text stringlengths 0 1.25M | meta stringlengths 47 1.89k |
|---|---|
double precision function cwig3j (j1,j2,j3,m1,m2,ient)
c wigner 3j coefficient for integers (ient=1)
c or semiintegers (ient=2)
c other arguments should be multiplied by ient
implicit double precision (a-h,o-z)
parameter (idim = 58)
character*512 slog
c dime... | {"hexsha": "c279aff19a08ed01894bc8cce73d07aeaff0ddf2", "size": 1992, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "src/MATH/cwig3j.f", "max_stars_repo_name": "xraypy/feff85exafs", "max_stars_repo_head_hexsha": "ec8dcb07ca8ee034d0fa7431782074f0f65357a5", "max_stars_repo_licenses": ["BSD-2-Clause"], "max_stars_c... |
import os
import numpy as np
import open3d as o3d
import glob
flip_transform = [[1, 0, 0, 0], [0, -1, 0, 0], [0, 0, -1, 0], [0, 0, 0, 1]]
def robust_kernel_fn(alpha, c):
if alpha == None:
# print('Binary truncation loss')
return lambda x: (np.abs(x) < c).astype(float)
if alpha == 2:
#... | {"hexsha": "34ab2798f114dbf51a5f8ba609a11c4c7ec17089", "size": 13117, "ext": "py", "lang": "Python", "max_stars_repo_path": "dataloader/common.py", "max_stars_repo_name": "Kwonyoung-Ryu/DeepGlobalRegistration", "max_stars_repo_head_hexsha": "0045118d96182047f4c09c4c4fe2a1b2b527cc5f", "max_stars_repo_licenses": ["MIT"],... |
% All comment lines start with %
% There are no multi-line comments
% LaTeX is NOT a "What You See Is What You Get" word processing software like
% MS Word, or OpenOffice Writer
% Every LaTeX command starts with a backslash (\)
% LaTeX documents start with a defining the type of document it's compiling
% Other docu... | {"hexsha": "d051795f4e26196501737de941155ea3b044b9dc", "size": 9934, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "samples/Misc/learn-latex.tex", "max_stars_repo_name": "MateoCerquetella/dark-plus-syntax", "max_stars_repo_head_hexsha": "c5afd61a0b7a0081b6e4c070a49cde71abfd3dce", "max_stars_repo_licenses": ["MIT"... |
# Problem: https://projecteuler.net/problem=227
# Define distance as the clockwise number of people between the dice.
# Use the distance between the dices as a state.
# Use Markov chain to track the probabilities.
# T[distance1][distance2] is the probability of transitioning from distance 1 to distance 2
impor... | {"hexsha": "b0aa6751c2d5f0e6b1fb3ed7b10e7e2ec35beb11", "size": 2199, "ext": "py", "lang": "Python", "max_stars_repo_path": "3rd_100/problem227_improved.py", "max_stars_repo_name": "takekoputa/project-euler", "max_stars_repo_head_hexsha": "6f434be429bd26f5d0f84f5ab0f5fa2bd677c790", "max_stars_repo_licenses": ["MIT"], "m... |
import os
import logging
os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3"
logging.getLogger("tensorflow").setLevel(logging.CRITICAL)
logging.getLogger("tensorflow_hub").setLevel(logging.CRITICAL)
import shutil
import numpy as np
from perform.constants import REAL_TYPE, PARAM_INPUTS, ROM_INPUTS
# Constants to use throughou... | {"hexsha": "c05a8d607bddab6a3eeadebdbcde66a638550f6b", "size": 9154, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/integration_tests/constants.py", "max_stars_repo_name": "cwentland0/perform", "max_stars_repo_head_hexsha": "e08771cb776a7e6518c43350746e2ca72f79b153", "max_stars_repo_licenses": ["MIT"], "m... |
#!/usr/bin/env python
"""
Author: Devansh Shukla
"""
import pandas as pd
import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
from matplotlib.animation import FuncAnimation, FFMpegWriter
import matplotlib.gridspec as gridspec
custom_rcparams = {
"axes.labelsize": 7,
"axes.titlesize": 8,
... | {"hexsha": "13be23a9d1afe060e4d8583ab13e4743506f3902", "size": 3019, "ext": "py", "lang": "Python", "max_stars_repo_path": "M6/AnimationParticle.py", "max_stars_repo_name": "devanshshukla99/MP409-Computational-Physics-Lab", "max_stars_repo_head_hexsha": "938318cb62f1b89a7bc57e07f9b5ac6f2b689fe4", "max_stars_repo_licens... |
import logging
import os
import pandas as pd
import numpy as np
import itertools as it
import xgboost as xgb
class XGB(object):
def __init__(self, obj):
self.master = obj
for key, val in vars(obj).items():
setattr(self, key, val)
base_for = "ACGT"
base_rev = "... | {"hexsha": "180746489c18e6c621d02998fa08b36af6095d1e", "size": 4099, "ext": "py", "lang": "Python", "max_stars_repo_path": "cctyper/xgb.py", "max_stars_repo_name": "ElementGenomicsInc/CRISPRCasTyper", "max_stars_repo_head_hexsha": "c1112b0bcaf3e4adc4bd89a693b84e7f9a185abf", "max_stars_repo_licenses": ["MIT"], "max_star... |
[STATEMENT]
lemma binsert_absorb[simp]: "binsert a (binsert a x) = binsert a x"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. binsert a (binsert a x) = binsert a x
[PROOF STEP]
by transfer simp | {"llama_tokens": 80, "file": null, "length": 1} |
###########################################################
###########################################################
## EMode - Python interface, by EMode Photonix LLC
###########################################################
## Copyright (c) 2021 EMode Photonix LLC
################################################... | {"hexsha": "f515f002621fe92a9ebd7bdca8224daca3277a5d", "size": 6825, "ext": "py", "lang": "Python", "max_stars_repo_path": "emodeconnection/emodeconnection.py", "max_stars_repo_name": "emode-photonix/emodeconnection", "max_stars_repo_head_hexsha": "d39b6a0c356bce8a3284087da37513c1c2ea3bf3", "max_stars_repo_licenses": [... |
int_or_bigint(x) = (y=mod(x,Int); x==y ? convert(Int,x) : convert(BigInt,y))
mutable struct UnpickleState
stack :: Stack
proto :: Int
pos :: Int
memo :: Memo
end
UnpickleState() = UnpickleState(Stack(), HIGHEST_PROTOCOL, 1, Memo())
function read_opcode(io::IO, state::UnpickleState)
ans = read(io, ... | {"hexsha": "0b6c729588ce151b21f2a65393042f3224019607", "size": 10391, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/PickleIO/unpickle.jl", "max_stars_repo_name": "cjdoris/PythonIO.jl", "max_stars_repo_head_hexsha": "cd2adb43c41ea55058beadb1ff2d9c0c57569f8e", "max_stars_repo_licenses": ["MIT"], "max_stars_co... |
import math
from typing import List
from typing import Sequence
from typing import Tuple
import matplotlib.axes
import matplotlib.pyplot as plt
import numpy as np
def prepare_axes(segments: List[str], columns_num: int, figsize: Tuple[int, int]) -> Sequence[matplotlib.axes.Axes]:
"""Prepare axes according to segm... | {"hexsha": "2684d74e46a6bc59c5dd24f2275e119dbf5dd10c", "size": 707, "ext": "py", "lang": "Python", "max_stars_repo_path": "etna/analysis/utils.py", "max_stars_repo_name": "martins0n/etna", "max_stars_repo_head_hexsha": "51e9cec5183da2499ca247b0e2db215507246ceb", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_cou... |
#!/usr/bin/python
#######################################################################
import torch
import torch.nn.functional as F
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import math
import pdb
from torch.autograd import Variable
######################################... | {"hexsha": "4e13d914807f15931122508d86dd693981f5f080", "size": 6259, "ext": "py", "lang": "Python", "max_stars_repo_path": "ASR_TransV1/Barlow_Twins.py", "max_stars_repo_name": "BUTSpeechFIT/ASR_Transformer", "max_stars_repo_head_hexsha": "814f720aa8265e9a377869f93dc65b251338e985", "max_stars_repo_licenses": ["MIT"], "... |
