text stringlengths 0 1.25M | meta stringlengths 47 1.89k |
|---|---|
#define HAS_HTTP_CLIENT_LOG
#include <boost/thread.hpp>
#include <boost/locale.hpp>
#include "asynchttpclient.h"
#include "synchttpclient.h"
#include "asyncdownload.h"
#include "syncdownload.h"
boost::asio::io_service g_io_service;
ProxyInfo g_proxy;
void handle_response(const ResponseInfo& r)
{
... | {"hexsha": "30d720eb71af9077272fdf0ecc81625af9be573a", "size": 3558, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "test.cpp", "max_stars_repo_name": "mkdym/httpclient", "max_stars_repo_head_hexsha": "2cadf455315a0031958712fb559ca43c6c3c5a71", "max_stars_repo_licenses": ["Unlicense"], "max_stars_count": 11.0, "ma... |
[STATEMENT]
lemma round_robin_Some_NoneD:
assumes rr: "round_robin n0 \<sigma> s = \<lfloor>(t, None, \<sigma>')\<rfloor>"
shows "\<exists>x ln n. thr s t = \<lfloor>(x, ln)\<rfloor> \<and> ln $ n > 0 \<and> \<not> waiting (wset s t) \<and> may_acquire_all (locks s) t ln"
[PROOF STATE]
proof (prove)
goal (1 subgoal... | {"llama_tokens": 3645, "file": "JinjaThreads_Execute_Round_Robin", "length": 26} |
```python
%matplotlib inline
```
```python
%run proof_setup
```
```python
import numpy as np
import sympy as sm
```
```python
def do_rotation(sinw, cosw, sini, cosi, x, y):
Rw = sm.Matrix([[cosw, -sinw, 0], [sinw, cosw, 0], [0, 0, 1]])
Ri = sm.Matrix([[1, 0, 0], [0, cosi, -sini], [0, sini, cosi]])
v0 ... | {"hexsha": "3e7ba43a0391e470b428a085afbfe75c4591c02b", "size": 104245, "ext": "ipynb", "lang": "Jupyter Notebook", "max_stars_repo_path": "paper/proofs/contact_impl.ipynb", "max_stars_repo_name": "exowanderer/exoplanet", "max_stars_repo_head_hexsha": "dfd4859525ca574f1936de7b683951c35c292586", "max_stars_repo_licenses"... |
\section{Universal coefficient theorem (and $\Hom$, adjointness)}
On Wednesday, we'll talk about the K\"{u}nneth theorem, and later we'll talk about the K\"{u}nneth theorem. We've been talking about tensor products of $R$-modules, but we can do something that's more natural in a way. That's the notion of $\Hom_R(M,N)$,... | {"hexsha": "055e850205e6b6fdcd4350b1ec1b4eeb4149915c", "size": 8621, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "old-905/lec-24-uct.tex", "max_stars_repo_name": "ichung/algtop-notes", "max_stars_repo_head_hexsha": "3f5d3189e2082716a69fccc1711d02ed848552d2", "max_stars_repo_licenses": ["MIT"], "max_stars_count"... |
SUBROUTINE blox(ll, al, bl, cl, s1, s3, srces)
! This subroutine forms the l-row block matrices and sources.
!
! On exit, s1 and s3 contain the sources for Legendre index ll
! a, b, c are the MPNT X MPNT Fourier blocks for this index
!
!-----------------------------------------------
! M o d u l e ... | {"hexsha": "85ea0b3915528eb14b5c32be3c877e634a6dc5b0", "size": 1931, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "DKES/Sources/General/blox.f", "max_stars_repo_name": "joseluisvelasco/STELLOPT", "max_stars_repo_head_hexsha": "e064ebb96414d5afc4e205f43b44766558dca2af", "max_stars_repo_licenses": ["MIT"], "max_... |
C
C
C ******************************************************************
C CHECK FOR STEAMBED BELOW CELL BOTTOM. RECORD REACHES FOR PRINTING
C ******************************************************************
MODULE GwfSfrCheckModule
USE GWFSFRMODULE,ONLY:ISTRM,STRM,NSTRM
USE GLOBAL,ONLY... | {"hexsha": "82d9e945d3b0009b9517b2721de8d29fb78a9eb3", "size": 1753, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "utils/mf5to6/src/MF2005/GwfSfrCheck.f", "max_stars_repo_name": "scharlton2/modflow6", "max_stars_repo_head_hexsha": "83ac72ee3b6f580aaffef6352cf15c1697d3ce66", "max_stars_repo_licenses": ["CC0-1.0... |
__author__ = 'rvuine'
from micropsi_core.nodenet.flow_netapi import FlowNetAPI
class TheanoNetAPI(FlowNetAPI):
"""
Theano extension of the NetAPI, giving native modules access to bulk operations and efficient
data structures for machine learning purposes.
"""
def announce_nodes(self, nodespace_u... | {"hexsha": "600398c6ca1795cf2e69bdea6a23dfd0dd03bdee", "size": 4009, "ext": "py", "lang": "Python", "max_stars_repo_path": "micropsi_core/nodenet/theano_engine/theano_netapi.py", "max_stars_repo_name": "Doik/micropsi2", "max_stars_repo_head_hexsha": "35ef3b48d9da255939e8e7af0e00bbcc98597602", "max_stars_repo_licenses":... |
module GDF_test
# greet() = print("Hello World!")
# include("tables.jl")
include("io.jl")
export GeoTable, gdf_from_geojson, read_gjt, read_shp
end # module
| {"hexsha": "c28a16dde5f8093891774b78444b61eb55fd7af3", "size": 160, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/GDF_test.jl", "max_stars_repo_name": "Sov-trotter/GDF_test.jl", "max_stars_repo_head_hexsha": "79d25fd44806cdfe17d9d351c6ebb5cef09686d6", "max_stars_repo_licenses": ["MIT"], "max_stars_count": n... |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import eegpy
from eegpy.misc import FATALERROR, debug
from eegpy.formats.iobase import EEG_file
try:
import scipy
except ImportError:
raise FATALERROR('SciPy or NumPy not found!\nPlease visit www.scipy.org or numeric.scipy.org for more information.')
__biosig_d... | {"hexsha": "0763a384d4f906678c0705ad9d36f3affb974fda", "size": 54363, "ext": "py", "lang": "Python", "max_stars_repo_path": "eegpy/formats/edf.py", "max_stars_repo_name": "thorstenkranz/eegpy", "max_stars_repo_head_hexsha": "0f9461456999874abbb774896ca832eb27740a9d", "max_stars_repo_licenses": ["BSD-2-Clause-FreeBSD"],... |
[STATEMENT]
lemma hfrefI[intro?]:
assumes "\<And>c a. P a \<Longrightarrow> hn_refine (fst RS a c) (f c) (snd RS a c) T (g a)"
shows "(f,g)\<in>hfref P RS T"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. (f, g) \<in> [P]\<^sub>a RS \<rightarrow> T
[PROOF STEP]
using assms
[PROOF STATE]
proof (prove)
using ... | {"llama_tokens": 384, "file": "Refine_Imperative_HOL_Sepref_Rules", "length": 3} |
import os
import numpy as np
import cv2
import lmdb
import argparse
cnt = 0
def filter_text(lang,text):
#print(lang,text)
unicode_range = {'odia':'[^\u0020-\u0040-\u0B00-\u0B7F]','kanada':'[^\u0020-\u0040-\u0C80-\u0CFF]',
'tamil':'[^\u0020-\u0040-\u0B80-\u0BFF]','malyalam':'[^\u0020-\u0040-\u0D00-\u0D7F]',
'... | {"hexsha": "03f0774fce9a08d4a11b3c223b1cf8698b64af1c", "size": 3453, "ext": "py", "lang": "Python", "max_stars_repo_path": "Recognition/prepare_data.py", "max_stars_repo_name": "prabhatrmishra/IDCardInfoExtr", "max_stars_repo_head_hexsha": "c59270f61a3251a6aff55bc7d81f2057c4663a37", "max_stars_repo_licenses": ["Apache-... |
import numpy as np
import pandas as pd
from sklearn import tree
from sklearn.model_selection import cross_val_score, train_test_split
from sklearn.linear_model import LogisticRegression
from statsmodels.imputation import mice
def clean(df):
d = {'Rural':0,'Semiurban':1,'Urban':2}
df['Property_Area'] =... | {"hexsha": "e503603d7603a36cede3471e8c96b946a439ab79", "size": 1568, "ext": "py", "lang": "Python", "max_stars_repo_path": "K-Fold Cross Validation of Prediction Model.py", "max_stars_repo_name": "energy-in-joles/Loan-Prediction-Model", "max_stars_repo_head_hexsha": "9689a4598f37cdf691ef1d9129a4a685bc41f016", "max_star... |
#!/usr/bin/env python
import rospy
import numpy as np
import random
import geometry_msgs
from geometry_msgs.msg import PoseWithCovarianceStamped
from nav_msgs.msg import Odometry
from geometry_msgs.msg import PoseArray
from laser_scan_get_map import MapClientLaserScanSubscriber
import tf_conversions
import tf
from ... | {"hexsha": "7d0c021cc6dd3fdec0e93695c3ef17ca0fc89fcf", "size": 9423, "ext": "py", "lang": "Python", "max_stars_repo_path": "particle_filter/particle_filter.py", "max_stars_repo_name": "or-tal-robotics/mcl_pi", "max_stars_repo_head_hexsha": "02d9b3bdd68c54afde36da320e1ce4bdc8d057d8", "max_stars_repo_licenses": ["Apache-... |
import numpy as np
import pandas as pd
label_name2id = {
'none' : -1,
'normal' : 0,
'snow' : 1,
'ice' : 2,
}
def sliding_windows(df, window_size, sliding_step):
"""slide windows to split data
Returns:
X (numpy.ndarray): shape: [windows_num, channels, H(1), W(window_siz... | {"hexsha": "71e58542f0b00ceb5ef5ef970c1d9d8f94b2d057", "size": 1338, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/dataset/process.py", "max_stars_repo_name": "BenbqZhang/ground-recognition", "max_stars_repo_head_hexsha": "09a03d46af6b59fd7b3cba25bb51bc923621c5e3", "max_stars_repo_licenses": ["MIT"], "max_... |
