""" Test that `binarise_mas... | {"hexsha": "cec2d6ac1e0ba42382e6df7067acb56371eaff67", "size": 1588, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/unit/detection/test_detection_mask.py", "max_stars_repo_name": "muminkoykiran/computervision-recipes", "max_stars_repo_head_hexsha": "b573f2600ebda68b1ab571d4122a32525b674587", "max_stars_re... |
[STATEMENT]
lemma analytically_valid_x:
assumes "analytically_valid s F j"
shows "(\<lambda>x. integral UNIV (\<lambda>y. ((partial_vector_derivative F j) (x, y)) * (indicator s (x, y)))) \<in> borel_measurable lborel"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. (\<lambda>x. integral UNIV (\<lambda>y. partial... | {"llama_tokens": 769, "file": "Green_Green", "length": 7} |
# Note that this script can accept some limited command-line arguments, run
# `julia build_tarballs.jl --help` to see a usage message.
using BinaryBuilder, Pkg
name = "Deno"
version = v"1.12.2"
release_url = "https://github.com/denoland/deno/releases/download/v$version"
# Collection of sources required to complete bu... | {"hexsha": "3fe8d1674c2da2c951cace3883c9ef556b60c499", "size": 1774, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "D/Deno/build_tarballs.jl", "max_stars_repo_name": "jameskermode/Yggdrasil", "max_stars_repo_head_hexsha": "75140f4a7b6bd8839b22da5a39d7ee68780d7d65", "max_stars_repo_licenses": ["MIT"], "max_stars_... |
# -------------------------------------------------------------------------
# MSDS R-CNN
# Licensed under The MIT License [see LICENSE for details]
# Written by Chengyang Li
# -------------------------------------------------------------------------
from __future__ import absolute_import
from __future__ import division... | {"hexsha": "729efa68c4c981dbc7bb368f3e03fc5c705a641f", "size": 1204, "ext": "py", "lang": "Python", "max_stars_repo_path": "lib/layer_utils/fusion_layer.py", "max_stars_repo_name": "Li-Chengyang/MSDS-RCNN", "max_stars_repo_head_hexsha": "9f141d7d381e81c915043f5b41949d462a47a570", "max_stars_repo_licenses": ["MIT"], "ma... |
\section{Throwing Booth}
\label{sec:hardware:throwing_booth}
The throwing booth is constructed from Item aluminum profiles.
The background of the images shall remain the same, regardless of the current location.
Therefore, a white side wall opposite the camera is used.
When creating the booth design, care is taken to ... | {"hexsha": "12b437e11412319aa61c946d4f614fcffafe80de", "size": 1700, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "doc/thesis/chapters/hardware/throwing_booth.tex", "max_stars_repo_name": "MuellerDominik/AIonFPGA", "max_stars_repo_head_hexsha": "f2379782660d4053a5bb60b9f6c6dea17363f96d", "max_stars_repo_licenses... |
#!/usr/bin/env python
#
# (c) 20018 team exitzero (goapsych0@exitzero.de)
#
# links:
# https://github.com/Gallopsled/pwntools
# http://docs.pwntools.com/en/stable/
# https://medium.com/bugbountywriteup/learn-pwntools-step-by-step-8c96f2dba61a
#
from __future__ import print_function
from pwn import *
# import ot... | {"hexsha": "498bd8af4aed6b4da0858bcfdd01987da3d1b244", "size": 936, "ext": "py", "lang": "Python", "max_stars_repo_path": "python/example.py", "max_stars_repo_name": "goapsych0/ctf", "max_stars_repo_head_hexsha": "7b9ae485de2c6d8f015c2a80b100feb14cafe090", "max_stars_repo_licenses": ["Unlicense"], "max_stars_count": nu... |
function sigmaEq = equivalentStress(sigma,varargin)
% von Mises equivalent stress
%
% Input
% sigma - @stressTensor
%
% Output
% s - double
%
s = sigma.deviatoricStress;
sigmaEq = sqrt(3/2 * s:s);
% the following code gives the same result
% is more efficient but less readable
% M = sigma.M;
% sigmaEq = sqrt(M(1,1... | {"author": "mtex-toolbox", "repo": "mtex", "sha": "f0ce46a720935e9ae8106ef919340534bca1adcb", "save_path": "github-repos/MATLAB/mtex-toolbox-mtex", "path": "github-repos/MATLAB/mtex-toolbox-mtex/mtex-f0ce46a720935e9ae8106ef919340534bca1adcb/TensorAnalysis/@stressTensor/equivalentStress.m"} |
!
