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__doc__ = \
"""
Calibrate multiple Intel RealSense D4XX cameras to a single global coordinate system using a defined checkerboard
Distributed as a module of DynaMo: https://github.com/anderson-cu-bioastronautics/dynamo_realsense-capture
"""
#############################################################################... | {"hexsha": "0f9cd90fdd904b62f2a4cb7ca3b2624833ac73c6", "size": 15911, "ext": "py", "lang": "Python", "max_stars_repo_path": "dynamo/calibration.py", "max_stars_repo_name": "anderson-cu-bioastronautics/dynamo_realsense-capture", "max_stars_repo_head_hexsha": "48f39afa41dfc18f444fe3b16ca451ab59ace62c", "max_stars_repo_li... |
"""."""
import time as _time
import numpy as np
from siriuspy.epics import PV
from siriuspy.devices import DCCT, SOFB
from ..optimization import PSO, SimulAnneal
class Septum:
"""."""
def __init__(self):
"""."""
self.sp = 'TB-04:PM-InjSept:Kick-SP'
self.rb = 'TB-04:PM-InjSept:Kick-... | {"hexsha": "5687e7577d0c01ab7522b6d64391df3a4be95d3f", "size": 7967, "ext": "py", "lang": "Python", "max_stars_repo_path": "apsuite/commisslib/posang_injbo_opt.py", "max_stars_repo_name": "carneirofc/apsuite", "max_stars_repo_head_hexsha": "1bbaa44ec6b89f50201790d6fab05c32729db6e1", "max_stars_repo_licenses": ["MIT"], ... |
import numpy as np
import wget
import logging
import requests
from bs4 import BeautifulSoup
from urllib import parse
from recolo import list_files_in_folder
import os
import pathlib
cwd = pathlib.Path(__file__).parent.resolve()
"""" This module provides data from an impact hammer experiment where the hammer was knoc... | {"hexsha": "8c45c01e537a093ab4a725407bd8dc7ae84b4a57", "size": 4815, "ext": "py", "lang": "Python", "max_stars_repo_path": "recolo/demoData/dataverseData.py", "max_stars_repo_name": "PolymerGuy/recon", "max_stars_repo_head_hexsha": "14d66e3d5cb5fcc4868df326b045952daf139291", "max_stars_repo_licenses": ["MIT"], "max_sta... |
import numpy as np
import pandas as pd
from pathlib import Path
import json
root_dir = Path('__file__').resolve().parent
data_dir = root_dir / "data" / "preprocessed"
data_name = "data.csv"
train_name = "train.csv"
test_name = "test.csv"
domain_name = "domain.json"
config_name = "config.json"
def write_data(config,... | {"hexsha": "c0c8a1ef58cecfdb2e6f04413e4f441a62b7b223", "size": 17886, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/script/generate_synthetic_data.py", "max_stars_repo_name": "FumiyukiKato/HDPView", "max_stars_repo_head_hexsha": "9e70ec567086375764fb4adf7ecd879947a48b1b", "max_stars_repo_licenses": ["Apach... |
import numpy as np
fish = np.genfromtxt("../input/day6.txt", delimiter=",")
fish = np.array([np.sum(fish == n) for n in range(0, 9)])
for n in [80, 256 - 80]:
for _ in range(n):
fish = np.roll(fish, -1)
fish[6] += fish[8]
print(np.sum(fish))
| {"hexsha": "0f949cf70417f831dafaa4e58cfc1d039b6e874d", "size": 269, "ext": "py", "lang": "Python", "max_stars_repo_path": "fifty-six/day6/day6.py", "max_stars_repo_name": "BasedJellyfish11/Advent-of-Code-2021", "max_stars_repo_head_hexsha": "9ed84902958c99c341ec2444d5db561c84348911", "max_stars_repo_licenses": ["MIT"],... |
// Boost.Geometry (aka GGL, Generic Geometry Library)
// Copyright (c) 2007-2012 Barend Gehrels, Amsterdam, the Netherlands.
// Use, modification and distribution is subject to the Boost Software License,
// Version 1.0. (See accompanying file LICENSE_1_0.txt or copy at
// http://www.boost.org/LICENSE_1_0.txt)
#ifnd... | {"hexsha": "a365ccf90a00600c1d6950f1916444f17202aa88", "size": 1756, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "CranApp/R-Portable/App/R-Portable/library/BH/include/boost/geometry/algorithms/detail/overlay/calculate_distance_policy.hpp", "max_stars_repo_name": "singhmanish979/Trend-Analytics", "max_stars_repo... |
import argparse
import multiprocessing
import os
import subprocess
import tempfile
from functools import partial
from pathlib import Path
from typing import Tuple
import matplotlib.pyplot as plt
import numpy
from mlxtk import plot, units
from mlxtk.cwd import WorkingDir
from mlxtk.inout.dmat2 import read_dmat2_gridre... | {"hexsha": "0136a00c3ee31e409550d55a631697f774948e5d", "size": 2920, "ext": "py", "lang": "Python", "max_stars_repo_path": "mlxtk/scripts/dmat2_gridrep_video.py", "max_stars_repo_name": "f-koehler/mlxtk", "max_stars_repo_head_hexsha": "373aed06ab23ab9b70cd99e160228c50b87e939a", "max_stars_repo_licenses": ["MIT"], "max_... |
/*
* 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)
*
* Copyright (c) 2009 Helge Bahmann
* Copyright (c) 2012 Tim Blechmann
* Copyright (c) 2013-2018, 2020-2021 Andrey Semashev
*/
/*!
* \file atomic/detail... | {"hexsha": "c5387f461a459be4507c59b00dad4be7859b0c34", "size": 5638, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "lib/boost_1.78.0/boost/atomic/detail/bitwise_cast.hpp", "max_stars_repo_name": "LaudateCorpus1/math", "max_stars_repo_head_hexsha": "990a66b3cccd27a5fd48626360bb91093a48278b", "max_stars_repo_licens... |
import math
import os
import random
import shutil
import time
from datetime import datetime
from logging import warning
import numpy as np
import pandas as pd
# spark_location = '/Users/Leo/spark-2.4.3-bin-hadoop2.7' # Set your own
# java8_location = '/Library/Java/JavaVirtualMachines/jdk1.8.0_151.jdk/Contents/Home/... | {"hexsha": "0fd03ddf12a6e48f76fc917e317042d806e96e56", "size": 31275, "ext": "py", "lang": "Python", "max_stars_repo_path": "brainex/experiments/harvest_setup.py", "max_stars_repo_name": "ebuntel/BrainExTemp", "max_stars_repo_head_hexsha": "991038155a6e9289af90da3d800210841ef23ff1", "max_stars_repo_licenses": ["MIT"], ... |
import numpy as np
import argparse
import cv2
from paddle.inference import Config
from paddle.inference import create_predictor
from paddle.inference import PrecisionType
# this is a simple resnet block for dynamci test.
def init_predictor(args):
config = Config('./model')
config.enable_memory_optim()
co... | {"hexsha": "3766fd5256a7e797c54c1bc4e778fd29c41f3157", "size": 3428, "ext": "py", "lang": "Python", "max_stars_repo_path": "python/paddle_trt/infer_trt_ernie.py", "max_stars_repo_name": "wangjiawei04/Paddle-Inference-Demo", "max_stars_repo_head_hexsha": "b00529f076ae628a8e0c95b10d9ce10259df1103", "max_stars_repo_licens... |
# Copyright (c) 2021 Oleg Polakow. All rights reserved.
# This code is licensed under Apache 2.0 with Commons Clause license (see LICENSE.md for details)
"""Base plotting functions.
Provides functions for visualizing data in an efficient and convenient way.
