text stringlengths 0 1.25M | meta stringlengths 47 1.89k |
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/* dyntest - fiddle with boost UTF init_unit_test_suite
CPATH=../libboost-1.55.inst/include/ g++ -c -o dyntest.o dyntest.cpp -DYYTEXT_POINTER=1 -fPIC -g -O0 -W -Wall -Wextra -Wnon-virtual-dtor -ansi -std=c++98 -pipe -Wno-empty-body -Wno-missing-field-initializers -Wwrite-strings -Wno-deprecated -Wno-unused -Wno-non-vi... | {"hexsha": "97a6d3416dfd361360e4195257ad5dcb111948e3", "size": 3116, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "tests/test_Trig.cpp", "max_stars_repo_name": "ericprud/SWObjects", "max_stars_repo_head_hexsha": "c2ceae74a9e20649dac84f1da1a4b0d2bd9ddce6", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 8.... |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
The :mod:`samplesizelib.linear.statistical` contains next classes:
- :class:`samplesizelib.linear.statistical.LagrangeEstimator`
- :class:`samplesizelib.linear.statistical.LikelihoodRatioEstimator`
- :class:`samplesizelib.linear.statistical.WaldEstimator`
"""
from __fu... | {"hexsha": "646d66880864443f2610d9442bb85d0f83203667", "size": 16230, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/samplesizelib/linear/statistical.py", "max_stars_repo_name": "andriygav/SampleSizeEstimation", "max_stars_repo_head_hexsha": "079959711a46201e08ae3e0d41815bcb70d7efc4", "max_stars_repo_licens... |
# ---
# jupyter:
# jupytext:
# text_representation:
# extension: .py
# format_name: light
# format_version: '1.5'
# jupytext_version: 1.11.2
# kernelspec:
# display_name: Python 3
# language: python
# name: python3
# ---
# + [markdown] colab_type="text" id="view-in-github"
#... | {"hexsha": "4ed7cd5b5cff2f457d68acfd20d6d88a658dcede", "size": 29411, "ext": "py", "lang": "Python", "max_stars_repo_path": "UFC_data_scraping.py", "max_stars_repo_name": "tylerlum/ufc_automated_scoring_system", "max_stars_repo_head_hexsha": "130e87365c2856f8dcc1bf00f5afbcf1159c41f3", "max_stars_repo_licenses": ["MIT"]... |
# Do not use packages that are not in standard distribution of python
import numpy as np
from ._base_network import _baseNetwork
class SoftmaxRegression(_baseNetwork):
def __init__(self, input_size=28*28, num_classes=10):
'''
A single layer softmax regression. The network is composed by:
a... | {"hexsha": "fd8cd9de24e22744179c9db52b6b89efb4526356", "size": 2629, "ext": "py", "lang": "Python", "max_stars_repo_path": "hw1 Two-layer-network/models/softmax_regression.py", "max_stars_repo_name": "mtang1001/ML-Exploration", "max_stars_repo_head_hexsha": "6fec422eca127210e948945e6d15526947bfae8e", "max_stars_repo_li... |
import wf_core_data.utils
import pandas as pd
import numpy as np
import inflection
import collections
import itertools
import copy
import os
import logging
logger = logging.getLogger(__name__)
TIME_FRAME_ID_VARIABLES = [
'school_year',
'term'
]
STUDENT_ID_VARIABLES = [
'legal_entity',
'student_id_nwe... | {"hexsha": "81aba13078c34d47b70fa337ffa7a00a55daf841", "size": 22003, "ext": "py", "lang": "Python", "max_stars_repo_path": "nwea_utils/analysis.py", "max_stars_repo_name": "WildflowerSchools/wf-nwea-utils", "max_stars_repo_head_hexsha": "f3b35baa5b03d36ea7b351c0173037055879d926", "max_stars_repo_licenses": ["MIT"], "m... |
#' Group input by rows
#'
#' \Sexpr[results=rd, stage=render]{dplyr:::lifecycle("questioning")}
#'
#' See [this repository](https://github.com/jennybc/row-oriented-workflows)
#' for alternative ways to perform row-wise operations
#'
#' `rowwise()` is used for the results of [do()] when you
#' create list-variables. It ... | {"hexsha": "21805c7cd3bc7bf0beedca6533bf24dd14bf48aa", "size": 3527, "ext": "r", "lang": "R", "max_stars_repo_path": "R/rowwise.r", "max_stars_repo_name": "rensa/dplyr", "max_stars_repo_head_hexsha": "1da8c88293bc5e26de3d100b83a9b92e216becf6", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_st... |
import numpy as np
from sklearn.metrics import classification_report
from preprocessing import Preprocessor
import time
class NaiveBayes:
def __init__(self):
self.labels = set()
self.word_counts = {}
self.priors = {}
self.likelihoods = {}
def train(self, train_x, train_y):
... | {"hexsha": "ef67cb40641b773595fde5a5c0f932e390840587", "size": 4190, "ext": "py", "lang": "Python", "max_stars_repo_path": "naive_bayes.py", "max_stars_repo_name": "radhe2205/abusive_lang_detection", "max_stars_repo_head_hexsha": "330066f505bb75222bdfcf95d29e105aa6282d11", "max_stars_repo_licenses": ["MIT"], "max_stars... |
\section{Results \& Discussion}
The aim of this work was to produce a well-parallelised software capable of quickly producing starting structures for later MD simulations of multiple micellar species from SAS data.
\subsection{Parallelisation}
The parallelisation of a software package is commonly assessed using two me... | {"hexsha": "121762a9df0d723d17e44d4ee1f04629b810fd3c", "size": 11522, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "reports/chapters/smallangle/discussion.tex", "max_stars_repo_name": "arm61/thesis", "max_stars_repo_head_hexsha": "4c76e837b1041472a5522427de0069a5a28d40c9", "max_stars_repo_licenses": ["CC-BY-4.0"... |
export Mesh3, get_ngauss, get_volume
@doc raw"""
`Mesh` holds data structures for an unstructured mesh.
- `nodes`: a $n_v \times 2$ coordinates array
- `edges`: a $n_{\text{edge}} \times 2$ integer array for edges
- `elems`: a $n_e \times 3$ connectivity matrix, 1-based.
- `nnode`, `nedge`, `nelem`: number of node... | {"hexsha": "a2706195b27932b6aba4ad99efc1a9383dbf5ddc", "size": 8846, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/MFEM3/MFEM.jl", "max_stars_repo_name": "kailaix/AdFem.jl", "max_stars_repo_head_hexsha": "77eabfeedb297570a42d1f26575c59f0712796d9", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 47, "... |
from ccdc.io import EntryReader
import pandas as pd
import numpy as np
csd_reader = EntryReader('CSD')
df = pd.read_csv('PATH TO CSV')
data = []
for refcode in df['Ref Codes']:
values_dict = {'Ref Code' : csd_reader.entry(refcode).identifier,
'Chemical Name': csd_reader.entry(refcode).chemical_n... | {"hexsha": "06bd93c2332dc5df19fe91fac1c95e12b4c5ea23", "size": 1182, "ext": "py", "lang": "Python", "max_stars_repo_path": "chemdataextractor_MOFs/web-scrape/csd_data_to_csv.py", "max_stars_repo_name": "peymanzmoghadam/DigiMOF-database-master-main", "max_stars_repo_head_hexsha": "62f11c41ca68a5ef4662b905d8a71c4bb111543... |
#
# Copyright (c) 2018 TECHNICAL UNIVERSITY OF MUNICH, DEPARTMENT OF MECHANICAL ENGINEERING, CHAIR OF APPLIED MECHANICS,
# BOLTZMANNSTRASSE 15, 85748 GARCHING/MUNICH, GERMANY, RIXEN@TUM.DE.
#
# Distributed under 3-Clause BSD license. See LICENSE file for more information.
#
"""
Tools for all elements.
