text stringlengths 0 1.25M | meta stringlengths 47 1.89k |
|---|---|
[STATEMENT]
lemma star_assnI:
assumes "(h,as)\<Turnstile>P" and "(h,as')\<Turnstile>Q" and "as\<inter>as'={}"
shows "(h,as\<union>as')\<Turnstile>P*Q"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. (h, as \<union> as') \<Turnstile> P * Q
[PROOF STEP]
using assms
[PROOF STATE]
proof (prove)
using this:
(h, as) \<... | {"llama_tokens": 344, "file": "Separation_Logic_Imperative_HOL_Assertions", "length": 3} |
"""Compute centroids of antenna coverage areas (cells)."""
import argparse
import numpy
import pandas as pd
import geopandas as gpd
import shapely.geometry
import matplotlib.patches
import matplotlib.pyplot as plt
import mobilib.voronoi
parser = argparse.ArgumentParser(
description=__doc__,
formatter_class=... | {"hexsha": "9f54aa6ec71bba67eb23e1a06c737426cc1f2109", "size": 5044, "ext": "py", "lang": "Python", "max_stars_repo_path": "antenna_centroids.py", "max_stars_repo_name": "simberaj/mobilib", "max_stars_repo_head_hexsha": "ae350d095a34f53704bd4aaaf7f45e573bda779a", "max_stars_repo_licenses": ["MIT"], "max_stars_count": n... |
import pandas as pd
import numpy as np
import torch
import torch.nn as nn
from torch.utils.data import DataLoader,WeightedRandomSampler
from torch.optim.lr_scheduler import ReduceLROnPlateau
import torchaudio
from settings import data_dir
import os
import json
from models.cnn.networks import groupNorm,resnet1d, wideres... | {"hexsha": "ded068319082ebca0afe243541de08840f69ed0e", "size": 8870, "ext": "py", "lang": "Python", "max_stars_repo_path": "models/baseline.py", "max_stars_repo_name": "pmwaniki/ppg-analysis", "max_stars_repo_head_hexsha": "ae1c76ca8b0eb95a51e3f48eccb8d0a76e7abfbf", "max_stars_repo_licenses": ["MIT"], "max_stars_count"... |
import numpy as np
import cv2 as cv
import matplotlib.pyplot as plt
class annotCats:
def __init__(self, image_path=None, label=None, xtickrange=100, ytickrange=100):
self.colors = self.cvColor()
self.label = label
try:
self.image = cv.imread(image_path)
self.image = ... | {"hexsha": "622737db943488bd657b6534933f99d5b94c3fa4", "size": 4726, "ext": "py", "lang": "Python", "max_stars_repo_path": "config/annots_cv.py", "max_stars_repo_name": "qkrwjdduf159/Waste-Recycling-Image-Segmentation", "max_stars_repo_head_hexsha": "eb84fbeb0aa946c04d2c6eb6254558afaf365abc", "max_stars_repo_licenses":... |
from __future__ import annotations
from typing import Any, Optional, Dict
from typing_extensions import Protocol
import numba
import numpy as np
from sunode.matrix import Sparse
from sunode.basic import lib, ffi
from sunode.dtypesubset import as_nested, DTypeSubset
class Problem(Protocol):
params_dtype: np.dty... | {"hexsha": "54d396faf7598cd1c2403f0598329088f02cdecc", "size": 18349, "ext": "py", "lang": "Python", "max_stars_repo_path": "sunode/problem.py", "max_stars_repo_name": "aseyboldt/pysundials-cffi", "max_stars_repo_head_hexsha": "ccdcd0fd0252285a5440c397619c57378a32d33a", "max_stars_repo_licenses": ["MIT"], "max_stars_co... |
(*
* Copyright 2014, NICTA
*
* This software may be distributed and modified according to the terms of
* the BSD 2-Clause license. Note that NO WARRANTY is provided.
* See "LICENSE_BSD2.txt" for details.
*
* @TAG(NICTA_BSD)
*)
(* License: BSD, terms see file ./LICENSE *)
theory ArrayAssertion
imports
"$L4V... | {"author": "CompSoftVer", "repo": "CSim2", "sha": "b09a4d77ea089168b1805db5204ac151df2b9eff", "save_path": "github-repos/isabelle/CompSoftVer-CSim2", "path": "github-repos/isabelle/CompSoftVer-CSim2/CSim2-b09a4d77ea089168b1805db5204ac151df2b9eff/CParser/tools/c-parser/umm_heap/ArrayAssertion.thy"} |
[STATEMENT]
lemma dom_sound: "dom i j \<Longrightarrow> dominate i j"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. local.dom i j \<Longrightarrow> dominate i j
[PROOF STEP]
by (fastforce simp add: dominate_def dest:path_entry_dom) | {"llama_tokens": 86, "file": "Dominance_CHK_Dom_Kildall_Correct", "length": 1} |
from __future__ import division
import numpy as np
import fidimag.extensions.clib as clib
import fidimag.common.helper as helper
import fidimag.common.constant as const
from .atomistic_driver import AtomisticDriver
class SLLG(AtomisticDriver):
"""
This class is the driver to solve the Stochastic Landau Lifs... | {"hexsha": "323437f784ae7a71793e161248d677ced2c2d62a", "size": 4297, "ext": "py", "lang": "Python", "max_stars_repo_path": "fidimag/atomistic/sllg.py", "max_stars_repo_name": "computationalmodelling/fidimag", "max_stars_repo_head_hexsha": "07a275c897a44ad1e0d7e8ef563f10345fdc2a6e", "max_stars_repo_licenses": ["BSD-2-Cl... |
import random
import numpy as np
import pytest
from pandas import DataFrame
from tests.utils import assert_dataframes_equals
from weaverbird.backends.pandas_executor.steps.rank import execute_rank
from weaverbird.pipeline.steps import RankStep
@pytest.fixture
def sample_df():
return DataFrame(
{'COUNTRY... | {"hexsha": "f126ce66dce68563a0fc82cfe7563db6abae2cee", "size": 1960, "ext": "py", "lang": "Python", "max_stars_repo_path": "server/tests/steps/test_rank.py", "max_stars_repo_name": "JeremyJacquemont/weaverbird", "max_stars_repo_head_hexsha": "e04ab6f9c8381986ab71078e5199ece7a875e743", "max_stars_repo_licenses": ["BSD-3... |
/*
* BikeInfo.hpp
*
* Created on: 18.02.2019
* Author: tomlucas
*/
#ifndef SIXDAYS_BIKEINFO_HPP_
#define SIXDAYS_BIKEINFO_HPP_
#include <Eigen/Core>
namespace zavi::sixdays{
struct BikeInfo{
Eigen::Vector3d imu_to_front, imu_to_back;
Eigen::Vector3d imuToFront(){
return imu_to_front;
}
Eigen:... | {"hexsha": "9073a9b0d6a141d820467f2679b944b4122a0964", "size": 415, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "SixdaysCode/Sixdays/BikeInfo.hpp", "max_stars_repo_name": "TomLKoller/ZaVI_TrackCycling", "max_stars_repo_head_hexsha": "7c23bc34e6e58c78ec249f6f55d4e70c7e91d315", "max_stars_repo_licenses": ["MIT"],... |
mutable struct TrackedState <: State
queues::Vector{Vector{Job}} #A vector of queues holding the jobs waiting in buffer
in_transit::BinaryMinHeap{Job} #Jobs in transit between queues
left_system::Vector{Job} #Jobs that have left the system
params::NetworkParameters #The parameters of the queueing syste... | {"hexsha": "d57bab2efb5a30366e22011b5304ef71b9535dd2", "size": 2747, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/state/tracking/tracked_state.jl", "max_stars_repo_name": "tcleugh/Thomas-Cleugh-and-Jason-Jones-2504-2021-PROJECT2", "max_stars_repo_head_hexsha": "243aa078a3f582c94d154100c0e143d910c264d4", "m... |
Require Import Algebra.SetoidCat.PairUtils Algebra.Utils Algebra.SetoidCat Algebra.Monad Algebra.Monoid Algebra.SetoidCat.SetoidUtils Tactics.
