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# -*- coding: utf-8 -*-
"""
Created on Mon Feb 19 14:21:56 2018
@author: Aditya Vikram
"""
# Importing the libraries
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
# Importing the dataset
dataset = pd.read_csv('Churn_Modelling.csv')
X = dataset.iloc[:, 3:13].values
y = dataset.il... | {"hexsha": "6f54665dfaabb4490a52926a3b2928b2695cc26e", "size": 5117, "ext": "py", "lang": "Python", "max_stars_repo_path": "churn_modelling_keras.py", "max_stars_repo_name": "vikadia7x/churn_modelling-using-keras", "max_stars_repo_head_hexsha": "e603a5b119bae8377cb34c72da3dc124c47ff609", "max_stars_repo_licenses": ["MI... |
#define BOOST_TEST_MODULE "test_bond_length_gocontact_interaction"
#ifdef BOOST_TEST_DYN_LINK
#include <boost/test/unit_test.hpp>
#else
#include <boost/test/included/unit_test.hpp>
#endif
#include <mjolnir/core/BoundaryCondition.hpp>
#include <mjolnir/core/SimulatorTraits.hpp>
#include <mjolnir/forcefield/local/GoCon... | {"hexsha": "b876f4bd0f789b907267072dddce6dd9e520f747", "size": 9822, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "test/core/test_contact_gocontact_interaction.cpp", "max_stars_repo_name": "yutakasi634/Mjolnir", "max_stars_repo_head_hexsha": "ab7a29a47f994111e8b889311c44487463f02116", "max_stars_repo_licenses": ... |
import functools
import operator
import os
import os.path
import sys
import numpy as np
# Bamboo utilities
current_file = os.path.realpath(__file__)
current_dir = os.path.dirname(current_file)
sys.path.insert(0, os.path.join(os.path.dirname(current_dir), 'common_python'))
import tools
# ==============================... | {"hexsha": "1644610a77ce342b1089eb0c66b38e0724a6e464", "size": 5461, "ext": "py", "lang": "Python", "max_stars_repo_path": "ci_test/unit_tests/test_unit_layer_one_hot.py", "max_stars_repo_name": "LLNL/LBANN", "max_stars_repo_head_hexsha": "8bcc5d461e52de70e329d73081ca7eee3e5c580a", "max_stars_repo_licenses": ["Apache-2... |
/*****************************************************************************
* {{name}}_test_base.f
*****************************************************************************/
{%set filename = "sim/tests/{{name}}_test_base.f" %}
+UVM_TESTNAME={{name}}_test_base
| {"hexsha": "bf557bf3fc44bd8c2795b1d5a8b4e3c7a5265378", "size": 272, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "templates/verif/uvm/subenv/sim/tests/base_test.f", "max_stars_repo_name": "mballance/vte", "max_stars_repo_head_hexsha": "6063be201412a69cd1fd681081a0ede77c455512", "max_stars_repo_licenses": ["Apa... |
// test cases for development purposes
#define BOOST_TEST_MODULE TestContext
#include <boost/test/unit_test.hpp>
#ifdef DEV_TESTS
#if defined(OPENCL_ENABLED)
#include "clfft_helper.hpp"
using namespace gearshifft::ClFFT;
struct Fixt {
cl_device_id device = 0;
cl_context ctx = 0;
Fixt() {
cl_platform_id... | {"hexsha": "55f6846ef925011d25a7d4934f088d7fc89015ed", "size": 4071, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "test/tests.cpp", "max_stars_repo_name": "psteinb/gearshifft", "max_stars_repo_head_hexsha": "fea380554a898191b57a1c0922174f5dd64c04db", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": ... |
import skimage.io as io
import skimage.transform as skt
import numpy as np
from PIL import Image
from src.models.class_patcher import patcher
from src.utils.imgproc import *
class patcher(patcher):
def __init__(self, body='./body/body_hakka.png', **options):
super().__init__('薄荷', body=body, pantie_positi... | {"hexsha": "1860e426db5afe6c765cbb31acc0c8c752545e12", "size": 2460, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/models/hakka.py", "max_stars_repo_name": "HhotateA/quiche_pantie_patch", "max_stars_repo_head_hexsha": "f50c4fd69bd43cccaeb38f026d486e3ccc3850d8", "max_stars_repo_licenses": ["CC-BY-4.0"], "ma... |
# Copyright 2018 DeepMind Technologies Limited. 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 required by ... | {"hexsha": "eb4cd276ec96403676ae131df2332b30e2de6872", "size": 4573, "ext": "py", "lang": "Python", "max_stars_repo_path": "acme/agents/jax/rnd/builder.py", "max_stars_repo_name": "contropist/deepmind-acme", "max_stars_repo_head_hexsha": "237d9c91531f184104a3f146d289ce8e0d41c807", "max_stars_repo_licenses": ["Apache-2.... |
import copy
import itertools
import numpy as np
import tensorflow as tf
def dRNN(cell, inputs, rate, scope='default'):
"""
This function constructs a layer of dilated RNN.
Inputs:
cell -- the dilation operations is implemented independent of the RNN cell.
In theory, any valid tensorflow... | {"hexsha": "a1ee27c3e2eab67b39c6563d81a00e9d03bf29ee", "size": 3852, "ext": "py", "lang": "Python", "max_stars_repo_path": "models/drnn.py", "max_stars_repo_name": "yuemind/DilatedRNN", "max_stars_repo_head_hexsha": "247a989977213dcb08a7db21dd16fea356b5be34", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count"... |
import time
from typing import List, Tuple
import os
import numpy as np
from src.utils.data_utils import ImdbDataset
from src.utils.config_loader import SentimentAnalysisConfigReader
from src.models.sentiment_analysis_rnn import RNNModel, DataPreprocessor
from src.models.sentiment_analysis_tfidf import DumbModel
def ... | {"hexsha": "330e14558357b2d7daaa5e69c0949349b2856cb8", "size": 3226, "ext": "py", "lang": "Python", "max_stars_repo_path": "marabou/train/src/scripts/train_sentiment_analysis.py", "max_stars_repo_name": "mmarouen/marabou", "max_stars_repo_head_hexsha": "2f3f7512714c94b8d8f3da12751895ea091fe5f6", "max_stars_repo_license... |
import numpy as np
from sklearn.naive_bayes import GaussianNB, BernoulliNB, MultinomialNB
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import (
ExtraTreesClassifier,
RandomForestClassifier,
GradientBoostingClassifier,
)
from sklearn.neighbors import KNeighborsClassifier
from sklear... | {"hexsha": "970e81c8c54466162fa704f5ce1b49379882d567", "size": 4821, "ext": "py", "lang": "Python", "max_stars_repo_path": "gama/configuration/testconfiguration.py", "max_stars_repo_name": "learsi1911/GAMA_pygmo_v4", "max_stars_repo_head_hexsha": "459807db352dd1c9f9c1e0e322f8c1e9b5abbca0", "max_stars_repo_licenses": ["... |
\chapter{Object-Oriented Analysis \& Design} \label{chapter:chapter4}
Object-Oriented Analysis (OOA) is a key activity in good software design as it facilitates the difficult transition between the problem domain and the solution domain. During this stage in the development process, the designer switches from a user-c... | {"hexsha": "c37162160c5d706da893f137a159f519fb391ecf", "size": 22029, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "chapters/chap4.tex", "max_stars_repo_name": "ChristinaKr/SoftwareEngineering", "max_stars_repo_head_hexsha": "0f8329336d86ba5f86e79eb25ca3ce8718246337", "max_stars_repo_licenses": ["BSD-2-Clause"],... |
[STATEMENT]
lemma [simp]:
shows assert_gpv_eq_Done: "assert_gpv b = Done x \<longleftrightarrow> b"
and Done_eq_assert_gpv: "Done x = assert_gpv b \<longleftrightarrow> b"
and Pause_neq_assert_gpv: "Pause out rpv \<noteq> assert_gpv b"
and assert_gpv_neq_Pause: "assert_gpv b \<noteq> Pause out rpv"
and assert... | {"llama_tokens": 386, "file": "CryptHOL_Generative_Probabilistic_Value", "length": 1} |
/*
* GridMapMsgHelpers.hpp
*
* Created on: Sep 8, 2014
* Author: Péter Fankhauser
* Institute: ETH Zurich, ANYbotics
*/
#include "grid_map_ros/GridMapMsgHelpers.hpp"
// Boost
#include <boost/assign.hpp>
namespace grid_map {
const int nDimensions()
{
return 2;
}
std::map<StorageIndices, std::string>... | {"hexsha": "a92f63564f728a1e7e08ba3b4972dee81a245103", "size": 473, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "grid_map/grid_map_ros/src/GridMapMsgHelpers.cpp", "max_stars_repo_name": "xplorer-robot/xplorer", "max_stars_repo_head_hexsha": "51e43613017455331864a166b1bb830819e823a0", "max_stars_repo_licenses": ... |
using Test
using Plots, Parameters, OrdinaryDiffEq