"""
__all__ = [... | {"hexsha": "15076c6f3d72e6934029db4f50578c6e3f89dc37", "size": 2405, "ext": "py", "lang": "Python", "max_stars_repo_path": "amfe/neumann/tools.py", "max_stars_repo_name": "ma-kast/AMfe", "max_stars_repo_head_hexsha": "99686cc313fb8904a093fb42e6cf0b38f8cfd791", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_cou... |
[STATEMENT]
lemma jumpF_poly_noroot:
assumes "poly p a\<noteq>0"
shows "jumpF_polyL q p a = 0" "jumpF_polyR q p a = 0"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. jumpF_polyL q p a = 0 &&& jumpF_polyR q p a = 0
[PROOF STEP]
subgoal
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. jumpF_polyL q p a = 0
[PROO... | {"llama_tokens": 798, "file": "Count_Complex_Roots_Extended_Sturm", "length": 11} |
import numpy as np
import meshio
load_width = 1
width = 16
height = 8
# This values are the values of the pixels from an image
# Since the image was of a FVM, the elements are being defined between the volumes
volXs = np.array([36,44,59,74,88,103,117,132,147,163,183,205,227,251,278,308,339,374,410,456,520,590,623], ... | {"hexsha": "c8cf1064af4bb29f812d7148acb0a485e4749842", "size": 1759, "ext": "py", "lang": "Python", "max_stars_repo_path": "meshes/scripts/uneq_strip_footing_x.py", "max_stars_repo_name": "Gustavo029/GridReader", "max_stars_repo_head_hexsha": "7edc950c469b06c3de0093e5fd8bf6cfd59af354", "max_stars_repo_licenses": ["MIT"... |
"""Test code for L2 norm"""
import numpy as np
import tvm
import topi
from topi.util import get_const_tuple
def l2norm_instance_python(a_np, eps, axis=None):
"""L2 norm operator in NCHW layout.
Parameters
----------
a_np : numpy.ndarray
4-D with shape [batch, in_channel, in_height, in_width]
... | {"hexsha": "182099ff93674df4a6fd40bf3956ef5b41b7c494", "size": 2256, "ext": "py", "lang": "Python", "max_stars_repo_path": "topi/tests/python/test_topi_l2norm.py", "max_stars_repo_name": "TaoLv/tvm", "max_stars_repo_head_hexsha": "11318966571f654f4e8bc550bfd9a293303e3000", "max_stars_repo_licenses": ["Apache-2.0"], "ma... |
import os
import PIL
import ipdb
import math
import torch
import argparse
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data import Dataset
import torchvision
import torchvision.transforms as transforms
import torch.distributed as dist
import torch.multiprocessing as mp
from... | {"hexsha": "32f201f6a965bf118770de52befc307dc5a5c882", "size": 7137, "ext": "py", "lang": "Python", "max_stars_repo_path": "preprocess/generate_tokens.py", "max_stars_repo_name": "Sunmingzhen/CogView", "max_stars_repo_head_hexsha": "6bc71b7cc07a209d258729674019f7d15a0ac4bb", "max_stars_repo_licenses": ["Apache-2.0"], "... |
[STATEMENT]
lemma z_eq_v1_solves:
assumes "z = v1"
shows "\<exists>paths. DisjointPaths G v0 v1 paths \<and> card paths = Suc sep_size"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. \<exists>paths. DisjointPaths G v0 v1 paths \<and> card paths = Suc sep_size
[PROOF STEP]
proof-
[PROOF STATE]
proof (state)
g... | {"llama_tokens": 2761, "file": "Menger_Y_eq_new_last", "length": 27} |
-- ---------------------------------------------------------------- [ Effs.idr ]
-- Module : Effs.idr
-- Copyright : (c) Jan de Muijnck-Hughes
-- License : see LICENSE
-- --------------------------------------------------------------------- [ EOH ]
module Sif.Effs
import public Effects
import public Effect.System... | {"hexsha": "31a678ce101fbc7f019668fca76304ddd3029765", "size": 3660, "ext": "idr", "lang": "Idris", "max_stars_repo_path": "Sif/Effs.idr", "max_stars_repo_name": "jfdm/sif-lang", "max_stars_repo_head_hexsha": "9554832d3de52a969f8866b4d6fd31fe44f93614", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": 1, ... |
# ------------------------------------------------------------------
# Licensed under the MIT License. See LICENSE in the project root.
# ------------------------------------------------------------------
"""
BallFolding(ball)
A method for creating folds from a spatial object that
are centers of balls.
"""
struct... | {"hexsha": "2a48b3917ef63acff1a03500c7666f43c254f94d", "size": 780, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/folding/ball.jl", "max_stars_repo_name": "mauro3/GeoStatsBase.jl", "max_stars_repo_head_hexsha": "98bd6c4c2f6ab4cbb228677329b95a6a3df3d95d", "max_stars_repo_licenses": ["MIT"], "max_stars_count"... |
[STATEMENT]
lemma inf_dense:
"x \<noteq> bot \<Longrightarrow> y \<noteq> bot \<Longrightarrow> x \<sqinter> y \<noteq> bot"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. \<lbrakk>x \<noteq> bot; y \<noteq> bot\<rbrakk> \<Longrightarrow> x \<sqinter> y \<noteq> bot
[PROOF STEP]
by (metis inf_selective) | {"llama_tokens": 123, "file": "Stone_Algebras_Lattice_Basics", "length": 1} |
""" Equal opportunity - Protected and unprotected False postives ratio"""
import math
import sys
import numpy
from metrics.utils import calc_fp_fn
from metrics.Metric import Metric
class EqOppo_fp_ratio(Metric):
def __init__(self):
Metric.__init__(self)
self.name = "EqOppo_fp_ratio"
def calc... | {"hexsha": "6d1f5f1ffb9c744b77afbd7608ca368ce8346c8c", "size": 940, "ext": "py", "lang": "Python", "max_stars_repo_path": "metrics/EqOppo_fp_ratio.py", "max_stars_repo_name": "Khumayun/FairDeepLearning", "max_stars_repo_head_hexsha": "e19947c17c282ce1e89ad105cc241ffc07190628", "max_stars_repo_licenses": ["MIT"], "max_s... |
import numpy as np
from .constants import WGS84
from .ellipsoid import Ellipsoid
def to_ecef(positions: np.ndarray, *, ellipsoid: Ellipsoid = WGS84) -> np.ndarray:
"""Convert positions to earth-centered, earth-fixed coordinates
Ported from
https://github.com/loicgasser/quantized-mesh-tile/blob/master/qu... | {"hexsha": "8874ce808e880bb4d2aa912f4e3416e4367a2efd", "size": 1503, "ext": "py", "lang": "Python", "max_stars_repo_path": "quantized_mesh_encoder/ecef.py", "max_stars_repo_name": "kylebarron/quantized-mesh-py", "max_stars_repo_head_hexsha": "98e9246ee14738a6665d7c87ce0883b6fe4b941e", "max_stars_repo_licenses": ["MIT"]... |
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
import pytest
import ray
from ray.tests.conftest import _ray_start_cluster
num_tasks_submitted = [10**n for n in range(0, 6)]
num_tasks_ids = ["{}_tasks".format(i) for i in num_tasks_submit... | {"hexsha": "b4225dec1a04a5e41ead263a42739a72c9f0c9d0", "size": 2851, "ext": "py", "lang": "Python", "max_stars_repo_path": "python/ray/tests/perf_integration_tests/test_perf_integration.py", "max_stars_repo_name": "tonymackinnon/ray", "max_stars_repo_head_hexsha": "14a1419682bdba40d2c8bf226e1727cf44abcaa4", "max_stars_... |
import numpy as np
import random
for i in range(10):
p = np.random.randint(11, 21)
print(p)
| {"hexsha": "0c334d6007790ec4e7d9377f3a4d60a308bf3b7c", "size": 101, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/test.py", "max_stars_repo_name": "owuordickson/swarm_gp", "max_stars_repo_head_hexsha": "0a6c6bdd51bc63fbf7e514207d3c367cebe72827", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null... |
//
// Copyright (c) 2019 Vinnie Falco (vinnie.falco@gmail.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)
//
// Official repository: https://github.com/CPPAlliance/http_proto
//
// Test that header file is ... | {"hexsha": "205b0566567aa3db850e6165a34f3a7f963aef9b", "size": 1330, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "test/unit/rfc/token_rule.cpp", "max_stars_repo_name": "alandefreitas/http_proto", "max_stars_repo_head_hexsha": "dc64cbdd44048a2c06671282b736f7edacb39a42", "max_stars_repo_licenses": ["BSL-1.0"], "m... |
#This is just used to see the webcam output
import time
import cv2
import zbar
import Image
import numpy as np
import pyqrcode
background_width = 1280
background_height = 720
padding = 10
qr_scale = 4
qr_unscaled_size = 21 # version 1
qr_size = qr_unscaled_size * qr_scale
