text stringlengths 0 1.25M | meta stringlengths 47 1.89k |
|---|---|
import copy
from ctypes import addressof
from pathlib import Path
import numpy as np
import torch
from torch.utils.data import random_split
from dataset.data_loading import BasicDataset
from networks.UNet.unet_model import UNet
from trainers.trainer import Trainer
class OutOfFoldTrainer(Trainer):
@staticmethod
... | {"hexsha": "6ed390430918c5ce3b74c44dd8101bfc4c7270e8", "size": 2935, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/trainers/oof_trainer.py", "max_stars_repo_name": "claudius-kienle/self-supervised-depth-denoising", "max_stars_repo_head_hexsha": "4dffb30e8ef5022ef665825d26f45f67bf712cfd", "max_stars_repo_li... |
/**
* @file semimprk.cc
* @brief NPDE homework SemImpRK code
* @author Unknown, Oliver Rietmann
* @date 04.04.2021
* @copyright Developed at ETH Zurich
*/
#include "semimprk.h"
#include <Eigen/Core>
#include <algorithm>
#include <cmath>
#include <iomanip>
#include <iostream>
#include <vector>
#include "../../.... | {"hexsha": "112af004c470d2b4bf989373a8ccb26854a2b49a", "size": 680, "ext": "cc", "lang": "C++", "max_stars_repo_path": "homeworks/SemImpRK/templates/semimprk.cc", "max_stars_repo_name": "0xBachmann/NPDECODES", "max_stars_repo_head_hexsha": "70a9d251033ab3d8719f0e221de4c2f4e9e8f4ea", "max_stars_repo_licenses": ["MIT"], ... |
#!/usr/bin/env python3
# naive_stereo.py
# This program performs block-based matching to create a depth map
# given 2 stereo images.
#Import the Image class from PIL (Pillow)
from PIL import Image, ImageOps, ImageDraw
import numpy as np
import math
###########
###########
MAX_DISPARITY = 50 ### PLAY AROUND WITH T... | {"hexsha": "c035be9c4d13d1f731f86af27549ab00f6923b84", "size": 3434, "ext": "py", "lang": "Python", "max_stars_repo_path": "lab5/stereo.py", "max_stars_repo_name": "nahushr/Computer-Vision", "max_stars_repo_head_hexsha": "a0f9867c416a5f734f0de4bfae11c0b6cbbd5f66", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_c... |
"""
这个例子将展示如何使用决策树进行分类.
"""
import sys
import numpy as np
import pandas as pd
import classicML as cml
DATASET_PATH = './datasets/西瓜数据集.tsv'
ATTRIBUTE_NAME = ['脐部', '色泽', '根蒂', '敲声', '纹理', '触感', '密度', '含糖率']
# 读取数据
dataframe = pd.read_csv(DATASET_PATH, sep='\t', index_col=0, header=0)
train_index = np.asarray([1, 2,... | {"hexsha": "2c71f18d66e8335e7e14bff5bf94b3e8f9736f25", "size": 929, "ext": "py", "lang": "Python", "max_stars_repo_path": "examples/decision_tree.py", "max_stars_repo_name": "sun1638650145/classicML", "max_stars_repo_head_hexsha": "7e0c2155bccb6e491a150ee689d3786526b74565", "max_stars_repo_licenses": ["Apache-2.0"], "m... |
import os.path
import math
import numpy as np
from typing import Tuple
from adaptiveleak.utils.constants import PERIOD, BT_FRAME_SIZE, BIG_NUMBER, LENGTH_SIZE
from adaptiveleak.utils.encryption import AES_BLOCK_SIZE, CHACHA_NONCE_LEN
from adaptiveleak.utils.data_utils import calculate_bytes, truncate_to_block
from ada... | {"hexsha": "ae7b492d5d1305c0de54edbd7151204a8fef101c", "size": 9398, "ext": "py", "lang": "Python", "max_stars_repo_path": "adaptiveleak/energy_systems/conversion.py", "max_stars_repo_name": "tejaskannan/adaptive-sensor-security", "max_stars_repo_head_hexsha": "4c6dd1eb55eb30a8330c4bf3537e06c7d7802c0b", "max_stars_repo... |
# -*- coding: utf-8 -*-
"""Python implementation of Tapir Lab.'s Acoustic Lip Synchronization.
This program extracts required statistics for training and matching phases
Functions are divided into two sub-groups as auxiliary and main functions
Main Functions
--------------
Voice Activity Detection:
... | {"hexsha": "f0a77215cc594f2517caa4a95124a52d72f41982", "size": 21836, "ext": "py", "lang": "Python", "max_stars_repo_path": "parameter_extraction.py", "max_stars_repo_name": "TapirLab/lip-sync", "max_stars_repo_head_hexsha": "f34c545597eaa946174c15bf4df0b91325deb5bd", "max_stars_repo_licenses": ["MIT"], "max_stars_coun... |
# Licensed under a 3-clause BSD style license - see LICENSE.rst
"""Common fit statistics used in gamma-ray astronomy.
see :ref:`fit-statistics`
"""
from __future__ import absolute_import, division, print_function, unicode_literals
import numpy as np
__all__ = [
"cash",
"cstat",
"wstat",
"get_wstat_mu... | {"hexsha": "0c099a36aeb95c4118d5b65a38016306bd34151e", "size": 9770, "ext": "py", "lang": "Python", "max_stars_repo_path": "gammapy/stats/fit_statistics.py", "max_stars_repo_name": "contrera/gammapy", "max_stars_repo_head_hexsha": "aa0a74baa977ee2477b5c63e036075f4219792a3", "max_stars_repo_licenses": ["BSD-3-Clause"], ... |
"""
"""
import re
import decorator
import numpy as np
import pandas as pd
import datetime
import time
try:
import cPickle as pickle
except ImportError:
import pickle
__author__ = 'Seung Hyeon Yu'
__email__ = 'rambor12@business.kaist.ac.kr'
def _memoize(func, *args, **kw):
# should w... | {"hexsha": "4d59bebb98b332564376f0979960f3268dc99f87", "size": 7069, "ext": "py", "lang": "Python", "max_stars_repo_path": "KSIF/core/utils.py", "max_stars_repo_name": "ksif/KSIF", "max_stars_repo_head_hexsha": "03d4a25b0ae30453ca8254a48641f1b9665f1d2c", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_st... |
import pyautogui
import PySimpleGUI as sg
import cv2
import numpy as np
"""
Demo program that displays a webcam using OpenCV
"""
def main():
sg.theme('Black')
# define the window layout
layout = [[sg.Text('OpenCV Demo', size=(40, 1), justification='center', font='Helvetica 20')],
[sg.Imag... | {"hexsha": "ed7b170507d5585ee1a7e2898a3bf5e74e202750", "size": 1718, "ext": "py", "lang": "Python", "max_stars_repo_path": "Camera.py", "max_stars_repo_name": "akpythonyt/Mystuffs", "max_stars_repo_head_hexsha": "9a7d4198ea0d3907af510e118ea8fa9ecdc5f4db", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 47, "max_... |
"""
Library Features:
Name: lib_data_io_binary
Author(s): Francesco Avanzi (francesco.avanzi@cimafoundation.org), Fabio Delogu (fabio.delogu@cimafoundation.org)
Date: '20210603'
Version: '1.0.0'
"""
#######################################################################################
# Li... | {"hexsha": "a1455e55c6243a2b48ee8572c73dd0df0b956605", "size": 4827, "ext": "py", "lang": "Python", "max_stars_repo_path": "tools/s3m_source2nc_converter/lib_data_io_binary.py", "max_stars_repo_name": "c-hydro/fp-s3m", "max_stars_repo_head_hexsha": "cbb1f347f558fbfaa8d564441931989bc833a02d", "max_stars_repo_licenses": ... |
import json
import pickle5 as pickle
import joblib
import numpy as np
__locations = None
__data_columns = None
__model = None
def load_saved_artifacts():
print("Loading server artifact ... Start")
global __data_columns
global __locations
global __model
with open("./artifacts/columns.json", 'r') a... | {"hexsha": "becd914f4c8fd3c91ee1da62f92df0d0c337fc75", "size": 1082, "ext": "py", "lang": "Python", "max_stars_repo_path": "util.py", "max_stars_repo_name": "hello5423/template-python-django", "max_stars_repo_head_hexsha": "0e08a9cc1e75ff38a82ee7e5d58c821fb7b2de89", "max_stars_repo_licenses": ["MIT"], "max_stars_count"... |
# -*- coding: utf-8 -*-
__all__ = ["Zero", "Constant"]
import theano.tensor as tt
class Zero:
def __call__(self, x):
return tt.zeros_like(x)
class Constant:
def __init__(self, value):
self.value = tt.as_tensor_variable(value)
def __call__(self, x):
return tt.zeros_like(x) + se... | {"hexsha": "48895537e8664223d4ab7da5fa633c000cd9c33f", "size": 329, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/exoplanet/gp/means.py", "max_stars_repo_name": "t-brandt/exoplanet", "max_stars_repo_head_hexsha": "68c567de27702190b41434c56d78315358f5c441", "max_stars_repo_licenses": ["MIT"], "max_stars_cou... |
# Copyright Shirin Yamani,2021
# Licensed under MIT licensed.
# See LICENSE.txt for more information.
import torch
import torchvision
import torchvision.transforms as transforms
import numpy as np
import time
import matplotlib.pyplot as plt
import argparse
import torch.optim as optim
from random import ... | {"hexsha": "98d23bf3c7ce3c3ba53bb555960af0f470498efb", "size": 8473, "ext": "py", "lang": "Python", "max_stars_repo_path": "train.py", "max_stars_repo_name": "shirinyamani/Translation_Machine", "max_stars_repo_head_hexsha": "ab4a6a2c73560ba5b281ec3ed6737ef7f208b955", "max_stars_repo_licenses": ["MIT"], "max_stars_count... |
!---------------------------------------------------!