# hip extensor parameters, single ramp from 0.5 to 1.0 over 0.1 s
cc_p = CCParameters(838.2, 645.0, 26.0, 7.94, 1.0, 1.0, 1.0, 4.93, 1.64)
sec_p = SECParameters(2854.48)
α_p = ActivationProfile(0.5, ActivationRamp(0.0, 0.1, 1.0))
## TorqueGenerator
include("torque_ge... | {"hexsha": "747b686fda99b671397b669dc6d2b205c7e08a08", "size": 4580, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "musclemodel/tests.jl", "max_stars_repo_name": "TomRottier/SprintingModel_julia", "max_stars_repo_head_hexsha": "86321d995872574998371f895627b4bb127c9499", "max_stars_repo_licenses": ["MIT"], "max_s... |
module TestUtils
using MLJBase
using Test
import LightGBM
@testset "mlj_to_kwargs removes classifier truncate_booster flag" begin
# Arrange
fixture = LightGBM.MLJInterface.LGBMClassifier()
# Act
output = LightGBM.MLJInterface.mlj_to_kwargs(fixture)
# Assert
@test :truncate_booster ∉ keys(... | {"hexsha": "4f04c248697be39a3114da40ee9d394785f75098", "size": 871, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/basic/test_mljinterface.jl", "max_stars_repo_name": "yaxxie/LightGBM.jl", "max_stars_repo_head_hexsha": "1234c5d3d383fb29967de6f047131b8139ba447f", "max_stars_repo_licenses": ["MIT"], "max_star... |
\documentclass[fleqn,10pt]{wlscirep}
\usepackage[utf8]{inputenc}
\usepackage{lineno}
\usepackage{adjustbox}
\usepackage{setspace}
\usepackage[normalem]{ulem}
\usepackage[T1]{fontenc}
\usepackage{pdfpages}
\usepackage{ulem}
\usepackage{array}
\newcolumntype{L}{>{\centering\arraybackslash}m{2cm}}
\title{Controlling for ... | {"hexsha": "31b7fa6006177b05c4814139bf23f21492a5aee3", "size": 44558, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "paper/main.tex", "max_stars_repo_name": "declan93/PGS-LMM", "max_stars_repo_head_hexsha": "51fa71c51a93ea85b325bf9dc2d343bbd62b98fb", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_... |
import numpy as np
import math
from tensorflow.keras import backend as K
from tensorflow.keras.layers import Layer,Dense, Activation
import tensorflow.keras as keras# as k
import tensorflow as t
from tensorflow.keras.models import Sequential
from tensorflow.keras.optimizers import Adam,SGD
from tensorflow.linalg impor... | {"hexsha": "ca3f7f078f6af4abb079063ac658a06b23d61945", "size": 1285, "ext": "py", "lang": "Python", "max_stars_repo_path": "grapa/layerfiles/gpool.py", "max_stars_repo_name": "psorus/grapa", "max_stars_repo_head_hexsha": "6af343bb35c466c971ded1876e7a9d00e77cef00", "max_stars_repo_licenses": ["MIT"], "max_stars_count": ... |
import cv2
import numpy
import math
from enum import Enum
class Pipeline:
"""
An OpenCV pipeline generated by GRIP.
"""
def __init__(self):
"""initializes all values to presets or None if need to be set
"""
self.__hsv_threshold_hue = [40.46762589928058, 108.78787878787878]... | {"hexsha": "13e9259e8c629af33c67aaa8d7455bcd9d49533a", "size": 5816, "ext": "py", "lang": "Python", "max_stars_repo_path": "grip_high_goal.py", "max_stars_repo_name": "4662FRCRobotics/InfiniteRechargeVision", "max_stars_repo_head_hexsha": "6f24b2e6b1af6b4ac7fe3281e18bcefb2d346f8c", "max_stars_repo_licenses": ["BSD-3-Cl... |
# -*- coding: utf-8 -*-
# This code is part of Qiskit.
#
# (C) Copyright IBM 2017.
#
# This code is licensed under the Apache License, Version 2.0. You may
# obtain a copy of this license in the LICENSE.txt file in the root directory
# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.
#
# Any modif... | {"hexsha": "9c838c149a0f0f7833c33911271b359c0ab8d7a9", "size": 7885, "ext": "py", "lang": "Python", "max_stars_repo_path": "qiskit/quantum_info/operators/measures.py", "max_stars_repo_name": "qinvador/qiskit-terra", "max_stars_repo_head_hexsha": "4e104de3c113c01688a0ed06b2f2cb1a958fce44", "max_stars_repo_licenses": ["A... |
c
c
c ###################################################
c ## COPYRIGHT (C) 1990 by Jay William Ponder ##
c ## All Rights Reserved ##
c ###################################################
c
c ################################################################
c ## ... | {"hexsha": "608325ff9c9070185973d16994fa0eaa7493b63e", "size": 2045, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "HCsbLib/HCsbLib/HTLib2.Bioinfo/External.Tinker/src/tinker-6.2.06/column.f", "max_stars_repo_name": "htna/HCsbLib", "max_stars_repo_head_hexsha": "dae7f4e3e5e2fbc3b6e619f2ea037f661a8ae097", "max_st... |
"""
The SIDDType 2.0 definition.
"""
__classification__ = "UNCLASSIFIED"
__author__ = "Thomas McCullough"
import logging
from typing import Union, Tuple
from collections import OrderedDict
from copy import deepcopy
import numpy
from sarpy.io.xml.base import Serializable
from sarpy.io.xml.descriptors import Serializ... | {"hexsha": "4a2eb7a028eaeb8a047830affaf26f29c31c4112", "size": 17707, "ext": "py", "lang": "Python", "max_stars_repo_path": "sarpy/io/product/sidd2_elements/SIDD.py", "max_stars_repo_name": "bombaci-vsc/sarpy", "max_stars_repo_head_hexsha": "3e31e9d7fca77612b60f2507f6f7068d1660a3e2", "max_stars_repo_licenses": ["MIT"],... |
import numpy as np
import matplotlib.pyplot as plt
import tensorflow as tf
class QciLinearRegression(object):
def __init__(self, learning_rate=0.01, epoch=50000, patience=10,
train_x=None, train_y=None,
validate_x=None, validate_y=None,
test_x=None, test_y=None):
... | {"hexsha": "89faf2a8062f8c390348b1d91cc8469e67461c7b", "size": 2980, "ext": "py", "lang": "Python", "max_stars_repo_path": "app/pqb/qic_linear_regression.py", "max_stars_repo_name": "yt7589/aqp", "max_stars_repo_head_hexsha": "c9c1c79facdea7ace73e2421e8a5868d87fb58dd", "max_stars_repo_licenses": ["Apache-2.0"], "max_st... |
# Copyright 2021 The Private Cardinality Estimation Framework 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 b... | {"hexsha": "75db64f4924735fb023fae8c8232f4bfa7cb7d31", "size": 2676, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/driver/tests/modeling_strategy_descriptor_test.py", "max_stars_repo_name": "pasin30055/planning-evaluation-framework", "max_stars_repo_head_hexsha": "ba5fc3b553fee0b4f5beb50076ecaa7b634dac23",... |
# -*- coding: utf-8 -*-
"""
"""
from __future__ import division, print_function, unicode_literals
from past.utils import old_div
import uncertainties
import uncertainties.umath
from .complex import Complex
import functools
import numpy as np
from . import dispatched
dispatched.module_by_type[uncertainties.AffineScal... | {"hexsha": "e3d868cbe35a3f2ade7ab853278c6825271d1349", "size": 1535, "ext": "py", "lang": "Python", "max_stars_repo_path": "phasor/math/uncert.py", "max_stars_repo_name": "mccullerlp/OpenLoop", "max_stars_repo_head_hexsha": "fe86dc6dec3740d4b6be6b88d8eef8566e2aa78d", "max_stars_repo_licenses": ["Apache-2.0"], "max_star... |
# This file is auto-generated by AWSMetadata.jl
using AWS
using AWS.AWSServices: efs
using AWS.Compat
using AWS.UUIDs
"""
create_access_point(client_token, file_system_id)
create_access_point(client_token, file_system_id, params::Dict{String,<:Any})
Creates an EFS access point. An access point is an applicati... | {"hexsha": "b2b686619e0761efa2641803a6da09a8c6753078", "size": 49347, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/services/efs.jl", "max_stars_repo_name": "wytbella/AWS.jl", "max_stars_repo_head_hexsha": "786e82ccd4d91ff0c9f51d7635045febb64d02f5", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, ... |
import json
import re
import string
import pandas as pd
import numpy as np
from .console import console, print_paper
def decode_line(line):
paper = json.loads(line)
return paper
def preprocess_title(text):
text = text.lower().replace("-", " ").replace("\n", "").replace(" ", " ").strip()
return te... | {"hexsha": "e25ac435c49a8055673aef726944ce891638277d", "size": 3743, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/ALreview/text.py", "max_stars_repo_name": "Deathn0t/ALReview", "max_stars_repo_head_hexsha": "816e9b04cde04816ae38a23271dd8e5cabf93f08", "max_stars_repo_licenses": ["BSD-2-Clause"], "max_stars... |
"""Functions for generating synthetic networks.