Require Import RelationClasses Relation_Definitions Morphisms SetoidClass.
Require Import Coq.Lists.List.
Open Scope type_scope.
Definition maybe_equiv {A} {SA : Setoid A} (... | {"author": "xu-hao", "repo": "CertifiedQueryArrow", "sha": "8db512e0ebea8011b0468d83c9066e4a94d8d1c4", "save_path": "github-repos/coq/xu-hao-CertifiedQueryArrow", "path": "github-repos/coq/xu-hao-CertifiedQueryArrow/CertifiedQueryArrow-8db512e0ebea8011b0468d83c9066e4a94d8d1c4/Algebra/SetoidCat/MaybeUtils.v"} |
[STATEMENT]
lemma prv_scnj_imp:
assumes "\<chi> \<in> fmla" and "F \<subseteq> fmla" "finite F"
and "\<phi> \<in> F" and "prv (imp \<phi> \<chi>)"
shows "prv (imp (scnj F) \<chi>)"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. prv (imp (scnj F) \<chi>)
[PROOF STEP]
unfolding scnj_def
[PROOF STATE]
proof (pr... | {"llama_tokens": 292, "file": "Syntax_Independent_Logic_Deduction", "length": 3} |
From Undecidability.TM Require Import Util.Prelim Util.TM_facts.
(* * 0-tape Turing machine that does nothing. *)
Section Mono_Nop.
Variable sig : finType.
Definition NullTM : TM sig 0 :=
{|
trans := fun '(q, s) => (q, Vector.nil _);
start := tt;
halt _ := true;
|}.
Definition Null ... | {"author": "uds-psl", "repo": "time-invariance-thesis-for-L", "sha": "41f4eb1f788cc4f096d9c7c286c9ca907588859f", "save_path": "github-repos/coq/uds-psl-time-invariance-thesis-for-L", "path": "github-repos/coq/uds-psl-time-invariance-thesis-for-L/time-invariance-thesis-for-L-41f4eb1f788cc4f096d9c7c286c9ca907588859f/theo... |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
from unittest import mock
import numpy as np
import pandas as pd
from ax.core.arm import Arm
from ax.core.data import Data
from ax.core.experiment import Experiment
from ax.core.metric import Metric
from ax.core.objective i... | {"hexsha": "bcee08c39b0ef06df51b1f89dfb47a237f2d434b", "size": 18889, "ext": "py", "lang": "Python", "max_stars_repo_path": "ax/modelbridge/tests/test_base_modelbridge.py", "max_stars_repo_name": "jlin27/Ax", "max_stars_repo_head_hexsha": "5eaa78f783d290027e23ee6b8d659b27eccf2ced", "max_stars_repo_licenses": ["MIT"], "... |
''' Implements the WEAT tests '''
import logging
import math
import itertools as it
import numpy as np
import scipy.special
import scipy.stats
# X and Y are two sets of target words of equal size.
# A and B are two sets of attribute words.
logger = logging.getLogger(__name__)
logging.basicConfig(format = '%(asctime... | {"hexsha": "300b572b7ab26aebd2552aee4df0eac6f06bcd2a", "size": 8207, "ext": "py", "lang": "Python", "max_stars_repo_path": "debias-BERT/experiments/weat.py", "max_stars_repo_name": "khatna/sent_debias", "max_stars_repo_head_hexsha": "b444c49475fedbbffa817717b1ff9484348a9fee", "max_stars_repo_licenses": ["MIT"], "max_st... |
import numpy as np
from nose.tools import assert_true, assert_false, assert_equal, assert_almost_equal, assert_raises
from numpy.testing import assert_array_equal, assert_array_almost_equal
from dipy.reconst.eit import DiffusionNablaModel, EquatorialInversionModel
from dipy.sims.voxel import SticksAndBall
from dipy.ut... | {"hexsha": "86a34c9ec28daeeacf490430e0584a340296f09e", "size": 4769, "ext": "py", "lang": "Python", "max_stars_repo_path": "dipy/reconst/tests/test_eit.py", "max_stars_repo_name": "Garyfallidis/dipy", "max_stars_repo_head_hexsha": "4341b734995d6f51ac9c16df26a7de00c46f57ef", "max_stars_repo_licenses": ["BSD-3-Clause"], ... |
[STATEMENT]
lemma signof_pm_one: "signof p \<in> {1, - 1}"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. of_int (sign p) \<in> {1::'a, - (1::'a)}
[PROOF STEP]
by (simp add: sign_def) | {"llama_tokens": 92, "file": "Jordan_Normal_Form_Missing_Misc", "length": 1} |
#!/usr/bin/python3
from os import path
import sys
import json
from matplotlib import pyplot, dates
from dateutil import parser
from datetime import timedelta
from scipy.optimize import curve_fit
from math import log
import numpy as np
DATA_DIR = "../COVID-19/dati-json/"
FILE_STATO = "dpc-covid19-ita-andamento-naziona... | {"hexsha": "ed7ce56862609ca6d49e4a63e643e7f4aa422f63", "size": 5705, "ext": "py", "lang": "Python", "max_stars_repo_path": "read_data.py", "max_stars_repo_name": "alexmogavero/py-COVID-19", "max_stars_repo_head_hexsha": "541554655de5a53c67c34ec0b3b28e51e867f739", "max_stars_repo_licenses": ["MIT"], "max_stars_count": n... |
