text stringlengths 0 1.25M | meta stringlengths 47 1.89k |
|---|---|
"""
output_type(f, arg_types...)
Return the output type of the specified function. Tries to be fast where possible.
"""
output_type(f, arg_types...) = Union{Base.return_types(f, arg_types)...}
output_type(::typeof(-), x) = x
# TODO more efficient versions for common exp & log cases
output_type(::typeof(+), x, y)... | {"hexsha": "9e0c7feb2c9ad1ab66b5a43ce18e2c8bd7b76621", "size": 742, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/output_type.jl", "max_stars_repo_name": "invenia/TimeDag.jl", "max_stars_repo_head_hexsha": "aa81512796f087815027c67dba4ab3766590a439", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 16,... |
Require Export RelDefinitions.
Require Export RelOperators.
Require Export Relators.
Require Import Delay.
(** ** The [monotonicity] tactic *)
(** The purpose of the [monotonicity] tactic is to automatically
select and apply a theorem of the form [Monotonic ?m ?R] in order to
make progress when the goal is an app... | {"author": "CertiKOS", "repo": "coqrel", "sha": "71699719ffd1cd8202e8ffeaf0faf890c765105a", "save_path": "github-repos/coq/CertiKOS-coqrel", "path": "github-repos/coq/CertiKOS-coqrel/coqrel-71699719ffd1cd8202e8ffeaf0faf890c765105a/Monotonicity.v"} |
@testset "Demultiplexer" begin
function randdna(n)
return LongDNASeq(rand([DNA_A, DNA_C, DNA_G, DNA_T, DNA_N], n))
end
function make_errors(seq, p=0.03)
seq = copy(seq)
nucs = DNA['A', 'C', 'G', 'T', 'N']
i = 1
while i ≤ lastindex(seq)
if rand() < p
... | {"hexsha": "fd9ab72d5fb1167491810b20c8e3253cf79a44e9", "size": 4142, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/demultiplexer.jl", "max_stars_repo_name": "cjprybol/BioSequences.jl", "max_stars_repo_head_hexsha": "01fbc26dc6209e86ec13cdee5f8598469e98ff35", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
[STATEMENT]
lemma fac_simp [simp]: "0 < i \<Longrightarrow> fac i = i * fac (i - 1)"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. 0 < i \<Longrightarrow> fac i = i * fac (i - 1)
[PROOF STEP]
by (cases i) simp_all | {"llama_tokens": 92, "file": "Simpl_ex_VcgExTotal", "length": 1} |
/*
* Copyright (c) Facebook, Inc. and its affiliates.
* All rights reserved.
*
* This source code is licensed under the BSD-style license found in the
* LICENSE file in the root directory of this source tree.
*/
#include "StructuredHeadersEncoder.h"
#include <boost/lexical_cast.hpp>
#include <boost/variant.hpp>
... | {"hexsha": "1155940fb00e4fb036aa963fadfe309432bcbfad", "size": 5418, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "proxygen/lib/http/structuredheaders/StructuredHeadersEncoder.cpp", "max_stars_repo_name": "yunyuncc/proxygen", "max_stars_repo_head_hexsha": "1ec10a60ab7634d6f804ccada7b3e123a93e23d6", "max_stars_re... |
function f = randarb(x,y)
% RANDARB generates a random observation from any arbitrary PDF defined by x,y
% function f = hfarbrand(x,y) x and y are vectors of length N that describe a PDF
% to some precision implicitly dictated by the size of N. Fhe returned scalar f
% is an observation from the set of x with a probabi... | {"author": "Sable", "repo": "mcbench-benchmarks", "sha": "ba13b2f0296ef49491b95e3f984c7c41fccdb6d8", "save_path": "github-repos/MATLAB/Sable-mcbench-benchmarks", "path": "github-repos/MATLAB/Sable-mcbench-benchmarks/mcbench-benchmarks-ba13b2f0296ef49491b95e3f984c7c41fccdb6d8/6506-obs-from-arbitrary-pdf/randarb.m"} |
[STATEMENT]
lemma getParts_nonempty_elems: "\<forall>w\<in>set (getParts rs). \<not> wordinterval_empty w"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. \<forall>w\<in>set (getParts rs). \<not> wordinterval_empty w
[PROOF STEP]
unfolding getParts_def
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. \<forall>w\<in>... | {"llama_tokens": 2388, "file": "Simple_Firewall_Service_Matrix", "length": 19} |
%-------------------------
% Resume in Latex
% Author : Ibrahim Eren Tilla
% License : MIT
%------------------------
\documentclass[letterpaper,11pt]{article}
\usepackage{latexsym}
\usepackage[empty]{fullpage}
\usepackage{titlesec}
\usepackage{marvosym}
\usepackage[usenames,dvipsnames]{color}
\usepackage{verbatim}
\... | {"hexsha": "60da26dbbcbda7494e4233d84846d90307485c65", "size": 7932, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "eren_tilla_resume.tex", "max_stars_repo_name": "erentilla/resume", "max_stars_repo_head_hexsha": "49104f3b00ee6db53b79f0b9092afe4193e783db", "max_stars_repo_licenses": ["MIT"], "max_stars_count": nu... |
[STATEMENT]
lemma infinite_term_UNIV[simp, intro]: "infinite (UNIV :: ('f,'v)term set)"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. infinite UNIV
[PROOF STEP]
proof -
[PROOF STATE]
proof (state)
goal (1 subgoal):
1. infinite UNIV
[PROOF STEP]
fix f :: 'f and v :: 'v
[PROOF STATE]
proof (state)
goal (1 subgoal):
... | {"llama_tokens": 598, "file": "Regular_Tree_Relations_Tree_Automata_Tree_Automata_Class_Instances_Impl", "length": 10} |
[STATEMENT]
lemma set_split: "{k. k<(Suc n)} = {k. k<n} \<union> {n}"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. {k. k < Suc n} = {k. k < n} \<union> {n}
[PROOF STEP]
apply auto
[PROOF STATE]
proof (prove)
goal:
No subgoals!
[PROOF STEP]
done | {"llama_tokens": 124, "file": "BDD_General", "length": 2} |
import numpy as np
from itertools import product
from scipy.stats import poisson
POISSON_UPPER_BOUND = 11
def state_value_compute(state, action, states_value, gamma=0.9):
state_value = 0.0
state_value -= 2 * abs(action)
for (lent1, lent2) in product(range(POISSON_UPPER_BOUND), range(POISSON_UPPER_BOUND)... | {"hexsha": "e7ba8a4bd4b2540025897440d17c7ab7722e648e", "size": 2361, "ext": "py", "lang": "Python", "max_stars_repo_path": "c4.py", "max_stars_repo_name": "renlikun1988/rl", "max_stars_repo_head_hexsha": "286749086011c60cb6d46dca0eac594ee7e255e3", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_... |
"""
Example usage:
baseline_df, base_reform_df, budget = get_data()
ubi_df = set_ubi(base_reform_df, budget, 0, 0, 0, 0, 0, np.zeros((12)), verbose=True)
"""
from openfisca_uk.tools.simulation import PopulationSim
import frs
import pandas as pd
import numpy as np
from rdbl import gbp
from openfisca_uk.tools.general ... | {"hexsha": "8ac53b0704168cefe2d682190da92cfd8327c18f", "size": 9865, "ext": "py", "lang": "Python", "max_stars_repo_path": "py/calc_ubi.py", "max_stars_repo_name": "DeepakSingh98/uk", "max_stars_repo_head_hexsha": "81f5f20d1198c3dcd4197ab574995816c257ac4a", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "... |
#imports
import numpy as np
import time
from RaDeep import *
x_list = [[[0, 0]], [[0, 1]], [[1, 0]], [[1, 1]]]
y_list = [[[0]], [[1]], [[1]], [[0]]]
x = [variable(x_item) for x_item in x_list]
y = [variable(y_item) for y_item in y_list]
hidden_size = 6
num_epochs = 500
lrate = 0.1
i2h_list = np.rando... | {"hexsha": "f956aa32d287af896870f5a60baa7d5eabd7705a", "size": 2175, "ext": "py", "lang": "Python", "max_stars_repo_path": "RaDeep/mlp.py", "max_stars_repo_name": "rajasekar-venkatesan/Deep_Learning", "max_stars_repo_head_hexsha": "c375dab303f44043a4dc30ea53b298d7eca1d5a7", "max_stars_repo_licenses": ["MIT"], "max_star... |
[STATEMENT]
lemma ESem_considers_fv': "\<lbrakk> e \<rbrakk>\<^bsub>\<rho>\<^esub> = \<lbrakk> e \<rbrakk>\<^bsub>\<rho> f|` (fv e)\<^esub>"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. \<lbrakk> e \<rbrakk>\<^bsub>\<rho>\<^esub> = \<lbrakk> e \<rbrakk>\<^bsub>\<rho> f|` fv e\<^esub>
[PROOF STEP]
proof (induct e a... | {"llama_tokens": 18844, "file": "Launchbury_Abstract-Denotational-Props", "length": 47} |
from es_distributed.policies import MujocoPolicy
from es_distributed.es import RunningStat
import gym, roboschool
import tensorflow as tf
import numpy as np
def jupyter_cell():
ob_stat = RunningStat(
env.observation_space.shape,
eps=1e-2 # eps to prevent dividing by zero at the beginning when com... | {"hexsha": "093ce099aece44aaa94986d03d39670e855d6a5a", "size": 1906, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/keras-test.py", "max_stars_repo_name": "spikingevolution/evolution-strategies", "max_stars_repo_head_hexsha": "21f6032df0d5aa5e5dcecedd2520b0492e2361f2", "max_stars_repo_licenses": ["MIT"], ... |