##
# This software was developed and / or modified by Raytheon Company,
# pursuant to Contract DG133W-05-CQ-1067 with the US Government.
#
# U.S. EXPORT CONTROLLED TECHNICAL DATA
# This software product contains export-restricted data whose
# export/transfer/disclosure is restricted by U.S. law. Dissemination
# to ... | {"hexsha": "29100997ad26392fdf9922359e00f2fd5bf26d3e", "size": 1871, "ext": "py", "lang": "Python", "max_stars_repo_path": "cave/com.raytheon.viz.gfe/localization/gfe/userPython/smartTools/RHTool.py", "max_stars_repo_name": "srcarter3/awips2", "max_stars_repo_head_hexsha": "37f31f5e88516b9fd576eaa49d43bfb762e1d174", "m... |
# @recipe function f(e::Event;coloring=[], labeling=[],show_pulses=true)
# if show_pulses==true
# n_wps = size(e.weighting_potentials,1)
# width = 800
# length = width+n_wps*width/2
# size --> (width,length)
# myheights = [width/length]
# for i in 1:n_wps
# ... | {"hexsha": "30d82e4481f9f34dabf4631c0c2d31a69d876dcb", "size": 2116, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/Event/plot_recipes.jl", "max_stars_repo_name": "UnofficialJuliaMirrorSnapshots/SolidStateDetectors.jl-71e43887-2bd9-5f77-aebd-47f656f0a3f0", "max_stars_repo_head_hexsha": "9c2c535181052a8e80165... |
using LightGraphs
using SparseArrays
import Base: show, eltype, copy
import LightGraphs: nv, has_edge, has_vertex, add_edge!, rem_edge!, rem_vertex!,
rem_vertices!, add_vertex!, add_vertices!, outneighbors, inneighbors, vertices, edges,
adjacency_matrix, src, dst, nv, edgetype
export AbstractMultigraph
# expo... | {"hexsha": "46b9331aabf55e6175bd2e75fef308eccda818e7", "size": 4116, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/abstract_multigraph.jl", "max_stars_repo_name": "QuantumBFS/Multigraphs.jl", "max_stars_repo_head_hexsha": "8d1c07598f5ce2083c18c358e5cded601b634a0a", "max_stars_repo_licenses": ["MIT"], "max_s... |
from sys import argv
from contextlib import redirect_stdout
from random import randint
import numpy as np
# generate two RxC matrices and their multiplication
# $ python c_array_gen.py 16 16 > data.txt
RS = 8
CS = 8
fm_np = np.random.randint(-128,127, size=(RS, CS))
sm_np = np.random.randint(-128,127, size=(RS, CS))
... | {"hexsha": "b407e4d05b2068dd6fcd9e6bbb3ab221e87d5366", "size": 1107, "ext": "py", "lang": "Python", "max_stars_repo_path": "verilog/dv/subservient_test/sw/py_tools/gen_c_array_2d.py", "max_stars_repo_name": "yongatek/caravel_yonga-serv-accelerator", "max_stars_repo_head_hexsha": "8efa2eeaadaeba3c78de0546ee40c83433a5632... |
import numpy as np
from numpy.linalg import inv
'''
Kalman smoothing of trajectories using INS transforms combined with
ICP scan matching
'''
def kf_pass(A0, B0, C1, d1, Q0, R1, mu00, Sigma00, u0, z1):
# Kalman filter forward pass
# Follows convention:
# x1 = A0*x0 + B0*u0 + eta, eta ~ N(0, Q0)
# z... | {"hexsha": "ee0a6a214bad30ab7cc30d900afe2f30dda2537f", "size": 7642, "ext": "py", "lang": "Python", "max_stars_repo_path": "process/KalmanSmoother.py", "max_stars_repo_name": "sameeptandon/sail-car-log", "max_stars_repo_head_hexsha": "0ee3d598bb09d389bcbd2ebf73cd4b2411e796be", "max_stars_repo_licenses": ["BSD-2-Clause"... |
#!/usr/bin/python3
import cv2
import imageio
import logging
import numpy as np
import os
from pathlib import Path
import pdb
import time
import torch
import torch.nn as nn
import torch.utils.data
import torch.backends.cudnn as cudnn
from typing import List, Tuple
import tqdm
import mmcv
from mseg.utils.dir_utils impo... | {"hexsha": "604d267d1a187632c0669c8cf99ed95b2f71a14d", "size": 24664, "ext": "py", "lang": "Python", "max_stars_repo_path": "mseg_semantic/tool/inference_task.py", "max_stars_repo_name": "Jokoe66/mseg-semantic", "max_stars_repo_head_hexsha": "d3aedcfe03ae66d07b9e7b0ba05e0d4b715d11ca", "max_stars_repo_licenses": ["MIT"]... |
""" Licensed under a 3-clause BSD style license - see LICENSE.rst
This script shows how to extract features from raw images.
The use of this script requires a mask file,
which has to be created with the script generate_mask.py
(c) 2020, Michael Mommert (mommermiscience@gmail.com)
"""
import os
import requests
import ... | {"hexsha": "68d5d43ec33f1723573b3b483777e660e26958b2", "size": 1632, "ext": "py", "lang": "Python", "max_stars_repo_path": "scripts/extract_features.py", "max_stars_repo_name": "mommermi/cloudynight", "max_stars_repo_head_hexsha": "6fcc72e4844075f65610b5dfb96857e87c507cf6", "max_stars_repo_licenses": ["BSD-3-Clause"], ... |
import numpy as np
def running_median(stream):
med = []
for i in range(1, len(stream)+1):
med.append(np.median(stream[:i]))
return med
if __name__ == '__main__':
print(running_median([2, 1, 4, 7, 2, 0, 5]))
# 2 1.5 2 3.0 2 2.0 2
| {"hexsha": "5c936070a9bdd811d6c2f2e3492e69664111546c", "size": 261, "ext": "py", "lang": "Python", "max_stars_repo_path": "day47/Solution.py", "max_stars_repo_name": "silvioedu/DailyInterview", "max_stars_repo_head_hexsha": "976aec8e001344931aed19f20ccffc605fe063fd", "max_stars_repo_licenses": ["MIT"], "max_stars_count... |
#!/usr/bin/env python3
import itertools
import time
from keithley2600b import SMU
import click
import zerorpc
import sys
import yaml
import numpy as np
import tempfile
from scipy import stats
from fabric import Connection
import matplotlib.pyplot as plt
from sklearn.linear_model import LinearRegression
import msgpack
i... | {"hexsha": "c97daf98062dc397d4bd4c7f2e83bedbb5ad6fea", "size": 6915, "ext": "py", "lang": "Python", "max_stars_repo_path": "extra/profile_frontend_measure.py", "max_stars_repo_name": "orgua/shepherd", "max_stars_repo_head_hexsha": "347af12740b008a78b7012ae748d9eb2b147274b", "max_stars_repo_licenses": ["MIT"], "max_star... |
# Copyright 2016-present CERN – European Organization for Nuclear Research
#
# 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... | {"hexsha": "7c4afee6bcb429d01794e16fab3ddf6dd6f70382", "size": 3734, "ext": "py", "lang": "Python", "max_stars_repo_path": "qf_lib_tests/unit_tests/common/utils/test_data_cleaner.py", "max_stars_repo_name": "webclinic017/qf-lib", "max_stars_repo_head_hexsha": "96463876719bba8a76c8269cef76addf3a2d836d", "max_stars_repo_... |
[STATEMENT]
lemma Assigno_undef: "(Assigno x \<theta> = undefg) = (\<theta>=undeft)"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. (Assigno x \<theta> = undefg) = (\<theta> = undeft)
[PROOF STEP]
by (metis Assigno.elims option.distinct(1)) | {"llama_tokens": 104, "file": "Differential_Game_Logic_USubst", "length": 1} |
import os
import gym
import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
from torch.autograd import Variable
from torch.distributions import Categorical
import matplotlib.pyplot as plt
env = gym.make("CartPole-v0")
env.reset()
class Policy(nn.Module):
... | {"hexsha": "81dd4f677837244ffa9caac9b8ba53f3b1826ac2", "size": 4994, "ext": "py", "lang": "Python", "max_stars_repo_path": "backpropagate_Cartpole/main.py", "max_stars_repo_name": "Forsenlol/Reinforced-training-simulating-the-work-of-neural-synapses", "max_stars_repo_head_hexsha": "21e70c3eb5fac8984adb78771bf25f1e5aef8... |