! Copyright (c) 2017 Shunsuke A. Sato !
! Released under the MIT license !
! https://opensource.org/licenses/mit-license.php !
!---------------------------------------------------!
subroutine mesh
use global_variables
use hpsi
... | {"hexsha": "d4ca8ca62812d79da7009cd45913b453e703e720", "size": 917, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "src/mesh.f90", "max_stars_repo_name": "shunsuke-sato/qm1d", "max_stars_repo_head_hexsha": "b09bbe97dc2987188fe635b27dce89ce82e5d1ee", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, ... |
import requests
import json
import pandas as pd
from pandas import json_normalize
import matplotlib.pyplot as plt
import seaborn as sns
import networkx as nx
from bokeh.io import show, output_file
from bokeh.models import Plot, Range1d, MultiLine, Circle, HoverTool, TapTool, BoxSelectTool, WheelZoomTool
from b... | {"hexsha": "863964681e9a3ab38be52fa9d4ad2eba7fc632e8", "size": 7608, "ext": "py", "lang": "Python", "max_stars_repo_path": "upwork-devs/lwasampijja-baker/issues.py", "max_stars_repo_name": "GiuseMSD/k8-data-visualization", "max_stars_repo_head_hexsha": "a14b20e843149eda946d764781efd75835ae7158", "max_stars_repo_license... |
# The MIT License (MIT)
#
# Copyright (c) 2016, Jack Liu
# All rights reserved.
#
# 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
... | {"hexsha": "6e6dfcc962f1a489b0168e34ecdc6599e004f8b8", "size": 19297, "ext": "py", "lang": "Python", "max_stars_repo_path": "stock.py", "max_stars_repo_name": "liu12295/stock", "max_stars_repo_head_hexsha": "4335ac1e85d27913d245f4b8663258f93a05b625", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_... |
!> Parameters to be used during calculations
!! such as conversion factors, imaginary number, \f$ \pi \f$....
MODULE parameters
USE kinds
IMPLICIT NONE
REAL(dp), PARAMETER :: pi=4.0_dp*DATAN(1.0_dp), & !4.0d0*atan(1.d0),
kb=1.3806488e-23_dp, & ! Boltzmann constant J/K
planck... | {"hexsha": "2ff9d36b15754cbdd0845f87fcf5987006ae142e", "size": 2544, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "mqds/src/general_src/parameters.f90", "max_stars_repo_name": "jprov410/mqds", "max_stars_repo_head_hexsha": "beead5c30aac77a7ae2d07e808d8c587cdd1c3ce", "max_stars_repo_licenses": ["BSD-3-Clause"... |
# Copyright 2020 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to... | {"hexsha": "b48ca58ff475e1820eb0eef839bd84cfc8ff5255", "size": 11310, "ext": "py", "lang": "Python", "max_stars_repo_path": "mindarmour/privacy/evaluation/membership_inference.py", "max_stars_repo_name": "mindspore-ai/mindarmour", "max_stars_repo_head_hexsha": "a5db0825fa06e4da870c0a850a18b374e8cdd086", "max_stars_repo... |
#include <stan/math/rev/scal.hpp>
#include <gtest/gtest.h>
#include <test/unit/math/rev/scal/fun/nan_util.hpp>
#include <test/unit/math/rev/scal/util.hpp>
#include <boost/math/special_functions/beta.hpp>
TEST(AgradRev,ibeta_vvv) {
using stan::math::var;
using stan::math::ibeta;
using stan::math::ibeta;
usin... | {"hexsha": "a46bac1f814593ca43c9899f84aaf34b0d816134", "size": 5480, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "cmdstan/stan/lib/stan_math/test/unit/math/rev/scal/fun/ibeta_test.cpp", "max_stars_repo_name": "yizhang-cae/torsten", "max_stars_repo_head_hexsha": "dc82080ca032325040844cbabe81c9a2b5e046f9", "max_s... |
// Copyright Rein Halbersma 2010-2020.
// 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)
#include <core/board/group.hpp> // axioms::is_realized, make
#include <dctl/core/board/angle.hp... | {"hexsha": "a374e5b8dc4a17ba63c62742fca3ad8a045645a4", "size": 1863, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "test/src/core/board/cyclic.cpp", "max_stars_repo_name": "sagarpant1/dctl", "max_stars_repo_head_hexsha": "b858fa139159eff73e8f3eec32da93ba077e0bd3", "max_stars_repo_licenses": ["BSL-1.0"], "max_star... |
import cv2
import sklearn
import numpy as np
from tensorflow.keras.preprocessing.image import img_to_array
from tensorflow.keras.preprocessing.image import ImageDataGenerator
def generateZoom(imgs, labs, samples):
rawGen = []
labelGen = []
n = imgs.shape[0]
for j in range(n):
img = imgs[j]
... | {"hexsha": "10ea05219446687ca433d6733ac0c8eed4498ec5", "size": 3164, "ext": "py", "lang": "Python", "max_stars_repo_path": "helper.py", "max_stars_repo_name": "oliverquintana/UUr-cGAN", "max_stars_repo_head_hexsha": "769e6e6c72f91f67efe58b6d68a0c302f8db95bb", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "m... |
import pandas as pd
import numpy as np
file1 = '../data/STRIDE_PATIENT.xlsx'
x1 = pd.ExcelFile(file1)
stride_patient = x1.parse('Sheet1')
file2 = '../data//SURGERY.xlsx'
x2 = pd.ExcelFile(file2)
surgery = x2.parse('Sheet1')
stride_patient_req = stride_patient
pat_surgery = pd.merge(stride_patient_req, surgery, on='P... | {"hexsha": "25ef5f11521f66d9ef3f67033db2f87ab7da8078", "size": 1205, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/feature vector creation/step1_patient_demographics.py", "max_stars_repo_name": "arjun-parthi/SSRI-Project", "max_stars_repo_head_hexsha": "62f610a594e5849ccf0f3c25cd6adcd63888ec2a", "max_stars... |
/////////////////////////////////////////////////////////////////////////////////////////////
// Copyright (c) 2021 Andreas Milton Maniotis.
//
// Email: andreas.maniotis@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/... | {"hexsha": "844f8d298291fa4dbf95f0675c03e262011165d1", "size": 1584, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "deprecated-material/test/test_find.cpp", "max_stars_repo_name": "aandriko/libaml", "max_stars_repo_head_hexsha": "9db1a3ac13ef8160a33ed03e861be5d8cc8ea311", "max_stars_repo_licenses": ["BSL-1.0"], "... |
<H1 style="text-align: center">ECMM426 - Computer Vision / ECMM441 - Machine Vision (Professional)</H1>
<H1 style="text-align: center"></H1>
<H2 style="text-align: center">Workshop 1</H2>
<H2 style="text-align: center">Image Processing</H2>
Simple examples of image processing concepts on OpenCV.
