text stringlengths 0 1.25M | meta stringlengths 47 1.89k |
|---|---|
[STATEMENT]
lemma stoch_proc_filt_eq_sets:
assumes "space M = space N"
shows "sets (stoch_proc_filt M X P n) = sets (stoch_proc_filt N X P n)"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. sets (stoch_proc_filt M X P n) = sets (stoch_proc_filt N X P n)
[PROOF STEP]
unfolding stoch_proc_filt_def
[PROOF STATE]
pr... | {"llama_tokens": 1096, "file": "DiscretePricing_Infinite_Coin_Toss_Space", "length": 10} |
# TODO add test cases for half, single, and longdouble functions.
# TODO add test cases for equivalent dtypes: bool_, bytes_, str_, int0, and uint0.
# TODO add test cases for mask operations: mask_or, mask_and, mask_xor, mask_andnot, mask_ori, mask_andi, mask_xori, and mask_andnoti.
# TODO fold all int, uint and flo... | {"hexsha": "1a0fc5a4210df0aa4ffd6e8e5c14f934dc0782b5", "size": 88713, "ext": "py", "lang": "Python", "max_stars_repo_path": "riptable/hypothesis_tests/test_riptide_numpy_equivalency.py", "max_stars_repo_name": "wenjuno/riptable", "max_stars_repo_head_hexsha": "7d519f6838b061dc66e38a7c2a54a1af9bbe4bb7", "max_stars_repo_... |
abstract type Barrier end
function drop(this::Barrier)
this.mEnabled = false
end
struct BufferBarrier <: Barrier
mEnabled::Bool
mTarget::vk.VkCommandBuffer
mSrcStage::vk.VkPipelineStageFlags
mDstStage::vk.VkPipelineStageFlags
mHandle::vk.VkBufferMemoryBarrier
function BufferBarrier(cmd::R... | {"hexsha": "2b45681a36f0932265cab870343a968dcce9dd45", "size": 2063, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/raii/Barriers.jl", "max_stars_repo_name": "gcmiao/Lava.jl", "max_stars_repo_head_hexsha": "fb9656944c63a9612927867cef9b0d0b33fe5f24", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "... |
import unittest
import numpy
from axis_speeds import AxisSpeeds
class TestAxisSpeeds(unittest.TestCase):
def test_zero(self):
speeds = AxisSpeeds()
speeds.axis_rotation_speeds[0] = 0 # roll axis
speeds.axis_rotation_speeds[1] = 0 # yaw axis
speeds.axis_rotation_speeds[2] = 0 # pitc... | {"hexsha": "199a026bd08fb33d00f2ac119c0f466cb1b19691", "size": 3059, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/python3/axis_speeds_test.py", "max_stars_repo_name": "timdelbruegger/freecopter", "max_stars_repo_head_hexsha": "0d7023ba0fdbdfe2609b49e86bf2545fb4c8b2da", "max_stars_repo_licenses": ["MIT"], ... |
# -*- coding: utf-8 -*-
import glob
import os
from sklearn_evaluation.plot import confusion_matrix as plot_cm
from data_load import load_data
import numpy
from keras import callbacks
from keras.models import Sequential
from keras.layers import Dense
from keras.layers import Dropout
from keras.constraints import maxno... | {"hexsha": "f168c26d7cc7f51f7f1fe5b94b57f9d2c349ec5c", "size": 7395, "ext": "py", "lang": "Python", "max_stars_repo_path": "mllc/cv_nnet.py", "max_stars_repo_name": "ipashchenko/mllc", "max_stars_repo_head_hexsha": "2bde6497432d23605b69534c6abf9119f11d3f8e", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, ... |
# !/usr/bin/env python3
# -*-coding:utf-8-*-
# @file:
# @brief:
# @author: Changjiang Cai, ccai1@stevens.edu, caicj5351@gmail.com
# @version: 0.0.1
# @creation date: 29-09-2019
# @last modified: Sun 26 Jan 2020 03:06:57 AM EST
# > see: http://jamesgregson.ca/bilateral-filtering-in-python.html
import numpy as np
import... | {"hexsha": "a18f9404d1c331cfb36bcc33c274d7982aac562a", "size": 15824, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/bilateral_filter.py", "max_stars_repo_name": "ccj5351/DAFStereoNets", "max_stars_repo_head_hexsha": "66b720a4abbac9097a794eacef034bab641771d9", "max_stars_repo_licenses": ["MIT"], "max_stars_... |
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
import os
from keras import backend as K
from keras.datasets import mnist
from keras.utils import np_utils
import keras
import numpy as np
import tensorflow as tf
os.e... | {"hexsha": "6d4e7d1cde20fdb96f73c5596f0fae125907c73a", "size": 2651, "ext": "py", "lang": "Python", "max_stars_repo_path": "EvadeML-Zoo/adv_get_output.py", "max_stars_repo_name": "RU-System-Software-and-Security/NIC", "max_stars_repo_head_hexsha": "35073b5ae1b87a465de156d780ccea3d16b5a5cb", "max_stars_repo_licenses": [... |
"""Image Classification Class for metrics for sail-on."""