# Power calculations for a binomial proportion
## Background
In an ideal statistical world no hypothesis test would be run before a [power analysis](https://en.wikipedia.org/wiki/Power_of_a_test) has been carried out to determine a reasonable [sample size](https://en.wikipedia.org/wiki/Sample_size_determination). Mos... | {"hexsha": "67d83fb190c05766dff3a366427d2423b979528b", "size": 287312, "ext": "ipynb", "lang": "Jupyter Notebook", "max_stars_repo_path": "_rmd/extra_binom_power/binomial_power.ipynb", "max_stars_repo_name": "erikdrysdale/erikdrysdale.github.io", "max_stars_repo_head_hexsha": "ff337117e063be7f909bc2d1f3ff427781d29f31",... |
function status = test_wfs_iir_prefilter(modus)
%TEST_WFS_IIR_PREFILTER tests the IIR WFS pre-equalization filter
%
% Usage: status = test_wfs_iir_prefilter(modus)
%
% Input parameters:
% modus - 0: numerical
% 1: visual (not available)
%
% Output parameters:
% status - true or false... | {"author": "sfstoolbox", "repo": "sfs-matlab", "sha": "02194f0243d1ead26572f760032c40527718919d", "save_path": "github-repos/MATLAB/sfstoolbox-sfs-matlab", "path": "github-repos/MATLAB/sfstoolbox-sfs-matlab/sfs-matlab-02194f0243d1ead26572f760032c40527718919d/validation/test_wfs_iir_prefilter.m"} |
[STATEMENT]
theorem reduce: "valid p \<longleftrightarrow> valid_in {1..card (props p)} p"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. valid p = valid_in {1..card (props p)} p
[PROOF STEP]
using valid_in_valid transfer
[PROOF STATE]
proof (prove)
using this:
\<lbrakk>card (props ?p) \<le> card ?U; valid_in ?U ?p\... | {"llama_tokens": 196, "file": "Paraconsistency_Paraconsistency", "length": 2} |
from pathlib import Path
import pandas as pd
import numpy as np
import pickle
from sklearn.feature_extraction.text import TfidfVectorizer
from nltk.corpus import stopwords
import argparse
def get_features(documents, max_features, min_df, max_df):
tfidfconverter = TfidfVectorizer(max_features=max_features, min_df=... | {"hexsha": "416da6b352f7879c897de3695a48841af0fd4213", "size": 2450, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/data/TFIDF_feature_extraction.py", "max_stars_repo_name": "nhutlong128/TopicClassification", "max_stars_repo_head_hexsha": "ef4ac58f7b8365a7c64ffa8197195db30de52620", "max_stars_repo_licenses"... |
import argparse
import os
import math
from time import time
from PIL import Image
import align.detect_face as detect_face
import cv2
import dlib
import numpy as np
import tensorflow as tf
from lib.face_utils import judge_side_face
from lib.utils import Logger, mkdir
from project_root_dir import project_dir
from src.sor... | {"hexsha": "ccb181a0c2a78b97924acdb1385749b3132e0bd7", "size": 15344, "ext": "py", "lang": "Python", "max_stars_repo_path": "tp1.py", "max_stars_repo_name": "ReignW/FaceDetection", "max_stars_repo_head_hexsha": "0b9f14da8ab866f173198f86a98fedd40db13e76", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max... |
module BifurcationsBase
using ..Continuations: AbstractContinuationCache, AbstractProblemCache,
AbstractContinuationSolver
include("timekind.jl")
include("statekind.jl")
include("contkind.jl")
include("problem.jl")
include("solution.jl")
include("solver.jl")
include("interface.jl")
include("tools.jl")
include("disp... | {"hexsha": "687319e75f8483dcc4113ac62f8f135b452304b9", "size": 343, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/base/base.jl", "max_stars_repo_name": "jhardenberg/Bifurcations.jl", "max_stars_repo_head_hexsha": "cc1bfe55389efdfa0c4e6a817707c235afe1aea8", "max_stars_repo_licenses": ["MIT"], "max_stars_coun... |
#!/usr/bin/python
import sys, os, argparse
import pickle
import numpy as np
import cv2
'''
CADA NUMERO REPRESENTA UM DEDO
menor = 1
anelar = 2
medio = 3
indicador = 4
polegar = 5
'''
parser = argparse.ArgumentParser()
parser.add_argument("-nf", "--frames", type=int, default=40)
parser... | {"hexsha": "4c3a0abc652e6bd2d8015c3ee437023e43f5db72", "size": 6442, "ext": "py", "lang": "Python", "max_stars_repo_path": "old/detmao.py", "max_stars_repo_name": "juniorfarrapo/Hand-Detector", "max_stars_repo_head_hexsha": "5e3bd0be63fd87e469dc6d9ddea4c822a6ba2c99", "max_stars_repo_licenses": ["Apache-2.0"], "max_star... |
function complex_example_AD()
% A basic example that shows how to define the cost funtion for
% optimization problems on complex manifolds.