## Imports
```pytho... | {"hexsha": "7dc6618979c6ac95f7e8993965f9309ae2843b1f", "size": 20160, "ext": "ipynb", "lang": "Jupyter Notebook", "max_stars_repo_path": "Notebooks/ECMM426_ECMM441_Image_Processing (1).ipynb", "max_stars_repo_name": "Aarif1430/monthly-challenges", "max_stars_repo_head_hexsha": "23990bb3e8930eeb9dc4308c73b43b77a6a3ab49"... |
(* This Isabelle theory is produced using the TIP tool offered at the following website:
https://github.com/tip-org/tools
This file was originally provided as part of TIP benchmark at the following website:
https://github.com/tip-org/benchmarks
Yutaka Nagashima at CIIRC, CTU changed the TIP output th... | {"author": "data61", "repo": "PSL", "sha": "2a71eac0db39ad490fe4921a5ce1e4344dc43b12", "save_path": "github-repos/isabelle/data61-PSL", "path": "github-repos/isabelle/data61-PSL/PSL-2a71eac0db39ad490fe4921a5ce1e4344dc43b12/UR/TIP_with_Proof/TIP15/TIP15/TIP_sort_BSortCount.thy"} |
! mpiexec -np 4 bin/Debug/WGFM_test2.exe
program matrices_mpi_test
use meshread_mod
use linalg_mod
use WGFM_matrices
use WGFM_matrices_mpi
use data_tools
use mpi
implicit none
complex(8), parameter :: IU = (0d0, 1d0)
! Variables
type(Mesh) :: msh
integer, parameter :: nNS =... | {"hexsha": "143774fb5f74f913edd5af0ad4cb7f20e21b0a44", "size": 3290, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "script/matrices_test/matrices_mpi_test.f90", "max_stars_repo_name": "Riarrieta/WindowedGreenFunctionMethod", "max_stars_repo_head_hexsha": "e6f965cac1acd8d9922e2bf94c33e3577ceb0dc9", "max_stars_... |
import time
import numpy as np
import pandas as pd
try:
import MySQLdb
except:
import pymysql
try:
db = MySQLdb.Connect(host="10.20.212.172", user="varientdb", passwd="varient2017", db="varientDB_new")
except:
db = pymysql.connect(host="10.20.212.172", user="varientdb", passwd="varient2017", db="vari... | {"hexsha": "ab9399f8bf8239163d969035a1e7b49c09f8c050", "size": 7004, "ext": "py", "lang": "Python", "max_stars_repo_path": "cut_file.py", "max_stars_repo_name": "luyu103713/variantDB_updater", "max_stars_repo_head_hexsha": "53cb390a63e7a392f628c566a87269dafdeeec29", "max_stars_repo_licenses": ["MIT"], "max_stars_count"... |
import numpy as np
def vindex(v):
if len(v.shape) > 2:
return len(v.shape) - 2
return 0
def vncomp(v):
return v.shape[vindex(v)]
def vcomplimit(v, n):
"""
Return a stack of vectors with the same shape as the input stack, but only
including the first n vector components.
:param v:... | {"hexsha": "30d9f6812489bbb143a2a58c1c8e8a8ced226370", "size": 9683, "ext": "py", "lang": "Python", "max_stars_repo_path": "kwanmath/vector.py", "max_stars_repo_name": "kwan3217/kwanmath", "max_stars_repo_head_hexsha": "c43f8209324cdb0c673b969b41b06d49c9d46e71", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_c... |
[STATEMENT]
lemma lms_contains_aref: "(list_contains, op_mset_contains) \<in> Id \<rightarrow> list_mset_rel \<rightarrow> bool_rel"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. (list_contains, op_mset_contains) \<in> Id \<rightarrow> list_mset_rel \<rightarrow> bool_rel
[PROOF STEP]
unfolding list_mset_rel_def li... | {"llama_tokens": 231, "file": "Refine_Imperative_HOL_IICF_Impl_IICF_List_Mset", "length": 2} |
% mnras_template.tex
%
% LaTeX template for creating an MNRAS paper
%
% v3.0 released 14 May 2015
% (version numbers match those of mnras.cls)
%
% Copyright (C) Royal Astronomical Society 2015
% Authors:
% Keith T. Smith (Royal Astronomical Society)
% Change log
%
% v3.0 May 2015
% Renamed to match the new package... | {"hexsha": "38da16ba35b8211078b101e9f55389f420f40287", "size": 100657, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "cc_cet/paper/submission_1/oldintro.tex", "max_stars_repo_name": "davidjwilson/pceb", "max_stars_repo_head_hexsha": "259cf4b18b51b7163d6ce84ab150c5f65f8cfdec", "max_stars_repo_licenses": ["MIT"], "... |
"""
ast_is(ast::VPtr, what::Symbol)::Bool
Helper for quickly recognizing kinds of ASTs
"""
ast_is(ast::VPtr, what::Symbol)::Bool = ccall(sbml(what), Cint, (VPtr,), ast) != 0
"""
parse_math_children(ast::VPtr)::Vector{Math}
Recursively parse all children of an AST node.
"""
parse_math_children(ast::VPtr)::Ve... | {"hexsha": "e7b750ec32d39684aa23b6317ccc57796530a215", "size": 2422, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/math.jl", "max_stars_repo_name": "anandijain/SBML.jl-1", "max_stars_repo_head_hexsha": "be679eabfeec4f0d79e5c98448387c188b7e4aa2", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": ... |
import math
import base64
import numpy as np
import tensorflow as tf
from .tflite_schema import Model as tflite_schema_model
from .tflite_schema import BuiltinOperator as tflite_schema_builtin_operator
from .tflite_schema import TensorType as tflite_schema_tensor_type
builtin_operator_code_lookup = {
code: name
... | {"hexsha": "9cbdd92ec01e6fd7ec9d3da2f696d0213b157185", "size": 8021, "ext": "py", "lang": "Python", "max_stars_repo_path": "nodeconfeu_watch/convert/export_tflite.py", "max_stars_repo_name": "coreml-models/nodeconfeu-gesture-models", "max_stars_repo_head_hexsha": "59545c091ed42d904c0ed41cc0a01d744959c48a", "max_stars_r... |
import random
import matplotlib.pyplot as plt
import numpy as np
from scipy import stats
"""
皮尔森相关系数
"""
def PCC(l1, l2):
return stats.pearsonr(l1, l2)
if __name__ == '__main__':
lis = []
height_score = {}
for i in range(1000):
l1 = [random.randint(0, 9) for _ in range(5)]
l2 = [ran... | {"hexsha": "adeddd9f929f8762cbb11859000855a4b8a5c161", "size": 1248, "ext": "py", "lang": "Python", "max_stars_repo_path": "NetRecorder/math_works/Pearson_CorrelationCoefficient.py", "max_stars_repo_name": "ga1008/net_tracfic_recorder", "max_stars_repo_head_hexsha": "adc837a62d772ca0a3142cf4efef17c34b908ab5", "max_star... |
import numpy as np
class Variable:
def __init__(self, data):
if data is not None:
if not isinstance(data, np.ndarray):
raise TypeError('{} is not supported'.format(type(data)))
self.data = data
self.grad = None
self.creator = None
def set_creator(s... | {"hexsha": "8a0ffe7606640fcc95f81840735b195d440dfad5", "size": 1681, "ext": "py", "lang": "Python", "max_stars_repo_path": "steps/step09.py", "max_stars_repo_name": "timwuu/deep-learning-from-scratch-3", "max_stars_repo_head_hexsha": "6f18dee8c1d764e16275ed68f90966bc85f0ae66", "max_stars_repo_licenses": ["MIT"], "max_s... |
'''
summary
Classes and functions for summarising Trips
Tightly coupled to CleanTrip
'''
import numpy as numpy
import pandas as pd
class TripSummaryStatistics(object):
'''
Summary statistics for a cleaned trip
'''
def __init__(self, clean_trip):
'''
Create an instance of TripSummarySt... | {"hexsha": "b82fc5d8b28eb50bdd23682c17c982da617dbedb", "size": 2143, "ext": "py", "lang": "Python", "max_stars_repo_path": "cats_analysis/summary.py", "max_stars_repo_name": "TomMonks/cats-time-series", "max_stars_repo_head_hexsha": "fb8ec51bc5805b070a7616e10b6aadd594915642", "max_stars_repo_licenses": ["MIT"], "max_st... |
import numpy as np
import torch
import math
class SVGD:
def __init__(self, distribution, kernel, optimizer):
self.P = distribution
self.K = kernel
self.optim = optimizer
def phi(self, X, *data):
X = X.detach().requires_grad_(True)
log_prob = self.P.log_prob(X, *data)
score_func = torch.a... | {"hexsha": "2427a00fec91b27edfc04282c9e20c540c622589", "size": 3011, "ext": "py", "lang": "Python", "max_stars_repo_path": "meta_learn/svgd.py", "max_stars_repo_name": "shlu2019/meta_learning_pacoh", "max_stars_repo_head_hexsha": "376349e66bdd782e3d06b4bac2ecb56a2a10bcf6", "max_stars_repo_licenses": ["MIT"], "max_stars... |
/*
* test_output.cpp
*
* Created: 11/2/2017
* Author: Michael E. Tryby
* US EPA - ORD/NRMRL
*
* Unit testing for SWMM outputapi using Boost Test.
*/
#define BOOST_TEST_MODULE "output"
#include <stdlib.h>
#include <stdio.h>
#include <string.h>
#include <math.h>
#include <boost/test/included/... | {"hexsha": "f1cc5bab749bde6f77cbee59897216b4247014b8", "size": 9432, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "tests/outfile/test_output.cpp", "max_stars_repo_name": "20-S2-2-C-Flood-Modelling/Stormwater-Management-Model", "max_stars_repo_head_hexsha": "c351f07713ae130f765b4e4a63def42dbc05998f", "max_stars_r... |
# -*- coding: utf-8 -*-
"""test_energycondensation.py
py Test:
Energy condensation on multi-group maco- and micro- parameters.