Each creates a figure widget that is compatible with ipywidg... | {"hexsha": "71d86ab4c588644f41e08be92337ca8f57a289c8", "size": 34072, "ext": "py", "lang": "Python", "max_stars_repo_path": "vectorbt/generic/plotting.py", "max_stars_repo_name": "lmservas/vectorbt", "max_stars_repo_head_hexsha": "be3ac88b7ed50834db599b3fd53a8421dfa480ed", "max_stars_repo_licenses": ["Apache-2.0"], "ma... |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
import os, sys
import time
BASE_DIR = os.path.dirname(o... | {"hexsha": "e4782ef67700d7ead54b633ce4ee1b5d157c40e2", "size": 13183, "ext": "py", "lang": "Python", "max_stars_repo_path": "models/cad_proposal_module.py", "max_stars_repo_name": "jeonghyunkeem/cs492h", "max_stars_repo_head_hexsha": "6fc550cf9e907e38d768315ea7ffbf239a215565", "max_stars_repo_licenses": ["MIT"], "max_s... |
# -*- coding: utf-8 -*-
"""
Created on Tue Apr 4 22:02:20 2017
@author: cvpr
Sort and save weighted patches
"""
import cv2
import numpy as np
import os
prewitt_img_path = '../data/Imageset/prewitt_images/' #path to gradient image
saliency_img_path = '../data/Imageset/saliency_images/' #path to salien... | {"hexsha": "f4d15ef7d982cb568812a30e9cb8204fcca5d2ef", "size": 1720, "ext": "py", "lang": "Python", "max_stars_repo_path": "tools/choose_patches.py", "max_stars_repo_name": "JayMarx/VSBIQA", "max_stars_repo_head_hexsha": "9dc949038163b9d584253312df54f9b667b995d1", "max_stars_repo_licenses": ["MIT"], "max_stars_count": ... |
import unittest
import numpy as np
import tensorflow as tf
from segelectri.data_loader.utils.manipulate_img_op import generate_crop_boxes, get_available_stuff, split_img_op
class TestManipulateImgOp(unittest.TestCase):
def test_get_available_stuff(self):
self.assertEqual([1], get_available_stuff(1024, 1... | {"hexsha": "5d504dedf480c40f649e44636a75a2b6b7d44a43", "size": 1707, "ext": "py", "lang": "Python", "max_stars_repo_path": "test/test_data_loader/test_utils/test_manipulate_img_op.py", "max_stars_repo_name": "imabackstabber/segment-with-nn", "max_stars_repo_head_hexsha": "0c45561be3d19fe37321716f526cf706f81ec42f", "max... |
import torch
import numpy as np
import torch.nn as nn
import pickle
import os
from sample_generator import sample_generator
from iterative_classifier import iterative_classifier
# Parameters
NR = 64
NT_list = np.arange(16, 33)
NT_prob = NT_list/NT_list.sum()
mod_n = 16
d_transmitter_encoding = NR
d_model = 512
n_head... | {"hexsha": "58af41aadf88f077cce417254a01801ba6689ce1", "size": 6989, "ext": "py", "lang": "Python", "max_stars_repo_path": "iid_channels/qam_16/re-mimo/train_classifier.py", "max_stars_repo_name": "dannis999/RE-MIMO", "max_stars_repo_head_hexsha": "199ddec7f142ba5bd87e76e0b5f7790c64e69b0c", "max_stars_repo_licenses": [... |
# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
#
# 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": "56e3b7bbba14fa94f4d029794c68ee3746c022bb", "size": 3793, "ext": "py", "lang": "Python", "max_stars_repo_path": "paddlevideo/loader/dataset/ms_tcn_dataset.py", "max_stars_repo_name": "Simon-liusheng/PaddleVideo", "max_stars_repo_head_hexsha": "6c35b68bc745c659813d6517eecade9c9508a628", "max_stars_repo_licens... |
[STATEMENT]
lemma JHS_Tr1_6:" \<lbrakk>Group G; 0 < r; 0 < s; compseries G r f; compseries G s g;
i \<le> r * s - Suc 0; Suc (rtos r s i) < r * s\<rbrakk> \<Longrightarrow>
((Gp G (cmp_rfn G r f s g i)) / (cmp_rfn G r f s g (Suc i))) \<cong>
((Gp G (g (Suc (rtos r s i div r)) \<diamondop>\<^bsub>G\<^esub>
(... | {"llama_tokens": 48341, "file": "Group-Ring-Module_Algebra3", "length": 71} |
#include <boost/local_function/aux_/add_pointed_const.hpp>
| {"hexsha": "47584a114af94a1a811e2b88ad3660355856d066", "size": 59, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "src/boost_local_function_aux__add_pointed_const.hpp", "max_stars_repo_name": "miathedev/BoostForArduino", "max_stars_repo_head_hexsha": "919621dcd0c157094bed4df752b583ba6ea6409e", "max_stars_repo_lice... |
import gtable as gt
import numpy as np
import pandas as pd
class TimeSuite:
def setup(self):
self.df1_s = pd.DataFrame({'A': ['A0', 'A1', 'A2', 'A3'],
'B': ['B0', 'B1', 'B2', 'B3'],
'C': ['C0', 'C1', 'C2', 'C3'],
... | {"hexsha": "263450ef31c532db1772467c4723505220692303", "size": 4179, "ext": "py", "lang": "Python", "max_stars_repo_path": "benchmarks/benchmark_outer_join.py", "max_stars_repo_name": "guillemborrell/gtable", "max_stars_repo_head_hexsha": "f03d9b208916c566e8033f7d8535dbc527b8dc38", "max_stars_repo_licenses": ["BSD-3-Cl... |
from __future__ import print_function, division
import numpy as np
import sounddevice as sd
import samplerate as sr
from fifo import FIFO
class OutputProcessor(object):
"""Basic output processor.
Passes samples through by multiplying with `input_gain` and `output_volume`.
"""
def __init__(self, in... | {"hexsha": "a369448579b26a1563911a34e7c15b65a978822e", "size": 4628, "ext": "py", "lang": "Python", "max_stars_repo_path": "soundbridge.py", "max_stars_repo_name": "tuxu/soundbridge", "max_stars_repo_head_hexsha": "31b4381e3d86b0bbe5d603a576cc1581a7fe8320", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "... |
#!/usr/bin/env python
__author__ = "Benjamin Quici, Ross J. Turner"
__date__ = "25/02/2021"
"""
Helper functions and classes for synchrofit's core modules.
"""
import logging
import numpy as np
logging.basicConfig(format="%(levelname)s (%(funcName)s): %(message)s")
logger = logging.getLogger(__name__)
logger.setLev... | {"hexsha": "6750cf1ab8619f2e21413d6e592129607b473037", "size": 15965, "ext": "py", "lang": "Python", "max_stars_repo_path": "sf/helpers.py", "max_stars_repo_name": "synchrofit/synchrofit", "max_stars_repo_head_hexsha": "9c60117b4753b9b599709c5bba4f38e992d15f20", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 12... |
#!/usr/bin/python
import os
import sys
import glob
import numpy as np
from Bio import SeqIO
from collections import defaultdict
from Bio.SeqUtils.ProtParam import ProteinAnalysis as PA
def Mut2ID(Mut, WTseq, residues):
ID = ''
for residue, aa in zip(residues, WTseq):
if str(residue) in Mut: ID += Mut.rsplit(st... | {"hexsha": "e12cd4f8cc203f7145b95a322aba6280351d89af", "size": 3896, "ext": "py", "lang": "Python", "max_stars_repo_path": "script/compile_fit_result.py", "max_stars_repo_name": "Wangyiquan95/NA_EPI", "max_stars_repo_head_hexsha": "140b3f1f6675717560c486830b9c099f22c300d6", "max_stars_repo_licenses": ["MIT"], "max_star... |
# -*- coding: utf-8 -*-
from functools import reduce
from itertools import zip_longest
from math import ceil
from math import floor
from math import log
from scipy import ndimage
import numpy as np
def morton_array(shape):
"""
Return array with Morton numbers.
Inspired by:
https://graphics.stanford... | {"hexsha": "e8570108c2af23f6389ee50dbc72cfa39f54cc83", "size": 5403, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/threedidepth/morton.py", "max_stars_repo_name": "nens/threedidepth", "max_stars_repo_head_hexsha": "dbe38acc745202f39741b607000f3d3b1611d434", "max_stars_repo_licenses": ["BSD-3-Clause"], "max... |
import random
import numpy as np
import tensorflow as tf
class ReplayMemory:
def __init__(self, capacity, transition_length):
self.size = capacity
self.memory = np.zeros((capacity, transition_length), dtype=np.float32)
self.pointer = 0
def remember(self, state, action, reward, next_... | {"hexsha": "8baee0c25d4dff03abbc987bebd0f809356db968", "size": 4576, "ext": "py", "lang": "Python", "max_stars_repo_path": "dqn/tic_tac_toe/agent.py", "max_stars_repo_name": "ViacheslavBobrov/ReinforcementLearning", "max_stars_repo_head_hexsha": "4e64231fb22d92b7bb8210be02a48cb4330ae65d", "max_stars_repo_licenses": ["M... |
"""
Implement Logistic regression/perceptron algorithm for classification. The implementation, should have a fit
method that accepts a list of lists of features, and a list of corresponding targets each a 1-hot encoded list
for the correct class.
The trained model should have a predict method that accepts a single set... | {"hexsha": "0a5958eece8b68fa55abfb3344a2a897af4637b4", "size": 5406, "ext": "py", "lang": "Python", "max_stars_repo_path": "python/logistic_regression.py", "max_stars_repo_name": "mohamedabdelbary/algo-exercises", "max_stars_repo_head_hexsha": "3b2b700453c010f61c0d4099762727e988e2b124", "max_stars_repo_licenses": ["MIT... |
# Licensed under a 3-clause BSD style license - see LICENSE.rst
"""
Astronomical and physics constants in SI units. See :mod:`astropy.constants`
for a complete listing of constants defined in Astropy.