2021, Xavier R. Hoffmann <xrhoffmann@gmail.com>
"""
import copy
import random
from typing import List, Sequence, Tuple, Dict
from scipy import special as sp_special # type: ignore
def configuration_model(
*, degrees: Sequence[int], max_trials: int = 10, max_fail... | {"hexsha": "78ab557091b34e6c5018cb1ed0027b8b20e76c22", "size": 4858, "ext": "py", "lang": "Python", "max_stars_repo_path": "networks/generation.py", "max_stars_repo_name": "xhoffmann/networks", "max_stars_repo_head_hexsha": "7fc699925af4053544c82f8c528a0b750d3336f7", "max_stars_repo_licenses": ["MIT"], "max_stars_count... |
module RegistryUtils
using Base: thispatch, thisminor, nextpatch, nextminor
import Base: convert
import LibGit2
import UUIDs
import LinearAlgebra: checksquare
import Pkg
using Pkg.Operations
using Pkg.Types
using Pkg.Types: uuid_package, uuid_registry, uuid5, VersionSpec, VersionRange, VersionBound
import Pkg: TOML
im... | {"hexsha": "16a446514286efc70ea87131130eccfd6c2982d3", "size": 610, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/RegistryUtils.jl", "max_stars_repo_name": "galenlynch/RegistryUtils.jl", "max_stars_repo_head_hexsha": "6944f98b3bc04d1a2cff5d4bf95b62634a25553b", "max_stars_repo_licenses": ["MIT"], "max_stars_... |
# Example of Naive Bayes implemented from Scratch in Python
import csv
import random
import math
import xgboost as xgb
import matplotlib.pyplot as plt
import numpy as np
def loadCsv(filename):
lines = csv.reader(open(filename, "r"))
dataset = list(lines)
for i in range(len(dataset)):
dataset[i] = [flo... | {"hexsha": "d7b9607848de2585415aac4ac43c5891e9257879", "size": 7928, "ext": "py", "lang": "Python", "max_stars_repo_path": "Final/prediction/nb1.py", "max_stars_repo_name": "NaguGowda/machin-learning-", "max_stars_repo_head_hexsha": "dd04f44a06d6f83e58ed6eb4d69db09620040e49", "max_stars_repo_licenses": ["Apache-2.0"], ... |
import numpy as np
import igraph as ig
import copy
import pickle
import os
def initialize_world(config):
g = ig.Graph()
hub_cities = config.get('hub_cities', [str(i) for i in range(5)])
for i, hub_city in enumerate(hub_cities):
hub_members = int(
config.get('hub_starting_members_avg',... | {"hexsha": "1e56642bd0fb96df32c3339cd041ce5f451c2943", "size": 9614, "ext": "py", "lang": "Python", "max_stars_repo_path": "simulation_code.py", "max_stars_repo_name": "keldLundgaard/ThousandNetwork_simulation", "max_stars_repo_head_hexsha": "d09cec2b2e56bb9945f513ec1b814da81a3ed90e", "max_stars_repo_licenses": ["MIT"]... |
#ifndef CANARD_NET_OFP_V13_HELLO_ELEMENTS_VERSIONBITMAP_HPP
#define CANARD_NET_OFP_V13_HELLO_ELEMENTS_VERSIONBITMAP_HPP
#include <cstddef>
#include <cstdint>
#include <algorithm>
#include <iterator>
#include <stdexcept>
#include <utility>
#include <boost/algorithm/cxx11/all_of.hpp>
#include <boost/container/vector.hpp... | {"hexsha": "14ca51f26a62c0a86d3d6ee53101eeea96bcc698", "size": 7459, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "include/canard/net/ofp/v13/hello_element/versionbitmap.hpp", "max_stars_repo_name": "amedama41/bulb", "max_stars_repo_head_hexsha": "2e9fd8a8c35cfc2be2ecf5f747f83cf36ffbbdbb", "max_stars_repo_licens... |
[STATEMENT]
lemma aboveS_decr:
assumes TRANS: "trans r" and ANTISYM: "antisym r" and
REL: "(a,b) \<in> r"
shows "aboveS r b \<le> aboveS r a"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. aboveS r b \<subseteq> aboveS r a
[PROOF STEP]
proof(unfold aboveS_def, auto)
[PROOF STATE]
proof (state)
goal (2 subgoa... | {"llama_tokens": 1265, "file": null, "length": 12} |
# Copyright 2016 The TensorFlow Authors. 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 required by applica... | {"hexsha": "31aa5e72e7def46e861ed65cd986fc1961302c0f", "size": 8594, "ext": "py", "lang": "Python", "max_stars_repo_path": "tensorflow/contrib/distributions/python/kernel_tests/multinomial_test.py", "max_stars_repo_name": "steven0820/tensorflow", "max_stars_repo_head_hexsha": "36ebbf1ddc3ed820b7a5572ff4ed8e9bc707b8e5",... |
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
from .encoder import GaussianEncoderBase
def he_init(m):
s = np.sqrt(2. / m.in_features)
m.weight.data.normal_(0, s)
class MaskedConv2d(nn.Conv2d):
def __init__(self, include_center=False, *args, **kwargs):
su... | {"hexsha": "2272d1c5c6c00f8a1a077276ac7a0faf94b7ecfa", "size": 3589, "ext": "py", "lang": "Python", "max_stars_repo_path": "modules/encoders/enc_resnet.py", "max_stars_repo_name": "tom-pelsmaeker/vae-lagging-encoder", "max_stars_repo_head_hexsha": "b190239019a94c85858d188a0853886eb48ce4be", "max_stars_repo_licenses": [... |
/-
Copyright (c) 2020 Kenny Lau. All rights reserved.
Released under Apache 2.0 license as described in the file LICENSE.
Authors: Kenny Lau
-/
import Mathlib.PrePort
import Mathlib.Lean3Lib.init.default
import Mathlib.algebra.big_operators.pi
import Mathlib.data.finsupp.default
import Mathlib.PostPort
universes u_1 u... | {"author": "AurelienSaue", "repo": "Mathlib4_auto", "sha": "590df64109b08190abe22358fabc3eae000943f2", "save_path": "github-repos/lean/AurelienSaue-Mathlib4_auto", "path": "github-repos/lean/AurelienSaue-Mathlib4_auto/Mathlib4_auto-590df64109b08190abe22358fabc3eae000943f2/Mathlib/algebra/big_operators/finsupp.lean"} |
import os
import warnings
import numpy as np
def nonzeros(m, row):
"""returns the non zeroes of a row in csr_matrix"""
for index in range(m.indptr[row], m.indptr[row + 1]):
yield m.indices[index], m.data[index]
_checked_blas_config = False
def check_blas_config():
"""checks to see if using Op... | {"hexsha": "d19ce9747e1994d85754dde995b30b45c658d1af", "size": 4459, "ext": "py", "lang": "Python", "max_stars_repo_path": "implicit/utils.py", "max_stars_repo_name": "bingxu01/implicit", "max_stars_repo_head_hexsha": "91215f1c278dd8ed455dd0dcff707af86f59371f", "max_stars_repo_licenses": ["MIT"], "max_stars_count": nul... |
import os
import sys
file_dir = os.path.dirname(__file__)
sys.path.append(file_dir)
import numpy as np
import torch
from pytorch_resnet import ResNet43
from e2cnn import gspaces
import torch.nn.functional as F
import e2cnn.nn as enn
import kornia as K
import torchvision
from matplotlib import pyplot as plt
class Tra... | {"hexsha": "bb91a2e3bd0f64e39d109dbb944c7375b0384b4c", "size": 7448, "ext": "py", "lang": "Python", "max_stars_repo_path": "networks/non_equi_transport.py", "max_stars_repo_name": "HaojHuang/Equivariant-Transporter-Net", "max_stars_repo_head_hexsha": "f3bd4bb0d669b54be9385a3246355a6f68a6bfea", "max_stars_repo_licenses"... |
# ---
# jupyter:
# jupytext:
# text_representation:
# extension: .py
# format_name: light
# format_version: '1.5'
# jupytext_version: 1.4.2
# kernelspec:
# display_name: Python 3
# language: python
# name: python3
# ---
# +
import pathlib
import os.path
import random
import ... | {"hexsha": "0269168038f06a32fce1e7ced409f022c7318c2f", "size": 976, "ext": "py", "lang": "Python", "max_stars_repo_path": "demo/preprocess_mini.py", "max_stars_repo_name": "CSCI4850/S20-team6-project", "max_stars_repo_head_hexsha": "b7968357518ec21a676460594d3912575c5d0606", "max_stars_repo_licenses": ["MIT"], "max_sta... |
from recurrentshop import*
from keras.layers import*
from keras.models import*
import numpy as np
import time
import sys
# Script for comparing performance of native keras and recurrentshop stacked RNN implementations
# We observe 20-30% speed ups on GPU
sys.setrecursionlimit(10000000)
# Params
rnn, rnn_cell = LS... | {"hexsha": "cef90ab877e59d13b3086387fb568000b6835246", "size": 1755, "ext": "py", "lang": "Python", "max_stars_repo_path": "examples/speed_test.py", "max_stars_repo_name": "arpitgogia/recurrentshop", "max_stars_repo_head_hexsha": "5cc58af3020c19ec9597944fdd4d33dfb29e9bec", "max_stars_repo_licenses": ["MIT"], "max_stars... |
import unittest
import itertools
# note: this is a Python 3.3 change, clean this up for OpenMDAO 3.x
try:
from collections.abc import Iterable
except ImportError:
from collections import Iterable
import numpy as np
import openmdao.api as om
from openmdao.utils.mpi import MPI
try:
from parameterized imp... | {"hexsha": "40086bb957c508246da13e83db39904bcf258178", "size": 13619, "ext": "py", "lang": "Python", "max_stars_repo_path": "openmdao/core/tests/test_deriv_transfers.py", "max_stars_repo_name": "gjkennedy/OpenMDAO", "max_stars_repo_head_hexsha": "06897b584403cce34bc106dd2840aa07eea69e96", "max_stars_repo_licenses": ["A... |
! This program calculates the matrix elements of the positronium-hydrogen
! matrices (similar to equation 2.15 of the Armour and Humberston article). Specifically,
! we are calculating elements of the form (phi_i, L phi_j) as in equation (3.22).