The condensation procedure can be applied on vectors, such as absorption xs,
as well as on scattering matrices.
Created on Mon Apr 11 11:00:00 2022 @author: Dan Kotlyar
Last updated on Mon A... | {"hexsha": "bce154900b7ad4cbbe5ede4c5ae2a9679876bf02", "size": 5594, "ext": "py", "lang": "Python", "max_stars_repo_path": "xsInterface/tests/test_energycondensation.py", "max_stars_repo_name": "CORE-GATECH-GROUP/xs-interface", "max_stars_repo_head_hexsha": "b391c0b3f5690a602f2f69a4cce137460d345413", "max_stars_repo_li... |
from json import loads
import json
import numpy as np
import math
from statistics import mean
#import re
elem_type = ['화속성', '명속성', '수속성', '암속성']
class SkillTree():
skill_db = None
passive_db = None
buffer_db = None
@classmethod
def initstatic(cls):
with open("./skill_db.json","r") as f:
... | {"hexsha": "d952835cf78e6d8b97084d2a0d9fa4bec1481a20", "size": 70537, "ext": "py", "lang": "Python", "max_stars_repo_path": "skilltree.py", "max_stars_repo_name": "dwlee08/dnfp-analyzer", "max_stars_repo_head_hexsha": "4ae4ec4d32c08288b997c83655a0c97c7d347216", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_coun... |
"""
This file contains code for a 2 layer fully connected neural network for classification; written from scratch.
"""
import torch
import numpy as np
from core.activations import activations
class SimpleFullyConnected(object):
"""
Simple 2 layer (single hidden) fully connected network for classification.
... | {"hexsha": "34b1533af7cfc7accceafb5a61f30d089bb3eac3", "size": 11593, "ext": "py", "lang": "Python", "max_stars_repo_path": "core/models/three_layer_fc.py", "max_stars_repo_name": "kalvare/machine_learning", "max_stars_repo_head_hexsha": "9710177515bcb22901eea5738364221fe4bf52b5", "max_stars_repo_licenses": ["MIT"], "m... |
\chapter{Vita}
This is another example of an appendix. Perhaps for listing your CV. Or giving
examples of having multiple appendices.
| {"hexsha": "3403a0c56f9607bd49d5d63e90563a403831c070", "size": 135, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "docs/Thesis tools/appendixB.tex", "max_stars_repo_name": "Malificiece/Leap-Motion-Thesis", "max_stars_repo_head_hexsha": "a87684c7e9c1d7250922d00f37f31ae242dcc363", "max_stars_repo_licenses": ["Unlic... |
#include "async_web_server_cpp/http_request_handler.hpp"
#include "async_web_server_cpp/http_connection.hpp"
#include "async_web_server_cpp/http_reply.hpp"
#include <boost/bind.hpp>
#include <boost/enable_shared_from_this.hpp>
#include <boost/noncopyable.hpp>
#include <boost/regex.hpp>
#include <boost/shared_ptr.hpp>... | {"hexsha": "b3b08a4f29f4611d35b09acd1255941319feecd0", "size": 4380, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/http_request_handler.cpp", "max_stars_repo_name": "PranavDhulipala/async_web_server_cpp", "max_stars_repo_head_hexsha": "7d810f35b684021c3c5e6d9349c22b51b6e6e9cb", "max_stars_repo_licenses": ["B... |
import sys
#Imports
import numpy as np
import math
import matplotlib.pyplot as plt
question = sys.argv[1]
def berkan_ozdamar_21602353_hw1(question):
if question == '1' :
A = np.array([[1, 0, -1, 2], [2, 1, -1, 5], [3, 3, 0, 9]])
b = np.array([1, 4, 9])
# Part a
print('Part a')
... | {"hexsha": "77a2d0185b80f2ff20d611bd6cae27b52d758b7b", "size": 9945, "ext": "py", "lang": "Python", "max_stars_repo_path": "Homework1/berkan_ozdamar_21602353_hw1.py", "max_stars_repo_name": "ozdamarberkan/Computational_Neuroscience", "max_stars_repo_head_hexsha": "8a8c02842968037fcbdcab15f203827642b6d816", "max_stars_r... |
{-# OPTIONS --without-K #-}
module hw1 where
open import Level using (_⊔_)
open import Function using (id)
open import Data.Nat using (ℕ; suc; _+_; _*_)
open import Data.Empty using (⊥)
open import Data.Sum using (_⊎_; inj₁; inj₂)
import Level
infix 4 _≡_
recℕ : ∀ {ℓ} → (C : Set ℓ) → C → (ℕ → C → C) → ℕ → C
recℕ C ... | {"hexsha": "e1b640d7174c2df0588dae3ed5260719015d37b2", "size": 11112, "ext": "agda", "lang": "Agda", "max_stars_repo_path": "hw1.agda", "max_stars_repo_name": "andmkent/misc-HoTT", "max_stars_repo_head_hexsha": "b05c58ffdaed99932ca2acc632deca8d14742b04", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_st... |
//------------------------------------------------------------------------------
/*
This file is part of rippled: https://github.com/ripple/rippled
Copyright (c) 2012, 2013 Ripple Labs Inc.
Permission to use, copy, modify, and/or distribute this software for any
purpose with or without fee is hereby ... | {"hexsha": "1b869f6a5dabcd80089a77ecd8a532309008619a", "size": 10676, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/ripple/server/impl/Port.cpp", "max_stars_repo_name": "shichengripple001/rippled", "max_stars_repo_head_hexsha": "7c66747d27869f9f3c96617bd4227038f1fa92b8", "max_stars_repo_licenses": ["ISC"], "... |
/*
* Copyright (c) 2013-2015, Michael Grey and Markus Theil
* All rights reserved.
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions
* are met:
* 1. Redistributions of source code must retain the above copyright
* notic... | {"hexsha": "6921bf9c2b56799ae07017e32d95185efe080e89", "size": 3812, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/db/SQLiteAreaPopulationReader.cpp", "max_stars_repo_name": "thillux/topoGen", "max_stars_repo_head_hexsha": "219901bda2df2594393dd5f52a6b6e961c59225e", "max_stars_repo_licenses": ["BSD-3-Clause"... |
subroutine svdfit(x,y,z,sig,ndata,a,ma,u,v,w,mp,np,chisq)
implicit real*8 (a-h,o-z)
parameter(nmax=327680,mmax=10,tol=1.e-12)
dimension x(ndata),y(ndata),z(ndata),sig(ndata),a(ma),v(np,np),
* u(mp,np),w(np),b(nmax),afunc(mmax)
c type *,'evaluating basis functions...'
do 12 i=1... | {"hexsha": "1bc6493f933151312cb4f4d09c38cf48e21f1352", "size": 3502, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "components/stdproc/stdproc/resampLib/src/svd.f", "max_stars_repo_name": "vincentschut/isce2", "max_stars_repo_head_hexsha": "1557a05b7b6a3e65abcfc32f89c982ccc9b65e3c", "max_stars_repo_licenses": [... |
import tensorflow as tf
import numpy as np
from tqdm import tqdm
def make_iterator(dataset,BATCH_SIZE):
with tf.name_scope('data'):
data = tf.data.Dataset.from_tensor_slices(dataset)
data = data.batch(BATCH_SIZE)
iterator = tf.data.Iterator.from_structure(data.output_types,data.output_shape... | {"hexsha": "00abff70c6dd2f5493ce2a29a8e99bdf88f3f073", "size": 2582, "ext": "py", "lang": "Python", "max_stars_repo_path": "CBLN/bnn/model_utils.py", "max_stars_repo_name": "Honglin20/Thesis", "max_stars_repo_head_hexsha": "ec344f82d8200ce006082ad32d75a905314be77e", "max_stars_repo_licenses": ["MIT"], "max_stars_count"... |
// Copyright 2011 Google Inc. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable... | {"hexsha": "f75f2bb5056dde55c75d023434bbe774c769f4f6", "size": 9209, "ext": "cc", "lang": "C++", "max_stars_repo_path": "3rdparty/rpcz/test/client_server_test.cc", "max_stars_repo_name": "marinadudarenko/bigartm", "max_stars_repo_head_hexsha": "c7072663581c59e970ef165a577dc4969810a19d", "max_stars_repo_licenses": ["Apa... |
import tempfile
import unittest
from pathlib import Path
import numpy as np
from dacbench.agents import StaticAgent
from dacbench.benchmarks import LubyBenchmark
from dacbench.logger import Logger, load_logs, log2dataframe
from dacbench.runner import run_benchmark
from dacbench.wrappers import EpisodeTimeWrapper
cl... | {"hexsha": "c63d5c2e74da656a6946706210cc6fecaa89e5ac", "size": 4330, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/wrappers/test_time_tracking_wrapper.py", "max_stars_repo_name": "ndangtt/LeadingOnesDAC", "max_stars_repo_head_hexsha": "953747d8702f179851d7973c65779a1f830e03a1", "max_stars_repo_licenses":... |
PROGRAM hello
print*, 'Hello, World!'