"""
import numpy as np
from .constant import Constant, EMConstant
# PHYSICAL CONSTANTS
class CODATA2014(Constant)... | {"hexsha": "f4604df2256b9280f0b86ade9b3a12aa1245b723", "size": 3680, "ext": "py", "lang": "Python", "max_stars_repo_path": "astropy/constants/codata2014.py", "max_stars_repo_name": "b1quint/astropy", "max_stars_repo_head_hexsha": "a170a74739e4356c169429a42e554f9777b53f4d", "max_stars_repo_licenses": ["BSD-3-Clause"], "... |
#%%
import imp
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import anthro.viz
import anthro.tessellation as tess
import shapely
import scipy.spatial
from shapely.geometry import LineString, MultiLineString, MultiPoint, Point
from shapely.geometry import Polygon, box, MultiPolygon
from shapel... | {"hexsha": "20edeaf3dda02dddeb555ab9e8097c8edb029c98", "size": 2220, "ext": "py", "lang": "Python", "max_stars_repo_path": "code/exploratory/voronoi_treemap.py", "max_stars_repo_name": "ilopezgp/human_impacts", "max_stars_repo_head_hexsha": "b2758245edac0946080a647f1dbfd1098c0f0b27", "max_stars_repo_licenses": ["MIT"],... |
[STATEMENT]
lemma distinct_list_of_dlist:
"distinct (list_of_dlist (dxs :: 'a set_dlist))"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. distinct (list_of_dlist dxs)
[PROOF STEP]
using list_of_dlist[of dxs] equal.equal_eq[OF equal_ceq]
[PROOF STATE]
proof (prove)
using this:
list_of_dlist dxs \<in> {xs. equal_bas... | {"llama_tokens": 216, "file": "Containers_DList_Set", "length": 2} |
import DataFrames
import PredictMD
import Test
x = Union{Missing, String}["foo", "bar", "foo", missing, "bar"]
Test.@test(
length(PredictMD.get_unique_values(x; skip_missings = true)) == 2
)
Test.@test(
length(PredictMD.get_unique_values(x; skip_missings = false)) == 3
)
Test.@test(
length(Predict... | {"hexsha": "13592b2756c2a509fb27f75da785714b9c33feff", "size": 1055, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/unit/toplevel/utils/constant_columns.jl", "max_stars_repo_name": "bcbi/PredictMD.jl", "max_stars_repo_head_hexsha": "7a68061b6e67ddbc217622c5432e73145c4a9392", "max_stars_repo_licenses": ["MIT... |
import numpy as np
import pytest
import numpy.testing as npt
from pulse2percept.implants.base import ProsthesisSystem
from pulse2percept.implants.bvt import BVT24, BVT44
@pytest.mark.parametrize('x', (-100, 200))
@pytest.mark.parametrize('y', (-200, 400))
@pytest.mark.parametrize('rot', (-45, 60))
@pytest.mark.parame... | {"hexsha": "7ff7c14537d7307104d73144f766863f44faec22", "size": 5324, "ext": "py", "lang": "Python", "max_stars_repo_path": "pulse2percept/implants/tests/test_bvt.py", "max_stars_repo_name": "narenberg/pulse2percept", "max_stars_repo_head_hexsha": "ca3aaf66672ccf3c9ee6a9a9d924184cdc6f031d", "max_stars_repo_licenses": ["... |
from __future__ import division
from simulate_queue import simulate_queue
from parameter_inference import param_inference
import numpy as np
import matplotlib.pyplot as plt
import pdb
import scipy.io
from adaptive_breakpoint_placement import adaptive_breakpoint_placement
from interp1 import interp1
from unique_last imp... | {"hexsha": "83c71fd30aa64ed5f253de23f7db4c0e6a39205d", "size": 2917, "ext": "py", "lang": "Python", "max_stars_repo_path": "occupant/occupancy/queueing/test_functions.py", "max_stars_repo_name": "YangyangFu/MPCPy", "max_stars_repo_head_hexsha": "c9980cbfe7b5ea21b003c2c0bab800099dccf3f1", "max_stars_repo_licenses": ["BS... |
import numpy as np
from scipy import stats
import matplotlib
matplotlib.use("PDF")
import matplotlib.pyplot as plt
if __name__ == "__main__":
timesteps = np.array([0.1,
0.05,
0.025,
0.01,
0.005,
... | {"hexsha": "a3a5c1374561c8570674ecf511affbc26cf45cf1", "size": 5520, "ext": "py", "lang": "Python", "max_stars_repo_path": "order_estimation.py", "max_stars_repo_name": "rnowling/integrator-experiments", "max_stars_repo_head_hexsha": "e8131f21f29ac2cec2cf28504634b49b2c20be98", "max_stars_repo_licenses": ["Apache-2.0"],... |
# 2017.12.16 by xiaohang
import sys
from caffenet import *
import numpy as np
import argparse
import torch.nn as nn
from torch.autograd import Variable
from torch.nn.parameter import Parameter
import time
def load_image(imgfile):
image = caffe.io.load_image(imgfile)
transformer = caffe.io.Transformer({'data': ... | {"hexsha": "02699dec068783e5ad42155542c00f64bd3f96e3", "size": 6507, "ext": "py", "lang": "Python", "max_stars_repo_path": "utils/caffe_to_pytorch/convertor_tool/pytorch_caffe/verify_deploy.py", "max_stars_repo_name": "best-of-acrv/unsupervised_depth_estimation", "max_stars_repo_head_hexsha": "4d28cd531988560b93ba7b499... |
% PURPOSE: Loads current ERP structure (if any) from de the workspace.
% Otherwise, load ALLERP(CURRENTERP); Otherwise ERP = [];
%
% To avoid clearing an already filled ERP structure after an interrupted
% process (for instance, after an error)
%
% *** This function is part of ERPLAB Toolbox ... | {"author": "ucdavis", "repo": "erplab", "sha": "e4f66f7a512c4dee2f7596982318e44bb1b72644", "save_path": "github-repos/MATLAB/ucdavis-erplab", "path": "github-repos/MATLAB/ucdavis-erplab/erplab-dd2f60aa41b01c866fcec342efafc48323523cc2/functions/preloadALLERP.m"} |
[STATEMENT]
lemma ad_fun_as2 [simp]: "kad (f \<circ>\<^sub>K g) \<squnion> kad (f \<circ>\<^sub>K kad (kad g)) = kad (f \<circ>\<^sub>K kad (kad g))"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. kad (f \<circ>\<^sub>K g) \<squnion> kad (f \<circ>\<^sub>K kad (kad g)) = kad (f \<circ>\<^sub>K kad (kad g))
[PROOF ST... | {"llama_tokens": 173, "file": "Transformer_Semantics_Kleisli_Quantaloid", "length": 1} |
from __future__ import unicode_literals
from StringIO import StringIO
import numpy
from pandas import DataFrame, pandas
from qcache.qframe.common import unquote, MalformedQueryException
from qcache.qframe.context import set_current_qframe
from qcache.qframe.query import query
from qcache.qframe.update import update_... | {"hexsha": "7a80f5db643232f51aba536c0ce8c57533ba2a03", "size": 3076, "ext": "py", "lang": "Python", "max_stars_repo_path": "qcache/qframe/__init__.py", "max_stars_repo_name": "tobgu/qcache", "max_stars_repo_head_hexsha": "331cd23f69a44824f86e0912f796cbc54e03b037", "max_stars_repo_licenses": ["MIT"], "max_stars_count": ... |
# This file is sometimes included as a standalone script, but we need some of
# the values from bundlepaths.jl
if !(@isdefined BUNDLES_PATH)
include("./bundlepaths.jl")
end
# Avoid namespace pollution with let
let
package_dir = dirname(@__DIR__)
# NodeJS isn't a hard requirement of WebIO, but is needed to... | {"hexsha": "9fd2a6de4fd114f0077de4b239d8c083d1acda08", "size": 2821, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "deps/_bundlejs.jl", "max_stars_repo_name": "UnofficialJuliaMirror/WebIO.jl-0f1e0344-ec1d-5b48-a673-e5cf874b6c29", "max_stars_repo_head_hexsha": "289ac08b2d64e0f4dd3d6fc33beabf63c3ce43a5", "max_star... |
import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np
from matplotlib2tikz import save as tikz_save
from enum import Enum
class PlotterType(Enum):
MATRIX = 1
SCATTER = 2
HISTOGRAM = 3
TRACE = 4
PLOT = 5
MULTITRACE = 6
BAR = 7
class Plotter:
def __init__(self, t... | {"hexsha": "ada3349a0b8d561beb71457fae0e01d77192754b", "size": 8497, "ext": "py", "lang": "Python", "max_stars_repo_path": "plotter/plotter.py", "max_stars_repo_name": "davidelbaze/plotter", "max_stars_repo_head_hexsha": "79871d87b6d1d35ce72159e664dd4e09eef559b7", "max_stars_repo_licenses": ["MIT"], "max_stars_count": ... |
\documentclass{article}
\usepackage{amssymb}
\usepackage{courier}
\usepackage{fancyhdr}
