text stringlengths 0 1.25M | meta stringlengths 47 1.89k |
|---|---|
import os
import numpy as np
import soundfile as sf
import matplotlib.pyplot as plt
import sys
sys.path.append('./')
import yamnet.params as yamnet_params
import yamnet.yamnet as yamnet_model
import tensorflow as tf
import subprocess
PATH_YAMNET_CLASSES = "./yamnet/yamnet_class_map.csv"
PATH_YAMNET_WEIGHTS = "./yam... | {"hexsha": "469cbcbbc4c3fabfb316c3f21746f3116cf24aea", "size": 3235, "ext": "py", "lang": "Python", "max_stars_repo_path": "python/yamnet/classifyAudio.py", "max_stars_repo_name": "6859-sp21/final-project-urbantransformationsharvardsq-covid-19", "max_stars_repo_head_hexsha": "ef1267b64401026b8032a35cd1951bcf48d2021b", ... |
"""
Programmer: Bitanu Chatterjee
Date of Development: 14/10/2020
This code has been developed according to the procedures mentioned in the following research article:
"Fathollahi-Fard, Amir Mohammad, Mostafa Hajiaghaei-Keshteli, and Reza Tavakkoli-Moghaddam.
'Red deer algorithm (RDA): a new nature-inspired meta-heuri... | {"hexsha": "0c580f0e421e674bee2e1b8233c8627bc1f7d4bc", "size": 15803, "ext": "py", "lang": "Python", "max_stars_repo_path": "Py_FS/wrapper/population_based/RDA.py", "max_stars_repo_name": "rishavpramanik/Feature-Selection", "max_stars_repo_head_hexsha": "afe96cb8271f1e86a77075d19ec107c37afbbff3", "max_stars_repo_licens... |
#NGSId1 NGSId2 SuperScaffoldId XmapGapLength AdjustedGapLength NGSLength1 NGSLength2
| {"hexsha": "04eeffec9333e2a612b0d25c262639fc8b40a42d", "size": 85, "ext": "gap", "lang": "GAP", "max_stars_repo_path": "tools/bionano/test-data/test_04.gap", "max_stars_repo_name": "pavanvidem/galaxytools", "max_stars_repo_head_hexsha": "339363f6c9d817bc2c35997b4dfdd3ca99a37055", "max_stars_repo_licenses": ["MIT"], "ma... |
"""
"""
struct ExtendedFESpace{S<:SingleFieldFESpace} <: SingleFieldFESpace
space::S
model::RestrictedDiscreteModel
partition::PosNegPartition
function ExtendedFESpace(space::SingleFieldFESpace,model::RestrictedDiscreteModel)
model_portion = model.model
@check get_triangulation(model_portion) === get_t... | {"hexsha": "75e666e02f7efe3cbaef5a35b4aaf7c5a343a185", "size": 4888, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/FESpaces/ExtendedFESpaces.jl", "max_stars_repo_name": "Omega-xyZac/Gridap.jl", "max_stars_repo_head_hexsha": "c9f0ca39a0c84646ea9e4b57fd39d3a6db59c044", "max_stars_repo_licenses": ["MIT"], "max... |
import numpy as np
import heapq
def neighbours(i, j, n, m):
full_list = [
(i - 1, j),
(i, j - 1),
(i + 1, j),
(i, j + 1)
]
return [(ii, jj) for ii, jj in full_list if 0 <= ii < n and 0 <= jj < m ]
def parse_input(filename):
with open(filename) as file:
ls = file... | {"hexsha": "3fba4743cd255e709c31a8414f7b3be558ad9028", "size": 1881, "ext": "py", "lang": "Python", "max_stars_repo_path": "py/day_09/solve.py", "max_stars_repo_name": "Thundzz/advent-of-code-2021", "max_stars_repo_head_hexsha": "e91cd8a5756566ad3f47e07a51b0beb5b9f5326c", "max_stars_repo_licenses": ["MIT"], "max_stars_... |
import ctypes as ct
import datetime
import os
import sys
import time
from ctypes.util import find_library
import cv2
import numpy as np
try:
__import__('aion.logger')
from aion.logger import lprint
except ModuleNotFoundError:
lprint = print
DISP_SW = os.environ.get('DISP_SW', '')
RETRY_MAX = 100
INTERVAL... | {"hexsha": "14bf8e406e8c11e5b7136e9a413aba05bb1b1db6", "size": 5246, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/thermo-camera/infrared_camera.py", "max_stars_repo_name": "latonaio/stream-usb-thermo-by-grpc", "max_stars_repo_head_hexsha": "32804c64e4dcef84c21f6cca19228e3b4eed13f2", "max_stars_repo_licens... |
from stingray.utils import jit
from math import gamma
import numpy as np
import matplotlib.pyplot as plt
from hendrics.base import r_det
@jit(nopython=True)
def sum(x):
s = 0
for el in x:
s += el
return s
@jit(nopython=True)
def factorial(n):
return gamma(n + 1)
@jit(nopython=True)
def fn(x,... | {"hexsha": "49c37337b9b3d26968aaf2d5214ffc843f4c61a4", "size": 3910, "ext": "py", "lang": "Python", "max_stars_repo_path": "notebooks/deadtime_model_zhang.py", "max_stars_repo_name": "astrojuan/HENDRICS", "max_stars_repo_head_hexsha": "79c85b281ca1f0933bfb0ec66ced73eb14ec5f0e", "max_stars_repo_licenses": ["BSD-3-Clause... |
#include <iostream>
#include <boost/multiprecision/cpp_int.hpp>
#include <sstream>
#include <unordered_map>
#include <map>
#include <limits>
using namespace std;
typedef boost::multiprecision::checked_cpp_int bigint;
namespace std {
template<> struct hash<bigint> {
size_t operator()(const bigint& x) const {
... | {"hexsha": "e96b717ef71bfe24f30cafe0fdc01d431dbd0d29", "size": 1865, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "crypto-013/pa5/middle.cpp", "max_stars_repo_name": "DouglasOrr/Snippets", "max_stars_repo_head_hexsha": "026e15a422b518ee7d9ce4849f971c4403ad9fe8", "max_stars_repo_licenses": ["MIT"], "max_stars_cou... |
(** In this file, we define matrices and prove many basic facts from linear algebra *)
Require Import Psatz.
Require Import String.
Require Import Program.
Require Import List.
Require Export Summation.
(* TODO: Use matrix equality everywhere, declare equivalence relation *)
(* TODO: Make all nat arguments to ma... | {"author": "inQWIRE", "repo": "QuantumLib", "sha": "d97ea40581961d7b53291a4a3dc7885fe7428060", "save_path": "github-repos/coq/inQWIRE-QuantumLib", "path": "github-repos/coq/inQWIRE-QuantumLib/QuantumLib-d97ea40581961d7b53291a4a3dc7885fe7428060/GenMatrix.v"} |
#/usr/bin/env python3
# -*- coding: utf-8 -*-
from functools import partial
import time
import cv2 as cv
import numpy as np
from PIL import Image
import copy
from chainer import datasets
from chainercv import transforms
from data_util.kitti_util.kitti_3d_detection_dataset import \
Kitti3dDetectionDataset
from da... | {"hexsha": "f30d20e2afd6381f9c19f26af6bc23ca0b9292f3", "size": 4628, "ext": "py", "lang": "Python", "max_stars_repo_path": "data_util/kitti_util/kitti_3d_transformed.py", "max_stars_repo_name": "yukitsuji/voxelnet_chainer", "max_stars_repo_head_hexsha": "5fc5b42c03ed46a40f3d53703b86c4d7dcccb2e6", "max_stars_repo_licens... |
program prog
if (i .eq. 0) then
write(6, *) 'i is zero.'
endif
end
| {"hexsha": "6a01363268010ba9d4c1096288b6ce9f9d5038e4", "size": 99, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "fable/test/valid/if_endif.f", "max_stars_repo_name": "rimmartin/cctbx_project", "max_stars_repo_head_hexsha": "644090f9432d9afc22cfb542fc3ab78ca8e15e5d", "max_stars_repo_licenses": ["BSD-3-Clause-LB... |
#!/usr/bin/env python
"""Downloads selected works from archiveofourown.org.
The script requires two inputs: csv-file and output_directory.
Through the csv_file the work IDs which should be downloaded are provided. It must hold a column 'id'
which holds the work-IDs (e.g. work_100).
