text stringlengths 0 1.25M | meta stringlengths 47 1.89k |
|---|---|
"""multimodel statistics.
Functions for multi-model operations
supports a multitude of multimodel statistics
computations; the only requisite is the ingested
cubes have (TIME-LAT-LON) or (TIME-PLEV-LAT-LON)
dimensions; and obviously consistent units.
It operates on different (time) spans:
- full: computes stats on fu... | {"hexsha": "f76425e4715744fef656ac497beed6ed0e9f5dd7", "size": 12433, "ext": "py", "lang": "Python", "max_stars_repo_path": "esmvalcore/preprocessor/_multimodel.py", "max_stars_repo_name": "Peter9192/ESMValCore", "max_stars_repo_head_hexsha": "febd96a39480cc837afbf4e1f5b0ef61571af76a", "max_stars_repo_licenses": ["Apac... |
#! python3
# -*- coding: utf-8 -*-
"""
################################################################################################
Implementation of 'PROGRESSIVE GROWING OF GANS FOR IMPROVED QUALITY, STABILITY, AND VARIATION'##
https://arxiv.org/pdf/1710.10196.pdf ... | {"hexsha": "7c5ba6b92ccb9bae556868e8b6c71ea1bf2a8d26", "size": 17816, "ext": "py", "lang": "Python", "max_stars_repo_path": "model.py", "max_stars_repo_name": "shanexn/pytorch-pggan", "max_stars_repo_head_hexsha": "8cb7dd97b0f0fb6b147b0ef83e9121b8f5fb5fff", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 7, "max... |
import time
import glob
from common import gym_interface
import pybullet as p
import os
import pybullet_data
import gym
import pybullet_envs
import shutil
import re
import numpy as np
import random
env = gym_interface.make_env(robot_body=900, render=True)()
obs = env.reset()
env.env._p.setGravity(0,0,-1)
a = env.actio... | {"hexsha": "4f9e55a47801e407d2cb2b2eb6f5055c87dcc8ca", "size": 570, "ext": "py", "lang": "Python", "max_stars_repo_path": "project/experiments/exp_016_arms/src/show_body_env.py", "max_stars_repo_name": "liusida/thesis-bodies", "max_stars_repo_head_hexsha": "dceb8a36efd2cefc611f6749a52b56b9d3572f7a", "max_stars_repo_lic... |
# Copyright (c) 2018-2020, NVIDIA CORPORATION.
from contextlib import ExitStack as does_not_raise
from sys import getsizeof
import cupy
import numpy as np
import pandas as pd
import pyarrow as pa
import pytest
from cudf import concat
from cudf.core import DataFrame, Series
from cudf.core.column.string import StringCo... | {"hexsha": "b9d0f4279340470cce8d4456111058f3e3b2d2ce", "size": 67231, "ext": "py", "lang": "Python", "max_stars_repo_path": "python/cudf/cudf/tests/test_string.py", "max_stars_repo_name": "eyal0/cudf", "max_stars_repo_head_hexsha": "b696d6064ba2b41be2a35fcdfa6fb4eebb4d3a9c", "max_stars_repo_licenses": ["Apache-2.0"], "... |
section \<open>Less-Equal or Fail\<close>
(* TODO: Move to Refinement Framework *)
theory Refine_Leof
imports Refine_Basic
begin
text \<open>A predicate that states refinement or that the LHS fails.\<close>
definition le_or_fail :: "'a nres \<Rightarrow> 'a nres \<Rightarrow> bool" (infix "\<le>\<^sub>n" 50) wher... | {"author": "data61", "repo": "PSL", "sha": "2a71eac0db39ad490fe4921a5ce1e4344dc43b12", "save_path": "github-repos/isabelle/data61-PSL", "path": "github-repos/isabelle/data61-PSL/PSL-2a71eac0db39ad490fe4921a5ce1e4344dc43b12/SeLFiE/Evaluation/Refine_Monadic/Refine_Leof.thy"} |
using Documenter
using Jedis
makedocs(
sitename="Jedis.jl Documentation",
# format = Documenter.HTML(prettyurls = false),
pages=[
"Home" => "index.md",
"Client" => "client.md",
"Commands" => "commands.md",
"Pipelining" => "pipeline.md",
"Pub/Sub" => "pubsub.md",
... | {"hexsha": "c59647fae30d2d5772a48d4501e41f659f9e67af", "size": 477, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "docs/make.jl", "max_stars_repo_name": "jacksoncalvert/Jedis.jl", "max_stars_repo_head_hexsha": "6df4c8f5da2d5841e8f0b7927894a5568cd6fc7e", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 8, "... |
"""Module for saving data in a AssetStore friendly way"""
##############################################################################
#
# redsky by Billinge Group
# Simon J. L. Billinge sb2896@columbia.edu
# (c) 2016 trustees of Columbia University in the City of
# ... | {"hexsha": "139a5479db1241d2fb8bdf98b39d7b572ba6c909", "size": 1817, "ext": "py", "lang": "Python", "max_stars_repo_path": "shed/savers.py", "max_stars_repo_name": "st3107/shed-streaming", "max_stars_repo_head_hexsha": "c632fc465d7e11fe0155fbc3e8add1965615dd51", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_c... |
import torch
from torch.utils.data import Dataset
import h5py
import numpy as np
from PIL import Image
import heatmap_generator
class Dataset(Dataset):
def __init__(self, h5file,std_filename,mean_filename):
with h5py.File(h5file, 'r') as hdf:
# Get the data
self.input_size = 3
... | {"hexsha": "8aea7e5dd88f78026042823d2df84602103dd8e2", "size": 1075, "ext": "py", "lang": "Python", "max_stars_repo_path": "models/model_source/v5.0.5/dataset.py", "max_stars_repo_name": "NicolasISEN/Facial_landmark_emotion_detection", "max_stars_repo_head_hexsha": "c7b8d7b0ea91a3496e3611bc1aab221709added4", "max_stars... |
import os
import sys
sys.path.append(os.getcwd())
import h5py
import common.vis_gui
import torch
import numpy as np
import skimage.transform
import time
import enum
import re
from PyQt5.QtCore import *
from PyQt5.QtWidgets import *
import common.utils as utils
import pyrenderer
from volnet.netw... | {"hexsha": "6a839f3abe5abb7f05bdeb6f72763cb208c41325", "size": 14675, "ext": "py", "lang": "Python", "max_stars_repo_path": "applications/volnet/vis_volnet.py", "max_stars_repo_name": "khoehlein/fV-SRN", "max_stars_repo_head_hexsha": "601f3e952b090df92e875c233c2c9ca646523948", "max_stars_repo_licenses": ["MIT"], "max_s... |
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.colors import LinearSegmentedColormap
def create_matrix(dots):
max_x = get_maximum_list_tuples(dots, 0)
max_y = get_maximum_list_tuples(dots, 1)
matrix = np.zeros((max_y+1, max_x+1))
for (x,y) in dots:
matrix[y][x] += 1
re... | {"hexsha": "35b829f31129b170bca9b808baf807c9aae0cb17", "size": 2596, "ext": "py", "lang": "Python", "max_stars_repo_path": "day 13/Martijn - Python/transparent_origami.py", "max_stars_repo_name": "AE-nv/aedvent-code-2021", "max_stars_repo_head_hexsha": "7ce199d6be5f6cce2e61a9c0d26afd6d064a86a7", "max_stars_repo_license... |
import tweepy
import os.path
import sys
import jsonpickle
import tweets_analyser
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import twitter_credentials
import calendar
import time
# Replace the API_KEY and API_SECRET with your application's key and secret.
API_KEY = twitter_credentials.CON... | {"hexsha": "43905b612f00592b1c36cbf15b67e4c0da7b60cf", "size": 3678, "ext": "py", "lang": "Python", "max_stars_repo_path": "NPP_tweets.py", "max_stars_repo_name": "Carlvinchi/MIA-GH-AI--Election_Prediction--2020", "max_stars_repo_head_hexsha": "36a9b9c573cc33c7126df924b01feb505216c4b3", "max_stars_repo_licenses": ["MIT... |
#!/usr/bin/python
import numpy as np
from scipy.ndimage import correlate
class CSC:
'Color Space Conversion'
def __init__(self, img, csc):
self.img = img
self.csc = csc
def execute(self):
img_h = self.img.shape[0]
img_w = self.img.shape[1]
img_c = self.img.shape[2]... | {"hexsha": "96a2e072e1e1409302a94ae0e1b29e0764fa3ced", "size": 1339, "ext": "py", "lang": "Python", "max_stars_repo_path": "model/csc.py", "max_stars_repo_name": "hdliu21/openISP", "max_stars_repo_head_hexsha": "30b0f20f689ef81df5a34639ccae5adbaa4bfa94", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max... |
import numpy as np
import matplotlib.pyplot as plt
"""
1 Topic Introduction
--------------------------
Given simultaneous nonlinear equations, find the roots using Newton-Rhapson.