from sail_on_client.evaluate.metrics import ProgramMetrics
from evaluate.metrics import M_acc, M_num, M_ndp, M_num_stats
from evaluate.metrics import M_ndp_failed_reaction
from evaluate.metrics import M_accuracy_on_novel
from evaluate.metrics import topk_accura... | {"hexsha": "5081c12ca61bb3c8346aea398371a0aee2ecd7c6", "size": 11573, "ext": "py", "lang": "Python", "max_stars_repo_path": "sail_on_client/evaluate/image_classification.py", "max_stars_repo_name": "darpa-sail-on/sail-on-client", "max_stars_repo_head_hexsha": "1fd7c0ec359469040fd7af0c8e56fe53277d4a27", "max_stars_repo_... |
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
import matplotlib.animation as animation
show_background = True
write_movie_file = False
plt.rcParams['animation.ffmpeg_path'] = 'C:\\Program Files\\ImageM... | {"hexsha": "9b60cb0c304a84a97bb43ed165a83902981e157e", "size": 4725, "ext": "py", "lang": "Python", "max_stars_repo_path": "embedding_visualization.py", "max_stars_repo_name": "txtData/embeddingVis", "max_stars_repo_head_hexsha": "c36619dcb1d8c0d82500c2badec63770fa222e88", "max_stars_repo_licenses": ["Apache-2.0"], "ma... |
//
// 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/json
//
// Test that header file is self-c... | {"hexsha": "883878aa6fefe65368717e3b224fb453e2eb129a", "size": 9081, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "test/parser.cpp", "max_stars_repo_name": "djarek/json", "max_stars_repo_head_hexsha": "e1bd8083ed547de26b567e7eef02a6f1142a9128", "max_stars_repo_licenses": ["BSL-1.0"], "max_stars_count": null, "ma... |
import cv2
import numpy
import openslide
import os
import uuid
from flask import jsonify, request, render_template
from flask_login import login_required, current_user
from Controller import manifest_controller
from Model import freehand_annotation_sqlite
from Model import manifest
original_data_root = 'static/data/... | {"hexsha": "6dd69f2f087ed1bf1620b58d768b5b881a1d2ae7", "size": 9674, "ext": "py", "lang": "Python", "max_stars_repo_path": "Server/freehand_annotation_server.py", "max_stars_repo_name": "llmay98/WSI-online-analysis-framework", "max_stars_repo_head_hexsha": "1f2ee084b7ed87d22f9fbf041a6101c600104bfc", "max_stars_repo_lic... |
from thyme.trajectory import Trajectory
import numpy as np
def replicate(trj, expand: tuple = (1, 1, 1)):
new_trj = Trajectory()
for idf in range(len(trj)):
_trj = trj.extract_frames([idf])
cell = _trj.cells[0]
new_positions = []
new_cells = []
new_forces = []
... | {"hexsha": "3f87b43a7d3de53d780a1c8aca209e5b24dabdc4", "size": 1291, "ext": "py", "lang": "Python", "max_stars_repo_path": "thyme/routines/manipulate.py", "max_stars_repo_name": "nw13slx/thyme", "max_stars_repo_head_hexsha": "b2a16aa1e6b0701adcfd2bd146f85b5c46b35254", "max_stars_repo_licenses": ["MIT"], "max_stars_coun... |
import errno
import json
import logging
import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
from pathlib import Path