The files are downloaded to the o... | {"hexsha": "2cd1487dc77097836a7e33bd2b67d06b33ff1ade", "size": 2371, "ext": "py", "lang": "Python", "max_stars_repo_path": "download_work.py", "max_stars_repo_name": "rrma/data_scraper", "max_stars_repo_head_hexsha": "a632e0e4b92f1b7d421c8a43c8a53b0601a11209", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null... |
#!/usr/bin/env python
import argparse
import shutil
import tempfile
import zipfile
from pathlib import Path
import tensorflow as tf
import numpy as np
from tqdm import tqdm
from hailo_model_zoo.utils import path_resolver
from hailo_model_zoo.utils.downloader import download_to_file, download_from_drive, download_fil... | {"hexsha": "259a05ca19ac9ba2adfabd0dcc22d68266a7a82c", "size": 4574, "ext": "py", "lang": "Python", "max_stars_repo_path": "hailo_model_zoo/datasets/create_aflw2k3d_tddfa_tfrecord.py", "max_stars_repo_name": "nadaved1/hailo_model_zoo", "max_stars_repo_head_hexsha": "42b716f337dde4ec602022a34d6a07a1bbd45539", "max_stars... |
import os
import re
import cv2
import numpy as np
import argparse
from sklearn.model_selection import train_test_split
def load_dataset(args):
datasetSize = args.size
genre = {
"Hip-Hop": 0,
"International": 1,
"Electronic": 2,
"Folk" : 3,
"Experimental": 4,
"Rock": 5,
... | {"hexsha": "b1ff0d103413643b961b42f78efa61f384ff880b", "size": 2611, "ext": "py", "lang": "Python", "max_stars_repo_path": "torch/process_data/load_train_data.py", "max_stars_repo_name": "namngduc/MiRemd", "max_stars_repo_head_hexsha": "6950058c3384890beec1d44ee606d2ca1bdf06e8", "max_stars_repo_licenses": ["MIT"], "max... |
import numpy as np
import pandas as pd
from .grid import Grid
from ..pvtpy.black_oil import Oil, Gas, Water
from ..krpy import KrWaterOil, KrGasOil
from ..wellpy.path import WellsGroup
from .numerical import Numerical
from .results import Results
from .initial_conditions import InitialConditions
class SimModel:
... | {"hexsha": "0354fa82eacaa2b6c3193e4d36d63b1f0bcfe2bc", "size": 3942, "ext": "py", "lang": "Python", "max_stars_repo_path": "reservoirpy/simulationpy/model.py", "max_stars_repo_name": "scuervo91/reservoirpy", "max_stars_repo_head_hexsha": "a4db620baf3ff66a85c7f61b1919713a8642e6fc", "max_stars_repo_licenses": ["MIT"], "m... |
import argparse
import collections
import sys
import math
import cPickle as pickle
from StringIO import StringIO
import scipy
import scipy.stats
import py_common
import sexpdata
import matplotlib
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from matplotlib.backends import backend_pdf
from s... | {"hexsha": "db977b9ce545400b5052eb4976836a2133d62d90", "size": 22535, "ext": "py", "lang": "Python", "max_stars_repo_path": "analysis/train_lasso_moe_neural_network.py", "max_stars_repo_name": "fyquah95/fyp-worker-dispatcher", "max_stars_repo_head_hexsha": "1ebf764e41202d18ccd1013ec2270caa6c44565a", "max_stars_repo_lic... |
Upcoming Events!
FALL SEED EXCHANGE!
Bring your favorite seeds at the EC Garden on October 5, 46pm
About the Collective
The Davis Seed Savers Alliance is a Community Organizations collective dedicated to saving and sharing seeds among the various gardening communities in Davis. Our vision is to establis... | {"hexsha": "a83cb5162c3762d7d445c20f7780d3dac733e8fc", "size": 6093, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "lab/davisWiki/Davis_Seed_Savers_Alliance.f", "max_stars_repo_name": "voflo/Search", "max_stars_repo_head_hexsha": "55088b2fe6a9d6c90590f090542e0c0e3c188c7d", "max_stars_repo_licenses": ["MIT"], "m... |
# -*- coding:utf-8 -*-
import numpy as np
import os
from utils import *
def compute_score_one_class(bbox1, bbox2, w_iou=1.0, w_scores=1.0, w_scores_mul=0.5):
# bbx: <x1> <y1> <x2> <y2> <class score>
n_bbox1 = bbox1.shape[0]
n_bbox2 = bbox2.shape[0]
# for saving all possible scores between each two bbxe... | {"hexsha": "df14051b50e9cd4414db2f2555746aa0d802b78f", "size": 9839, "ext": "py", "lang": "Python", "max_stars_repo_path": "eval_results.py", "max_stars_repo_name": "nguyentunglam9229/JAAD", "max_stars_repo_head_hexsha": "e593c71183e8314fe2316fbbb6602c0329ce0362", "max_stars_repo_licenses": ["MIT"], "max_stars_count": ... |
[STATEMENT]
lemma digraph_map[intro]: "digraph_map G M"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. digraph_map G M
[PROOF STEP]
by unfold_locales | {"llama_tokens": 65, "file": "Planarity_Certificates_Planarity_Graph_Genus", "length": 1} |
"""
eom_var1(x, p, n) -> Function
Equations of motion for a vector autoregressive system where X₁ → X₂ → X₃.
"""
function eom_var1(x, p, n)
σ₁, σ₂, σ₃ = p[1], p[2], p[3]
x₁, x₂, x₃ = x[1], x[2], x[3]
θ = rand(Normal(0, σ₁))
η = rand(Normal(0, σ₂))
ϵ = rand(Normal(0, σ₃))
dx₁ = θ
dx₂ = ... | {"hexsha": "9c5a138fcf63db057f4a2c5d331ef7461d98d5f6", "size": 694, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/systems/discretemaps/var1.jl", "max_stars_repo_name": "UnofficialJuliaMirrorSnapshots/CausalityTools.jl-5520caf5-2dd7-5c5d-bfcb-a00e56ac49f7", "max_stars_repo_head_hexsha": "93935b3bc73738c52b00... |
# -*- coding: utf-8 -*-
"""
Created on Wed Feb 28 08:13:49 2018
@author: adrian
"""
import math
import sympy as sy
# Define the variable and the function to approximate
x = sy.Symbol('x')
f = sy.exp(x)
# Taylor approximation at x0 of the function 'function'
def taylor(function,x0,n):
i = 0
p = 0
while i... | {"hexsha": "d1e65fef3776e3f39d9c5fe0b407a795c482a1f4", "size": 1447, "ext": "py", "lang": "Python", "max_stars_repo_path": "Parcial 1/punto2.py", "max_stars_repo_name": "adgarciaar/NumericalAnalysis", "max_stars_repo_head_hexsha": "0f1cc6878dfb6c51052ee82298dd1e3512f74fde", "max_stars_repo_licenses": ["MIT"], "max_star... |
# -*- coding: utf-8 -*-
"""
Created on Mon Jun 13 21:18:45 2016
@author: Fang Ren
"""
import numpy as np
import matplotlib.pyplot as plt
import os.path
def save_texture_plot_csv(Q, chi, cake, imageFilename, save_path):
Q, chi = np.meshgrid(Q, chi)
plt.figure(3)
plt.title('texture')
keep = (cake ... | {"hexsha": "810df186cf5f86249ecdd69f91acba5ac13d9043", "size": 1394, "ext": "py", "lang": "Python", "max_stars_repo_path": "scripts/on_the_fly_assessment/save_texture_plot_csv.py", "max_stars_repo_name": "fang-ren/on_the_fly_assessment", "max_stars_repo_head_hexsha": "102a7985d1765b11e6a7fdc1a11ac973cbc5fe3d", "max_sta... |
import sys
import numpy as np
import matplotlib.pyplot as plt
class Register:
# address is a 3 bit string
def __init__(self, address):
self.value = 0;
self.address = address
def addr(self):
return self.address
def val(self):
return self.value
class Flag_Register:
... | {"hexsha": "ff0ec4f878de88d643af07c0c6688660bcda6f6e", "size": 21440, "ext": "py", "lang": "Python", "max_stars_repo_path": "simulator.py", "max_stars_repo_name": "AshwinSheoran02/Assembler-and-Simulator-using-Python3", "max_stars_repo_head_hexsha": "23c728870a5385cba63a6e02f3168935fd9f98fc", "max_stars_repo_licenses":... |
using Primes
function getmersenneprimes(n)
t1 = time()
count = 0
i = 2
while(n > count)
if(isprime(i) && ismersenneprime(2^BigInt(i) - 1))
println("M$i, cumulative time elapsed: $(time() - t1) seconds")
count += 1
end
i += 1
end
end
getmersenneprimes... | {"hexsha": "9b924b27a1d50c0b1793addddfd0d7e898e5fd75", "size": 325, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "lang/Julia/lucas-lehmer-test.jl", "max_stars_repo_name": "ethansaxenian/RosettaDecode", "max_stars_repo_head_hexsha": "8ea1a42a5f792280b50193ad47545d14ee371fb7", "max_stars_repo_licenses": ["MIT"], ... |
"""
Helper function for input/output operations.