# Copyright (c) Materials Virtual Lab.
# Distributed under the terms of the BSD License.
"""
Functions for creating supercells for NEB calculations
"""
import logging
from typing import List, Tuple, Union, Optional
import numpy as np
# from ase.build import find_optimal_cell_shape, get_deviation_from_optimal_cell_sha... | {"hexsha": "e6e2d6091447057acf10c5ad6320c3c8c7dff62f", "size": 7097, "ext": "py", "lang": "Python", "max_stars_repo_path": "pymatgen/analysis/diffusion/utils/supercells.py", "max_stars_repo_name": "materialsvirtuallab/pymatgen-analysis-diffusion", "max_stars_repo_head_hexsha": "fddb01992bc8288cdbaba77b3b064b106a1cd274"... |
import os
import h5py
import argparse
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
from scipy.spatial import distance
parser = argparse.ArgumentParser()
parser.add_argument(
"--partition_file",
type=str,
default="data/partition_files/wikiner_pa... | {"hexsha": "c5077433c32a18aa143cfae20215d79ad4a5ee38", "size": 4263, "ext": "py", "lang": "Python", "max_stars_repo_path": "app/fednlp/data/advanced_partition/util/visualization_distplot.py", "max_stars_repo_name": "ray-ruisun/FedML", "max_stars_repo_head_hexsha": "24ff30d636bb70f64e94e9ca205375033597d3dd", "max_stars_... |
#! /usr/bin/env python
from __future__ import print_function
from timeit import default_timer as time
import sys
import numpy as np
from numba import dppl
import dppl.ocldrv as ocldrv
@dppl.kernel
def data_parallel_sum(a, b, c):
i = dppl.get_global_id(0)
j = dppl.get_global_id(1)
c[i,j] = a[i,j] + b[i,j]... | {"hexsha": "0ff945c5ed86b4069f942bcea204c218ac845513", "size": 1408, "ext": "py", "lang": "Python", "max_stars_repo_path": "numba/dppl/examples/sum2D.py", "max_stars_repo_name": "AlexanderKalistratov/numba", "max_stars_repo_head_hexsha": "f5c5ba339b980830e73f1dc76efb6b043adcddbb", "max_stars_repo_licenses": ["BSD-2-Cla... |
# -*- noplot -*-
"""
https://matplotlib.org/examples/pylab_examples/ginput_manual_clabel.html
This provides examples of uses of interactive functions, such as ginput,
waitforbuttonpress and manual clabel placement.
This script must be run interactively using a backend that has a
graphical user interface (for example, ... | {"hexsha": "076bde9e6067c690c30a352a9ce62a499e4d7706", "size": 2567, "ext": "py", "lang": "Python", "max_stars_repo_path": "tutorial/matplotlib-tutorial/ginput_manual_clabel.py", "max_stars_repo_name": "zixia/python-facenet", "max_stars_repo_head_hexsha": "d86e0c49a9ce413bef6e58a19a9f723aadcef968", "max_stars_repo_lice... |
import glob
import matplotlib.pyplot as plt
import numpy as np
from icecube import dataio
import simweights
corsika_dataset_dir = "/data/sim/IceCube/2016/filtered/level2/CORSIKA-in-ice/20904/"
corsika_filelist = list(
glob.glob(corsika_dataset_dir + "0000000-0000999/Level2_IC86.2016_corsika.020904.00000*.i3.zst"... | {"hexsha": "fa103e9e258875d138cd4e83dfbe42a625a46e15", "size": 4210, "ext": "py", "lang": "Python", "max_stars_repo_path": "examples/tutorial_corsika.py", "max_stars_repo_name": "icecube/simweights", "max_stars_repo_head_hexsha": "f8a7c35a8f54a7cca17ff1e1cd73cc3424b57980", "max_stars_repo_licenses": ["OLDAP-2.5"], "max... |
import torch
import torch.nn as nn
import numpy as np
class _ModeNormalization(nn.Module):
def __init__(self, dim, n_components, eps):
super(_ModeNormalization, self).__init__()
self.eps = eps
self.dim = dim
self.n_components = n_components
self.alpha = nn.Parameter(torch.... | {"hexsha": "a09cb89d50da0a40f75e172184d4336496c4b583", "size": 3966, "ext": "py", "lang": "Python", "max_stars_repo_path": "models/mn.py", "max_stars_repo_name": "Buhua-Liu/ShapeTextureDebiasedTraining", "max_stars_repo_head_hexsha": "f0f564214e2b0e8fc0dbb98cbd58994e7ef5b23e", "max_stars_repo_licenses": ["MIT"], "max_s... |
from running_metrics.running_metric import RunningMetric
import numpy as np
class RunningConfusionMatrix(RunningMetric):
def __init__(self, classes):
super().__init__(classes)
self.num_classes = len(classes)
self.cm = []
self.eye = np.eye(self.num_classes, self.num_classes)
de... | {"hexsha": "efc55e482016a99bd2092b3d37a237ac63411d77", "size": 1512, "ext": "py", "lang": "Python", "max_stars_repo_path": "evaluation/running_metrics/running_confusion_matrix.py", "max_stars_repo_name": "amithjkamath/stochastic_segmentation_networks", "max_stars_repo_head_hexsha": "295f9322f78e4407ad13438ed6e93b77e3ff... |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