# -*- coding: utf-8 -*-
#
# example exs4a
# ----------------------------------------------------------------
# PURPOSE
# Analysis of a plane truss using loops.
# ----------------------------------------------------------------
# REFERENCES
# P-E Austrell 1994-03-08
# K-G Olsson 1995-09-28
# O Dahlblom 2... | {"hexsha": "a1ef88434b5ccc3090b6123d01b327bb72daa7ec", "size": 2113, "ext": "py", "lang": "Python", "max_stars_repo_path": "examples/exs_bar2_la.py", "max_stars_repo_name": "CALFEM/calfem-py", "max_stars_repo_head_hexsha": "26d4082ca6b907c48ad814733c733ae30a959657", "max_stars_repo_licenses": ["MIT"], "max_stars_count"... |
% Options for packages loaded elsewhere
\PassOptionsToPackage{unicode}{hyperref}
\PassOptionsToPackage{hyphens}{url}
%
\documentclass[
]{book}
\usepackage{lmodern}
\usepackage{amssymb,amsmath}
\usepackage{ifxetex,ifluatex}
\ifnum 0\ifxetex 1\fi\ifluatex 1\fi=0 % if pdftex
\usepackage[T1]{fontenc}
\usepackage[utf8]{... | {"hexsha": "8f6cd0f30638f595c77229b090ac91022229ca47", "size": 50082, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "bookdown-demo.tex", "max_stars_repo_name": "AnkaS/recipes", "max_stars_repo_head_hexsha": "eed59420e131d453591797d1769ab8eb4b1bb267", "max_stars_repo_licenses": ["CC0-1.0"], "max_stars_count": null... |
\section{Task}
Build a speaker recognition system
| {"hexsha": "b561ee41e20150ec3e56300bbc7f237662811628", "size": 51, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "doc/Midterm-Report/task.tex", "max_stars_repo_name": "juliia5m/knu_voice", "max_stars_repo_head_hexsha": "1f5d150ded23af4c152b8d20f1ab4ecec77b40e1", "max_stars_repo_licenses": ["Apache-2.0"], "max_sta... |
# -*- coding: utf-8 -*-
"""
Created on Wed Dec 15 21:41:25 2021
@author: Oscar
"""
import math
import numpy as np
def regularFalsi(f, a, b, Tol, N):
i = 1
fa = f(a)
fb = f(b)
print("%-20s %-20s %-20s %-20s %-20s" %
("n","a_n","b_n","p_n","f(p_n)"))
while(i <= N):
... | {"hexsha": "74ffcfcca57c2af0a9c420e7f28082d41cc5dcbc", "size": 919, "ext": "py", "lang": "Python", "max_stars_repo_path": "False Position Code.py", "max_stars_repo_name": "oliver779/Computational_Methods_Course", "max_stars_repo_head_hexsha": "e3d96d97ae0b3acaa1b61474eb18b3fbbf8edc9c", "max_stars_repo_licenses": ["MIT"... |
subroutine Bamp_mmp(q,mc,ms,Bmmp)
c--- u + g -> c + s + d (t-channel single-charm)
************************************************************************
* *
* AUTHORS: R. FREDERIX AND F. TRAMONTANO *
* DATE :... | {"hexsha": "5db6888ae702887bc0181be56ca4ee35d6c0ac6e", "size": 21460, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "MCFM-JHUGen/src/Stopb/Bmmp.f", "max_stars_repo_name": "tmartini/JHUGen", "max_stars_repo_head_hexsha": "80da31668d7b7eb5b02bb4cac435562c45075d24", "max_stars_repo_licenses": ["Apache-2.0"], "max_... |
function part1(input)
state = parse_input(input, 3)
for i = 1:6
state = simulate_cycle(state)
end
return length(state)
end
function part2(input)
state = parse_input(input, 4)
for i = 1:6
state = simulate_cycle(state)
end
return length(state)
end
function parse_input(inp... | {"hexsha": "694a0fc89c0ff46c4b80407e70f887cae086a283", "size": 1229, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "2020/day17.jl", "max_stars_repo_name": "GunnarFarneback/AdventOfCode.jl", "max_stars_repo_head_hexsha": "2f60011747bfe5d27e954f914f39b4ea2f7b0722", "max_stars_repo_licenses": ["MIT"], "max_stars_co... |
dyn.load('/Library/Java/JavaVirtualMachines/jdk1.8.0_131.jdk/Contents/Home/jre/lib/server/libjvm.dylib')
setwd("/Users/mengmengjiang/all datas/voltage")
library(xlsx)
par(mfrow = c(2,2), mar = c(1.8,2.2,0.8,1), oma = c(1,1,1,1))
### 32G-18nl/min ###
k1<-read.xlsx("he-32g.xlsx", sheetName = "2kv18", header = TRUE)
k2... | {"hexsha": "9ff959e9ccaca46910635248d0f1abd26aa6b7ba", "size": 4727, "ext": "r", "lang": "R", "max_stars_repo_path": "thesis/chap5/chap5-fig5-21.r", "max_stars_repo_name": "shuaimeng/r", "max_stars_repo_head_hexsha": "94fa0cce89c89a847b95000f07e64a0feac1eabe", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null... |
#include <boost/fusion/container/map/map_iterator.hpp>
| {"hexsha": "1ddbd3ba01c4aff7dbd5fa8fb732c0aef0440247", "size": 55, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "src/boost_fusion_container_map_map_iterator.hpp", "max_stars_repo_name": "miathedev/BoostForArduino", "max_stars_repo_head_hexsha": "919621dcd0c157094bed4df752b583ba6ea6409e", "max_stars_repo_licenses... |
function [varargout]=gpatch(varargin)
% function [h]=gpatch(F,V,C,CE,A,L)
% ------------------------------------------------------------------------
% This function is a short-hand version of the |patch| command. The inputs
% for |gpatch| are the faces (F), the vertices (V), the color description
% (C), the edge color... | {"author": "MultiDIC", "repo": "MultiDIC", "sha": "d363c3ea74673e58df275d4a4c8e528ef5472acb", "save_path": "github-repos/MATLAB/MultiDIC-MultiDIC", "path": "github-repos/MATLAB/MultiDIC-MultiDIC/MultiDIC-d363c3ea74673e58df275d4a4c8e528ef5472acb/lib_ext/GIBBON/lib/gpatch.m"} |
#!/usr/bin/python
##############################################
###Python template
###Author: Elizabeth Lee
###Date: 6/9/13
###Function:
#### 1) create scatter of OR by zipcode vs. urban metro RUCC avg 2013
###Import data: zipcode_bysseas_cl.csv
###Command Line: python
############################################... | {"hexsha": "94eaf2f6f272544f781038dd60857a1ff3d600a9", "size": 11462, "ext": "py", "lang": "Python", "max_stars_repo_path": "scripts/OR_urbanmetro_v6-7-13.py", "max_stars_repo_name": "eclee25/flu-SDI-exploratory-age", "max_stars_repo_head_hexsha": "2f5a4d97b84d2116e179e85fe334edf4556aa946", "max_stars_repo_licenses": [... |
from __future__ import print_function
import numpy as np
import argparse
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torchvision import datasets, transforms
from torch.autograd import Variable
class QAlexNet(nn.Module):
def __init__(self, num_classes=10):
... | {"hexsha": "f05f903315b385e6ea385806809eca7f9ec9bd60", "size": 6093, "ext": "py", "lang": "Python", "max_stars_repo_path": "models/model.py", "max_stars_repo_name": "LinjianMa/neuralODE-282", "max_stars_repo_head_hexsha": "28d7b520beeeeb773e651110d1faedda6cb835e7", "max_stars_repo_licenses": ["MIT"], "max_stars_count":... |
function res = adjD(y)
res = zeros(size(y,1),size(y,2));
%y1 = ones(imsize)*y(1)/sqrt(prod(imsize));
%yx = (reshape(y(2:prod(imsize)+1), imsize(1), imsize(2)));
%yy = (reshape(y(prod(imsize)+2:end), imsize(1), imsize(2)));
res = adjDx(y(:,:,1)) + adjDy(y(:,:,2));
return;
function res = adjDy(x)
res = x(:,[1,1:end... | {"author": "thomaskuestner", "repo": "CS_MoCo_LAB", "sha": "a26e8e483624b2e4ee669e7a069ba9c74d2d2e4b", "save_path": "github-repos/MATLAB/thomaskuestner-CS_MoCo_LAB", "path": "github-repos/MATLAB/thomaskuestner-CS_MoCo_LAB/CS_MoCo_LAB-a26e8e483624b2e4ee669e7a069ba9c74d2d2e4b/reconstruction/matlab/CS_LAB_matlab/@TVOP/pri... |
import torch
import numpy as np
from torch import nn
from torch.nn import functional as F
from torch import optim
from metalearning.learner import Learner
from utils import get_performance
from copy import deepcopy
class Meta(nn.Module):
def __init__(self, args):
super(Meta, self).__init__()
sel... | {"hexsha": "cf9248a769d68171e24266e95a35c4f4274da545", "size": 8386, "ext": "py", "lang": "Python", "max_stars_repo_path": "metalearning/meta_update.py", "max_stars_repo_name": "yyyqqq5/MetaHG", "max_stars_repo_head_hexsha": "fa5fa36fdda4a491d68c8e66b70e2e34a7eb356d", "max_stars_repo_licenses": ["MIT"], "max_stars_coun... |
import numpy as np
import cvxpy as cvx
import util
def set_contains_array(S, a):
"""
:param S: list of np.ndarray
:param a: np.ndarray
:return: contains, 0 or 1
"""
contains = 0
for b in S:
if not (a - b).any(): # if a contained in S
contains = 1
return contains
... | {"hexsha": "8c3ed5966f5972419e367c22f46baad392ad9753", "size": 8958, "ext": "py", "lang": "Python", "max_stars_repo_path": "pomdp.py", "max_stars_repo_name": "gongjue/pocm", "max_stars_repo_head_hexsha": "1f8ae819aaa7fa5f25878a0662a23cb457c1180b", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_... |
!---------------------------------------------------------------------!