"""
import pandas as pd
import numpy as np
def read_item_processing_descriptions_from_excel(item_overview_path):
item_processing =\
pd.read_excel(item_overview_path, sheet_name='item_processing', keep_default_na=False, usecols='A:C,T,AA:AP',
... | {"hexsha": "fc610d483adedcf42a62fdfba7337abcfd6ad6b4", "size": 982, "ext": "py", "lang": "Python", "max_stars_repo_path": "helper/io.py", "max_stars_repo_name": "stefanhgm/Interpretable-3-day-ICU-readmission-prediction", "max_stars_repo_head_hexsha": "050e09b27e7596fc6276ce3276aab7cbe265a9a5", "max_stars_repo_licenses"... |
import sys
import pandas as pd
pd.options.display.max_columns = 30
import numpy as np
from time import time
#
import warnings
warnings.filterwarnings('ignore')
#
import nltk
from nltk.tokenize import TweetTokenizer
tokenizer = TweetTokenizer()
#from nltk.corpus import stopwords
#stop_words = set(stopwords.words('spani... | {"hexsha": "2d2d30e8b014568fed75652020d5c70b02f1ff8d", "size": 6141, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/training.py", "max_stars_repo_name": "mmaguero/textcat-josa", "max_stars_repo_head_hexsha": "9bcd6fe86dbf2d1b7500df22a875850ad1d691cb", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1... |
#%%
import sys
import os
os.chdir(os.path.dirname(os.getcwd())) # make directory one step up the current directory
sys.path.append('/Users/mwinding/repos/maggot_models')
from pymaid_creds import url, name, password, token
import pymaid
rm = pymaid.CatmaidInstance(url, token, name, password)
import matplotlib.pyplot ... | {"hexsha": "6b8d391f1e607755ff4bfd82b736c864e1bd6c27", "size": 23438, "ext": "py", "lang": "Python", "max_stars_repo_path": "interhemisphere/hemisphere_cascades.py", "max_stars_repo_name": "mwinding/connectome_tools", "max_stars_repo_head_hexsha": "0392f6b1e924194299ea7760d8386eb01f3371a3", "max_stars_repo_licenses": [... |
"""Test spatial depence of feature extraction methods
Extracts quadrants of input images such that a certain percentage of
training examples are present in those quadrants. The hope is to
select training sets where training pixels and test pixels are not
spatially interspersed.
"""
from collections import namedtuple
i... | {"hexsha": "a0fbd4205a6a2c8b0aa629c85d94ca2f380d3ffa", "size": 4626, "ext": "py", "lang": "Python", "max_stars_repo_path": "vector_partition.py", "max_stars_repo_name": "ilyakava/pyfst", "max_stars_repo_head_hexsha": "7c8daa5493e22a12bf0c37201c0887550036ab25", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 3, "... |
dyn.load("tlpreg")
# multiple regression
library(foreach)
fit.mtlpreg1 <- function(Y, X, b.init = NULL, pen.fac = rep(1,ncol(X)),
tau=0.5*sqrt(log(p)/n), K=NULL, nfold=10, tol=1e-4, dc.maxit=as.integer(max(5,1+log2(n/log(p)))), cd.maxit=1e+4) {
n <- nrow(X)
p <- ncol(X)
if (n < nfol... | {"hexsha": "20cf010a17a0cf3f8689e3d1205289c5f54d9d12", "size": 1203, "ext": "r", "lang": "R", "max_stars_repo_path": "mtlpreg1.r", "max_stars_repo_name": "chunlinli/tlpreg", "max_stars_repo_head_hexsha": "474ba017429de60c5e7699db9d5ebf26cc771978", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_... |
The Lofts is a mixeduse building that includes street level retail with business offices on the second floor, and apartments on the third floor. All of the apartments are zoned as commercial and residential space. The apartments are lofts, each with one and a half bathrooms, an upstairs bedroom area, a kitchen area, a... | {"hexsha": "12926742a335799f0f387142bbfd8f7b00dbd206", "size": 8346, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "lab/davisWiki/The_Lofts.f", "max_stars_repo_name": "voflo/Search", "max_stars_repo_head_hexsha": "55088b2fe6a9d6c90590f090542e0c0e3c188c7d", "max_stars_repo_licenses": ["MIT"], "max_stars_count": ... |
program hycom_month_day
implicit none
c
c input: ddd (ordinal day)
c output: ddd, months that span the day, and their weights (linear interpolation)
c
c example: echo 360 | hycom_month_day
c result: 360 12 01 0.6967211 0.3032789
c
integer jday,mon0,mon1
real*4 w0,w1,x
c
... | {"hexsha": "e20d61039699a41e9d9f1e89737852f45214623a", "size": 572, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "bin/hycom_month_day.f", "max_stars_repo_name": "TillRasmussen/HYCOM-tools", "max_stars_repo_head_hexsha": "7d26b60ce65ac9d785e0e36add36aca05c0f496d", "max_stars_repo_licenses": ["MIT"], "max_stars_... |
#!/usr/bin/env python
# coding: utf-8
import pandas as pd
import numpy as np
from scipy.stats import multivariate_normal
import csv
import pathlib
from .config_paths import jpg_path, eyetracking_dataset_path
from joblib import Parallel, delayed
def get_gaussian(y,x,sy,sx, sizey,sizex, shown_rects_image_spa... | {"hexsha": "df1a7b201284dce7cb553d0c5c55c265a12bd48c", "size": 3891, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/generate_heatmap_eyetracking.py", "max_stars_repo_name": "ricbl/etsaliencymaps", "max_stars_repo_head_hexsha": "a8447f366491b166975bae17ec85f8c362d51170", "max_stars_repo_licenses": ["MIT"], "... |
% \iffalse meta-comment
%
% Copyright (C) 1993-2019
% The LaTeX3 Project and any individual authors listed elsewhere
% in this file.
%
% This file is part of the LaTeX base system.
% -------------------------------------------
%
% It may be distributed and/or modified under the
% conditions of the LaTeX Project Publ... | {"hexsha": "dfa9e23d7803578aac32ff5a1aada7442376ccf8", "size": 5621, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "base/doc/ltnews05.tex", "max_stars_repo_name": "JUnland420/latex", "max_stars_repo_head_hexsha": "abb82c1299b37a12ab40779ee4b64db717f6004b", "max_stars_repo_licenses": ["LPPL-1.3c"], "max_stars_coun... |
import math
import keras
from keras import models
from keras import layers
import numpy as np
## Basic convolutional block
def basic_block(y,K,args,ishape=0,residual=0,tlist=[]):
if (residual):
x=y
str=np.ones(args.autonconv)
if (residual):
str[args.autonconv-1]=2
str=np.int32(str)
... | {"hexsha": "0c9847b84aa6d6f83e648f2691d87ffd36e0af0b", "size": 2525, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/automodel.py", "max_stars_repo_name": "RParedesPalacios/GILA", "max_stars_repo_head_hexsha": "dd45f9618bdc429b7751fb46d581b30bbadd6f20", "max_stars_repo_licenses": ["MIT"], "max_stars_count": ... |
function noiseWriteParamsToFID(noise, FID)
% NOISEWRITEPARAMSTOFID Write the noise parameters to a stream.
% FORMAT
% DESC writes noise parameters to a file stream.
% ARG noise : the noise structure that is being written.
% ARG FID : the file ID of the stream that is being written.
%
% COPYRIGHT : Neil D. Lawrence, 2... | {"author": "SheffieldML", "repo": "GPmat", "sha": "4b5914a38ecbad9fb7a13a3392970bfc28c9d911", "save_path": "github-repos/MATLAB/SheffieldML-GPmat", "path": "github-repos/MATLAB/SheffieldML-GPmat/GPmat-4b5914a38ecbad9fb7a13a3392970bfc28c9d911/noise/noiseWriteParamsToFID.m"} |
# coding: utf-8
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
import os
import edward as ed
from edward.models import Bernoulli, Normal, Categorical,Empirical
from edward.util import Progbar
from keras.layers import Dense
from scipy.misc import imsave
import matplotlib.pyplot as pl... | {"hexsha": "b72021d7347e8d411ccb25b6f6a6b431fcb15cfa", "size": 6005, "ext": "py", "lang": "Python", "max_stars_repo_path": "MNIST/MCMC/Train.py", "max_stars_repo_name": "matthewwicker/StatisticalGuarenteesForBNNs", "max_stars_repo_head_hexsha": "1f585636c152b8489e331641c743ff628c2b7cc7", "max_stars_repo_licenses": ["BS... |
import numpy as np
import cv2
from scipy.special import logit, expit
from nav_msgs.msg import OccupancyGrid
from .sonar import *
from .utils.conversions import *
from . import pcl
class Submap(object):
def __init__(self):
# index
self.k = 0
# gtsam.Pose2
self.pose = None
... | {"hexsha": "20ae7d1616acc42ca46e2cbc662ef6bd5bd208f4", "size": 21323, "ext": "py", "lang": "Python", "max_stars_repo_path": "bruce_slam/src/bruce_slam/mapping.py", "max_stars_repo_name": "jinkunw/bruce", "max_stars_repo_head_hexsha": "8059222dc79cd160ab844420b379726e7c4ee947", "max_stars_repo_licenses": ["BSD-3-Clause"... |
#Import Dependencies
import pandas as pd
import numpy as np
import sklearn
from datetime import datetime
import pickle
df = pd.read_csv('ipl.csv')
#Data Cleaning
columns_remove = ['mid','venue','batsman','bowler','striker','non-striker']
df.drop(labels=columns_remove, axis=1, inplace=True)
# Keeping only consistent ... | {"hexsha": "faac9f5ed2110dc25720cffedce4b326869eaa07", "size": 2452, "ext": "py", "lang": "Python", "max_stars_repo_path": "model_development.py", "max_stars_repo_name": "rohitpathak18/IPL-first-Innings-Score-prediction", "max_stars_repo_head_hexsha": "6796b9dd69ccd5e82bfdb6f3047c6a58ea459fbf", "max_stars_repo_licenses... |
//
// Copyright Jason Rice 2017
// 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)
//
#ifndef NBDL_CONCEPT_ENDPOINT_HPP
#define NBDL_CONCEPT_ENDPOINT_HPP
#include <nbdl/concept/HasImpl.hpp>
#include <boost/hana/co... | {"hexsha": "e2d3c1e78e1d551ce59be97b4e6691a6dfd09c2a", "size": 933, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "include/nbdl/concept/Endpoint.hpp", "max_stars_repo_name": "ricejasonf/nbdl", "max_stars_repo_head_hexsha": "ae63717c96ab2c36107bc17b2b00115f96e9d649", "max_stars_repo_licenses": ["BSL-1.0"], "max_st... |
#!/usr/bin/env python
import numpy as np
import rospy
from geometry_msgs.msg import PoseStamped, TwistStamped
from styx_msgs.msg import Lane, Waypoint
from scipy.spatial import KDTree
from dbw_mkz_msgs.msg import ThrottleCmd, SteeringCmd, BrakeCmd
from std_msgs.msg import Bool
from std_msgs.msg import Int32
import ma... | {"hexsha": "3ed43b3036a7ef62fe246c05f3e8a6f677f71771", "size": 9065, "ext": "py", "lang": "Python", "max_stars_repo_path": "ros/src/waypoint_updater/waypoint_updater.py", "max_stars_repo_name": "SmartDriveTeam/SmartDrive", "max_stars_repo_head_hexsha": "c0fbbcec602b0438ba299eff31cab89ebf171322", "max_stars_repo_license... |
(* This file implements various helper functions and proofs *)
From Coq Require Import ZArith.