# Copyright (c) 2016-2017, Cabral, Juan; Luczywo, Nadia
# All rights reserved.
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
# * Redistributions of source code must retai... | {"hexsha": "cad899f0a0e1eccda3385a0462773488bb876f04", "size": 11957, "ext": "py", "lang": "Python", "max_stars_repo_path": "skcriteria/plot/__init__.py", "max_stars_repo_name": "dand-oss/scikit-criteria", "max_stars_repo_head_hexsha": "1ca7667e08e79d551f8241278c939f604800d81b", "max_stars_repo_licenses": ["BSD-3-Claus... |
"""
打包成图片
"""
import os
from PIL import Image
import numpy as np
def chunks(l, n):
for i in range(0, len(l), n):
yield l[i:i + n]
def packimg(array, i, file_path):
data = list(chunks(array, 28))
data = np.array(data)
data = np.matrix(data)
img = Image.fromarray(data.astype(np.uint8))
... | {"hexsha": "0f94bba49b6f3001d7624a7d74fda12c98839e16", "size": 388, "ext": "py", "lang": "Python", "max_stars_repo_path": "img.py", "max_stars_repo_name": "wlnyx/-", "max_stars_repo_head_hexsha": "9a7ea36b4c04afabd426b8c48d7874d1738e4c5c", "max_stars_repo_licenses": ["MulanPSL-1.0"], "max_stars_count": null, "max_stars... |
[STATEMENT]
lemma subst_LLq[simp]:
assumes [simp]: "t1 \<in> atrm" "t2 \<in> atrm" "s \<in> atrm" "x \<in> var"
shows "subst (LLq t1 t2) s x = LLq (substT t1 s x) (substT t2 s x)"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. subst (LLq t1 t2) s x = LLq (substT t1 s x) (substT t2 s x)
[PROOF STEP]
proof-
[PROOF STA... | {"llama_tokens": 1583, "file": "Syntax_Independent_Logic_Syntax_Arith", "length": 9} |
module AuthenticationsController
using Genie, Genie.Renderer, Genie.Router, Genie.Sessions, Genie.Helpers
using SearchLight, SearchLight.QueryBuilder
using GenieAuthentication
function show_login()
html!(:authentications, :show_login, context = @__MODULE__)
end
function login()
query = (from(User) + where("usern... | {"hexsha": "89e0e413cba53fa5c15e0e8de69a6bd1ea9360ab", "size": 746, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "app/resources/authentications/AuthenticationsController.jl", "max_stars_repo_name": "tetsugps/julia", "max_stars_repo_head_hexsha": "715a511bba2be07f99e9c7a6671e8d2200cf5df8", "max_stars_repo_licens... |
//
// Copyright (c) 2016,2018 CNRS
//
#ifndef __pinocchio_joint_generic_hpp__
#define __pinocchio_joint_generic_hpp__
#include "pinocchio/multibody/joint/joint-collection.hpp"
#include "pinocchio/multibody/joint/joint-composite.hpp"
#include "pinocchio/multibody/joint/joint-basic-visitors.hxx"
#include "pinocchio/con... | {"hexsha": "282a9c1edd4bcd354b3b22d00fe6bbf5288f96de", "size": 8618, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "src/multibody/joint/joint-generic.hpp", "max_stars_repo_name": "mkatliar/pinocchio", "max_stars_repo_head_hexsha": "b755b9cf2567eab39de30a68b2a80fac802a4042", "max_stars_repo_licenses": ["BSD-2-Clau... |
from base import *
import numpy as np
from typing import List
def numpy_heavy_mult(a_b: List[np.array], base):
start = time.time() - base
np.multiply(*a_b)
stop = time.time() - base
return start, stop
DIMS = 9000
a = np.random.rand(DIMS, DIMS)
b = np.random.rand(DIMS, DIMS)
a_b_arr = [(a, b)] * 8
... | {"hexsha": "28fd8331506fc0eca7e5d8fae29fd82722b01744", "size": 356, "ext": "py", "lang": "Python", "max_stars_repo_path": "5.py", "max_stars_repo_name": "broccoli-smuggler/lightning-talk-snakes", "max_stars_repo_head_hexsha": "661a6306c7b5e35480984440caa430cc87dbea26", "max_stars_repo_licenses": ["MIT"], "max_stars_cou... |
import matplotlib.pyplot as plt
import networkx as nx
from agents.AgentBase import AgentBase
import logger
from agentNet.agent_net import AgentNet
from agents.Offer import Offer, OfferType
import numpy as np
import queue
def get_id(jid=''):
jid = str(jid)
return int(jid[len('agent_'):-(len(AgentBase.HOST) + 1... | {"hexsha": "bbed082616a8101f063a9c87ecad56d7b53799e1", "size": 4039, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/agentPlot/display_graph.py", "max_stars_repo_name": "warbarbye/SAG-2019Z", "max_stars_repo_head_hexsha": "b2dbdac80228a83680b4245c1ef30e1940bd8ee7", "max_stars_repo_licenses": ["MIT"], "max_st... |
# Example : 5.3A Chapter : 5.3 Page No: 277
# Nullspace of matrix as transpose of Cofactor matrix
nullspacebasis<-function(A){
C<-matrix(c(1:9),ncol=3)
for(i in 1:3){
for(j in 1:3){
if((i+j)%%2==0){
x<-1
}
else{
x<--1
}
C[i,j]<-x*det(A[-i,-j])
... | {"hexsha": "ac70393c89916b4e7f29d1233a0c7cb324501cd3", "size": 653, "ext": "r", "lang": "R", "max_stars_repo_path": "Introduction_To_Linear_Algebra_by_Gilbert_Strang/CH5/EX5.3.a/Ex5_5.3A.r", "max_stars_repo_name": "prashantsinalkar/R_TBC_Uploads", "max_stars_repo_head_hexsha": "b3f3a8ecd454359a2e992161844f2fb599f8238a"... |
\chapter{Introduction}
\label{ch:introduction}
\section{}
\section{}
\section{}
| {"hexsha": "16a98065e253753998235de5dde254d290805d6f", "size": 84, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "Chapters/chap00.tex", "max_stars_repo_name": "fmr42/MasterThesis", "max_stars_repo_head_hexsha": "0ff0d2100d6547afb67af40c0f355bec1a3c9150", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null... |
from argparse import ArgumentParser
from pathlib import Path
import numpy as np
DIR_HERE = Path(__file__).resolve().parent
def parse_args(args=None):
argparser = ArgumentParser()
argparser.add_argument('--shape', nargs='+', type=int, default=[1000, 800])
argparser.add_argument('--nattrs', nargs='+', type... | {"hexsha": "320683b8f987caf74197efc649357bb812c04212", "size": 4300, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/make_examples.py", "max_stars_repo_name": "pedroilidio/hyper_tree", "max_stars_repo_head_hexsha": "f637f01398ddbe6f7f8600f2d72c5424939de98e", "max_stars_repo_licenses": ["BSD-3-Clause"], "ma... |
# -*- coding: utf-8 -*-
# Morra project: Base parser
#
# Copyright (C) 2020-present by Sergei Ternovykh
# License: BSD, see LICENSE for details
"""
Base classes for the project.
"""
from collections import OrderedDict, defaultdict
from copy import deepcopy
from math import isclose
import pickle
import random
from rando... | {"hexsha": "e24815d16327a4a4b68f0953c260989d1e1c2402", "size": 36624, "ext": "py", "lang": "Python", "max_stars_repo_path": "morra/base_parser.py", "max_stars_repo_name": "fostroll/morra", "max_stars_repo_head_hexsha": "6363c74f6575c061347e0c9372348f83792f5128", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_c... |
import numpy as np
import scipy.io as sio
from pyplfv.wavelet import save_waveleted_signal_with_farray
from pyplfv.tve import save_normalized_tve_with_farray
from pyplfv.plv import save_plv_with_farray
from pyplfv.utility import save_data
from pyplfv.utility import load_data
from pyplfv.plv import plv_with_farray
from ... | {"hexsha": "48affb2e9c6e26aceb279faaea8611e0498c1e20", "size": 2448, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/plv_test.py", "max_stars_repo_name": "SakuragiYoshimasa/EEG_DataProcessing", "max_stars_repo_head_hexsha": "6d41db008964bbe4bd27eb3312aba78fd4db9608", "max_stars_repo_licenses": ["MIT"], "ma... |
from sklearn.feature_extraction.text import CountVectorizer
from sklearn import preprocessing
scaler = preprocessing.StandardScaler();
from sklearn import svm
from scipy.sparse import coo_matrix, hstack
def combineFeatures(f1,f2):
f1 = coo_matrix(f1)
f2 = coo_matrix(f2)
return hstack([f1,f2]).toarray()
in... | {"hexsha": "2968d662b9e20e2aac5a815d370fcbf3b9b240b3", "size": 2594, "ext": "py", "lang": "Python", "max_stars_repo_path": "Supervised and Unsupervised learning/n-grams.py", "max_stars_repo_name": "prernaa/NLPCourseProj", "max_stars_repo_head_hexsha": "77027b3adac06d2e99af7f6ff50265f77aa70cfb", "max_stars_repo_licenses... |
/-
Copyright (c) 2021 Adam Topaz. All rights reserved.
Released under Apache 2.0 license as described in the file LICENSE.
Authors: Adam Topaz
-/
import topology.category.Profinite
import topology.locally_constant.basic
import topology.discrete_quotient
/-!