# Watershed Se detection function
# This function is based on code contributed by Suxing Liu, Arkansas State University.
# For more information see https://github.com/lsx1980/Leaf_count
import cv2
import os
import numpy as np
from scipy import ndimage as ndi
from skimage.feature import peak_local_max
from skimage.segm... | {"hexsha": "b73261aa3445a5ef7d723578ad59245dc62bffb7", "size": 2975, "ext": "py", "lang": "Python", "max_stars_repo_path": "plantcv/plantcv/watershed.py", "max_stars_repo_name": "typelogic/plantcv", "max_stars_repo_head_hexsha": "f375982cd87e0b54e49c09422abaad51ff74c64b", "max_stars_repo_licenses": ["MIT"], "max_stars_... |
import numpy as np
import pandas as pd
import os
abs_dir = os.path.dirname(__file__)
data_path = os.path.join(abs_dir, "../../../data/")
def sample_beta_binomial(n, p, k, size=None):
p = np.random.beta(k/(1-p), k/p, size=size)
r = np.random.binomial(n, p)
return r
def name2nis(name):
"""
A functi... | {"hexsha": "a5dcefd82cbc2cc3a125b8ea04fcbb0fba30b9ad", "size": 1805, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/covid19model/models/utils.py", "max_stars_repo_name": "JennaVergeynst/COVID19-Model", "max_stars_repo_head_hexsha": "9446147a71ead91a3c32c17680967f61b57c418a", "max_stars_repo_licenses": ["MIT... |
include("../src/lstm_g/network.jl")
include("../src/lstm_g/viz.jl")
nin = 3
nhidden = 2
nout = 1
# basic ANN
inputlayer = gatedlayer(3, tag=:input)
hiddenlayer = gatedlayer(2, tag=:hidden)
outputlayer = gatedlayer(1, tag=:output)
@show inputlayer hiddenlayer outputlayer
ci = connect(inputlayer, hiddenlayer)
co = c... | {"hexsha": "6c5884db4d1608ec915698565d14109c013f8e97", "size": 746, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/test_lstm_g.jl", "max_stars_repo_name": "tbreloff/OnlineAI.jl", "max_stars_repo_head_hexsha": "b6c2fcb4066c071683c89b519a8760ace27cdbd4", "max_stars_repo_licenses": ["MIT"], "max_stars_count": ... |
# ****************************************************************************
#### randomized inputs w/ scaled flow
# ****************************************************************************
# ****************************************************************************
#### standard imports
# ********************... | {"hexsha": "8608fee963c0ff32350cb40651577713b00052f5", "size": 8927, "ext": "py", "lang": "Python", "max_stars_repo_path": "examples/example_randominputs_R3.py", "max_stars_repo_name": "GeoDesignTool/GeoDT", "max_stars_repo_head_hexsha": "dea9bfa982213d45328fec339f0ca2cfa130df3d", "max_stars_repo_licenses": ["BSD-3-Cla... |
from __future__ import division
from __future__ import print_function
from pathlib import Path
from random import random
import sys
project_path = Path(__file__).resolve().parents[1]
sys.path.append(str(project_path))
import tensorflow as tf
import os
import scipy.sparse as sp
import numpy as np
from cor... | {"hexsha": "068af80fc93f008dc8c8532122a15ef102953425", "size": 8736, "ext": "py", "lang": "Python", "max_stars_repo_path": "validation/2_cluster_geometric_ap.py", "max_stars_repo_name": "omarmaddouri/GCNCC_1", "max_stars_repo_head_hexsha": "ec858bbe8246e4af15f7b870ca0ccafdea93d627", "max_stars_repo_licenses": ["MIT"], ... |
import numpy as np
from astropy.table import Table
import astropy.units as u
from astropy.nddata import StdDevUncertainty
from astropy.utils.exceptions import AstropyUserWarning
import warnings
import logging
from specutils.spectra import Spectrum1D
def spectrum_from_column_mapping(table, column_mapping, wcs=None):
... | {"hexsha": "4a0b23dffa318b2c52542937334c5a4f960ab07a", "size": 8445, "ext": "py", "lang": "Python", "max_stars_repo_path": "specutils/io/parsing_utils.py", "max_stars_repo_name": "hamogu/specutils", "max_stars_repo_head_hexsha": "b873f2ac9b3c207c9e670246d102f46a9606d6ed", "max_stars_repo_licenses": ["BSD-3-Clause"], "m... |
# --------------
#Importing header files
import pandas as pd
from sklearn.model_selection import train_test_split
# Code starts here
data=pd.read_csv(path)
#X=data[data.columns.difference(['customer.id','paid.back.loan'])]
#print(X.head(10))
#y=data['paid.back.loan']
X=data.drop(['customer.id','paid.back.loan'],1)
... | {"hexsha": "86bed80edce7d3160f7901d4cba5bf54b59c9164", "size": 4244, "ext": "py", "lang": "Python", "max_stars_repo_path": "DecisionTree/code.py", "max_stars_repo_name": "anuragswain63/ga-learner-dsmp-repo", "max_stars_repo_head_hexsha": "24c73eacf50e31f90393e388f0b7e66f10d7268b", "max_stars_repo_licenses": ["MIT"], "m... |
# Copyright (c) 2021 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": "3919994d6fa4be6c6b1b0d8c44ecb5fd98b3b5e8", "size": 5501, "ext": "py", "lang": "Python", "max_stars_repo_path": "apps/drug_target_interaction/moltrans_dti/preprocess.py", "max_stars_repo_name": "agave233/PaddleHelix", "max_stars_repo_head_hexsha": "e5578f72c2a203a27d9df7da111f1ced826c1429", "max_stars_repo_l... |
#!/usr/bin/env python3
#
# This Python script provides an example usage of RFFGPC class which is a class for
# Gaussian process classifier using RFF. Interface of RFFGPC is quite close to
# sklearn.gaussian_process.GaussianProcessClassifier.
#
# Author: Tetsuya Ishikawa <tiskw111@gmail.com>
# Date : January 29, 2021
#... | {"hexsha": "9645c26532b6fddc957ba8ed40f8fa19e0e5d64c", "size": 5395, "ext": "py", "lang": "Python", "max_stars_repo_path": "examples/gpc_for_mnist/train_gpc_for_mnist.py", "max_stars_repo_name": "Linleaf1996/random-fourier-features", "max_stars_repo_head_hexsha": "6638280e4ff0f334a973a238d5d14ffac6ede9c8", "max_stars_r... |
!> @brief This subroutine reads in coordinates (in degrees) on the surface of Earth and a heading (in degrees) and a distance (in metres) it then calculates the coordinates (in degrees) that are at the end of the vector.
!>
!> @note https://en.wikipedia.org/wiki/Vincenty%27s_formulae
!>
!> @note https://www.movable-typ... | {"hexsha": "635db23b2cf58652d7f89decfa0ab2fbb1e2f40a", "size": 7208, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "mod_safe/sub_calc_loc_from_loc_and_bearing_and_dist.f90", "max_stars_repo_name": "Guymer/fortranlib", "max_stars_repo_head_hexsha": "30e27b010cf4bc5acf0f3a63d50f11789640e0e3", "max_stars_repo_li... |
Require Export Lib.