2 Topic Theory/Approach
--------------------------
Algebrically manipulate the given functions to find a function of:
x... | {"hexsha": "c80cd83c41828f9bd0588e30ff4cd790989d374b", "size": 2406, "ext": "py", "lang": "Python", "max_stars_repo_path": "Examples/Simultaneous_nonlinear_equationsp2.py", "max_stars_repo_name": "atv32/Scientific-Computations", "max_stars_repo_head_hexsha": "d2796ccf05685d0da8e04fb8d4a34937a97177d9", "max_stars_repo_l... |
from typing import Tuple
import numpy as np
from genrl.core.bandit import MultiArmedBandit
class GaussianMAB(MultiArmedBandit):
"""
Contextual Bandit with categorial context and gaussian reward distribution
:param bandits: Number of bandits
:param arms: Number of arms in each bandit
:param rewa... | {"hexsha": "116be04e92a33b4980b16d9c9dc54eadfeb6fdbf", "size": 1442, "ext": "py", "lang": "Python", "max_stars_repo_path": "genrl/agents/bandits/multiarmed/gaussian_mab.py", "max_stars_repo_name": "matrig/genrl", "max_stars_repo_head_hexsha": "25eb018f18a9a1d0865c16e5233a2a7ccddbfd78", "max_stars_repo_licenses": ["MIT"... |
'`matflow_abaqus.main.py`'
import numpy as np
from abaqus_parse import materials
from abaqus_parse.parts import generate_compact_tension_specimen_parts
from abaqus_parse.steps import generate_compact_tension_specimen_steps
from abaqus_parse.writers import write_inp
from abaqus_parse.generate_MK_mesh import generate_MK... | {"hexsha": "ebaff0bb2d543a4dcdb7292a57a5738766b68712", "size": 6771, "ext": "py", "lang": "Python", "max_stars_repo_path": "matflow_abaqus/main.py", "max_stars_repo_name": "maria-yankova/matflow-abaqus", "max_stars_repo_head_hexsha": "387dd5654b586c014f7cdff26e844698192dd13b", "max_stars_repo_licenses": ["MIT"], "max_s... |
#
# Copyright (c) 2021, NVIDIA CORPORATION. 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 appl... | {"hexsha": "292e03f51b2cd42426969b94e8c6c06f9a73e649", "size": 4060, "ext": "py", "lang": "Python", "max_stars_repo_path": "tools/pytorch-quantization/tests/optim_helper_test.py", "max_stars_repo_name": "martellz/TensorRT", "max_stars_repo_head_hexsha": "f182e83b30b5d45aaa3f9a041ff8b3ce83e366f4", "max_stars_repo_licens... |
# This is python script for Metashape Pro. Scripts repository: https://github.com/agisoft-llc/metashape-scripts
#
# Based on https://colab.research.google.com/github/tensorflow/lucid/blob/master/notebooks/differentiable-parameterizations/style_transfer_3d.ipynb
# Modifications:
# 1. Taking into account cameras position... | {"hexsha": "f224da8ff9a5ef200899d21524789844cef41a32", "size": 22320, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/model_style_transfer.py", "max_stars_repo_name": "rafulweber/metashape-scripts", "max_stars_repo_head_hexsha": "d38ff73e4a7879de5710c27f24149ad6554d4bab", "max_stars_repo_licenses": ["MIT"], ... |
module comm
use,intrinsic:: iso_fortran_env, only: stdout=>output_unit, stderr=>error_unit, wp=>real32, dp=>real64
implicit none
public
real(dp),parameter :: pi = 4._dp*atan(1._dp)
real(dp),parameter :: deg2rad = pi/180._dp
real(dp),parameter :: rad2deg = 180/pi
integer, parameter :: npt=500
logical :: debug=.false... | {"hexsha": "2abef8e971691764e899c6ca140760b81c2b9191", "size": 415, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "dir.source/dir.include/comm.f90", "max_stars_repo_name": "space-physics/transcar", "max_stars_repo_head_hexsha": "a9305bd29723beb45004a8882627fa518d8a1bb6", "max_stars_repo_licenses": ["Apache-2.... |
(** Generated by coq-of-ocaml *)
Require Import OCaml.OCaml.
Local Set Primitive Projections.
Local Open Scope string_scope.
Local Open Scope Z_scope.
Local Open Scope type_scope.
Import ListNotations.
Unset Positivity Checking.
Unset Guard Checking.
Inductive nat : Set :=
| O : nat
| S : nat -> nat.
Inductive natu... | {"author": "yalhessi", "repo": "lemmaranker", "sha": "53bc2ad63ad7faba0d7fc9af4e1e34216173574a", "save_path": "github-repos/coq/yalhessi-lemmaranker", "path": "github-repos/coq/yalhessi-lemmaranker/lemmaranker-53bc2ad63ad7faba0d7fc9af4e1e34216173574a/benchmark/clam/_lfind_clam_lf_goal33_distrib_100_plus_assoc/goal33con... |
#include "mod_passauth/provider.hpp"
#include <algorithm>
#include <iterator>
#include <iostream>
#include <sstream>
#include <boost/foreach.hpp>
#include <boost/scope_exit.hpp>
#include <boost/make_shared.hpp>
#include <boost/algorithm/string/predicate.hpp>
#include <boost/algorithm/string.hpp>
#include <boost/lexic... | {"hexsha": "29c7dd7de8b77741db8a8199a13aa840083bf66e", "size": 6283, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "provider.cpp", "max_stars_repo_name": "Trree/Apache_C-", "max_stars_repo_head_hexsha": "7c308b306bb4153f9d99a4e5a5cc33a208e91831", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": null,... |
import cv2
import mediapipe as mp
import numpy as np
from game.game import *
import argparse
import pygame
import random
import time
from utils import *
from body_part_angle import BodyPartAngle
## setup agrparse
ap = argparse.ArgumentParser()
ap.add_argument("-t",
"--game_type",
... | {"hexsha": "aabfeb1d5375d4eede82a1610403b6682598a937", "size": 8793, "ext": "py", "lang": "Python", "max_stars_repo_path": "modelSelection.py", "max_stars_repo_name": "liuzihau/OutdoorAtHome", "max_stars_repo_head_hexsha": "aaf928c8e8e347b5ed9809e20b4536250236eca5", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars... |
import nltk
import numpy as np
from flask import Flask, render_template, request
from tensorflow.keras.models import load_model
from src.api.Postprocessing import Postprocessing
from src.api.PredictionPipeline import PredictionPipeline
from src.api.Preprocessing import Preprocessing
app = Flask(__name__, template_fol... | {"hexsha": "a5a4f7d31cb611f311825325ac9f83db485e1323", "size": 1869, "ext": "py", "lang": "Python", "max_stars_repo_path": "app.py", "max_stars_repo_name": "ikrizanic/Twitter-Sentiment-Analysis", "max_stars_repo_head_hexsha": "08fda7f61f5f3e548cb1e65ec06aa35dc0489af3", "max_stars_repo_licenses": ["MIT"], "max_stars_cou... |
#################################################################################################
# Functions
#################################################################################################
function get_ϕs_eff_rng(eff_type, config)
# Pick max values
if eff_type == "mml"
ϕ1_max = [... | {"hexsha": "1a0f966025338fef15c856ee0f2ae99d000fd6aa", "size": 18273, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "calls/PaperSimulations/Aim-5/Aim-5-Source.jl", "max_stars_repo_name": "jordiabante/CpelNano.jl", "max_stars_repo_head_hexsha": "a5cd558b30113f05f93305a515ab24db8633e9a5", "max_stars_repo_licenses"... |
/-
Runs theorem naming evaluation.