From Coq Require Import List.
From Coq Require Import Permutation.
From Coq Require Import Morphisms.
From Coq Require Import Psatz.
From Coq Require Import Eqdep_dec.
Require Import Automation.
Import ListNotations.
Fixpoi... | {"author": "malthelange", "repo": "CLVM", "sha": "e80aef02c3112b5b62db79bc2b233020367b0bde", "save_path": "github-repos/coq/malthelange-CLVM", "path": "github-repos/coq/malthelange-CLVM/CLVM-e80aef02c3112b5b62db79bc2b233020367b0bde/execution/theories/Extras.v"} |
# Copyright 2020 Amazon.com, Inc. or its affiliates. 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. A copy of
# the License is located at
#
# http://aws.amazon.com/apache2.0/
#
# or in the "license" file acc... | {"hexsha": "a14f1d3eca98922a0c33d27bc3ac305dea6e8774", "size": 15995, "ext": "py", "lang": "Python", "max_stars_repo_path": "test/integration/sagemaker/test_spark.py", "max_stars_repo_name": "shubhamjain2706/sagemaker-spark-container", "max_stars_repo_head_hexsha": "9a957c8a7f5a360a1b32661bc0ba951b35e263bc", "max_stars... |
# test module for the derived RelPerm class located in resqpy.olio.dataframe.py
import os
import numpy as np
import pandas as pd
import pytest
from pandas.testing import assert_frame_equal
import resqpy.model as rq
from resqpy.olio.relperm import (RelPerm, relperm_parts_in_model, text_to_relperm_dict)
def test_col... | {"hexsha": "068f9a3b5b25e44c5cd351c91903164e4fb57a0c", "size": 6864, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/test_relperm.py", "max_stars_repo_name": "cflynn3/resqpy", "max_stars_repo_head_hexsha": "d1d34972ee95526265bb7bde96ca232f98f69c77", "max_stars_repo_licenses": ["MIT"], "max_stars_count": nu... |
import unittest
import numpy.testing as testing
import numpy as np
import fitsio
import tempfile
import os
from redmapper import ColorBackground
from redmapper import ColorBackgroundGenerator
from redmapper import Configuration
class ColorBackgroundTestCase(unittest.TestCase):
"""
Tests for the redmapper.Colo... | {"hexsha": "ab38ae2d8c17a7a5df07314f47034bda8a636085", "size": 3845, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/test_color_background.py", "max_stars_repo_name": "erykoff/redmapper", "max_stars_repo_head_hexsha": "23fb66c7369de784c67ce6c41ada2f1f51a84acb", "max_stars_repo_licenses": ["Apache-2.0"], "m... |
Require ClassicalEpsilon.
Require Import Reals Psatz.
From stdpp Require Import tactics.
From mathcomp Require Import ssrfun ssreflect eqtype ssrbool seq fintype choice bigop.
From discprob.basic Require Import base sval order monad bigop_ext nify.
From discprob.idxval Require Import pival_dist pival ival_dist ival iva... | {"author": "jtassarotti", "repo": "polaris", "sha": "c7873f05214351d54cacf3d8482625ee33ad3288", "save_path": "github-repos/coq/jtassarotti-polaris", "path": "github-repos/coq/jtassarotti-polaris/polaris-c7873f05214351d54cacf3d8482625ee33ad3288/proba/theories/idxval/irrel_equiv.v"} |
import os
import numpy as np
import glob
import torch
from torch_geometric.data import Dataset
from torch_geometric.data import Data
import pickle
from .helper_ply import write_ply, read_ply
from .helper_tool import DataProcessing as DP
import importlib
if importlib.util.find_spec("valeodata") is not None:
valeodat... | {"hexsha": "606044cf3480c3e5e8a79354e65a92bb2132c0e7", "size": 8921, "ext": "py", "lang": "Python", "max_stars_repo_path": "lightconvpoint/datasets/semantic_kitti.py", "max_stars_repo_name": "valeoai/POCO", "max_stars_repo_head_hexsha": "c6ab56b1b7f01c51d1bc6987eae0a8c79725e63f", "max_stars_repo_licenses": ["Apache-2.0... |
using SimpleCG
plane_g = Plane(Vector3(0, 1, 0), 0);
sphere1_g = Sphere(Vector3(-10, 10, -10), 10);
sphere2_g = Sphere(Vector3(10, 10, -10), 10);
plane = GeometryWithMaterial(plane_g, CheckerMaterial(0.1, 0.5))
sphere1 = GeometryWithMaterial(sphere1_g, PhongMaterial(Red, White, 16, 0.25))
sphere2 = GeometryWithMateria... | {"hexsha": "a65be4d0e7d841f22f7c79b5d22b47144566fe56", "size": 588, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "examples/union_reflect.jl", "max_stars_repo_name": "sunoru/SimpleeCG.jl", "max_stars_repo_head_hexsha": "07a1fb747973e6b09ec71dcceba66e1f93bcbc7c", "max_stars_repo_licenses": ["MIT"], "max_stars_cou... |
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.widgets import Slider, Button
fig, ax = plt.subplots()
plt.subplots_adjust(bottom=0.35)
# Create and plot sine wave
t = np.arange(0.0, 1.0, 0.001)
s = 5 * np.sin(2 * np.pi * 3 * t)
l, = plt.plot(t, s)
# Create axes for frequency and amplitude slid... | {"hexsha": "7fcab62e87aee63f4572ca89096c5c2f90de4d8c", "size": 1007, "ext": "py", "lang": "Python", "max_stars_repo_path": "Lecture8/slider_demo.py", "max_stars_repo_name": "Astro-330/Astro-330.github.io", "max_stars_repo_head_hexsha": "e7ba5d1db0f369a110419e939d9ed2d29c9d7020", "max_stars_repo_licenses": ["MIT"], "max... |
from typing import Tuple
import numpy as np
import ray
from ray.rllib import MultiAgentEnv
from ray.rllib.utils import override
from ray.rllib.utils.typing import MultiAgentDict
from rlgym.gym import Gym
def _action_dict_to_numpy(action_dict: MultiAgentDict):
action_array = np.zeros((len(action_dict), 8))
f... | {"hexsha": "42f9bd14dcb2cb7cc5fe058a37ed0874a3958c93", "size": 1291, "ext": "py", "lang": "Python", "max_stars_repo_path": "rlgym_tools/rllib_utils/rllib_env.py", "max_stars_repo_name": "jboardman367/rlgym-tools-update-log-reward", "max_stars_repo_head_hexsha": "db786d02ca86c4081bf38261b4223915370ff4de", "max_stars_rep... |
"""
sub_tss(h, metaV, metaE; opt=false)
Compute the target set exploiting a subtractive approach.