Require Export Coq.Classes.RelationClasses.
Require Export Coq.Setoids.Setoid.
Parameter set : Type.
Parameter In : set → set → Prop.
Notation "x ∈ X" := (In x X) (at level 69).
Notation "x ∉ X" := (not (In x X)) (at level 69).
Definition Subq (X Y : set) : Prop := ∀ x : set, x ∈ X → x ∈ Y.
Not... | {"author": "jwiegley", "repo": "set-theory", "sha": "ae9714a5b7355fb23b7383a0bc4c90dc00c50264", "save_path": "github-repos/coq/jwiegley-set-theory", "path": "github-repos/coq/jwiegley-set-theory/set-theory-ae9714a5b7355fb23b7383a0bc4c90dc00c50264/Axioms.v"} |
import matplotlib.pyplot as plt
import numpy as np
import time
import matplotlib.cm as cm
import sys
plt.ion()
class GEMDistribution():
def __init__(self, alpha):
self.alpha = alpha
self.cutoffs = [0]
def sample(self):
rand = np.random.uniform()
while self.cutoffs[-1] < rand:... | {"hexsha": "3617fc45daaacfcabb94238e3cf9d354b5c0d225", "size": 2736, "ext": "py", "lang": "Python", "max_stars_repo_path": "stick_break.py", "max_stars_repo_name": "alexandrwang/6882demos", "max_stars_repo_head_hexsha": "7d5b100f348180dfd58a5f73fb839698bfabe436", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 3... |
"""
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, distribute, sublicense, and/or sell
copies of ... | {"hexsha": "86d6a1c8794db6775bfb30ec3357109ffde08edc", "size": 6975, "ext": "py", "lang": "Python", "max_stars_repo_path": "aggregation/mcs.py", "max_stars_repo_name": "jacgraz/aggregation", "max_stars_repo_head_hexsha": "e6eb7e39d49d6c278befb191b36dfb41500a5ff2", "max_stars_repo_licenses": ["MIT"], "max_stars_count": ... |
/*
* systools.cpp
*
* Created on: Sep 25, 2016
* Author: george
*/
#define BOOST_FILESYSTEM_NO_DEPRECATED
#include <boost/filesystem/operations.hpp>
#include <boost/filesystem/path.hpp>
#include <ctime>
#include <stdexcept>
#include "systools.hpp"
#include "stringtools.hpp"
using namespace std;
namespace... | {"hexsha": "dab4cbcf2f3ca87ba817d7842129ea9bd7f5f113", "size": 3776, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/tools/systools.cpp", "max_stars_repo_name": "forsyde/DeSyDe", "max_stars_repo_head_hexsha": "48c55861ed78dd240451787258ee286b0f46aea5", "max_stars_repo_licenses": ["BSD-2-Clause"], "max_stars_co... |
/*
* MIT License
*
* Copyright (c) 2020 Koki Shinjo
*
* 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, mo... | {"hexsha": "dbc2ac26230c5c958d082832f8d57e477cc4ea3c", "size": 6240, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/pointcloud_accumulator.cpp", "max_stars_repo_name": "sktometometo/pointcloud_accumulator", "max_stars_repo_head_hexsha": "31a5030992eda338aea2b2d85e410bee55602d12", "max_stars_repo_licenses": ["... |
[STATEMENT]
lemma pmdl_idI:
assumes "0 \<in> B" and "\<And>b1 b2. b1 \<in> B \<Longrightarrow> b2 \<in> B \<Longrightarrow> b1 + b2 \<in> B"
and "\<And>c t b. b \<in> B \<Longrightarrow> monom_mult c t b \<in> B"
shows "pmdl B = B"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. pmdl B = B
[PROOF STEP]
proof
... | {"llama_tokens": 1604, "file": "Polynomials_MPoly_Type_Class", "length": 22} |
//! \file
//!
//! Primary Author: Dylan Leeman
#include <algorithm>
#include <list>
#include <boost/lambda/lambda.hpp>
#include <boost/bind.hpp>
#include <autonomy/action_handler.hpp>
namespace autonomy
{
template < typename EntityT >
void entity_base<EntityT>::clear_actions(size_t which_queue)
{
... | {"hexsha": "fcafa043fc66f92339263dc96c913cc2652d714e", "size": 1648, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/autonomy/entity.inc.cpp", "max_stars_repo_name": "medlefsen/autonomy", "max_stars_repo_head_hexsha": "ed9da86e9be98dd2505a7f02af9cd4db995e6baf", "max_stars_repo_licenses": ["Artistic-2.0"], "max... |
import numpy as np
# Load data
programs = []
filename = "day12.txt"
with open(filename) as f:
# Find the number of programs
N = len(f.readlines())
# Build adjacency matrix
pipes = np.zeros([N, N])
with open(filename) as f:
# Populate the adjacency matrix
for line in f.readlines():
lin... | {"hexsha": "296b2fe41f43b7ab64682e9f9c8a2edd931cd6b4", "size": 903, "ext": "py", "lang": "Python", "max_stars_repo_path": "2017/day12-1.py", "max_stars_repo_name": "alvaropp/AdventOfCode2017", "max_stars_repo_head_hexsha": "2827dcc18ecb9ad59a1a5fe11e469f31bafb74ad", "max_stars_repo_licenses": ["MIT"], "max_stars_count"... |
# -*- coding: utf-8 -*-
from __future__ import division
from __future__ import print_function
from builtins import input
from builtins import map
from builtins import str
from builtins import zip
from builtins import range
from past.builtins import basestring
from past.utils import old_div
from builtins impor... | {"hexsha": "8c2f1a7e859472696c52136fe070895d86c6113f", "size": 16255, "ext": "py", "lang": "Python", "max_stars_repo_path": "libraries/utilities_general_v2.py", "max_stars_repo_name": "CortanaIntelligenceGallery/imageclassificationcntklaptop", "max_stars_repo_head_hexsha": "b9327fc279c5ba020f128faee93eb0e9fc077fa1", "m... |
import numpy as np
from PIL import Image
from typing import Tuple
SQUARE_COLOR = (255, 0, 0, 255) # Let's make a red square
ICON_SIZE = (512, 512) # The recommended minimum size from WordPress
def generate_pixels(resolution: Tuple[int, int]) -> np.ndarray:
"""Generate pixels of an image with the provi... | {"hexsha": "8f9ffc384df81c97c24e1aa9866cf76a0ed5b6c3", "size": 1086, "ext": "py", "lang": "Python", "max_stars_repo_path": "prototypes/red_square.py", "max_stars_repo_name": "vix597/mp3-to-image", "max_stars_repo_head_hexsha": "e9ee5d7453f274440d81a987c793de5a8a49ec2b", "max_stars_repo_licenses": ["Apache-2.0"], "max_s... |
# name this file 'solutions.py'
"""Volume II Lab 13: Optimization Packages I (scipy.optimize)
<Name>
<Class>
<Date>
"""
import scipy.optimize as opt
import numpy as np
from matplotlib import pyplot as plt
from mpl_toolkits.mplot3d import axes3d
from blackbox_function import blackbox
# Problem 1: use scipy.optimize.min... | {"hexsha": "9ccfd527d662367f3940d6b321c4e94d5e874c70", "size": 3665, "ext": "py", "lang": "Python", "max_stars_repo_path": "Vol2B/scipyoptimize/spec.py", "max_stars_repo_name": "joshualy/numerical_computing", "max_stars_repo_head_hexsha": "9f474e36fe85ae663bd20e2f2d06265d1f095173", "max_stars_repo_licenses": ["CC-BY-3.... |
\chapter{Installation}
\paragraph{Janne Spijkervet}
Installation README: \url{https://gitlab.com/uva-robotics/uva-robotics}
\paragraph{Uva Robotics}\label{uva-robotics}
Repository for the development of intelligent systems for the UvA Intelligent Robotics Lab. Most programs use either ROS or our own Redis/Websockets... | {"hexsha": "5275ea2f7db909a1c2953f8ecd62473b2282690b", "size": 3542, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "chapters/preface/installation.tex", "max_stars_repo_name": "uva-robotics/labbook", "max_stars_repo_head_hexsha": "90a29f73a7a1d7603823d7a10822a12dfb1d4d17", "max_stars_repo_licenses": ["Apache-2.0"]... |
############### IMPORT PACKAGES ##################
import numpy as np
from numpy.linalg import inv as inv #Used in kalman filter
#Used for naive bayes decoder
try:
import statsmodels.api as sm
except ImportError:
print("\nWARNING: statsmodels is not installed. You will be unable to use the Naive Bayes Decoder... | {"hexsha": "501601b484c2fe12641a9af70be3fe0268a65253", "size": 59370, "ext": "py", "lang": "Python", "max_stars_repo_path": "Neural_Decoding/decoders.py", "max_stars_repo_name": "esiabri/Neural_Decoding", "max_stars_repo_head_hexsha": "c4077d0c90876da260e9416d55e785ebefae369c", "max_stars_repo_licenses": ["BSD-3-Clause... |
[STATEMENT]
lemma atm_of_map_literal[simp]: "atm_of (map_literal f l) = f (atm_of l)"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. atm_of (map_literal f l) = f (atm_of l)
[PROOF STEP]
by (cases l; simp) | {"llama_tokens": 101, "file": "Functional_Ordered_Resolution_Prover_Executable_Subsumption", "length": 1} |
Address(Puma Court) is a residential Culdesacs culdesac in South Davis.