x_qr_interval = background_width - (padding ... | {"hexsha": "23b57fab2645b4e3a3338e11dfaa0fda1cef4e29", "size": 5830, "ext": "py", "lang": "Python", "max_stars_repo_path": "qr_code_temp.py", "max_stars_repo_name": "GemHunt/RealTimeCoinID", "max_stars_repo_head_hexsha": "26449a1cc79f0698f7d4fd5b8dbb000a6c25f7c8", "max_stars_repo_licenses": ["MIT"], "max_stars_count": ... |
//
// Copyright (c) 2013-2017 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_IMPL_STATIC_STREAMBUF_IPP
#define BEAST_IMPL_STATIC_STREAMBUF_IPP
... | {"hexsha": "90a4834625868a42e6b3ac01874326ba05f969c5", "size": 6021, "ext": "ipp", "lang": "C++", "max_stars_repo_path": "src/beast/include/beast/core/impl/static_streambuf.ipp", "max_stars_repo_name": "MassICTBV/casinocoind", "max_stars_repo_head_hexsha": "81d6a15a0578c086c1812dd2203c0973099b0061", "max_stars_repo_lic... |
<table border="0">
<tr>
<td>
</td>
<td>
</td>
</tr>
</table>
# Orthogonal Random Forest: Use Cases and Examples
Orthogonal Random Forest (ORF) combines orthogonalization,
a technique that effectively removes the confounding effect in two-stage estimati... | {"hexsha": "135048af3b4c1d18f12907c443683ce35c1a575d", "size": 157843, "ext": "ipynb", "lang": "Jupyter Notebook", "max_stars_repo_path": "notebooks/Orthogonal Random Forest Examples.ipynb", "max_stars_repo_name": "bquistorff/EconML", "max_stars_repo_head_hexsha": "73a21bfe3470e7f0d1702a6db71efd0892cfee9d", "max_stars_... |
# 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 applic... | {"hexsha": "8a623aa24c0c986ca583632606167e2c9964689f", "size": 35482, "ext": "py", "lang": "Python", "max_stars_repo_path": "paddlex/cv/models/classifier.py", "max_stars_repo_name": "fanweiya/PaddleX", "max_stars_repo_head_hexsha": "4258ec623d24db6c5a755357430cbb4455391731", "max_stars_repo_licenses": ["Apache-2.0"], "... |
import argparse
import io
import pathlib
import sys
import PIL.Image
import numpy as np
def load_image(image_filepath, image_size, scale, subtract_value, bgr):
image = PIL.Image.open(image_filepath)
image = image.resize((image_size, image_size))
image = np.array(image, dtype=np.float32) / 255
image *=... | {"hexsha": "c5ad88465e3881486ec75d64c8650788af476c88", "size": 1263, "ext": "py", "lang": "Python", "max_stars_repo_path": "modelutils/commands/image.py", "max_stars_repo_name": "shonohs/modelutils", "max_stars_repo_head_hexsha": "24df495ce5372c3f8a1f064f163b51150517e2de", "max_stars_repo_licenses": ["MIT"], "max_stars... |
# Tencent is pleased to support the open source community by making ncnn available.
#
# Copyright (C) 2020 THL A29 Limited, a Tencent company. All rights reserved.
#
# Licensed under the BSD 3-Clause License (the "License"); you may not use this file except
# in compliance with the License. You may obtain a copy of the... | {"hexsha": "5b886ebaf6ee7198b92f931b871e241f2114fd3f", "size": 3878, "ext": "py", "lang": "Python", "max_stars_repo_path": "python/ncnn/utils/functional.py", "max_stars_repo_name": "fzyzcjy/ncnn", "max_stars_repo_head_hexsha": "42e71609508fde1bd54d9d9de6ca5522ee3bcf37", "max_stars_repo_licenses": ["BSD-3-Clause"], "max... |
import argparse
from torch.utils.data import DataLoader
from resnet import get_pretrained_resnet
import torch
from resnet import ResNet
import logging
from enum import Enum
import numpy as np
import torchvision
class CLI(Enum):
DATA = 'path_data'
CHECKPOINT = 'path_model_checkpoint'
CHECKPOINT_FREQUENCY =... | {"hexsha": "70e0f76f6d31d3b06c8266b104f987ac67def29b", "size": 7766, "ext": "py", "lang": "Python", "max_stars_repo_path": "stanford-augmented-image-classification/a_resnet_training_common_cli.py", "max_stars_repo_name": "meghanaravikumar/sigopt-examples", "max_stars_repo_head_hexsha": "e2d938928384f340d77efb52b226f678... |
import cv2
import numpy
#'''The class colorImage is created and the cases are defined where the user inserts or not the path of the image'''
class colorImage:
def __init__ (self, route = None): #Initialization
if route is None: # When the user does not enter the path of the image, it is inserted by default... | {"hexsha": "d9cc136fd468e7119a9460ea3f852e3d1cc855c6", "size": 3197, "ext": "py", "lang": "Python", "max_stars_repo_path": "colorImage.py", "max_stars_repo_name": "alejandraavendano/colorImage.AAC", "max_stars_repo_head_hexsha": "23b3d176ea13e7c76fc97da94b515bb574335ef2", "max_stars_repo_licenses": ["CC0-1.0"], "max_st... |
!##############################################################################
!# Tutorial 002a: Memory management, 1D arrays
!##############################################################################
module tutorial002a
! Include basic Feat-2 modules
use fsystem
use genoutput
use storage
implicit no... | {"hexsha": "0f025b0fd77c5c3a561f16b0d242fcadc2edfb0b", "size": 2145, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "tutorials/tutorial01/src/tutorial002a.f90", "max_stars_repo_name": "trmcnealy/Featflow2", "max_stars_repo_head_hexsha": "4af17507bc2d80396bf8ea85c9e30e9e4d2383df", "max_stars_repo_licenses": ["I... |
open import Data.Bool using ( Bool ; true ; false ; _∧_ )
open import Data.Product using ( _×_ )
open import Relation.Binary.PropositionalEquality using ( _≡_ )
open import Relation.Unary using ( _∈_ )
open import Web.Semantic.DL.Concept using
( Concept ; ⟨_⟩ ; ¬⟨_⟩ ; ⊤ ; ⊥ ; _⊓_ ; _⊔_ ; ∀[_]_ ; ∃⟨_⟩_ ; ≤1 ; >1 )
ope... | {"hexsha": "cbdc0a4377dd63906550e7b914280550364c5520", "size": 3227, "ext": "agda", "lang": "Agda", "max_stars_repo_path": "src/Web/Semantic/DL/TBox/Minimizable.agda", "max_stars_repo_name": "agda/agda-web-semantic", "max_stars_repo_head_hexsha": "8ddbe83965a616bff6fc7a237191fa261fa78bab", "max_stars_repo_licenses": ["... |
%!TEX TS-program = XeLaTeX
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Hieu Do - Resume
% 7/26/2016
%
% Reference:
% Debarghya Das (http://debarghyadas.com)
\documentclass[]{hieudo-build}
\usepackage{enumitem}
\begin{document}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%
% TITLE NAME
%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%... | {"hexsha": "b551fc48f1b28ec1a1ee204215791d2c65e79d1b", "size": 8300, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "Resume/Pritthijit_Nath_Resume.tex", "max_stars_repo_name": "nathzi1505/CVandResume", "max_stars_repo_head_hexsha": "47d46d44040f98cd5d8a5c4eaa845bc5567383f1", "max_stars_repo_licenses": ["MIT"], "ma... |
import numpy as np
import h5py
from scipy.special import comb
class BullseyeData:
def __init__(self, n, eps, copies=1, scale_4=False):
eps_list = [0.025, 0.05, 0.075, 0.1, 0.125, -1, -2]
assert eps in eps_list
a,b = 0.25, 0.5
c,d = 0.75, 1.0
self.n = n
self.copies =... | {"hexsha": "c1bcfe4a41f7bf532ac1d50b12aa7346d7ca1cd1", "size": 4146, "ext": "py", "lang": "Python", "max_stars_repo_path": "bullseye/bullseye.py", "max_stars_repo_name": "syanga/model-augmented-mutual-information", "max_stars_repo_head_hexsha": "a7c0ccb3b32320e9c45c266d668a879e240d39e3", "max_stars_repo_licenses": ["MI... |
[STATEMENT]
lemma space_in_measure_of[simp]: "\<Omega> \<in> sets (measure_of \<Omega> M \<mu>)"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. \<Omega> \<in> sets (measure_of \<Omega> M \<mu>)
[PROOF STEP]
by (subst sets_measure_of_conv) (auto simp: sigma_sets_top) | {"llama_tokens": 108, "file": null, "length": 1} |
from copy import deepcopy
from itertools import product
import numpy as np
with open("day17.txt", "r") as f:
data = np.array([list(line) for line in f.read().splitlines()])
space = np.zeros(data.shape, dtype=int)
space[np.where(data == "#")] = 1
space = np.expand_dims(space, axis=0)
neighbours = np.array(
[... | {"hexsha": "8e910fd03a1fbabd743a664e793fe7a7e9c0b657", "size": 1323, "ext": "py", "lang": "Python", "max_stars_repo_path": "2020/day17-1.py", "max_stars_repo_name": "alvaropp/AdventOfCode2017", "max_stars_repo_head_hexsha": "2827dcc18ecb9ad59a1a5fe11e469f31bafb74ad", "max_stars_repo_licenses": ["MIT"], "max_stars_count... |
export bfgs_bl
export bfgs_rc
export bfgsH
"""
Modelos básicos...