# Cofiltered limits of profinite sets.
This file contains so... | {"author": "jjaassoonn", "repo": "projective_space", "sha": "11fe19fe9d7991a272e7a40be4b6ad9b0c10c7ce", "save_path": "github-repos/lean/jjaassoonn-projective_space", "path": "github-repos/lean/jjaassoonn-projective_space/projective_space-11fe19fe9d7991a272e7a40be4b6ad9b0c10c7ce/src/topology/category/Profinite/cofiltere... |
#!/usr/bin/env python3
# -*- encoding: utf8 -*-
import json
import numpy as np
from ref_finder import RefFinder
class GlyphModel(object):
def _build_glyph_model(self):
# A helper function to convert a control point representation to a list
as_list = lambda pt: [ pt['x'], pt['y'] ]
# On points of the con... | {"hexsha": "d8be2262d8206d69042d1f0f148001dfbc1e337a", "size": 4284, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/cv/glyph_model.py", "max_stars_repo_name": "lvwzhen/glow-sans", "max_stars_repo_head_hexsha": "715f35148a9e023568839b9740908fe1feccbd19", "max_stars_repo_licenses": ["MIT"], "max_stars_count":... |
# 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": "658cb4edcda86c18c161ee3c181da102807287eb", "size": 7615, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/unit_tests/pandas_postprocessing/test_pivot.py", "max_stars_repo_name": "7vikpeculiar/superset", "max_stars_repo_head_hexsha": "800ced5e257d5d83d6dbe4ced0e7318ac40d026f", "max_stars_repo_lic... |
[STATEMENT]
lemma DF_unfold : "DF A = (\<sqinter> z \<in> A \<rightarrow> DF A)"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. DF A = (\<sqinter>z\<in>A \<rightarrow> DF A)
[PROOF STEP]
by(simp add: DF_def, rule trans, rule fix_eq, simp) | {"llama_tokens": 106, "file": "HOL-CSP_Assertions", "length": 1} |
import copy
import random
from dataclasses import dataclass
from typing import Dict
import numpy as np
import tensorflow as tf
import torch
from torch import nn, optim
from thesis import memory, networks_torch, utils
def egreedy_act(
num_actions: int, state: np.ndarray, q_net: nn.Module, eps: float = 0.01
) -> ... | {"hexsha": "88803335a233ae5d929b30275264acf9f9e82c6b", "size": 4572, "ext": "py", "lang": "Python", "max_stars_repo_path": "dopamine/thesis/scratch/dqn_torch.py", "max_stars_repo_name": "xqz-u/dopamine", "max_stars_repo_head_hexsha": "d562750a58bcf681a6f8b590f4e4dfb263654b5e", "max_stars_repo_licenses": ["Apache-2.0"],... |
import os
from flask import Flask
def create_app(test_config=None, dbfile=None):
# create and configure the app
app = Flask(__name__, instance_relative_config=True)
app.config.from_mapping(
SECRET_KEY='dev',
DATABASE=os.path.join(app.instance_path, dbfile),
)
if test_config is None... | {"hexsha": "e824e789b789543167005e82de8f632b6f0076f5", "size": 1387, "ext": "py", "lang": "Python", "max_stars_repo_path": "server/flaskr/create.py", "max_stars_repo_name": "Intel-OpenVINO-Edge-AI-Scholarship/arcface-project", "max_stars_repo_head_hexsha": "86458a207c8e265bfc231736234ec38e4e70588b", "max_stars_repo_lic... |
[STATEMENT]
lemma synth_trans: "[| X\<in> synth G; G \<subseteq> synth H |] ==> X\<in> synth H"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. \<lbrakk>X \<in> synth G; G \<subseteq> synth H\<rbrakk> \<Longrightarrow> X \<in> synth H
[PROOF STEP]
by (drule synth_mono, blast) | {"llama_tokens": 113, "file": null, "length": 1} |
# coding: utf-8
# Copyright (c) Pymatgen Development Team.
# Distributed under the terms of the MIT License.
"""
This module implements a core class LammpsData for generating/parsing
LAMMPS data file, and other bridging classes to build LammpsData from
molecules. This module also implements a subclass CombinedData for... | {"hexsha": "9ed502eb52422307dfe3eb751f27ec76ac451144", "size": 89784, "ext": "py", "lang": "Python", "max_stars_repo_path": "pymatgen/io/lammps/data.py", "max_stars_repo_name": "mmbliss/pymatgen", "max_stars_repo_head_hexsha": "0d2e39bb6406d934c03e08919f2cd4dedb41bc22", "max_stars_repo_licenses": ["MIT"], "max_stars_co... |
# -*- coding: utf-8 -*-
"""
Module to produce cartesian gridded traveltime look-up tables.
"""
import math
import warnings
import pickle
import struct
from copy import copy
import os
import skfmm
import pyproj
import numpy as np
import pandas as pd
from scipy.interpolate import RegularGridInterpolator, griddata, int... | {"hexsha": "15ee55bd8b69acad36299909ae856ecda075bcce", "size": 51755, "ext": "py", "lang": "Python", "max_stars_repo_path": "QMigrate/core/model.py", "max_stars_repo_name": "TomWinder/QuakeMigrate", "max_stars_repo_head_hexsha": "c5d2de4104a113192dd3d722adcaf58e90dd0c4f", "max_stars_repo_licenses": ["MIT"], "max_stars_... |
include("src/test.jl")
| {"hexsha": "46de126a5c3bdb31c891bf001211410fad210ce9", "size": 23, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "runtests.jl", "max_stars_repo_name": "silky/Cosmology.jl", "max_stars_repo_head_hexsha": "34a98aefa0d5f1bef407f990107a940b57aed651", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_sta... |
import os
import pickle
import sys
import time
from collections import OrderedDict
import sklearn.metrics as skm
from taggers.lample_lstm_tagger.utils import create_input
from taggers.lample_lstm_tagger.utils import models_path
from taggers.lample_lstm_tagger.loader import word_mapping, char_mapping
from taggers.lamp... | {"hexsha": "6c5004a4a9860693c36ebecfa286a2806ed6a8cc", "size": 12090, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/taggers/lample_lstm_tagger/lstm_wrapper.py", "max_stars_repo_name": "anbasile/arxiv2018-bayesian-ensembles", "max_stars_repo_head_hexsha": "52e2741540ce0466666aaca9fe9dd148c144123a", "max_sta... |
# calculation of time (in seconds) that elapsed between the stimulation is applied and the VAS
# score is register
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
# set path
path = '../data/data_sub.xlsx'
dataFrame = pd.read_excel(path, header=2, sheet_name='trials_noTime'... | {"hexsha": "ed67579df11e7362ed1efebcca102dfd8e623111", "size": 10926, "ext": "py", "lang": "Python", "max_stars_repo_path": "py/deprecated/data_swarm.py", "max_stars_repo_name": "EdgardoCS/Arduino_tesis", "max_stars_repo_head_hexsha": "0e7278834fe59c4e54aff45c1152acb9c21b2a24", "max_stars_repo_licenses": ["MIT"], "max_... |
[STATEMENT]
lemma project_constrains:
"(project h C F \<in> A co B) =
(F \<in> (C \<inter> extend_set h A) co (extend_set h B) & A \<subseteq> B)"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. (project h C F \<in> A co B) = (F \<in> C \<inter> extend_set h A co extend_set h B \<and> A \<subseteq> B)... | {"llama_tokens": 856, "file": null, "length": 6} |
import itertools as it
import time
from contextlib import contextmanager
import git
import numpy as np
from recsys.log_utils import get_logger
logger = get_logger()
@contextmanager
def timer(name):
t0 = time.time()
yield
logger.info(f"[{name}] done in {time.time() - t0:.0f} s")
def group_lengths(group... | {"hexsha": "a2f0274a50bb49f3797d513eef48940d25af2c54", "size": 2905, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/recsys/utils.py", "max_stars_repo_name": "atakanerbas/recsys2019", "max_stars_repo_head_hexsha": "c0caed220056d3758d7e8b0032e89429fc07f8cf", "max_stars_repo_licenses": ["Apache-2.0"], "max_sta... |
# coding=utf-8
"""
Copyright 2021 Huawei Technologies Co., Ltd
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 agre... | {"hexsha": "1ad53111535df6d52aa6c4cec6afc59ce8a23b17", "size": 3208, "ext": "py", "lang": "Python", "max_stars_repo_path": "research/audio/fcn-4/infer/utils/audio2melgram.py", "max_stars_repo_name": "leelige/mindspore", "max_stars_repo_head_hexsha": "5199e05ba3888963473f2b07da3f7bca5b9ef6dc", "max_stars_repo_licenses":... |
from collections.abc import Mapping, Iterable
import warnings
import numpy as np
from sklearn.utils import check_random_state
from sklearn.utils.random import sample_without_replacement
from sklearn.model_selection import ParameterGrid
class BatchParameterSampler:
def __init__(self, param_distribution, n_iter, ... | {"hexsha": "395b84717f4175c9e74e3e92c12253966d959666", "size": 2974, "ext": "py", "lang": "Python", "max_stars_repo_path": "mango/domain/batch_parameter_sampler.py", "max_stars_repo_name": "jashanmeet-collab/mango", "max_stars_repo_head_hexsha": "ed1fb80fda35d00f6cdfc06e71f55b1a0a9cf4b3", "max_stars_repo_licenses": ["A... |
import torch
import numpy as np
import torch.nn as nn
import torchvision
from numpy import linalg as la
import time
class HighTkd2ConvRSvd(nn.Module):
def __init__(self, conv_nn_module, k11, k12, r31, r32, r4):
def simple_randomized_torch_svd(M, k=10):
B = torch.tensor(M).cuda(0)
... | {"hexsha": "e39c1443a6980548ba08ca6876a0f9ac60246233", "size": 8514, "ext": "py", "lang": "Python", "max_stars_repo_path": "decomp/hotcake_rsvd.py", "max_stars_repo_name": "RuiLin0212/HOTCAKE", "max_stars_repo_head_hexsha": "f973d8a39971209f4e10cdb9ff89e9ca194bacc4", "max_stars_repo_licenses": ["MIT"], "max_stars_count... |
// Copyright (c) 2012-2018 The Elastos Open Source Project
// Distributed under the MIT software license, see the accompanying
// file COPYING or http://www.opensource.org/licenses/mit-license.php.