! PHAML !
! !
! The Parallel Hierarchical Adaptive MultiLevel code for solving !
! linear elliptic partial differ... | {"hexsha": "1134461b3f82f228c5df4c79f8fb1111e0710c96", "size": 30793, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "src/lapack_solve.f90", "max_stars_repo_name": "qsnake/phaml", "max_stars_repo_head_hexsha": "8925b4c32657bbd9f81cd5f8f9d6739151c66fec", "max_stars_repo_licenses": ["mpich2"], "max_stars_count":... |
import Aoc
import Data.Fin
import Data.List
import Data.List1
import Data.Nat
import Data.Strings
import Data.Vect
%default total
Passport : Type
Passport = List (String, String)
||| Split a string at the first colon in it: "abc:def" -> ("abc", "def").
splitColon : String -> (String, String)
splitColon s = let (p, q)... | {"hexsha": "dbcbf3764d1c2ac7bcd2deac8415a1b220ccdef6", "size": 1891, "ext": "idr", "lang": "Idris", "max_stars_repo_path": "day-4.idr", "max_stars_repo_name": "lynn/aoc-2020", "max_stars_repo_head_hexsha": "a66b9cac109f9c3b51f18f5fb2d54ebdf395bb10", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 4, "max_stars_r... |
"""
MIT License
Copyright (c) 2017 Ragini Sharma
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish,... | {"hexsha": "238cf3a1e3716035c486b881d96778de2d37b894", "size": 4313, "ext": "py", "lang": "Python", "max_stars_repo_path": "regressor.py", "max_stars_repo_name": "raginisharma14/Deep-Learning", "max_stars_repo_head_hexsha": "df201ff8db9f2f0f252c53f81dd4ace2a1372bd0", "max_stars_repo_licenses": ["MIT"], "max_stars_count... |
#! /usr/bin/env python3
import numpy as np
def readcloudsfile(fname):
'''
Format is this:
4 rows of comments
A row of nlat, nlon, nlayers
A row of lat, lon, level, alt (m), p (bar), T (K), t_std (K),
EW vel (m/s), NS vel (m/s), vert vel (m/s), clouds 1-13 vis tau (650 nm),
cloud total vis ... | {"hexsha": "8e0ed6c455dc590ebd12faafa193109d5322f18c", "size": 7127, "ext": "py", "lang": "Python", "max_stars_repo_path": "theresa/scripts/calcq.py", "max_stars_repo_name": "rychallener/theresa", "max_stars_repo_head_hexsha": "886c6b74bee2edef7df9b6b54ce6d97de4aa4421", "max_stars_repo_licenses": ["MIT"], "max_stars_co... |
import unittest
from pyscses.set_of_sites import SetOfSites
from pyscses.defect_species import DefectSpecies
from pyscses.defect_at_site import DefectAtSite
from pyscses.site import Site, LabelError
from unittest.mock import Mock, patch
from pyscses.constants import fundamental_charge
import numpy as np
def create_moc... | {"hexsha": "3fde155cd46a47abea1b1dbce3dccb9f597c7774", "size": 8969, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/test_site.py", "max_stars_repo_name": "j-m-dean/pyscses", "max_stars_repo_head_hexsha": "6c2875cb87a8f91ae7aed382922c34b0e611ba85", "max_stars_repo_licenses": ["MIT"], "max_stars_count": nul... |
module timestep_mod
use globals_mod
real(dp), save :: DT_ITER
contains
function calc_timestep(mesh) result(timestep)
use mesh_mod, only: mesh_t
use mesh_mod, only: mesh_iterate_cells
type(mesh_t), intent(inout) :: mesh
real(dp) :: timestep
DT_ITER = HUGE(1.0)
call mesh_iterate_cells(me... | {"hexsha": "845d36f813783e510e03fcfd749d88672dfc674c", "size": 1009, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "source/equations/euler/timestep_mod.f90", "max_stars_repo_name": "jmark/nemo2d", "max_stars_repo_head_hexsha": "a508f192d0f6da49e485ee9c8d1c049dbb81d033", "max_stars_repo_licenses": ["MIT"], "ma... |
# Copyright (c) Facebook, Inc. and its affiliates.
import numpy as np
import torch
import torch.nn as nn
from torch.nn import LayerNorm
from torch.nn import TransformerEncoder, TransformerEncoderLayer
from torch.nn import TransformerDecoder, TransformerDecoderLayer
from torch.nn.init import xavier_uniform_
from fairm... | {"hexsha": "e78504ac8549ce976b55d122784e13033af4915f", "size": 6632, "ext": "py", "lang": "Python", "max_stars_repo_path": "fairmotion/models/transformer.py", "max_stars_repo_name": "martoko/fairmotion", "max_stars_repo_head_hexsha": "13b24dc3f184d1af7874ee68e78ba74f2be83973", "max_stars_repo_licenses": ["BSD-3-Clause"... |
import matplotlib.pyplot as plt
import numpy as np
import matplotlib.colors as mcolors
def make_colormap(seq):
"""Return a LinearSegmentedColormap
seq: a sequence of floats and RGB-tuples. The floats should be increasing
and in the interval (0,1).
from : http://stackoverflow.com/questions/168348... | {"hexsha": "850af61737ea1cc7f7f7d728e753ed3859e41cba", "size": 2934, "ext": "py", "lang": "Python", "max_stars_repo_path": "helpers/lib/custom_cmap.py", "max_stars_repo_name": "scrasmussen/icar", "max_stars_repo_head_hexsha": "88c59fed7595b176a81127993785fdeb514f28a3", "max_stars_repo_licenses": ["MIT"], "max_stars_cou... |
import argparse
from augur.utils import write_json
import Bio.SeqIO
from collections import OrderedDict
import hdbscan
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import re
from scipy.spatial.distance import squareform, pdist
import seaborn as sns
from sklearn.decomposition import PCA
from sk... | {"hexsha": "969f9e95f44522202f869c675b6eec427d148afe", "size": 8809, "ext": "py", "lang": "Python", "max_stars_repo_path": "notebooks/scripts/embed.py", "max_stars_repo_name": "blab/cartography", "max_stars_repo_head_hexsha": "36ef6408e7bdb73b59003166ad7725662fd8fbe6", "max_stars_repo_licenses": ["MIT"], "max_stars_cou... |
double precision function fdisc(blockno,xc,yc)
implicit none
double precision xc,yc, xp, yp, zp
integer blockno
integer*8 cont, get_context
double precision pi, pi2
common /compi/ pi, pi2
double precision beta, theta(2)
common /annulus_comm/ beta, theta
d... | {"hexsha": "64c795200f6a3b6a2563b5cce0f2376cf23640c5", "size": 750, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "applications/paper/annulus/fdisc.f", "max_stars_repo_name": "scivision/forestclaw", "max_stars_repo_head_hexsha": "4dae847a8abd1055e70acda9c2973cd3dd77d3f1", "max_stars_repo_licenses": ["BSD-2-Clau... |
import pandas as pd
from ecommercetools.utilities import tools
import numpy as np
np.seterr(divide='ignore')
def get_products(transaction_items, days=None):
"""Return a Pandas DataFrame of products from a Pandas DataFrame of transaction items.
Args:
transaction_items (object): Pandas DataFrame.