bfgs_bl (busca linear)
bfgs_rc (região de confiança - Steihaug Toint)
Options:
- atol: absolute tolerance for the first order condition (default: 1e-6)
- rtol: relative tolerance for the first order condition (default: 1e-6)
- max_eval: maximum number ... | {"hexsha": "0b2b62301222dcbd7c3ba279b3d37b0d788a0b16", "size": 9262, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/bfgs_basicos.jl", "max_stars_repo_name": "FKrukoski/Projeto2Solvers.jl", "max_stars_repo_head_hexsha": "8d48093e9bc1fca9470f568c2415df7bdbaa1672", "max_stars_repo_licenses": ["MIT"], "max_stars... |
//
// TDF SDK
//
// Created by Sujan Reddy on 2019/03/28.
// Copyright 2019 Virtru Corporation
//
#define BOOST_TEST_MODULE test_key_access_object_suite
#include "asym_decryption.h"
#include "asym_encryption.h"
#include "crypto/bytes.h"
#include "crypto/crypto_utils.h"
#include "crypto/rsa_key_pair.h"
#include "en... | {"hexsha": "decf18dfe616b46c841bff2ad5389017b26342aa", "size": 67562, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/tests/test_tdfbuilder_v2.cpp", "max_stars_repo_name": "opentdf/client-cpp", "max_stars_repo_head_hexsha": "9c6dbc73a989733e30371555aa7a24ff496a62f1", "max_stars_repo_licenses": ["MIT"], "max_st... |
function [Y, R, E] = IsomapII(D, n_fcn, n_size, options)
% ISOMAPII Computes Isomap embedding using an advanced version of
% the algorithm in Tenenbaum, de Silva, and Langford (2000),
% which can take advantage of sparsity in the graph and
% redundancy in the distances.
%
% [Y, ... | {"author": "vigente", "repo": "gerardus", "sha": "4d7c5195b826967781f1bb967872410e66b7cd3d", "save_path": "github-repos/MATLAB/vigente-gerardus", "path": "github-repos/MATLAB/vigente-gerardus/gerardus-4d7c5195b826967781f1bb967872410e66b7cd3d/matlab/ThirdPartyToolbox/IsomapII.m"} |
from typing import Any, Protocol, Sized, Tuple, TypeVar, Union, Type
import numpy as np
Num = Union[float, int, complex]
R = TypeVar('R', bound='MathRelation')
R2 = TypeVar('R2', bound='MathRelation')
SP = TypeVar('SP', bound='MathSpectrum')
S = TypeVar('S', bound='MathSignal')
SPRN = Union['MathSpectrum', '... | {"hexsha": "43b7bf97064d0bc75cca96b2ba3eb811f5640d27", "size": 5390, "ext": "py", "lang": "Python", "max_stars_repo_path": "compose_signals/math_protocols.py", "max_stars_repo_name": "Omnivanitate/sweep_design", "max_stars_repo_head_hexsha": "00c20066d83a2eebf8402294b413737f49a97564", "max_stars_repo_licenses": ["MIT"]... |
C
real*4 function ct_lin_inv_grey( rgb, ci, cimin, cimax )
C --------------------------------------------------------
C
C Function defining a standard linear inverted grey-scale.
C
C ct_lin_inv_grey = (cimax-ci)/(cimax-cimin)
C
*-
integer rgb, ci, cimin, cimax
ct_lin_inv_grey = (fl... | {"hexsha": "137592fb37fc80a80dc9a2d561c1e84bbbf1ea60", "size": 365, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "graphic_lib/ct_lin_inv_grey.f", "max_stars_repo_name": "CavendishAstrophysics/anmap", "max_stars_repo_head_hexsha": "efb611d7f80a3d14dc55e46cd01e8a622f6fd294", "max_stars_repo_licenses": ["BSD-3-Cl... |
staticserver(dir::AbstractString="."; cache::Int=0) =
(r::Resource, req, id) -> begin
filepath = joinpath(dir, req[:path]...)
ext = splitext(filepath)[2][2:end]
isfile(filepath) || return Response(404)
mt = mtime(filepath) |> Dates.unix2datetime
mt -= Dates.Millisecond(Date... | {"hexsha": "90c721e65ce417ebac8a5eeb4af1aad9431a7abb", "size": 900, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/utils/staticserver.jl", "max_stars_repo_name": "UnofficialJuliaMirror/Restful.jl-a0979ab6-dee4-51c8-812d-69046455aaa6", "max_stars_repo_head_hexsha": "b84b54bac6987176d926abc1b319fd69648b59a7", ... |
// copyright (c) 2013 the dzcoin core developers
// distributed under the mit software license, see the accompanying
// file copying or http://www.opensource.org/licenses/mit-license.php.
//
// unit tests for alert system
//
#include "alert.h"
#include "chain.h"
#include "chainparams.h"
#include "clientversion.h"
#i... | {"hexsha": "2d9cc1ebe5e8dcf9c61c7fc2033f352cd4dadafa", "size": 7888, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/test/alert_tests.cpp", "max_stars_repo_name": "dzcoin/DzCoinMiningAlgorithm", "max_stars_repo_head_hexsha": "b0294cf5ac893fe907b08105f1aa826c3da464cf", "max_stars_repo_licenses": ["MIT"], "max_s... |
/*
* Copyright 2014 Matthias Fuchs
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to... | {"hexsha": "857e7ca2d58052c5bc2ab96d61550e39827c5c8c", "size": 1876, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "stromx/raspi/test/GpioTriggerTest.cpp", "max_stars_repo_name": "uboot/stromx-raspi", "max_stars_repo_head_hexsha": "38411b6a479c1e82adffea3e5bde2fdf246d920c", "max_stars_repo_licenses": ["Apache-2.0... |
halve <- function(a) floor(a/2)
double <- function(a) a*2
iseven <- function(a) (a%%2)==0
ethiopicmult<-function(x,y){
res<-ifelse(iseven(y),0,x)
while(!y==1){
x<-double(x)
y<-halve(y)
if(!iseven(y)) res<-res+x
}
return(res)
}
print(ethiopicmult(17,34))
| {"hexsha": "d105b32b1d17f57101f52179769e2e14da8c0318", "size": 266, "ext": "r", "lang": "R", "max_stars_repo_path": "Task/Ethiopian-multiplication/R/ethiopian-multiplication-2.r", "max_stars_repo_name": "LaudateCorpus1/RosettaCodeData", "max_stars_repo_head_hexsha": "9ad63ea473a958506c041077f1d810c0c7c8c18d", "max_star... |
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
!
! EVB-QMDFF - RPMD molecular dynamics and rate constant calculations on
! black-box generated potential energy surfaces
!
! Copyright (c) 2021 by Julien Steffen (steffen@pctc.uni-kiel.de)
! Stefa... | {"hexsha": "82873909abc44bef81b69304153c50c6ea430cb4", "size": 3267, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "src/readaa.f90", "max_stars_repo_name": "Trebonius91/EVB-QMDFF", "max_stars_repo_head_hexsha": "8d03e1ad073becb0161b0377b630d7b65fe3c290", "max_stars_repo_licenses": ["MIT", "Unlicense"], "max_s... |
import numpy as np
from numpy.testing import (TestCase, assert_array_equal, assert_equal,
assert_almost_equal, assert_array_almost_equal,
assert_raises)
from numpy.testing.decorators import knownfailureif
import astropy.cosmology
from astropy import units as u
try:... | {"hexsha": "1c1a3f2deab05cd6f7d6c05a27e0486fe24e5796", "size": 6499, "ext": "py", "lang": "Python", "max_stars_repo_path": "NFW/tests/test_mass_concentration.py", "max_stars_repo_name": "joergdietrich/NFW", "max_stars_repo_head_hexsha": "58b0ff6b5382461e6053e12c75d35543dd3f8b13", "max_stars_repo_licenses": ["BSD-2-Clau... |
import multiprocessing
import os
import re
import _pickle as pickle
import tensorflow as tf
import tensorflow.contrib.slim.nets
import numpy as np
from models import spotify
PATH_MAGNATAGATUNE = 'datasets/magnatagatune'
INPUT_SHAPE = (628, 128)
CLASSES = [
'classical', 'instrumental', 'electronica', 'techno',
... | {"hexsha": "4b4365f85b71ef96203cd313f0f8e1cef72f11b1", "size": 5689, "ext": "py", "lang": "Python", "max_stars_repo_path": "project/train_magnatagatune.py", "max_stars_repo_name": "miguelfrde/cs231n", "max_stars_repo_head_hexsha": "c0dc0a505d7a8a6af3439fad33068dfe1428d2e4", "max_stars_repo_licenses": ["MIT"], "max_star... |