! Copyright (c) 2019 National Technology & Engineering Solutions of
! Sandia, LLC (NTESS). Under the terms of Contract DE-NA0003525 with
! NTESS, the U.S. Government retains certain rights in this software.
!
!****************************************************************************
!
! PROGRAM: MatMCNP... | {"hexsha": "7ec51dd5050bb72febb90fedc6a9491078663870", "size": 12471, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "source/MatMCNP.f90", "max_stars_repo_name": "krdepri/MatMCNP", "max_stars_repo_head_hexsha": "84f2f1e97f325b29cb8474679a1fe57ef7320e7d", "max_stars_repo_licenses": ["Intel", "X11"], "max_stars_... |
# -*- coding: utf-8 -*-
# Copyright (C) 2015-2018 by Brendt Wohlberg <brendt@ieee.org>
# All rights reserved. BSD 3-clause License.
# This file is part of the SPORCO package. Details of the copyright
# and user license can be found in the 'LICENSE.txt' file distributed
# with the package.
r"""Compute the nuclear norm ... | {"hexsha": "627a65a8f82d2149df7418fcc10b857263ff3081", "size": 1432, "ext": "py", "lang": "Python", "max_stars_repo_path": "sporco/prox/_nuclear.py", "max_stars_repo_name": "cwitkowitz/sporco", "max_stars_repo_head_hexsha": "3a0bf16f1fc6aee8f323653781ea4bc8dd79845b", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_st... |
# #############################################################################
# #############################################################################
#Analysis of ray
#This file contains functions for analyzing and computing properties of a given ray.
# ######################################################... | {"hexsha": "55c51753847a721072bb1ee4ef661b902c5c3574", "size": 2418, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/ray_analysis.jl", "max_stars_repo_name": "amyascwk/CavChaos.jl", "max_stars_repo_head_hexsha": "f4a2f9801af5e67a5648d37901a4e5cd377c064d", "max_stars_repo_licenses": ["MIT"], "max_stars_count":... |
"An abstract type for the problems from [NSDE.jl](https://github.com/antonuccig/NSDE.jl)."
abstract type AbstractNSDEProblem end
"An abstract type for the solvers from [NSDE.jl](https://github.com/antonuccig/NSDE.jl)."
abstract type AbstractNSDESolver end
"An abstract type for the solutions from [NSDE.jl](https://git... | {"hexsha": "6ba93e1b6e623eaacaa9f6defb302f19a0d407e6", "size": 996, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/abstract.jl", "max_stars_repo_name": "antonuccig/NSDEBase.jl", "max_stars_repo_head_hexsha": "49f22b25ae80a2d861634fc4503fa9635ddd5142", "max_stars_repo_licenses": ["MIT"], "max_stars_count": nu... |
# Copyright 2022 Deep Learning on Flink Authors
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable l... | {"hexsha": "618323d89582cdc4534e0cb0e78ecdbb3c05456a", "size": 2696, "ext": "py", "lang": "Python", "max_stars_repo_path": "dl-on-flink-examples/src/test/python/mnist_data_setup.py", "max_stars_repo_name": "Sxnan/dl-on-flink", "max_stars_repo_head_hexsha": "5151eed9bde850eb07062a084f72096ff7b07027", "max_stars_repo_lic... |
import numpy as np