Intersecting Streets
El Macero Drive
| {"hexsha": "154c9f24404925660c60ac1c3040ed041c3553a3", "size": 116, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "lab/davisWiki/Puma_Court.f", "max_stars_repo_name": "voflo/Search", "max_stars_repo_head_hexsha": "55088b2fe6a9d6c90590f090542e0c0e3c188c7d", "max_stars_repo_licenses": ["MIT"], "max_stars_count": ... |
from sklearn.impute import SimpleImputer
from sklearn.preprocessing import StandardScaler
from sklearn.pipeline import Pipeline
from sklearn.model_selection import train_test_split
from sklearn.experimental import enable_halving_search_cv # noqa
from sklearn.metrics import mean_squared_error, r2_score, mean_squared_lo... | {"hexsha": "80784dfcdb379663be171ab447fe63076eb95bde", "size": 11622, "ext": "py", "lang": "Python", "max_stars_repo_path": "modeler/modeler.py", "max_stars_repo_name": "ajmal017/longshot", "max_stars_repo_head_hexsha": "0978fb107ab83034372e0e633483d381ac06f25f", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2... |
# This file is a part of JuliaFEM.
# License is MIT: see https://github.com/JuliaFEM/ModelReduction.jl/blob/master/LICENSE
using ModelReduction
@testset "Perform Guyan Reduction" begin
K = [
1.0 -1.0 0.0 0.0 0.0
-1.0 2.0 -1.0 0.0 0.0
0.0 -1.0 2.0 -1.0 0.0
0.0 0.0 -1.0 ... | {"hexsha": "c2864a61ca5bafb6fb7c05f28994b1805e9891bc", "size": 530, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/test_guyan_reduction.jl", "max_stars_repo_name": "UnofficialJuliaMirror/ModelReduction.jl-d4b734c2-cabc-57bd-991d-11b386a60271", "max_stars_repo_head_hexsha": "84c4db7b461e0383025c69f38f7b73e9c... |
# Evaluate precision of image classification in a given image region
# Instructions:
# a) Set folder of images in Image_Dir
# c) Set folder for ground truth Annotation in AnnotationDir
# The Label Maps should be saved as png image with same name as the corresponding image and png ending. The value of each pixel corr... | {"hexsha": "1c465eea594f4a857f85aba181b0c6af1aa42352", "size": 5672, "ext": "py", "lang": "Python", "max_stars_repo_path": "EvaluateAccuracy.py", "max_stars_repo_name": "sagieppel/Classification-of-the-material-given-region-of-an-image-using-a-convolutional-neural-net-with-attent", "max_stars_repo_head_hexsha": "2c78f0... |
import os
import json
import numpy as np
import librosa
from inputs import get_bit_rates_and_waveforms
from inputs import get_truth_ds_filename_pairs
import tensorflow as tf
from models import deep_residual_network
data_settings = os.path.join('settings', 'data_settings.json')
model_settings_file = os.path.join('setti... | {"hexsha": "8391a0e5dfcc8064e3472d569a1da9ce92417c7c", "size": 3622, "ext": "py", "lang": "Python", "max_stars_repo_path": "upsample_audio_file.py", "max_stars_repo_name": "Tom-Evers/EnglishSpeechUpsampler", "max_stars_repo_head_hexsha": "779d534e61575cf63fa420274accb0edef6b1597", "max_stars_repo_licenses": ["MIT"], "m... |
SUBROUTINE PLTSET
C
C COMMENTS FROM G.C. -
C THE DRIVER FOR DMAP MODULE PLTSET IS DPLTST
C THIS ROUTINE HAS NOTHING TO DO WITH DPLTST. IT IS CALLED ONLY
C BY PARAM (IN MODULE PLOT), XYPLOT, AND SEEMAT
C
C
LOGICAL TAPBIT
INTEGER CHRWRD,PBFSIZ,PBUFSZ,PDATA,PLTDA... | {"hexsha": "0de08bef769ce63713ab3fca404c9ddb1fb8d81b", "size": 3117, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "mis/pltset.f", "max_stars_repo_name": "ldallolio/NASTRAN-95", "max_stars_repo_head_hexsha": "6d2c175f5b53ebaec4ba2b5186f7926ef9d0ed47", "max_stars_repo_licenses": ["NASA-1.3"], "max_stars_count": ... |
/*******************************************************************************
Copyright (c) 2017, Honda Research Institute Europe GmbH
Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are
met:
1. Redistributions of sour... | {"hexsha": "929c99a2491c0b522d044a358cac1cd10b1fc362", "size": 15402, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/RcsCore/Rcs_eigen.cpp", "max_stars_repo_name": "famura/Rcs", "max_stars_repo_head_hexsha": "4f8b997d2649a2cd7a1945ea079e07a71ee215fc", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_co... |
'''
140819:
move lr into fit
add time recording
add pred
add prob
2014/09/03:
change the last layer into softmax
2015/02/01:
normalize the w
'''
import cPickle
import gzip
import os
import sys
import time
import numpy
from numpy import *
import theano
import... | {"hexsha": "e04ce3bdbbf6c8d7929e543bb43a061776d919c5", "size": 13318, "ext": "py", "lang": "Python", "max_stars_repo_path": "ML/adder/mnist_mlp.py", "max_stars_repo_name": "PiscesDream/Ideas", "max_stars_repo_head_hexsha": "9ba710e62472f183ae4525f35659cd265c71392e", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars... |
import cirq
import numpy as np
from cirq.ops.common_gates import ZPowGate
from cirq.circuits.qasm_output import QasmUGate
#Got flags from: https://github.com/alexandrupaler/fondq
def is_op_with_decomposed_flag(op, gate_type):
if op == gate_type:
return hasattr(op, "decomposed")
return False
def reset... | {"hexsha": "8610c918d4027b84535686024417b317df2c5188", "size": 3327, "ext": "py", "lang": "Python", "max_stars_repo_path": "VOQC/interop/cirq/decompose_cirq_gates.py", "max_stars_repo_name": "taorunz/SQIR", "max_stars_repo_head_hexsha": "e826699c9063be390f7e9d25b6f3c0b52ee5d25c", "max_stars_repo_licenses": ["MIT"], "ma... |
OPEN (F,STATUS = 'SCRATCH') !Temporary disc storage.
| {"hexsha": "a0a0918f45b6db980ced377ea13ebef9971160ab", "size": 63, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "Task/Secure-temporary-file/Fortran/secure-temporary-file.f", "max_stars_repo_name": "LaudateCorpus1/RosettaCodeData", "max_stars_repo_head_hexsha": "9ad63ea473a958506c041077f1d810c0c7c8c18d", "max_s... |
// Boost.Geometry (aka GGL, Generic Geometry Library)
// Copyright (c) 2015 Barend Gehrels, Amsterdam, the Netherlands.