# ___________________________________________________________________________
#
# EGRET: Electrical Grid Research and Engineering Tools
# Copyright 2019 National Technology & Engineering Solutions of Sandia, LLC
# (NTESS). Under the terms of Contract DE-NA0003525 with NTESS, the U.S.
# Government retains certain r... | {"hexsha": "dbbff64d6ac2ba73c45eb72549961987483a8402", "size": 5313, "ext": "py", "lang": "Python", "max_stars_repo_path": "egret/model_library/unit_commitment/status_vars.py", "max_stars_repo_name": "bknueven/Egret", "max_stars_repo_head_hexsha": "37567c1ec3bc0072b61124ce46ceb28add9ad539", "max_stars_repo_licenses": [... |
[STATEMENT]
lemma Der_conc [simp]:
shows "Deriv c (A @@ B) = (Deriv c A) @@ B \<union> (if [] \<in> A then Deriv c B else {})"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. Deriv c (A @@ B) = Deriv c A @@ B \<union> (if [] \<in> A then Deriv c B else {})
[PROOF STEP]
unfolding Deriv_def conc_def
[PROOF STATE]
pr... | {"llama_tokens": 276, "file": "Regular-Sets_Regular_Set", "length": 2} |
import sys
sys.path.insert(0, "./../")
sys.path.insert(0, "./")
import os
import subprocess
import json
import warnings
from netCDF4 import Dataset
import pytest
import pprint as pp
import numpy as np
import main as scampy
import common as cmn
# list of possible test cases
case_list = ['Bomex', 'life_cycle_Tan2018... | {"hexsha": "82ea71a48127710f6b91e1a5f4c3b836f8613ecb", "size": 2733, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/unit/test_mean.py", "max_stars_repo_name": "jiahe23/SCAMPy", "max_stars_repo_head_hexsha": "0f8e9656b043e98c6bd316fda45bcf146bddbcbd", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_c... |
import os
import numpy as np
from tqdm import tqdm
def convert_transforms(root_path):
file = os.path.join(root_path, 'transforms.npy')
poses_path = os.path.join(root_path, 'poses')
os.makedirs(poses_path, exist_ok=True)
pose_file = os.path.join(poses_path, '{}.npy')
poses_file = os.path.join(root... | {"hexsha": "ee61f867e52f47f250053b7b7a242b470e088533", "size": 858, "ext": "py", "lang": "Python", "max_stars_repo_path": "convert_transforms_to_npy.py", "max_stars_repo_name": "federicocunico/ObjectDatasetTools", "max_stars_repo_head_hexsha": "c7418c588bfe2d1615bcd8aa96271394eb854b85", "max_stars_repo_licenses": ["MIT... |
""" See detailed analysis about maxout via links below:
https://github.com/Duncanswilson/maxout-pytorch/blob/master/maxout_pytorch.ipynb
https://cs231n.github.io/neural-networks-1/
Detailed descriptions about arch of MaxoutConv:
https://github.com/paniabhisek/maxout/blob/master/maxout.json... | {"hexsha": "a6224ee9b7c9a0224c3bd8feceea062b032649bc", "size": 5181, "ext": "py", "lang": "Python", "max_stars_repo_path": "nets/maxout.py", "max_stars_repo_name": "zhuangzi926/KnowledgeDistillation-pytorch", "max_stars_repo_head_hexsha": "4785bd9afa5d79a744c127851e316caf8469a10e", "max_stars_repo_licenses": ["MIT"], "... |
#ifndef SM_TRANSFORMATION_HPP
#define SM_TRANSFORMATION_HPP
#include <sm/kinematics/quaternion_algebra.hpp>
#include <boost/serialization/nvp.hpp>
#include <sm/eigen/serialization.hpp>
#include "HomogeneousPoint.hpp"
#include <boost/serialization/split_member.hpp>
#include <boost/serialization/version.hpp>
#include <... | {"hexsha": "ad8d3b6ba03e716f918f7f9f32482a07e6682b12", "size": 5922, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "Schweizer-Messer/sm_kinematics/include/sm/kinematics/Transformation.hpp", "max_stars_repo_name": "PushyamiKaveti/kalibr", "max_stars_repo_head_hexsha": "d8bdfc59ee666ef854012becc93571f96fe5d80c", "m... |
[STATEMENT]
lemma token_time_finite_pair_rule:
fixes A :: "(nat \<times> nat) set"
fixes B :: "nat set"
assumes unique: "\<And>x y z. P x y \<Longrightarrow> P x z \<Longrightarrow> y = z"
and existsA: "\<And>x. x \<in> A \<Longrightarrow> (\<exists>y. P x y)"
and existsB: "\<And>y. y \<in> B \<Longr... | {"llama_tokens": 3574, "file": "LTL_to_DRA_Mojmir", "length": 32} |
# Copyright 2018 The TensorFlow Probability 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 o... | {"hexsha": "bc6786872099a99b4589b964d700193ecc959c9a", "size": 23626, "ext": "py", "lang": "Python", "max_stars_repo_path": "tensorflow_probability/python/distributions/distribution_properties_test.py", "max_stars_repo_name": "awav/probability", "max_stars_repo_head_hexsha": "c833ee5cd9f60f3257366b25447b9e50210b0590", ... |
from __future__ import division
import math
import torch
import torch.utils.data
from collections import defaultdict
import onmt
from onmt.speech.Augmenter import Augmenter
from onmt.modules.dropout import switchout
import numpy as np
from .batch_utils import allocate_batch
"""
Data management for sequence-to-sequenc... | {"hexsha": "54790773a680227ca2deabea66e5ae8e11295cdc", "size": 22892, "ext": "py", "lang": "Python", "max_stars_repo_path": "onmt/data/dataset.py", "max_stars_repo_name": "tuannamnguyen93/NMTGMinor", "max_stars_repo_head_hexsha": "acde3454343bda7060fae541c110d0ad1a8ac4f4", "max_stars_repo_licenses": ["MIT"], "max_stars... |
# -*- coding: utf-8 -*-
"""
Created on Wed Feb 15 16:42:48 2012
Show an animated sine function and measure frames per second (FPS)
"""
import sys
sys.ps1 = 'Ciao'
import time
import numpy as np
import matplotlib
matplotlib.use('qt4agg')
import matplotlib.pyplot as plt
x = np.random.randn(10)
print('rea... | {"hexsha": "f87fa755342cad7b66639a1a4c449c4575103995", "size": 624, "ext": "py", "lang": "Python", "max_stars_repo_path": "dsp_fpga/00_py_examples/running_sine_1.py", "max_stars_repo_name": "chipmuenk/python_snippets", "max_stars_repo_head_hexsha": "20ea4ad1436cfaa7debcbc9c87cdef375cea996b", "max_stars_repo_licenses": ... |
""" Tests for the utility functions in has_traits_utils module. """
from unittest import skipUnless, TestCase
import numpy as np
from uuid import UUID
try:
from scimath.units.api import UnitArray, UnitScalar
SCIMATH_AVAILABLE = True
except ImportError:
SCIMATH_AVAILABLE = False
from traits.api import Arr... | {"hexsha": "84651a11fdb5efc4a1b7501a7e9acda36e7d6663", "size": 7585, "ext": "py", "lang": "Python", "max_stars_repo_path": "app_common/traits/tests/test_has_traits_utils.py", "max_stars_repo_name": "KBIbiopharma/app_common", "max_stars_repo_head_hexsha": "bd913e24741fb070aad058a0f90cbb2c64d8b106", "max_stars_repo_licen... |
# https://tel.archives-ouvertes.fr/tel-00641678/document
import pytest
from sympy import *
from sympy import symbols, conjugate
from sympy import sin, cos
from sympy.abc import a, b, c, d, x, y, z, w, theta
from sympy.algebras.quaternion import Quaternion
from context import DualQuaternion
from sympy import simplify
f... | {"hexsha": "d607c9d9a517412bf73120fe57a6794f2f3b7a8a", "size": 1075, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/Proof_DH_diff_for_2_32.py", "max_stars_repo_name": "wdfalfred/SymDQ", "max_stars_repo_head_hexsha": "82d858f1df9057c100fc35adc8bea2793c34a0f1", "max_stars_repo_licenses": ["MIT"], "max_stars... |
#Combine vectors by rows and columns
v1 = c(1,3,5,7,9)
v2 = c(2,4,6,8,10)
print("Original vectors:")
print(v1)
print(v2)
print("Combines the said two vectors by columns:")
result = cbind(v1,v2)
print(result)
print("Combines the said two vectors by rows:")
result = rbind(v1,v2)
print(result) | {"hexsha": "c319ca650a90b07fa4a14206feb9f5ec137eedae", "size": 304, "ext": "r", "lang": "R", "max_stars_repo_path": "combine2vectorsbyrowandcol.r", "max_stars_repo_name": "maansisrivastava/Practice-code-R", "max_stars_repo_head_hexsha": "24f1469908195050472831db7b1ebe83744d422c", "max_stars_repo_licenses": ["MIT"], "ma... |
import os
import sys
from os.path import join,basename,dirname,splitext
from pathlib import Path
import numpy as np
import scipy
from scipy import io
import scipy.sparse as sp
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib as mpl
from pprint import pprint
import argparse
from collections import d... | {"hexsha": "baeaba41eabc0ed55207a20e1e38ecdbcb9c0c85", "size": 1977, "ext": "py", "lang": "Python", "max_stars_repo_path": "tmp_test_overwrite_tensorboard.py", "max_stars_repo_name": "lzx325/vision_transformer", "max_stars_repo_head_hexsha": "8397a05f7b234fa5e0ede347d9061527b901dc68", "max_stars_repo_licenses": ["Apach... |
"""
nlcmap - a nonlinear cmap from specified levels
Copyright (c) 2006-2007, Robert Hetland <hetland@tamu.edu>
Release under MIT license.