import re
import statsmodels.stats.multitest as multitest
from biclust_comp import logging_utils
def threshold_float_to_str(threshold_float):
threshold_str = f"_thresh... | {"hexsha": "834cdf08e9af136ad69e22189c4cef5b26e19ccd", "size": 9916, "ext": "py", "lang": "Python", "max_stars_repo_path": "biclust_comp/utils.py", "max_stars_repo_name": "nichollskc/biclust_comp", "max_stars_repo_head_hexsha": "1b01d44e8a4ab4f40cf015e60bd39afb8616f89d", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
# Copyright 2019 The TensorFlow 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 applica... | {"hexsha": "d30e349a6c7ba3faeea5ec9871bbb722cee13bca", "size": 1516, "ext": "py", "lang": "Python", "max_stars_repo_path": "tensorflow_model_optimization/python/core/sparsity_tf2/prunable_model_test.py", "max_stars_repo_name": "xwinxu/model-optimization", "max_stars_repo_head_hexsha": "c3d78bff440e1cb9f237d9b31f1047b36... |
import numpy as np
import copy
import operator
import matplotlib.pyplot as plt
from .sampler import Sampler
class QRBM:
def __init__(self,
n_visible,
n_hidden,
err_function='mse',
use_tqdm=True,
tqdm=None):
if err_funct... | {"hexsha": "eeb744ad8f54b94f587d6e1f499bfaff1a384c04", "size": 16070, "ext": "py", "lang": "Python", "max_stars_repo_path": "qrbm/qrbm.py", "max_stars_repo_name": "mercari/CFQIRBM", "max_stars_repo_head_hexsha": "fc785a0a62726ee47812aa26692984ee2c8f3d41", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": 17... |
import auxiliary.processthread as process
import pandas as pd
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
import json
import keywords
import argparse
options = keywords.getarguments()
plt.rcParams['svg.fonttype'] = 'none'
plt.rcParams['pdf.fonttype... | {"hexsha": "b37b0ac4bd08852ff6972c0e57e57220f16a7fba", "size": 4140, "ext": "py", "lang": "Python", "max_stars_repo_path": "hpc/processing/plot_fusion_over_time.py", "max_stars_repo_name": "samvonderdunk/artistoo", "max_stars_repo_head_hexsha": "890fad12ee42d7fed78ea28d144ad3b2be321da5", "max_stars_repo_licenses": ["MI... |
[STATEMENT]
lemma locLength_is_length_loc:
"P\<^bsub>wf\<^esub>,P\<^bsub>\<Phi>\<^esub> \<turnstile> (None, h, (stk, loc, C, M, pc) # frs') \<surd> \<Longrightarrow> locLength P C M pc = length loc"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. P\<^bsub>wf\<^esub>,P\<^bsub>\<Phi>\<^esub> |- (None, h, (stk, loc, C... | {"llama_tokens": 195, "file": "Slicing_JinjaVM_SemanticsWF", "length": 1} |
@testset "unsqueeze" begin
x = randn(2, 3, 2)
@test @inferred(unsqueeze(x; dims=1)) == reshape(x, 1, 2, 3, 2)
@test @inferred(unsqueeze(x; dims=2)) == reshape(x, 2, 1, 3, 2)
@test @inferred(unsqueeze(x; dims=3)) == reshape(x, 2, 3, 1, 2)
@test @inferred(unsqueeze(x; dims=4)) == reshape(x, 2, 3, 2, 1... | {"hexsha": "298b9fc3406f7c8ec517797006ca03748dfd18ab", "size": 3502, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/utils.jl", "max_stars_repo_name": "dmolina/MLUtils.jl", "max_stars_repo_head_hexsha": "4ad22745693c05363a03172fbbf14725e606020d", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "... |