-/
import all
import backends.bfs.openai
import utils.util
import evaluation
section main
meta structure TheoremNamingEvalResult : Type :=
(decl_nm : name) -- name of top-level theorem (i.e. ground truth)
(decl_tp : expr) -- goal of top-level theorem
(predictions : list (string ×... | {"author": "jesse-michael-han", "repo": "lean-tpe-public", "sha": "87c7bb8dfb8271d8fcf917aae0e731600c4f4c6c", "save_path": "github-repos/lean/jesse-michael-han-lean-tpe-public", "path": "github-repos/lean/jesse-michael-han-lean-tpe-public/lean-tpe-public-87c7bb8dfb8271d8fcf917aae0e731600c4f4c6c/src/tools/theorem_naming... |
#!/usr/bin/env python
#
# Copyright 2019 DFKI GmbH.
#
# 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, merg... | {"hexsha": "ee6fa9127c704cb69a7b990879c76b5ebaf9086d", "size": 4682, "ext": "py", "lang": "Python", "max_stars_repo_path": "vis_utils/graphics/plot_manager.py", "max_stars_repo_name": "eherr/vis_utils", "max_stars_repo_head_hexsha": "b757b01f42e6da02ad62130c3b0e61e9eaa3886f", "max_stars_repo_licenses": ["MIT"], "max_st... |
module RcSetup
using Parameters
import DataStructures: OrderedDict
export Setup, Port, all_ports, OrderedDict
module Port
export PressPorts, all_ports
@enum PressPorts in1 in2 in3 in4 out1 out2 out3 out4 ice1 ice2 ice3 ice4
const all_ports = [in1, in2, in3, in4, ice1, ice2, ice3, ice4, out1, out2, out3, out4]
end
"Dis... | {"hexsha": "b536cb8bb8e1690d5dad5404f2a575ecf094784f", "size": 1253, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/setup/RcSetup.jl", "max_stars_repo_name": "mauro3/RchannelImages.jl", "max_stars_repo_head_hexsha": "738ad3d318cebf1e2d3afebf16ad60f70b08a6cf", "max_stars_repo_licenses": ["MIT"], "max_stars_co... |
from data import Data
import argparse
import numpy as np
import os
import sys
import matplotlib.pyplot as plt
from power_law import y_function
def main(args, imbalanced=False):
print(args)
dataObj = Data(dataset=args.dataset, args=args)
if args.dataset == "IMAGENET":
# Imagenet is loaded from args... | {"hexsha": "c4b1a6ee371d6914ef225765738803e25762a53f", "size": 7035, "ext": "py", "lang": "Python", "max_stars_repo_path": "al_utils/partition_data.py", "max_stars_repo_name": "PrateekMunjal/TorchAL", "max_stars_repo_head_hexsha": "ec60b093333c66e4c8862d128d81680060fddf36", "max_stars_repo_licenses": ["MIT"], "max_star... |
# @package hubzero-simtool
# @file params.py
# @copyright Copyright (c) 2019-2021 The Regents of the University of California.
# @license http://opensource.org/licenses/MIT MIT
# @trademark HUBzero is a registered trademark of The Regents of the University of California.
#
import os
import sys
i... | {"hexsha": "8b5a02dcfd02499918134484933a60e627d9b129", "size": 24710, "ext": "py", "lang": "Python", "max_stars_repo_path": "simtool/params.py", "max_stars_repo_name": "hubzero/simtool", "max_stars_repo_head_hexsha": "8763a0cafbd608f36f156051d3cf44c679a23c5f", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null... |
import re as re
from typing import Union, Callable
import numpy as np
__author__ = "piveloper"
__copyright__ = "26.03.2020, piveloper"
__version__ = "1.0"
__email__ = "piveloper@gmail.com"
__doc__ = """This script includes helpful functions to extended PyOpenCl functionality."""
from pyopencl_extension.types.auto_ge... | {"hexsha": "45cfc287c00ebcfa8424b38b2c03e795b4545b83", "size": 7185, "ext": "py", "lang": "Python", "max_stars_repo_path": "pyopencl_extension/types/utilities_np_cl.py", "max_stars_repo_name": "piveloper/pyopencl-extension", "max_stars_repo_head_hexsha": "0f9fede4cfbb1c3f6d99c5e0aa94feddb23a5d4c", "max_stars_repo_licen... |
Traditionally a drinking establishment of AngloIrish background, but can mean any gentrified bar that also serves food and features live entertainment.
In Davis
de Veres Irish Pub
In Sacramento
Fox & Goose Pub
Streets of London Pub
Shady Lady Saloon
| {"hexsha": "cf687f76dc69a49f061101f74c714dea8ffa97e1", "size": 271, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "lab/davisWiki/Pubs.f", "max_stars_repo_name": "voflo/Search", "max_stars_repo_head_hexsha": "55088b2fe6a9d6c90590f090542e0c0e3c188c7d", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, ... |
import numpy as np
def rand_uniform_bool(loc=0.5) -> bool:
""" Generates a boolean derived from a uniform distribution biased towards the value of loc
Args:
loc: The bias, if higher tends towards producing false more and vice versa
Returns:
bool
"""
return np.random.uniform() > l... | {"hexsha": "fcf21d2b835b96b923105605c905d8977f865586", "size": 325, "ext": "py", "lang": "Python", "max_stars_repo_path": "opticverge/core/generator/bool_generator.py", "max_stars_repo_name": "opticverge/scikit-evolution", "max_stars_repo_head_hexsha": "fdd69468c0aacfba4c1ff82a6619f20abfd5f77b", "max_stars_repo_license... |
import unittest
import numpy as np
try:
import nifty
except ImportError:
nifty = None
# TODO try importing from dsb
# try:
# from cremi_tools.metrics import adapted_rand, voi
# except ImportError:
# adapted_rand, voi = None, None
class TestMatching(unittest.TestCase):
# TODO implement download o... | {"hexsha": "f2286c0f8706525b04e65a5b57d31b23af996b02", "size": 1139, "ext": "py", "lang": "Python", "max_stars_repo_path": "test/evaluation/test_matching.py", "max_stars_repo_name": "boykovdn/elf", "max_stars_repo_head_hexsha": "93e35509da82213e227f7abdbf88c8b5648e7674", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
import pandas as pd
import csv
import numpy as np
geiger = pd.DataFrame.from_csv('data/geiger_proteomics.csv')
names = list({l.split(' ')[1][:-2]+'_geiger' for l in geiger.columns})
genes = pd.DataFrame.from_csv('data/human_length.csv').index
geiger_out = pd.DataFrame(index=genes, columns=names)
for i in np.arange(0,... | {"hexsha": "00bb95f92c5640482dd602ff74c4350d0ce6817e", "size": 890, "ext": "py", "lang": "Python", "max_stars_repo_path": "protein_length/split_geiger.py", "max_stars_repo_name": "dandanvidi/weighted-proteins", "max_stars_repo_head_hexsha": "1c3b8dc2b1a53404c59f5a443d3c14dbfcffac26", "max_stars_repo_licenses": ["MIT"],... |
import numpy as np
import salem
import torch
from combine2d.core.dynamics import run_forward_core
def create_cost_func(gdir, data_logger=None, surface_noise=None,
bed_measurements=None):
"""
Creates a cost function based on the glacier directory.
Parameters
----------
gdir: ... | {"hexsha": "1cefe259308c51f62fa1bfe201cbcf46a8aaeaf3", "size": 18642, "ext": "py", "lang": "Python", "max_stars_repo_path": "combine2d/core/cost_function.py", "max_stars_repo_name": "phigre/cobi", "max_stars_repo_head_hexsha": "bb6cd9a49eb22862be6d87f0a2b0c8baf65cadb5", "max_stars_repo_licenses": ["BSD-3-Clause"], "max... |
"""Object Detection task"""
import copy
import pickle
import platform
import logging
import warnings
import os
import pandas as pd
import numpy as np
from autogluon.common.utils.log_utils import set_logger_verbosity, verbosity2loglevel
from autogluon.core.utils import get_gpu_count_all
from .._gluoncv import ObjectDet... | {"hexsha": "5b00e88d53ffbd876610b500d18c5ba2498bd38f", "size": 27452, "ext": "py", "lang": "Python", "max_stars_repo_path": "vision/src/autogluon/vision/detector/detector.py", "max_stars_repo_name": "huibinshen/autogluon", "max_stars_repo_head_hexsha": "18c182c90df89762a916128327a6792b8887c5c6", "max_stars_repo_license... |
"""Landlab component that simulates potential evapotranspiration rate.
Potential Evapotranspiration Component calculates spatially distributed
potential evapotranspiration based on input radiation factor (spatial
distribution of incoming radiation) using chosen method such as constant
or Priestley Taylor. Ref: ASCE-EW... | {"hexsha": "4b967bc41018863990a01d5307ad003aca80caa4", "size": 2891, "ext": "py", "lang": "Python", "max_stars_repo_path": "landlab/components/pet/__init__.py", "max_stars_repo_name": "SiccarPoint/landlab", "max_stars_repo_head_hexsha": "4150db083a0426b3647e31ffa80dfefb5faa5a60", "max_stars_repo_licenses": ["MIT"], "ma... |
import torch
import torch.nn as nn
import torch.nn.function as F
from torch.autograd import Variable
from torch import autograd
from numpy import pi
from numpy import log as np_log
from base_neural_process import BaseNeuralProcess
from distributions import sample_diag_gaussians, local_repeat
log_2pi = np_log(2*pi)
... | {"hexsha": "f2d629eb61c01a0129ec8d5d8b4904f0cc632752", "size": 5495, "ext": "py", "lang": "Python", "max_stars_repo_path": "neural_processes/enc_free_neural_process.py", "max_stars_repo_name": "yifan-you-37/rl_swiss", "max_stars_repo_head_hexsha": "8b0ee7caa5c1fa93860916004cf4fd970667764f", "max_stars_repo_licenses": [... |
"""Contains transition functions and corresponding helper functions.
Below the signature and purpose of a transition function and its helper
functions is explained with a transition function called example_func:
>
**example_func(** *states, params**)**:
The actual transition function.