END
| {"hexsha": "4b34eeda6556cfcc74fd2536d09cde99732fff7a", "size": 62, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "source/pkgsrc/lang/gcc44/files/hello.f", "max_stars_repo_name": "Scottx86-64/dotfiles-1", "max_stars_repo_head_hexsha": "51004b1e2b032664cce6b553d2052757c286087d", "max_stars_repo_licenses": ["Unlic... |
#include "util/atomic_queue.hpp"
#include <boost/python/errors.hpp>
#include <iostream>
#include "util/errors.hpp"
namespace cvisual {
void
atomic_queue_impl::push_notify()
{
empty = false;
}
} // !namespace cvisual
| {"hexsha": "afd21987c62911f9800d90737a466243306a8143", "size": 236, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/core/util/atomic_queue.cpp", "max_stars_repo_name": "lebarsfa/vpython-wx", "max_stars_repo_head_hexsha": "38df062e5532b79f632f4f2a1abae86754c264a9", "max_stars_repo_licenses": ["BSL-1.0"], "max_s... |
"""
** deeplean-ai.com **
created by :: GauravBh1010tt
contact :: gauravbhatt.deeplearn@gmail.com
"""
import time
import math
import pandas as pd
import numpy as np
import torch
import os
def asMinutes(s):
m = math.floor(s / 60)
s -= m * 60
return '%dm %ds' % (m, s)
def timeSince(since, percent):
no... | {"hexsha": "36fc9bd99aa200539601b907aa8502efc8165ffc", "size": 4172, "ext": "py", "lang": "Python", "max_stars_repo_path": "sketch_generation/data_load.py", "max_stars_repo_name": "louis-gautier/Project-DL-Seq2Seq", "max_stars_repo_head_hexsha": "25fca13803b1a70dc9c4f5be2cf5c85a75496feb", "max_stars_repo_licenses": ["M... |
program flotante
use, intrinsic :: iso_fortran_env, only: sp=>real32, dp=>real64
implicit none
real(sp) :: flotante32
real(dp) :: flotante64
flotante32 = 1.0_sp
!Sufijo explicito para constantes literales
flotante64 = 1.0_dp
print *,'flotante32 = '... | {"hexsha": "85d2870207beb14ae3219de7422995ce9e745479", "size": 487, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "Programas/flotante.f90", "max_stars_repo_name": "Marc-xyz/InicioRapidoEnFortran", "max_stars_repo_head_hexsha": "8cfe017877061bbfbb2bef66e16c2afc9375243d", "max_stars_repo_licenses": ["MIT"], "ma... |
# coding:utf-8
import logging
import time
import pyaudio
import numpy
import platform
import itertools
from threading import Timer, Event
from queue import Queue, Empty
from .base import BaseOperation
class EnterGate(BaseOperation):
def __init__(self, stoped, tasksQueue):
"""
副本顶门
"""
... | {"hexsha": "ea87076c784ca8acaa79c18275b7c3268e6580ac", "size": 1368, "ext": "py", "lang": "Python", "max_stars_repo_path": "slavewg/entergate.py", "max_stars_repo_name": "lamter/slavewg", "max_stars_repo_head_hexsha": "c3a4098b6c2cbfd232f8ed2290141b7f61d7db6f", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_coun... |
// Copyright (c) 2016 Alexander Gallego. All rights reserved.
//
#include "codegen.h"
#include <iostream>
#include <boost/filesystem.hpp>
#include <glog/logging.h>
#include "cpp_generator.h"
#include "go_generator.h"
#include "python_generator.h"
namespace smf_gen {
codegen::codegen(std::string ifname, std::strin... | {"hexsha": "fd6f865f091d5208ecdc1a291c95642419567e6d", "size": 3393, "ext": "cc", "lang": "C++", "max_stars_repo_path": "src/smfc/codegen.cc", "max_stars_repo_name": "mmaslankaprv/smf", "max_stars_repo_head_hexsha": "1ff81eeb69a73faa42f7b178521748e1747d55f9", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count"... |
"""
This module tests the predictive posterior for missing data in the Bayesian GP-LVM, MRD, and DP-GP-LVM models. This
module uses PoseTrack data to perform these tests.
"""
from distributions.normal import mvn_log_pdf
from models.dp_gp_lvm import dp_gp_lvm
from models.gaussian_process import bayesian_gp_lvm, manifol... | {"hexsha": "0677dd24751a1821f54fdb49473d39f451738b50", "size": 33673, "ext": "py", "lang": "Python", "max_stars_repo_path": "test/posetrack_missing_data_test.py", "max_stars_repo_name": "AndrewRLawrence/dp_gp_lvm", "max_stars_repo_head_hexsha": "b0d4c776714f22e83de31127fbfbbd511f017dcd", "max_stars_repo_licenses": ["MI... |
"""Template to turn the .csv files in data/ into work-precision plots."""
import matplotlib.pyplot as plt
import numpy as np
from _styles import LINESTYLES, MARKERS
from probnumeval.timeseries import chi2_confidence_intervals
plt.style.use(
[
"./visualization/stylesheets/fontsize/7pt.mplstyle",
".... | {"hexsha": "28c93f02450197d7551eedc739facb157080c669", "size": 2026, "ext": "py", "lang": "Python", "max_stars_repo_path": "visualization/prior_samples.py", "max_stars_repo_name": "feimeng93/probabilistic-bvp-solver", "max_stars_repo_head_hexsha": "d6b38d4ff7b3ab6cf3003de30eb2f6eeb42c0beb", "max_stars_repo_licenses": [... |
import faiss
import numpy as np
from sklearn import preprocessing
from utils import load_image, get_image_paths
class Analyst:
def __init__(self, V_dict):
self.classes = list(V_dict)
self.V_dict = V_dict
self._V_dict = V_dict # save a copy that won't be
@property
def V_norm_dic... | {"hexsha": "a2a744ed03ad8975ff805aa12867a13db01807a4", "size": 5659, "ext": "py", "lang": "Python", "max_stars_repo_path": "style_stack/analysts.py", "max_stars_repo_name": "TheAustinator/style_similarity_search", "max_stars_repo_head_hexsha": "3f7b35ca0255eb93f5e109f05b31032576ee9c1b", "max_stars_repo_licenses": ["MIT... |
InstallMethod( JupyterRender, [ IsRecord ],
r -> Objectify( JupyterRenderableType
, rec( data := rec( text\/plain := String(r) )
, metadata := rec() ) ) );
# This is still an ugly hack, but its already much better than before!
BindGlobal("J... | {"hexsha": "09889f62a487f049fcc48b2d1e6378789eb1c06f", "size": 4412, "ext": "gi", "lang": "GAP", "max_stars_repo_path": "gap/JupyterUtil.gi", "max_stars_repo_name": "ZachNewbery/JupyterKernel", "max_stars_repo_head_hexsha": "5bf0e17031271bc641c4e604c9562eb48dd33633", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_st... |
# -*- coding: utf-8 -*-
"""TASK#1.ipynb
Automatically generated by Colaboratory.