from stl import mesh
def mesh_location_zero(my_mesh):
midPosRel = (my_mesh.max_ - my_mesh.min_)/2
my_mesh.x = my_mesh.x - (midPosRel[0] + my_mesh.min_[0])
my_mesh.y = my_mesh.y - (midPosRel[1] + my_mesh.min_[1])
my_mesh.z = my_mesh.z - (midPosRel[2] + my_mesh.min_[2])
return my_m... | {"hexsha": "50d6e25b009af8f288305c235bc447fc90bb36c2", "size": 323, "ext": "py", "lang": "Python", "max_stars_repo_path": "HolePlateMaker/mesh_location_zero.py", "max_stars_repo_name": "henjin0/HolePlateMaker", "max_stars_repo_head_hexsha": "daf7ef5269b03f2ac2fdc9a8132f945b14177aa0", "max_stars_repo_licenses": ["MIT"],... |
program t
print *,'ok'
end program t
module m
contains
subroutine s
print *,'bad'
end subroutine s
end module m
| {"hexsha": "3ebdbf10855150fa93c81ed2b92483a4e9c4329d", "size": 123, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "tests/t0220r/t.f90", "max_stars_repo_name": "maddenp/ppp", "max_stars_repo_head_hexsha": "81956c0fc66ff742531817ac9028c4df940cc13e", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": ... |
# -*- coding: UTF-8 -*-
"""
Copyright 2021 Tianshu AI Platform. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless req... | {"hexsha": "ede3dc7656bab9b6af9effaad3c9d2edbb24939a", "size": 28590, "ext": "py", "lang": "Python", "max_stars_repo_path": "tsvis/logger/utils.py", "max_stars_repo_name": "hdu-jinminghu/TS-VIS-master", "max_stars_repo_head_hexsha": "c717722f86ee6996afc052ec946fbaefc1b5d3dc", "max_stars_repo_licenses": ["Apache-2.0"], ... |
// Copyright (C) 2014-2017 Internet Systems Consortium, Inc. ("ISC")
//
// This Source Code Form is subject to the terms of the Mozilla Public
// License, v. 2.0. If a copy of the MPL was not distributed with this
// file, You can obtain one at http://mozilla.org/MPL/2.0/.
#include <config.h>
#include <asiolink/io_add... | {"hexsha": "3ec3fd5cca0d2988210ac6dd172e00b5c55ce0cc", "size": 17466, "ext": "cc", "lang": "C++", "max_stars_repo_path": "src/lib/dhcpsrv/parsers/host_reservation_parser.cc", "max_stars_repo_name": "nchaigne/kea", "max_stars_repo_head_hexsha": "2badfd4d9b4f2420b0e9683db5da16a3ab90dd81", "max_stars_repo_licenses": ["Apa... |
'''
Computes the magnetic coordinates of each packet
Also determines whether the packet is in shadow
'''
import numpy as np
def xyz_to_magcoord(t, x, inputs, planet):
if planet.object == 'Mercury':
magcoord = None
else:
# Need to add magnetic coordinates for Jupiter and Saturn
assert ... | {"hexsha": "d08c10737638c21b72988a3fc8be15c71b2a097a", "size": 381, "ext": "py", "lang": "Python", "max_stars_repo_path": "nexoclom/modelcode/xyz_to_magcoord.py", "max_stars_repo_name": "mburger-stsci/NExoCloM", "max_stars_repo_head_hexsha": "c0c81eeb04c5571662f3d86337d84a18f1cd0dcf", "max_stars_repo_licenses": ["BSD-3... |
#!/usr/bin/env python3
import numpy as np
from .base_converter import BaseConverter
from ..egograph import EgoGraph
from typing import Tuple, Dict
NID = '_NID'
EID = '_EID'
UID = '_UID'
VID = '_VID'
class Ego2Tensor(BaseConverter):
"""
An object that convert ego-graph into tensor.