"""
function sub_tss(h, metaV, metaE; opt=false, printme=false)
Vsub = Dict{Int,Tuple{Int,Int}}() # node => (degree, threshold)
Esub = Dict{Int,Tuple{Int,Int}}() # edge => (size, threshold)
nCount=0
e... | {"hexsha": "9ad78df783f29a69c1b9a42cf3e63d803d3874ad", "size": 11209, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/heuristics/subtractive.jl", "max_stars_repo_name": "pszufe/LTMSim.jl", "max_stars_repo_head_hexsha": "fa721f9b288ab3c6cfa65ca902ecfb0a5d9013c2", "max_stars_repo_licenses": ["MIT"], "max_stars_... |
import numpy as np
import ACO
import DP
import GA
import utils
algorithmList = ["动态规划算法", "蚁群算法", "遗传算法", "蚁群算法优化测试", "数据集测试", "自动化算法测试"]
def functionChoose(choice):
print("现在使用的是{}".format(algorithmList[int(choice) - 1]))
if int(choice) < len(algorithmList) - 1:
cityNum, coordinate, point = utils.cityInit(T... | {"hexsha": "f4d07321a92815b72b6cf9d99ba7ea58fa1de215", "size": 6119, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/TSP/main.py", "max_stars_repo_name": "ox4f5da2/TSP", "max_stars_repo_head_hexsha": "b395806b44fdc2e2b5d7488b77356ccbf30eef21", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max... |
from neuronmi.simulators.solver.embedding import EmbeddedMesh
from neuronmi.simulators.solver.transferring import SubMeshTransfer
import dolfin as df
import numpy as np
import unittest
class TestCases(unittest.TestCase):
def test_to_DG0(self):
subdomains = (df.CompiledSubDomain('near(x[0], 0.5)'), df.Dom... | {"hexsha": "5d2b09b0e0d597820e9e1db3eb4ee487bc77b01b", "size": 5268, "ext": "py", "lang": "Python", "max_stars_repo_path": "test/simulators/solver/test_transferring.py", "max_stars_repo_name": "MiroK/nEuronMI", "max_stars_repo_head_hexsha": "227b26598fa2cde5aabec68db898f308fb44aa31", "max_stars_repo_licenses": ["Apache... |
# 未加入平仄规则
import random
import sys
import os
import numpy as np
from tensorflow import keras
# import plaidml.keras
# plaidml.keras.install_backend()
# import os
# os.environ["KERAS_BACKEND"] = "plaidml.keras.backend"
# import keras
import warnings
warnings.filterwarnings('ignore')
class Config_5(object):
poetry... | {"hexsha": "65243d16c289f12b77e9bdadc4779597ac141ab5", "size": 13954, "ext": "py", "lang": "Python", "max_stars_repo_path": "code.py", "max_stars_repo_name": "x5g/AutoWritePoems", "max_stars_repo_head_hexsha": "634d30587bb8e5cf15d8287d259ac22fe0798762", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_... |
# -*- coding: utf-8 -*-
from datetime import datetime,timezone,timedelta
import requests
import urllib.request as req
import os
import time
import cv2
from PIL import Image
dt1 = datetime.utcnow().replace(tzinfo=timezone.utc)
dt2 = dt1.astimezone(timezone(timedelta(hours=8))) # 轉換時區
# print('UTC \t%s\nUTC+8\t%s'%(dt1,d... | {"hexsha": "2afb22a66e43dd4e05946d7a093cfdd2b4c56896", "size": 7082, "ext": "py", "lang": "Python", "max_stars_repo_path": "Forcast_1hour_rainfall.py", "max_stars_repo_name": "Jwander0820/Forecast-typhoon-rainfall-Unet", "max_stars_repo_head_hexsha": "c2acd911a91344a55d8a78f9bd8426a61a6e6879", "max_stars_repo_licenses"... |
# First, document and link test case
export vdpol
vdpol = let
tcname = :vdpol
T = Float64 # the datatype used
Tarr = Matrix
dof = 2 # degrees of freedom
dae=0
# stiffness of system, one of the three constants
stiffness = [nonstiff, mildlystiff, stiff][3]
mu = 1e3
eps = 1/mu^2 # re... | {"hexsha": "418df5e1215d6b2879b66aed42829c976b75293f", "size": 1749, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/testcases/vdpol.jl", "max_stars_repo_name": "pwl/IVPTestSuite.jl", "max_stars_repo_head_hexsha": "15d92b5b943c35493ce29e8aa8def14619d96760", "max_stars_repo_licenses": ["MIT"], "max_stars_count... |
import sympy
HI, HII, HM, HeI, HeII, HeIII, H2I, H2II, de = sympy.sympify("HI, HII, HM, HeI, HeII, HeIII, H2I, H2II, de")
k1, k2, k3, k4, k5, k6, k7, k8, k9, k10, k11, k12, k13, k14, k15, k16, k17, k18, k19, k20, k21, k22, k23, k24, k25, k26, k27, k28, k29, k30, k31, k32 = sympy.sympify("k1, k2, k3, k4, k5, k6, k... | {"hexsha": "5a468e1736acc47d5023f6ccaee0b439b45d0bdf", "size": 2115, "ext": "py", "lang": "Python", "max_stars_repo_path": "RHS_eqs.py", "max_stars_repo_name": "emilyng/Chemistry-Solver", "max_stars_repo_head_hexsha": "f4f21dd37898d35d669f9d0223674e251a4c58dd", "max_stars_repo_licenses": ["MIT"], "max_stars_count": nul... |
# http://github.com/timestocome
# classifier in tf using linear regression
# meh, best cost is still about 22% error rate predicting if Nasdaq will go up or down tomorrow
# when using VIX, GDP % chg, Gold, 1yr Treas, 10 Bond, GDP actual, Unemployment rate
import pandas as pd
import numpy as np
import tensorflow as ... | {"hexsha": "81995a23a6ac3cc70059cef6e72d4f0147546afd", "size": 4524, "ext": "py", "lang": "Python", "max_stars_repo_path": "StockMarketLinearRegression/LogisticRegression_Classifier.py", "max_stars_repo_name": "ag88/Test-stock-prediction-algorithms", "max_stars_repo_head_hexsha": "c8995b3aa787fb63d9b1d8fc097ececa9d32dc... |
# -*- coding: utf-8 -*-
"""
Created on Wed Aug 15 11:47:47 2018
@author: Victor Onink
"""
from netCDF4 import Dataset
import numpy as np
from mpl_toolkits.basemap import Basemap
import matplotlib.pyplot as plt
from datetime import datetime, timedelta
import matplotlib.dates as mdates
def AreaCalc(sizeLat,sizeLon): ... | {"hexsha": "017021690a47dbe243a709e2a76f33edd39349a1", "size": 7292, "ext": "py", "lang": "Python", "max_stars_repo_path": "Figures/EKE_PacificAtlantic.py", "max_stars_repo_name": "OceanParcels/SKIM-garbagepatchlocations", "max_stars_repo_head_hexsha": "3c028e3ceba902ff79f52e31b83bed811bde1133", "max_stars_repo_license... |
"""Simple layer profile plots."""
import os
import numpy as np
import nibabel as nb
import matplotlib.pyplot as plt
# Output nifti from 01_simulate_layers
FILE1 = "/media/faruk/Seagate Backup Plus Drive/DATA_MRI_NIFTI/derived/sub-05/T1/07_register_to_T2s/sub-05_ses-T1_MP2RAGE_uni_crop_ups2X_avg_reg.nii.gz"
# Metric ... | {"hexsha": "bf6342bfa41f5d9086aa4c6418703a51b924172a", "size": 3097, "ext": "py", "lang": "Python", "max_stars_repo_path": "scripts/unused/unused-whole_brain_segmentation/09_plot_profiles.py", "max_stars_repo_name": "ofgulban/meso-MRI", "max_stars_repo_head_hexsha": "15ef8e19aae6218833a06bf01418d3d83eafd8c7", "max_star... |
"""
Name: Einstein-Maxwell Field
References: Stephani (20.9a) p221
Coordinates: Cylindrical
Symmetry:
- Cylindrical
- Static
Notes: Angular Magnetic Field
"""
from sympy import cosh, diag, log, symbols
coords = symbols("t rho phi z", real=True)
variables = symbols("a b m", constant=True)
functions = ()
t, rh, ... | {"hexsha": "793e0e5b6c84ba69ffc5585f0f1f863a85742aee", "size": 512, "ext": "py", "lang": "Python", "max_stars_repo_path": "riccipy/metrics/einstein_maxwell_1.py", "max_stars_repo_name": "cjayross/riccipy", "max_stars_repo_head_hexsha": "2cc0ca5e1aa4af91b203b3ff2bb1effd7d2f4846", "max_stars_repo_licenses": ["MIT"], "max... |
import timeit
from logging import DEBUG, basicConfig, getLogger
import numpy as np
import pandas as pd
from scipy.stats import rankdata
from sklearn.model_selection import train_test_split
from sklearn.neural_network import MLPClassifier, MLPRegressor
basicConfig(
format="[%(asctime)s] %(name)s %(levelname)s: %(m... | {"hexsha": "35a865f6355bccc6b5e49a188ff0ba6c8811fb5a", "size": 3372, "ext": "py", "lang": "Python", "max_stars_repo_path": "mlpensemble/mlp.py", "max_stars_repo_name": "maskot1977/MLPensemble", "max_stars_repo_head_hexsha": "6ef4dc14a05e4936c69157ae6498d67dc7653491", "max_stars_repo_licenses": ["MIT"], "max_stars_count... |
#!/usr/bin/env python
# -*- coding: UTF-8 -*-
import numpy as np
from brian2 import second, mV, plt
from matplotlib import colors
from mpl_toolkits.mplot3d import Axes3D
from matplotlib import cm
def plot_kernels(kernels, lx, ly, kwargs={}, ax=None):
"""Plots the given kernels.