Some hacks added 2012 noted in code (@MRR)
"""
from pylab import *
from numpy import *
from matplotlib.colors import LinearSegmentedColormap
class nlcmap(LinearSegmentedColormap)... | {"hexsha": "6351f90a0f5f4f6e1afab2b65644d0853cbec8e0", "size": 1544, "ext": "py", "lang": "Python", "max_stars_repo_path": "scripts/data-viz/nonlinear_colormap.py", "max_stars_repo_name": "TravisWheelerLab/MMOREseqs", "max_stars_repo_head_hexsha": "492eda6efa4fd95ac0a787405a40db5a860bf3dc", "max_stars_repo_licenses": [... |
(* *********************************************************************)
(* *)
(* The Quantitative CompCert verified compiler *)
(* *)
(* Tahin... | {"author": "academic-archive", "repo": "pldi14-veristack", "sha": "9edcd8752ae2e1e6377bfb33589a377cc39c04ca", "save_path": "github-repos/coq/academic-archive-pldi14-veristack", "path": "github-repos/coq/academic-archive-pldi14-veristack/pldi14-veristack-9edcd8752ae2e1e6377bfb33589a377cc39c04ca/qcompcert/driver/Compleme... |
/*
* Copyright 2016 C. Brett Witherspoon
*/
#include <algorithm>
#include <chrono>
#include <cmath>
#include <complex>
#include <iostream>
#include <stdexcept>
#include <random>
#include <boost/preprocessor/stringize.hpp>
#include <boost/program_options.hpp>
#include <boost/compute/core.hpp>
#include <signum/openc... | {"hexsha": "5d54952cebc9def9dd192153bba9fd9ceb15c9d2", "size": 2745, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "examples/fft_benchmark.cpp", "max_stars_repo_name": "spoonb/libcomm", "max_stars_repo_head_hexsha": "5638dac889bddb16420d8321067c783438a5deaf", "max_stars_repo_licenses": ["0BSD"], "max_stars_count"... |
// Copyright Louis Dionne 2013-2017
// Distributed under the Boost Software License, Version 1.0.
// (See accompanying file LICENSE.md or copy at http://boost.org/LICENSE_1_0.txt)
#include <boost/hana/type.hpp>
namespace hana = boost::hana;
template <typename ...> struct F { struct type; };
struct M { templ... | {"hexsha": "2d08bc9a2ba43ea99928d88f1b009d51208793e7", "size": 709, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "REDSI_1160929_1161573/boost_1_67_0/libs/hana/test/issues/clang_20046.cpp", "max_stars_repo_name": "Wultyc/ISEP_1718_2A2S_REDSI_TrabalhoGrupo", "max_stars_repo_head_hexsha": "eb0f7ef64e188fe871f47c2ef... |
C
C
*$ 2) Routines Producing New Images
* --------------------------------
C
C
*+ image_convolve
subroutine image_convolve(nix,niy,in_data,ncx,ncy,icx,icy,array,
* null,out_data,status )
C ----------------------------------------------------------------
C
C C... | {"hexsha": "40916b82ab66d661619ac74b4a8f16584fc537ed", "size": 1872, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "image_lib/image_convolve.f", "max_stars_repo_name": "CavendishAstrophysics/anmap", "max_stars_repo_head_hexsha": "efb611d7f80a3d14dc55e46cd01e8a622f6fd294", "max_stars_repo_licenses": ["BSD-3-Clau... |
# Numerics.py
#
# Created:
# Modified: Feb 2016, Andrew Wendorff
# ----------------------------------------------------------------------
# Imports
# ----------------------------------------------------------------------
from Conditions import Conditions
from SUAVE.Methods.Utilities.Chebyshev import chebyshev_da... | {"hexsha": "c716dcfde36a746b4f49fe46f0af48bc51fba156", "size": 1383, "ext": "py", "lang": "Python", "max_stars_repo_path": "References/Geovana Neves/TCC_Geovana_Neves_GitHub/SUAVE_modifications/SUAVE-feature-constant_throttle_EAS/trunk/SUAVE/Analyses/Mission/Segments/Conditions/Numerics.py", "max_stars_repo_name": "Vin... |
import numpy as np
import cv2
import matplotlib.pyplot as plt
from sklearn.datasets import fetch_olivetti_faces
from sklearn.cluster import MiniBatchKMeans
from sklearn.decomposition import LatentDirichletAllocation
from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer
from sklearn.neighbors i... | {"hexsha": "d1f16660b2fcf04ea721e7d5f77ed498f481f9aa", "size": 9664, "ext": "py", "lang": "Python", "max_stars_repo_path": "visual_words/visual_words.py", "max_stars_repo_name": "vsmolyakov/cv", "max_stars_repo_head_hexsha": "dd4f5d02a82df5cd5342797d184ebf2722e6562e", "max_stars_repo_licenses": ["MIT"], "max_stars_coun... |
# coding: utf-8
"""
This module for defining chemical reaction objects was originally sourced from
pymatgen and streamlined for the reaction-network code.
"""
import re
from functools import cached_property
from itertools import chain, combinations
from typing import Dict, List, Optional
import numpy as np
from monty... | {"hexsha": "1119bd60ce39b526ded56a1feea780984853a73d", "size": 14720, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/rxn_network/reactions/basic.py", "max_stars_repo_name": "bigboyabhisthi/reaction-network", "max_stars_repo_head_hexsha": "b84f16b7261ecd62d7aa8e2681907f6ea0c35565", "max_stars_repo_licenses":... |
import neural_net as nn
import numpy as np
LEARNING_RATE = 0.8
ACTIVATION = nn.Sigmoid
RANDOM_WEIGHTS = True
LOSS_FN = nn.CrossEntropyLoss
LAYERS = (2, 2, 1)
INPUTS = np.array([[0, 0, 1, 1], [0, 1, 0, 1]])
OUTPUTS = np.array([[0, 1, 1, 0]])
a = nn.StochasticNet(layers=LAYERS, activation=ACTIVATION, loss=LOSS_FN, lr=... | {"hexsha": "70447262f79031e816f408c84db7b971b2e21936", "size": 807, "ext": "py", "lang": "Python", "max_stars_repo_path": "test.py", "max_stars_repo_name": "Youyoun/numpy_neural_network", "max_stars_repo_head_hexsha": "3a13971f8877e72bb244fd0a9ba17ca6dd4ddaf5", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_coun... |
import os
import time
import threading
import multiprocessing
import math
from pylab import *
import PIL.Image as im
import csv
import sys
from imutils import face_utils
import numpy as np
import argparse
import imutils
import dlib
import cv2
import numpy
from PIL import ImageFont
from PIL import Image
from PIL import... | {"hexsha": "d84489a8d8a5d6c9c15985bea5f95435c36b6a22", "size": 6976, "ext": "py", "lang": "Python", "max_stars_repo_path": "calcolo_ragnatela.py", "max_stars_repo_name": "s-corso-98/SpiderGenderProject", "max_stars_repo_head_hexsha": "cd08a1141654be9489b5a9668c06254ce2dfac22", "max_stars_repo_licenses": ["MIT"], "max_s... |
[STATEMENT]
lemma lt_list_encode: \<open>n [\<in>] ns \<Longrightarrow> n < list_encode ns\<close>
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. n [\<in>] ns \<Longrightarrow> n < list_encode ns
[PROOF STEP]
proof (induct ns)
[PROOF STATE]
proof (state)
goal (2 subgoals):
1. n [\<in>] [] \<Longrightarrow> n < list... | {"llama_tokens": 701, "file": "FOL_Seq_Calc3_Encoding", "length": 7} |
Require Import Coq.Reals.Rdefinitions.