\usepackage{fancyvrb}
\usepackage[T1]{fontenc}
\usepackage[top=.75in, bottom=.75in, left=.75in,right=.75in]{geometry}
\usepackage{graphicx}
\usepackage{lastpage}
\usepackage{listings}
\lstset{basicstyle=\small\ttfamily}
\usepackage{... | {"hexsha": "947f3ac211a0a0622e492e9e917c85cafaf9735b", "size": 6047, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "static/w18/advanced/c4cs-wk7-advanced.tex", "max_stars_repo_name": "yuqijin/c4cs-2.github.io", "max_stars_repo_head_hexsha": "b7921a0f480d2e0f7747eea1662b24bd90fde500", "max_stars_repo_licenses": ["... |
from torchvision import transforms
from torch.utils.data import Dataset
from .data_utils import get_onehot
from .augmentation.randaugment import RandAugment
import torchvision
from PIL import Image
import numpy as np
import copy
class BasicDataset(Dataset):
"""
BasicDataset returns a pair of image and labels... | {"hexsha": "8a6ec7955077249cc985dc817997675359089741", "size": 7892, "ext": "py", "lang": "Python", "max_stars_repo_path": "datasets/dataset.py", "max_stars_repo_name": "JacobGump/TorchSSL", "max_stars_repo_head_hexsha": "bb6978d6e67940eb3d28d99c45d0f2960355b972", "max_stars_repo_licenses": ["MIT"], "max_stars_count": ... |
# NLP written by GAMS Convert at 04/21/18 13:52:28
#
# Equation counts
# Total E G L N X C B
# 801 801 0 0 0 0 0 0
#
# Variable counts
# x b i s1s s2s sc ... | {"hexsha": "ecd8ead66f88d429cd0b546ecfaa76f28106bcd8", "size": 137585, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/examples/minlplib/lnts200.py", "max_stars_repo_name": "ouyang-w-19/decogo", "max_stars_repo_head_hexsha": "52546480e49776251d4d27856e18a46f40c824a1", "max_stars_repo_licenses": ["MIT"], "m... |
import os
import argparse
import numpy as np
import torch
from torch.utils.data import Dataset
class SmallSynthData(Dataset):
def __init__(self, data_path, mode, params):
self.mode = mode
self.data_path = data_path
if self.mode == 'train':
path = os.path.join(data_path, 'train... | {"hexsha": "86653dcbaba5e85426bbf64d0d9e979eb384b20e", "size": 7347, "ext": "py", "lang": "Python", "max_stars_repo_path": "locs/datasets/small_synth_data.py", "max_stars_repo_name": "mkofinas/locs", "max_stars_repo_head_hexsha": "4cb0ab9e989ebfee42d1d2850bdf3360336b5c1c", "max_stars_repo_licenses": ["MIT"], "max_stars... |
# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not u... | {"hexsha": "12edbdac5f23dfe4182a3c396f98b9a834c34fc9", "size": 5661, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/python/relay/benchmarking/benchmark_vm.py", "max_stars_repo_name": "XiaoSong9905/tvm", "max_stars_repo_head_hexsha": "48940f697e15d5b50fa1f032003e6c700ae1e423", "max_stars_repo_licenses": ["... |
import unittest
import numpy
import nlcpy as vp
from nlcpy import testing
nan_dtypes = (
numpy.float32,
numpy.float64,
numpy.complex64,
numpy.complex128,
)
shapes = (
(10,),
)
@testing.parameterize(*(
testing.product({
'shape': shapes,
})
))
class TestCorr(unittest.TestCase):
... | {"hexsha": "ab5eef7a80435f4ef544b0b127815c526ab71479", "size": 1007, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/pytest/statistics_tests/test_correlate.py", "max_stars_repo_name": "SX-Aurora/nlcpy", "max_stars_repo_head_hexsha": "0a53eec8778073bc48b12687b7ce37ab2bf2b7e0", "max_stars_repo_licenses": ["B... |
#gup_zmat_mini.py
import numpy as np
import pyopencl as cl
import pyopencl.array as cl_array
import pyopencl.clrandom as clrand
import pyopencl.tools as cltools
from pyopencl.scan import GenericScanKernel
import matplotlib.pyplot as plt
import time
def sim_health_index(n_runs):
# Set up OpenCL context and command qu... | {"hexsha": "f525f72485b4788c713b2a223ab1c0438eb51ce0", "size": 3292, "ext": "py", "lang": "Python", "max_stars_repo_path": "Assignment1/gpu_zmat_mini.py", "max_stars_repo_name": "cindychu/LargeScaleComputing_S20", "max_stars_repo_head_hexsha": "913b0155f47914c258b503df677067a510dd23f5", "max_stars_repo_licenses": ["MIT... |
#------------------------------------------------------------------------------#
# This script prints out info on the specified model.
# Can give you:
# a) Input output dimensions
# b) Sample Computation times for various input dimensions on your GPU
# c) FLOPS for the computation
#
# Author : Manohar Ku... | {"hexsha": "515862231ae95716ca1b5d2ff29b083c8f2fa3e6", "size": 3888, "ext": "py", "lang": "Python", "max_stars_repo_path": "util_kerasmodel_info.py", "max_stars_repo_name": "galsh17/cartwheel_train", "max_stars_repo_head_hexsha": "a50abe18cfe8c1f0f24267c3efa8537ecf211e72", "max_stars_repo_licenses": ["MIT"], "max_stars... |
module Multinomials
export +, *, -, ^
include("structures.jl")
include("algorithms.jl")
end
| {"hexsha": "029d60046e5b9289218d0b86c30a36ffd1cfe775", "size": 99, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/Multinomials.jl", "max_stars_repo_name": "WilCrofter/Multinomials.jl", "max_stars_repo_head_hexsha": "f5904d2b47e631ea2f9bf3a21222fe0bdf440c0a", "max_stars_repo_licenses": ["MIT"], "max_stars_cou... |
import cv2
import torch
import numpy as np
import albumentations as albu
from albumentations.pytorch import ToTensorV2
def get_transform(image_size: int = 512):
transform = albu.Compose([
albu.LongestMaxSize(max_size=image_size),
albu.PadIfNeeded(min_height=image_size, min_width=image_size, value=... | {"hexsha": "b523ddfe6764c87dba8352a0830477fc5d67d299", "size": 697, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/transforms.py", "max_stars_repo_name": "Inkln/StyleTransferWithCatalyst", "max_stars_repo_head_hexsha": "c3181ecdfd32160907efc2d9d917a55925c25c11", "max_stars_repo_licenses": ["Apache-2.0"], "m... |
\subsection{Temporal difference learning}
| {"hexsha": "3f68a4e3589337a4a39d2f2d085cd7a1230b3dea", "size": 45, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "src/pug/theory/ai/reinforcement/02-01-temporalDifference.tex", "max_stars_repo_name": "adamdboult/nodeHomePage", "max_stars_repo_head_hexsha": "266bfc6865bb8f6b1530499dde3aa6206bb09b93", "max_stars_re... |
"""
Some signal functions implemented using mpmath.
"""
from __future__ import division
try:
import mpmath
except ImportError:
mpmath = None
def _prod(seq):
"""Returns the product of the elements in the sequence `seq`."""
p = 1
for elem in seq:
p *= elem
return p
def _relative_degr... | {"hexsha": "49cc2e3532820312ea27df1ad3bb24763fc4a8db", "size": 3341, "ext": "py", "lang": "Python", "max_stars_repo_path": "scipy/signal/tests/mpsig.py", "max_stars_repo_name": "magnusja/scipy", "max_stars_repo_head_hexsha": "c4a5a1f984e28840010f20a7e41caa21b8f41979", "max_stars_repo_licenses": ["FSFAP"], "max_stars_co... |
import torch as tr
import torch.nn as nn
import pandas as pd
import numpy as np
import os
from torch.utils.data import Subset, DataLoader
from src.dataset import DDataset
from src.sampler import DSampler
from src.model import Model
from src.augmentator import Augmentator
batch_size = 24
n_classes = 16
ids = list(ran... | {"hexsha": "46e2b44335359657f78632e21e65604829be2d14", "size": 1483, "ext": "py", "lang": "Python", "max_stars_repo_path": "eval_test.py", "max_stars_repo_name": "cyones/ECI2019-Competencia_Despegar", "max_stars_repo_head_hexsha": "42328ffcacf7eb31ffb99d80ebbdd4f51ed71333", "max_stars_repo_licenses": ["MIT"], "max_star... |
#############GET CHESSBOARD CORNERS AND CALIBRATE/UNDISTORT CAMERA###################
#Recommended to use at least 20 images to obtain a reliable calibration
#Use glob API to read in all images of .jpg format
#We know the chessboard corners should appear rectangularly i.e. on a lattice.