... | {"hexsha": "5536942fd2fce8bece6f08faf5f52f8ada50383d", "size": 6860, "ext": "py", "lang": "Python", "max_stars_repo_path": "ecommercetools/products/products.py", "max_stars_repo_name": "ruber0id/ecommercetools", "max_stars_repo_head_hexsha": "c34582bf6ad3392fe9a3d05c92cf96dbf435d0a1", "max_stars_repo_licenses": ["MIT"]... |
#
# File: CNN_inferece.py
# Date:11.08.2018
# Author: Denis Tananaev
#
#
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import tensorflow as tf
from tensorflow.python.framework import ops
import scipy.misc
from numpy import newaxis
from PIL import Image
#... | {"hexsha": "1110f6ea6cf510ead8247784d0eb09981717ebff", "size": 3607, "ext": "py", "lang": "Python", "max_stars_repo_path": "CNN_inference.py", "max_stars_repo_name": "Dtananaev/tf_lstm_depth", "max_stars_repo_head_hexsha": "94f83e8671e8928eba24eac6936a02cd9d123686", "max_stars_repo_licenses": ["BSD-2-Clause"], "max_sta... |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
__author__ = "Han"
__email__ = "liuhan132@foxmail.com"
import logging
import numpy as np
import torch.nn
import torch.cuda
from game.models.layers import *
from utils.functions import compute_mask, del_zeros_right, compute_top_layer_mask
from game.tokenizer import Vocabul... | {"hexsha": "0e41183e623af0e1acc95d1600bc9caf64b61d96", "size": 10165, "ext": "py", "lang": "Python", "max_stars_repo_path": "game/models/doc_rep_pt.py", "max_stars_repo_name": "laddie132/MD3", "max_stars_repo_head_hexsha": "3df45918e33437e9a2309f7965f34f3a75621059", "max_stars_repo_licenses": ["MIT"], "max_stars_count"... |
using SymPy
wmotor1 = Sym("wmotor1");
wmotor2 = Sym("wmotor2");
wmotor3 = Sym("wmotor3");
wmotor4 = Sym("wmotor4");
## Propeller Forces
keng = 6.11e-3;
f1_body = [0;0;-keng*wmotor1];
f2_body = [0;0;-keng*wmotor2];
f3_body = [0;0;-keng*wmotor3];
f4_body = [0;0;-keng*wmotor4];
## Propeller Moments
kmeng = 1.5e-4;
m1_... | {"hexsha": "6888150c592b510b3f92d351aa6157a9494a8d84", "size": 1101, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "example/QuadRotor/ComputeMixingLogic.jl", "max_stars_repo_name": "jg110/Aerospace.jl", "max_stars_repo_head_hexsha": "48998ecaabf7b052793a44205e5482301ac32822", "max_stars_repo_licenses": ["MIT"], ... |
import os
import pickle as pkl
from tqdm import tqdm
import numpy as np
from scipy import stats
import matplotlib.pyplot as plt
import pandas as pd
import ujson as json
import nltk
from nltk.tokenize import word_tokenize
from time import time
np.random.seed(int(time()))
SPACE = ' '
def stat_length(seq_length):
... | {"hexsha": "be8bc31ec262c7ee031c00356d8cae4a016cbe4c", "size": 21636, "ext": "py", "lang": "Python", "max_stars_repo_path": "preprocess/torch_preprocess.py", "max_stars_repo_name": "shiningliang/Multi-level-Causality-Detection-Network", "max_stars_repo_head_hexsha": "126a11735781224aaa31af37728e6dd0b2e511c2", "max_star... |
#The MIT License
#
#Copyright (c) 2017 DYNI machine learning & bioacoustics team - Univ. Toulon
#
#Permission is hereby granted, free of charge, to any person obtaining a copy of
#this software and associated documentation files (the "Software"), to deal in
#the Software without restriction, including without limitatio... | {"hexsha": "1d343fd3c65745dc60ae4efdc8853a247fb02969", "size": 18957, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/test_utils.py", "max_stars_repo_name": "dynilib/dynibatch", "max_stars_repo_head_hexsha": "b647a50e64796d0d4558cc554060aa82427d468b", "max_stars_repo_licenses": ["MIT"], "max_stars_count": ... |
"""Side profiles of common trees in their bare (leafless) form."""
import matplotlib.pyplot as plt
import numpy as np
# ---------- Tree parameters ------------
# Overall profile dimensions parameters:
height = 100.0
width = 100.0
# Angles:
initial_angle = 0.5 * np.pi
angle_between_segments = 0.05 * np.pi
# Trunk a... | {"hexsha": "590195e366c79cefb5df2d33bac09c14d54fa8cd", "size": 1988, "ext": "py", "lang": "Python", "max_stars_repo_path": "tree-profiles/tree_profiles.py", "max_stars_repo_name": "sadielbartholomew/creative-matplotlib", "max_stars_repo_head_hexsha": "b881025b63e9fcfdeea8e41a8f212a2c83c64a24", "max_stars_repo_licenses"... |
__precompile__(false)
module Buildkite
import HTTP
import JSON
export BuildkiteAPI
abstract type AbstractBuildkiteAPI end
Base.@kwdef struct BuildkiteAPI <: AbstractBuildkiteAPI
base_url::HTTP.URI = HTTP.URI("https://api.buildkite.com/v2/")
access_token::String = ""
end
function buildkite_api_uri(api::Bui... | {"hexsha": "9e5b2bbe850dcb784be668e59d9b08ba1c1f3bbf", "size": 4480, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/Buildkite.jl", "max_stars_repo_name": "jakebolewski/Buildkite.jl", "max_stars_repo_head_hexsha": "ea5870badc29f5605e48084823c41982b07701bf", "max_stars_repo_licenses": ["MIT"], "max_stars_count... |
[STATEMENT]
lemma (in mut_m) handshake_invL_eq_imp:
"eq_imp (\<lambda>(_::unit) s. (AT s (mutator m), s\<down> (mutator m), sys_hs_type s\<down>, sys_hs_pending m s\<down>, mem_store_buffers (s\<down> sys) (mutator m)))
handshake_invL"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. eq_imp (\<lambda>_ s. ... | {"llama_tokens": 1065, "file": "ConcurrentGC_Phases", "length": 2} |
""" Dependências """
import numpy as np
import pandas as pd
import multiprocessing
from tqdm import tqdm
from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import StandardScaler
from sklearn.decomposition import PCA
from sklearn.metrics import confusion_matrix
from scipy import sparse
import oper... | {"hexsha": "334156dd623a6dc612cac8393f7b6552f6441bcf", "size": 51522, "ext": "py", "lang": "Python", "max_stars_repo_path": "nsc/neurongraph.py", "max_stars_repo_name": "GuilhermeToso/masters-project", "max_stars_repo_head_hexsha": "01d5acfddaedb3cbf7fa9247a88108530547e155", "max_stars_repo_licenses": ["MIT"], "max_sta... |
// Copyright (c) 2006 Johan Rade
// Copyright (c) 2011 Paul A. Bristow To incorporate into Boost.Math
// 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)
// test_nonfinite_trap.cpp
#ifdef _MSC_VER
# pragma warni... | {"hexsha": "819833e1a021efe0113134397e8b1edf98b968b0", "size": 7084, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "boost/libs/math/test/test_nonfinite_trap.cpp", "max_stars_repo_name": "randolphwong/mcsema", "max_stars_repo_head_hexsha": "eb5b376736e7f57ff0a61f7e4e5a436bbb874720", "max_stars_repo_licenses": ["BS... |
from flare.rbcm import RobustBayesianCommitteeMachine
from flare.gp import GaussianProcess
import os as os
import numpy as np
from flare.struc import Structure
from flare.env import AtomicEnvironment
from flare.gp_algebra import get_kernel_vector
TEST_DIR = os.path.dirname(__file__)
TEST_FILE_DIR = os.path.join(TEST_D... | {"hexsha": "027648a8f6daed9fed13a52884d10fa00147d177", "size": 4696, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/test_rbcm.py", "max_stars_repo_name": "aaronchen0316/flare", "max_stars_repo_head_hexsha": "47a2a89af635dfec6b41a873625ac2411da14ebb", "max_stars_repo_licenses": ["MIT"], "max_stars_count": ... |
[STATEMENT]
lemma free_eta: "s \<rightarrow>\<^sub>\<eta> t \<Longrightarrow> loose_bvar1 t i = loose_bvar1 s i"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. s \<rightarrow>\<^sub>\<eta> t \<Longrightarrow> loose_bvar1 t i = loose_bvar1 s i
[PROOF STEP]
apply (induct arbitrary: i set: eta)
[PROOF STATE]
proof (pro... | {"llama_tokens": 763, "file": "Metalogic_ProofChecker_EtaNorm", "length": 4} |
import numpy as np
import plotly.offline as pyo
import plotly.graph_objs as go
np.random.seed(42)
random_x = np.random.randint(1, 101, 100)
random_y = np.random.randint(1, 101, 100)
print(type(random_x))