Args:
* states... | {"hexsha": "a1c0b95bbed7765f7475e593bb05df450e7d75c6", "size": 4570, "ext": "py", "lang": "Python", "max_stars_repo_path": "skillmodels/transition_functions.py", "max_stars_repo_name": "suri5471/skillmodels", "max_stars_repo_head_hexsha": "8ceeeae7892cbec859c5725e4e169f2b6d025be4", "max_stars_repo_licenses": ["BSD-2-Cl... |
import numpy as np
x = []
y = []
nrOfDegs = 15
wielomiany = []
with open("C:\\Users\\User\\Desktop\\Studia\\Jezyk C\\Zadania\\python\\w.txt", "r") as file1:
for line in file1.readlines():
f_list = [float(i) for i in line.split("\t")]
x.append(f_list[0])
y.append(f_list[1])
nr... | {"hexsha": "967c4edd8a3d74b56281dc7c5a21483a94ea69f4", "size": 1438, "ext": "py", "lang": "Python", "max_stars_repo_path": "Python/numerki_zad_20.py", "max_stars_repo_name": "Armillion/Exercises", "max_stars_repo_head_hexsha": "d20c1949a8b185305eb388e2b21bf52de5f438cb", "max_stars_repo_licenses": ["Apache-2.0"], "max_s... |
"""
Generate content wise reports for an aggregated, and status wise views
"""
import re
import sys, time
import os
import requests
import numpy as np
import pandas as pd
from datetime import date, datetime
from pathlib import Path
from string import Template
from dataproducts.util.utils import get_tenant_info, creat... | {"hexsha": "5b8bac0ab6f6388496fdffcf20fb2e9cc798edbd", "size": 19408, "ext": "py", "lang": "Python", "max_stars_repo_path": "python-scripts/src/main/python/dataproducts/services/etb/content_progress.py", "max_stars_repo_name": "keshavprasadms/sunbird-data-products", "max_stars_repo_head_hexsha": "3e154484a5543976df537d... |
import numpy as np
import pandas as pd
ENSEMBL_2_GENE_SYMBOLS = '/local/scratch/rv340/hugo/genes_ENSEMBL_to_official_gene.csv'
def ENSEMBL_to_gene_symbols(ENSEMBL_symbols, file=ENSEMBL_2_GENE_SYMBOLS):
def _ENSEMBL_to_gene_symbols(file):
df = pd.read_csv(file, header=None)
df.columns = ['ensemble... | {"hexsha": "1afd7e28168c7735958a6e0e0c23ae22c8f37fa1", "size": 2571, "ext": "py", "lang": "Python", "max_stars_repo_path": "data/data_utils.py", "max_stars_repo_name": "899la/GTEx-imputation", "max_stars_repo_head_hexsha": "6272548f606877a030ac42138aa11cce079f1b13", "max_stars_repo_licenses": ["MIT"], "max_stars_count"... |
# -*- coding: utf-8 -*-
"""
Wrapper for evaluating different kernels
"""
import numpy as np
from ..utils.log_util import LogInfo
class Evaluator:
def __init__(self, model, sess, ob_batch_num=100, show_detail=True):
self.model = model
self.sess = sess
self.ob_batch_num = ob_batch_num
... | {"hexsha": "f84030aa59542da733aa6eb7077ef7d7ece1aeec", "size": 5955, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/kbqa/learner/evaluator.py", "max_stars_repo_name": "xuehuiping/TransDG", "max_stars_repo_head_hexsha": "ca55744594c5c8d6fe045bed499df72110880366", "max_stars_repo_licenses": ["MIT"], "max_star... |
data Nat : Set where
zero : Nat
suc : Nat → Nat
interleaved mutual
data Even : Nat → Set
data Odd : Nat → Set
-- base cases: 0 is Even, 1 is Odd
constructor
even-zero : Even zero
odd-one : Odd (suc zero)
-- step case: suc switches the even/odd-ness
constructor
even-suc : ∀ {n} → Odd n... | {"hexsha": "9a2b124bed86aa312bef0696f2fd8ec2629d76fc", "size": 380, "ext": "agda", "lang": "Agda", "max_stars_repo_path": "test/Succeed/Issue2858-EvenOdd.agda", "max_stars_repo_name": "shlevy/agda", "max_stars_repo_head_hexsha": "ed8ac6f4062ea8a20fa0f62d5db82d4e68278338", "max_stars_repo_licenses": ["BSD-3-Clause"], "m... |
# plots.py
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.manifold import TSNE
import ast
from util import Util
import glob_conf
import seaborn as sns
import numpy as np
class Plots():
def __init__(self):
"""Initializing the util system"""
self.util = Util()
def describe... | {"hexsha": "87458713afe4e4072d7764ef3c006d7a8f69a183", "size": 2432, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/plots.py", "max_stars_repo_name": "bagustris/nkululeko", "max_stars_repo_head_hexsha": "87a4918b37e2a8599b81c4752c6750fc8adaa079", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, ... |
[STATEMENT]
lemma invh_baldL_invc:
"\<lbrakk> invh l; invh r; bheight l + 1 = bheight r; invc r \<rbrakk>
\<Longrightarrow> invh (baldL l a r) \<and> bheight (baldL l a r) = bheight l + 1"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. \<lbrakk>invh l; invh r; bheight l + 1 = bheight r; invc r\<rbrakk> \<Lon... | {"llama_tokens": 233, "file": "Priority_Search_Trees_PST_RBT", "length": 1} |
'''
root/code/individual_definitions/individual_mutate.py
Overview:
overview of what will/should be in this file and how it interacts with the rest of the code
Rules:
mention any assumptions made in the code or rules about code structure should go here
'''
### packages
from abc import ABC, abstractmethod
from numpy ... | {"hexsha": "b5703a380f40f4fbbd8788e28f32046e2280a70f", "size": 4252, "ext": "py", "lang": "Python", "max_stars_repo_path": "codes/individual_definitions/individual_mutate.py", "max_stars_repo_name": "roddtalebi/ezCGP", "max_stars_repo_head_hexsha": "a93df7ae91fd5905df368661b86ae653c3d08869", "max_stars_repo_licenses": ... |
include("./qol.jl")
include("./timer.jl") | {"hexsha": "91c5c69fd2a7abb2a5ad58bda0eac7187f90068e", "size": 41, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/utilities/utilitiesMain.jl", "max_stars_repo_name": "IsaacRudich/PnB_SOP", "max_stars_repo_head_hexsha": "4d28dc183bcb1427e68ed8d9d8f0030d195c10c1", "max_stars_repo_licenses": ["MIT"], "max_stars... |
"""Observation Classes for BDGym Highway Env """
from typing import TYPE_CHECKING, List, Optional, Tuple
import numpy as np
from gym import spaces
from highway_env.road.lane import AbstractLane
from highway_env.envs.common.observation import \
KinematicObservation, ObservationType
import bdgym.envs.utils as util... | {"hexsha": "62c96b1d0fc6e7967fd7bc1a262604049920ee2c", "size": 14964, "ext": "py", "lang": "Python", "max_stars_repo_path": "bdgym/envs/driver_assistant/observation.py", "max_stars_repo_name": "RDLLab/benevolent-deception-gym", "max_stars_repo_head_hexsha": "4d04e097609097e0f07c661aac221184ebdec2fe", "max_stars_repo_li... |
SUBROUTINE DQDEV ( device, iunit, iatyp, iret )
C************************************************************************
C* DQDEV *
C* *
C* This subroutine returns the current plot device identifier, unit *
C* number and access type. If no device is set, a blank is returned. *
C* DEVICE has traditio... | {"hexsha": "828fea0bcfa7a69a7772631434033fdb31db700e", "size": 1561, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "gempak/source/gpltdev/control/dqdev.f", "max_stars_repo_name": "oxelson/gempak", "max_stars_repo_head_hexsha": "e7c477814d7084c87d3313c94e192d13d8341fa1", "max_stars_repo_licenses": ["BSD-3-Clause... |
from distutils.core import Extension,setup
from Cython.Build import cythonize
import numpy
ext_modules = [
Extension(
"julia",
["julia.pyx"],
include_dirs=[numpy.get_include()],
)
]
setup(
ext_modules=cythonize(ext_modules)
) | {"hexsha": "13cc692892caf13e92961732515183160313ff4c", "size": 262, "ext": "py", "lang": "Python", "max_stars_repo_path": "Cython/vs_numba/setup.py", "max_stars_repo_name": "royqh1979/python_libs_usage", "max_stars_repo_head_hexsha": "57546d5648d8a6b7aca7d7ff9481aa7cd4d8f511", "max_stars_repo_licenses": ["MIT"], "max_s... |
#! /usr/bin/env python
# embedding_in_qt.py --- Simple Qt application embedding matplotlib canvases
#
# Copyright (C) 2005 Florent Rougon
#
# This file is an example program for matplotlib. It may be used and
# modified with no restriction; raw copies as well as modified versions
# may be distributed without limitatio... | {"hexsha": "311fd9e961f783827188cdd747fc7b195331e250", "size": 4389, "ext": "py", "lang": "Python", "max_stars_repo_path": "examples/user_interfaces/embedding_in_qt.py", "max_stars_repo_name": "pierre-haessig/matplotlib", "max_stars_repo_head_hexsha": "0d945044ca3fbf98cad55912584ef80911f330c6", "max_stars_repo_licenses... |
function adjacency_structure = gr_adjacency_structure ( node_num, ...
node_coordinates, edge_num, edge_nodes )
%*****************************************************************************80
%
%% GR_ADJACENCY_STRUCTURE returns the adjacency structure of a graph.