// Use, modification and distribution is subject to the Boost Software License,
// Version 1.0. (See accompanying file LICENSE_1_0.txt or copy at
// http://www.boost.org/LICENSE_1_0.txt)
#i... | {"hexsha": "9c8cef1c5391cfc546325de7dca7cce731ff5768", "size": 10039, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "build/lua-reader/include/boost-1.60.0/boost/geometry/algorithms/detail/overlay/handle_colocations.hpp", "max_stars_repo_name": "LazyPlanet/MX-Client", "max_stars_repo_head_hexsha": "07ac4ada4507fb7... |
### A Pluto.jl notebook ###
# v0.14.2
using Markdown
using InteractiveUtils
# This Pluto notebook uses @bind for interactivity. When running this notebook outside of Pluto, the following 'mock version' of @bind gives bound variables a default value (instead of an error).
macro bind(def, element)
quote
loc... | {"hexsha": "9449f88a26390bc8eb1778a732f91cc75fd38b36", "size": 43511, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "notebooks/week1/images.jl", "max_stars_repo_name": "rfhklwt/18S191_notebook", "max_stars_repo_head_hexsha": "beb6001d2fc92a317cb9e578a60d83222512bebb", "max_stars_repo_licenses": ["MIT"], "max_sta... |
from pytest import approx
import pytest
import numpy as np
from tests.context import LexRankSummarizer
from sadedegel.tokenize import Doc
@pytest.mark.skip()
def test_lxr_summarizer_all_lower():
summ = LexRankSummarizer("log_norm", "smooth", normalize=False)
assert summ.predict(Doc('ali topu tut. oya ip atla... | {"hexsha": "c9d9b5e0d23ec6b9aa2a24be9d355456cdda8d98", "size": 793, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/summarizer/test_lxr.py", "max_stars_repo_name": "GlobalMaksimum/sadedegel", "max_stars_repo_head_hexsha": "8e28dbeabc3bf0d6f2222089ac5e3a849f9d3a6b", "max_stars_repo_licenses": ["MIT"], "max_... |
from __future__ import print_function, division
from random import randint, uniform, choice, sample
from sklearn.metrics import roc_curve, auc
from sklearn.metrics import confusion_matrix
import math
import numpy as np
PRE, REC, SPEC, FPR, NPV, ACC, F1 = 7, 6, 5, 4, 3, 2, 1
def _randint(a=0,b=0):
if a < b:
... | {"hexsha": "61601a714ff4227079bb0e4dc5a84dcd94d50a0b", "size": 4472, "ext": "py", "lang": "Python", "max_stars_repo_path": "utilities.py", "max_stars_repo_name": "snaraya7/simplifying-software-analytics", "max_stars_repo_head_hexsha": "446a687f5d32ab66d53132d0889ce39eaca13ea6", "max_stars_repo_licenses": ["MIT"], "max_... |
(************************************************************************)
(* * The Coq Proof Assistant / The Coq Development Team *)
(* v * INRIA, CNRS and contributors - Copyright 1999-2018 *)
(* <O___,, * (see CREDITS file for the list of authors) *)
(* \VV/ *********... | {"author": "princeton-vl", "repo": "CoqGym", "sha": "0c03a6fba3a3ea7e2aecedc1c624ff3885f7267e", "save_path": "github-repos/coq/princeton-vl-CoqGym", "path": "github-repos/coq/princeton-vl-CoqGym/CoqGym-0c03a6fba3a3ea7e2aecedc1c624ff3885f7267e/coq/test-suite/ssr/have_transp.v"} |
[STATEMENT]
lemma unique_declared_in:
"\<lbrakk>G\<turnstile>m declared_in C; G\<turnstile>n declared_in C; memberid m = memberid n\<rbrakk>
\<Longrightarrow> m = n"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. \<lbrakk>G\<turnstile> m declared_in C; G\<turnstile> n declared_in C; memberid m = memberid n\<rbrakk... | {"llama_tokens": 181, "file": null, "length": 1} |
import atexit
import csv
import os
import platform
import psutil
import random
import matplotlib.pyplot as plt
import numpy as np
from .util.EpisodeScheduler import EpisodeScheduler
from .util.StateSet import StateSet, Select, SelectPolicy
def get_random_port():
rand = random.Random()
while True:
po... | {"hexsha": "fe07307ee21bb4874312d17759c9ad5bfe59421f", "size": 7821, "ext": "py", "lang": "Python", "max_stars_repo_path": "minos/lib/common.py", "max_stars_repo_name": "ZhuFengdaaa/minos", "max_stars_repo_head_hexsha": "b710e9fbad67b18bafedd0d528a7559d8379b9cb", "max_stars_repo_licenses": ["MIT"], "max_stars_count": n... |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Sat Jul 25 14:52:15 2020
@author: cxue2
"""
from datetime import datetime
import numpy as np
from sklearn.metrics import confusion_matrix
from sklearn.metrics import roc_auc_score, roc_curve, auc
from sklearn.metrics import precision_recall_curve, average_p... | {"hexsha": "c4d843cad53d0408b55fcf053412644c596f52a8", "size": 4554, "ext": "py", "lang": "Python", "max_stars_repo_path": "azrt2021/misc.py", "max_stars_repo_name": "vkola-lab/azrt2021", "max_stars_repo_head_hexsha": "a75c1302434c4578daf4cde119cfa50f552a9a43", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2, ... |
"""Signal detection capabilities for amodem."""
import collections
import itertools
import logging
import numpy as np
from . import dsp
from . import equalizer
from . import common
log = logging.getLogger(__name__)
class Detector:
COHERENCE_THRESHOLD = 0.9
CARRIER_DURATION = sum(equalizer.prefix)
CA... | {"hexsha": "7677e90dbd47270b07d5dc3141dcb467bc7456ef", "size": 4044, "ext": "py", "lang": "Python", "max_stars_repo_path": "amodem/detect.py", "max_stars_repo_name": "Matthew-MK/amodem", "max_stars_repo_head_hexsha": "a75dda9ab0f7445589a036357e604703ccb34726", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 766,... |
import os
import random
import time
import cv2
import numpy as np
import tensorflow as tf
from tensorflow.keras.callbacks import CSVLogger
from tensorflow.keras.metrics import Accuracy, CategoricalAccuracy
from tensorflow.keras.models import load_model
from tensorflow.python.keras.callbacks import LearningRateSchedule... | {"hexsha": "162d4db3235aa0f22a77e3c7280725de879bf93f", "size": 3750, "ext": "py", "lang": "Python", "max_stars_repo_path": "models/densenet/model.py", "max_stars_repo_name": "SchiffFlieger/semantic-segmentation-master-thesis", "max_stars_repo_head_hexsha": "f54b8321a9e0828e492bc6847acbff80c1a75d7c", "max_stars_repo_lic... |
#
# Copyright 2020 Antoine Sanner
#
# ### MIT license
#
# 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, me... | {"hexsha": "a218731124beda9fb8e32bf37cbbed5536f7bfb9", "size": 9884, "ext": "py", "lang": "Python", "max_stars_repo_path": "test/ReferenceSolutions/test_sphere_jkr.py", "max_stars_repo_name": "ContactEngineering/Adhesion", "max_stars_repo_head_hexsha": "acc46ad9bfe49fec667cb9a116ebde426faa38c4", "max_stars_repo_license... |
import statistics
import numpy as np
import pandas as pd
def upSampling(data, column, bins=100, target_samples="mean"):
"""
This function will down-up sampling data to mean samples of column's data.
data: pandas DataFrame
column: name of column in DataFrame
"""
is_warned = False
# Get target column from DataFr... | {"hexsha": "0c36e2bb7de339034cf90a399f4a0757df3b727b", "size": 2812, "ext": "py", "lang": "Python", "max_stars_repo_path": "utilities/sampling/upSampling.py", "max_stars_repo_name": "TeaKatz/Binary_Search_Optimization", "max_stars_repo_head_hexsha": "728d17ce5ca91aef62497a749e7dd75475c2bd5a", "max_stars_repo_licenses":... |
'''
imports and config for image recognition
'''
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import argparse
import sys
import time
import numpy as np
import tensorflow as tf
from PIL import Image # for image resizing
UPLOAD_FOLDER = '/home/sidearmjoh... | {"hexsha": "a48e2afe58f76f7cdf2598c5f6511a85dd23153b", "size": 4711, "ext": "py", "lang": "Python", "max_stars_repo_path": "main.py", "max_stars_repo_name": "john-running/GardenSpy", "max_stars_repo_head_hexsha": "42a3a2797fbc5ca1da1e4fb6c0d597f8a3479e0e", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": n... |
using Lazy
using BenchmarkTools
function fib(n)
a,b = 0,1
for i in 1:(n-1)
a,b = b,a+b
end
return a
end
fibs = @lazy 0:1:(fibs + drop(1, fibs));
println(@benchmark fib(20))
println(@benchmark take(20,fibs))
| {"hexsha": "f95f84ed350dfcbe4f0dc68a4fcf50f5409f34e2", "size": 222, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "fib-bench.jl", "max_stars_repo_name": "vmchale/julia-problem-solving", "max_stars_repo_head_hexsha": "beac7fcf80f551b3f88eb190e12dead42ccf098a", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_sta... |
import os
import random
import sys
import catboost
import numpy as np
import pandas
import pandas as pd
MODELS_PATH = os.path.dirname(os.path.realpath(__file__))
SEED = 42
def reseed(seed=SEED):
np.random.seed(seed)
random.seed(seed)
def preprocess(df):
df['longitude'] = df['longitude'].astype(np.flo... | {"hexsha": "4de21370596af5edc85df245ff8b278258eff078", "size": 1234, "ext": "py", "lang": "Python", "max_stars_repo_path": "catboost_simple/predict.py", "max_stars_repo_name": "vladsam80/wildfire", "max_stars_repo_head_hexsha": "ee8ec6bf6ee9eb490115d2221b1c303422193e69", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
# ---
# title: 889. Construct Binary Tree from Preorder and Postorder Traversal
# id: problem889
# author: Tian Jun
# date: 2020-10-31
# difficulty: Medium
# categories: Tree
# link: <https://leetcode.com/problems/construct-binary-tree-from-preorder-and-postorder-traversal/description/>
# hidden: true
# ---
#
# Return... | {"hexsha": "df63aa608173503783179a101607efb4c1bd7bf4", "size": 943, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/unresolved/889.construct-binary-tree-from-preorder-and-postorder-traversal.jl", "max_stars_repo_name": "noob-data-analaysis/LeetCode.jl", "max_stars_repo_head_hexsha": "94d91b295e988948e77e737c1... |
!