%
% Note that automatic differentiation for complex numbers is not supported
% for Matlab R2021a or earlier. To fully exploit the convenience of AD,
% please update to the latest ... | {"author": "NicolasBoumal", "repo": "manopt", "sha": "b8b54a6af8b965f7ae572972ba0d15787427744b", "save_path": "github-repos/MATLAB/NicolasBoumal-manopt", "path": "github-repos/MATLAB/NicolasBoumal-manopt/manopt-b8b54a6af8b965f7ae572972ba0d15787427744b/manopt/autodiff/basic_examples_AD/complex_example_AD.m"} |
#include <unsupported/Eigen/MatrixFunctions>
#include <Eigen/Eigenvalues>
#include "BlockMatrix.h"
BlockMatrix::BlockMatrix(int L) {
init(L);
}
BlockMatrix::BlockMatrix(const BlockMatrix& bm) {
init(bm._L);
for (int i = 0; i < bm.size(); i++) {
for (int j = 0; j < bm.size(); j++) {
_mats[i][j] = bm._mats[i][j... | {"hexsha": "dc25df2afa326870ba55615f35ff7054dec219b1", "size": 13013, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/BlockMatrix.cpp", "max_stars_repo_name": "gaolichen/otoc4n4sym", "max_stars_repo_head_hexsha": "b504f9eb6efdf52567b3655a2caff6238559ba64", "max_stars_repo_licenses": ["MIT"], "max_stars_count":... |
import os
import sys
import simulation
import numpy as np
from multiprocessing import Pool
from test_model import *
from rational_model import *
n_players = 1
pars = 100*(0.97)**np.array(range(500))
light_fields = ['0-1en01', '1-1en01', '2-1en01', '3-1en01']
out_dir = '../../modeling/'
try:
os.makedirs(out_d... | {"hexsha": "f7b6182cbd2d98b55fddccdcecb02ea7fe475b85", "size": 975, "ext": "py", "lang": "Python", "max_stars_repo_path": "simulations/old/player_model/simulate_parameter_sweep.py", "max_stars_repo_name": "hawkrobe/fish", "max_stars_repo_head_hexsha": "2000e46c397f7c95bba8ecb0c6afd26013929ff8", "max_stars_repo_licenses... |
import logging
import os
import time
import unittest
import hydra
import numpy as np
import pytorch_lightning as pl
import torch
from hydra.core.global_hydra import GlobalHydra
from src.lib.config import register_configs
from src.utils import utils
class TestMainFile(unittest.TestCase):
@classmethod
def set... | {"hexsha": "b09ece9745900d2d54eba682faee8c689a7816bc", "size": 4013, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/test_main_functionality.py", "max_stars_repo_name": "judithernandez/hydra_test", "max_stars_repo_head_hexsha": "239207afbaee00a27f8d87ac14905dfff3e1c502", "max_stars_repo_licenses": ["BSD-3-... |
from collections import Counter
import csv
import logging
import numpy as np
import pandas as pd
import random
from scipy import stats
from sklearn.metrics import f1_score
from sklearn.model_selection import GridSearchCV
import sys
import os
__author__ = "Christopher Potts"
__version__ = "CS224u, Stanford, Spring 2019... | {"hexsha": "072bcffb6698ac2cfa7768b075751285781d0880", "size": 8468, "ext": "py", "lang": "Python", "max_stars_repo_path": "utils.py", "max_stars_repo_name": "yiyang7/cs224u", "max_stars_repo_head_hexsha": "3e360a15640f3ba5fc24f34ad45fc8284aabd26d", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": null, "m... |
import argparse
import os
import toml
import tensorflow as tf
import numpy as np
from ohnomore_seq2seq import read_config
from ohnomore_seq2seq import Numberer
from ohnomore_seq2seq import read_unique_tokens
from ohnomore_seq2seq import py2numpy, sample_n_batches, get_batches
from ohnomore_seq2seq import Mode, BasicM... | {"hexsha": "a6726dc8a9ff27026f1f83ee70dbe60cf3c58ee1", "size": 8803, "ext": "py", "lang": "Python", "max_stars_repo_path": "ohnomore_seq2seq_rs/python/train.py", "max_stars_repo_name": "twuebi/ohnomore_seq2seq_rs", "max_stars_repo_head_hexsha": "f9d1517cf4bacefe589ed49118a6ba1921065217", "max_stars_repo_licenses": ["Ap... |
\section{Sylvester's Law, Sesquilinear Forms}
We start by looking at some immediate corollaries of \ref{bilinear_diag}.
\begin{corollary}
For a finite dimensional complex vector space $V$ and a symmetric bilinear form $\phi$ on $V$, there is a basis $B$ of $V$ such that
$$[\phi]_B=\begin{pmatrix}
I... | {"hexsha": "e46827a19949d5960b5d14baf35880eb0e0c0d6e", "size": 4073, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "19/sylvester.tex", "max_stars_repo_name": "david-bai-notes/IB-Linear-Algebra", "max_stars_repo_head_hexsha": "5a499f7ed33ef0110facb27323e13f42883aa0c5", "max_stars_repo_licenses": ["MIT"], "max_star... |
from stuff import *
import sys,re,argparse,pickle
from scipy.optimize import minimize
from scipy.stats import norm
from scipy.special import gammaln
from math import log,exp,sqrt,sin,pi
import numpy as np
from subprocess import Popen,PIPE
from datetime import datetime
# (Make it auto download files?)
# Get ltla.csv fr... | {"hexsha": "a1fa2ecb6910e236b506bfecc716f7f47ba6e799", "size": 19767, "ext": "py", "lang": "Python", "max_stars_repo_path": "VOCgrowth/vocfit2.py", "max_stars_repo_name": "alex1770/Covid-19", "max_stars_repo_head_hexsha": "0212593bf5d9bcbb7009c7d1fb1710116ad8bf32", "max_stars_repo_licenses": ["MIT"], "max_stars_count":... |
import click
import pandas
import dendropy
import networkx as nx
from pathlib import Path
from sketchy.sketchy import LineageIndex
from sketchy.utils import MutuallyExclusiveOption
@click.command()
@click.option(
'--ssh',
'-s',
default=None,
type=Path,
required=True,
help... | {"hexsha": "3be4dfda3c68f16a70eabcae7a272f25241b4835", "size": 2715, "ext": "py", "lang": "Python", "max_stars_repo_path": "sketchy/terminal/survey/popmap/commands.py", "max_stars_repo_name": "mbhall88/sketchy", "max_stars_repo_head_hexsha": "5ed26d28f104710f6d425053eae41fd0e99f8760", "max_stars_repo_licenses": ["MIT"]... |
(*-------------------------------------------*
| CSP-Prover on Isabelle2005 |
| February 2006 |
| April 2006 (modified) |
| March 2007 (modified) |
| ... | {"author": "pefribeiro", "repo": "CSP-Prover", "sha": "8967cc482e5695fca4abb52d9dc2cf36b7b7a44e", "save_path": "github-repos/isabelle/pefribeiro-CSP-Prover", "path": "github-repos/isabelle/pefribeiro-CSP-Prover/CSP-Prover-8967cc482e5695fca4abb52d9dc2cf36b7b7a44e/FNF_F/FNF_F_sf_seq.thy"} |