:param graph: the gra... | {"hexsha": "b40890ef2bd3d3305996d7d02935ed48b3830516", "size": 3901, "ext": "py", "lang": "Python", "max_stars_repo_path": "python/graph2tensor/converter/ego2tensor.py", "max_stars_repo_name": "deepest-stack/graph2tensor", "max_stars_repo_head_hexsha": "4258bd7fff68348c98a77cff88afef039c1d44ba", "max_stars_repo_license... |
import h5py
#from mpi4py import MPI
import numpy as np
import time
from netCDF4 import Dataset as DS
import os
def writetofile(src, dest, channel_idx, varslist):
if os.path.isfile(src):
batch = 2**6
rank = MPI.COMM_WORLD.rank
Nproc = MPI.COMM_WORLD.size
Nimgtot = 1460#src_shape[0]
... | {"hexsha": "331e34aa34f00e06f5e60999b73257eea80d916e", "size": 2498, "ext": "py", "lang": "Python", "max_stars_repo_path": "scripts/tp_tigge_nc2hdf5.py", "max_stars_repo_name": "pzharrington/WeatherBench", "max_stars_repo_head_hexsha": "244b8452c928d825af388ba62e0c3c21affb32d3", "max_stars_repo_licenses": ["MIT"], "max... |
# PREAMBLE
# PKG_SETUP
# ## Setup
using RoboDojo
# ## Initial conditions
q1 = nominal_configuration(quadruped)
v1 = zeros(quadruped.nq)
# ## Time
h = 0.01
T = 100
# ## Simulator
s = Simulator(quadruped, T, h=h)
# ## Simulate
simulate!(s, q1, v1)
# ## Visualizer
vis = Visualizer()
render(vis)
# ## Visualize
v... | {"hexsha": "2c9f285d8491af46d2817c5cc874a0dc91689014", "size": 338, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "examples/quadruped/quadruped.jl", "max_stars_repo_name": "mcx/RoboDojo.jl", "max_stars_repo_head_hexsha": "b31fa17ee84285f45b76de78d9e660a83f5ddc9e", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
import numpy as np
import matplotlib.pyplot as plt
from convolution_matrices import convmat2D as cm
from RCWA_functions import run_RCWA_simulation as rrs
import cmath
from numpy.linalg import cond
plt.close("all")
'''
RCWA testing with a metal, which should match the spectra we showed in the RCWA_1D code.
as in, if you... | {"hexsha": "0d6981fb19ff5b2e8045d2d63464fd0c81c5676a", "size": 3746, "ext": "py", "lang": "Python", "max_stars_repo_path": "RCWA_2D_examples/RCWA_metal_square_grid.py", "max_stars_repo_name": "zhaonat/RCWA", "max_stars_repo_head_hexsha": "a28fdf90b5b5fc0fedacc8bb44a0a0c2f2a02143", "max_stars_repo_licenses": ["MIT"], "m... |
import numpy as np
from scipy.ndimage import convolve
def find_nearest_index(array, value):
idx = (np.abs(np.array(array) - value)).argmin()
return idx
def get_min_max_xy(pos_array):
min_x = 10000
max_x = -1
min_y = 10000
max_y = -1
for xy in pos_array:
[_x, _y] = xy
if ... | {"hexsha": "fefc9a5a59f617f09f3f20fa0b914ba11f254566", "size": 1950, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/iBeatles/utilities/array_utilities.py", "max_stars_repo_name": "ornlneutronimaging/iBeatles", "max_stars_repo_head_hexsha": "0a6ca1e18780cf08ad97b6cedede5a23f52bc953", "max_stars_repo_licenses... |
from sympy import diag
import rclpy
from rclpy.node import Node
from rclpy.qos import QoSPresetProfiles
from cv_bridge import CvBridge
from stereo_msgs.msg import DisparityImage
from std_msgs.msg import Header
from sensor_msgs.msg import PointCloud2, CameraInfo, PointField
import cv2
from time import time
import numpy... | {"hexsha": "68df36d4baab16e16f5ea636362556cbdf9bb792", "size": 3837, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/stereo_cam/stereo_cam/pcd_publisher.py", "max_stars_repo_name": "frank20a/collaborative-sats", "max_stars_repo_head_hexsha": "9d26d3c8f66cf43bbd514f02434851439e746797", "max_stars_repo_license... |
# -*- coding: utf-8 -*-
################################################################################
#Importing Libraries
################################################################################
global debug, interactive
debug = 0
interactive = 1
from pylab import *
from math import *
import matplotlib