Args:
kernels (list ... | {"hexsha": "ed810617e267430f4f91e5c7eb147bf875751ed7", "size": 13277, "ext": "py", "lang": "Python", "max_stars_repo_path": "v1_simple_cell/plots.py", "max_stars_repo_name": "ghadj/Cat-V1-Simple-Cell", "max_stars_repo_head_hexsha": "04d16b93f9ed48053c843ed756310382a158c5e3", "max_stars_repo_licenses": ["MIT"], "max_sta... |
from nav_msgs.msg import OccupancyGrid
from geometry_msgs.msg import Pose
import tf
import tf_conversions
import tf2_ros
import tf2_geometry_msgs
import transforms3d, transforms3d.euler
import numpy as np
from params import RosParams
class LocalOccupancyGridParams(RosParams):
_params = [('occupancy_threshold'... | {"hexsha": "1d59064524618a4eb2a970d0898585726c5d69c2", "size": 5112, "ext": "py", "lang": "Python", "max_stars_repo_path": "scripts/occupancy_grid.py", "max_stars_repo_name": "vdot/mdet", "max_stars_repo_head_hexsha": "34e666ffb5811e27872614602d9665b913ab1059", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 9, ... |
import GPy
import numpy as np
#import GPy.util.choleskies # TODO use GPy's cholskies module
import choleskies
import plotting
from special_einsum import special_einsum
class Layer(GPy.core.parameterization.Parameterized):
"""
A general Layer class, the base for hidden, input and output layers.
"""
def ... | {"hexsha": "ed3653194911061552cbff78cd218b490383f0ba", "size": 22608, "ext": "py", "lang": "Python", "max_stars_repo_path": "layers.py", "max_stars_repo_name": "jameshensman/deepGPy", "max_stars_repo_head_hexsha": "9ebd9fc0ff016cbaac89599f34ee3922b7007f6d", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count"... |
# Module to run tests on pyigm.utils
from __future__ import print_function, absolute_import, division, unicode_literals
# TEST_UNICODE_LITERALS
import pytest
import numpy as np
from astropy.cosmology import Planck15 as cosmo
from astropy.coordinates import SkyCoord
from astropy import units as u
import astropy
fro... | {"hexsha": "5e4ce32fcc129713973ffde526e0605f03f6b689", "size": 3298, "ext": "py", "lang": "Python", "max_stars_repo_path": "pyigm/tests/test_utils.py", "max_stars_repo_name": "pyigm/pyigm", "max_stars_repo_head_hexsha": "8b4bc7f7f1c9f1c280720a4cc0693cd7cb79e9cb", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_... |
/*
* Copyright (C) 2016 by Gerrit Daniels <gerrit.daniels@gmail.com>
* All rights reserved.
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions are met:
*
* 1. Redistributions of source code must retain the above copyright no... | {"hexsha": "20e05931b9a9bcdab4210b7355f48ec9c6d91de6", "size": 2414, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "include/serialize_base_classes.hpp", "max_stars_repo_name": "gerritdaniels/reflection", "max_stars_repo_head_hexsha": "1b4638d3541f1a554f154c626590e08b6de58b65", "max_stars_repo_licenses": ["BSD-2-C... |
[STATEMENT]
lemma Proj_ortho_compl:
"Proj (- X) = id_cblinfun - Proj X"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. Proj (- X) = id_cblinfun - Proj X
[PROOF STEP]
by (transfer, auto) | {"llama_tokens": 89, "file": "Complex_Bounded_Operators_Complex_Bounded_Linear_Function", "length": 1} |
import numpy as np
class Board:
def __init__(self, width, height):
self.board = np.zeros((width, height), 'uint8')
self.counters = np.zeros((width, height), 'uint8')
self.width = width
self.height = height
def change(self, i, j):
self.board[i, j] = (self.board[i, j] ==... | {"hexsha": "eb57f66a9d05a8f867350e1824cfcaed71b4ef9d", "size": 2004, "ext": "py", "lang": "Python", "max_stars_repo_path": "game of life/window.py", "max_stars_repo_name": "droidroot1995/DAFE_Python_914", "max_stars_repo_head_hexsha": "0de65a84ab7f4c8f24b83a5747f71f52d57ecc20", "max_stars_repo_licenses": ["Unlicense"],... |
import numpy, scipy
from statsmodels.base.model import GenericLikelihoodModel as gll
def _ll_stdpoisson(y, x, beta):
mu = numpy.exp(numpy.dot(x, beta))
pr = numpy.exp(-mu) * numpy.power(mu, y) / scipy.special.factorial(y)
ll = numpy.log(pr)
return(ll)
def stdpoisson(Y, X):
class stdpoisson(gll):
def __i... | {"hexsha": "151553fbc24d5cd1cca32cc7af54d197970aa655", "size": 979, "ext": "py", "lang": "Python", "max_stars_repo_path": "codes/stdpoisson.py", "max_stars_repo_name": "statcompute/py_countreg", "max_stars_repo_head_hexsha": "3f62b8f16b95be5be46cacb93f544bbca6b1ec55", "max_stars_repo_licenses": ["MIT"], "max_stars_coun... |
import unittest
import contextlib
import numpy as np
import BioPlate.utilitis as bpu
from pathlib import Path, PurePath
from BioPlate import BioPlate
from BioPlate.database.plate_db import PlateDB
from BioPlate.database.plate_historic_db import PlateHist
from string import ascii_uppercase
from tabulate import tabulate... | {"hexsha": "8b325f543cbd96c89621c0ed78777fa396ed7a46", "size": 23783, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/test_plate.py", "max_stars_repo_name": "jecker7/BioPlate", "max_stars_repo_head_hexsha": "2339cca2187af522cba9b1bbc8791bdbf8e0a6bd", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 8... |
from urllib.request import urlopen
from bs4 import BeautifulSoup
import pandas as pd
import re
from bs4.element import NavigableString, Tag
import datetime
import urllib
import requests
import scipy.stats as stats
import math
import matplotlib.pyplot as plt
# Function to scrape strings from html
def scrape_string(res... | {"hexsha": "a92f074a05d9d9a30c85a8d8b5b436ee1fd701cc", "size": 7260, "ext": "py", "lang": "Python", "max_stars_repo_path": "hw2_source_final.py", "max_stars_repo_name": "mmrosek/CSCI-3360", "max_stars_repo_head_hexsha": "bd3ae7ba4030f1ace93bb7e2e7ed0dceda3823e7", "max_stars_repo_licenses": ["MIT"], "max_stars_count": n... |
!! Copyright (C) Stichting Deltares, 2012-2016.
!!
!! This program is free software: you can redistribute it and/or modify
!! it under the terms of the GNU General Public License version 3,
!! as published by the Free Software Foundation.
!!
!! This program is distributed in the hope that it will be useful,
!! b... | {"hexsha": "bba584700e75c9d69b71b69216df5efce6ea8f6f", "size": 10600, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "docker/water/delft3d/tags/v6686/src/engines_gpl/part/packages/kernel_f/src/extout.f90", "max_stars_repo_name": "liujiamingustc/phd", "max_stars_repo_head_hexsha": "4f815a738abad43531d02ac66f5bd... |
Load LFindLoad.
Load LFindLoad.
From adtind Require Import goal57.
From lfind Require Import LFind.
Require Import Extraction.
Extract Inductive nat => nat [ "(O)" "S" ].
Extract Inductive list => list [ "Nil" "Cons" ].
Extraction "/home/yousef/lemmafinder/benchmark/_lfind_clam_lf_goal57_theorem0_37_lem/goal57_lfind_o... | {"author": "yalhessi", "repo": "lemmaranker", "sha": "53bc2ad63ad7faba0d7fc9af4e1e34216173574a", "save_path": "github-repos/coq/yalhessi-lemmaranker", "path": "github-repos/coq/yalhessi-lemmaranker/lemmaranker-53bc2ad63ad7faba0d7fc9af4e1e34216173574a/benchmark/clam/_lfind_clam_lf_goal57_theorem0_37_lem/lfind_ml_generat... |
using LocalRegistry, Pkg
const __topdir__ = dirname(@__DIR__)
"""
bump_version(version::VersionNumber)
Bump the version number of all packages in the repository to `version`.
"""
function bump_version(version::VersionNumber)
for dir in (__topdir__,
joinpath(__topdir__, "SnoopCompileCore"))
... | {"hexsha": "21b694701758d7dc265674c8313f645397ef6bb7", "size": 1548, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "utils/register.jl", "max_stars_repo_name": "ranocha/SnoopCompile.jl", "max_stars_repo_head_hexsha": "3d31919388e2ac5beb2ff23d06695cadc3cc6e65", "max_stars_repo_licenses": ["MIT"], "max_stars_count"... |
import networkx as nx
import pandas as pd
from IPython.display import Image, display
import scphylo as scp
from scphylo.pl._helper import (
_add_barplot,
_add_chromplot,
_clonal_cell_mutation_list,
_get_tree,
_newick_info2_mutation_list,
)
def clonal_tree(
tree,
muts_as_number=False,
... | {"hexsha": "6441f8f5c653a85524e7b3eb5b63fb1c518d1164", "size": 14243, "ext": "py", "lang": "Python", "max_stars_repo_path": "scphylo/pl/_trees.py", "max_stars_repo_name": "faridrashidi/scphylo-tools", "max_stars_repo_head_hexsha": "4574e2c015da58e59caa38e3b3e49b398c1379c1", "max_stars_repo_licenses": ["BSD-3-Clause"], ... |
#################
#### IMPORTS ####
import click, os, pandas as pd, numpy as np, subprocess, sys, shutil
from pybedtools import BedTool
from pyfaidx import Fasta
from itertools import combinations
import scipy.sparse as sps
import glob, re
from random import randint
from Bio import SeqIO, Phylo
from Bio.Phylo.TreeCons... | {"hexsha": "1d91ac3b21ab1014aa2637931037ec84d9e4dd90", "size": 88623, "ext": "py", "lang": "Python", "max_stars_repo_path": "JoshuaTree2.py", "max_stars_repo_name": "jlevy44/JoshuaTree2", "max_stars_repo_head_hexsha": "c643deabe687e4a5901579a6182a88ec1cc73ba2", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2, ... |
import numpy as np
from abc import ABC, abstractmethod
from utils import HandState, Move, Hand
class Player(ABC):
def __init__(self, number_chips):
self.chip_stack = number_chips
self.__hole_cards = ['..', '..']