using Plots
using LinearAlgebra
include("mess.jl")
using SparseArrays
using Arpack
#komvos1 komvos2 komvos3
xy = [ 0 0;
0 1]; #orismos komvon san xyz
nodes = size(xy)[2]; #arithmos komvon
#komvos1_start komvos1_end
numel = [1 2
]; #orismos ton stixion os grammes me toys akraious komvous
N = siz... | {"hexsha": "e7b2e839b96a77da807584f1103de70b679e3cbe", "size": 5197, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "axnai julia/test.jl", "max_stars_repo_name": "billyon/first", "max_stars_repo_head_hexsha": "fba58ba3832d0b65fd6f3f3a7537d109d1e32eff", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "ma... |
import numpy as np
from multiprocessing import sharedctypes, Pool, process
import itertools
import sys
from netCDF4 import Dataset
import os
import argparse
import timeit
sys.path.append('/home/robinmid/repos/harvey_scaling/')
sys.path.append('/home/robin/repos/harvey_scaling/')
from analysis.utils import WORLD_REGION... | {"hexsha": "14e21e6dcda5c8e2fb44e01184231e82f9ec5a1b", "size": 10258, "ext": "py", "lang": "Python", "max_stars_repo_path": "analysis/DataReader.py", "max_stars_repo_name": "rmiddelanis/harvey_scaling", "max_stars_repo_head_hexsha": "a94064996fb200c26a90482cc63804dcdc3cf6dd", "max_stars_repo_licenses": ["MIT"], "max_st... |
import abc
import math
from os import name, stat
from typing import overload
import tensorflow as tf
from src.utils import sigma2snr, snr2sigma
import tensorflow_probability as tfp
tfd = tfp.distributions
from tensor_annotations import tensorflow as ttf
from tensor_annotations import axes
import numpy as np
from src.c... | {"hexsha": "905050b3abeeae342b8297ea323dbc182a46a123", "size": 7928, "ext": "py", "lang": "Python", "max_stars_repo_path": "turbo-codes/src/channelcoding/channels.py", "max_stars_repo_name": "tripods-xai/isit-2022", "max_stars_repo_head_hexsha": "024a0ccb59f7d4b2c9e88ef96d4a9c57712d6dfd", "max_stars_repo_licenses": ["M... |
// Copyright (c) 2018-2019 The Unit-e developers
// Distributed under the MIT software license, see the accompanying
// file COPYING or http://www.opensource.org/licenses/mit-license.php.
#include <proposer/block_builder.h>
#include <consensus/tx_verify.h>
#include <consensus/validation.h>
#include <test/test_unite.... | {"hexsha": "a907583a9260116c0759e8ca52ffde9c1d304554", "size": 5358, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/test/tx_verify_tests.cpp", "max_stars_repo_name": "frolosofsky/unit-e", "max_stars_repo_head_hexsha": "d3d12508b915986841bd19c4dee9e50dd662a112", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
(*
Copyright (C) 2020 Susi Lehtola
This Source Code Form is subject to the terms of the Mozilla Public
License, v. 2.0. If a copy of the MPL was not distributed with this
file, You can obtain one at http://mozilla.org/MPL/2.0/.