impo... | {"hexsha": "56ca067eb92481a1f6d0165695a46120057f39c4", "size": 22173, "ext": "py", "lang": "Python", "max_stars_repo_path": "Young_Massive_Stars/Code_v31_multiple/galaxy_generator/aux/gameFunctions31.py", "max_stars_repo_name": "lhquirogan/Galactic_Maser_Simulator", "max_stars_repo_head_hexsha": "cb74afd40b6d99429219c4... |
#!/usr/bin/env python
#
# Created 19-Mar-2013 by Daniel Margala (University of California, Irvine) <dmargala@uci.edu>
#
# usage:
# Import libraries
import numpy as np
import os
#import yanny
import pyfits as pf
import sys
import re
import math
class CAMERA: pass
class RED_CAMERA (CAMERA): pass
class BLUE_CAMERA (C... | {"hexsha": "5ae24c262fd35892a131468ba28a8f9d93fb97ec", "size": 7629, "ext": "py", "lang": "Python", "max_stars_repo_path": "python/old/bosslya.py", "max_stars_repo_name": "dmargala/blupe", "max_stars_repo_head_hexsha": "317ca35f554ae25605310cddd3e67e9fdad7b2ba", "max_stars_repo_licenses": ["MIT"], "max_stars_count": nu... |
import pandas as pd
import numpy as np
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_absolute_error
from sklearn.model_selection import train_test_split
from sklearn.pipeline import Pipeline
from sklearn.compose import ColumnTransformer
from sklearn.preprocessing import OneHotEncod... | {"hexsha": "8718b10aad5b5f67a7f2543af25415c32b892c44", "size": 2015, "ext": "py", "lang": "Python", "max_stars_repo_path": "AI & Domains (ML etc) Library/Titanic_kaggle/pipeline2.py", "max_stars_repo_name": "hammad1201/Hacktoberfest-2021", "max_stars_repo_head_hexsha": "b4b86792755c7b86d5bcc94ac8159d8825ed169e", "max_s... |
module common_debug_oceanmodel
! This module is for debugging, but also for standard checks
! that should be maintained to catch any problems immediately and exit
! rather than continuing with bad values.
!
! Author: Steve Penny, June 2017 (visiting scientist, ECMWF)
USE common, ONLY: r_size
IMPLICIT NONE
PUBLIC :: ... | {"hexsha": "6fcd7c366c81f5e0b2c8dab4018830cf9e718550", "size": 6921, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "src/model_specific/hycom/common_debug_hycom.f90", "max_stars_repo_name": "GEOS-ESM/Ocean-LETKF", "max_stars_repo_head_hexsha": "a7c4bbf86cdbff078212914dcc059d0b1450accf", "max_stars_repo_license... |
#!/usr/bin/env python3
# Copyright 2019 Alexander Meulemans
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable... | {"hexsha": "beb55b6f9dbfeca1f16d5c6ba4d94bb21c67842b", "size": 11781, "ext": "py", "lang": "Python", "max_stars_repo_path": "lib/builders.py", "max_stars_repo_name": "lebrice/theoretical_framework_for_target_propagation", "max_stars_repo_head_hexsha": "d177493a5b96f6563fc6bad18ac03d512f2b217f", "max_stars_repo_licenses... |
#!/usr/bin/env python
# Authors: Junior Costa de Jesus #
import rospy
import os
import json
import numpy as np
import random
import time
import sys
sys.path.append(os.path.dirname(os.path.abspath(os.path.dirname(__file__))))
from collections import deque
from std_msgs.msg import Float32
from environment_stage_1 import... | {"hexsha": "c6208565c6a0397b397934f67b0e9e3771743d49", "size": 13674, "ext": "py", "lang": "Python", "max_stars_repo_path": "SAC/sac_stage_1.py", "max_stars_repo_name": "Crawford-fang/ROS_pytorch_RL", "max_stars_repo_head_hexsha": "2d3476f15d51aa1f5b5ae9edc5d7f4c776e5de9f", "max_stars_repo_licenses": ["Apache-2.0"], "m... |
[STATEMENT]
lemma istate_\<Delta>1:
assumes B: "B vl vl1"
shows "\<Delta>1 istate vl istate vl1"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. \<Delta>1 istate vl istate vl1
[PROOF STEP]
using B
[PROOF STATE]
proof (prove)
using this:
B vl vl1
goal (1 subgoal):
1. \<Delta>1 istate vl istate vl1
[PROOF STEP]
unfol... | {"llama_tokens": 649, "file": "CoCon_Paper_Confidentiality_Paper_Aut_PC", "length": 3} |
using HTTP, Joseki, JSON, Sockets, Test
@testset "Integration tests" begin
# Simple server
function pow(req::HTTP.Request)
j = HTTP.queryparams(HTTP.URI(req.target))
if !(haskey(j, "x")&haskey(j, "y"))
return error_responder(req, "You need to specify values for x and y!")