data = [go.Scatter(x=random_x,
y=random_y,
mode='markers',
... | {"hexsha": "643c61787cc691269655088935c5f107a6fb6e38", "size": 791, "ext": "py", "lang": "Python", "max_stars_repo_path": "Plotly/scatter_plots.py", "max_stars_repo_name": "jorge-garciadiego/Visualization-Dash", "max_stars_repo_head_hexsha": "68fe99ff5c3ecc99eea3845f38849669449d89d2", "max_stars_repo_licenses": ["MIT"]... |
import pandas as pd
import numpy as np
if __name__ == '__main__':
import argparse
parser = argparse.ArgumentParser(prog='augment_snp_effects.py',
description='''
Augment SNP effects generated by simulate_effect_sizes.py
''')
parser.add_argument('--snp_effect_parquet', help='''
... | {"hexsha": "f45b4903f5f312d399c0232ed7cbf8c5427fcb74", "size": 1607, "ext": "py", "lang": "Python", "max_stars_repo_path": "submission/simulation/simulate_phenotypes/augment_snp_effects.py", "max_stars_repo_name": "liangyy/ukb_idp_genetic_arch", "max_stars_repo_head_hexsha": "562612af6c82684f13e0cf2cbe2adc9a8f31b29d", ... |
import numpy as np
class GameInfo(object):
def __init__(self,
frame,
robot_action=None,
human_action=None,
scored=None,
puck_was_hit=False,
puck_is_at_the_bottom=False,
distance_decreased=False,
... | {"hexsha": "cedb0a483680e4dcbaf67c37bf2ab5f242ab6d31", "size": 1072, "ext": "py", "lang": "Python", "max_stars_repo_path": "air_hockey/game_info.py", "max_stars_repo_name": "Gabo-Tor/air-hockey", "max_stars_repo_head_hexsha": "fc949075812da9c5fb5bbb511a717c4aa51640b3", "max_stars_repo_licenses": ["MIT"], "max_stars_cou... |
//
// Copyright (c) 2002--2010
// Toon Knapen, Karl Meerbergen, Kresimir Fresl,
// Thomas Klimpel and Rutger ter Borg
//
// 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)
//
// THIS FILE IS AUTOMATICALLY GENERATED
... | {"hexsha": "ef08398e9274664a6b60791de17e8d1bf0b31f52", "size": 5362, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "externals/numeric_bindings/boost/numeric/bindings/lapack/driver.hpp", "max_stars_repo_name": "ljktest/siconos", "max_stars_repo_head_hexsha": "85b60e62beca46e6bf06bfbd65670089e86607c7", "max_stars_r... |
import numpy as np
from .myqt import QT
import pyqtgraph as pg
from .cataloguecontroller import CatalogueController
from .traceviewer import CatalogueTraceViewer
from .peaklists import PeakList, ClusterPeakList
from .ndscatter import NDScatter
from .waveformviewer import WaveformViewer
from .similarity import SpikeSi... | {"hexsha": "b9b1ea8c986a30498e0764c11494f11d061c21e9", "size": 11968, "ext": "py", "lang": "Python", "max_stars_repo_path": "tridesclous/gui/cataloguewindow.py", "max_stars_repo_name": "rdarie/tridesclous", "max_stars_repo_head_hexsha": "178c0a67d7b3ac88be8e4383001396c1e0f976c2", "max_stars_repo_licenses": ["MIT"], "ma... |
#include <fstream>
#include <set>
#include <sstream>
#include <string>
#include <boost/test/unit_test.hpp>
#include "utilities.h"
#include "relation_buffer.h"
using namespace cflr;
using namespace std;
BOOST_AUTO_TEST_SUITE( relation_buffer_test )
BOOST_AUTO_TEST_CASE( int_buffer ){
typedef relation_bu... | {"hexsha": "630f633b35cc39c20ffd2b19de5feed1ef8f444c", "size": 8393, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/test/cpp/test_relation_buffer.cpp", "max_stars_repo_name": "cauliflower-cflr/cauliflower", "max_stars_repo_head_hexsha": "dc8ed39c60bcd8fd700d9da48aba124dd9d6d115", "max_stars_repo_licenses": ["... |
"""
Test cases for functions/classes in nn_comparator.py
-- kandasamy@cs.cmu.edu
"""
# pylint: disable=no-member
# pylint: disable=invalid-name
import numpy as np
# Local imports
from . import otmann
from .unittest_neural_network import generate_cnn_architectures, \
generate_m... | {"hexsha": "d28f7f24278b313a7a2eda3b820a9b07f82b1a6d", "size": 19090, "ext": "py", "lang": "Python", "max_stars_repo_path": "dragonfly/nn/unittest_otmann.py", "max_stars_repo_name": "hase1128/dragonfly", "max_stars_repo_head_hexsha": "4be7e4c539d3edccc4d243ab9f972b1ffb0d9a5c", "max_stars_repo_licenses": ["MIT"], "max_s... |
"""
Tests for datatype and galaxycluster
"""
from numpy.testing import assert_raises, assert_equal
from clmm import GCData
from clmm import Cosmology
def test_init():
gcdata = GCData()
assert_equal(None, gcdata.meta['cosmo'])
def test_update_cosmo():
# Define inputs
cosmo1 = Cosmology(H0=70.0, Omeg... | {"hexsha": "4503f7a9e776a39f9049f07e6e7bfb6fcb3a25ea", "size": 6144, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/test_gcdata.py", "max_stars_repo_name": "96RadhikaJadhav/CLMM", "max_stars_repo_head_hexsha": "cd0508f82f9a6a4692fe785277ac25c73e89d0d7", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_st... |
# Copyright 2019 Google Inc. 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 applicable law or ... | {"hexsha": "317e9ac178a54833b44028edd640ce6922b03712", "size": 5155, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/trainer.py", "max_stars_repo_name": "AlbaraaKhayat/e3d_lstm", "max_stars_repo_head_hexsha": "3caa74fb203fbfc0857a20088d559fec23ba8d99", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_co... |
from skimage import io, transform
import glob
import os
import tensorflow as tf
import numpy as np
import time
import matplotlib.pyplot as plt
import random
plt.rcParams['font.sans-serif'] = ['SimHei'] # 用来正常显示中文标签
plt.rcParams['axes.unicode_minus'] = False # 用来正常显示负号
'''
数据集来源
@misc{e-VDS,
author = {Culurcie... | {"hexsha": "4a99c6abfa8fe22c002c07be0bf04e38473c6948", "size": 9387, "ext": "py", "lang": "Python", "max_stars_repo_path": "Python/cnn.py", "max_stars_repo_name": "2016gary/RealTimeIR", "max_stars_repo_head_hexsha": "77ae3b673307821febbcf89c3e4d2da1834f5119", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count"... |
#!/usr/bin/env python
'''
Copyright (C) 2013- Swedish Meteorological and Hydrological Institute (SMHI)
This file is part of RAVE.
RAVE is free software: you can redistribute it and/or modify
it under the terms of the GNU Lesser General Public License as published by
the Free Software Foundation, either version 3 of t... | {"hexsha": "8ae4d3daed6812889788950149a8e2e5223eb551", "size": 11952, "ext": "py", "lang": "Python", "max_stars_repo_path": "rave_ec/Lib/odc_hac.py", "max_stars_repo_name": "DanielMichelson/drqc_article", "max_stars_repo_head_hexsha": "cd7df2f7290adedb557bbc6ba484d30039a23ce2", "max_stars_repo_licenses": ["CC-BY-4.0"],... |
"""
The ComplementPOVMEffect class and supporting functionality.
"""
#***************************************************************************************************
# Copyright 2015, 2019 National Technology & Engineering Solutions of Sandia, LLC (NTESS).
# Under the terms of Contract DE-NA0003525 with NTESS, the ... | {"hexsha": "76788c766fab1ff456b47ab376d741cf20b4f45f", "size": 8839, "ext": "py", "lang": "Python", "max_stars_repo_path": "pygsti/modelmembers/povms/complementeffect.py", "max_stars_repo_name": "pyGSTi-Developers/pyGSTi", "max_stars_repo_head_hexsha": "bfedc1de4d604f14b0f958615776fb80ddb59e33", "max_stars_repo_license... |
# import modules
import itertools
import os
import re
import sys
import xml.etree.ElementTree as ET
import numpy as np
import pandas as pd
from arcgis.features import GeoAccessor
import arcpy
if sys.version_info > (3, 0):
import winreg
else:
import _winreg as winreg
class BA_Data:
def __init__(self):
... | {"hexsha": "2b979221d7533470c5432e722727ca1cebba3e96", "size": 18558, "ext": "py", "lang": "Python", "max_stars_repo_path": "ba_data_paths/ba_data.py", "max_stars_repo_name": "knu2xs/ba_data_paths", "max_stars_repo_head_hexsha": "c161feec529882a2edfb2ed88b8a89cf07ec3243", "max_stars_repo_licenses": ["Apache-2.0"], "max... |
From Mtac2 Require Import Mtac2 CompoundTactics.