function [sx, dsdx, dsdp] = VBA_sparsifyPrior (x, logExponent, smoothness)
% // VBA toolbox //////////////////////////////////////////////////////////
%
% [sx, dsdx, dsdp] = VBA_sparsifyPrior (x, varargin)
% parameter transformation that emulates Laplace priors (L1-norm)
%
% IN:
% - x: input value to be transformed
%... | {"author": "MBB-team", "repo": "VBA-toolbox", "sha": "01ff63f43ef7a6473bc5e3f28dd9ffa58fcfb414", "save_path": "github-repos/MATLAB/MBB-team-VBA-toolbox", "path": "github-repos/MATLAB/MBB-team-VBA-toolbox/VBA-toolbox-01ff63f43ef7a6473bc5e3f28dd9ffa58fcfb414/utils/VBA_sparsifyPrior.m"} |
# --------------
# Importing header files
import numpy as np
# Path of the file has been stored in variable called 'path'
path
#New record
new_record=[[50, 9, 4, 1, 0, 0, 40, 0]]
data = np.genfromtxt(path, delimiter=",", skip_header=1)
print("\nData: \n\n", data)
print("\nType of data: \n\n", ty... | {"hexsha": "8e2fadd5f0ed77246611ed811d4f0bbb54852cd1", "size": 1676, "ext": "py", "lang": "Python", "max_stars_repo_path": "GreyAto/code.py", "max_stars_repo_name": "maheshgnc2010/ga-learner-dsmp-repo", "max_stars_repo_head_hexsha": "70933159b1a9f2da6b0953142c615e8a855dced4", "max_stars_repo_licenses": ["MIT"], "max_st... |
"""
Created on Sun Apr 22 17:24:47 2018
@author: Steven
"""
from radioxenon_ml.read_in import ml_matrix_composition as mlmc
from radioxenon_ml.solve import variance as v
import numpy as np
#for some reason the following import must occur in order to refresh any changes to variance
#if I try to import it earlier it d... | {"hexsha": "c5039b0f2f30da00a054f2ae0b1409d923c51a22", "size": 4380, "ext": "py", "lang": "Python", "max_stars_repo_path": "radioxenon_ml/test_files/test_functions.py", "max_stars_repo_name": "manninosi/radioxenon_ml", "max_stars_repo_head_hexsha": "e901a2465bcbe491184cefc58db021a9321b9555", "max_stars_repo_licenses": ... |
from pynq import DefaultIP
from pynq import DefaultHierarchy
from pynq import Xlnk
import numpy as np
from scipy import signal
import matplotlib.pyplot as plt
class BandwidthSelector(DefaultHierarchy):
def __init__(self, description, pkt_config=1, pkt_reload=128): # Find out the correct length of config and reloa... | {"hexsha": "493dab8a3ff04c1befba73256e7eec766d88935c", "size": 3302, "ext": "py", "lang": "Python", "max_stars_repo_path": "rfsoc_sam/bandwidth_selector.py", "max_stars_repo_name": "plysaght/rfsoc_sam", "max_stars_repo_head_hexsha": "e299303fce67ba5f682b7fc96c582ec25af739f9", "max_stars_repo_licenses": ["BSD-3-Clause"]... |
import json
import numpy as np
import pkg_resources
import random
import re
import scipy.misc as m
from mnist import MNIST
from copy import deepcopy
SIZE = 28
PARTITIONS = 5
TRAINING_EXAMPLES = 5000
TEST_EXAMPLES = 100
def main():
mndata = MNIST(pkg_resources.resource_filename('fuzzi', 'd... | {"hexsha": "f1e92742dfd33993f3bf57ababf59e1418cd509f", "size": 2045, "ext": "py", "lang": "Python", "max_stars_repo_path": "fuzzi-gen/fuzzi/processing/pate.py", "max_stars_repo_name": "hengchu/fuzzi-impl", "max_stars_repo_head_hexsha": "bcf49c42d2dd4e1e3c1fe8b85fa7f845ea8fd016", "max_stars_repo_licenses": ["BSD-3-Claus... |
/-
Copyright (c) 2022 Anand Rao, Rémi Bottinelli. All rights reserved.
Released under Apache 2.0 license as described in the file LICENSE.
Authors: Anand Rao, Rémi Bottinelli
-/
import combinatorics.simple_graph.ends.defs
import data.finite.set
import data.finset.basic
/-!
# Properties of the ends of graphs
This file ... | {"author": "0art0", "repo": "Freudenthal-Hopf", "sha": "1fefe94a6f0432686f21f97e4916efcbb81fec9d", "save_path": "github-repos/lean/0art0-Freudenthal-Hopf", "path": "github-repos/lean/0art0-Freudenthal-Hopf/Freudenthal-Hopf-1fefe94a6f0432686f21f97e4916efcbb81fec9d/src/properties.lean"} |
recursive function evapol(tu,nu,tv,nv,c,rad,x,y) result(e_res)
implicit none
real*8 :: e_res
c function program evacir evaluates the function f(x,y) = s(u,v),
c defined through the transformation
c x = u*rad(v)*cos(v) y = u*rad(v)*sin(v)
c and where s(u,v) is a bicubic spline ( 0<=u<=1 , -p... | {"hexsha": "f02569a4018eb869b9699ab41992a0c0614ecb26", "size": 2719, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "scipy/interpolate/fitpack/evapol.f", "max_stars_repo_name": "jcampbell/scipy", "max_stars_repo_head_hexsha": "a11eef3a29ad200649edd32c0de0d0210001acb4", "max_stars_repo_licenses": ["BSD-3-Clause"]... |
import logging
import boost_histogram as bh
import numpy as np
import pytest
from cabinetry import histo
class ExampleHistograms:
"""a collection of different kinds of histograms"""
@staticmethod
def normal():
bins = [1, 2, 3]
yields = [1.0, 2.0]
stdev = [0.1, 0.2]
retur... | {"hexsha": "a1e607fc16dcb9af0bb77697cbc14e9691892411", "size": 8341, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/test_histo.py", "max_stars_repo_name": "ExternalRepositories/cabinetry", "max_stars_repo_head_hexsha": "2261b603786dd3a5c5963b4e365d04ff250ba012", "max_stars_repo_licenses": ["BSD-3-Clause"]... |
import os
import sys
import h5py
import numpy as np
import shutil
job_repeat_attempts = 5
def check_file(filename):
if not os.path.exists(filename):
return False
# verify the file has the expected data
import h5py
f = h5py.File(filename, 'r')
fkeys = f.keys()
f.close()
if set(fkeys... | {"hexsha": "63f68e9ce9221a3ff04952eea58eb0e04483e360", "size": 2575, "ext": "py", "lang": "Python", "max_stars_repo_path": "Relabeling/create_global_map.py", "max_stars_repo_name": "Rhoana/rhoana", "max_stars_repo_head_hexsha": "b4027a57451d175ea02c2c7ef472cf9c4e1a0efc", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
# Matern 5/2 isotropic covariance function
"""
Mat52Iso <: MaternIso
Isotropic Matern 5/2 kernel (covariance)
```math
k(x,x') = σ²(1+√5|x-x'|/ℓ + 5|x-x'|²/(3ℓ²))\\exp(- √5|x-x'|/ℓ)
```
with length scale ``ℓ`` and signal standard deviation ``σ``.