#Currently there is a deviatio... | {"hexsha": "b2348a389ed6e4817211ad7b3aca3a680646e99d", "size": 3105, "ext": "py", "lang": "Python", "max_stars_repo_path": "camera_cal.py", "max_stars_repo_name": "davecerr/advanced-lane-detection", "max_stars_repo_head_hexsha": "cb30de13613167bffe259aa7813a92f5f311b84a", "max_stars_repo_licenses": ["MIT"], "max_stars_... |
#------------------------------------------------------------------------------
# Libraries
#------------------------------------------------------------------------------
# Standard
from sklearnex import patch_sklearn
patch_sklearn()
from abc import ABC, abstractmethod
import numpy as np
import pandas as pd
from sklea... | {"hexsha": "0509bd56664f84f1ff5ef72c23cfd23309d20c3d", "size": 14962, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/mlregression/base/base_mlreg.py", "max_stars_repo_name": "muhlbach/ml-regression", "max_stars_repo_head_hexsha": "59dfa5acc9841729d632030492e029bb329ce3ed", "max_stars_repo_licenses": ["MIT"]... |
# ------------- Lifetime utility
"""
$(SIGNATURES)
Lifetime utility as a function of lifetime income (earnings + assets).
NOT counting retirement income.
Gives minimum consumption to make negative incomes feasible.
"""
function lifetime_utility(w :: Worker, workStartAge :: Integer, ltIncome)
T = cons_periods(... | {"hexsha": "33672bc67bf4bd23b827b94ea49aad606761e929", "size": 5740, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/lifetime_utility.jl", "max_stars_repo_name": "hendri54/CollegeStratWorker", "max_stars_repo_head_hexsha": "93e95ab73118ec31080fc072953381f09be27029", "max_stars_repo_licenses": ["MIT"], "max_st... |
import sys
from itertools import combinations
import numpy as np
from scipy.sparse import csr_matrix
from scipy.sparse.csgraph import floyd_warshall
I = np.array(sys.stdin.read().split(), dtype=np.int64)
n, m, R = I[:3]
r = I[3 : 3 + R] - 1
a, b, c = I[3 + R :].reshape(-1, 3).T
a -= 1
b -= 1
graph = csr_... | {"hexsha": "27d03883f6c73606043558c92afc44452535fc03", "size": 618, "ext": "py", "lang": "Python", "max_stars_repo_path": "jp.atcoder/abc073/abc073_d/9106590.py", "max_stars_repo_name": "kagemeka/atcoder-submissions", "max_stars_repo_head_hexsha": "91d8ad37411ea2ec582b10ba41b1e3cae01d4d6e", "max_stars_repo_licenses": [... |
[STATEMENT]
theorem integral_substitution:
assumes integrable: "set_integrable lborel {g a..g b} f"
assumes derivg: "\<And>x. x \<in> {a..b} \<Longrightarrow> (g has_real_derivative g' x) (at x)"
assumes contg': "continuous_on {a..b} g'"
assumes derivg_nonneg: "\<And>x. x \<in> {a..b} \<Longrightarrow> g' x \<g... | {"llama_tokens": 11319, "file": null, "length": 64} |
#!/usr/bin/env python
# coding: utf-8
# ## Process the results of tests
# In[1]:
import matplotlib
import numpy as np
import os, sys, getopt, math
import pandas as pd
import matplotlib.pyplot as plt
from ipywidgets import interact, interactive, fixed, interact_manual
# In[2]:
class Analysis:
def __init__(s... | {"hexsha": "3f9cc6052401f3791420f09dacc69f64a4612b0e", "size": 6593, "ext": "py", "lang": "Python", "max_stars_repo_path": "scripts/statsAnalysis.py", "max_stars_repo_name": "dcs-chalmers/pilisco", "max_stars_repo_head_hexsha": "da59496dd4204a3bf5b379d292b0ad93eee79421", "max_stars_repo_licenses": ["Apache-2.0"], "max_... |
from __future__ import print_function
from __future__ import division
import time
import sys
import numpy as np
from numpy import *
from scipy.ndimage.filters import gaussian_filter1d
#import config
import functionLib as lib
def getNotesToKeyMatrix(noteList, weights):
matrix = np.zeros([12, len(noteList)])
fo... | {"hexsha": "167f56c6dc630a6b77b9e49ecf84ffd91f9f700b", "size": 5605, "ext": "py", "lang": "Python", "max_stars_repo_path": "ar/music.py", "max_stars_repo_name": "dgole/audioReactiveFadeCandy", "max_stars_repo_head_hexsha": "aa54325052efb6b1a09741ea4d26b824a1183c5e", "max_stars_repo_licenses": ["MIT"], "max_stars_count"... |
#ifndef PROTOC_SERIALIZATION_SERIALIZATION_HPP
#define PROTOC_SERIALIZATION_SERIALIZATION_HPP
///////////////////////////////////////////////////////////////////////////////
//
// http://protoc.sourceforge.net/
//
// Copyright (C) 2013 Bjorn Reese <breese@users.sourceforge.net>
//
// Permission to use, copy, modify, a... | {"hexsha": "f5875dd926c63347864737a6ea5d003259990281", "size": 2403, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "include/protoc/serialization/serialization.hpp", "max_stars_repo_name": "skyformat99/protoc", "max_stars_repo_head_hexsha": "f0a72275c92bedc8492524cb98cc24c5821c4f11", "max_stars_repo_licenses": ["B... |
import numpy as np
print(np.__version__)
n = int(input())
Z = np.zeros(n)
print(Z)
params = input().split()
t = 'float64' if params[-1].isdigit() else params.pop()
Z = np.zeros(tuple(map(int, params)), dtype=t)
print(Z)
Z = np.zeros((10,10))
print(Z.size * Z.itemsize)
np.info(np.add)
np.info(np.array)
Z = n... | {"hexsha": "2bc18cc8a656956ed34332fc3293dacf2cf723db", "size": 746, "ext": "py", "lang": "Python", "max_stars_repo_path": "math and python workshop/6/6-3.py", "max_stars_repo_name": "DzmitrySakalenka/stepik_courses", "max_stars_repo_head_hexsha": "7c43ac35cd921e8f6f96fb4f15f77ace38cc2d21", "max_stars_repo_licenses": ["... |
/*
* Copyright (c) 2013 Juniper Networks, Inc. All rights reserved.
*/
#include <boost/program_options.hpp>
#include <sandesh/sandesh.h>
#include "base/logging.h"
#include "cmn/agent_cmn.h"
#include "init/agent_param.h"
#include "discovery_agent.h"
#include "controller/controller_init.h"
#include "cmn/agent_cmn.h"
... | {"hexsha": "f6ba18e143118df1f1e9fe8e2ba7e7a4ad1f9067", "size": 6880, "ext": "cc", "lang": "C++", "max_stars_repo_path": "src/vnsw/agent/cfg/discovery_agent.cc", "max_stars_repo_name": "amitkg29/contrail-controller", "max_stars_repo_head_hexsha": "be71b50f185a68338ea54d6f8088623ab88c2bf6", "max_stars_repo_licenses": ["A... |
import gc
import os
import time
import boto3
import dask
import fsspec
import joblib
import numpy as np
import pandas as pd
import rasterio as rio
import rioxarray
import utm
import xarray as xr
import xgboost as xgb
from pyproj import CRS
from rasterio.session import AWSSession
from s3fs import S3FileSystem
import c... | {"hexsha": "ec42621735accbc14b3e699a45655c0437f85255", "size": 9928, "ext": "py", "lang": "Python", "max_stars_repo_path": "carbonplan_trace/v1/inference.py", "max_stars_repo_name": "carbonplan/trace", "max_stars_repo_head_hexsha": "5cf113891bdefa29c2afd4478dff099e0458c82c", "max_stars_repo_licenses": ["MIT"], "max_sta... |
#!/usr/bin/env python
# Written by Greg Ver Steeg
# See readme.pdf for documentation
# Or go to http://www.isi.edu/~gregv/npeet.html
import scipy.spatial as ss
from scipy.special import digamma
from math import log
import numpy.random as nr
import numpy as np
import random
# CONTINUOUS ESTIMATORS
def entropy(x, k=... | {"hexsha": "139198b01e8c955789ac072d23a319600d0cc1f1", "size": 10494, "ext": "py", "lang": "Python", "max_stars_repo_path": "lm_pretrain/analysis/entropy_estimators.py", "max_stars_repo_name": "dillondaudert/pssp_lstm", "max_stars_repo_head_hexsha": "d5e00a9a99b912ed6d9e33966077ce1d782a8dfb", "max_stars_repo_licenses":... |
from deepquantum.circuit import Circuit
from deepquantum.utils import dag
import torch
import torch.nn as nn
import numpy as np
class QuPoolXYZ(nn.Module):
"""Quantum Pool layer.