#include <vector>
#include <map>
#include <boost/scoped_ptr.hpp>
#include "SidechainSubWallet.h"
#include "ELACoreExt/EL... | {"hexsha": "8f270c5875749742f647ed6127e276076da6dff1", "size": 4654, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "SDK/Implement/SidechainSubWallet.cpp", "max_stars_repo_name": "RainFallsSilent/Elastos.ELA.SPV.Cpp", "max_stars_repo_head_hexsha": "f2663de380b413efd6e10430cdada3701cb84698", "max_stars_repo_license... |
\SetAPI{J-C}
\section{cache.child.onupdate.overwritetomany}
\label{configuration:CacheChildOnupdateOverwritetomany}
\ClearAPI
Defines whether during an update of the cache the to-many relations should be resetted. Valid values are "true" and "false".
%% GENERATED USAGE REFERENCE - DO NOT EDIT
\begin{longtable}{ l l } \... | {"hexsha": "6198e88fd4df9f26d612c0c0913b7d0d936e7432", "size": 864, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "doc/reference-manual/tex/configuration/CacheChildOnupdateOverwritetomany.tex", "max_stars_repo_name": "Dennis-Koch/ambeth", "max_stars_repo_head_hexsha": "8552b210b8b37d3d8f66bdac2e094bf23c8b5fda", "... |
import requests
import pandas as pd
import numpy as np
class Federation():
def __init__(self) -> None:
self._cartola_base_url = "https://api.cartolafc.globo.com/"
self._market_status_url = self._cartola_base_url + "mercado/status"
self._market_highlights_url = self._cartola_base_url + "mer... | {"hexsha": "782ce05c42739bc9595e9bc3dff80b21575be12e", "size": 7034, "ext": "py", "lang": "Python", "max_stars_repo_path": "pytolafc/Federation.py", "max_stars_repo_name": "gabrielsouzaesilva/pytolafc", "max_stars_repo_head_hexsha": "09ec865c300341b7ddc57cb3a5dec88bbf195553", "max_stars_repo_licenses": ["MIT"], "max_st... |
cdis Forecast Systems Laboratory
cdis NOAA/OAR/ERL/FSL
cdis 325 Broadway
cdis Boulder, CO 80303
cdis
cdis Forecast Research Division
cdis Local Analysis and Prediction Branch
cdis LAPS
cdis
cdis This software and its documentation are in the public domain and
cdis are furnished "as is... | {"hexsha": "be8e2ac1313f976c105203376ad0cc79c4538862", "size": 4284, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "src/ingest/radar/wsiremap/vrc/table/genvrclut.f", "max_stars_repo_name": "maxinye/laps-mirror", "max_stars_repo_head_hexsha": "b3f7c08273299a9e19b2187f96bd3eee6e0aa01b", "max_stars_repo_licenses":... |
"""
"""
import pydiffvg
import torch
import os
import numpy as np
import cv2
import skimage
import skimage.io
import matplotlib.pyplot as plt
import random
import argparse
import math
import errno
from tqdm import tqdm
from torch.optim.lr_scheduler import CosineAnnealingLR
from torch.nn.functional import adaptive_avg_p... | {"hexsha": "f676f05bb7906c9235679eb6952013f7a6e4a1f3", "size": 6663, "ext": "py", "lang": "Python", "max_stars_repo_path": "pair/generate/generate_template.py", "max_stars_repo_name": "13952522076/diffvg", "max_stars_repo_head_hexsha": "2c5af9ecf470b1c7071e821583e5ba09cb2c4622", "max_stars_repo_licenses": ["Apache-2.0"... |
[STATEMENT]
lemma connectedin_path_image: "pathin X g \<Longrightarrow> connectedin X (g ` ({0..1}))"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. pathin X g \<Longrightarrow> connectedin X (g ` {0..1})
[PROOF STEP]
by (simp add: path_connectedin_imp_connectedin path_connectedin_path_image) | {"llama_tokens": 107, "file": null, "length": 1} |
import numpy as np
from funcs_thermo import pv_c, pd_c, sv_c, sd_c, cpm_c, exner_c
def convert_forcing_entropy(p_0, q_tot, q_vap, T, q_tot_tendency, T_tendency):
p_vap = pv_c(p_0, q_tot, q_vap)
p_dry = pd_c(p_0, q_tot, q_vap)
return cpm_c(q_tot) * T_tendency/T + (sv_c(p_vap,T)-sd_c(p_dry,T)) * q_tot_tenden... | {"hexsha": "49fd4e50f183184ba33d7fc331c7f75b822135e8", "size": 437, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/funcs_forcing.py", "max_stars_repo_name": "charleskawczynski/SCAMPy", "max_stars_repo_head_hexsha": "b43c8a639b6b3e185ac667cb64359de9029bc3af", "max_stars_repo_licenses": ["Apache-2.0"], "max_s... |
from __future__ import print_function, division
import svgpathtools
import numpy as np
from matplotlib import pyplot as plt
PAGE_HEIGHT = 32000
PAGE_WIDTH = 32000
OFFSET_WIDTH = 3200
OFFSET_HEIGHT = 3200
DEFAULT_X, DEFAULT_Y = 0, 0
def get_paths(file_path, debug=False):
_paths, attributes = svgpathtools.svg2pa... | {"hexsha": "ea39bd99349eb4bf8190f643ccd20a1ace7cd886", "size": 3054, "ext": "py", "lang": "Python", "max_stars_repo_path": "main.py", "max_stars_repo_name": "damo-da/cnc-rpi-drawing-robot", "max_stars_repo_head_hexsha": "5bd3285267ae740f2472a4458500e96375260611", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1... |
module Main where
import Criterion.Main
import Statistics.Sample.Histogram.Magnitude
import Data.Functor.Identity
main = defaultMain
[ bgroup "resolution" [ bench "1" . nf (foldHist 1) $ Identity (1 :: Double)
, bench "2" . nf (foldHist 2) $ Identity (1 :: Double)
,... | {"hexsha": "c369aeb74dd1b08da4b34ce8eb91fc0b2482224e", "size": 660, "ext": "hs", "lang": "Haskell", "max_stars_repo_path": "benchmark/Bench.hs", "max_stars_repo_name": "skedgeme/histogram-magnitude", "max_stars_repo_head_hexsha": "a8417dfdb002f99dba8740925f2eaa58de8509ea", "max_stars_repo_licenses": ["BSD-3-Clause"], "... |
[STATEMENT]
lemma C_eq_id:
"wellformed_policy1_strong p \<Longrightarrow> C(list2FWpolicy (insertDeny p)) = C (list2FWpolicy p)"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. wellformed_policy1_strong p \<Longrightarrow> C (list2FWpolicy (insertDeny p)) = C (list2FWpolicy p)
[PROOF STEP]
by (rule ext) (auto int... | {"llama_tokens": 145, "file": "UPF_Firewall_FWNormalisation_NormalisationIntegerPortProof", "length": 1} |
import json
import os
import sys
from typing import Text, List, Dict, Any
import numpy as np
import matplotlib.pyplot as plt
import torch
from torch import nn
from mixin import NameMixIn
class BaseModel(nn.Module):
"""
Tracks objects attributes and provides methods for model serialization/deserialization
... | {"hexsha": "fb8e70165e26abb340172365f7f844703393d961", "size": 4181, "ext": "py", "lang": "Python", "max_stars_repo_path": "base.py", "max_stars_repo_name": "bvezilic/Transformer", "max_stars_repo_head_hexsha": "99a8a4687dceb3d08140d009b1295748f37632ca", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": nul... |
"""
Facilities to interface with the Heliophysics Events Knowledgebase.