! This does use OpenMP to speed up computation on multicore processors,... | {"hexsha": "648aeec2f89ad6dbc100a864365f2de470d0f460", "size": 44989, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "General Code/Short-Range/Quadruple Precision/PsHMain.f90", "max_stars_repo_name": "DentonW/Ps-H-Scattering", "max_stars_repo_head_hexsha": "943846d1deadbe99a98d2c2e26bcebf55986d8e7", "max_stars... |
# -*- coding:utf-8 -*-
from __future__ import division, absolute_import
import numpy as np
from simple_ml.base.base_error import *
from simple_ml.base.base_model import BaseTransform
__all__ = ['PCA', 'SuperPCA']
class PCA(BaseTransform):
def __init__(self, top_n):
super(PCA, self).__init__()
... | {"hexsha": "1bf8380cd3c5417c15203471b6005d8a4252bc27", "size": 3119, "ext": "py", "lang": "Python", "max_stars_repo_path": "simple_ml/pca.py", "max_stars_repo_name": "Yangruipis/simple_ml", "max_stars_repo_head_hexsha": "09657f6b017b973a5201aa611774d6ac8f0fc0a2", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2... |
great_ne(q1,h1).
great_ne(t1,hh1).
great_ne(v1,w1).
great_ne(kk1,ff1).
great_ne(bb1,f1).
great_ne(jj1,f1).
great_ne(m1,c1).
great_ne(jj1,aa1).
great_ne(p1,ii1).
great_ne(p1,dd1).
great_ne(t1,m1).
great_ne(v1,i1).
great_ne(dd1,bb1).
great_ne(jj1,k1).
great_ne(cc1,kk1).
great_ne(aa1,i1).
great_ne(m1,v1).
great_ne(cc1,y1)... | {"hexsha": "13ce3a30e97be9870d0d5231d7680b33c5aa6cda", "size": 604, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "foldsCreator/files/datasets/alzheimer_anime_noiseless/test10.f", "max_stars_repo_name": "manoelfranca/cilppp", "max_stars_repo_head_hexsha": "732189159a64e56cd32a5ad8a9010d928f4d41c6", "max_stars_r... |
import os.path
import random
import numpy as np
from matplotlib import pyplot as plt
import mnist
from network import NeuralNetwork
# Download dataset
if(not os.path.exists('mnist.pkl')): mnist.get()
# Load dataset
training_data, training_labels, testing_data, testing_labels = mnist.load()
# Create NN
nn = NeuralN... | {"hexsha": "eb55a1ec805b40571728fbdd8ed6e9bdb33e4573", "size": 910, "ext": "py", "lang": "Python", "max_stars_repo_path": "test.py", "max_stars_repo_name": "TheGameDevDude/DigitRecognition", "max_stars_repo_head_hexsha": "59269d640b89f991ab32b50042e5a26179f89b2d", "max_stars_repo_licenses": ["MIT"], "max_stars_count": ... |
# Diffusion maps
# --------------
# Diffusion maps,
# Coifman, R. & Lafon, S., Applied and Computational Harmonic Analysis, Elsevier, 2006, 21, 5-30
#### DiffMap type
struct DiffMap{T <: AbstractFloat} <: SpectralResult
t::Int
ɛ::Float64
K::AbstractMatrix{T}
proj::Projection{T}
DiffMap{T}(t::Int, ... | {"hexsha": "16eb59e45769b7106e83ff0bfb848ac999f4caf7", "size": 1581, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/diffmaps.jl", "max_stars_repo_name": "simonschoelly/ManifoldLearning.jl", "max_stars_repo_head_hexsha": "ac0f75fd0ea71a625e939d36d3f2ae118fdb2069", "max_stars_repo_licenses": ["MIT"], "max_star... |
import os
extension_modules = {}
directory = 'src/xxdata_11'
sources = ['xxdata_11.for', 'xxrptn.for', 'i4unit.for',
'i4fctn.for', 'xxword.for', 'xxcase.for', 'xfelem.for', 'xxslen.for',
'../xxdata_11.pyf', '../helper_functions.for']
extension_modules['_xxdata_11'] = dict(sources=sources, directory=directory)... | {"hexsha": "717dbf662027141d041c7f93c7269922a2586b17", "size": 1230, "ext": "py", "lang": "Python", "max_stars_repo_path": "setup.py", "max_stars_repo_name": "wagdav/atomic", "max_stars_repo_head_hexsha": "c54225abdb607c53a9d35658d381635403c751fb", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 3, "max_stars_re... |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
#
# Copyright (c) 2019 Ryan L. Collins <rlcollins@g.harvard.edu>
# and the Talkowski Laboratory
# Distributed under terms of the MIT license.
"""
Create single BED file of pext scores per base per gene
"""
import json
from pandas import to_numeric
from numpy import nanm... | {"hexsha": "344315eb327c3f2c441564e0a509f8b3e96b105d", "size": 3989, "ext": "py", "lang": "Python", "max_stars_repo_path": "data_curation/gene/process_pext.py", "max_stars_repo_name": "talkowski-lab/rCNV2", "max_stars_repo_head_hexsha": "fcc1142d8c13b58d18a37fe129e9bb4d7bd6641d", "max_stars_repo_licenses": ["MIT"], "ma... |
line_defaults() = Dict{Symbol, Any}([
:labelV => nothing
]);
"""
$(SIGNATURES)
Line graph. Simple wrapper around `lines`.