*)
(* type: mgga_exc *)
(* prefix:
mgga_x_jk_params *params;
assert(p->params !=... | {"hexsha": "6e8101cb218a017d25bbaf8c7fa1e097e5aa3d6e", "size": 662, "ext": "mpl", "lang": "Maple", "max_stars_repo_path": "libxc-5.1.6/maple/mgga_exc/mgga_x_jk.mpl", "max_stars_repo_name": "pwang234/lsms", "max_stars_repo_head_hexsha": "6044153b6138512093e457bdc0c15c699c831778", "max_stars_repo_licenses": ["BSD-3-Claus... |
"""
Created: May 2018
@author: JerryX
Find more : https://www.zhihu.com/people/xu-jerry-82
"""
import numpy as np
import sys
import os
curPath = os.path.abspath(os.path.dirname(__file__))
sys.path.append(curPath)
from xDLUtils import Tools
#Tools = xnnUtils.Tools()
# 全连接类
class FCLayer(object):
... | {"hexsha": "8fc2034f0644b29f89328813362022468e3df07b", "size": 3078, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/xDLbase/fc.py", "max_stars_repo_name": "AskyJx/xDeepLearning", "max_stars_repo_head_hexsha": "98d875a34c6b4f4a51dcb825998028fd36260848", "max_stars_repo_licenses": ["MIT"], "max_stars_count": ... |
import matplotlib as mpl
import matplotlib.pyplot as plt
import matplotlib.cm as cm
from skimage.transform import pyramid_gaussian
from PIL import Image
import numpy as np
from struct import unpack
import os
import sys
import re
import gc
import fs_utility
from logger import Logger
log = Logger(__name__)
log.logger... | {"hexsha": "e5402fbbcf07f84dbe9244ea5791bd7da31d21b3", "size": 20257, "ext": "py", "lang": "Python", "max_stars_repo_path": "code/python/src/utility/depthmap_utils.py", "max_stars_repo_name": "manurare/360monodepth", "max_stars_repo_head_hexsha": "43347063f1fa521ea3e0866ff85bbcb734b64393", "max_stars_repo_licenses": ["... |
from concurrent import futures
import nevergrad.optimization as optimization
from nevergrad import instrumentation as instru
import argparse
import torch
from train_loop import TrainLoop
import torch.optim as optim
import torch.utils.data
import model as model_
import numpy as np
from data_load import Loader, Loader_va... | {"hexsha": "d424d4edff1b259e4df801390cd4ee6b10bbd3e3", "size": 8618, "ext": "py", "lang": "Python", "max_stars_repo_path": "hp_search.py", "max_stars_repo_name": "joaomonteirof/multitask_asv", "max_stars_repo_head_hexsha": "16795628e4bcac8c7b83b6edeacee1a739495092", "max_stars_repo_licenses": ["MIT"], "max_stars_count"... |
from gensim.models import Word2Vec
import json
import logging
import numpy as np
logging.basicConfig(format='%(asctime)s : %(message)s', level=logging.INFO)
word2vec_params = {
'sg': 1, # 0 ： CBOW； 1 : skip-gram
"size": 300,
"alpha": 0.01,
"min_alpha": 0.0005,
'window': 10,
'min_... | {"hexsha": "8544f2616ff531a0a981609fd0a60ba3eb07934c", "size": 4575, "ext": "py", "lang": "Python", "max_stars_repo_path": "Negative Sampling and LSTM/word2vec_gensim.py", "max_stars_repo_name": "yu45020/Pytorch_Learning_Note", "max_stars_repo_head_hexsha": "b4b89b604fc2effd1ac16359f842b2f143019309", "max_stars_repo_li... |
# -*- coding: utf-8 -*-
"""
make connectome matrices for each measure, sorted by infomap modules
Created on Sun Jun 21 15:58:15 2015
@author: poldrack
"""
import os
import numpy
import nilearn.plotting
import scipy.stats
from myconnectome.utils.load_parcel_data import load_parcel_data
import matplotlib.pyplot as plt
... | {"hexsha": "b5965a0816a8c8f5919e82fa7d1ac64313e4f918", "size": 5863, "ext": "py", "lang": "Python", "max_stars_repo_path": "myconnectome/rsfmri/mk_sorted_adjmatrices.py", "max_stars_repo_name": "poldrack/myconnectome", "max_stars_repo_head_hexsha": "201f414b3165894d6fe0be0677c8a58f6d161948", "max_stars_repo_licenses": ... |