self.community_cards = []
def update_stack(self, amount):
if -amou... | {"hexsha": "883534cfd440e5d3b0d008533bd1e0220ca898f3", "size": 2354, "ext": "py", "lang": "Python", "max_stars_repo_path": "player.py", "max_stars_repo_name": "leonlufkin/poker-bot", "max_stars_repo_head_hexsha": "60faaa4ed4c877bbb5c337ada4656649e1bdd89c", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "m... |
//--------------------------------------------------------------------------------
//This is a file from Arkengine
//
//
//Copyright (c) arkenthera.All rights reserved.
//
//BasicRenderWindow.cpp
//--------------------------------------------------------------------------------
#include "Core/YumeHeaders.h"
#include ... | {"hexsha": "8c4caeeabb36211d9a6ed1b176b0dc43ecc591cd", "size": 5745, "ext": "cc", "lang": "C++", "max_stars_repo_path": "Samples/ModelViewer/ModelViewer.cc", "max_stars_repo_name": "rodrigobmg/YumeEngine", "max_stars_repo_head_hexsha": "67c525c84616a5167b5bae45f36641e90227c281", "max_stars_repo_licenses": ["MIT"], "max... |
import matplotlib.pyplot as plt
import numpy as np
from heuslertools.magnetism import Crystal, Layer
if __name__ == "__main__":
CuMnSb = Crystal(a=6.09, n=4, mu_eff=5.4, t_cw=-160)
sample = Layer(l=4.5e-3, w=3.5e-3, h=80e-9, crystal=CuMnSb)
print('')
print('µ_bohr per unit formula:', sample.emu_to_mubo... | {"hexsha": "d9df7a4204653d73503aa2c89be7a125ad41beab", "size": 899, "ext": "py", "lang": "Python", "max_stars_repo_path": "examples/magnetism/CuMnSb_pm_moment.py", "max_stars_repo_name": "LukeSkywalker92/heuslertools", "max_stars_repo_head_hexsha": "58108511eec4a027f7d42888e66b50b2dc8d7612", "max_stars_repo_licenses": ... |
\par
\vfill \eject
\section{MPI Solution of $A X = Y$ using an $LU$ factorization}
\label{section:LU-MPI}
\par
Unlike the serial and multithreaded environments where the data
structures are global, existing under one address space,
in the MPI environment, data is local, each process or processor
has its own distinct a... | {"hexsha": "40d283348ac53b23e64098a9cc0303e872540030", "size": 15084, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "ccx_prool/SPOOLES.2.2/documentation/AllInOne/LU_MPI.tex", "max_stars_repo_name": "alleindrach/calculix-desktop", "max_stars_repo_head_hexsha": "2cb2c434b536eb668ff88bdf82538d22f4f0f711", "max_stars... |
using GtkIDE
using Test, Gtk
###############
## EDITOR
sleep_time = 0.5
function _test_completion_232_(x::Int64, y::Float64)
end
@testset "Editor" begin
main_window = GtkIDE.main_window
editor = main_window.editor
console = GtkIDE.current_console(main_window)
cd(joinpath(GtkIDE.HOMEDIR,".."))
GtkIDE.update_pathEn... | {"hexsha": "dd720e0bfe3b20156f0a21cb1f3e8bb6ad638663", "size": 2807, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/runtests.jl", "max_stars_repo_name": "jonathanBieler/GtkIDE.jl", "max_stars_repo_head_hexsha": "9112e8e2a504146d72528fedb170a8c68e2e1659", "max_stars_repo_licenses": ["MIT"], "max_stars_count"... |
\section{Performance Evaluation}
The three dimensions of library evaluation are (i) scalability (ii) reliability or availability (iii) reasonable efficient. Standard methodology for evaluating performance has been found in literature. Evaluation methodology for the other two aspects, unfortunately, requires more stu... | {"hexsha": "80630fe231ad4ab55e75676124aa1cb3c4ade29e", "size": 1601, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "s1024484/ReviewReport/Aug2012/evaluation.tex", "max_stars_repo_name": "Jiansen/TAkka", "max_stars_repo_head_hexsha": "d2410190552aeea65c1da5f0ae05f08ba1f4d102", "max_stars_repo_licenses": ["BSD-Sour... |
\filetitle{irisstartup}{Start an IRIS session}{config/irisstartup}
\paragraph{Syntax}\label{syntax}
\begin{verbatim}
irisstartup
irisstartup -shutup
\end{verbatim}
\paragraph{Description}\label{description}
We recommend that you keep the IRIS root directory on the permanent
Matlab search path. Each time you ... | {"hexsha": "f1f7272207f40a393c9544f9b57565385811f7b0", "size": 1452, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "-help/config/irisstartup.tex", "max_stars_repo_name": "OGResearch/IRIS-Toolbox-For-Octave", "max_stars_repo_head_hexsha": "682ea1960229dc701e446137623b120688953cef", "max_stars_repo_licenses": ["BSD... |
# 1. Preprocess text using torchtext
# 1. Preprocess text
# ^^^^^^^^^^^^^^^^^^^^^^^
import pandas as pd
import numpy as np
import torch
from torchtext.data import Field
from torchtext.data import TabularDataset
# from torchtext.vocab import Vectors, GloVe
def latin2utf(fn):
with open(fn, 'r', encoding='latin1') as... | {"hexsha": "b355903af89641c7a55b49a6ffafe9ee452a47c4", "size": 1969, "ext": "py", "lang": "Python", "max_stars_repo_path": "task_six/process_text.py", "max_stars_repo_name": "leekev/cnn-text-classification-pytorch", "max_stars_repo_head_hexsha": "16c65b5428cb1d6cd583e327dcde05120afbdbf2", "max_stars_repo_licenses": ["A... |
\documentclass{article} % For LaTeX2e
\usepackage{iclr2020_conference,times}
% Optional math commands from https://github.com/goodfeli/dlbook_notation.
\input{math_commands.tex}
\usepackage{hyperref}
\usepackage{url}
\title{Squaring Deep Neural Networks for interpretability \\ Decision Trees as Surrogate Model for... | {"hexsha": "e0e5a769b3629a8246f07a6f908aefbb97e53901", "size": 17700, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "papers/iclr2020/iclr2020_conference.tex", "max_stars_repo_name": "kmunve/ml-workshop", "max_stars_repo_head_hexsha": "96a42e663bb656e97231eff17ef4ca21e2a14b0e", "max_stars_repo_licenses": ["MIT"], ... |
import numpy as np
import pandas as pd
from sklearn.metrics import accuracy_score
from sklearn.metrics import confusion_matrix
from sklearn.neighbors import KNeighborsClassifier
# read training data from excel file
data_columns= pd.read_excel("data.xlsx",parse_cols = 4)
# convert training data to matrix
data=np.array(... | {"hexsha": "94dbee25c3c7bfd3793f9f5f281200c515582137", "size": 1545, "ext": "py", "lang": "Python", "max_stars_repo_path": "Computatinal Intelligence/KNN_Algorithm/KNN.py", "max_stars_repo_name": "tolgahanakgun/School-Projects", "max_stars_repo_head_hexsha": "3aecfa3887bc69f3fff44bd9509ff355c99ab1f4", "max_stars_repo_l... |
################################################################################
# Copyright 2020, Tom Van Acker #
################################################################################
# MultiStateSystems.jl ... | {"hexsha": "5d4af3cbb34b71fc69af8aee62ed7eca7e792200", "size": 4341, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "examples/anholt_wind_farm/elements/wind_turbine.jl", "max_stars_repo_name": "timmyfaraday/MultiStateSystems.jl", "max_stars_repo_head_hexsha": "766962cd4e4af62544965dcc7d60b1d2fca2286f", "max_stars... |
@doc """
Euler method
Step Propagation using the Euler formula.
""" ->
immutable QuEuler <: QuPropagatorMethod
end
@doc """
Crank Nicolson Method
Step Propagation using the Crank Nicolson formula.
""" ->
immutable QuCrankNicolson <: QuPropagatorMethod
end
@doc """
Krylov subspace Method
Step Propagation using the ... | {"hexsha": "6100e395ee9c615651ee86bbeda6dfbf3c71b344", "size": 2984, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/propstepsolvers.jl", "max_stars_repo_name": "amitjamadagni/QuDynamics.jl", "max_stars_repo_head_hexsha": "7241df499e2155ef3dccfde3646320c690645c4f", "max_stars_repo_licenses": ["MIT"], "max_sta... |
#' fmlref
#'
#' @description
#' A reference implementation of the fmlr API. The implementation only uses base
#' R functions.