"""
function line_plot(xV, yV :: AbstractVector{F};
fig = blank_plot(), pos = (1,1), kwargs ...) where F
args = merge(line_defaults(), kwargs);
ax = make_axis(fig, pos; args.... | {"hexsha": "b20f660699d8a84ff6b076ccbc4d635e003aac46", "size": 2334, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/line_graphs.jl", "max_stars_repo_name": "hendri54/CollegeStratMakie", "max_stars_repo_head_hexsha": "ece1b3be11b2a8528ae7b4eba78b9e9e1b457ec0", "max_stars_repo_licenses": ["MIT"], "max_stars_co... |
# Inspired by Self-Driving-Car Nano Degree from Udacity
# Assuming that we know the region which is of interest to us
# Eg: This could be our knowledge of how and where the camera is mounted, therefore what part of the image would have
# the road
import os
import matplotlib.pyplot as plt
import matplotlib.image as m... | {"hexsha": "db90d9531243f73018719802d29296aeacafb0f3", "size": 1710, "ext": "py", "lang": "Python", "max_stars_repo_path": "image_processing/basics/002_region_selection.py", "max_stars_repo_name": "eshanmherath/AV-Perception", "max_stars_repo_head_hexsha": "ec56065621141c436d8be39094f4505a6971e796", "max_stars_repo_lic... |
[STATEMENT]
theorem load_after_alloc_2:
assumes "alloc h c s = Success (h', cap)"
and "|t|\<^sub>\<tau> \<le> s"
and "block_id cap \<noteq> block_id cap'"
shows "load h' cap' t = load h cap' t"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. load h' cap' t = load h cap' t
[PROOF STEP]
using assms
[PROOF S... | {"llama_tokens": 1021, "file": "CHERI-C_Memory_Model_CHERI_C_Concrete_Memory_Model", "length": 3} |
from sympy import Symbol, integrate, Eq, Mul, Add, Float, cos, sin, Pow, Unequality, core
import sympy
import re
"""First try to read general sin or cos functions from arguments"""
def gchsnew(funct, der, leng, symbo):
inte = 0
inte2 = 1
print(('in gcf'))
if re.search("sin", str(funct)) is not None or ... | {"hexsha": "c3299999beda3d1ee5a23fd0e1e7b82ce9800ce4", "size": 7823, "ext": "py", "lang": "Python", "max_stars_repo_path": "System_of_Beams/testing_further_possibilties/integ_general.py", "max_stars_repo_name": "ChairOfStructuralMechanicsTUM/Mechanics_Apps", "max_stars_repo_head_hexsha": "b064a42d4df3fa9bde62a5cff9cb27... |
from model import create_model
from ..data import DataLoader
from pathlib import Path
import numpy as np
import os
import matplotlib.pyplot as plt
import itertools
from sklearn.metrics import confusion_matrix
def shuffle(x_train, y_train):
idx = np.arange(x_train.shape[0])
np.random.shuffle(idx)
return x... | {"hexsha": "8facd435d8b201a8ca8750cc44ea5ae355d3b794", "size": 4892, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/models/train_model.py", "max_stars_repo_name": "akash-harijan/cataract-detection", "max_stars_repo_head_hexsha": "ccb7045290a7a002bba1ff68220d19ec3a79ea2d", "max_stars_repo_licenses": ["MIT"],... |
[STATEMENT]
lemma \<Gamma>\<^sub>A\<^sub>O\<^sub>D\<^sub>V_skeleton_wf [simp]:
"wellformed \<Gamma>\<^sub>A\<^sub>O\<^sub>D\<^sub>V_skeleton"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. wellformed \<Gamma>\<^sub>A\<^sub>O\<^sub>D\<^sub>V_skeleton
[PROOF STEP]
proof (rule, intro allI)
[PROOF STATE]
proof (state)... | {"llama_tokens": 459, "file": "AODV_Aodv", "length": 5} |
#include <CExprI.h>
#include <boost/math/special_functions/erf.hpp>
#include <CMathGen.h>
#include <CInvNorm.h>
#include <COSNaN.h>
#include <cmath>
#include <ccomplex>
#include <cstdlib>
#include <cstring>
// NOTE: types are only needed if normal conversion rules don't handle the type correctly
#ifdef GNUPLOT_EXPR
... | {"hexsha": "2e8b57b3bb6c997d99ecd4740a75a301d6f271c6", "size": 26651, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/CExprFunction.cpp", "max_stars_repo_name": "colinw7/CQGnuPlot", "max_stars_repo_head_hexsha": "8001b0a0d40c1fde8e5efe05ebe0c9b0541daa94", "max_stars_repo_licenses": ["MIT"], "max_stars_count": ... |
"""
_to_union(datatype)
Make a minimal `Union` type from a collection of data types.
"""
_to_union(datatype) = Union{(isa(datatype, Type) ? [datatype] : datatype)...}
"""
_find_rand_argmax(d::DictionaryView)
Compute `argmax` of `d` and select one element randomly.
"""
function _find_rand_argmax(d::DictionaryVi... | {"hexsha": "27c143498cc7bfbc698cfde58ed7467ea1a4b03b", "size": 818, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/utils.jl", "max_stars_repo_name": "JuliaConstraints/LocalSearchSolvers.jl", "max_stars_repo_head_hexsha": "cd34500d131b05afdef4383f9a3b32a7e0c4ec41", "max_stars_repo_licenses": ["MIT"], "max_sta... |
import pandas
class LiteralCache:
"""class which stores literals
and corresponding truth values
e.g. [
"food=banana": [True, True, False, False, True],
"food=apple" : [True, True, True, True, False]
]
"""
def __init__(self):
self.__cache = {}
def insert(s... | {"hexsha": "2a7c6a6be2b6d0e86c04d8bdedf5377bc6ecd0eb", "size": 10187, "ext": "py", "lang": "Python", "max_stars_repo_path": "build/lib/pyarc/qcba/data_structures/quant_dataset.py", "max_stars_repo_name": "jirifilip/CBA", "max_stars_repo_head_hexsha": "59168ef6fb4c9e319475f9a7498446ba5ff306e1", "max_stars_repo_licenses"... |
# -*- coding: utf-8 -*-
# --------------------------------------------------------
# Tensorflow TIN
# Licensed under The MIT License [see LICENSE for details]
# --------------------------------------------------------
from __future__ import absolute_import
from __future__ import division
from __future__ import print_f... | {"hexsha": "a9f45b2dc2f00774d784f3850af0496aa2a8631d", "size": 35705, "ext": "py", "lang": "Python", "max_stars_repo_path": "lib/networks/TIN_HICO_with_part.py", "max_stars_repo_name": "enlighten0707/Transferable-Interactiveness-Network", "max_stars_repo_head_hexsha": "5ffbf1d0779702225bac37d6cc5d3ddf9b17a1cd", "max_st... |
import pandas as pd
import numpy as np
import os
import time
import regex as re
import math
from underthesea import word_tokenize
from utils import remove_html, remove_emojis, covert_unicode, lowercase_remove_noise_character
def clean_review(review_str):
clean_string = review_str.replace("\n","")
clean_st... | {"hexsha": "0bd7599b9bafb7f88382f3595698ab168b0b3ef6", "size": 890, "ext": "py", "lang": "Python", "max_stars_repo_path": "Model/preprocess.py", "max_stars_repo_name": "Haiduongcable/DataScience-SentimentAnalysis", "max_stars_repo_head_hexsha": "2500b70d2ef66202d47e5277c8bd6ebcb8a6a905", "max_stars_repo_licenses": ["Ap... |
from keras.preprocessing.image import ImageDataGenerator, array_to_img, img_to_array, load_img
from keras.models import Model
from keras.layers import Conv2D, MaxPooling2D
from keras.layers import Activation, Dropout, Flatten, Dense, BatchNormalization
from keras.applications import MobileNet
import os
import numpy as ... | {"hexsha": "b9dd9e73252395dbca1691e25725df8c454e2328", "size": 4022, "ext": "py", "lang": "Python", "max_stars_repo_path": "T2/src/task2.py", "max_stars_repo_name": "pedromsfernandes/VCOM1920", "max_stars_repo_head_hexsha": "c50874c32e1e470bd30bed5b732737ac55ef40a5", "max_stars_repo_licenses": ["MIT"], "max_stars_count... |
import numpy as np
from sklearn.svm import SVC
def run(x_train, y_train, x_test, y_test, clf):
clf.fit(x_train, y_train)
return clf.score(x_test, y_test)
def split(x,y,k,m):
ns = int(y.shape[0]/m)
s = []
for i in range(m):
s.append([x[(ns*i):(ns*i+ns)], y[(ns*i):(ns*i+ns)]])
x_test, y... | {"hexsha": "15b4bab6c47fac7f74056bd62e3e4f1c59fb3d46", "size": 1520, "ext": "py", "lang": "Python", "max_stars_repo_path": "chapter_07/bc_rbf_svm_search.py", "max_stars_repo_name": "haloway13/PracticalDeepLearningPython", "max_stars_repo_head_hexsha": "c3760b17945c9389421c2970a3d16c6528fb7af6", "max_stars_repo_licenses... |
# This third demo shows how a robot swarm can autonomously choose an open curve shape and form
# the shape in a distributed way. This simulation shares the same strategy with second demo in
# organizing the robots, but it needs no role assignment on the open curve.
# input arguments:
# '-n': number of robots
# '--manu... | {"hexsha": "be0ae99da0ea54f21092034b028e6b9dffd4751c", "size": 60234, "ext": "py", "lang": "Python", "max_stars_repo_path": "demo_3.py", "max_stars_repo_name": "yangliu28/swarm_formation_sim", "max_stars_repo_head_hexsha": "5c6dd025667338103500c35b2ecee7aceec886a1", "max_stars_repo_licenses": ["MIT"], "max_stars_count"... |
\documentclass[12pt,titlepage]{article}
\setlength{\oddsidemargin}{0in}
\setlength{\evensidemargin}{0in}
\setlength{\textwidth}{6.5in}
%
\setlength{\textheight}{9in}
\setlength{\topmargin}{0in}
\setlength{\headsep}{0in}
\setlength{\topskip}{0in}
\setlength{\headheight}{0in}
\usepackage{graphicx}
\usepackage{times}
\u... | {"hexsha": "2b1f8781b61c0fd34e9a5ee757a0b87a19ac002c", "size": 6859, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "reports/2019Q3/Couch_Q3_CY2019.tex", "max_stars_repo_name": "smcouch/INCITE_2018", "max_stars_repo_head_hexsha": "dd87ad70e934d4c274ab90e37b90b3c7dc2f61e7", "max_stars_repo_licenses": ["BSD-3-Clause... |
import Mathlib.Data.Int.Basic
import Mathlib.Data.Nat.Prime
import Mathlib.Tactic.LibrarySearch
import Mathlib.Tactic.Linarith
import Aesop
import Mathlib.Data.Set.Basic
/-
Lean is a language that we will be using in CS22 this year.