"""
import json
import codecs
import urllib
import inspect
from itertools import chain
import astropy.table
from astropy.table import Row
import sunpy.net._attrs as core_attrs
from sunpy import log
from sunpy.net import attr
from sunpy.net.base_c... | {"hexsha": "86edffcc0bc50f054c4ba494ac25b0d5e04886d3", "size": 7273, "ext": "py", "lang": "Python", "max_stars_repo_path": "sunpy/net/hek/hek.py", "max_stars_repo_name": "jgieseler/sunpy", "max_stars_repo_head_hexsha": "9eb01ce9eea43512cc928b17c6d79ac06dce0ece", "max_stars_repo_licenses": ["BSD-2-Clause"], "max_stars_c... |
# coding: utf-8
# Copyright (c) Pymatgen Development Team.
# Distributed under the terms of the MIT License.
from __future__ import unicode_literals
import numpy as np
from pymatgen.util.coord import Simplex
from functools import cmp_to_key
from scipy.spatial import HalfspaceIntersection, ConvexHull
from pymatgen.an... | {"hexsha": "2e24b0c0a26b36a89154433df2a8ad8206ebd40c", "size": 14473, "ext": "py", "lang": "Python", "max_stars_repo_path": "pymatgen/analysis/pourbaix/analyzer.py", "max_stars_repo_name": "frssp/pymatgen", "max_stars_repo_head_hexsha": "bdd977f065b66191557c7398b31a1571bc541fdb", "max_stars_repo_licenses": ["MIT"], "ma... |
import numpy as np
import sys, time
# time.clock() is cpu time of current process
# time.time() is wall time
# to see what this does, try
# for x in progprint_xrange(100):
# time.sleep(0.01)
# TODO there are probably better progress bar libraries I could use
def progprint_xrange(*args,**kwargs):
xr = xrange... | {"hexsha": "7bcc02f9b5f314c1607f929b872d2b29ee5372ae", "size": 1503, "ext": "py", "lang": "Python", "max_stars_repo_path": "core/util/text.py", "max_stars_repo_name": "Ardavans/sHDP", "max_stars_repo_head_hexsha": "4fc18a86668b1c1eebd416857184d60079db2ed6", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 74, "ma... |
# -*- coding: utf-8 -*-
#
#################
# This script takes as input the EIA sectorial energy consumption data
# in .csv format, stripped of the explanatory text,
# and returns a csv file with the processed time series.
# Original Data are provided in BTU per year, and are converted here to TW.
#
# Last updated: No... | {"hexsha": "80296b74f7158e3e0a6b5c2417597bfce1507441", "size": 1608, "ext": "py", "lang": "Python", "max_stars_repo_path": "data/energy/EIA_energy_consumption_by_sector/code/get_sectorial_trends.py", "max_stars_repo_name": "ilopezgp/human_impacts", "max_stars_repo_head_hexsha": "b2758245edac0946080a647f1dbfd1098c0f0b27... |
accum_type(::Type{T}) where {T<:Integer} = Int
accum_type(::Type{Float32}) = Float32
accum_type(::Type{T}) where {T<:Real} = Float64
accum_type(::Type{T}) where {T<:FixedPoint} = floattype(T)
accum_type(::Type{C}) where {C<:Colorant} = base_colorant_type(C){accum_type(eltype(C))}
accum_type(... | {"hexsha": "b6dbfc0d70a3c11e0d6f3e0a37d0bf53c6002cac", "size": 13760, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/core.jl", "max_stars_repo_name": "JuliaImages/ImageSegmentation.jl", "max_stars_repo_head_hexsha": "252e083626481ebf2260605b95ff19a5c8039e83", "max_stars_repo_licenses": ["MIT"], "max_stars_co... |
import gym
import numpy as np
from gym import spaces
from gym.utils import seeding
from .agent import PusherActions
from .minigrid import COLOR_NAMES, CELL_PIXELS, Grid
class PusherGridEnv(gym.Env):
"""
2D grid world game environment
"""
metadata = {
'render.modes': ['human', 'rgb_array', 'p... | {"hexsha": "0093fd68338f27d7eadaf4172cde9f5726b43273", "size": 12044, "ext": "py", "lang": "Python", "max_stars_repo_path": "gym_minigrid/pushergrid.py", "max_stars_repo_name": "Rockett8855/gym-minigrid", "max_stars_repo_head_hexsha": "e8f50fae6f0eb64b2c5a91db2164cd8114a7bf4a", "max_stars_repo_licenses": ["BSD-3-Clause... |
#!/usr/bin/env python
# -*- coding: UTF-8 -*-
import socket
import threading
from numpy import true_divide
def get_ip_x():
s = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)
try:
s.connect(('10.255.255.255', 1))
IP = s.getsockname()[0]
print("rmip:"+IP)
except:
IP = '127... | {"hexsha": "013a1d7d8f2db659931f9c48a6ee0379fc1d7306", "size": 1068, "ext": "py", "lang": "Python", "max_stars_repo_path": "nodes/tianbot_mini_scan_ip.py", "max_stars_repo_name": "tianbot/abc_swarm", "max_stars_repo_head_hexsha": "ac0c3b42e6c76a19403b93aeeb887c0c85237508", "max_stars_repo_licenses": ["BSD-3-Clause"], "... |
import mysql.connector
import requests
import json
import os
import numpy as np
import wikipedia
# /index.py
from flask import Flask, request, jsonify, render_template, url_for
from fuzzywuzzy import fuzz
from fuzzywuzzy import process
class queryFunctions():
def __init__(self, api_keys):
self.API... | {"hexsha": "2fd57644acfedb0fbc6f7db756e5b3fc8d47524e", "size": 8832, "ext": "py", "lang": "Python", "max_stars_repo_path": "queryFunctions.py", "max_stars_repo_name": "NickDST/Interactive-Assistant-Winter", "max_stars_repo_head_hexsha": "7b4ea5bea45201a8a091134cdfab9e8bd3419d65", "max_stars_repo_licenses": ["MIT"], "ma... |
import timeit
import time
import matplotlib.pyplot as plt
import numpy as np
# Find target 22 (i.e. return its index)in a sorted list
# Here we use Binary Search algorithm because its time complexity is O(log n)
def binarySearch(lst, search):
lower_bound = 0
upper_bound = len(lst) - 1
while True:
... | {"hexsha": "68e1a7747b5b672f99b949e823f373ffdfbd6105", "size": 2513, "ext": "py", "lang": "Python", "max_stars_repo_path": "bigO_presentation/src/Searching.py", "max_stars_repo_name": "ypraw/bigOPYID", "max_stars_repo_head_hexsha": "82fc45595bbb650379b97c220981b5118647d9d8", "max_stars_repo_licenses": ["MIT"], "max_sta... |
C Copyright(C) 2011 Sandia Corporation. Under the terms of Contract
C DE-AC04-94AL85000 with Sandia Corporation, the U.S. Government retains
C certain rights in this software
C
C Redistribution and use in source and binary forms, with or without
C modification, are permitted provided that the following conditions are... | {"hexsha": "8d7079ad208db58d2cd2e136f6706b746acbc5e4", "size": 3582, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "packages/seacas/applications/gen3d/g3_relblk.f", "max_stars_repo_name": "milljm/seacas", "max_stars_repo_head_hexsha": "4990651554b336901e260304067ff91c7284531f", "max_stars_repo_licenses": ["NetC... |
#ifndef HEADER_GUARD_a51a7157f86a31d744e2508bdac9271d
#define HEADER_GUARD_a51a7157f86a31d744e2508bdac9271d
#include <boost/range/reference.hpp>
#include "jbms/is_contiguous.hpp"
#include <cstring>
#include <type_traits>
#include "jbms/print_fwd.hpp"
#include "jbms/enable_if.hpp"
namespace jbms {
template <class T>
... | {"hexsha": "083cc0f6a76bdfc299af1c9d682849b4e2052b5f", "size": 9217, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "src/jbms/array_view.hpp", "max_stars_repo_name": "ezhu5121/array_view", "max_stars_repo_head_hexsha": "58e8eab6957066033f1c967c8db5317d42a596a8", "max_stars_repo_licenses": ["BSL-1.0"], "max_stars_c... |
# -*- coding: utf-8 -*-
# @Author : Tek Raj Chhetri
# @Email : tekraj.chhetri@sti2.at
# @Web : http://tekrajchhetri.com/
# @File : Download.py
# @Software: PyCharm
import urllib.parse
import requests
import pandas as pd
import json
import csv
from itertools import zip_longest
import urllib.parse
import reque... | {"hexsha": "49586dde4b109df4f450efbf4a1a7e8373b60ae0", "size": 14962, "ext": "py", "lang": "Python", "max_stars_repo_path": "Download.py", "max_stars_repo_name": "tekrajchhetri/combined-system-metrics-to-cloud-services-reliability", "max_stars_repo_head_hexsha": "770828747b70bb763339f4aa5e7bc0ba143ad095", "max_stars_re... |
"""
Author: Johnny Lu
Date: 6th/July/2020
Copyright: Johnny Lu, 2020
email: joh@johdev.com
website: https://johdev.com
"""
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor
from sklearn.model_selection import cross_va... | {"hexsha": "8714b8e34578f4bf3854de4f0d7568bc006f661b", "size": 3017, "ext": "py", "lang": "Python", "max_stars_repo_path": "main.py", "max_stars_repo_name": "JL1829/EmployeeSalaryPrediction", "max_stars_repo_head_hexsha": "55ba4209f23e2ed5fac47c46fc7a112b82d78e14", "max_stars_repo_licenses": ["MIT"], "max_stars_count":... |
import data_decoder as dc
import numpy as np
import pickle
from sklearn.neural_network import MLPClassifier
from sklearn.preprocessing import StandardScaler
from sklearn.metrics import confusion_matrix, classification_report, accuracy_score, f1_score
from sklearn.model_selection import GridSearchCV
from Game import G... | {"hexsha": "0c850e10385f2d7f4944fdfeb7a850123276cccb", "size": 2949, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/main.py", "max_stars_repo_name": "koodimonsteri/DigitClassifier", "max_stars_repo_head_hexsha": "fc9e772f713587aa45813e51183392cada0a1dec", "max_stars_repo_licenses": ["MIT"], "max_stars_count... |
subroutine dredge(nmmax ,lsedtot,nst , &
& cdryb ,dps ,dbodsd ,kfsed , &
& s1 ,timhr ,morhr ,gdp )
!----- GPL ---------------------------------------------------------------------
!