放置4个量子门，2个参数。
"""
def __init__(self, n_qubits, gain=2 ** 0.5, use_wscale=True, lrmul=1):
super().__init__()
... | {"hexsha": "cdbce3fd8eff5726f37554c2bd0b66944c68ba34", "size": 2380, "ext": "py", "lang": "Python", "max_stars_repo_path": "deepquantum/nn/qupool.py", "max_stars_repo_name": "TuringQ/VisualQ", "max_stars_repo_head_hexsha": "42793d364a8838bfb32128e165048c7bb0b6ea5d", "max_stars_repo_licenses": ["MIT"], "max_stars_count"... |
import itertools as it
import operator as op
from functools import reduce, wraps
from typing import Callable, Iterable, Optional, Tuple
import moderngl
import numpy as np
from ... import config
from ...constants import *
from ...mobject.opengl_mobject import OpenGLMobject, OpenGLPoint
from ...utils.bezier import (
... | {"hexsha": "6ab3d7c34b9f63cd31419412d0756b4f6c1163b1", "size": 43546, "ext": "py", "lang": "Python", "max_stars_repo_path": "manim/mobject/types/opengl_vectorized_mobject.py", "max_stars_repo_name": "fj/manim", "max_stars_repo_head_hexsha": "ba786fa02c9280d3fb02270ce324549de595ccc7", "max_stars_repo_licenses": ["MIT"],... |
#!/usr/bin/env python
###############################################################################
# binning.py - A binning algorithm spinning off of the methodology of
# Lorikeet
###############################################################################
# ... | {"hexsha": "d5b2c07585aaabacbcda2ac5841078fea2c655c9", "size": 49236, "ext": "py", "lang": "Python", "max_stars_repo_path": "flight/rosella/validating.py", "max_stars_repo_name": "rhysnewell/flock", "max_stars_repo_head_hexsha": "269555b850382b64041e3f131b6e63059c4425b3", "max_stars_repo_licenses": ["BSD-3-Clause"], "m... |
# Generate sample bounding boxes.
# matlab code:
# https://github.com/hellbell/ADNet/blob/master/utils/gen_samples.m
from options.general import opts
import numpy as np
import numpy.matlib
from utils.my_math import normal_round as round
def gen_samples(type, bb, n, opts, trans_f, scale_f):
# type => sampling met... | {"hexsha": "6a0eaa300fa05a30fec7e96c186b16adb30d5002", "size": 4801, "ext": "py", "lang": "Python", "max_stars_repo_path": "projects/adnet/utils/gen_samples.py", "max_stars_repo_name": "hizb-resume/LTD-local-track-to-detect-for-VID", "max_stars_repo_head_hexsha": "7147ac7c6cd4b22a956aaaabaa151e5ed5410c68", "max_stars_r... |
from os.path import join
from topology import load_persistence_diagram_dipha, load_persistence_diagram_json
from utilities import parse_filename, get_patient_ids_and_times
import numpy as np
from glob import glob
import seaborn as sns
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt
from tqdm im... | {"hexsha": "a16b4e93caac6dc7e0058b181a8a565bb8115770", "size": 3711, "ext": "py", "lang": "Python", "max_stars_repo_path": "ephemeral/plot_persistence_diagrams.py", "max_stars_repo_name": "BorgwardtLab/fMRI_Cubical_Persistence", "max_stars_repo_head_hexsha": "5a4773333a33f86c17bbd0604cc48a361fc9a35f", "max_stars_repo_l... |
subroutine test_RTS_Main (ifltab1, messageUnit, status)
!
!
! Purpose: Calls all test functions for regular interval time series data
!
implicit none
!
integer messageUnit, status
!
!
integer(8) ifltab1(600)
common /lchk/ lcheck
logical lcheck
! test_RTS_Basic ... | {"hexsha": "c0355111d5afad769a6d9bb2930f839cd21cc16a", "size": 7029, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "test/Fortran/source/test_RTS_Main.f90", "max_stars_repo_name": "HydrologicEngineeringCenter/heclib", "max_stars_repo_head_hexsha": "dd3111ee2a8d0c80b88d21bd529991f154fec40a", "max_stars_repo_lic... |
[STATEMENT]
lemma vid_on_vdoubleton[simp]: "vid_on (set {a, b}) = set {\<langle>a, a\<rangle>, \<langle>b, b\<rangle>}"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. vid_on (set {a, b}) = set {\<langle>a, a\<rangle>, \<langle>b, b\<rangle>}
[PROOF STEP]
by (auto simp: vinsert_set_insert_eq) | {"llama_tokens": 128, "file": "CZH_Foundations_czh_sets_CZH_Sets_BRelations", "length": 1} |
#!/Users/tkirke/anaconda/bin/python
# -*- coding: utf-8 -*-
import re,sys,os,codecs
from time import sleep
from math import sqrt,log,pi,sin,atan2
import cmath
from scipy import signal,fft
import numpy, matplotlib
from lame import *
matplotlib.use('qt4agg')
import matplotlib.pyplot as plt
from tone_est import *
show_p... | {"hexsha": "b12ef2c4d8ea9c5879b4209b92fc3d4608200d7c", "size": 3764, "ext": "py", "lang": "Python", "max_stars_repo_path": "test_notch.py", "max_stars_repo_name": "amikey/audio_scripts", "max_stars_repo_head_hexsha": "3c6adc3c4e2a338590bb69e2a13c954bfd8cec46", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 6, "... |
\section{Results and Conclusions}
\subsection{Re-evaluation of the design}
\subsubsection{Reliability}
\begin{itemize}
\item The message sent by network devices are receivable to the receivers.
\item Our system might have packet loss. This might be occurred in the first route or ping because the network need to fil... | {"hexsha": "4e48d11ba403728c7382d3fa26d443e5a5d935a0", "size": 3457, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "report/step6.tex", "max_stars_repo_name": "Smithienious/CO3094-asg2", "max_stars_repo_head_hexsha": "8c301fcbcbe2a7deceeb50105ed5d6624e6ad413", "max_stars_repo_licenses": ["MIT"], "max_stars_count":... |
from scipy import spatial
def calculate_cosine_similarity(v1, v2):
try:
cosine = 1 - spatial.distance.cosine(v1, v2)
except ValueError:
cosine = 0
finally:
pass
return cosine
| {"hexsha": "1fee06e0d0559395b26454c1c2c0ba67f969a0e2", "size": 217, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/shared/functions.py", "max_stars_repo_name": "matifq/eve", "max_stars_repo_head_hexsha": "3a7e7ad3270cfe4f771c6480cc36b536f37d7bbc", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 9, "m... |
import numpy as np
from NN3_tf import NN3_tf
from sklearn.model_selection import train_test_split
from nn_utils import crossover, Type, sort_by_fittest, read_dataset
X, Y = read_dataset(180, 500)
train_x, test_x, train_y, test_y = train_test_split(
X, Y, test_size=0.3, random_state=1)
X, Y = read_dataset(180, 5... | {"hexsha": "cb7d2a1e81d6afc89d1b23a6bfa078facb0d862d", "size": 1469, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/GDNN3_tf.py", "max_stars_repo_name": "gmaggiotti/genetic_deep_learning", "max_stars_repo_head_hexsha": "3bade857fa7a1564d2c6ef9519bcd37cfcd2d1a4", "max_stars_repo_licenses": ["MIT"], "max_star... |
import datetime
import warnings
import numpy as np
import matplotlib
from hapiclient.plot.datetick import datetick
def timeseries(t, y, **kwargs):
'''Plot a time series
'''
opts = {
'logging': False,
'title': '',
'xlabel': '',
'ylabel': ''... | {"hexsha": "2bcbae6818c1b75e834c8f5aa158479e54a78594", "size": 4534, "ext": "py", "lang": "Python", "max_stars_repo_path": "hapiclient/plot/timeseries.py", "max_stars_repo_name": "lkilcommons/client-python", "max_stars_repo_head_hexsha": "7f8f895bd6b43aa12c4531e4498d1bab91c30691", "max_stars_repo_licenses": ["BSD-3-Cla... |
'''
Created with love by Sigmoid
@Author - Stojoc Vladimir - vladimir.stojoc@gmail.com
'''
import numpy as np
import pandas as pd
import random
import sys
from random import randrange
import math
from .erorrs import NotBinaryData, NoSuchColumn
def warn(*args, **kwargs):
pass
import warnings
warnin... | {"hexsha": "3acf705f0ee63514df2165d01acd4087ef1bb157", "size": 8569, "ext": "py", "lang": "Python", "max_stars_repo_path": "crucio/SLS.py", "max_stars_repo_name": "SigmoidAI/crucio", "max_stars_repo_head_hexsha": "fd646a2e3be9e79572dd8a24f6a41dbb7267e028", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "m... |
# flake8: noqa
### USAGE:
# conda activate ve-AICO
# python print_system.py
### Terminal log:
# Platform: Linux-5.11.0-36-generic-x86_64-with-glibc2.17
# PyTorch: 1.9.1
# NumPy: 1.20.3
# Python: 3.8.11 (default, Aug 3 2021, 15:09:35)
# ... | {"hexsha": "6f171c2aaf0eb82e4e82e515f37704ef3c8e4b77", "size": 544, "ext": "py", "lang": "Python", "max_stars_repo_path": ".github/print_system.py", "max_stars_repo_name": "gomezalberto/pretus", "max_stars_repo_head_hexsha": "63b05419f70ecf8b28ccbb6191eab79637c051ac", "max_stars_repo_licenses": ["MIT"], "max_stars_coun... |
The combination of several tools can significantly contribute to contribute to addressing this issue in developing and underdeveloped countries. developing countries. One tool would be satellite imagery such as Synthetic Aperture Radar.