# Copyright Hugh Perkins 2016, 2017
"""
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, sof... | {"hexsha": "3524dfd3a8afde17d10fdcaf1746f8c5a9ef5c64", "size": 6178, "ext": "py", "lang": "Python", "max_stars_repo_path": "test/test_atomic.py", "max_stars_repo_name": "dendisuhubdy/coriander", "max_stars_repo_head_hexsha": "7df182981e5c4a8e043fea25d272d025a953f06d", "max_stars_repo_licenses": ["Apache-2.0"], "max_sta... |
using Random
Random.seed!(1974)
println("Seed 1974: ",rand(),"\t", rand(), "\t", rand())
Random.seed!(1975)
println("Seed 1975: ",rand(),"\t", rand(), "\t", rand())
Random.seed!(1974)
println("Seed 1974: ",rand(),"\t", rand(), "\t", rand())
| {"hexsha": "f7896349fb38c51b5fde5748caaf8f2b6ad9a445", "size": 242, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "1_chapter/seedExample.jl", "max_stars_repo_name": "Yoshinobu-Ishizaki/StatsWithJuliaBook", "max_stars_repo_head_hexsha": "4c704e96d87b91e680122a6b6fa2d2083c70ea88", "max_stars_repo_licenses": ["MIT"... |
<p align="center">
</p>
## Interactive Spatial Estimation with Strings of Data Demonstration
### Michael Pyrcz, Associate Professor, University of Texas at Austin
##### [Twitter](https://twitter.com/geostatsguy) | [GitHub](https://github.com/GeostatsGuy) | [Website](http://michaelpyrcz.com) | [GoogleScholar]... | {"hexsha": "460f96e1b813815b68c01c7150d2777ce1c95c74", "size": 31983, "ext": "ipynb", "lang": "Jupyter Notebook", "max_stars_repo_path": "Interactive_String_Effect.ipynb", "max_stars_repo_name": "AndrewAnnex/PythonNumericalDemos", "max_stars_repo_head_hexsha": "11a7dec09bf08527b358fa95119811b6f73023b5", "max_stars_repo... |
function flg = checkDictUse
% Since MATLAB 7.3(2006A) and 7.4(2006B) doesnot support dicom dictionary
% this function checks the matlab version and returns the flag to allow the
% use of dictionary.
%
% DK
%
% Copyright 2010, Joseph O. Deasy, on behalf of the CERR development team.
%
% This file is part of The Comput... | {"author": "cerr", "repo": "CERR", "sha": "d320754abad9dcb78508ab69f33ae9f644202114", "save_path": "github-repos/MATLAB/cerr-CERR", "path": "github-repos/MATLAB/cerr-CERR/CERR-d320754abad9dcb78508ab69f33ae9f644202114/CERR_core/Importing/checkDictUse.m"} |
#This software is Copyright 2012 The Regents of the University of California. All Rights Reserved.
#Permission to use, copy, modify, and distribute this software and its documentation for educational, research and non-profit purposes for non-profit institutions, without fee, and without a written agreement is hereby gr... | {"hexsha": "fa30778099bed618a7f24238a61ca0040fd414d4", "size": 4754, "ext": "py", "lang": "Python", "max_stars_repo_path": "cytoseg/neural_network.py", "max_stars_repo_name": "slash-segmentation/DP2", "max_stars_repo_head_hexsha": "6f768e4b8a75a3ab2bf1359ae94704332426a4d6", "max_stars_repo_licenses": ["Unlicense"], "ma... |
# SPDX-License-Identifier: MIT
from enum import IntEnum
from math import log2
from collections import Counter
from sys import stderr
try:
from scipy.stats import chi2, kstest, uniform
except ImportError:
print('the scipy library is not installed. \n'
'Use `pip install scipy` to install it', file=std... | {"hexsha": "f67df7f5f5999535c02ad069cd1b70a7f925bc89", "size": 7046, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/analysis.py", "max_stars_repo_name": "malon43/entropy-visualization", "max_stars_repo_head_hexsha": "f5a2da84b105ac00fc39b94eff32394c7a43819b", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
using BufferedStreams
using Test
struct InfiniteStream <: IO
byte::UInt8
end