If you're in this class, you've most likely used a programming language before.
Lean... | {"author": "robertylewis", "repo": "leanclass", "sha": "f609276675431388632d46619581bdb7c557be50", "save_path": "github-repos/lean/robertylewis-leanclass", "path": "github-repos/lean/robertylewis-leanclass/leanclass-f609276675431388632d46619581bdb7c557be50/BrownCs22/Demos/01-intro.lean"} |
import easycorrector.ngram_model.load_model as load_model
import numpy as np
import easycorrector.common.common as common
model_name = "ngram_model"
def correct(text):
lm = load_model.get_char_ngram_lm_model()
maybe_errors = []
if not text.strip():
return maybe_errors
ngram_avg_scores = []
... | {"hexsha": "3f086fdf2c8d8c08eb7e43d7442b1c010e69d795", "size": 2535, "ext": "py", "lang": "Python", "max_stars_repo_path": "easycorrector/ngram_model/ngram_correct.py", "max_stars_repo_name": "milter001/text_corrector", "max_stars_repo_head_hexsha": "ca28fe0ebc008c1c9b1c640eacb78e876e9a3e84", "max_stars_repo_licenses":... |
import os
import time
import json
from textblob import TextBlob
import htmllib
import difflib
import pandas as pd
import numpy as np
import sklearn
from bs4 import BeautifulSoup
from src.helper.collection import handle_error, light_error_handle, get_response
from src.helper.constant import ANSWER, QUESTION, TAG
cla... | {"hexsha": "08807f646e803dcbfb95945906db2526e48e4794", "size": 2273, "ext": "py", "lang": "Python", "max_stars_repo_path": "Python-Server/src/module/ml_handler.py", "max_stars_repo_name": "MCD-50/OpenEd", "max_stars_repo_head_hexsha": "e6cf6d05b3c002de21bdd69b3adbe9c6602d3cc3", "max_stars_repo_licenses": ["MIT"], "max_... |
#!/usr/bin/python
# -*- coding: utf-8 -*-
"""
CSV File for NAB
Usage:
nabcsv.py [--cmd cmdtype] --threshold threshold --input inputfile --output outputfile
cmdtype ; score by default, to convert nab result file
; flag, to convert flaginfo.csv
; prune, to remove datapoints not in gree... | {"hexsha": "862ea5b8ed58bc0289746d10cf93d8bd486946e7", "size": 5866, "ext": "py", "lang": "Python", "max_stars_repo_path": "docs/report/fa20-523-349/project/RankNet/indycar/nabcsv.py", "max_stars_repo_name": "mikahla1/cybertraining-dsc.github.io", "max_stars_repo_head_hexsha": "168cadb2f755cb6ad4907e5656bd879d57e01e43"... |
library(tidyverse)
df <- read_csv("Top2000_extra_columns_spotify_genre.csv")
df$...1 <- NULL
colnames(df) <- tolower(colnames(df))
audio_features <- c("tempo","danceability","energy","valence","loudness","instrumentalness")
col_years <- colnames(df)[grepl("[0-9]{4}", colnames(df))]
non_col_years <- colnames(df)[!grep... | {"hexsha": "e61703d3607cbff99bc00cbaf431ecb86aa2c3f8", "size": 906, "ext": "r", "lang": "R", "max_stars_repo_path": "shiny/global.r", "max_stars_repo_name": "AzucenaMV/top2000-dashboard", "max_stars_repo_head_hexsha": "d1fa465469024e7b97d8db8160ae85199b8f5642", "max_stars_repo_licenses": ["MIT"], "max_stars_count": nul... |
#!/usr/bin/python
import cv2
from Brain import Brain
from os import listdir
import numpy as np
imageSize = Brain.IMAGE_SIZE
# tools
def read_image(path, scale_size=imageSize):
img = cv2.resize(cv2.imread(path, 0), scale_size)
d = np.asarray(img)
d = d.reshape((1, scale_size[0] * scale_size[1]))
retu... | {"hexsha": "cac46b5a64415a03e254ca0739bc84016cf0549e", "size": 2515, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/modules/ml/mmlcore.py", "max_stars_repo_name": "ArefMq/SoccerBallDetection", "max_stars_repo_head_hexsha": "3df55ed96fb42c5bc85d10f69a21eaa5bc3a948e", "max_stars_repo_licenses": ["MIT"], "max_... |
# code for running scalability experiments in JAIR submission
import sys
import numpy as NP
import random
import math
import time
import scipy
from tensorlog import comline
from tensorlog import dataset
from tensorlog import declare
from tensorlog import expt
from tensorlog import funs
from tensorlog import interp
fr... | {"hexsha": "c01ea3c7d501217fcafe986e90d1f985677a3433", "size": 4991, "ext": "py", "lang": "Python", "max_stars_repo_path": "datasets/grid/bigexpt.py", "max_stars_repo_name": "saraswat/TensorLog", "max_stars_repo_head_hexsha": "c56cebfa33b5123d5340a7b429e333da09d223d8", "max_stars_repo_licenses": ["Apache-2.0"], "max_st... |
submodule (real_transform_routines) real_initialization_routines
contains
module subroutine rfft1i(n, wsave, lensav, ierror)
!
! rfft1i: initialization for rfft1b and rfft1f.
!
! purpose:
!
! rfft1i initializes array wsave for use in its companion routines
... | {"hexsha": "df9d1394ad6b65124afb63689ebeca644be0b9ce", "size": 14053, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "src/real_initialization_routines.f90", "max_stars_repo_name": "jbdv-no/modern_fftpack", "max_stars_repo_head_hexsha": "6909d44988925dcae1ee478c06be31e5605d3974", "max_stars_repo_licenses": ["MI... |
[STATEMENT]
lemma sameDom_sym:
"sameDom inp inp' = sameDom inp' inp"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. sameDom inp inp' = sameDom inp' inp
[PROOF STEP]
unfolding sameDom_def
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. (\<forall>i. (inp i = None) = (inp' i = None)) = (\<forall>i. (inp' i = None) = ... | {"llama_tokens": 162, "file": "Binding_Syntax_Theory_Preliminaries", "length": 2} |
#coding:utf-8
import numpy as np
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.feature_extraction.text import TfidfTransformer
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn import svm
from sklearn.neighbors import KNeighborsClassifier
from sklearn.linear_model impo... | {"hexsha": "b87be4f8ec1f43d0c87370f86c8a1a45b3382db5", "size": 4569, "ext": "py", "lang": "Python", "max_stars_repo_path": "tfidf.py", "max_stars_repo_name": "ezhouyang/class", "max_stars_repo_head_hexsha": "6bef374a78bdef8df946680c283df9643ac91ed7", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": 1, "max... |
# String matching functions for Aaron
using StringDistances
# Fairly arbitrarily chosen list of stop words
const stopwords = [
"the",
"is",
"at",
"which",
"on",
"in",
"for",
"with"
]
function clean_string(x)
xlwr = lowercase(x)
xcln = replace(xlwr, r"[^-a-z]", " ") # keep ... | {"hexsha": "c41d11ea11add4a81167bc273785d6d40ca51c83", "size": 3668, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "string_matching.jl", "max_stars_repo_name": "paulstey/string_matching", "max_stars_repo_head_hexsha": "2cd307eecd732d3ff674764f0e80e34fbc5658bb", "max_stars_repo_licenses": ["MIT"], "max_stars_coun... |
import numpy as np
#from gym.envs.mujoco import mujoco_env
#from gym import utils
import os
import gym
from meta_mb.logger import logger
from gym.envs.mujoco.mujoco_env import MujocoEnv
from meta_mb.meta_envs.base import MetaEnv
from meta_mb.meta_envs.base import RandomEnv
class FetchJellyEnv(RandomEnv, gym.utils.EzPi... | {"hexsha": "c5a79aa1470ede48b0c7f0a43ff6ee1745f80c0e", "size": 2971, "ext": "py", "lang": "Python", "max_stars_repo_path": "meta_mb/envs/jelly/fetch_jelly.py", "max_stars_repo_name": "iclavera/meta-mb", "max_stars_repo_head_hexsha": "a1204e573c1415161129403cfb287bf120488fd0", "max_stars_repo_licenses": ["MIT"], "max_st... |
import numbers
import time
import numpy as np
import scipy
from sklearn.utils.extmath import safe_sparse_dot
from sklearn.decomposition.nmf import _beta_divergence, _beta_loss_to_float
from scipy.special import expit
from scipy.sparse import issparse
USE_CYTHON = False # currently, cython is disabled due to unsolved... | {"hexsha": "69671c4d6a40be260c03099c6cc0d08c58d76dec", "size": 22938, "ext": "py", "lang": "Python", "max_stars_repo_path": "pycmf/cmf_solvers.py", "max_stars_repo_name": "smn-ailab/PyCMF", "max_stars_repo_head_hexsha": "f2c3b7117b2b3b59d73d472f11c9bb1a6d537fe0", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2... |
"""Numpy based linear algebra backend."""