Require Import TLA.TLA.
Require Import TLA.ProofRules.
Require Import Examples.System.
Open Scope HP_scope.
Section SensorWithError.
Variable err : R.
Definition Sense : Formula :=
"Xmax" <= "Xmin" + err //\\ "Xmin" <= "x" <= "Xmax".
Definition SenseSafe : Formula :... | {"author": "dricketts", "repo": "quadcopter", "sha": "62bb21915612a141e1ffabc73df3dc2d931c54ce", "save_path": "github-repos/coq/dricketts-quadcopter", "path": "github-repos/coq/dricketts-quadcopter/quadcopter-62bb21915612a141e1ffabc73df3dc2d931c54ce/oldexamples/SensorWithError.v"} |
# -*- coding: utf-8 -*-
"""
Created on Sun May 28 14:01:01 2017
@author: xin
https://gist.github.com/stewartpark/187895beb89f0a1b3a54
"""
from keras import backend as K
from keras.models import Sequential
from keras.layers import Dense
from keras.optimizers import SGD
import numpy as np
batch_size = 1
num_classes = ... | {"hexsha": "9426d467369788e2acd7970daa0c9680eb3ffb71", "size": 1528, "ext": "py", "lang": "Python", "max_stars_repo_path": "keras/keras_xor.py", "max_stars_repo_name": "OnlyBelter/MachineLearning_examples", "max_stars_repo_head_hexsha": "c2d766540aacb0aea1a4892c97c5dd509bf2a62f", "max_stars_repo_licenses": ["MIT"], "ma... |
<h1>IBM Quantum Challenge Africa 2021</h1>
<p style="font-size:xx-large;">Introduction and the Crop-Yield Problem</p>
Quantum Computing has the potential to revolutionize computing, as it can solve problems that are not possible to solve on a classical computer. This extra ability that quantum computers have is called... | {"hexsha": "3fa0448cbfc2f2c779bc9a269bbb7d9f7974cc7e", "size": 425605, "ext": "ipynb", "lang": "Jupyter Notebook", "max_stars_repo_path": "qiskit/challenges/IBMQuantumChallenge_Africa_2021/content/lab1/lab1.ipynb", "max_stars_repo_name": "mickahell/quantum_experiences", "max_stars_repo_head_hexsha": "4f94d9e536f4906e79... |
import sys, os
sys.path.append(os.path.join(os.path.dirname(__file__), '..'))
import argparse, json
import matplotlib.pyplot as plt
import numpy as np
from sklearn.manifold import TSNE
from matplotlib.offsetbox import *
from PIL import Image
from utils.experiments import load_data
def load_image(path... | {"hexsha": "d2b92fd97ea319df14fac922323ee093c15cbdcd", "size": 7134, "ext": "py", "lang": "Python", "max_stars_repo_path": "run/tsne_analysis.py", "max_stars_repo_name": "yamad07/vjvae", "max_stars_repo_head_hexsha": "dd8d6607f5ec6c46df1794f903b42aee890d970b", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 30, ... |
{"mathlib_filename": "Mathlib.Tactic.TryThis", "llama_tokens": 0} | |
// Boost.Geometry (aka GGL, Generic Geometry Library)
// Copyright (c) 2007-2014 Barend Gehrels, Amsterdam, the Netherlands.
// Copyright (c) 2008-2014 Bruno Lalande, Paris, France.
// Copyright (c) 2009-2014 Mateusz Loskot, London, UK.
// Copyright (c) 2013-2014 Adam Wulkiewicz, Lodz, Poland.
// This file was... | {"hexsha": "a86bcc56f703cf593ce06730f73cb6654defcbf4", "size": 8456, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "ReactAndroid/build/third-party-ndk/boost/boost_1_57_0/boost/geometry/algorithms/detail/disjoint/segment_box.hpp", "max_stars_repo_name": "kimwoongkyu/react-native-0-36-1-woogie", "max_stars_repo_hea... |
import scipy.linalg
import numpy as np
def make_lapack_inverse(size):
# The identity matrix
iden = np.eye(size)
# Optimises some of the matrix inverting by only doing some things once and by bypassing sanity checks
def lapack_inverse(A):
results = scipy.linalg.lapack.dgesv(A, iden)
if... | {"hexsha": "cff5c13b46232dd1b4f79e108189ddc5a6a16a31", "size": 449, "ext": "py", "lang": "Python", "max_stars_repo_path": "Initial Testing/utils.py", "max_stars_repo_name": "MrAttoAttoAtto/CircuitSimulatorC2", "max_stars_repo_head_hexsha": "4d821c86404fe3271363fd8c1438e4ca29c17a13", "max_stars_repo_licenses": ["MIT"], ... |
import numpy as np
import matplotlib.pyplot as plt
import utils as ut
import os
from test_funcs import eval_alignment_by_div_embed
from param import P
def read_all_file(dir_path):
all_data = []
all_file = os.listdir(dir_path)
for path in all_file:
with open(dir_path + "/" + path, "r") as f:
... | {"hexsha": "0c24a47c510026dbf8381a53f10342c388ee121c", "size": 9210, "ext": "py", "lang": "Python", "max_stars_repo_path": "6/master/src/openea/expriment/data_analyse.py", "max_stars_repo_name": "smurf-1119/knowledge-engeneering-experiment", "max_stars_repo_head_hexsha": "7fd3647bfc5b05e5fd6f93fea324e7ec0d55d4a1", "max... |
# -*- coding: utf-8 -*-
"""
Output file exporting system.
"""
import json
import os
import shutil
from typing import Optional
import cv2
import numpy as np
import pandas as pd
from perceptree.common.configuration import Config
from perceptree.common.configuration import Configurable
from perceptree.common.logger im... | {"hexsha": "a48407a22375244d27ad921d785cf10db839d539", "size": 14309, "ext": "py", "lang": "Python", "max_stars_repo_path": "PerceptualMetric/psrc/perceptree/data/exporter.py", "max_stars_repo_name": "PolasekT/ICTree", "max_stars_repo_head_hexsha": "d13ad603101805bcc288411504ecffd6f2e1f365", "max_stars_repo_licenses": ... |
import os
import json
from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split
from sklearn.metrics import roc_auc_score
import xgboost as xgb
import argparse
CLI=argparse.ArgumentParser()
CLI.add_argument("--trainFile", type=str, default="")
CLI.add_argument("--valFile", t... | {"hexsha": "a3f515c0dffba08fc76806800b71e17e06f75a10", "size": 1843, "ext": "py", "lang": "Python", "max_stars_repo_path": "Perform Hyper-Parameter Tuning of XGBoost models using Watson Machine Learning Accelerator/train_xgb_default.py", "max_stars_repo_name": "helena-k/wmla-assets", "max_stars_repo_head_hexsha": "4fd3... |
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from mmcv.cnn import ConvModule
from mmcv.runner import BaseModule, force_fp32
from mmdet.core import build_bbox_coder, multi_apply, multiclass_nms
from mmdet.models.builder import HEADS, build_loss
from mmdet.models.losses import ac... | {"hexsha": "07c542ef144ca664516f2a5b87913c6d92a75405", "size": 25025, "ext": "py", "lang": "Python", "max_stars_repo_path": "downstream/tinypersons/mmdet/models/roi_heads/bbox_heads/sabl_head.py", "max_stars_repo_name": "bwconrad/solo-learn", "max_stars_repo_head_hexsha": "ec510d803a4428d7d8803b90fa1484c42cb9cb52", "ma... |
#redirect Sage Environmental
| {"hexsha": "49f14cb1bddb9b3826c62bab904af9815c0942cd", "size": 29, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "lab/davisWiki/Fritsch_Environmental.f", "max_stars_repo_name": "voflo/Search", "max_stars_repo_head_hexsha": "55088b2fe6a9d6c90590f090542e0c0e3c188c7d", "max_stars_repo_licenses": ["MIT"], "max_star... |
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from wordcloud import WordCloud, STOPWORDS
replacements = {'éèêë':'e', 'àâä':'a', 'ùûü':'u', 'ôö':'o', 'îï':'i'}
sep = {',', ';', '(', "'", ')', ';', '\\', ' ', '’'}
unwanted = {'y', 'c', 'de', 'le', 'd', 'l', 'du', 'la', 'un', 'une', 'des',... | {"hexsha": "2aafdff05818a00391efe5d8369d12aebf7e5430", "size": 3881, "ext": "py", "lang": "Python", "max_stars_repo_path": "Emma/main_insight4.py", "max_stars_repo_name": "D4GGrenoble/finding_associations", "max_stars_repo_head_hexsha": "ccee4d9814365d3e71f963013fc412887f03491f", "max_stars_repo_licenses": ["MIT"], "ma... |
/*********************************************************************
* Software License Agreement (BSD License)
*
* Copyright (c) 2008, Willow Garage, Inc.