%
% Discussion:
%
% Since we are using MATLAB, we... | {"author": "johannesgerer", "repo": "jburkardt-m", "sha": "1726deb4a34dd08a49c26359d44ef47253f006c1", "save_path": "github-repos/MATLAB/johannesgerer-jburkardt-m", "path": "github-repos/MATLAB/johannesgerer-jburkardt-m/jburkardt-m-1726deb4a34dd08a49c26359d44ef47253f006c1/graph_representation/gr_adjacency_structure.m"} |
#!/usr/bin/python
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import matplotlib.colors as mcolors
import tensorflow as tf
from segnet import segnet
from tensorflow.contrib.keras import backend as K
from PIL import Image
import numpy as np
import pdb
import cv2
from vis.visualization import v... | {"hexsha": "fbaee9d4c6815d0b08800fb8fe1ad4b381c03bfd", "size": 3692, "ext": "py", "lang": "Python", "max_stars_repo_path": "over_lay_filter_heatmap_nnpreimage.py", "max_stars_repo_name": "yukikawana/git_analyze", "max_stars_repo_head_hexsha": "9c143a954b09551dc48da35f6a0ef60cb1229e94", "max_stars_repo_licenses": ["MIT"... |
from ase import *
from gpaw import *
from ase.io import *
import numpy as npy
xc='LDA'
slab, calc = restart('out_LCAO_'+xc+'.gpw')#,txt = 'dos_out.txt')
#e_fermi = calc.get_fermi_level()
#num_at = slab.get_number_of_atoms()
b=53
wf = calc.get_pseudo_wave_function(band=b)
fname= 'wf_'+str(b)+'-HOMO.cube'
print 'wri... | {"hexsha": "151c0bdaa2c2e64ec499728e625e65b115348ace", "size": 558, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/4N-coronene/plot_frontiers.py", "max_stars_repo_name": "Probe-Particle/PPSTM", "max_stars_repo_head_hexsha": "4434739bd737e58a2fc556dff24e8e7d6eab084e", "max_stars_repo_licenses": ["MIT"], "m... |
#! /usr/bin/env python3
# -*- coding: utf-8 -*-
import numpy as np
import random
import matplotlib.pyplot as plt
n = int(input("Ile ruchów? "))
x = y = 0
lx = [0]
ly = [0]
for i in range(0, n):
# wylosuj kąt i zamień go na radiany
rad = float(random.randint(0, 360)) * np.pi / 180
x = x + np.cos(rad) # w... | {"hexsha": "8380d81065642e4a4c188256ec6c74ce39f77078", "size": 783, "ext": "py", "lang": "Python", "max_stars_repo_path": "docs/pylab/rbrowna03.py", "max_stars_repo_name": "damiankarol7/python101", "max_stars_repo_head_hexsha": "1978a9402a8fb0f20c4ca7bd542cb8d7d4501b9b", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
# coding: utf-8
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.pylab as pylab
import matplotlib.cm as cm
import scipy.misc
from PIL import Image
import scipy.io as sio
import os
import cv2
import time
import pdb
import numpy as np
# Make sure that caffe is on the python path:
caffe_root = '../../... | {"hexsha": "6a3c2c2f6f88f2137f19a6428d64f0975369e90d", "size": 2097, "ext": "py", "lang": "Python", "max_stars_repo_path": "examples/ofnet/PIOD/deploy_ofnet_piod.py", "max_stars_repo_name": "buptlr/LuRui-OFNet", "max_stars_repo_head_hexsha": "e76937c8c24c6012cdf7edbbf09431d9ef7aa163", "max_stars_repo_licenses": ["MIT"]... |
#!/usr/bin/env python
__author__ = 'Simon_2'
# ======================================================================================================================
# Extract results from .txt files generated by " " and draw a plot comparing the automatic metric extraction method
# (called "binary") to manua... | {"hexsha": "0c26250e3d1b12b6e9a98531288aa72f72cd1a5c", "size": 16394, "ext": "py", "lang": "Python", "max_stars_repo_path": "dev/atlas/validate_atlas/plot_auto_vs_manual.py", "max_stars_repo_name": "valosekj/spinalcordtoolbox", "max_stars_repo_head_hexsha": "266bfc88d6eb6e96a2c2f1ec88c2e185c6f88e09", "max_stars_repo_li... |
import numpy
from btypes.big_endian import *
import gx
import logging
logger = logging.getLogger(__name__)
class Header(Struct):
magic = ByteString(4)
section_size = uint32
shape_count = uint16
__padding__ = Padding(2)
shape_offset = uint32
index_offset = uint32
unknown0_offset = uint32
... | {"hexsha": "24e4ffc05a779618663a446a2d66005c4cf8a50f", "size": 9815, "ext": "py", "lang": "Python", "max_stars_repo_path": "j3d/shp1.py", "max_stars_repo_name": "blank63/j3dview", "max_stars_repo_head_hexsha": "498225e12119a9a2b4af9beed1be95f28d8410e2", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 13, "max_st... |
import json
import os
import numpy as np
from tqdm import tqdm
from mmhuman3d.core.conventions.keypoints_mapping import convert_kps
from mmhuman3d.data.data_converters.base_converter import BaseConverter
from mmhuman3d.data.data_converters.builder import DATA_CONVERTERS
from mmhuman3d.data.data_structures.human_data ... | {"hexsha": "cb1f9108c341cf7ad9efffde1ec896bbc704ddbd", "size": 8908, "ext": "py", "lang": "Python", "max_stars_repo_path": "mmhuman3d/data/data_converters/pw3d_hybrik.py", "max_stars_repo_name": "ykk648/mmhuman3d", "max_stars_repo_head_hexsha": "26af92bcf6abbe1855e1a8a48308621410f9c047", "max_stars_repo_licenses": ["Ap... |
# -*- coding: utf-8 -*-
"""
Created on Tue Aug 4 11:01:16 2015
@author: hehu
"""
import matplotlib.pyplot as plt
import numpy as np
from sklearn.neighbors import KNeighborsClassifier
from sklearn.lda import LDA
from sklearn.svm import SVC, LinearSVC
from sklearn.linear_model import LogisticRegression
from sklearn.na... | {"hexsha": "732eaf7f9aabea9b85ea5b8f89c8f70d75d596d9", "size": 5901, "ext": "py", "lang": "Python", "max_stars_repo_path": "code/LRExample.py", "max_stars_repo_name": "mahehu/SGN-41007", "max_stars_repo_head_hexsha": "c8ed169a0a5f70fb87b99448e39a573c0df584b2", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 61, ... |
(* NIZK Proof System - Useful functions by Remi Bazin *)
(* Imports *)
Require Import Arith.
Require Import NPeano.
Require Import Le.
Require Import List.
(* Various utils *)
Fixpoint repeat_fn (n:nat) (A:Type) (f:A -> A) (s:A) : A :=
match n with
| O => s
| S m => repeat_fn m A f (f s)
end
.
Lemm... | {"author": "baz1", "repo": "GS-NIZK-Proofs", "sha": "4808405aedabcfa97cd3603f3a794c4015bfc455", "save_path": "github-repos/coq/baz1-GS-NIZK-Proofs", "path": "github-repos/coq/baz1-GS-NIZK-Proofs/GS-NIZK-Proofs-4808405aedabcfa97cd3603f3a794c4015bfc455/proofs/utils.v"} |
import inspect
import numpy as np
def assert_shape(test, reference):
assert test.shape == reference.shape, "Shape mismatch: {} and {}".format(
test.shape, reference.shape)
class ConfusionMatrix:
"""Helper class to hold confusion matrix values, so we don't have to recompute when evaluating multiple... | {"hexsha": "457a5c14368d603a502e76ec2c6cded7815df23f", "size": 16706, "ext": "py", "lang": "Python", "max_stars_repo_path": "probunet/eval.py", "max_stars_repo_name": "jenspetersen/probabilistic-unet", "max_stars_repo_head_hexsha": "ce4708045a3fa3c9d23d44300920e2177fea7140", "max_stars_repo_licenses": ["MIT"], "max_sta... |
\section{Author contribution statement}
TG proposed the research. JCHL developed the method of analysis. The idea to
analyze the bid-ask spread impact was due to JCHL. JCHL carried out the
analysis. Both authors contributed equally to analyzing the results and writing
the paper.