! Copyri... | {"hexsha": "09c53bd14b8b44cac517532270f22dd455e7b2e1", "size": 69529, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "docker/water/delft3d/tags/v6686/src/engines_gpl/flow2d3d/packages/kernel/src/compute_sediment/dredge.f90", "max_stars_repo_name": "liujiamingustc/phd", "max_stars_repo_head_hexsha": "4f815a738a... |
import numpy as np
from qiskit import QuantumCircuit, ClassicalRegister, QuantumRegister
from qiskit import execute, BasicAer
from qiskit.compiler import transpile
from qiskit.quantum_info.operators import Operator, Pauli
from qiskit.quantum_info import process_fidelity
from qiskit.extensions import RXGate, XGate, CX... | {"hexsha": "b1b90ff0142edd4b57ee62d8845519bbc0fb29fd", "size": 4335, "ext": "py", "lang": "Python", "max_stars_repo_path": "2. Advanced Circuits/operators.py", "max_stars_repo_name": "apcarrik/qiskit", "max_stars_repo_head_hexsha": "bfad886cacb80a7dae80f67dfadd80d12bcb3b13", "max_stars_repo_licenses": ["MIT"], "max_sta... |
from builtins import str
import unittest
import copy
import os
import numpy as num
from anuga.file_conversion.grd2array import grd2array
#Aux for fit_interpolate.fit example
def linear_function(point):
point = num.array(point)
return point[:,0]+3*point[:,1]
#return point[:,1... | {"hexsha": "a301553df2a40c66f83a4b27c6371f2e645a3723", "size": 8914, "ext": "py", "lang": "Python", "max_stars_repo_path": "anuga/file_conversion/tests/test_grd2array.py", "max_stars_repo_name": "samcom12/anuga_core", "max_stars_repo_head_hexsha": "f4378114dbf02d666fe6423de45798add5c42806", "max_stars_repo_licenses": [... |
/*
Copyright 2019 Tenable, Inc. ... | {"hexsha": "cd8ee621debb8a6b241a53cc18dfdecc5efa54b0", "size": 9270, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "cleaner_wrasse/src/makemessage.hpp", "max_stars_repo_name": "Bram-Wel/routeros", "max_stars_repo_head_hexsha": "21d721384c25edbca66a3d52c853edc9faa83cad", "max_stars_repo_licenses": ["BSD-3-Clause"]... |
[STATEMENT]
theorem load_after_free_2:
assumes "free h c = Success (h', cap)"
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 free_cond[OF assms(1)]
[PROOF STATE]
proof (pro... | {"llama_tokens": 1426, "file": "CHERI-C_Memory_Model_CHERI_C_Concrete_Memory_Model", "length": 3} |
# https://people.csail.mit.edu/rivest/pubs/RST01.pdf
| {"hexsha": "8a68690c19fb00cc6cf6330dfff70d0e3840018e", "size": 53, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/ringsignatures.jl", "max_stars_repo_name": "UnofficialJuliaMirrorSnapshots/CryptoSignatures.jl-35cc5888-0c46-470e-89c7-eafcaf79a1aa", "max_stars_repo_head_hexsha": "2b1a4e92bf93b285f7ab4b5b5900d1... |
#!/usr/bin/env python
from nose.tools import assert_equal, assert_true, assert_is, assert_is_not
from numpy.testing import assert_array_less, assert_array_almost_equal
import numpy as np
from six.moves import range
from heat import BmiHeat
def test_get_initial_value():
model = BmiHeat()
model.initialize()
... | {"hexsha": "9c9e2646c4d9df88d1ccdc4e91f0720211ba43f5", "size": 1725, "ext": "py", "lang": "Python", "max_stars_repo_path": "test/heat/tests/test_get_value.py", "max_stars_repo_name": "franklinzhanggis/model-interoperable-engine", "max_stars_repo_head_hexsha": "40b724813bec9af16f4ca95e36f8ff16be787315", "max_stars_repo_... |
import logging
import ast
import os
import struct
import sys
import math
import random
import time
from os.path import join as pjoin
from collections import deque, namedtuple
import numpy as np
import tensorflow as tf
from tensorflow import keras
from .timer import Timer
logger = logging.getLogger("MCDose."+__name__... | {"hexsha": "83e050c6dc88d10d06c673b08e13afdb3c90e456", "size": 9850, "ext": "py", "lang": "Python", "max_stars_repo_path": "MC simulation/mcdose/mcdose/dataloader.py", "max_stars_repo_name": "qihuilyu/P2T", "max_stars_repo_head_hexsha": "6b8a24a632354d70c8ba44df717291573a5e0bd2", "max_stars_repo_licenses": ["MIT"], "ma... |
@testset "Unconstrained" begin
include("double_msd.jl")
include("AJPR14e54.jl")
include("AP12e21.jl")
include("JCG14e61.jl")
include("JCG14e63.jl")
include("PJ08e28.jl")
include("PJ08e54.jl")
end
@testset "Constrained" begin
include("PEDJ16s4.jl")
end
| {"hexsha": "63dd8e3a96bb9e02a3876bcbf2e7e49923718651", "size": 284, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/discrete.jl", "max_stars_repo_name": "UnofficialJuliaMirrorSnapshots/SwitchOnSafety.jl-ceb7f16a-07bf-5f4a-9354-b68f01b1610f", "max_stars_repo_head_hexsha": "e9fefe2cb8f45f27ed9ea95d3edee725e8b8... |
c
c file resc2.f
c
c . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
c . .
c . copyright (c) 1999 by UCAR .
c . .