... | {"hexsha": "ec199af9b6e9d1ea8d752a240dd4091d1ca67941", "size": 1525, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/integration-tests.jl", "max_stars_repo_name": "UnofficialJuliaMirrorSnapshots/Joseki.jl-b588beb9-536a-5a7d-a241-c127386fde06", "max_stars_repo_head_hexsha": "1a065dc90399c1009dfb4792457dde2542... |
#!usr/bin/env python
#coding=utf-8
import pyaudio
import wave
import numpy
#define stream chunk
chunk = 1024
#open a wav format music
f = wave.open(r"crash_cymbal.wav","rb")
snare = wave.open(r"snare.wav","rb")
bass_drum = wave.open(r"bass_drum.wav","rb")
crash_cymbal = wave.open(r"crash_cymbal.wav","... | {"hexsha": "235764aea1c2f884f61f17f0dbf4136b1fe8616b", "size": 1265, "ext": "py", "lang": "Python", "max_stars_repo_path": "audio/readWawe.py", "max_stars_repo_name": "valschneider/lauzhack2017", "max_stars_repo_head_hexsha": "36fe0bb043165fa788a28863298332d70a95a57a", "max_stars_repo_licenses": ["MIT"], "max_stars_cou... |
import argparse
import sys
import os
import tensorflow as tf
from utils.config import load_config, gan_from_config
from classifiers.cifar_model import Model
from blackbox import get_cached_gan_data
from utils.network_builder import model_a, DefenseWrapper
from cleverhans.utils_tf import model_train, model_eval
from ut... | {"hexsha": "3b6b7f52441b27c2b935603d2826ddf5f98bae64", "size": 5434, "ext": "py", "lang": "Python", "max_stars_repo_path": "classification.py", "max_stars_repo_name": "killianlevacher/defenseInvGAN-src", "max_stars_repo_head_hexsha": "8fa398536773c5bc00c906562d2d9359572b8157", "max_stars_repo_licenses": ["MIT"], "max_s... |
[STATEMENT]
lemma transaction_strand_memberD[dest]:
assumes "x \<in> set (transaction_strand T)"
shows "x \<in> set (transaction_receive T) \<or> x \<in> set (transaction_checks T) \<or>
x \<in> set (transaction_updates T) \<or> x \<in> set (transaction_send T)"
[PROOF STATE]
proof (prove)
goal (1 subgoal)... | {"llama_tokens": 305, "file": "Automated_Stateful_Protocol_Verification_Transactions", "length": 2} |
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import inspect
from histogram import Hist
# read in data for testing from 'test_data_1.csv'
# file has 1 column of data with heading called 'col_1'
# save to pandas dataframe 'df_1'
df_1 = pd.read_csv(r'C:\projects_learning\ringvision\pypi... | {"hexsha": "08825ecd103757d4ba1afb5f5049539b0b296921", "size": 1152, "ext": "py", "lang": "Python", "max_stars_repo_path": "test_hist_class.py", "max_stars_repo_name": "brianRingler/EDA-Tools-", "max_stars_repo_head_hexsha": "1870e786f1cd009f03a51243177e5b22a98bb921", "max_stars_repo_licenses": ["MIT"], "max_stars_coun... |
#include <set>
#include <iterator>
#include <boost/test/unit_test.hpp>
#include <dracosha/validator/validator.hpp>
#include <dracosha/validator/prevalidation/resize_validated.hpp>
using namespace DRACOSHA_VALIDATOR_NAMESPACE;
BOOST_AUTO_TEST_SUITE(TestPrevalidation)
BOOST_AUTO_TEST_CASE(CheckResizeValidated)
{
... | {"hexsha": "b8a3d51c35d02be2143d95d5990abc0dac122d3f", "size": 9089, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "test/testresizevalidated.cpp", "max_stars_repo_name": "evgeniums/cpp-validator", "max_stars_repo_head_hexsha": "e4feccdce19c249369ddb631571b60613926febd", "max_stars_repo_licenses": ["BSL-1.0"], "ma... |
# -*- coding: utf-8 -*-
# @Author: sf942274
# @Date: 2020-04-01 08:09:19
# @Last Modified by: Weiyue Ji
# @Last Modified time: 2021-06-06 17:52:02
import os, sys, pickle, datetime, time, warnings
import pandas as pd
import numpy as np
import skimage.io as skiIo