One of us (JCHL) acknowledges financia... | {"hexsha": "d426ec2759323ad93d06fef2e6e95b4b6454dcca", "size": 592, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "paper/forex_response_spread_paper/sections/10_paper_contributions.tex", "max_stars_repo_name": "juanhenao21/forex_response_spread_year", "max_stars_repo_head_hexsha": "251ccccfc9a49f546db5e325ea6b594... |
using EndpointRanges
using Test
@testset "One dimensional" begin
r1 = -3:7
r2 = 2:5
@test ibegin(r1) == -3
@test ibegin(r2) == 2
@test iend(r1) == 7
@test iend(r2) == 5
@test (ibegin+3)(r1) == 0
@test (ibegin+2)(r2) == 4
@test (iend+3)(r1) == 10
@test (iend-2)(r2) == 3
for... | {"hexsha": "ca1180dfc186196c330405e8c673feabd96d0dfb", "size": 1523, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/runtests.jl", "max_stars_repo_name": "UnofficialJuliaMirrorSnapshots/EndpointRanges.jl-340492b5-2a47-5f55-813d-aca7ddf97656", "max_stars_repo_head_hexsha": "95e0f790f11460cac4699e6e59fa1487289... |
[STATEMENT]
lemma polyadd_normh: "isnpolyh p n0 \<Longrightarrow> isnpolyh q n1 \<Longrightarrow> isnpolyh (polyadd p q) (min n0 n1)"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. \<lbrakk>isnpolyh p n0; isnpolyh q n1\<rbrakk> \<Longrightarrow> isnpolyh (p +\<^sub>p q) (min n0 n1)
[PROOF STEP]
proof (induct p q arb... | {"llama_tokens": 43852, "file": "Taylor_Models_Polynomial_Expression", "length": 130} |
"""
Parse the incoming model into a standardized representation.
"""
import numpy as np
from .parser_cb import parse_cb_ensemble
from .parser_lgb import parse_lgb_ensemble
from .parser_sk import parse_skhgbm_ensemble
from .parser_sk import parse_skgbm_ensemble
from .parser_sk import parse_skrf_ensemble
from .parser_xg... | {"hexsha": "0062f2defcce873fd75c8a0b399d2d59e8afda01", "size": 2430, "ext": "py", "lang": "Python", "max_stars_repo_path": "tree_influence/explainers/parsers/__init__.py", "max_stars_repo_name": "jjbrophy47/tree_influence", "max_stars_repo_head_hexsha": "245ff369ed3f4df3ddba243c7e3172423f385505", "max_stars_repo_licens... |
[STATEMENT]
lemma infinite_psubset_coinduct[case_names infinite, consumes 1]:
assumes "R A"
assumes "\<And> A. R A \<Longrightarrow> \<exists> B \<subset> A. R B"
shows "infinite A"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. infinite A
[PROOF STEP]
proof
[PROOF STATE]
proof (state)
goal (1 subgoal):
... | {"llama_tokens": 390, "file": "Probabilistic_Timed_Automata_library_Basic", "length": 7} |
import os
import json
import numpy as np
import matplotlib.pyplot as plt
def compute_iou(box_1, box_2):
'''
This function takes a pair of bounding boxes and returns intersection-over-
union (IoU) of two bounding boxes.
'''
'''
BEGIN YOUR CODE
'''
# print(box_1)
tl_row_1,tl_col_1,br_r... | {"hexsha": "7a85d09bb45124bee7ec676265a8ecd665a3c74e", "size": 6474, "ext": "py", "lang": "Python", "max_stars_repo_path": "eval_detector.py", "max_stars_repo_name": "ruillercoaster/caltech-ee148-spring2020-hw02", "max_stars_repo_head_hexsha": "606382394e9d2037e5989794d896b1d80f128987", "max_stars_repo_licenses": ["MIT... |
# Parameterize by T so that way it can be Vector{Expression} which is defined after
struct Operation <: AbstractOperation
op::Function
args::Vector{Expression}
end
# Recursive ==
function Base.:(==)(x::Operation,y::Operation)
x.op == y.op && length(x.args) == length(y.args) && all(isequal.(x.args,y.args))
... | {"hexsha": "6e5c53d4a1dc6a33f6364b5a917e9048173e41f2", "size": 2052, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/operations.jl", "max_stars_repo_name": "Vaibhavdixit02/ModelingToolkit.jl", "max_stars_repo_head_hexsha": "774217709e377733b10fb3ffe0671bd9a95ad24d", "max_stars_repo_licenses": ["MIT"], "max_st... |
import torch
import numpy as np
from torch.utils.data import Dataset, DataLoader
class MetaQADataSet(Dataset):
def __init__(self, entity_embed_path, entity_dict_path, relation_embed_path, relation_dict_path, qa_dataset_path,
split):
"""
create MetaQADataSet
:param enti... | {"hexsha": "770008373683d2bbdf4251923e30de5931933c7a", "size": 9693, "ext": "py", "lang": "Python", "max_stars_repo_path": "relational_chain_reasoning_module/dataloader.py", "max_stars_repo_name": "albert-jin/Rec-KGQA", "max_stars_repo_head_hexsha": "38c6ca0980ce78a5b67b1cec75a0c40425de5440", "max_stars_repo_licenses":... |
using LinearAlgebra
X = randn(40_000,40_000);
XX = X'X;
XX1 = copy(XX)
@time F1 = eigen(XX1)
@show F1.values[end]
XX2 = copy(XX)
@time F2 = eigvals(XX2)
@show F2[end]
XX3 = copy(XX)
@time F3 = LAPACK.syev!('N', 'U', XX3) #!! will change XX3 inplace
@show F3[end]
XX4 = copy(XX)
using Arpack
eigs(XX4,nev=1)[1][1]... | {"hexsha": "4dc9d95128bb2b1d73eb65d9703ab3abfcee0284", "size": 940, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "eigen.jl", "max_stars_repo_name": "zhaotianjing/JuliaTips", "max_stars_repo_head_hexsha": "29b1095563c28380924a6a436db55bee63a35e9c", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2, "max_s... |
import json
import librosa
import lws
import numpy as np
class LwsAudioProcessor:
def __init__(self, audio_config):
params = self._load_params(audio_config)
self._params = params
self._mel_basis = self._compute_mel_basis()
self._lws_processor = self._build_lws_processor()
@sta... | {"hexsha": "bdd043b7fca9ad132ec527c8d0aa27aa22ca262c", "size": 1856, "ext": "py", "lang": "Python", "max_stars_repo_path": "pytorch/audio_lws.py", "max_stars_repo_name": "vladbataev/nv-wavenet", "max_stars_repo_head_hexsha": "2e16155cef2a460bb7862df674a1b8fa074a5cab", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_s... |
import numpy as np
import torch
from torch.autograd import Variable
from receptor.core.rollout import Rollout, RolloutParallel
from receptor.utils import discount_rewards
def test_rollouts():
rollout = Rollout()
obs = np.ones((2, 4, 3))
for i in range(10):
rollout.add(obs * i, action=5, reward=1,... | {"hexsha": "f64495b904c59c09abeefe8ebabbed809fe2b81e", "size": 4582, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/core/test_rollout.py", "max_stars_repo_name": "dbobrenko/receptor", "max_stars_repo_head_hexsha": "b6447491250c1d41da704b989c751ff2c8a045e6", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
# Simple model using double exponentials
```python
from sympy import *
```
```python
from IPython.display import display, Markdown
```
```python
init_printing()
```
```python
t, P, e_r, e_d, delta_e, rho_e, g_e, i_r, i_d, delta_i, rho_i, g_i, b = symbols('t P \\tau_{er} \\tau_{ed} \\delta_e \\rho_e \\bar{g}_e \... | {"hexsha": "15a2914ebdf961755bf6d7b223dad29bd192fc41", "size": 356820, "ext": "ipynb", "lang": "Jupyter Notebook", "max_stars_repo_path": "model/Model_double_exps.ipynb", "max_stars_repo_name": "elifesciences-publications/linearity", "max_stars_repo_head_hexsha": "777769212ac43d854d23d5b967c6323747c56c09", "max_stars_r... |
from typing import Tuple, List, Any, Union
import numpy as np
import torch.nn as nn
from iatransfer.toolkit.base_matching import Matching
class DPMatching(Matching):
"""Dynamic programming matching algorithm for IAT.