function Base.eof(stream::InfiniteStream)
return false
end
function Base.read(stream::InfiniteStream, ::Type{UInt8})
return stream.byte
end
if isdefined(Base, :unsafe_read)
function Base.unsafe_read(stream::InfiniteStream, ... | {"hexsha": "efc6182986da19e8cef5ad91208a5403db2a8132", "size": 16859, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/runtests.jl", "max_stars_repo_name": "UnofficialJuliaMirror/BufferedStreams.jl-e1450e63-4bb3-523b-b2a4-4ffa8c0fd77d", "max_stars_repo_head_hexsha": "b85ff82efcd95d3a4d72a4021f0833b579554bec",... |
import random
from typing import Union, Tuple, Any, Mapping
import numpy as np
from albumentations.core.transforms_interface import (
ImageOnlyTransform,
)
from .functional import channel_dropout
__all__ = ["ChannelDropout"]
class ChannelDropout(ImageOnlyTransform):
"""Randomly Drop Channels in the input I... | {"hexsha": "45d42c76d0a70c4b6dee8817c3955d4b0b1b141e", "size": 2280, "ext": "py", "lang": "Python", "max_stars_repo_path": "albumentations/augmentations/dropout/channel_dropout.py", "max_stars_repo_name": "Multihuntr/albumentations", "max_stars_repo_head_hexsha": "d2db7d7d8859f7090f6fc39a37d6c8858db218d6", "max_stars_r... |
[STATEMENT]
lemma p1_has_only_Says' [rule_format]: "evs \<in> p1 \<Longrightarrow>
ev \<in> set evs \<longrightarrow> (\<exists>A B X. ev=Says A B X)"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. evs \<in> p1 \<Longrightarrow> ev \<in> set evs \<longrightarrow> (\<exists>A B X. ev = Says A B X)
[PROOF STEP]
by (er... | {"llama_tokens": 146, "file": null, "length": 1} |
// Copyright (c) 2013 Damond Howard
//
// 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 <hpx/hpx.hpp>
#include <hpx/runtime/components/component_factory.hpp>
#include <hpx/util/portable_binary_iarchive.hpp>... | {"hexsha": "d7af4f6da983f416106c4f940ca9a4c0020bb489", "size": 1635, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "cuda/kernel.cpp", "max_stars_repo_name": "josephwinston/hpxcl", "max_stars_repo_head_hexsha": "ebd18f245beea46be943ffa22e2fb8145b0b2836", "max_stars_repo_licenses": ["BSL-1.0"], "max_stars_count": n... |
from manimlib.animation.animation import Animation
from manimlib.utils.rate_functions import linear
import numpy as np
class Simulate(Animation):
CONFIG = {
"rate_func" : linear
}
def __init__(self, scene, mobject, **kwargs):
self.physic_object = mobject
self.scene = mobject
... | {"hexsha": "3625acc2421e3fcffa0ad71c1180afd43706b1ad", "size": 858, "ext": "py", "lang": "Python", "max_stars_repo_path": "manim_demo/manimlib/animation/physics.py", "max_stars_repo_name": "shujunge/manim_tutorial", "max_stars_repo_head_hexsha": "8e320373f0404dcc0a200ab3750ee70784dc1345", "max_stars_repo_licenses": ["M... |
# BSD 3-Clause License; see https://github.com/scikit-hep/awkward-1.0/blob/master/LICENSE
from __future__ import absolute_import
import sys
import copy
import pytest
import numpy
import awkward1
def test():
np_array = numpy.array([0.0, 1.1, 2.2, 3.3, 4.4, 5.5, 6.6, 7.7, 8.8, 9.9])
one = awkward1.Array(np_... | {"hexsha": "fae4e37a5ea0145f72dec01a0e09ad2a587ac92f", "size": 1103, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/test_0511-copy-and-deepcopy.py", "max_stars_repo_name": "uccross/awkward-1.0", "max_stars_repo_head_hexsha": "af774dee5efa3ced4c478e2172f4cb71057ace33", "max_stars_repo_licenses": ["BSD-3-Cl... |
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import argparse
from torch.utils.data import DataLoader
import torch
import torch.nn as nn
from torch.autograd import Variable
import torchvision
import torchvision.transforms as transforms