import numpy as np
def det(*args, **kwargs):
return np.linalg.det(*args, **kwargs)
def norm(*args, **kwargs):
return np.linalg.norm(*args, **kwargs)
def inv(*args, **kwargs):
return np.linalg.inv(*args, **kwargs)
def matrix_rank(*args, **kwargs):
return... | {"hexsha": "7e99c3e06e97a25c70fa2ecb9b7c447f47a5024e", "size": 715, "ext": "py", "lang": "Python", "max_stars_repo_path": "geomstats/backend/numpy_linalg.py", "max_stars_repo_name": "effigies/geomstats", "max_stars_repo_head_hexsha": "0d6979a15cefcf98f7f92bade9d0e4abee3dde14", "max_stars_repo_licenses": ["MIT"], "max_s... |
# -*- coding: utf-8 -*-
"""\
Copyright (c) 2015-2018, MGH Computational Pathology
"""
from __future__ import print_function
from numpy.random.mtrand import RandomState
from calicoml.core.utils import with_numpy_arrays, format_p_value
import numpy as np
import pandas as pd
import sklearn
from scipy.stats import pe... | {"hexsha": "38659f7da7f72ca443e2135d119f5426d5b0e444", "size": 8089, "ext": "py", "lang": "Python", "max_stars_repo_path": "calicoml/core/metrics.py", "max_stars_repo_name": "MGHComputationalPathology/CalicoML", "max_stars_repo_head_hexsha": "e296f1cc0a78c4bf05e719aae96d8ea2e9d6c03c", "max_stars_repo_licenses": ["BSD-3... |
//----------------------------------*-C++-*----------------------------------//
/**
* @file Material.hh
* @author Jeremy Roberts
* @brief Material class definition.
*/
//---------------------------------------------------------------------------//
#ifndef detran_material_MATERIAL_HH_
#define detran_material_... | {"hexsha": "e91828d43df5049e8086a77f4e5f5ae8fe428131", "size": 9008, "ext": "hh", "lang": "C++", "max_stars_repo_path": "src/material/Material.hh", "max_stars_repo_name": "baklanovp/libdetran", "max_stars_repo_head_hexsha": "820efab9d03ae425ccefb9520bdb6c086fdbf939", "max_stars_repo_licenses": ["MIT"], "max_stars_count... |
function [d,fp,dt,tc,t]=readhtk(file)
%READHTK read an HTK parameter file [D,FP,DT,TC,T]=(FILE)
%
% d is data, fp is frame period in seconds
% dt is data type, tc is full type code, t is a text version of the full typecode
% tc is the sum of the following values:
% 0 WAVEFORM
% 1 LPC
% 2 LPREFC
% 3 LPCEPST... | {"author": "stephencwelch", "repo": "Perceptual-Coding-In-Python", "sha": "2993f57570663768c02745019185091a23f021fe", "save_path": "github-repos/MATLAB/stephencwelch-Perceptual-Coding-In-Python", "path": "github-repos/MATLAB/stephencwelch-Perceptual-Coding-In-Python/Perceptual-Coding-In-Python-2993f57570663768c02745019... |
from util import *
from util.functions import *
from util.plotting import *
import tensorflow.keras as keras
from keras.models import Sequential
from keras.layers import Dense
from keras.callbacks import LearningRateScheduler
from keras import initializers
from sklearn.cluster import KMeans
from sklearn.manifold import... | {"hexsha": "23165e8ce15cf7e5928ddd6b09c9195cc90f3c0f", "size": 13266, "ext": "py", "lang": "Python", "max_stars_repo_path": "util/methods.py", "max_stars_repo_name": "narutatsuri/deep_embedded_clustering", "max_stars_repo_head_hexsha": "b38be4742b31d651cf4a04152dd7fc4fd049b312", "max_stars_repo_licenses": ["MIT"], "max... |
/** @file InterpolationTest.cpp
*
* Copyright (c) 2020 IACE
*/
#define BOOST_TEST_MODULE InterpolationTest
#include "Interpolation.h"
#include <boost/test/unit_test.hpp>
BOOST_AUTO_TEST_CASE( LinearInterpolatorBoundaryTest ) {
double dx[2] = {1, 2};
double dy[2] = {2, 4};
LinearInterpolator li(dx, d... | {"hexsha": "562bb763258a4cb5d63dba66ee751188ea0712db", "size": 2232, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "utils/InterpolationTest.cpp", "max_stars_repo_name": "ummerland/tool-libs", "max_stars_repo_head_hexsha": "8c2d635e067e827b67678b45d17670dd62ef83a0", "max_stars_repo_licenses": ["BSD-3-Clause"], "ma... |
import matplotlib
matplotlib.use("agg")
from matplotlib import pyplot as plt
plt.style.use("ggplot")
import seaborn as sns
from networkx import DiGraph
from IPython.core.display import Image
from .export import to_agraph
from .AnalysisGraph import AnalysisGraph
from .utils.misc import _insert_line_breaks
from functoo... | {"hexsha": "41c72a42d06204820a617fda41e523a9b8781ff2", "size": 2101, "ext": "py", "lang": "Python", "max_stars_repo_path": "delphi/visualization.py", "max_stars_repo_name": "cthoyt/delphi", "max_stars_repo_head_hexsha": "3df2de639905453f5d28d7a7b3b9f7e5a7a1fb0d", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_co... |
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as seabornInstance
from sklearn.linear_model import LassoLars
from sklearn import metrics
from sklearn.model_selection import train_test_split
dataset = pd.read_csv('Weather.csv')
X = dataset['MinTemp'].values.reshape(-1, ... | {"hexsha": "dcecba5aa0605b42dfea86c3ca5c57ee7248ff4c", "size": 1033, "ext": "py", "lang": "Python", "max_stars_repo_path": "Sklearn_Regression_Portfolio/Reg_LassoLars.py", "max_stars_repo_name": "KiLJ4EdeN/Sklearn_Regression_Portfolio", "max_stars_repo_head_hexsha": "bc779b82b35583c6f75abe69739dc6b698934047", "max_star... |
SUBROUTINE SCANINT( STRING, VALUE, NCHARS, NDIGITS )
C***********************************************************************
C Version "$Id: scanint.f 1 2017-06-10 18:05:20Z coats $"
C EDSS/Models-3 I/O API.
C Copyright (C) 1992-2002 MCNC and Carlie J. Coats, Jr., and
C (C) 2003-2010 by Baron Advanced Meteor... | {"hexsha": "a93c0eaf69f233684341e13037afdb0af9fdb7c5", "size": 3504, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "JPS_CITY_CHEM/citychem-1.3/preproc/bconcc2.2/ioapi3.2/ioapi/scanint.f", "max_stars_repo_name": "mdhillmancmcl/TheWorldAvatar-CMCL-Fork", "max_stars_repo_head_hexsha": "011aee78c016b76762eaf511c78f... |
"""
Parses Resume and returns skill,education,work experience
"""
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import spacy
import pickle
import random
import sys, fitz
import docx
import docx2txt
import os
from utils import constants as cs
import re
de... | {"hexsha": "5a7291bf89b0d39e4e9f94f186ac9601fcb102fc", "size": 4182, "ext": "py", "lang": "Python", "max_stars_repo_path": "resume_parser/resume_parser.py", "max_stars_repo_name": "ExtremelySunnyYK/Resume-Parser-Name-Entity-Recognition", "max_stars_repo_head_hexsha": "3ebab6eacf01d20fc90325c6696781c14286e1bf", "max_sta... |
#== # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # #
#
# Description
#
# Functions related to the analysis of the Right Ascension of the Ascending
# Node (RAAN).
#
# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # ==#
export compute_RAAN_lt, sim_RAAN_J2
"""
c... | {"hexsha": "e125e7a73afa63d3dd9f5996d386052b0c4b9251", "size": 2122, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/analysis/raan.jl", "max_stars_repo_name": "disberd/SatelliteToolbox.jl", "max_stars_repo_head_hexsha": "441470938af978e9d5653a9c4b36ccc107023960", "max_stars_repo_licenses": ["MIT"], "max_stars... |
# Copyright (c) 2020 Hartmut Kaiser
#
# Distributed under the Boost Software License, Version 1.0. (See accompanying
# file LICENSE_1_0.txt or copy at http://www.boost.org/LICENSE_1_0.txt)
# #1267: np.random.random_sample is not available
from phylanx import Phylanx
import numpy as np
@Phylanx
def test_random_in... | {"hexsha": "e335eb2163ecdbb2672e40752da69fa52e96b463", "size": 1075, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/regressions/python/1267_random_integers.py", "max_stars_repo_name": "NanmiaoWu/phylanx", "max_stars_repo_head_hexsha": "295b5f82cc39925a0d53e77ba3b6d02a65204535", "max_stars_repo_licenses": ... |
from typing import List, Optional
import copy
import numpy as np
class RuleBasedAgentWrapper(object):
def __init__(
self,
ruleBasedAgent:object,
player_idx:int,
nbr_actors:int
):
self.nbr_actors = nbr_actors
self.action_space_dim = ruleBasedAgent.action_s... | {"hexsha": "798b3a4e335c204c9e0c79c99b9ac28e42f0a10e", "size": 5621, "ext": "py", "lang": "Python", "max_stars_repo_path": "symbolic_behaviour_benchmark/utils/agent_wrappers.py", "max_stars_repo_name": "Near32/SymbolicBehaviourBenchmark", "max_stars_repo_head_hexsha": "d1f9f14ed186292e22802781f4737e6747cd8c64", "max_st... |
import numpy as np
import matplotlib.pyplot as plt
from photutils import Background2D, MedianBackground
from astropy.stats import SigmaClip
from skimage.transform import hough_circle, hough_circle_peaks
from skimage.feature import canny
from skimage.draw import circle_perimeter
from skimage import util, filters, morpho... | {"hexsha": "cfed6962168fd9faf4969c247f08594102893d71", "size": 12703, "ext": "py", "lang": "Python", "max_stars_repo_path": "script/vincent.py", "max_stars_repo_name": "bioimage-analysis/microarray_analysis_vincent", "max_stars_repo_head_hexsha": "ed3401a895c06550f2f12e74cbaf971a7578963c", "max_stars_repo_licenses": ["... |
# -*- coding: utf-8 -*-
"""
Created on Thu Aug 16 13:30:45 2018
@author: Lionel Massoulard
"""
import pandas as pd
import numpy as np
from sklearn.datasets import make_classification, make_regression
from sklearn.model_selection import train_test_split
from tests.helpers.testing_help_models import verif_model
fro... | {"hexsha": "94828cef8528be1c1572fa3a593df18acada9cfe", "size": 7297, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/models/test_rotation_forest.py", "max_stars_repo_name": "gfournier/aikit", "max_stars_repo_head_hexsha": "23257f365a4f387cbb86f0ed3994b696a81b57c6", "max_stars_repo_licenses": ["BSD-2-Clause... |
###############################################################################
#
# File: opencv_optical_flow.py
#
# Wrap OpenCV's optical flow functions to make them even easier to use
#
# History:
# 08-05-20 - Levi Burner - Created file
#
###############################################################################... | {"hexsha": "848532fe0ebc0825a719beed1f4d191c232b4d7b", "size": 1248, "ext": "py", "lang": "Python", "max_stars_repo_path": "motion_illusions/opencv_optical_flow.py", "max_stars_repo_name": "agganu/motion_illusions", "max_stars_repo_head_hexsha": "a5343bada7678827a53551e637e21fcd1a189a0d", "max_stars_repo_licenses": ["B... |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
# @Date : 2021/8/5 3:12 下午
# @File : filter_wrong.py
# @Author: johnson
# @Desc : 使用多个随机数种子训练模型，然后过滤出所有预测错误的样本，供以后进行分析
import argparse
import json
import os
import pandas as pd
from experiments.myexample.mydata_prepro import do_prepro, absa_source_file, dem8_source_fil... | {"hexsha": "7337e522a80c417c587adda707c51c76773b4b27", "size": 28595, "ext": "py", "lang": "Python", "max_stars_repo_path": "filter_wrong.py", "max_stars_repo_name": "johnson7788/mt-dnn", "max_stars_repo_head_hexsha": "26e5c4a5bfdbf1a1dd1c903e606db1c070568237", "max_stars_repo_licenses": ["MIT"], "max_stars_count": nul... |
! Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
! See https://llvm.org/LICENSE.txt for license information.
! SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
!** Test checking msv-vector-bits are passed correctly
! REQUIRES: aarch64-registered-target
! REQUIRES: llvm-13
! RUN: ... | {"hexsha": "fe1c2bd59643a9ded6ab7ba85ae142fabf6e3b95", "size": 1961, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "test/llvm_ir_correct/vscale-mbits.f90", "max_stars_repo_name": "vigbalu/flang", "max_stars_repo_head_hexsha": "d47f3a626687453df7d3dede01021f5891c2dacd", "max_stars_repo_licenses": ["Apache-2.0"... |
import sys
import PyQt5
from PyQt5.QtWidgets import QMainWindow, QApplication, QToolBar, QFileDialog, QMessageBox, QColorDialog
from PyQt5.QtCore import Qt
from PyQt5.QtGui import QImage, QPixmap, QColor
from labelseg.mainwindow import Ui_MainWindow
import cv2 as cv
from enum import Enum
import os
from pathlib import P... | {"hexsha": "d42b79fff4f4bc3370b43b40ef4a12f91fc99ef7", "size": 29072, "ext": "py", "lang": "Python", "max_stars_repo_path": "labelseg/app.py", "max_stars_repo_name": "1010098686/labelseg", "max_stars_repo_head_hexsha": "8602c55bd2dadc60fb9da5d193ecdf26dc24d398", "max_stars_repo_licenses": ["MIT"], "max_stars_count": nu... |
[STATEMENT]
lemma has_integral_cexp_minus_to_infinity:
fixes a::complex\<comment>\<open>TODO: generalize\<close>
assumes a: "0 < Re a"
shows "((\<lambda>x. exp (x *\<^sub>R - a)) has_integral exp (c *\<^sub>R - a) / a) {c..}"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. ((\<lambda>x. exp (x *\<^sub>R - a)) h... | {"llama_tokens": 542, "file": "Laplace_Transform_Existence", "length": 3} |
import cv2
import PIL.Image
import time
import numpy
import random
import string
from typing import List, Tuple
from types import ModuleType
import os
import multiprocessing
import multiprocessing.synchronize
from scriptorium.ocr import OCR
class CameraManager(multiprocessing.Process):
title = "Press any key to s... | {"hexsha": "dd3978beb25a52840fd74a18aa26a15fa2d92a73", "size": 3455, "ext": "py", "lang": "Python", "max_stars_repo_path": "scriptorium/camera.py", "max_stars_repo_name": "sevagh/Scriptorium", "max_stars_repo_head_hexsha": "26c1e59e2d96936e2f68bc66ce46a2805fde4bd4", "max_stars_repo_licenses": ["MIT"], "max_stars_count"... |
(* memory_model.thy *)
(* William Mansky *)
(* Memory model locales for PTRANS. *)
theory memory_model
imports "$AFP/List-Infinite/ListInfinite" AxiomaticModel
begin
(*
print_locale "ord"
instantiation option :: (ord) ord
begin
fun less_eq_option where
"(None \<le> None) = True"
| "(None \<le> (Some _ )) = True"... | {"author": "liyili2", "repo": "timed-relaxed-memory-model", "sha": "6d85bc75d8b04228b3e581b945e3f672395f0c66", "save_path": "github-repos/isabelle/liyili2-timed-relaxed-memory-model", "path": "github-repos/isabelle/liyili2-timed-relaxed-memory-model/timed-relaxed-memory-model-6d85bc75d8b04228b3e581b945e3f672395f0c66/me... |
\section{Model specification}
\label{sec:model_spec}
The model specification is shown in Table~\ref{tab:model_specification} for all the experiments in Section~\ref{sec:experiments}.
CIRAR10 ResNet uses the regular ResNet units while CIFAR100 ResNet uses the bottleneck units. Only the convolutional layers are shown wi... | {"hexsha": "f050e14d1c648e91fa9db6eac5f6c117ee67634e", "size": 2808, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "model_spec.tex", "max_stars_repo_name": "mitliagkas/dshs", "max_stars_repo_head_hexsha": "6d5262af72288dd06544c2d5831d0c198db251bb", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max... |
/*
Copyright 2010 Intel Corporation
Use, modification and distribution are subject to the Boost Software License,
Version 1.0. (See accompanying file LICENSE_1_0.txt or copy at
http://www.boost.org/LICENSE_1_0.txt).
*/
//layout_database.hpp
#ifndef BOOST_POLYGON_TUTORIAL_LAYOUT_DATABASE_HPP
#define BOOST_POLYGON_TUTO... | {"hexsha": "d3cc2b36b36f29c07a75c3374525fadd81964934", "size": 1387, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "boost/libs/polygon/doc/tutorial/layout_database.hpp", "max_stars_repo_name": "randolphwong/mcsema", "max_stars_repo_head_hexsha": "eb5b376736e7f57ff0a61f7e4e5a436bbb874720", "max_stars_repo_licenses... |
/********************************************************************************
* Copyright 2009 The Robotics Group, The Maersk Mc-Kinney Moller Institute,
* Faculty of Engineering, University of Southern Denmark
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file exce... | {"hexsha": "228c147c195642865d9e77b8c47cf0aff1b35c98", "size": 1441, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "RobWork/src/rw/core/Message.cpp", "max_stars_repo_name": "ZLW07/RobWork", "max_stars_repo_head_hexsha": "e713881f809d866b9a0749eeb15f6763e64044b3", "max_stars_repo_licenses": ["Apache-2.0"], "max_st... |
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