"""
def match(self, from_module: List[Union[nn.Module, List[nn.Module]]],
... | {"hexsha": "4e23e96eaa8d1f0906e9f1c69c66eb1a31e1180e", "size": 5173, "ext": "py", "lang": "Python", "max_stars_repo_path": "iatransfer/toolkit/matching/dp_matching.py", "max_stars_repo_name": "KamilPiechowiak/iatransfer", "max_stars_repo_head_hexsha": "d7607662a2d2f7d1a16164c813e8721a0563552b", "max_stars_repo_licenses... |
from datetime import datetime
from io import BytesIO
import numpy as np
import streamlit as st
from PIL import Image
from pydicom import dcmread
import constants as const
import functions as fun
st.set_page_config(
page_title="CT Simulator",
page_icon=":computer:",
layout="wide",
init... | {"hexsha": "d6c9c7cac1d46786259cb64b8127e6a043bcf48a", "size": 6776, "ext": "py", "lang": "Python", "max_stars_repo_path": "ct_simulator.py", "max_stars_repo_name": "xfredeq/CT-simulator", "max_stars_repo_head_hexsha": "b7cde915ed56fc6170c25f534d5cc4fd80217ba2", "max_stars_repo_licenses": ["MIT"], "max_stars_count": nu... |
import argparse
import numpy as np
import models.ensemble as e
import utils.load as l
import utils.metrics as m
import utils.wrapper as w
def get_arguments():
"""Gets arguments from the command line.
Returns:
A parser with the input arguments.
"""
# Creates the ArgumentParser
parser =... | {"hexsha": "bae17f83cfa22d7f27fe4b757cd88fd629bd9dd6", "size": 2591, "ext": "py", "lang": "Python", "max_stars_repo_path": "ensemble_learning_with_umda.py", "max_stars_repo_name": "gugarosa/evolutionary_ensembles", "max_stars_repo_head_hexsha": "4f73f26bbdfd58bc06ba96fb4d22624dd5a65b5a", "max_stars_repo_licenses": ["MI... |
theory Assertions
imports "../Algebra/BBI" Heap "$ISABELLE_HOME/src/HOL/Eisbach/Eisbach"
begin
no_notation
times (infixl "*" 70) and
sup (infixl "+" 65) and
bot ("\<bottom>")
notation plus (infixl "+" 65)
type_synonym 'a pred = "('a \<times> heap) set"
(*****************************************************... | {"author": "victorgomes", "repo": "veritas", "sha": "d0b50770f9146f18713a690b87dc8fafa6a87580", "save_path": "github-repos/isabelle/victorgomes-veritas", "path": "github-repos/isabelle/victorgomes-veritas/veritas-d0b50770f9146f18713a690b87dc8fafa6a87580/SL/Assertions.thy"} |
#!/opt/conda/envs/ih8life/bin/python
# coding: utf-8
import sys
import os
import json
import time
from copy import deepcopy
import matplotlib.pyplot as plt
import numpy as np
import cv2
import torch
import torch.nn as nn
from torch.utils.data import Dataset, DataLoader
from torchvision import transforms, models
gpu... | {"hexsha": "8d7c044b7d1f980a4d3708710c1d85df59b37d89", "size": 10500, "ext": "py", "lang": "Python", "max_stars_repo_path": "train_model.py", "max_stars_repo_name": "rebeccaayu/masked-emotions", "max_stars_repo_head_hexsha": "dc3ebb935a245484d8a9ad1580ab1c44a7bfa322", "max_stars_repo_licenses": ["MIT"], "max_stars_coun... |
#include <k52/optimization/params/continuous_parameters_array.h>
#ifdef BUILD_WITH_MPI
#include <boost/mpi.hpp>
#include <boost/serialization/vector.hpp>
#include <k52/parallel/mpi/constants.h>
#endif
#include <stdexcept>
namespace k52
{
namespace optimization
{
ContinuousParametersArray::ContinuousParametersArra... | {"hexsha": "ae1c2b2dd20e74075a67d4c9007ffc2208de2f7b", "size": 1649, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/optimization/params/continuous_parameters_array.cpp", "max_stars_repo_name": "wfoperihnofiksnfvopjdf/k52", "max_stars_repo_head_hexsha": "2bbbfe018db6d73ec9773f29e571269f898a9bc0", "max_stars_re... |
import os
import numpy as np
import pytest
from jina.flow import Flow
from jina.proto import jina_pb2
from jina.types.ndarray.generic import NdArray
from tests import validate_callback
NUM_DOCS = 100
cur_dir = os.path.dirname(os.path.abspath(__file__))
@pytest.fixture
def multimodal_documents():
docs = []
... | {"hexsha": "9c52aa34263b31634800d65613184a194c50a4db", "size": 4090, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/integration/multimodal/test_multimodal_parallel.py", "max_stars_repo_name": "yuanl/jina", "max_stars_repo_head_hexsha": "989d0689353bbbcd2c7bf11928b652224c3d4bf7", "max_stars_repo_licenses":... |
"""
sum_gp(first_term, ratio, num_terms)
Finds sum of n terms in a geometric progression
# Input parameters
- first_term : first term of the series
- raio : common ratio between consecutive terms -> a2/a1 or a3/a2 or a4/a3
- num_terms : number of terms in the series till which we count sum
# Example
```j... | {"hexsha": "8ed5455a8c3ce633840373e3e54670e0b39e0353", "size": 871, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/math/sum_of_geometric_progression.jl", "max_stars_repo_name": "Whiteshark-314/Julia", "max_stars_repo_head_hexsha": "3285d8d6b7585cc1075831c2c210b891151da0c2", "max_stars_repo_licenses": ["MIT"]... |
# Standard Library
import argparse
import random
# Third Party
import mxnet as mx
import numpy as np
from mxnet import autograd, gluon
from mxnet.gluon import nn
import os
def parse_args():
parser = argparse.ArgumentParser(
description="Train a mxnet gluon model for FashonMNIST dataset"
)
parser.... | {"hexsha": "6a840a313ad44edb587209bc812639bcf2c41e5e", "size": 4778, "ext": "py", "lang": "Python", "max_stars_repo_path": "sagemaker-debugger/mxnet_spot_training/mxnet_gluon_spot_training.py", "max_stars_repo_name": "jpmarques19/tensorflwo-test", "max_stars_repo_head_hexsha": "0ff8b06e0415075c7269820d080284a42595bb2e"... |
[STATEMENT]
lemma Form_imp_wf_dbfm:
assumes "Form x" obtains A where "wf_dbfm A" "x = \<lbrakk>quot_dbfm A\<rbrakk>e"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. (\<And>A. \<lbrakk>wf_dbfm A; x = \<lbrakk>quot_dbfm A\<rbrakk>e\<rbrakk> \<Longrightarrow> thesis) \<Longrightarrow> thesis
[PROOF STEP]
by (metis as... | {"llama_tokens": 165, "file": "Incompleteness_Coding_Predicates", "length": 1} |
using Test, LinearAlgebra
using DiffEqSensitivity, StochasticDiffEq
using ForwardDiff, Zygote
using Random
@info "SDE Non-Diagonal Noise Adjoints"
seed = 100
Random.seed!(seed)
tstart = 0.0
tend = 0.1
dt = 0.005
trange = (tstart, tend)
t = tstart:dt:tend
tarray = collect(t)
function g(u,p,t)
sum(u.^2.0/2.0)
end
... | {"hexsha": "ba5dbe9656128d36e281b591d3a3c0530ece73cb", "size": 17948, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/sde_nondiag_stratonovich.jl", "max_stars_repo_name": "JuliaDiffEq/DiffEqSensitivity.jl", "max_stars_repo_head_hexsha": "4ca100b4c6dd87fb5a6c43abde500b61f04928d7", "max_stars_repo_licenses": [... |
import pandas as pd
import geopandas as gp
import numpy as np
from shapely.geometry import Point, LineString, MultiLineString
def to2D(geometry):
"""Flatten a 3D line to 2D.
Parameters
----------
geometry : LineString
Input 3D geometry
Returns
-------
LineString
Output 2D... | {"hexsha": "5e792f7ba106ee935edcf181cc684805f281be86", "size": 8102, "ext": "py", "lang": "Python", "max_stars_repo_path": "nhdnet/geometry/lines.py", "max_stars_repo_name": "brendan-ward/nhd-barriers", "max_stars_repo_head_hexsha": "4a469149456a15001db1ccd852924da08a71272c", "max_stars_repo_licenses": ["MIT"], "max_st... |
import os
import time
from collections import OrderedDict
import numpy as np
from env.jaco.two_jaco import TwoJacoEnv
from env.transform_utils import quat_dist, up_vector_from_quat, forward_vector_from_quat
def cos_dist(a, b):
return np.dot(a, b) / np.linalg.norm(a) / np.linalg.norm(b)
class TwoJacoPickEnv(Tw... | {"hexsha": "43008f84ef3150c1cf235ac71d4f0d467b5a187b", "size": 15615, "ext": "py", "lang": "Python", "max_stars_repo_path": "env/jaco/primitives/two_jaco_pick.py", "max_stars_repo_name": "clvrai/coordination", "max_stars_repo_head_hexsha": "2b1bc8a6817b477f49c0cf6bdacd9c2f2e56f692", "max_stars_repo_licenses": ["MIT"], ... |
import utils
import data.vector data.list data.int.basic tactic.omega data.fin
tactic.linarith tactic.apply
open utils
section grids
open list
class relative_grid (α : Type*) :=
(carrier : Type)
(rows : α → ℕ)
(cols : α → ℕ)
(nonempty : Πg, rows g * cols g > 0)
(data : Πg, fin (rows g)... | {"author": "frankSil", "repo": "CAExtensions", "sha": "f5c74fd9a806696c73497d9abd45b7315f45379f", "save_path": "github-repos/lean/frankSil-CAExtensions", "path": "github-repos/lean/frankSil-CAExtensions/CAExtensions-f5c74fd9a806696c73497d9abd45b7315f45379f/src/grid.lean"} |