from skimage import exposure, img_as_float, filter... | {"hexsha": "bf8e84e58351d7b97cb34230162628e5471e59e6", "size": 16557, "ext": "py", "lang": "Python", "max_stars_repo_path": "image_quantification/src/main/data_quantification_main.py", "max_stars_repo_name": "AltschulerWu-Lab/nsp_extension", "max_stars_repo_head_hexsha": "e656063c345ddd5d8bbec50cdb1dc779af2f719e", "max... |
#!/usr/bin/env python3
import os
import numpy as np
import matplotlib.pyplot as plt
def main():
plt.rcParams.update({"font.size": 6})
fig = plt.figure(constrained_layout=True, figsize=(80/25.4, 35/25.4))
gs = fig.add_gridspec(1, 3)
axs = gs.subplots()
for d in [0, 1, 2]:
errors = np.l... | {"hexsha": "795cc410967f76b8f01969dd837c8df7d05c5a11", "size": 958, "ext": "py", "lang": "Python", "max_stars_repo_path": "figures/performance_error.py", "max_stars_repo_name": "stefaniaebli/paper-snn-neurips2020tda", "max_stars_repo_head_hexsha": "935658c9fa93897b4e288918e6e9c3fb0a0bee3e", "max_stars_repo_licenses": [... |
"""
Created on September 5th, 2018
@author: itailang
"""
# import system modules
import os.path as osp
import sys
import argparse
import numpy as np
# add paths
parent_dir = osp.dirname(osp.dirname(osp.dirname(osp.abspath(__file__))))
if parent_dir not in sys.path:
sys.path.append(parent_dir)
# import modules
f... | {"hexsha": "51088d4482d38e28dd61cc657effc8e5f759ede8", "size": 6228, "ext": "py", "lang": "Python", "max_stars_repo_path": "reconstruction/autoencoder/evaluate_ae.py", "max_stars_repo_name": "Pandinosaurus/learning_to_sample", "max_stars_repo_head_hexsha": "99e977e1c53ec0fa8b2b8a5151a56d0d088f6f78", "max_stars_repo_lic... |
/* Copyright (c) 2012, Julian Straub <jstraub@csail.mit.edu>
* Licensed under the MIT license. See LICENSE.txt or
* http://www.opensource.org/licenses/mit-license.php */
#pragma once
#include "baseMeasure.hpp"
#include "probabilityHelpers.hpp"
#include <stddef.h>
#include <stdint.h>
#include <typeinfo>
#include ... | {"hexsha": "4339a8fbbefce275dad1d7e8753791653c159eb1", "size": 4237, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "include/hdp_base.hpp", "max_stars_repo_name": "jstraub/bnp", "max_stars_repo_head_hexsha": "11cd28b49e9cf1db96f349181aff57a17672b6a4", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 12.0, "m... |
# This source code is part of the Biotite package and is distributed
# under the 3-Clause BSD License. Please see 'LICENSE.rst' for further
# information.
"""
This module provides functionality for pseudoknot detection.
"""
__name__ = "biotite.structure"
__author__ = "Tom David Müller"
__all__ = ["pseudoknots"]
impo... | {"hexsha": "6424449dba9bda91843dc8b189690c879853ec77", "size": 22943, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/biotite/structure/pseudoknots.py", "max_stars_repo_name": "danijoo/biotite", "max_stars_repo_head_hexsha": "22072e64676e4e917236eac8493eed4c6a22cc33", "max_stars_repo_licenses": ["BSD-3-Claus... |
import pickle
import ray # TODO: Add ray to dependencies.
import tensorflow as tf
import numpy as np
from rllab.misc.overrides import overrides
from rllab.misc import logger
from . import tf_utils
from .sampler import Sampler, rollout
# TODO: Make the remote sampler correctly use the initial exploration policy, as ... | {"hexsha": "1aa9a2943bc163a8164c950c8f880c84a2451473", "size": 2664, "ext": "py", "lang": "Python", "max_stars_repo_path": "maci/misc/remote_sampler.py", "max_stars_repo_name": "bbrito/mapr2", "max_stars_repo_head_hexsha": "5aa1a4c85c28918d9f16e5544793bf5574d7c49e", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars... |
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