c . UNIVERSITY CORPORA... | {"hexsha": "fd8becab67879da4119f6f47348957f7f2b788b7", "size": 4437, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "external/mudpack/resc2.f", "max_stars_repo_name": "utastudents/selalib", "max_stars_repo_head_hexsha": "84af43d0e82d4686a837c64384bbd173412df50e", "max_stars_repo_licenses": ["CECILL-B"], "max_sta... |
[STATEMENT]
lemma starfun_n_eq [simp]: "( *fn* f) (star_of n) = star_n (\<lambda>i. f i n)"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. (*fn* f) (star_of n) = star_n (\<lambda>i. f i n)
[PROOF STEP]
by (simp add: starfun_n star_of_def) | {"llama_tokens": 112, "file": null, "length": 1} |
import Lean
def checkWithMkMatcherInput (matcher : Lean.Name) : Lean.MetaM Unit :=
Lean.Meta.Match.withMkMatcherInput matcher fun input => do
let res ← Lean.Meta.Match.mkMatcher input
let origMatcher ← Lean.getConstInfo matcher
if not <| input.matcherName == matcher then
throwError "matcher name not recons... | {"author": "subfish-zhou", "repo": "leanprover-zh_CN.github.io", "sha": "8b2985d4a3d458ceda9361ac454c28168d920d3f", "save_path": "github-repos/lean/subfish-zhou-leanprover-zh_CN.github.io", "path": "github-repos/lean/subfish-zhou-leanprover-zh_CN.github.io/leanprover-zh_CN.github.io-8b2985d4a3d458ceda9361ac454c28168d92... |
#include "lexer.hpp"
#include "args-parser.hpp"
#include <exception>
#include <iostream>
#include <map>
#include <regex>
#include <boost/algorithm/string/split.hpp>
#include <boost/algorithm/string/classification.hpp>
const std::map<TokenType, std::regex> tokenRegexMap {
{ TokenType::WORD, std::regex("(\\b[\\w_][\... | {"hexsha": "50e77c08cd25c66c8c6125b33ac1be867a68e818", "size": 8597, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "engines/cpp/src/lexer.cpp", "max_stars_repo_name": "muditg317/Bokay", "max_stars_repo_head_hexsha": "0fd7717d05f7885f8e2e6448f3f45899fbb6d1cb", "max_stars_repo_licenses": ["MIT"], "max_stars_count":... |
import numbers
import numpy as np
import pandas as pd
def cusum_filter(raw_time_series, threshold, timestamps=True):
"""
Snippet 2.4, page 39, The Symmetric CUSUM Filter.
:return:
"""
if not isinstance(threshold, numbers.Number):
return cusum_filter_dynamic(raw_time_series, threshold, t... | {"hexsha": "baa949e1f3a74aa3287bbf35515cfc531e6db71c", "size": 1920, "ext": "py", "lang": "Python", "max_stars_repo_path": "adv_finance/labeling/filters.py", "max_stars_repo_name": "cw-jang/adv_finance", "max_stars_repo_head_hexsha": "240ce03e53fc6eead469a1ce7a220510a78c437e", "max_stars_repo_licenses": ["BSD-3-Clause"... |
module DistributionsAD
using PDMats,
ForwardDiff,
Zygote,
LinearAlgebra,
Distributions,
Random,
Combinatorics,
SpecialFunctions,
StatsFuns,
Compat
using Tracker: Tracker, TrackedReal, TrackedVector, TrackedMatrix, TrackedArray,
TrackedVecOrMa... | {"hexsha": "4bf5fc8018d02200715f8ade1be60c11d725042e", "size": 1560, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/DistributionsAD.jl", "max_stars_repo_name": "AStupidBear/DistributionsAD.jl", "max_stars_repo_head_hexsha": "d5cb1f9ead07122d4e3b51f0207053554e2b7446", "max_stars_repo_licenses": ["MIT"], "max_... |
! The Basic Model Interface ISO_C_BINDINGING compatible free functions
!
! @author: Nels Frazier
! @email: nels.frazier@noaa.gov
! Date: August 23, 2021
!
! This module provides a set of ISO_C_BINDING compatable functions
! that allow a Fortran BMI compatible model to interoperate with a C program, given that the
! BMI... | {"hexsha": "50cea2313e41c2318a084685acdd80f9613e1000", "size": 66328, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "iso_c_bmi.f90", "max_stars_repo_name": "zhengtaocui/bmi-fortran", "max_stars_repo_head_hexsha": "1729d684ad6e8aa0da0f85c92ac501032feb0f39", "max_stars_repo_licenses": ["MIT"], "max_stars_count"... |
from scipy.signal import lfilter
import numpy as np
from scipy.signal.signaltools import lfilter
from scipy.stats import linregress
from datetime import timedelta
# import matplotlib.pyplot as plt
########################################################
# automated extraction of the baseflow recession coefficient k ... | {"hexsha": "527ea0b4fe1d3eaffe73ee89a18161d60b35cbc4", "size": 4477, "ext": "py", "lang": "Python", "max_stars_repo_path": "hydrographSeparation.py", "max_stars_repo_name": "maseology/pyDrology", "max_stars_repo_head_hexsha": "eb8b677c077c50a309110a8a3a2bd0dd63b041e2", "max_stars_repo_licenses": ["MIT"], "max_stars_cou... |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Mon Feb 10 09:32:34 2020
@author: ravinderjit
Reject trials with large p2p deflections
Calculate system functions from EEG data for Dynamic Binaural Msequence project
Also compute noise floors
"""
import matplotlib.pyplot as plt
import matplotlib.colors a... | {"hexsha": "506d5bf7a423bbda65f1865e266f9b21644f26dd", "size": 4250, "ext": "py", "lang": "Python", "max_stars_repo_path": "EEG_analysis/DynBin_sysFuncs_PCA.py", "max_stars_repo_name": "Ravinderjit-S/DynamicBinauralProcessing", "max_stars_repo_head_hexsha": "ceb4c6aa6e9f903eb6e3d4f41ff8b1ea292e6f5c", "max_stars_repo_li... |
#include <nameclaim.h>
#include <core_io.h>
#include <boost/test/unit_test.hpp>
#include <primitives/transaction.h>
#include <test/test_bitcoin.h>
BOOST_FIXTURE_TEST_SUITE(nameclaim_tests, BasicTestingSetup)
BOOST_AUTO_TEST_CASE(calc_min_claimtrie_fee)
{
CMutableTransaction tx;
tx.vout.resize(1);
tx.vout... | {"hexsha": "d928c2265cfb216223c9b71609e044e6d0a45ad8", "size": 2517, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/test/nameclaim_tests.cpp", "max_stars_repo_name": "AlessandroSpallina/lbrycrd", "max_stars_repo_head_hexsha": "51618ce73bdf8798c70a12fe48484358f2df4b06", "max_stars_repo_licenses": ["MIT"], "max... |
import numpy as np
import gym
import time
# Execution
def execute(env, policy, gamma=1.0, render=False):
"""
Args:
policy: [S,A] shaped matrix representing the policy
env: OpenAI gym env
env.P represents the transition probabilities
of the environment
env.P[... | {"hexsha": "681e984907d6ab224962bc02dc2d3c74b5cae074", "size": 3985, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/dynamic_programming/value_policy_iteration/policy_iteration.py", "max_stars_repo_name": "johannesharmse/move_37_course", "max_stars_repo_head_hexsha": "a2060129cbc6fb651113aa18f1a6ea2673845182... |
#
# Copyright 2016 Quantopian, 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 agreed to in wr... | {"hexsha": "37c7f35307819005a10faaad053d48027b0b4977", "size": 25621, "ext": "py", "lang": "Python", "max_stars_repo_path": "zipline/utils/calendars/trading_calendar.py", "max_stars_repo_name": "nathanwolfe/zipline-minute-bars", "max_stars_repo_head_hexsha": "bcc6532731503c4521c6f7c4f9ee5e7ee545c013", "max_stars_repo_l... |
C#####################################################################
C
C FILE -
C
C Source code for geological applications using x3dgen
C Original file was adrivgen.f by Carl Gable
C
C CHANGE HISTORY -
C
C Original version - Carl Gable - 97
C error checking and extensions - T.Cherry -... | {"hexsha": "cb3c12a3eb2a2419ce220f6d9a3293bd3b767e0e", "size": 27523, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "src/tempgable.f", "max_stars_repo_name": "millerta/LaGriT-1", "max_stars_repo_head_hexsha": "511ef22f3b7e839c7e0484604cd7f6a2278ae6b9", "max_stars_repo_licenses": ["CNRI-Python"], "max_stars_coun... |
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
from itertools import chain
from utils.misc import convert_to_one_hot
from IPython import embed
MODULE_INPUT_NUM = {
'_NoOp': 1,
'_Find': 0,
'_Transform': 1,
'_Filter': 1,
'_And': 2,
'_Describe': 1,
}
MODULE_... | {"hexsha": "f07d67958a80174af504c750dac2b33e55247ee6", "size": 9085, "ext": "py", "lang": "Python", "max_stars_repo_path": "exp_vqa/model/composite_modules.py", "max_stars_repo_name": "qiuyue1993/XNM-Net", "max_stars_repo_head_hexsha": "1c4a16fd745d9e90e0d7a08b21e7efca4d2c6195", "max_stars_repo_licenses": ["MIT"], "max... |
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