* All rights reserved.
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following condit... | {"hexsha": "d264f96cea17c6773ff2db6dd05b609d10a4a7c9", "size": 45320, "ext": "cc", "lang": "C++", "max_stars_repo_path": "src/sdf/interface/parser_deprecated.cc", "max_stars_repo_name": "nherment/gazebo", "max_stars_repo_head_hexsha": "fff0aa30b4b5748e43c2b0aa54ffcd366e9f042a", "max_stars_repo_licenses": ["ECL-2.0", "A... |
##########################################################
# POSTPROCESSING FUNCTIONS #
##########################################################
# rcATT is a tool to prediction tactics and techniques
# from the ATT&CK framework, using multilabel text
# classification and post proce... | {"hexsha": "bb9e7a2035814096bad5ea187d3e5876ee659cb2", "size": 11596, "ext": "py", "lang": "Python", "max_stars_repo_path": "unsupported/attack-predictor/1.0.0/src/classification_tools/postprocessing.py", "max_stars_repo_name": "sais7/python-apps", "max_stars_repo_head_hexsha": "3cef25e95216843ec461897c489d22d0a4cf4a19... |
# Copyright 2022 Google LLC.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, ... | {"hexsha": "cefcf0e247de70fb3608c8a929165dd3b28ff421", "size": 14955, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/custom_model.py", "max_stars_repo_name": "google/crystalvalue", "max_stars_repo_head_hexsha": "719226fb302d414e94fcdb3ac4b468977f3529ec", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars... |
import numpy as np
from neuraxle.hyperparams.space import HyperparameterSamples
from neuraxle.steps.flow import OptionalStep
from neuraxle.steps.numpy import MultiplyByN
def test_optional_should_disable_wrapped_step_when_disabled():
p = OptionalStep(MultiplyByN(2), nullified_return_value=[]).set_hyperparams(Hype... | {"hexsha": "ade912977db829f3a5af3d2a70be281213a66013", "size": 820, "ext": "py", "lang": "Python", "max_stars_repo_path": "testing/test_optional.py", "max_stars_repo_name": "Kimoby/Neuraxle", "max_stars_repo_head_hexsha": "af96f79d4f770f50174e2edf40da4147cdb8a5b5", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_... |
/**
* \file boost/numeric/ublasx/operation/empty.hpp
*
* \brief Check for emptiness a ginve vector/matrix expression.
*
* Copyright (c) 2010, Marco Guazzone
*
* 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... | {"hexsha": "9ddf3fa7ee2e70d8b62e11cc896bb95c5a53a1bf", "size": 1785, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "boost/numeric/ublasx/operation/empty.hpp", "max_stars_repo_name": "comcon1/boost-ublasx", "max_stars_repo_head_hexsha": "290b92b643a944825df99bece3468a4f81518056", "max_stars_repo_licenses": ["BSL-1... |
import torch
import utils
import random
import logging
import sys
import json
import numpy as np
from utils import parse_arguments
from hyperpyyaml import load_hyperpyyaml
logger = logging.getLogger(__name__)
class VoxCelebDataset(torch.utils.data.Dataset):
def __init__(self, hparams, csv_data_file):
sup... | {"hexsha": "1357986f64bea0cdaba0f579918dff6570cbdf4e", "size": 3423, "ext": "py", "lang": "Python", "max_stars_repo_path": "VoxCelebDataset.py", "max_stars_repo_name": "akaver/speaker-embed-augm", "max_stars_repo_head_hexsha": "eef03926d1bc18a2463260b3bc4a7c12c624fee6", "max_stars_repo_licenses": ["Apache-2.0"], "max_s... |
SUBROUTINE genmul(n,p,ncat,ix)
C**********************************************************************
C
C SUBROUTINE GENMUL( N, P, NCAT, IX )
C GENerate an observation from the MULtinomial distribution
C
C
C Arguments
C
C
C N --> Number of events that will be class... | {"hexsha": "75377beadf840d7cb995efdca2fb70650d164f01", "size": 2464, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "Ions/Source/v_1.3/Sampler/Ranlib/src/genmul.f", "max_stars_repo_name": "ppernot/MC-ChemDB", "max_stars_repo_head_hexsha": "376c0b7e4596d8652833b5ff2ebe6316039587c9", "max_stars_repo_licenses": ["M... |
[STATEMENT]
lemma iso_image: "mono ((`) f)"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. mono ((`) f)
[PROOF STEP]
by (simp add: image_mono monoI) | {"llama_tokens": 70, "file": "Order_Lattice_Props_Order_Duality", "length": 1} |
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
#
# Author: Pengcheng He (penhe@microsoft.com)
# Date: 05/15/2019
#
import os
import numpy as np
import math
import sys
from torch.utils.data import Sampler
__all__=['BatchSampler', 'Distribut... | {"hexsha": "1aec3c2b4298503556aef1b8d4f0b2abb934f5fa", "size": 2003, "ext": "py", "lang": "Python", "max_stars_repo_path": "DeBERTa/data/data_sampler.py", "max_stars_repo_name": "tirkarthi/DeBERTa", "max_stars_repo_head_hexsha": "c558ad99373dac695128c9ec45f39869aafd374e", "max_stars_repo_licenses": ["MIT"], "max_stars_... |
from __future__ import division
#Implements K-means algorithm
import numpy as np
from random import randint
from copy import deepcopy
from time import time
from misc import inf
#@dist is the distance used to compute calculus
#@elementSet is the set of elements to cluster
#@k is the number of clusters required (it wil... | {"hexsha": "84f54cc1db734d6d7cde28128124cf048c9508ed", "size": 5647, "ext": "py", "lang": "Python", "max_stars_repo_path": "kMeans.py", "max_stars_repo_name": "kuredatan/taxocluster", "max_stars_repo_head_hexsha": "acec6219ae5b7bd8e3831d71ee79ecfeebb53c8b", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "... |
import unittest
import numpy as np
import torch
from qmctorch.utils import set_torch_double_precision
from qmctorch.scf import Molecule
from qmctorch.wavefunction import SlaterJastrow
class TestGTO2STOFit(unittest.TestCase):
def setUp(self):
torch.manual_seed(101)
np.random.seed(101)
... | {"hexsha": "8aea1c7851d1bb94a491c47a90579f299486bd68", "size": 1418, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/scf/test_gto2sto_fit.py", "max_stars_repo_name": "NLESC-JCER/QMCTorch", "max_stars_repo_head_hexsha": "c56472cd3e9cc59f2e01a880e674b7270d2cdc2b", "max_stars_repo_licenses": ["Apache-2.0"], "... |
import torch.nn as nn
import time
import numpy as np
import torch
import math
from config import *
class sp500_element: # the element of the sp500 variant dataset.
def __init__(self, features, label):
self.features, self.label = features, label
class dataset_element: # the element of the trivial dataset.
... | {"hexsha": "eb986c983c8769c01aaac17ac6a770f3a77f9e11", "size": 5722, "ext": "py", "lang": "Python", "max_stars_repo_path": "synthetic_linear_programming/util.py", "max_stars_repo_name": "PredOptwithSoftConstraint/PredOptwithSoftConstraint", "max_stars_repo_head_hexsha": "c0ec41a8c2c48034851cf04cd848013ceba1dd40", "max_... |
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