"""
mutable struct Mat52Iso <: MaternIso
"Length scale"
ℓ::F... | {"hexsha": "3ef155a4e6be944a831555d5dad1da78329508ef", "size": 1186, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/kernels/mat52_iso.jl", "max_stars_repo_name": "jbrea/GaussianProcesses.jl", "max_stars_repo_head_hexsha": "732063745067a803429415100f2b08bb396a1df8", "max_stars_repo_licenses": ["MIT"], "max_st... |
"""
Multitask Lasso Model
Details: https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.MultiTaskLasso.html
"""
from sklearn.linear_model import MultiTaskLasso, Lasso
from joblib import Parallel, delayed
import numpy as np
class LassoMultitask_wo_subx():
""" XGBoost models for ... | {"hexsha": "594e23dc8563a5e763c0694f1c0710fb5ec968dc", "size": 4519, "ext": "py", "lang": "Python", "max_stars_repo_path": "SSF_mip/model/lassomultitask_wo_subx.py", "max_stars_repo_name": "Sijie-umn/SSF-MIP", "max_stars_repo_head_hexsha": "89636846cc11950c9fd851e58ea410cef72d9b90", "max_stars_repo_licenses": ["MIT"], ... |
import numpy as np
#%matplotlib inline
import matplotlib.pyplot as plt
from scipy import special
np.info(special)
x = np.linspace(0,20,100)
for ii in range(5):
plt.plot(x, special.jn(ii, x))
plt.grid(True)
| {"hexsha": "88dc3dc89eb887850455c2203660087901f378c6", "size": 213, "ext": "py", "lang": "Python", "max_stars_repo_path": "MyScripts/030-SciPy.py", "max_stars_repo_name": "diegoomataix/Curso_AeroPython", "max_stars_repo_head_hexsha": "c2cf71a938062bc70dbbf7c2f21e09653fa2cedd", "max_stars_repo_licenses": ["CC-BY-4.0"], ... |
map_tt_t(f, tt::Type{<:Tuple}) = Base.tuple_type_cons(f(Base.tuple_type_head(tt)), map_tt_t(f, Base.tuple_type_tail(tt)))
map_tt_t(f, tt::Type{Tuple{}}) = Tuple{}
function mapreduce_tt(f, op, v0, tt)
op(f(Base.tuple_type_head(tt)), mapreduce_tt(f, op, v0, Base.tuple_type_tail(tt)))
end
mapreduce_tt(f, op, v0, tt::... | {"hexsha": "b3cd052cf81fe33e8902c40bfce7181398f24ef1", "size": 499, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/tuple_types.jl", "max_stars_repo_name": "ettersi/IterTools.jl", "max_stars_repo_head_hexsha": "f7dbe509d201098e65c6fc2be18547047d10eeb2", "max_stars_repo_licenses": ["MIT"], "max_stars_count": n... |
# Copyright 2020 Deepmind Technologies Limited.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agr... | {"hexsha": "3ef8c5cb192c530c73bdfd51ff2f406cfb8ee705", "size": 3567, "ext": "py", "lang": "Python", "max_stars_repo_path": "adversarial_robustness/jax/eval.py", "max_stars_repo_name": "jamestwebber/deepmind-research", "max_stars_repo_head_hexsha": "2e866f19371695ac351a4f2023ad3987997d7e84", "max_stars_repo_licenses": [... |
import numpy as np
from scipy import fftpack
from scipy import interpolate
def fourier_transform(X,Y):
'''
Function for determining concentration amplitudes from data.
'''
# interpolate axes to get even time steps
f = interpolate.interp1d(X, Y, kind = "linear")
# get timestep from average of t... | {"hexsha": "88ed7a93a2c0c0a8af867dc1c431b814bf940573", "size": 855, "ext": "py", "lang": "Python", "max_stars_repo_path": "SCRIPTS/helpers/parameter_determination_helper.py", "max_stars_repo_name": "huckgroup/formose-2021", "max_stars_repo_head_hexsha": "f1c5e809e0cbbbc744a4fe636069cdfc83ad6091", "max_stars_repo_licens... |
theory AltFlatLemma
imports AltDropEnv AltCutEnv
begin
definition infl_use_env where
"infl_use_env r_s r_x = (\<lambda> x. if r_s x = OwnPerm \<and> r_x x = NoPerm then OwnPerm else NoPerm)"
lemma infl_disj_use_env: "\<lbrakk> leq_use_env r_ex r_x \<rbrakk> \<Longrightarrow> disj_use_env r_ex (infl_use_... | {"author": "anon-ef", "repo": "perm_lang_ef2", "sha": "0fcb6e4c175193cc7b94f297a8aaa605f502d711", "save_path": "github-repos/isabelle/anon-ef-perm_lang_ef2", "path": "github-repos/isabelle/anon-ef-perm_lang_ef2/perm_lang_ef2-0fcb6e4c175193cc7b94f297a8aaa605f502d711/perm_ref/AltFlatLemma.thy"} |
function plotData(X, y)
%PLOTDATA Plots the data points X and y into a new figure
% PLOTDATA(x,y) plots the data points with + for the positive examples
% and o for the negative examples. X is assumed to be a Mx2 matrix.
% Create New Figure
figure; hold on;
% ====================== YOUR CODE HERE ===============... | {"author": "1094401996", "repo": "machine-learning-coursera", "sha": "e53d1021a08b0f2ab7e0840d9807ab14e24ea9bb", "save_path": "github-repos/MATLAB/1094401996-machine-learning-coursera", "path": "github-repos/MATLAB/1094401996-machine-learning-coursera/machine-learning-coursera-e53d1021a08b0f2ab7e0840d9807ab14e24ea9bb/p... |
# -*- coding: utf-8 -*-
"""
Created on Sat Nov 28 13:32:18 2020
@author: qtckp
"""
import sys
sys.path.append('..')