Import T.
Import T.notations.
Import CT.
Import CT.notations.
Example exabs (x : nat) : x = 1 -> 1 = x.
MProof.
intro H.
simple_rewrite H.
reflexivity.
Qed.
Example exabs2 (x : nat) : S x = 1 -> 1 = S x.
MProof.
intro H.
simple_rewrite H.
reflexivity.
Qed.
Require Imp... | {"author": "Mtac2", "repo": "Mtac2", "sha": "d16c2e682d5ab18ed77b13b4fd60a42a65c4f958", "save_path": "github-repos/coq/Mtac2-Mtac2", "path": "github-repos/coq/Mtac2-Mtac2/Mtac2-d16c2e682d5ab18ed77b13b4fd60a42a65c4f958/tests/comptactics.v"} |
"""
* GTDynamics Copyright 2020, Georgia Tech Research Corporation,
* Atlanta, Georgia 30332-0415
* All Rights Reserved
* See LICENSE for the license information
*
* @file test_four_bar.py
* @brief Unit tests for inverse dynamics of a four bar linkage.
* @author Frank Dellaert, Varun Agrawal, Mandy Xie, Alejan... | {"hexsha": "a3a69f14b1156a6ae26b955bc39ae05511c53bf2", "size": 3957, "ext": "py", "lang": "Python", "max_stars_repo_path": "python/tests/test_four_bar.py", "max_stars_repo_name": "danbarla/GTDynamics", "max_stars_repo_head_hexsha": "0448b359aff9e0e784832666e4048ee01c8b082d", "max_stars_repo_licenses": ["BSD-2-Clause"],... |
[STATEMENT]
lemma wtpd_res:
"\<lbrakk> method (G, D) (md, pTs) = Some (D, rT, (pns, lvars, blk, res));
wf_prog wf_java_mdecl G; is_class G D \<rbrakk>
\<Longrightarrow> wtpd_expr (env_of_jmb G D (md, pTs)) res"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. \<lbrakk>method (G, D) (md, pTs) = Some (D, rT, pns,... | {"llama_tokens": 1071, "file": null, "length": 4} |
#include "version.h"
#include "githash.h"
#include "fastqlib.h"
#include <boost/program_options.hpp>
namespace po = boost::program_options;
using namespace std;
string percent(int num,int den) {
char buffer[100];
sprintf(buffer,"%d / %d\t( %.2f %% )\t",num,den,100. * float(num)/float(den));
return(buffer);
}
i... | {"hexsha": "d3c15343cc73da15a8170e4de55b1c8c26a0fc43", "size": 2104, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "mergeReads.cpp", "max_stars_repo_name": "CarolineOhrman/NxTrim", "max_stars_repo_head_hexsha": "c47acfa0a5fb086b7d470e9566490d838b7be1a7", "max_stars_repo_licenses": ["BSD-2-Clause"], "max_stars_cou... |
# 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": "071968ce2b1fa4fd3020bd516724858533f979ad", "size": 5147, "ext": "py", "lang": "Python", "max_stars_repo_path": "tutorials/language/extern_op.py", "max_stars_repo_name": "baowenlei/tvm", "max_stars_repo_head_hexsha": "6b2e18ed96fad26b4a5e5f8a6dcbedf9206c9a65", "max_stars_repo_licenses": ["Apache-2.0"], "max_... |
[STATEMENT]
lemma len_Suc_mult2[simp]: "len (Suc (2 * x)) = Suc (len x)"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. len (Suc (2 * x)) = Suc (len x)
[PROOF STEP]
proof (induct x rule: len.induct)
[PROOF STATE]
proof (state)
goal (3 subgoals):
1. len (Suc (2 * 0)) = Suc (len 0)
2. len (Suc (2 * Suc 0)) = Suc (le... | {"llama_tokens": 530, "file": "Formula_Derivatives_Presburger_Formula", "length": 4} |
[STATEMENT]
lemma conjuncts: "(\<forall> q\<in> set (conjuncts p). Ifm bs q) = Ifm bs p"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. (\<forall>q\<in>set (conjuncts p). Ifm bs q) = Ifm bs p
[PROOF STEP]
by (induct p rule: conjuncts.induct) auto | {"llama_tokens": 111, "file": null, "length": 1} |
export Problem, loss, lossgrad, lossngrad, jacobian, valueandjacobian
export project, normcs, sizen, sizep, predictor, prediction
type Problem{L<:AbLoss,
B<:AbBasis,
CS<:AbConstraintSet}
X :: Matrix
y :: Vector
loss :: L
ϕ :: Type{B}
Ω :: CS
end
Problem(X, y, l, B, ::Type{BasisL2Co... | {"hexsha": "728072f84d7ca791ae9d7966ac9f6583e7ad5159", "size": 2789, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/Tproblem.jl", "max_stars_repo_name": "rakeshvar/AnyBoost.jl", "max_stars_repo_head_hexsha": "f2477b1737e8834d622b1e3b7269bd133b25e544", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 10... |
# STUMPY
# Copyright 2019 TD Ameritrade. Released under the terms of the 3-Clause BSD license. # noqa: E501
# STUMPY is a trademark of TD Ameritrade IP Company, Inc. All rights reserved.
from collections import deque
import numpy as np
def atsc(IL, IR, j):
"""
Compute the anchored time series chain (ATSC).... | {"hexsha": "7c3e59c3a5c561399c0fb25d7ac161821c66aeb5", "size": 2625, "ext": "py", "lang": "Python", "max_stars_repo_path": "stumpy/chains.py", "max_stars_repo_name": "abbasnikbakht/stumpy", "max_stars_repo_head_hexsha": "3d0f5e165ade7dfdd4dad19ba5f71026345c551c", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_... |
[STATEMENT]
lemma mono2mono:
assumes "monotone ordb leq (\<lambda>y. f y)" "monotone orda ordb (\<lambda>x. t x)"
shows "monotone orda leq (\<lambda>x. f (t x))"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. monotone orda leq (\<lambda>x. f (t x))
[PROOF STEP]
using assms
[PROOF STATE]
proof (prove)
using this:... | {"llama_tokens": 206, "file": null, "length": 2} |
import random
import numpy as np
class Dataset:
"""
A mapping from column names to immutable arrays of equal length.
"""
def __init__(self, **data):
self._data = {}
self._length = None
super().__init__()
for column, data in data.items():
self[... | {"hexsha": "b890ea8f1dfb10602452f7f081689d7b823747de", "size": 3250, "ext": "py", "lang": "Python", "max_stars_repo_path": "sets-master/sets-master/sets/core/dataset.py", "max_stars_repo_name": "FedericoMolinaChavez/tesis-research", "max_stars_repo_head_hexsha": "d77cc621d452c9ecf48d9ac80349b41aeb842412", "max_stars_re... |
import numpy as np
from PIL import Image
import settings as globalv
import pca_utils as pca
from matplotlib import pyplot as plt
import glob
from matplotlib.image import imread
import warnings # Disable warning: https://stackoverflow.com/questions/41001533/how-to-ignore-python-warnings-for-complex-numbers
warnings.fil... | {"hexsha": "d435ebd4722f1cc05e6f21a9620ab1ae10d22ee0", "size": 2436, "ext": "py", "lang": "Python", "max_stars_repo_path": "hw3/references/simple-PCA/image_pca.py", "max_stars_repo_name": "ardihikaru/mlsp", "max_stars_repo_head_hexsha": "db38972bcceac7b95808132457c4de9170546c9d", "max_stars_repo_licenses": ["Apache-2.0... |
# Copyright (c) 2018 PaddlePaddle 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 app... | {"hexsha": "ebe820cb90ae235c4a95819f823b74afbc98e3b3", "size": 2000, "ext": "py", "lang": "Python", "max_stars_repo_path": "python/paddle/fluid/tests/unittests/test_executor_and_mul.py", "max_stars_repo_name": "zmxdream/Paddle", "max_stars_repo_head_hexsha": "04f042a5d507ad98f7f2cfc3cbc44b06d7a7f45c", "max_stars_repo_l... |
[STATEMENT]
lemma listrelp_imp_listsp1:
assumes H: "listrelp (\<lambda>x y. P x) xs ys"
shows "listsp P xs"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. listsp P xs
[PROOF STEP]
using H
[PROOF STATE]
proof (prove)
using this:
listrelp (\<lambda>x y. P x) xs ys
goal (1 subgoal):
1. listsp P xs
[PROOF STEP]
by... | {"llama_tokens": 147, "file": null, "length": 2} |
\ Tools -- Formatted unsigned single hex number output, not using BASE
\ an-17jan2022
decimal
: .1HX ( x -- ) \ Print last digit of x in hex
15 and \ lowest nibble
9 over < 7 and + \ for A..F
[char] 0 + emit ;
: .NHX ( x n -- ) \ Print last n digits of x in hex
... | {"hexsha": "4d5b5b53c2995465471f1a4c2c895710faea6737", "size": 898, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "Programming-Pearls/print-hex/print-hex.f", "max_stars_repo_name": "embeddingforth/embeddinForth", "max_stars_repo_head_hexsha": "b1921074c6f63be9570c7345e8196a6a4ff44b93", "max_stars_repo_licenses"... |
\section{FLL}
\pulpissimo containts 3 FLLs. One FLL is meant for generating the clock for the
peripheral domain, one for the core domain (core, memories, event unit etc) and
one is meant for the cluster. The latter is not used.