Original file is located at
https://colab.research.google.com/drive/1u-kmG1USY74r39hqZBePjvvD3GZOJkcJ
# **LetsGrowMore**
---
---
# ***Data Science Internship***
---
---
## `Author: UMER FAROOQ`
## `Task Level: Beginner Leve... | {"hexsha": "4d030e9615684503b23c3033d4dcf31a0c1eff62", "size": 6767, "ext": "py", "lang": "Python", "max_stars_repo_path": "task_1.py", "max_stars_repo_name": "Umer86/IRIS_FLOWER_Classfication_ML_Project", "max_stars_repo_head_hexsha": "18b0ae5385cb23dff296b7b0fb1f3db6756deaac", "max_stars_repo_licenses": ["MIT"], "max... |
/*
Script for converting the mask images to the mmsegmentation form
*/
import glob
from PIL import Image
import os
import numpy as np
# src_class="pldu"
src_dir = "C:/Users/Xyedj/Desktop/datasets/wires/PLDU/gt/aug_gt/90.0_0" #image dir with default mask images
dst_dir = "C:/Users/Xyedj/Desktop/datasets/wires/PLDU/gt... | {"hexsha": "87191240d4f9d31fda32d068bf8bcbd810660e6b", "size": 1137, "ext": "py", "lang": "Python", "max_stars_repo_path": "mmseg/utils/convertmode.py", "max_stars_repo_name": "rubeea/pl_mmseg_point_rend", "max_stars_repo_head_hexsha": "2b92b03a4d0356871f2bd18b79ed34d4594a6470", "max_stars_repo_licenses": ["Apache-2.0"... |
import h5py
import numpy as np
from pyVHR.datasets.dataset import Dataset
from pyVHR.signals.bvp import BVPsignal
class COHFACE(Dataset):
"""
Cohface dataset structure:
-----------------
datasetDIR/
|
|-- subjDIR_1/
| |-- vidDIR1/
| |-- videoFile1.avi
... | {"hexsha": "a84bb310c7fcb9a1956bdbbcb02b5cb359b61630", "size": 1534, "ext": "py", "lang": "Python", "max_stars_repo_path": "pyVHR/pyVHR/datasets/cohface.py", "max_stars_repo_name": "chiccheung/PRD_rPPG_method_3DCNN", "max_stars_repo_head_hexsha": "c2da5c523dc960644b444c14b1417a8ec86eba25", "max_stars_repo_licenses": ["... |
[STATEMENT]
lemma floor_eq_iff: "\<lfloor>x\<rfloor> = a \<longleftrightarrow> of_int a \<le> x \<and> x < of_int a + 1"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. (\<lfloor>x\<rfloor> = a) = (of_int a \<le> x \<and> x < of_int a + (1::'a))
[PROOF STEP]
using floor_correct floor_unique
[PROOF STATE]
proof (prove... | {"llama_tokens": 300, "file": null, "length": 2} |
import numpy as np
import matplotlib.pyplot as plt
import os
import getopt
import sys
HISTORY_FILE = 'model_history.npy'
def main(argv):
global HISTORY_FILE
if(argv):
inputFile = argv[0]
else:
print("History file or model directory needed")
sys.exit(0)
if(os.path.isdir(inputFi... | {"hexsha": "d93c471033fa42b4e9f36ae74c292d75be88a90f", "size": 1491, "ext": "py", "lang": "Python", "max_stars_repo_path": "graph_training.py", "max_stars_repo_name": "MichaelSeaman/char-rnn", "max_stars_repo_head_hexsha": "68e2763b3f904873bc580af6f465300c415fa575", "max_stars_repo_licenses": ["MIT"], "max_stars_count"... |
import numpy as np
import pandas as pd
import json
# rand_post = pd.read_excel(
# 'D:\Epay\Epay\Dashboard\Python code\Proxy Payday Loan Data Corrected.xlsx', sheet_name='Rand Post Data')
def gini_total(loc,sheet_name):
data = pd.read_excel(loc, sheet_name, na_values=0)
data.sort_values(by=['PD'], ascendin... | {"hexsha": "83257a19809e7c6cb89cff23db954fee8a04b1dd", "size": 996, "ext": "py", "lang": "Python", "max_stars_repo_path": "python/payday/gini_data.py", "max_stars_repo_name": "LiLyBabe/dashboard_prototype", "max_stars_repo_head_hexsha": "4857844f24a4cb87fda5e9b0386cfe84faf7028a", "max_stars_repo_licenses": ["Apache-2.0... |
#include "ros/ros.h"
#include <iostream>
#include <stdio.h> /* printf, scanf, puts, NULL */
#include <stdlib.h> /* srand, rand */
#include <time.h>
#include <opencv2/core/core.hpp>
#include <opencv2/contrib/contrib.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <opencv2/imgproc/imgproc.hpp>
#include... | {"hexsha": "8621eb727c7ce75c26f1d910805c2a81bdcade9d", "size": 16704, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "catkin_ws/src/imaging_sonar_sim/src/imaging_sonar_sim.cpp", "max_stars_repo_name": "SyllogismRXS/syllo-gazebo", "max_stars_repo_head_hexsha": "81fe461c97ae42104a3d4591084c5b8616841bf5", "max_stars_... |
from jax.config import config
config.update("jax_enable_x64", True)
import jax.numpy as jnp
from jax import jit, grad, vmap
from jax import random
from jax.ops import index, index_update
from jax.flatten_util import ravel_pytree
from jax.scipy.special import logsumexp
from slicereparam.functional import setup_slice_... | {"hexsha": "7c4e8eaaa7a4dc7e9bb61bfa71b15f3d7af336e7", "size": 9167, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/tests.py", "max_stars_repo_name": "PrincetonLIPS/slicereparam", "max_stars_repo_head_hexsha": "d393a4e0f052b8c420dcb890db10e62731d29f57", "max_stars_repo_licenses": ["MIT"], "max_stars_count... |
function read_map(map::String)
map_dir = joinpath(dirname(@__DIR__), "maps")
datac = RGB24.(PNGFiles.load(joinpath(map_dir,map)*".png"))
datah = Float32.(reinterpret(UInt8,PNGFiles.load(joinpath(map_dir,MAP_LIST[map])*".png")))
if size(datac,1) == 2*size(datah,1) # some height maps need to be upsampled
... | {"hexsha": "01f6748aaa488963e07925c62c216bcee1a0e1ea", "size": 1521, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/map_utils.jl", "max_stars_repo_name": "jebej/VoxelSpace", "max_stars_repo_head_hexsha": "7fa2e92cacf00a010970e5198d5efb197d053eab", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, ... |
import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split, StratifiedKFold
from sklearn import svm
from sklearn import metrics
def classification(latent_code, random_seed=42, ten_fold=False):
tumour_type = pd.read_csv('data/PANCAN/GDC-PANCAN_both_samples_tumour_type.tsv', sep='\t... | {"hexsha": "4b485d13de04e9ab78e5a24bfbc9c508de3e7e56", "size": 3618, "ext": "py", "lang": "Python", "max_stars_repo_path": "classification.py", "max_stars_repo_name": "biprodip/multi_omic_vae", "max_stars_repo_head_hexsha": "e174bfd97f9a47b2b916390fcf46830fccae063b", "max_stars_repo_licenses": ["MIT"], "max_stars_count... |
module neutraldata2Daxisymmobj
use, intrinsic :: iso_fortran_env, only: stderr=>error_unit
use phys_consts, only: wp,debug,pi,Re
use meshobj, only: curvmesh
use config, only: gemini_cfg
use inputdataobj, only: inputdata
use neutraldataobj, only: neutraldata
use neutraldata2Dobj, only: neutraldata2D
use reader, only: g... | {"hexsha": "d0b8bbd01227a600a92b4ceeaf9d43ab29560d8f", "size": 11553, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "src/inputdata/neutraldata2Daxisymmobj.f90", "max_stars_repo_name": "gemini3d/GEMINI", "max_stars_repo_head_hexsha": "4655db755101a127bf1bfeddefd6c021f39b1bdb", "max_stars_repo_licenses": ["Apac... |
module ReturnTypes
using BinaryTraits: has_proper_return_type
using Test
import Base: +
struct MyInt <: Integer
value::Int
end
+(x::MyInt, y::Integer) = x.value + y
function test()
# concrete return type, zero ambiguity
f1(x::Int) = x + 1
@test has_proper_return_type(f1, Tuple{Int}, Int)
@test ha... | {"hexsha": "6b458719de936052cd5eaae2bc72691e0d642eb5", "size": 1795, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/test_return_types.jl", "max_stars_repo_name": "tk3369/BinaryTraits.jl", "max_stars_repo_head_hexsha": "cdac46ec724112b45ef66200fc4e9fd471d31700", "max_stars_repo_licenses": ["MIT"], "max_stars... |
#include <Rcpp.h>
#include <RcppEigen.h>
#include <Eigen/Dense>
#include <queue>
// #include<Eigen/SparseCore>
using namespace Rcpp;
using namespace Eigen;
using namespace std;
// [[Rcpp::depends(RcppEigen)]]
//
using Eigen::Map; // 'maps' rather than copies
using Eigen::Matrix; ... | {"hexsha": "9cc12e26adf6ec47edc26d1b60dd394212928456", "size": 11309, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "fspcaC_sept_passD.cpp", "max_stars_repo_name": "denis-rinfret/gioden", "max_stars_repo_head_hexsha": "39f5fab1311420e4b6f9b74e67eb24e9b6a0ab77", "max_stars_repo_licenses": ["Apache-2.0"], "max_star... |
/**
* Project: The Stock Libraries
*
* File: utilities.cpp
* Created: Jun 25, 2012
*
* Author: Abhinav Sarje <abhinav.sarje@gmail.com>
*
* Copyright (c) 2012-2017 Abhinav Sarje
* Distributed under the Boost Software License.
* See accompanying LICENSE file.
*/
#include <iostream>
#include <cmath>
#in... | {"hexsha": "699698811de216e8f559f7bbc3f93d6897e9ddba", "size": 13746, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "image/utilities.cpp", "max_stars_repo_name": "mywoodstock/woo", "max_stars_repo_head_hexsha": "7a6e39b2914ec8ff5bf52c3aa5217214532390e4", "max_stars_repo_licenses": ["BSL-1.0"], "max_stars_count": ... |
# -*- coding: utf-8 -*-
from __future__ import absolute_import, division, print_function, unicode_literals
import numpy as np
import gdspy
import uuid
import picwriter.toolkit as tk
class Taper(gdspy.Cell):
""" Taper Cell class (subclass of gdspy.Cell).