! CRTM_LowFrequency_MWSSEM
!
! Module containgin routines to compute microwave ocean emissivity components
! (FWD, TL, and AD) for low frequencies.
!
!
! CREATION HISTORY:
! Written by: Masahiro Kazumori, JCSDA 31-Jul-2006
! Masahiro.Kazumori@noaa.gov
! Quanhua Li... | {"hexsha": "ef3e200756e0aacb4db9efbbb8f0ce1b9bcea9c6", "size": 45299, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "src/MW/Water/CRTM_FASTEM/CRTM_LowFrequency_MWSSEM.f90", "max_stars_repo_name": "JCSDA-internal/CSEM1.0.0", "max_stars_repo_head_hexsha": "e1adf653b66f129c7c1f8f116b36779b10b7fd44", "max_stars_r... |
# -*- python -*-
# -*- coding: utf-8 -*-
#
# eric m. gurrola <eric.m.gurrola@jpl.nasa.gov>
#
# (c) 2018-2021 jet propulsion laboratory
# (c) 2018-2021 california institute of technology
# all rights reserved
#
# United States Government Sponsorship acknowledged. Any commercial use must be negotiated with
# the Office o... | {"hexsha": "b779132510a161ee0ff573ac061978270af24e79", "size": 14938, "ext": "py", "lang": "Python", "max_stars_repo_path": "models/cdm/cdm/libcdm.py", "max_stars_repo_name": "lijun99/altar", "max_stars_repo_head_hexsha": "92c2915de3de0c51138d382c8192ead7d6eed1a1", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_star... |
//---------------------------------------------------------------------------//
//!
//! \file tstDynamicOutputFormatterFactory.cpp
//! \author Alex Robinson
//! \brief The dynamic output formatter factory unit tests
//!
//---------------------------------------------------------------------------//
// Std Lib Inclu... | {"hexsha": "3af11f70762b31c9b5664cb600cdbcfffc9e14ff", "size": 24594, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "packages/utility/core/test/tstDynamicOutputFormatterFactory.cpp", "max_stars_repo_name": "bam241/FRENSIE", "max_stars_repo_head_hexsha": "e1760cd792928699c84f2bdce70ff54228e88094", "max_stars_repo_... |
#!/usr/bin/env python
# coding: utf-8
# In[1]:
import astropy
import subprocess
import shlex
import pandas as pd
import numpy as np
from astropy.table import Table
from astropy.table import Column
import os
import glob2
import matplotlib.pyplot as plt
from matplotlib.backends.backend_pdf import PdfPages
import SNID_A... | {"hexsha": "1630c522064a0773ae91e4d0fe845b7c0d8ff2c3", "size": 1632, "ext": "py", "lang": "Python", "max_stars_repo_path": "SNID_Multi_Analysis_AllTemplates.py", "max_stars_repo_name": "adamamiller/supernova-spectrum-analysis", "max_stars_repo_head_hexsha": "1f7816bdc7dadb1a9a2ee3a97a1f77dd6f0c06dd", "max_stars_repo_li... |
# coding=utf-8
# Copyright 2021 The Ravens Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or... | {"hexsha": "f36beaa02a48cb6c68edf44e27f6c48b6f313d78", "size": 30459, "ext": "py", "lang": "Python", "max_stars_repo_path": "ravens/agents/transporter.py", "max_stars_repo_name": "YunchuZhang/Learning-to-use-different-tools-for-objects-rearrangement", "max_stars_repo_head_hexsha": "3759664cd77b5810834937c478a9a44ad36ac... |
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License.
import pandas as pd
import numpy as np
from reco_utils.common.constants import (
DEFAULT_USER_COL,
DEFAULT_ITEM_COL,
DEFAULT_RATING_COL,
DEFAULT_LABEL_COL
)
def user_item_pairs(
user_df,
item_df,
... | {"hexsha": "576234118ec9b6afcaef5f24d044ebff9e8fb89e", "size": 9338, "ext": "py", "lang": "Python", "max_stars_repo_path": "reco_utils/dataset/pandas_df_utils.py", "max_stars_repo_name": "kecoli/Recommenders", "max_stars_repo_head_hexsha": "892d0d8a3c866eca0b8f55e6f6a58d2b15a56b4c", "max_stars_repo_licenses": ["MIT"], ... |
# -*- coding: UTF-8 -*-
"""
ANTARES Object class specification
"""
from __future__ import absolute_import
from __future__ import unicode_literals
import warnings
import numpy as np
from . import constants
from .features.base import BaseMixin
from astropy.stats import sigma_clip
import extinction
__all__ = ['LAobject'... | {"hexsha": "16d3245a3189c51d777ae363f18e0b878a45a160", "size": 16041, "ext": "py", "lang": "Python", "max_stars_repo_path": "astrorapid/ANTARES_object/LAobject.py", "max_stars_repo_name": "mcoughlin/astrorapid", "max_stars_repo_head_hexsha": "ec0cf99d2bca25f617eed4b36185c25ef3015c5b", "max_stars_repo_licenses": ["MIT"]... |
#!/usr/bin/env python3
#
# Copyright 2015 Signal Processing Devices Sweden AB. All rights reserved.
#
# Description: ADQ14 FWDAQ streaming example
# Documentation:
#
import numpy as np
import scipy.signal as signal
import ctypes as ct
import matplotlib.pyplot as plt
import sys
import time
import os
sys.path.insert... | {"hexsha": "913e6b796258f4b8da39a9282fd3c2ac8276a073", "size": 10499, "ext": "py", "lang": "Python", "max_stars_repo_path": "HHGMonitor/ADQ14_FWSDR_streaming_example.py", "max_stars_repo_name": "thomasbarillot/DAQ", "max_stars_repo_head_hexsha": "20126655f74194757d25380680af9429ff27784e", "max_stars_repo_licenses": ["M... |
import numpy as np
import pytest
from sklearn.exceptions import NotFittedError
from deslib.static.static_selection import StaticSelection
from sklearn.utils.estimator_checks import check_estimator
from sklearn.tree import DecisionTreeClassifier
def test_check_estimator():
check_estimator(StaticSelection)
# Tes... | {"hexsha": "730c4f2a14e23807aeb5875676d2a5d1021d2737", "size": 2722, "ext": "py", "lang": "Python", "max_stars_repo_path": "deslib/tests/static/test_static_selection.py", "max_stars_repo_name": "mrtrunghieu1/Mutil-DesLib-Algorithm", "max_stars_repo_head_hexsha": "4fd82c553adc34561f6698b18a08ad89a58deee6", "max_stars_re... |
import copy
import itertools
import logging
import os.path as osp
from collections import defaultdict
from typing import Any, Dict, Iterable, List, NamedTuple, Optional, Tuple
import numpy as np
import torch
from torch.nn.modules import Module
from torch.nn.parallel import DataParallel, DistributedDataParallel
from i... | {"hexsha": "562e00b190b47aa2c3f6a299356c14f28c4a5898", "size": 13125, "ext": "py", "lang": "Python", "max_stars_repo_path": "imix/utils/checkpoint.py", "max_stars_repo_name": "linxi1158/iMIX", "max_stars_repo_head_hexsha": "af87a17275f02c94932bb2e29f132a84db812002", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars... |
//
// Copyright (c) 2015-2016 Vinnie Falco (vinnie dot falco at gmail dot com)
//
// 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 BEAST_CORE_FILE_HPP
#define BEAST_CORE_FILE_HPP
#include <beast/config.h... | {"hexsha": "8b3eecc874a0294b4aa72a0a599273ee92a3ff4e", "size": 888, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "src/beast/include/beast/core/file.hpp", "max_stars_repo_name": "Py9595/Backpack-Travel-Underlying-Code", "max_stars_repo_head_hexsha": "c5758792eba7cdd62cb6ff46e642e8e4e4e5417e", "max_stars_repo_lice... |
import numpy as np
import sys
import sklearn
from sklearn import metrics
import matplotlib
matplotlib.use('agg')
from matplotlib import pyplot as plt
models = sys.argv[1:]
cnt = 0
maker = ['<','|','^','s','d','x','p','*','o']
#color = ['b','g','c','#FF00FF','y','m','#FF0000']
for model_name in models:
x = np.load... | {"hexsha": "6a27cb62f0fbbf1ae26cda9d01b361f22e3caf22", "size": 3905, "ext": "py", "lang": "Python", "max_stars_repo_path": "algorithm/draw_plot.py", "max_stars_repo_name": "CrisJk/PA-TRP", "max_stars_repo_head_hexsha": "e1d846667153787070aaf0fe83414c3d38795941", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_cou... |
"""
Unit tests for trust-region iterative subproblem.