All the FLLs can be bypassed by writing to the JTAG register before the reset
signal is as... | {"hexsha": "9d89bd16cb0e47312906fdd60c2e049e55ed6cec", "size": 4240, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "doc/datasheet/content/peripherals_fll.tex", "max_stars_repo_name": "davideschiavone/pulpissimo", "max_stars_repo_head_hexsha": "c0e29f22ed947ce5330d950d8432085937136e72", "max_stars_repo_licenses": ... |
"""
"""
import numpy as np
from jax import numpy as jax_np
from jax import jit as jax_jit
from jax import value_and_grad
from ..utils import jax_inverse_sigmoid, jax_sigmoid, jax_adam_wrapper
def test_inverse_sigmoid_actually_inverts():
""""""
x0, k, ylo, yhi = 0, 5, 1, 0
xarr = np.linspace(-1, 1, 100)
... | {"hexsha": "a1ce5b82bd690b7cabdcd42e272af39fec6cdb32", "size": 1408, "ext": "py", "lang": "Python", "max_stars_repo_path": "diffmah/tests/test_utils.py", "max_stars_repo_name": "ArgonneCPAC/diffmah", "max_stars_repo_head_hexsha": "867d11def6284b07e58753f0d4590adc21495e3d", "max_stars_repo_licenses": ["BSD-3-Clause"], "... |
# coding=utf-8
import os
import re
import json
import numpy as np
import pandas as pd
import html
import urllib
from itertools import combinations
import jieba
import jieba.posseg as pseg
from collections import defaultdict
from .word_discoverer import WordDiscoverer
from .sent_dict import SentDict
from .resources impo... | {"hexsha": "142d312d20d6e08e4ea2f35c9ee1103b070ef548", "size": 44553, "ext": "py", "lang": "Python", "max_stars_repo_path": "harvesttext/harvesttext.py", "max_stars_repo_name": "wainshine/HarvestText", "max_stars_repo_head_hexsha": "391aed784d27b182cbf4e2e25d8526ee48f23377", "max_stars_repo_licenses": ["MIT"], "max_sta... |
import re
import numpy as np
import pytest
from ctc_decoder import BKTree
from ctc_decoder import LanguageModel
from ctc_decoder import beam_search
from ctc_decoder import best_path
from ctc_decoder import lexicon_search
from ctc_decoder import loss
from ctc_decoder import prefix_search_heuristic_split
from ctc_decod... | {"hexsha": "69bfcc49dd3e50caca02368b75b9ff9469038340", "size": 2884, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/test_real_example.py", "max_stars_repo_name": "TenaciousC22/CTCDecoder", "max_stars_repo_head_hexsha": "8a52aa0557f41bb8674643f0a5ae8071224aec79", "max_stars_repo_licenses": ["MIT"], "max_st... |
# # Variational problems
#
# In this example, we will numerically simulate an entropy-regularised Wasserstein gradient flow
# approximating the Fokker-Planck and porous medium equations.
#
# The connection between Wasserstein gradient flows and (non)-linear PDEs is due to Jordan, Kinderlehrer and Otto [^JKO98], and... | {"hexsha": "d6bc7cdb078010a03f55d18fa65e3e3f10e4dba1", "size": 13544, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "examples/variational/script.jl", "max_stars_repo_name": "zsteve/ot.jl", "max_stars_repo_head_hexsha": "a82e3fb19b00839ba809225e76a4ea43b49a315d", "max_stars_repo_licenses": ["MIT"], "max_stars_cou... |
using HTTP
using JSON
export RESTAPI
struct RESTAPI
baseurl
end
function (m::RESTAPI)(cmd)
try
url = "$(m.baseurl)/$cmd"
response = HTTP.get(url)
return JSON.parse(String(response.body))
catch e
return "error for $url occurred: $e"
end
end
| {"hexsha": "692109dc3f31a4d9d9ead35eb155f30b4b74a3dc", "size": 292, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/utils.jl", "max_stars_repo_name": "dhonza/Loudspeakers.jl", "max_stars_repo_head_hexsha": "cae99792aaa822b4c74fe2f018e546c20784e35b", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null,... |
'''
Utility functions.
'''
import numpy as np
from functools import partial
def fwht(x):
"""Recursive implementation of the 1D Cooley-Tukey FFT"""
# x = np.asarray(x, dtype=float)
N = x.shape[0]
if N == 1:
return x
else:
X_even = fwht(x[0:(N//2)])
X_odd = fwht(x[(N//2):])
... | {"hexsha": "4f2171b61df7960f20ec78f4ff5fb57756884bb8", "size": 2236, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/utils.py", "max_stars_repo_name": "aditya-sengupta/spright", "max_stars_repo_head_hexsha": "8c369c6eb33e0a677afaf039973faa11da8b458b", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1,... |
import os
import numpy as np
import tensorflow as tf
import soundfile as sf
from tqdm import tqdm
import pandas as pd
class Trainer():
def __init__(self, model, optimizer,loss, strategy, path_experiment, args):
self.model=model
print(self.model.summary())
... | {"hexsha": "cdaf2225ed42997b6c0285c181a3ecb9091494ba", "size": 2378, "ext": "py", "lang": "Python", "max_stars_repo_path": "trainer.py", "max_stars_repo_name": "IgnacioIrigaray/denoising-historical-recordings", "max_stars_repo_head_hexsha": "12f3f070e6131c58fbab810e0fc33f4a2cd8fb51", "max_stars_repo_licenses": ["MIT"],... |
Require Import Coq.Lists.List. Import ListNotations.
Require Import
Coq.Bool.Bool
Coq.Strings.Ascii
Coq.Strings.String.
Local Open Scope string.
Local Open Scope char.
From SRC Require Export CStrings.
(* Act II - Scene 0, Before Semirings *)
Inductive regex : Type :=
| Eps : regex
| Sy... | {"author": "mtrsk", "repo": "Regex-Play-Coq", "sha": "6f04e9299cfaa85377cf50d0470750d95abbb0b9", "save_path": "github-repos/coq/mtrsk-Regex-Play-Coq", "path": "github-repos/coq/mtrsk-Regex-Play-Coq/Regex-Play-Coq-6f04e9299cfaa85377cf50d0470750d95abbb0b9/src/EffMatching.v"} |
using DynamicalSystemsBase
function eom_ar1_unidir(x, p, n)
a₁, b₁, c_xy, σ = (p...,)
x, y = (x...,)
ξ₁ = rand(Normal(0, σ))
ξ₂ = rand(Normal(0, σ))
dx = a₁*x + ξ₁
dy = b₁*y + c_xy*x + ξ₂
return SVector{2}(dx, dy)
end
function ar1_unidir(;uᵢ = rand(2), a₁ = 0.90693, b₁ = 0.40693, c_xy... | {"hexsha": "faf5917f642bdf0022ce4e7422362d0749b71dbb", "size": 1512, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/resampling/binning/test_bin_BinnedWeightedResampling.jl", "max_stars_repo_name": "JuliaTagBot/UncertainData.jl", "max_stars_repo_head_hexsha": "4d9dc513b97f04a1d761e0a94eab3e3b11cc4c8a", "max_... |
#! format: off
abstract type AbstractRegulationFormulation <: AbstractDeviceFormulation end
struct ReserveLimitedRegulation <: AbstractRegulationFormulation end
struct DeviceLimitedRegulation <: AbstractRegulationFormulation end
get_variable_sign(_, ::Type{PSY.RegulationDevice{PSY.ThermalStandard}}, ::DeviceLimitedRe... | {"hexsha": "4105c5d7f3ca37474ddc7d0408aaca4d3d508f35", "size": 14206, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/devices_models/devices/regulation_device.jl", "max_stars_repo_name": "sambuddhac/PowerSimulations.jl", "max_stars_repo_head_hexsha": "5e485fab40dcd16d1cb4ed497b07373171ee0aa7", "max_stars_repo... |
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