#! /usr/bin/env python
# -*- coding: utf-8 -*-
# vim:fenc=utf-8
#
# Copyright © 2017 Takuma Yagi <tyagi@iis.u-tokyo.ac.jp>
#
# Distributed under terms of the MIT license.
from __future__ import print_function
from __future__ import division
from six.moves import range
import os
import numpy as np
import cv2
import m... | {"hexsha": "2fae8617056b27a3f302f817f1687323dc763fd1", "size": 19147, "ext": "py", "lang": "Python", "max_stars_repo_path": "utils/plot.py", "max_stars_repo_name": "takumayagi/fpl", "max_stars_repo_head_hexsha": "c357ee74c6ec9ef446f5e8a26b31215199a78602", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 51, "max_... |
// Boost.Geometry
// QuickBook Example
// Copyright (c) 2020, Aditya Mohan
// 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)
//[cross_product
//` Calculate the cross product of two... | {"hexsha": "ca2906be120da7b8f052443f9dc63fe5328dfef8", "size": 1476, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "doc/src/examples/arithmetic/cross_product.cpp", "max_stars_repo_name": "jkerkela/geometry", "max_stars_repo_head_hexsha": "4034ac88b214da0eab8943172eff0f1200b0a6cc", "max_stars_repo_licenses": ["BSL... |
#include <cradle/io/generic_io.hpp>
#include <boost/numeric/conversion/cast.hpp>
#include <boost/filesystem/operations.hpp>
#include <boost/shared_array.hpp>
#include <json/json.h>
#include <cradle/date_time.hpp>
#include <cradle/encoding.hpp>
#include <cradle/io/compression.hpp>
#include <cradle/io/crc.hpp>
#includ... | {"hexsha": "380ab6ed8c502075824575643e6a555e572934fd", "size": 24353, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "cradle/src/cradle/io/generic_io.cpp", "max_stars_repo_name": "dotdecimal/open-cradle", "max_stars_repo_head_hexsha": "f8b06f8d40b0f17ac8d2bf845a32fcd57bf5ce1d", "max_stars_repo_licenses": ["MIT"], ... |
#! /usr/bin/env python
# -*- encoding: utf-8 -*-
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
# import matplotlib.mlab as mlab 已弃用
import scipy.stats
import random
np.random.seed(0)
# 6.2 深入理解伯努利分布
def pro_test1():
# 二项分布实现例程
# 同时抛掷5枚硬币，出现正面朝上的次数——试验10次
print(np.random.binomial(5... | {"hexsha": "eb3297154d3661d49edcb8525c270ce628db94a4", "size": 15783, "ext": "py", "lang": "Python", "max_stars_repo_path": "six_probability.py", "max_stars_repo_name": "hollo08/stockstrategy", "max_stars_repo_head_hexsha": "09ece2457d653439a8ace80a6ac7dd4da9813846", "max_stars_repo_licenses": ["MIT"], "max_stars_count... |
function OneWayAnova()
return OneWayAnova(())
end
function anova_f_value(obj::OneWayAnova, arg0::Collection)
return jcall(obj, "anovaFValue", jdouble, (Collection,), arg0)
end
function anova_p_value(obj::OneWayAnova, arg0::Collection)
return jcall(obj, "anovaPValue", jdouble, (Collection,), arg0)
end
fun... | {"hexsha": "f873a05a9905d136539343ce9231c96a9ee56216", "size": 635, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "gen/HipparchusWrapper/StatWrapper/InferenceWrapper/one_way_anova.jl", "max_stars_repo_name": "JuliaAstrodynamics/Orekit.jl", "max_stars_repo_head_hexsha": "e2dd3d8b2085dcbb1d2c75471dab42d6ddf52c99",... |
import pandas as pd
import numpy as np
from sklearn.preprocessing import LabelEncoder, OneHotEncoder
from sklearn.metrics import confusion_matrix
import matplotlib.pyplot as plt
import tensorflow as tf
from keras.models import Sequential
from keras.optimizers import Adam
from keras.layers import Conv2D, ZeroPadding2D, ... | {"hexsha": "a45d16c8b5744da268608df06450d19b3ad04583", "size": 4581, "ext": "py", "lang": "Python", "max_stars_repo_path": "Training CancerSiamese/MET500/MET_siam.py", "max_stars_repo_name": "MMostavi/CancerSiamese", "max_stars_repo_head_hexsha": "9538eafac489bd4d2fe8fc06b6a72b852ca0bb29", "max_stars_repo_licenses": ["... |
!! Copyright (C) Stichting Deltares, 2012-2016.
!!
!! This program is free software: you can redistribute it and/or modify
!! it under the terms of the GNU General Public License version 3,
!! as published by the Free Software Foundation.
!!
!! This program is distributed in the hope that it will be useful,
!! b... | {"hexsha": "e23f7c141ced88ab15afd20c024e826968280f3d", "size": 5019, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "docker/water/delft3d/tags/v6686/src/engines_gpl/waq/packages/waq_io/src/waq_io/check.f", "max_stars_repo_name": "liujiamingustc/phd", "max_stars_repo_head_hexsha": "4f815a738abad43531d02ac66f5bd0d... |
# This Class utilizes the functions
# from ds_utilites.py as methods of the class
import pandas as pd
class DsHelper(): # Classes usually use CamelCase DsUtilitesClass
def __init__(self):
# __init__ is defined but is empty. Use 'pass' if nothing is needed
pass
def enlarge(self, n):
... | {"hexsha": "89adcaa90210bf42fb50ba7236aed6b19aa2a055", "size": 8836, "ext": "py", "lang": "Python", "max_stars_repo_path": "my_lambdata/ds_utilities_class.py", "max_stars_repo_name": "JeffreyAsuncion/Lambdata-JeffreyAsuncion", "max_stars_repo_head_hexsha": "b325178e5e2580090e53b1f81074c7e0886dfccc", "max_stars_repo_lic... |
# -*- coding: utf-8 -*-
"""
Created on Fri Jan 21 16:54:16 2022
@author: amasilva
"""
import duneevolution as devo
import numpy as np
# import matplotlib.pyplot as plt
# =============================================================================
# Read the model results based on the information of th... | {"hexsha": "0d23e8c1cb7c1cdfc1146b2965212091ab8f0b22", "size": 3239, "ext": "py", "lang": "Python", "max_stars_repo_path": "Example.py", "max_stars_repo_name": "AnaNobre/Dune-Evolution", "max_stars_repo_head_hexsha": "e8bf33a2fb624ad2c15acad5fd586bf00113c257", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null... |
from unittest import TestCase
from pyksburden.genereader import GeneReader
import os
import logging
import numpy as np
logging.basicConfig(level=logging.DEBUG)
class TestGeneReader(TestCase):
def setUp(self):
assert os.path.isdir('data')
self.plink_file = 'data/chr22_rare_test_data'
self... | {"hexsha": "35b6c006f6a0d8764855338bbe246eb2d0636952", "size": 769, "ext": "py", "lang": "Python", "max_stars_repo_path": "pyksburden/tests/test_geneReader.py", "max_stars_repo_name": "rmporsch/pyksburden", "max_stars_repo_head_hexsha": "e9bdca6ffc472fca67b2c37acc940710d96cfd7b", "max_stars_repo_licenses": ["MIT"], "ma... |
#include <glog/logging.h>
#include <pcl/common/io.h>
#include <pcl/common/time.h>
#include <pcl/registration/correspondence_rejection_sample_consensus.h>
#include <v4r/common/graph_geometric_consistency.h>
#include <v4r/common/miscellaneous.h>
#include <boost/graph/biconnected_components.hpp>
#include <boost/gra... | {"hexsha": "8b1f45c041c3385bc5212952440fb1874d187979", "size": 35647, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "modules/common/src/graph_geometric_consistency.cpp", "max_stars_repo_name": "v4r-tuwien/v4r", "max_stars_repo_head_hexsha": "ff3fbd6d2b298b83268ba4737868bab258262a40", "max_stars_repo_licenses": ["... |
[STATEMENT]
lemma CARD_eq: "CARD('a) = nat n"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. CARD('a) = nat n
[PROOF STEP]
proof -
[PROOF STATE]
proof (state)
goal (1 subgoal):
1. CARD('a) = nat n
[PROOF STEP]
have "CARD('a) = card (Abs ` {0..<n})"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. CARD('a) = card (... | {"llama_tokens": 795, "file": null, "length": 13} |
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