To run it in its simplest form::
nosetests test_optimize.py
"""
from __future__ import division, print_function, absolute_import
import numpy as np
from scipy.optimize._trustregion_exact import (
estimate_smallest_singular_value,
singular_leading_submat... | {"hexsha": "92ffe91873c4c0e6694cad77a8fccb65c37fd9dd", "size": 13044, "ext": "py", "lang": "Python", "max_stars_repo_path": "scipy/optimize/tests/test_trustregion_exact.py", "max_stars_repo_name": "ririw/scipy", "max_stars_repo_head_hexsha": "680ecf8c52966343827903e6b7983b1ef7323fe2", "max_stars_repo_licenses": ["BSD-3... |
\documentclass[12pt]{article}
\begin{document}
\author{Luyu Liu}
\newcounter{para}
\newcommand\para{\par\refstepcounter{para}\thepara\space}
\section*{CSE 5194 WEEK3- Basic idea}
\title{CSE 5194 WEEK3 - Basic idea}
\paragraph{Define-by-run and define-and-run}
Which of them has better performance:
... | {"hexsha": "ceb8ed2bb116b4b1c0eaad4bc94c955fdc788d74", "size": 2783, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "notes/WEEK3_BasicIdeasContinue.tex", "max_stars_repo_name": "luyuliu/CSE-5194", "max_stars_repo_head_hexsha": "52970106c21b30e64d4cf1df26bec09929494060", "max_stars_repo_licenses": ["MIT"], "max_sta... |
# License: BSD 3 clause
import numpy as np
from .base import SolverFirstOrderSto
from .build.solver import SGDDouble as _SGDDouble
from .build.solver import SGDFloat as _SGDFloat
__author__ = "Stephane Gaiffas"
dtype_class_mapper = {
np.dtype('float32'): _SGDFloat,
np.dtype('float64'): _SGDDouble
}
# TODO:... | {"hexsha": "0af29642979613ca9a864d1637aee1ed5ca3f513", "size": 5714, "ext": "py", "lang": "Python", "max_stars_repo_path": "tick/solver/sgd.py", "max_stars_repo_name": "sumau/tick", "max_stars_repo_head_hexsha": "1b56924a35463e12f7775bc0aec182364f26f2c6", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": ... |
/*
Copyright 2010 Larry Gritz and the other authors and contributors.
All Rights Reserved.
Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are
met:
* Redistributions of source code must retain the above copyright
noti... | {"hexsha": "299a7cd6bae76407465e38af3d3fda5ef212e237", "size": 12626, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/libutil/colortransfer.cpp", "max_stars_repo_name": "ndubey/oiio", "max_stars_repo_head_hexsha": "fb00e178be048c31f478076977d17434c310472c", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_sta... |
/**********************************************************\
Original Author: Richard Bateman and Georg Fritzsche
Created: December 3, 2009
License: Dual license model; choose one of two:
New BSD License
http://www.opensource.org/licenses/bsd-license.php
- or -
GNU... | {"hexsha": "548e6a71ef3c984b9054622d5c145db127d668f0", "size": 1998, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "chrome/plugin/examples/FBTestPlugin/SimpleMathAPI.cpp", "max_stars_repo_name": "Faham/bric-n-brac", "max_stars_repo_head_hexsha": "c886e0855869a794700eb385171bbf5bfd595aed", "max_stars_repo_licenses... |
#----------------------- Artificial Neural Network for classification --------------------#
#importing required libraries
import numpy as np
import pandas as pd
import tensorflow as tf
from sklearn.compose import ColumnTransformer
from sklearn.preprocessing import OneHotEncoder
from sklearn.preprocessing import LabelEn... | {"hexsha": "ca9af6e0b8e6544c066119019b6aecc0066d6cb4", "size": 4863, "ext": "py", "lang": "Python", "max_stars_repo_path": "Projects/Image-Recognition-Dog,Cat/Model/Artificial_Neural_Network_Case_Study.py", "max_stars_repo_name": "Raj123-github/Data_Science_portfolio", "max_stars_repo_head_hexsha": "4733f733c0121cef598... |
# Standard library
import os
from os import path
import sys
# Third-party
from astropy.table import Table, hstack
import astropy.units as u
import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
from isochrones import StarModel
from isochrones.observation import Source, Observation, ObservationTr... | {"hexsha": "460babdb8710a62ef86a86c7a3e322c110619d4b", "size": 4968, "ext": "py", "lang": "Python", "max_stars_repo_path": "scripts/run_isochrones_sample.py", "max_stars_repo_name": "adrn/dr2-lmc-cluster", "max_stars_repo_head_hexsha": "9e90cf4c130d35f8c79a327cef978acf99c3b2bf", "max_stars_repo_licenses": ["MIT"], "max... |
import numpy
import sys
import nmslib
import time
import math
from multiprocessing import Process
from xclib.data import data_utils
def write_knn_out(out_dir,write_dist,num_inst,nbrs,batch_no,metric_space):
with open('%s/%d'%(out_dir,batch_no),'w') as fp:
fp.write('%d %d\n'%(len(nbrs),num_inst))
if write_dist == ... | {"hexsha": "a67bea23eeccce984afba270d8db02b4599d2f2f", "size": 2111, "ext": "py", "lang": "Python", "max_stars_repo_path": "ANNS/test_hnsw.py", "max_stars_repo_name": "WestbrookGE/SliceSparse", "max_stars_repo_head_hexsha": "a6387763d2e919a995a0b9c9c3e5a3bffef0cf95", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_st... |
using LoopVectorization
using Base: OneTo
using LinearAlgebra: stride1
using Test
add_1_dim(x::AbstractArray) = reshape(x, size(x)..., 1)
check_finite(x::AbstractArray) = all(isfinite.(x)) || throw(error("x not finite!"))