Args:
* **wgt** (WaveguideTemplate): Wa... | {"hexsha": "a6f3e068a923a0a155484bce9dd28fdef4c1b2d8", "size": 4941, "ext": "py", "lang": "Python", "max_stars_repo_path": "picwriter/components/taper.py", "max_stars_repo_name": "crioso/PICwriter", "max_stars_repo_head_hexsha": "24b4ca37361899cba9d23c057b14429055a3da0f", "max_stars_repo_licenses": ["MIT"], "max_stars_... |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
import sys
import time
import numpy as np
import pandas as pd
from hurry.filesize import size
from sklearn.cluster import KMeans
from sklearn.metrics import adjusted_rand_score, calinski_harabasz_score
from tqdm import tqdm
from dpu_kmeans import KMeans as DPU_KMeans
fr... | {"hexsha": "460c9937bef3be455d60ede3b57c50018ff67082", "size": 6652, "ext": "py", "lang": "Python", "max_stars_repo_path": "benchmarks/higgs/CPU+DPU.py", "max_stars_repo_name": "upmem/dpu_kmeans", "max_stars_repo_head_hexsha": "cd84ca1cd9b49116aeb22c500f7d1c78600b3269", "max_stars_repo_licenses": ["MIT"], "max_stars_co... |
import numpy
class CharsLengthExtractor:
metrics = {
'mean': numpy.mean,
'min': numpy.min,
'max': numpy.max
}
def __init__(self, metric):
self.metric = metric
def dfw(self, nodes, lengths):
for node in nodes:
lengths.append(len(node['chars']))
... | {"hexsha": "4649e2175a08029af59fa7c5d92b6529ac31bc7d", "size": 561, "ext": "py", "lang": "Python", "max_stars_repo_path": "lib/FeatureExtraction/Features/CharsLengthExtractor.py", "max_stars_repo_name": "PetukhovVictor/ast2vec", "max_stars_repo_head_hexsha": "f6420d11583caa3e92e0df25fee1d2149da5e7ca", "max_stars_repo_l... |
function [R, t] = read4PCSResults(filename)
M = dlmread(filename);
R = M(1:3, 1:3);
t = M(1:3, 4);
end | {"author": "intellhave", "repo": "SDRSAC", "sha": "b081721e9dfd7843d75aa12f30025b2bd7c8f024", "save_path": "github-repos/MATLAB/intellhave-SDRSAC", "path": "github-repos/MATLAB/intellhave-SDRSAC/SDRSAC-b081721e9dfd7843d75aa12f30025b2bd7c8f024/utils/read4PCSResults.m"} |
program rand_test
use, intrinsic :: iso_fortran_env, only : sp => REAL32, dp => REAL64
use random_mod, only : random_normal_number
implicit none
integer, parameter :: N = 10000
real(kind=dp) :: r
integer :: i
do i = 1, N
call random_normal_number(r)
print *, r
end do
en... | {"hexsha": "f46834121ada0f6c3c6ba261ca4d62e39df40e83", "size": 340, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "Fortran/Types/rand_test.f90", "max_stars_repo_name": "Gjacquenot/training-material", "max_stars_repo_head_hexsha": "16b29962bf5683f97a1072d961dd9f31e7468b8d", "max_stars_repo_licenses": ["CC-BY-4... |
C
C $Id: pcsetr.f,v 1.16 2008-07-27 00:17:20 haley Exp $
C
C Copyright (C) 2000
C University Corporation for Atmospheric Research
C All Rights Reserved
C
C The use of this Software is governed by a License Agreem... | {"hexsha": "9f7bb4f550f581a4f03bdce746f6208b0f48e91e", "size": 6447, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "ncarg2d/src/libncarg/plotchar/pcsetr.f", "max_stars_repo_name": "tenomoto/ncl", "max_stars_repo_head_hexsha": "a87114a689a1566e9aa03d85bcf6dc7325b47633", "max_stars_repo_licenses": ["Apache-2.0"],... |
# ---
# jupyter:
# jupytext:
# text_representation:
# extension: .jl
# format_name: light
# format_version: '1.5'
# jupytext_version: 1.5.2
# kernelspec:
# display_name: Julia 1.5.0
# language: julia
# name: julia-1.5
# ---
using LinearAlgebra
include("SOneTo.jl")
"""
... | {"hexsha": "a8858d891a3dd032238b4e62957c436287832acd", "size": 5806, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/StaticArray.jl/util.jl", "max_stars_repo_name": "terasakisatoshi/Gomagorithm.jl", "max_stars_repo_head_hexsha": "2a120a8298e011ffd016c88d6626eecd4cd86510", "max_stars_repo_licenses": ["MIT"], "... |
from __future__ import division
from builtins import range
import numpy as np
from numpy import newaxis as na
from inspect import getargspec
from functools import wraps
import itertools
from nose.plugins.attrib import attr
from pyhsmm import models as m, distributions as d
##########
# util #
##########
def likeli... | {"hexsha": "58af94b5fd8da7161675cfa3db440247328a1eee", "size": 5078, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/test_hmm_likelihood.py", "max_stars_repo_name": "edfincham/pyhsmm", "max_stars_repo_head_hexsha": "e6cfde5acb98401c2e727ca59a49ee0bfe86cf9d", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
from __future__ import absolute_import, division, print_function
import math
import itertools
import operator
import pytest
from datetime import datetime, date
from cytoolz import pluck
import datashape
import blaze
from blaze.compute.python import (nunique, mean, rrowfunc, rowfunc,
... | {"hexsha": "b7714909cad5ccdaf4556dd21b1f0b30b019b556", "size": 21384, "ext": "py", "lang": "Python", "max_stars_repo_path": "blaze/compute/tests/test_python_compute.py", "max_stars_repo_name": "vitan/blaze", "max_stars_repo_head_hexsha": "0cddb630ad1cf6be3967943337529adafa006ef5", "max_stars_repo_licenses": ["BSD-3-Cla... |
using Metrics
using Test
using Random
Random.seed!(0)
@testset "Metrics.jl" begin
include("regression.jl")
include("classification.jl")
include("rank.jl")
include("nlp.jl")
include("cv.jl")
end
| {"hexsha": "33d45f41b14b52876900033ee0ed0ae4577fd1d6", "size": 216, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/runtests.jl", "max_stars_repo_name": "yuehhua/Metrics.jl", "max_stars_repo_head_hexsha": "6dc6fd6155afe551dd6424debdf7f034e68acb29", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 14, "... |
""" Code to train diagnosis classification models. """
from dermosxai import datasets
from dermosxai import transforms
from dermosxai import utils
from dermosxai import models
from dermosxai import train_abl
import numpy as np
import h5py
from os import path
# Set directory to save results
DDSM_dir = '/src/dermosxai/... | {"hexsha": "7d62ccccc5bf76ca0836983d8108154ba1ba010c", "size": 32118, "ext": "py", "lang": "Python", "max_stars_repo_path": "dermosxai/train_classifier.py", "max_stars_repo_name": "ecobost/dermosxai", "max_stars_repo_head_hexsha": "c851edaf6edaae063c3fd52749994a0225d4a7d1", "max_stars_repo_licenses": ["MIT"], "max_star... |
#impoting dataset
dataset = read.csv('Experience.csv')
# dataset = dataset[, 2:3]
#Splitting the Dataset into Training set and Test set
#install.packages('caTools')
library(caTools)
set.seed(123)
split = sample.split(dataset$Salary, SplitRatio = 2/3)
training_set = subset(dataset, split == TRUE)
test_set = s... | {"hexsha": "e694350b53e378a77c60f2aa7ace31af8381312c", "size": 1492, "ext": "r", "lang": "R", "max_stars_repo_path": "R/Regression/Simple_Linear_Regression/Simple_Linear_Regression.r", "max_stars_repo_name": "luther001/ML", "max_stars_repo_head_hexsha": "d83696e9f9bdc3d2f58de1754f69dab658e029be", "max_stars_repo_licens... |
/*
* Copyright (C) 2014 Martin Preisler <martin@preisler.me>
*
* This file is part of oclcrypto.
*
* Permission is hereby granted, free of charge, to any person obtaining
* a copy of this software and associated documentation files (the
* "Software"), to deal in the Software without restriction, including
* wit... | {"hexsha": "ff092b026fa67ae52b00462d54255a25bea3d721", "size": 4941, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "tests/BLOWFISH_ECB.cpp", "max_stars_repo_name": "wdv4758h/oclcrypto", "max_stars_repo_head_hexsha": "fe66b6f0aa523bd5ea5f24039c1e70def3460ad8", "max_stars_repo_licenses": ["MIT"], "max_stars_count":... |
! @@name: threadprivate.5f
! @@type: F-fixed
! @@compilable: yes
! @@linkable: yes
! @@expect: success
PROGRAM INC_GOOD2
INTEGER, ALLOCATABLE, SAVE :: A(:)
INTEGER, POINTER, SAVE :: PTR
INTEGER, SAVE :: I
INTEGER, TARGET :: TARG
LOGICAL :: FIRSTIN = .TRUE.