#'
#' @docType package
#' @name fmlref-package
#' @author Drew Schmidt
#' @keywords package
NULL
| {"hexsha": "6d8e12a618cfe9ff0932e01af03926228b6b3aa7", "size": 225, "ext": "r", "lang": "R", "max_stars_repo_path": "R/fmlref-package.r", "max_stars_repo_name": "fml-fam/fmlref", "max_stars_repo_head_hexsha": "a74ae07811c5c3d54bdaaa46e97b2321b299b3ff", "max_stars_repo_licenses": ["BSL-1.0"], "max_stars_count": null, "m... |
import math
import numpy as np
import tf
from abc import ABCMeta, abstractmethod
STATE_SIZE = 3 # State size [x,y,yaw]
LM_SIZE = 2 # LM state size [x,y]
INF = 1e6
# Covariance for EKF simulation
Q = np.diag([
0.2, # variance of location on x-axis
0.2, # variance of location on y-axis
math.radians(3.0)... | {"hexsha": "41662e021ce5e06bb73a72af683d3e42bf159dbb", "size": 2264, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/course_agv_slam/scripts/ekf.py", "max_stars_repo_name": "ShawHaines/learnGit", "max_stars_repo_head_hexsha": "83cc1746ab4f59adde67ff7fe4f42e3513680b11", "max_stars_repo_licenses": ["MIT"], "ma... |
Require Export Gen.
Definition even (n:nat) : Prop :=
evenb n = true.
Definition even_n__even_SSn (n:nat) : Prop :=
(even n) -> (even (S (S n))).
Definition true_for_zero (P:nat -> Prop) : Prop :=
P 0.
Definition preserved_by_S (P:nat -> Prop) : Prop :=
forall n', P n' -> P (S n').
Definition true_for_al... | {"author": "egejjespersen", "repo": "software_foundation_exercise", "sha": "e2f788ff88b4b6a6cefc3f413e646c8a733232b2", "save_path": "github-repos/coq/egejjespersen-software_foundation_exercise", "path": "github-repos/coq/egejjespersen-software_foundation_exercise/software_foundation_exercise-e2f788ff88b4b6a6cefc3f413e6... |
import tensorflow as tf
import numpy as np
class FaceDetector:
def __init__(self, model_path, gpu_memory_fraction=0.25, visible_device_list='0'):
"""
Arguments:
model_path: a string, path to a pb file.
gpu_memory_fraction: a float number.
visible_device_list: a ... | {"hexsha": "81cf91e1929f66d0d592668f58c259c7cb1a879a", "size": 2623, "ext": "py", "lang": "Python", "max_stars_repo_path": "face_detector.py", "max_stars_repo_name": "beingnothing/FaceTrack_by_FaceBoxes", "max_stars_repo_head_hexsha": "5b84c163fd9851bf6b9bd2764798c292bee04fa7", "max_stars_repo_licenses": ["MIT"], "max_... |
/**
* @file crossprod.cc
* @brief NPDE homework CrossProd code
* @author Unknown, Oliver Rietmann
* @date 31.03.2021
* @copyright Developed at ETH Zurich
*/
#include "crossprod.h"
#include <Eigen/Geometry>
#include <iomanip>
#include <iostream>
#include <vector>
namespace CrossProd {
/* SAM_LISTING_BEGIN_0 */... | {"hexsha": "0eb902c8cc83a046e06c224c9dfe54767f888c89", "size": 916, "ext": "cc", "lang": "C++", "max_stars_repo_path": "homeworks/CrossProd/templates/crossprod.cc", "max_stars_repo_name": "kryo4096/NPDECODES", "max_stars_repo_head_hexsha": "3498c0e4abec6ba21447849ba2ddc9286c068ea1", "max_stars_repo_licenses": ["MIT"], ... |
import argparse
import uuid
import pandas as pd
import os
from collections import OrderedDict
import sys
import numpy as np
import datetime
import getpass
import re, string
import warnings
import scipy
warnings.filterwarnings("ignore")
class query:
def __init__(self,query_bed):
df = pd.read_csv(qu... | {"hexsha": "23a017fdfc3e34cdac8090bd8a848a85ed1ee12c", "size": 7028, "ext": "py", "lang": "Python", "max_stars_repo_path": "tf_target_finder/utils.py", "max_stars_repo_name": "YichaoOU/TF_target_finder", "max_stars_repo_head_hexsha": "727f0aab0622c97658805f545aad267c1cafe382", "max_stars_repo_licenses": ["MIT"], "max_s... |
import os, sys
import logging
import numpy as np
import pandas as pd
import argparse
import glob
import torchaudio
import torch
import re
import json
import librosa
from datasets import DatasetDict
import torchvision.transforms as T
import torchvision
from transformers import (
set_seed,
Wav2Vec2Processor,
... | {"hexsha": "11d7279b2abd1782ea64a5b0708f8eb0537da510", "size": 13662, "ext": "py", "lang": "Python", "max_stars_repo_path": "train.py", "max_stars_repo_name": "HLTCHKUST/CI-AVSR", "max_stars_repo_head_hexsha": "158d5b3efabd29bf445545eaeedb5ad6b751f30d", "max_stars_repo_licenses": ["CC0-1.0"], "max_stars_count": 20, "ma... |
[STATEMENT]
lemma not_fwd_if_skip1:
"\<lbrakk>\<not> forward_arcs (y#x#x'#xs); forward_arcs (x#x'#xs)\<rbrakk> \<Longrightarrow> \<not> forward_arcs (y#x'#xs)"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. \<lbrakk>\<not> forward_arcs (y # x # x' # xs); forward_arcs (x # x' # xs)\<rbrakk> \<Longrightarrow> \<not>... | {"llama_tokens": 165, "file": "Query_Optimization_IKKBZ_Examples", "length": 1} |
# Authors: Thomas Moreau <thomas.moreau@inria.fr>
import time
import numpy as np
from scipy import sparse
from . import check_random_state
from .dictionary import get_D_shape
from ..loss_and_gradient import gradient_zi
from .convolution import _choose_convolve_multi
def _coordinate_descent_idx(Xi, D, constants, reg... | {"hexsha": "b98120882754e4c8b7d300c6c627558fd29543a9", "size": 9085, "ext": "py", "lang": "Python", "max_stars_repo_path": "alphacsc/utils/coordinate_descent.py", "max_stars_repo_name": "vishalbelsare/alphacsc", "max_stars_repo_head_hexsha": "7b7f2f3b0456ab338e95924c76828a26b3b8e4b2", "max_stars_repo_licenses": ["BSD-3... |
PROGRAM POSTSCRIPT
C *** LAST REVISED ON 15-MAR-1988 08:33:33.75
C *** SOURCE FILE: [DL.GRAPHICS.LONGLIB]POSTSCRIPT.FOR
C
C CREATED: DGL 4-APR-1985
C
C THIS PROGRAM CONVERTS THE PRINTER GRAPHICS FILE
C PRODUCED BY THE LONGLIB GRAPHICS LIBRARY
C TO THE POSTSCRIPT LANGUAGE.
C
CHARACTER*80 NAME
C
C VAX DEPENDENT ROUTIN... | {"hexsha": "e586f62400f417a3e9b5c4b9e76362ee9eea665e", "size": 4421, "ext": "for", "lang": "FORTRAN", "max_stars_repo_path": "npo-17443/dl/graphics/longlib/postscript.for", "max_stars_repo_name": "SteveDoyle2/nasa-cosmic", "max_stars_repo_head_hexsha": "c8015a9851a04f0483b978d92c2cbaee31c81fe3", "max_stars_repo_license... |
#include <algorithm>
#include <iostream>
#include <locale>
#include <cmath>
#include <vector>
#include <iterator>
#include <set>
#include <boost/lexical_cast.hpp>
#include <boost/fusion/include/for_each.hpp>
#include <boost/fusion/adapted/boost_tuple.hpp>
#include <boost/fusion/adapted/std_pair.hpp>
#include <boost/var... | {"hexsha": "0fe2e47fa3b8d9c2d9be4013341e1025f2a1ba3d", "size": 783, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/lexical_cast.cpp", "max_stars_repo_name": "blindij/boostkata", "max_stars_repo_head_hexsha": "5a0509c0413bddac54af94d2c00f9cb24b172279", "max_stars_repo_licenses": ["MIT"], "max_stars_count": nul... |
import theano
from scalmulop import ScalMulV1
from doubleop import DoubleOp
import opt
def test_scalmul_double():
x = theano.tensor.matrix()
y = ScalMulV1(2)(x)
f = theano.function([x], y)
assert not any(isinstance(n.op, ScalMulV1)
for n in f.maker.fgraph.toposort())
assert any... | {"hexsha": "c38efcf4c8439b7f53ca12b1808a8be1159c790c", "size": 400, "ext": "py", "lang": "Python", "max_stars_repo_path": "test_opt.py", "max_stars_repo_name": "kaileepm/tutorial_theano", "max_stars_repo_head_hexsha": "15699d23775af663c8a6db13b32e4796a175061c", "max_stars_repo_licenses": ["BSD-2-Clause"], "max_stars_co... |
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