"Given k-dimensional array `x` where `n=size(x,k)`, compute multinomial logistic Pr(i ∈ 1:n | x[... | {"hexsha": "17b0f475731644777747d6d6fa789a9206f40935", "size": 5401, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "example/softmax3test.jl", "max_stars_repo_name": "magerton/ShaleDrillingLikelihood.jl", "max_stars_repo_head_hexsha": "cd883f82ec381d3c6d87db59224e4a35c09c1551", "max_stars_repo_licenses": ["MIT"],... |
# -*- coding: utf-8 -*-
"""
Created on Wed Apr 25 11:34:44 2018
@author: conte
"""
import sys
import cv2
import skimage
import numpy
import approaches.approach0.approach0 as a0
import approaches.approach1.approach1 as a1
import approaches.approach2.approach2 as a2
import approaches.approach3.approach3 as a3
import app... | {"hexsha": "37b7444ead7f44f1162ffc932716f780ea1e0c92", "size": 2438, "ext": "py", "lang": "Python", "max_stars_repo_path": "virtualHeartRate.py", "max_stars_repo_name": "Aurielws/2SRPy", "max_stars_repo_head_hexsha": "292d36faca8e57ed41635dba1d055e82fb27508f", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null... |
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import warnings
warnings.filterwarnings("ignore")
import yfinance as yf
yf.pdr_override()
import datetime as dt
from sklearn.metrics import accuracy_score
from sklearn.linear_model import LinearRegression
import datetime as dt
symbol = 'AAPL'
star... | {"hexsha": "59954340de4a56634c93e27e3753a0da6cb165eb", "size": 1338, "ext": "py", "lang": "Python", "max_stars_repo_path": "Machine_Learning/stock_simple_ml.py", "max_stars_repo_name": "vhn0912/Finance", "max_stars_repo_head_hexsha": "39cf49d4d778d322537531cee4ce3981cc9951f9", "max_stars_repo_licenses": ["MIT"], "max_s... |
"""
The pycity_scheduling framework
Copyright (C) 2022,
Institute for Automation of Complex Power Systems (ACS),
E.ON Energy Research Center (E.ON ERC),
RWTH Aachen University
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
documentation files (the "Softwa... | {"hexsha": "5030a6988ac208bd2d3118b05fed4523515306f8", "size": 11526, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/pycity_scheduling/classes/timer.py", "max_stars_repo_name": "ElsevierSoftwareX/SOFTX-D-20-00087", "max_stars_repo_head_hexsha": "d2d3f1effda2c0499cb05abf87435375a21379e3", "max_stars_repo_lic... |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Mon Dec 9 18:15:20 2019
@author: diyixuan
"""
from VorDiff.reverse_operator import ReverseOperator as rop
from VorDiff.reverse_autodiff import ReverseAutoDiff as rad
import numpy as np
import math
x = rad.reverse_scalar(0.5)
c = 0.5
def test_sin():
... | {"hexsha": "bc42b7ccbaa128e8edbd0c5fcdef7240b252d16e", "size": 2896, "ext": "py", "lang": "Python", "max_stars_repo_path": "VorDiff/tests/test_reverse_operator.py", "max_stars_repo_name": "VoraciousFour/VorDiff", "max_stars_repo_head_hexsha": "9676462b028e532b10ebf38989b5014763b7260e", "max_stars_repo_licenses": ["MIT"... |
#include <iostream>
#include <cmath>
#include <algorithm>
#include <vector>
#include <boost/multiprecision/cpp_int.hpp>
#include <boost/lexical_cast.hpp>
using namespace boost::multiprecision;
using namespace std;
void solve(cpp_int x, vector<cpp_int> &v) {
if (x > 3234566667) return;
v.push_back(x);
cpp_in... | {"hexsha": "354a4ea955cbc0ec09da053b491b72a385d4eaeb", "size": 622, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "AtCoder/abc161/d/main.cpp", "max_stars_repo_name": "H-Tatsuhiro/Com_Pro-Cpp", "max_stars_repo_head_hexsha": "fd79f7821a76b11f4a6f83bbb26a034db577a877", "max_stars_repo_licenses": ["MIT"], "max_stars_... |
[STATEMENT]
lemma append_rows_access1 [simp]:
assumes "i < dim_row A"
assumes "dim_col A = dim_col B"
shows "row (A @\<^sub>r B) i = row A i"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. row (A @\<^sub>r B) i = row A i
[PROOF STEP]
proof
[PROOF STATE]
proof (state)
goal (2 subgoals):
1. \<And>ia. ia < dim_v... | {"llama_tokens": 2120, "file": "Linear_Programming_More_Jordan_Normal_Forms", "length": 19} |
import pygame
from neuron import Neuron
import numpy as np
class NeuronG(Neuron):
def __init__(self, pos, v_r = 0, R_m = 1, tau = 1, threshold = 0.2, scale = 1, **kwargs):
super().__init__(v_r, R_m, tau, threshold, **kwargs)
self.pos = np.array(pos)
self.scale = scale
self.unit_scale = 20
val = int(self.v /... | {"hexsha": "ef5d341ed87f20065cb472ff5bae657ab6d20c7a", "size": 2036, "ext": "py", "lang": "Python", "max_stars_repo_path": "final/neurongraphics.py", "max_stars_repo_name": "SamKG/bic-final", "max_stars_repo_head_hexsha": "f29f21c4b207e7d84f81f451f3eb677c0be98f0c", "max_stars_repo_licenses": ["MIT"], "max_stars_count":... |
from __future__ import print_function
import os
import logging
import numpy as np
import scipy.misc
try:
from StringIO import StringIO # Python 2.7
except ImportError:
from io import BytesIO # Python 3.x
def set_logger(log_path, log_name='training'):
if log_path is None:
print('log_path is empty'... | {"hexsha": "b92811e92f3e3aae1f86e916db3a936523ba9011", "size": 862, "ext": "py", "lang": "Python", "max_stars_repo_path": "framework/logbase.py", "max_stars_repo_name": "AIM3-RUC/video-paragraph", "max_stars_repo_head_hexsha": "072e7447a6bc12080f0baa3d41e9e96d3d240221", "max_stars_repo_licenses": ["MIT"], "max_stars_co... |
% Options for packages loaded elsewhere
\PassOptionsToPackage{unicode}{hyperref}
\PassOptionsToPackage{hyphens}{url}
%
\documentclass[
man]{apa6}
\usepackage{amsmath,amssymb}
\usepackage{lmodern}
\usepackage{iftex}
\ifPDFTeX
\usepackage[T1]{fontenc}
\usepackage[utf8]{inputenc}
\usepackage{textcomp} % provide eu... | {"hexsha": "8b477a6982724287f0926e8a372e09ccedb3d7a2", "size": 24733, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "studies/complexity_benchmark/manuscript.tex", "max_stars_repo_name": "danibene/NeuroKit", "max_stars_repo_head_hexsha": "df0ab6696e7418cf8b8dcd3ed82dbf879fa61b3a", "max_stars_repo_licenses": ["MIT"... |
import numpy as np;
from .covariance_target import CovarianceTarget;
class AdaptiveCovariance(CovarianceTarget):
def __init__(self, decay_rate= 1.0, percentile=.50):
self.gamma = decay_rate;
self.percentile = percentile;
def _reset(self, initial_mean, initial_covariance):
self._covari... | {"hexsha": "064b58662165c6012c4d5844b42057afc0af2be3", "size": 1512, "ext": "py", "lang": "Python", "max_stars_repo_path": "learnedevolution/targets/covariance/adaptive_covariance.py", "max_stars_repo_name": "realtwister/LearnedEvolution", "max_stars_repo_head_hexsha": "2ec49b50a49acae9693cfb05ac114dfbcc4aa337", "max_s... |
import os, time, glob, imageio
import torch
import torch.nn as nn
from torch.utils.data import DataLoader, Dataset
import numpy as np
from util import run_model, get_args
from dataloader import KneeData, KneeManager
from collections import Counter
import cv2, glob, scipy.ndimage
def get_label(path):
"""
label... | {"hexsha": "313c3a6a42a93fcc705702b3390fd8dada15934d", "size": 13754, "ext": "py", "lang": "Python", "max_stars_repo_path": "frontend.py", "max_stars_repo_name": "vkola-lab/mrm2019", "max_stars_repo_head_hexsha": "0f0c73e3b4f95b7a5eb06356043455390b8e9109", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 3, "max_... |
module CellMLToolkit
using MathML
using SymbolicUtils: FnType, Sym, operation
using ModelingToolkit
using EzXML
include("cellml.jl")
"""
reads a CellML path or io and returns an ODESystem
"""
function read_cellml(path, tspan)
xml = readxml(path)
ml = CellModel(xml, process_cellml_xml(xml))
ODEProble... | {"hexsha": "d32e5b1002e09d7bd5ae54c247787784dc17c039", "size": 2574, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/CellMLToolkit.jl", "max_stars_repo_name": "anandijain/CellMLToolkit.jl", "max_stars_repo_head_hexsha": "adf6c38d1f3c125306d8212f5e17d3190a9f6539", "max_stars_repo_licenses": ["MIT"], "max_stars... |
[STATEMENT]
lemma n_gt_1: "n > 1"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. 1 < n
[PROOF STEP]
using kyber_spec_axioms kyber_spec_def
[PROOF STATE]
proof (prove)
using this:
kyber_spec TYPE('a) TYPE('k) n q k n'
kyber_spec TYPE(?'a) TYPE(?'k) ?n ?q ?k ?n' \<equiv> ((?n = 2 ^ ?n' \<and> 0 < ?n') \<and> 2 < ?q \<... | {"llama_tokens": 283, "file": "CRYSTALS-Kyber_Kyber_spec", "length": 2} |
/******************************************************************
*
* mUPnP for C
*
* Copyright (C) Satoshi Konno 2005
* Copyright (C) 2006 Nokia Corporation. All rights reserved.
*
* This is licensed under BSD-style license, see file COPYING.
*
***************************************************************... | {"hexsha": "11a9425983d19d5f3ae5ef3dae2cab5c9fc6e771", "size": 1435, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "app/src/main/cpp/mupnp/src/tests/DeviceTest.cpp", "max_stars_repo_name": "ytxhao/CyberLink2Android", "max_stars_repo_head_hexsha": "3f9777fce21382c32572c524de3b2a9dfeac623f", "max_stars_repo_license... |
import math
import numpy
class FeatureMapper(object):
def __init__(self, features):
self.features = features
def map(self, fv):
raise NotImplementedError
def __call__(self, doc):
for chain in doc.chains:
for c in chain.candidates:
c.fv = self.map(nu... | {"hexsha": "f85f1170730cc9dd0ec97b456f9f454490b8d4ae", "size": 1503, "ext": "py", "lang": "Python", "max_stars_repo_path": "nel/features/mapping.py", "max_stars_repo_name": "psyML/nel", "max_stars_repo_head_hexsha": "5ce1f0b2ef6246f27113ffe2d80d9b95f05f884e", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 196, ... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.