\subsection{Flood mapping}
The flood map was obtained by processing Sentinel-1 GRD... | {"hexsha": "e8dcd2ede25b5fedcdab3705626fb387d0c76fde", "size": 8789, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "IAC2021/content/remote_sensing.tex", "max_stars_repo_name": "mudesire/stea_igcp", "max_stars_repo_head_hexsha": "8496597dfcb5cddafe9c581f65a3c8a1b0fd3d8a", "max_stars_repo_licenses": ["MIT"], "max_s... |
import numpy as np
def t():
list=[1,2,3,4,5]
list=np.array(list)
list2=[2,3]
print(list[list2])
if __name__ == '__main__':
t() | {"hexsha": "80faffcc2e22ae56301f0177535ed8f857b1a55a", "size": 147, "ext": "py", "lang": "Python", "max_stars_repo_path": "ava/teat.py", "max_stars_repo_name": "Achuttarsing/Slow-Fast-pytorch-implementation", "max_stars_repo_head_hexsha": "4c47c8dce3096ec6a274bdd1d20d1e4fba789e48", "max_stars_repo_licenses": ["Apache-2... |
import pandas as pd
import math
import numpy as np
import cv2
import os
def make_datalist(arg, data, ch):
s_size = int(arg.train_batch_size / 1.5)
d_size = arg.train_batch_size - s_size
train_a = data.sample(frac=1).reset_index(drop=True)
train_ad = train_a.iloc[int(len(data) * s_size / a... | {"hexsha": "72677ccae2ecb1a30398c03b878c0c374e4e4382", "size": 5417, "ext": "py", "lang": "Python", "max_stars_repo_path": "train/util/Make_random_pairs.py", "max_stars_repo_name": "changsukim-ku/order-learning", "max_stars_repo_head_hexsha": "9878b0376d04edae74ea8f3df97a9bd79f5a906b", "max_stars_repo_licenses": ["MIT"... |
function bubble_sort(seq::Vector{Int64})::Vector{Int64}
l = length(seq)
for _ = 0:l-1
for n = 1:l-1
if seq[n+1] < seq[n]
seq[n], seq[n+1] = (seq[n+1], seq[n])
end
end
end
return seq
end
if abspath(PROGRAM_FILE) == @__FILE__
unsorted = [14, 11... | {"hexsha": "04d17960bb38f14c18cf8bf420fbad8e40ca47af", "size": 475, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "tests/expected/julia/bubble_sort.jl", "max_stars_repo_name": "MiguelMarcelino/py2many", "max_stars_repo_head_hexsha": "9b040b2a157e265df9c053eaf3e5cd644d3e30d0", "max_stars_repo_licenses": ["MIT"], ... |
# The Process
Not everytime is there a situation where $Ax = b$ has exact solutions , such is the case when we are trying to find a best fit line for a given set of datapoints , there is no direct solution for it , instead we are in search for the best possible solution.So instead of finding solutions for $Ax = b$ , w... | {"hexsha": "5aefb101235bd1f06d103563648bdc0e8a72945d", "size": 18907, "ext": "ipynb", "lang": "Jupyter Notebook", "max_stars_repo_path": "Linearreg.ipynb", "max_stars_repo_name": "B20204/Professorpy", "max_stars_repo_head_hexsha": "572c735c360c19c55f7554245c65975505e5e237", "max_stars_repo_licenses": ["MIT"], "max_star... |
import os
import random
import logging
import numpy as np
import tensorflow as tf
import utils
import data_loader
from model import NLR_model
from hyper_params import HyperParams
def evaluate(sess, model, users, hist_items, scores, labels,
user_2_id, item_2_id, test_ratio=0.5, topk=5):
count = 0
... | {"hexsha": "c74d6e09b598f7a02cea26a030ef1227c16f2c2f", "size": 6836, "ext": "py", "lang": "Python", "max_stars_repo_path": "train.py", "max_stars_repo_name": "Scagin/NeuralLogicReasoning", "max_stars_repo_head_hexsha": "2128352ebc98bea60f640e7420d80f276b07bb94", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 21... |
```python
# 3x + 2y = 1jj0
# x + 3y = 8
import sympy as sm
sm.Matrix([[3,2],[1,3]]).inv()*sm.Matrix([10,8])
```
$\displaystyle \left[\begin{matrix}2\\2\end{matrix}\right]$
---
# $\color{red}{\text{differential coefficiant}}$ is the $\color{magenta}{\text {cause effect}}$
> ### wrt (with respect to)
> ### $1x =: ... | {"hexsha": "c820059b15407d0c98abe6f3178489289a9de55c", "size": 9241, "ext": "ipynb", "lang": "Jupyter Notebook", "max_stars_repo_path": "python/GeometryAG/01-intro.ipynb", "max_stars_repo_name": "karng87/nasm_game", "max_stars_repo_head_hexsha": "a97fdb09459efffc561d2122058c348c93f1dc87", "max_stars_repo_licenses": ["M... |
__author__ = 'Maria Khodorchenko'
from pathos.multiprocessing import ProcessPool as Pool
import numpy as np
import random
from qparallel.helpers import (
split_data
)
"""
Graph should be passed as list of weighted edges
in the form [node1, node2, weight]
Nodes numbering should begin from 0
"""
class Graph:
... | {"hexsha": "a85c3b806dceddbea8814b160d7923fc99bb7650", "size": 3707, "ext": "py", "lang": "Python", "max_stars_repo_path": "qparallel/graph/graphalgs.py", "max_stars_repo_name": "KirovVerst/qclustering", "max_stars_repo_head_hexsha": "4fc1265819f7db31ce63320dbaead6ca4e7798ba", "max_stars_repo_licenses": ["MIT"], "max_s... |
(* -*- coding: utf-8; mode:coq -*-
* Auto-generated - Do not edit or overwrite!
*)
Require Import Arith. (* ouvre droit, notamment, à 'auto with arith' *)
Section exo2.
Parameter a b : nat.
(* Programme annoté :
{ (x = a and y = b) }
Auto0:t <- x
Auto1:x <- y
Auto2:y <- t
{ (x = b a... | {"author": "adud", "repo": "prog-l3", "sha": "cca449d82f22c714e3703984aed39307ee0c3657", "save_path": "github-repos/coq/adud-prog-l3", "path": "github-repos/coq/adud-prog-l3/prog-l3-cca449d82f22c714e3703984aed39307ee0c3657/tp6/exo2.v"} |
import numpy as np
path = './seg_train_dat/train_0000.dat'
fp = open(path,'rb')
a = np.fromfile(fp, np.uint8, -1)
fp.close()
a = a.reshape((1216, 1936, 1))
print(a.shape)
import cv2
resize_size_x = 512
resize_size_y = 256
img = cv2.resize(a), (resize_size_x, resize_size_y), interpolation=cv2.INTER_NEAREST)
print(img.... | {"hexsha": "55c76ded6729913ef99b822c71d7b9c0288facfb", "size": 787, "ext": "py", "lang": "Python", "max_stars_repo_path": "utils/gen_signate_dat/script/verify_dat.py", "max_stars_repo_name": "Vertical-Beach/ai-edge-contest4", "max_stars_repo_head_hexsha": "4b5211a9adb383756acade42c8a8b104f6fd7363", "max_stars_repo_lice... |
using StaticArrays
if PLOT == :winston
using Winston
import Winston: plot, oplot
function plot(Y::SamplePath{SVector{2,Float64}}, args...; keyargs...)