!$OMP THREADPRI... | {"hexsha": "d0086c3a05e732b736679b5ee01e53b5311afcff", "size": 983, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "test/openmp_examples/sources/Example_threadprivate.5f.f", "max_stars_repo_name": "kammerdienerb/flang", "max_stars_repo_head_hexsha": "8cc4a02b94713750f09fe6b756d33daced0b4a74", "max_stars_repo_lic... |
/*==============================================================================
Copyright (c) 2009 Peter Dimov
Copyright (c) 2005-2010 Joel de Guzman
Copyright (c) 2010 Thomas Heller
Distributed under the Boost Software License, Version 1.0. (See accompanying
file LICENSE_1_0.txt or copy at ... | {"hexsha": "5843b8ee98077b34794e6b26bbf847eefa16c38b", "size": 999, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "REDSI_1160929_1161573/boost_1_67_0/libs/phoenix/test/boost_bind_compatibility/bind_ref_test.cpp", "max_stars_repo_name": "Wultyc/ISEP_1718_2A2S_REDSI_TrabalhoGrupo", "max_stars_repo_head_hexsha": "eb... |
"""obstacle_avoid_test controller."""
from robot_manager import RobotManager
from args_manager import ArgsManager
import numpy as np
np.random.seed(13482737)
# np.random.seed(13482736)
argsManager = ArgsManager()
robotManager = RobotManager(argsManager.process_args())
robotManager.execute()
| {"hexsha": "3b0cc784687590c4010b5abd5d97bc5f03ba9277", "size": 296, "ext": "py", "lang": "Python", "max_stars_repo_path": "webots-project/controllers/pos-prediction/pos-prediction.py", "max_stars_repo_name": "joangerard/webots-thesis", "max_stars_repo_head_hexsha": "c18c53b281af6c68431b9b3abde07d1934c37dd9", "max_stars... |
#!/usr/bin/env python
# coding: utf-8
import pandas as pd
import pandas_profiling
from sklearn.preprocessing import LabelEncoder
from tqdm import tqdm
from joblib import Parallel,delayed
import numpy as np
import json
import re
import time
from sklearn.utils import shuffle
# tqdm.pandas()
def time_transform(t):
... | {"hexsha": "b39a80e2cb962bfa185f007e44cac7f7c04688ed", "size": 35914, "ext": "py", "lang": "Python", "max_stars_repo_path": "DataLoader.py", "max_stars_repo_name": "JiayinL/Dropout-Prediction", "max_stars_repo_head_hexsha": "fd1de819579b641ff8c7aa416c1fb5cb6c6a7114", "max_stars_repo_licenses": ["MIT"], "max_stars_count... |
import unittest
from prime.input import Field, Parametrization, Intertwiner, InverseIntertwiner
from prime.utils import phis, to_tensor
from prime.output import ConstantOutputCoefficient
import sympy as sp
import numpy as np
class GenerateConstantOutputCoefficient(unittest.TestCase):
# Setup
phis = phis(6)
... | {"hexsha": "c34e17ddcbfccd45312beeb59cb6642dd06f8d3f", "size": 4013, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/output/test_constant_coefficient.py", "max_stars_repo_name": "florianwolz/prime", "max_stars_repo_head_hexsha": "5ac29c523c5730e1d50c5d4703658427fdddfac3", "max_stars_repo_licenses": ["Apach... |
from __future__ import division
from collections import defaultdict
from skimage import io
import numpy as np
import os, glob, sys
"""This script requests all of the tiles required to build up a
given jp2 image, and after fetching each of those tiles, rebuilds
the retrieved images into one large jp2 file"""
def requ... | {"hexsha": "a433ff394ea73e7c0356153aef798ac29ff1afc7", "size": 3429, "ext": "py", "lang": "Python", "max_stars_repo_path": "app/utils/request_yale_map_image_tiles.py", "max_stars_repo_name": "YaleDHLab/gathering-a-building", "max_stars_repo_head_hexsha": "f8ad3f2be0e93090ab4e50755ef81f3b412901c7", "max_stars_repo_licen... |
import pandas as pd
import numpy as np
import scipy.stats
import scipy.special
import pysam
import h5py
import multiprocessing as mp
import itertools as it
import math
import os
from DIGDriver.sequence_model import sequence_tools
from DIGDriver.sequence_model import nb_model
DNA53 = 'NTCGA'
DNA35 = 'NAGCT'
trans = DN... | {"hexsha": "8ecd78a5fe4a78c8ad8452d1c4047f168402cc5e", "size": 25159, "ext": "py", "lang": "Python", "max_stars_repo_path": "DIGDriver/sequence_model/genic_driver_tools.py", "max_stars_repo_name": "maxwellsh/DIGDriver", "max_stars_repo_head_hexsha": "1f8503c8c22861d6f9b601fd8c5a131e3dc31fc1", "max_stars_repo_licenses":... |
[STATEMENT]
lemma the_pw_cat_lKe_colimit_components:
shows "the_pw_cat_lKe_colimit \<alpha> \<KK> \<TT> \<FF> c\<lparr>UObj\<rparr> = \<FF>\<lparr>ObjMap\<rparr>\<lparr>c\<rparr>"
and "the_pw_cat_lKe_colimit \<alpha> \<KK> \<TT> \<FF> c\<lparr>UArr\<rparr> = op_ntcf
(
the_pw_cat_rKe_limit \<alpha> (... | {"llama_tokens": 1016, "file": "CZH_Universal_Constructions_czh_ucategories_CZH_UCAT_PWKan", "length": 2} |
"""
WeightedBinaryLoss{L,W} <: SupervisedLoss
Can an be used to represent a re-weighted version of some type of
binary loss `L`. The weight-factor `W`, which must be in `[0, 1]`,
denotes the relative weight of the positive class, while the
relative weight of the negative class will be `1 - W`.
For example: To crea... | {"hexsha": "da0f655b00571f28f36cc0bea99d87937fee415d", "size": 3538, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/supervised/weightedbinary.jl", "max_stars_repo_name": "johnnychen94/LossFunctions.jl", "max_stars_repo_head_hexsha": "ad125ad0749e1f99e4a82164d0eb2e631e91aa53", "max_stars_repo_licenses": ["MIT... |
# This should probably be its own package
_cufunc(f,x) = f
_cufunc(f,x,xs...) = _cufunc(f, xs...)
using MacroTools
isbcastop(x) = isexpr(x, :call) && x.args[1] in :[.*,./,.+,.-].args
broadcast_inputs(ex) =
ex isa Symbol ? [ex] :
@capture(ex, f_.(args__)) ? vcat(broadcast_inputs.(args)...) :
isbcastop(ex) ? vc... | {"hexsha": "a431e0d6e819a6b925a938283922860e8e32981e", "size": 715, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/cubroadcast.jl", "max_stars_repo_name": "gustafsson/NNlib.jl", "max_stars_repo_head_hexsha": "6b58df43a4de71bda3246e6cac2e073af10da899", "max_stars_repo_licenses": ["MIT"], "max_stars_count": nu... |
module m_prms_streamflow
use variableKind
use prms_constants
! use, intrinsic :: iso_c_binding, only: c_sizeof
use, intrinsic :: iso_fortran_env, only: output_unit
use Control_class, only: Control
use Simulation_class, only: Simulation
use PRMS_BASIN, only: Basin
use PRMS_MUSKINGUM, ... | {"hexsha": "874f55b88b23d1af079c5f781ce4a11ca5dda685", "size": 9662, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "src/m_prms_streamflow.f90", "max_stars_repo_name": "nhm-usgs/bmi-prms6-streamflow", "max_stars_repo_head_hexsha": "f670f20e3da156e3b1935cf23245d4c13fd0fbc9", "max_stars_repo_licenses": ["CC0-1.0... |
[STATEMENT]
lemma fMin_eqI: "(\<And>y. y |\<in>| A \<Longrightarrow> x \<le> y) \<Longrightarrow> x |\<in>| A \<Longrightarrow> fMin A = x"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. \<lbrakk>\<And>y. y |\<in>| A \<Longrightarrow> x \<le> y; x |\<in>| A\<rbrakk> \<Longrightarrow> fMin A = x
[PROOF STEP]
by trans... | {"llama_tokens": 141, "file": null, "length": 1} |
//
// Copyright © 2017 Arm Ltd. All rights reserved.
// SPDX-License-Identifier: MIT
//
#include "../TestUtils.hpp"
#include <Optimizer.hpp>
#include <boost/test/unit_test.hpp>
using namespace armnn;
BOOST_AUTO_TEST_SUITE(Optimizer)
using namespace armnn::optimizations;
BOOST_AUTO_TEST_CASE(PermuteAsReshapeTest)... | {"hexsha": "b44331c9fcdc1ba207749813b366c16451514042", "size": 2155, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/armnn/test/optimizations/PermuteAsReshapeTests.cpp", "max_stars_repo_name": "tom-gall/armnn", "max_stars_repo_head_hexsha": "a21620d32a8a0a8d527c061e2a22d51009d75877", "max_stars_repo_licenses":... |
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