from torch.utils.data... | {"hexsha": "6a50f5d09b296e0436216ca9e548733dbace8b0d", "size": 7068, "ext": "py", "lang": "Python", "max_stars_repo_path": "main.py", "max_stars_repo_name": "NSLRP/IIQ-CNN", "max_stars_repo_head_hexsha": "09d8d9e5dd704fb317769b7e8c9faa6617b2e23d", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": null, "max... |
import json
from bs4 import BeautifulSoup
import urllib3
import numpy as np
import pandas as pd
import re
http = urllib3.PoolManager()
rows = 20
def get_data(start: int, rows: int):
url = f"http://nemo-solrcloud-cluster1.springer-sbm.com:8983/solr/nemo_smed_live-2019-10-17-10/select?wt=json&q=*:*&fq=-productGr... | {"hexsha": "7d4c845ff3065061360d6dad81594db694aae668", "size": 1832, "ext": "py", "lang": "Python", "max_stars_repo_path": "export_from_solr/export_smed_articles.py", "max_stars_repo_name": "lukas-abegg/nemo-elasticbert-service", "max_stars_repo_head_hexsha": "3d1d47ce7525b85b2f86f570d550022ac297ca6d", "max_stars_repo_... |
""" Test Logistic Regression Model
Since Logistic Regression model is from scikit-learn, we test it by checking if
it has the same result as the model in scikit-learn on iris dataset.
"""
import unittest
from numpy.testing import assert_array_equal
from sklearn import datasets
try:
from sklearn.model_selection im... | {"hexsha": "16c3268161aca46853d12bbf4fa8f66805d94ac8", "size": 1562, "ext": "py", "lang": "Python", "max_stars_repo_path": "libact/models/tests/test_logistic_regression.py", "max_stars_repo_name": "joequant/libact", "max_stars_repo_head_hexsha": "4fbf4d59fd0d4e23858b264de2f35f674c50445b", "max_stars_repo_licenses": ["B... |
# https://towardsdatascience.com/step-by-step-r-cnn-implementation-from-scratch-in-python-e97101ccde55
# https://github.com/1297rohit/RCNN
import os
import cv2
import pandas as pd
import numpy as np
# Intersection over Union https://www.pyimagesearch.com/2016/11/07/intersection-over-union-iou-for-object-detection/... | {"hexsha": "29d950b08c95e3ef41b2dcef2c8ef4b2414178dd", "size": 6489, "ext": "py", "lang": "Python", "max_stars_repo_path": "CategoricalRCNN/categorical_image_generator.py", "max_stars_repo_name": "vladiant/ObjectDetectSamples", "max_stars_repo_head_hexsha": "ac82d0d8284f6fdbcebf1ff429d2aec18cf03fb0", "max_stars_repo_li... |
"""Tests for the TimeChangedBrownianMotion class."""
import numpy as np
import pytest
from processes.processes import TimeChangedBrownianMotion
@pytest.fixture(scope="module")
def process():
return TimeChangedBrownianMotion(time_change=lambda t: np.tanh(t))
@pytest.mark.parametrize("time_change", [1, "a", la... | {"hexsha": "39bcade892118141133197a749eff0b86e2c9b9d", "size": 1430, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/test_time_changed_brownian_motion.py", "max_stars_repo_name": "martinstefanik/processes", "max_stars_repo_head_hexsha": "44f95d99995ec0a2dc530f25d3adf69ebfb69841", "max_stars_repo_licenses":... |
import unittest
import six
import cupy
from cupy import core
from cupy import testing
@testing.gpu
class SimpleReductionFunction(unittest.TestCase):
def setUp(self):
self.my_int8_sum = core.create_reduction_func(
'my_sum', ('b->b',), ('in0', 'a + b', 'out0 = a', None))
@testing.numpy_c... | {"hexsha": "7c9f21ca435400a230b961ecb91e256487878bd5", "size": 3605, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/cupy_tests/core_tests/test_reduction.py", "max_stars_repo_name": "yutiansut/cupy", "max_stars_repo_head_hexsha": "d460ed81d1deb01db7a55c0f57804856299f491d", "max_stars_repo_licenses": ["MIT"... |
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