yy = Bridge.mat(Y.yy)
plot(yy[1,:], yy[2,:], args...; keyargs...)
end
function plot(Y::SamplePath{SVector{1,Float64}}, args...; keyargs...)
yy = Brid... | {"hexsha": "4fcdc6be42499c5c0a6c3d6a1893a1d5fd7126f0", "size": 1605, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "example/plot.jl", "max_stars_repo_name": "UnofficialJuliaMirror/Bridge.jl-2d3116d5-4b8f-5680-861c-71f149790274", "max_stars_repo_head_hexsha": "e0a387537c4761c95bc91c29a9656dfb5265fe53", "max_stars... |
#%%
#importing some useful packages
import numpy as np
import cv2
#import math
#%matplotlib inline
#%%
def grayscale(img):
"""Applies the Grayscale transform
This will return an img with only one color channel
but NOTE: to see the returned img as grayscale
you should call plt.imshow(gray, cma... | {"hexsha": "1b17788066d1796437291d8615713c9761f7d807", "size": 7529, "ext": "py", "lang": "Python", "max_stars_repo_path": "Lane_Lines_Finding.py", "max_stars_repo_name": "tahir1069/Finding-Lane-Lines-SDVND", "max_stars_repo_head_hexsha": "f96d9440f27e05979c561465a224850a2aad13a4", "max_stars_repo_licenses": ["BSD-3-Cl... |
from spearmint.models.gp import GP
from spearmint.acquisition_functions.predictive_entropy_search_multiobjective import sample_gp_with_random_features
from spearmint.utils.parsing import parse_config_file
from spearmint.tasks.input_space import InputSpace
from spearmint.tasks.input_space import param... | {"hexsha": "b870332e4370fc4af49698c3fe13e2ca417c2c79", "size": 2956, "ext": "py", "lang": "Python", "max_stars_repo_path": "examples/mooc_synthetic_6d_noisy/random/synthetic_problem.py", "max_stars_repo_name": "fernandezdaniel/Spearmint", "max_stars_repo_head_hexsha": "3c9e0a4be6108c3d652606bd957f0c9ae1bfaf84", "max_st... |
# -*- coding: utf-8 -*-
from __future__ import print_function
import sys
import numpy as np
from qsrlib_io.world_qsr_trace import World_QSR_Trace
def apply_median_filter(qsr_world, params):
"""
Function to apply a median filter to the QSRLib World Trace
..seealso:: For further details about Filters, refer... | {"hexsha": "2147d13f98020c006546dffaddeb24f6aa539bd2", "size": 5201, "ext": "py", "lang": "Python", "max_stars_repo_path": "qsrlib/src/qsrlib_utils/filters.py", "max_stars_repo_name": "alexiatoumpa/QSR_Detector", "max_stars_repo_head_hexsha": "ff92a128dddb613690a49a7b4130afeac0dd4381", "max_stars_repo_licenses": ["MIT"... |
import io
import random
import os
import seaborn as sns
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
from itertools import product
from tensorflow.python.keras.preprocessing.image import load_img, img_to_array
from sklearn.metrics import recall_score, f1_score, precision_score, accuracy_score... | {"hexsha": "1abc4cc56d12b4e0626c2f54976e23d97f6a381d", "size": 37392, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/data_viz/visualizacion_resultados.py", "max_stars_repo_name": "jbustospelegri/breast_cancer_diagnosis", "max_stars_repo_head_hexsha": "38eb990ef716912c6acabb443e6eb5c21d9b4e0d", "max_stars_re... |
/*******************************************************************************
Copyright 2021 by Greg Landrum and the Shape-it contributors
This file is part of Shape-it.
Permission is hereby granted, free of charge, to any person obtaining a copy of
this software and associated documentation files (the "Software"... | {"hexsha": "981615a93dc698220e91117f3c27f838cbaf3a99", "size": 2143, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/Wrap/cpyshapeit.cpp", "max_stars_repo_name": "silicos-it/shape-it", "max_stars_repo_head_hexsha": "d9850ce7b0d3d5f2e0c928501ea5a86b9d2eb421", "max_stars_repo_licenses": ["MIT"], "max_stars_count... |
import tensorflow as tf
import numpy as np
import cv2
import time
from train_config import config as cfg
from lib.core.model.facebox.net import FaceBoxes
class FaceDetector:
def __init__(self, model_path):
"""
Arguments:
model_path: a string, path to the model params file.
"""
... | {"hexsha": "b64d685291cc6b9e7e602cb83ae4237050a3b83e", "size": 3965, "ext": "py", "lang": "Python", "max_stars_repo_path": "lib/core/api/face_detector.py", "max_stars_repo_name": "610265158/faceboxes-tensorflow", "max_stars_repo_head_hexsha": "700714ec77c4bc23cb478b3e4fa9230e1559417a", "max_stars_repo_licenses": ["Apac... |
#!/usr/bin/env python3
import networkx as nx
import os, os.path
import statistics
import sys
import utils
from argparse import ArgumentParser
# Prints a CSV table of the nodes in common amongst the provided GraphML files,
# based on a given property
class Options:
def __init__(self):
self._init_parser()... | {"hexsha": "b396a64dc07e973642b3ae8c2713e229b9e1563c", "size": 4228, "ext": "py", "lang": "Python", "max_stars_repo_path": "nodes_in_common.py", "max_stars_repo_name": "weberdc/find_hccs", "max_stars_repo_head_hexsha": "43fcb151901f48765ea8e4ccf0b82dbb726762a3", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_cou... |
import argparse
import re
import numpy as np
import pandas as pd
from tools.text import clean_text
def parse_arguments(parser):
parser.add_argument('--test_file', type=str, default=None)
parser.add_argument('--train_file', type=str, default=None)
parser.add_argument('--output_dir', type=str, default=Non... | {"hexsha": "5696376463b8b1ce091e0559017d6595773c0fa9", "size": 2980, "ext": "py", "lang": "Python", "max_stars_repo_path": "1-rayvanve/code/src/preprocessing.py", "max_stars_repo_name": "NASA-Tournament-Lab/CDC-NLP-Occ-Injury-Coding", "max_stars_repo_head_hexsha": "7012b6726c9178ba4914b3de25578a75ad7d3e87", "max_stars_... |
# The MIT License (MIT)
#
# Copyright (c) 2018 PyBER
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, mer... | {"hexsha": "e86ba59b27e6dbeb36683496cd7cae2e5abddfb2", "size": 14356, "ext": "py", "lang": "Python", "max_stars_repo_path": "fileExplorer.py", "max_stars_repo_name": "aff3ct/PyBER", "max_stars_repo_head_hexsha": "c84e1f63c193c5661ab8917a97264e4b899b7be0", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 10, "max_... |
# syntax: proto3
using ProtoBuf
import ProtoBuf.meta
const APIVersion = (;[
Symbol("APIVERSION_UNSPECIFIED") => Int32(0),
Symbol("V1") => Int32(1),
]...)
export APIVersion
| {"hexsha": "a95114976ce2f855522a47f7a3ced47b9fd097a7", "size": 182, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/proto/src/generated/service_invocation/apiversion_pb.jl", "max_stars_repo_name": "oolong-dev/Dapr.jl", "max_stars_repo_head_hexsha": "133460f4ccf7c86313089c65e1efa7155afa4c91", "max_stars_repo_l... |
\documentclass[12pt]{article}
\input{./ref/structure.tex}
\title{Introductory Backtesting Notes \\ for Quantitative Trading Strategies \\[2ex]
\large Useful Metrics and Common Pitfalls}
\author{Leo Wong (QFIN \& COSC, HKUST)}
\date{December, 2019}
\begin{document}
\begin{titlingpage}
\maketitle
\... | {"hexsha": "08fce7801607901d85ce917339e74426eb55e87a", "size": 17146, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "src/backtest.tex", "max_stars_repo_name": "exnight/COMP4971C", "max_stars_repo_head_hexsha": "a0c7fbce8af44f785ffbdfc0e41f63cd94e1b2a2", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null,... |
using Base.Broadcast: BroadcastFunction, broadcasted, materialize
ChainRulesCore.@non_differentiable Base.getindex(m::AbstractAttenMask, I::Integer...)
ChainRulesCore.@non_differentiable Base.getindex(m::MaskIndexer, I::Integer...)
ChainRulesCore.@non_differentiable Base.getindex(m::AbstractAttenMask, I::Tuple)
ChainR... | {"hexsha": "2436cfb85872b2e006c14be64d9ab0bb8bfe0efc", "size": 2538, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/mask/grad.jl", "max_stars_repo_name": "chengchingwen/NeuralAttentionlib.jl", "max_stars_repo_head_hexsha": "e8f5adbb52902d148fb87f1e5c5f8a67727ab3cb", "max_stars_repo_licenses": ["MIT"], "max_s... |
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