import numpy as np
from geneticalgorithm2 import geneticalgorithm2 as ga
import matplotlib.pyplot as plt
dim = 25
def f(X):
return np.sum(X)
varbound = np.array([[0,10]]*dim)
start_ge... | {"hexsha": "34067a32322378c99f6038943fb2f63edf4ca43f", "size": 1015, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/Standard GA vs. Elitist GA.py", "max_stars_repo_name": "mudesire/geneticalgorithm2", "max_stars_repo_head_hexsha": "165d87b182a0b56e035f7a37a17a16950de20ddd", "max_stars_repo_licenses": ["MI... |
import matplotlib.pyplot as plt
import numpy as np
from scipy.stats import norm
import matplotlib.colors as colors
from matplotlib.patches import Polygon
from matplotlib import cm
from matplotlib import rc
__author__ = 'ernesto'
# if use latex or mathtext
rc('text', usetex=False)
rc('mathtext', fontset='cm')
# aux... | {"hexsha": "fc71d6690bc52a2714646b390c57b8d6981ad0f4", "size": 7352, "ext": "py", "lang": "Python", "max_stars_repo_path": "figuras/Pycharm_Papoulis_Probability_Report/mean_and_distribution.py", "max_stars_repo_name": "bor9/estudiando_el_papoulis", "max_stars_repo_head_hexsha": "ef40ac18d7aece3415cd9ce72d1f9684c762d6df... |
data("USArrests") # Loading the data set
df <- scale(USArrests) # Scaling the data
# View the firt 3 rows of the data
head(df, n = 3)
#x: numeric matrix, numeric data frame or a numeric vector
#centers: Possible values are the number of clusters (k) or a set of initial (distinct) cluster centers. If a... | {"hexsha": "fb8f06d17ea3ac452559d50783742bac7e7d8852", "size": 1265, "ext": "r", "lang": "R", "max_stars_repo_path": "kmeans.r", "max_stars_repo_name": "sujayr/R_basics", "max_stars_repo_head_hexsha": "06f2cc05fbed7302d9399d40cfeccaf491dede38", "max_stars_repo_licenses": ["Unlicense"], "max_stars_count": null, "max_sta... |
# Solution {-}
A random variable $X$ whose probability density function is given as:
\begin{equation*}
f_X(x)=
\begin{cases}
\alpha e^{-\alpha x}, &x \geq 0 \\
0, &x < 0 \\
\end{cases}
\end{equation*}
This density function is used to describe the failure of equipment components. The probability that a ... | {"hexsha": "a4300da2bc12b3cd38d8290e86eaa7db9adbb4b5", "size": 2269, "ext": "ipynb", "lang": "Jupyter Notebook", "max_stars_repo_path": "Problem 1.15.ipynb", "max_stars_repo_name": "mfkiwl/GMPE340", "max_stars_repo_head_hexsha": "3602b8ba859a2c7db2cab96862472597dc1ac793", "max_stars_repo_licenses": ["MIT"], "max_stars_... |
import gym
import numpy as np
import pytest
from baselines.common.vec_env import DummyVecEnv
import gym3
from gym3.types_np import multimap, sample, zeros
def gym3_rollout(e):
for _ in range(10):
rew, ob, done = e.observe()
print(multimap(lambda x: x.shape, ob), rew.shape, done.shape)
e.a... | {"hexsha": "5bff6601d6581fc2faf479ce62a0e593a0fc7ad8", "size": 1913, "ext": "py", "lang": "Python", "max_stars_repo_path": "gym3/interop_test.py", "max_stars_repo_name": "christopher-hesse/gym3", "max_stars_repo_head_hexsha": "2ed9d344ede8bbd96b6280e9fbbbf55861ea33a9", "max_stars_repo_licenses": ["MIT"], "max_stars_cou... |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
# Filename:str_format.py
"I am : doestr.__doc__"
import imp
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from matplotlib import animation
from urllib import request
from urllib.request import urlopen
from bs4 import BeautifulSo... | {"hexsha": "16655e86cb4f814f2d8ca15fe2bfafd1fa828d5a", "size": 4195, "ext": "py", "lang": "Python", "max_stars_repo_path": "hello1.py", "max_stars_repo_name": "8055aa/Python3Code", "max_stars_repo_head_hexsha": "ac832241b87c02a3b3e1b216dbcd3cfbd0293b7c", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": nul... |
import time
from typing import Tuple, List, Dict, Any
import cupy as np
from dataset import Dataset
from dataset_type import DatasetType
from plot_drawer import PlotDrawer
from simple_framework.layers.dense_layer import DenseLayer
from simple_framework.layers.dropout_layer import DropoutLayer
from simple_framework.la... | {"hexsha": "035b8ecc107e6c419d68184a9ecb14551d5650dc", "size": 6924, "ext": "py", "lang": "Python", "max_stars_repo_path": "model.py", "max_stars_repo_name": "VictorAtPL/MNIST_FCDN_NumPy", "max_stars_repo_head_hexsha": "f30e010a3d69afcc19c49e16ed3f004ae64be32d", "max_stars_repo_licenses": ["MIT"], "max_stars_count": nu... |
# This module will perform the box count algorithm on a BMP image object
from m_filter import intensity # should i define this locally?
from math import log
from statistics import variance
from datetime import datetime
import numpy as np
import matplotlib.pyplot as plt
# --------------------------------------------... | {"hexsha": "581d87639f6ed90cb59de2f3115993ff8db8b7c7", "size": 6459, "ext": "py", "lang": "Python", "max_stars_repo_path": "m_boxcount.py", "max_stars_repo_name": "mcm7f/thesis", "max_stars_repo_head_hexsha": "412f35de9b1ce25ed4447d077a2a5292a57063f9", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_star... |
# Machine Learning: Basic Principles 2018
# Round 2 - Regression
## Learning goals
In this exercise you will learn how to use **linear regression**, in order to predict a quantity of interest based on data. The implementation of linear regression amounts to the minimization of a function (**the empirical risk**) whic... | {"hexsha": "5c6abdb66accb16e2d92de5a615973c25e807866", "size": 104623, "ext": "ipynb", "lang": "Jupyter Notebook", "max_stars_repo_path": "Regression - Exercise.ipynb", "max_stars_repo_name": "Oltier/ml-practice-practice2", "max_stars_repo_head_hexsha": "cb66580c1fea447a06674ebb5819ff292114f2fd", "max_stars_repo_licens... |
import pytest
from astropy.table import Table
from b_to_zooniverse.do_upload import manifest
TEST_EXAMPLES_DIR = 'python/test_examples'
@pytest.fixture()
def joint_catalog():
return Table([
{
'nsa_id': 'example_nsa_id',
'iauname': 'example_iauname',
'ra': 147.45674,
... | {"hexsha": "f1a0fcf140ed60ae935b43f129560d54a2e7c9dc", "size": 6277, "ext": "py", "lang": "Python", "max_stars_repo_path": "decals/b_to_zooniverse/do_upload/manifest_test.py", "max_stars_repo_name": "zooniverse/decals", "max_stars_repo_head_hexsha": "ce0086fa7fc3597b12f90d7ce9dd92d1cbcf8e66", "max_stars_repo_licenses":... |
#---------------------------------------------------------------
# 03_Run_Regression_and_Suitability.py
#---------------------------------------------------------------
import os, pdb
import pandas as pd
import numpy as np
import Urban_Tree_Functions as urbanTreeLib
tableDir = r'/Volumes/Seagate Backup Plus Drive/... | {"hexsha": "73e4f81b98fb67f8da9accbc423d4504a1fd6f26", "size": 9001, "ext": "py", "lang": "Python", "max_stars_repo_path": "03_Run_Regression_and_Suitability.py", "max_stars_repo_name": "leahscampbell/CUTI-Scripts", "max_stars_repo_head_hexsha": "9261daa4a2020faa03e7aa61872532cdcbdc0fd7", "max_stars_repo_licenses": ["M... |
[STATEMENT]
lemma quotient_rep_ex : "H \<in> (carrier (G LMod (stabiliser x))) \<Longrightarrow> rep H \<in> H"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. H \<in> carrier (G LMod stabiliser x) \<Longrightarrow> rep H \<in> H
[PROOF STEP]
proof -
[PROOF STATE]
proof (state)
goal (1 subgoal):
1. H \<in> carrier ... | {"llama_tokens": 1326, "file": "Orbit_Stabiliser_Orbit_Stabiliser", "length": 16} |
/*
Copyright (c) 2009, Yoav Aviram
All rights reserved.
Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions
are met:
* Redistributions of source code must retain the above copyright
notice, this list of conditions and the fol... | {"hexsha": "d5c07889b9aaecb5a2f87ad1a288ac0a6ab428db", "size": 4380, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "include/feedreader/Feed.hpp", "max_stars_repo_name": "thawkins/feed-reader-lib", "max_stars_repo_head_hexsha": "504eb9f95d87757c7044cdac062ed55efb14f849", "max_stars_repo_licenses": ["BSD-3-Clause"]... |
#include "stdafx.h"
#include "UnistdLoader.h"
#include <fstream>
#include <sstream>
#include <boost/filesystem.hpp>
namespace fs = boost::filesystem;
inline std::string trim(std::string& str)
{
str.erase(0, str.find_first_not_of(' ')); //prefixing spaces
str.erase(str.find_last_not_of(' ')+1); //sur... | {"hexsha": "2830ca4119c43af3058f6fbb6b596f11010b51d4", "size": 2268, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/ArmSVC/UnistdLoader.cpp", "max_stars_repo_name": "azerg/armsvc-for-ida", "max_stars_repo_head_hexsha": "87a8feff3549f69bc74b06f6eea9d48d83f340f8", "max_stars_repo_licenses": ["MIT"], "max_stars_... |
import cv2 as cv
import numpy as np
### TARGET: reduce noise using erode/dilate
img = cv.imread("data/images/sea.jpg")
cv.imshow("Image", img)
# Transform to HSV to achieve greater accuracy
hsv = cv.cvtColor(img, cv.COLOR_BGR2HSV)
mask = cv.inRange(hsv, (30, 50, 10), (90, 255, 255))
cv.imshow("Mask", mask)
# Fir... | {"hexsha": "237c8644602641b8ca409a229b77cc957fe92f32", "size": 815, "ext": "py", "lang": "Python", "max_stars_repo_path": "python/cv/ErodeDilate/main.py", "max_stars_repo_name": "knyazer/lessons", "max_stars_repo_head_hexsha": "2ff0ecc4be53d56d4709f5b0e0de2b5a3cc2d0cc", "max_stars_repo_licenses": ["MIT"], "max_stars_co... |
[GOAL]
M : Type u_1
inst✝¹ : Monoid M
X : Type u
inst✝ : MulAction M X
⊢ Category.{?u.1482, u} (ActionCategory M X)
[PROOFSTEP]
dsimp only [ActionCategory]
[GOAL]
M : Type u_1
inst✝¹ : Monoid M
X : Type u
inst✝ : MulAction M X
⊢ Category.{?u.1482, u} (Functor.Elements (actionAsFunctor M X))
[PROOFSTEP]
infer_instance
[... | {"mathlib_filename": "Mathlib.CategoryTheory.Action", "llama_tokens": 10049} |
"""Functions for control of LTI systems with multiplicative noise."""
# Author: Ben Gravell
import numpy as np
from numpy import linalg as la
from .matrixmath import is_pos_def, vec, sympart, kron, dlyap, mdot
from .extramath import quadratic_formula
import warnings
from warnings import warn
def dlyap_m... | {"hexsha": "572a2ba0ec41abe8155efc2e5a431dde66426aa4", "size": 11555, "ext": "py", "lang": "Python", "max_stars_repo_path": "utility/ltimult.py", "max_stars_repo_name": "TSummersLab/robust-adaptive-control-multinoise", "max_stars_repo_head_hexsha": "4ad22c72f8f45b6ef7cb61c33b5100bea5d88873", "max_stars_repo_licenses": ... |
# Copyright 2020-present NAVER Corp. Under BSD 3-clause license
"""
Colmap database import as basic kapture objects functions
"""
from kapture.io.tar import TarCollection
import logging
import numpy as np
from tqdm import tqdm
from typing import Tuple, Optional
# kapture
import kapture
import kapture.io.features
# l... | {"hexsha": "5c8de4633e833d12763654bfda609effd726bcc5", "size": 14767, "ext": "py", "lang": "Python", "max_stars_repo_path": "kapture/converter/colmap/import_colmap_database.py", "max_stars_repo_name": "v-mehta/kapture", "max_stars_repo_head_hexsha": "b95a15b83032d667282ab96fa5be5327b2c99ec7", "max_stars_repo_licenses":... |
#include <chrono>
#include <string>
#include <istream>
#include <ostream>
#include <iostream>
#include <algorithm>
#include <boost/asio.hpp>
using boost::asio::ip::tcp;
template<class T>
void check_error(T error)
{
if(error) {
std::stringstream sstream;
sstream << error;
throw std::runtime... | {"hexsha": "98c4d77981e0623c4ebd380a32b4d0a0deff42fb", "size": 3959, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "asio/async_speed_server/client.cpp", "max_stars_repo_name": "proydakov/cppzone", "max_stars_repo_head_hexsha": "2cee5523df8fadbd087746c16bbf386360c8114a", "max_stars_repo_licenses": ["MIT"], "max_st... |
import numpy as np
import yaml
import os, sys
import copy
from functools import reduce
import random
from timeloop_env import TimeloopEnv
from multiprocessing.pool import Pool
from multiprocessing import cpu_count
import shutil
from functools import cmp_to_key, partial
class GammaTimeloopEnv(object):
def __init__... | {"hexsha": "a173392b5aff72ab8af19464267b1bce5af17501", "size": 11536, "ext": "py", "lang": "Python", "max_stars_repo_path": "gamma_timeloop_src/gamma_timeloop_env.py", "max_stars_repo_name": "maestro-project/gamma", "max_stars_repo_head_hexsha": "fe4ba02a90d554c8dd49989c986082d27093d964", "max_stars_repo_licenses": ["M... |
# LabTrade - A visual tool to support the development of strategies in Quantitative Finance - by fab2112
import sys
import numpy as np
import pandas as pd
import pyqtgraph as pg
from PyQt5 import QtCore, QtGui
class labtrade:
def __init__(self):
# Variables Logic
self.showplt1 = 1 # Performance /... | {"hexsha": "2ac2b2100311ccf333a8f5f9f23e78808b97fc64", "size": 50632, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/labtrade.py", "max_stars_repo_name": "fab2112/labtrade", "max_stars_repo_head_hexsha": "012e7c56f6992f8510cfcebd9ba38fc5a16565ae", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "m... |
# -*- coding: utf-8 -*-
"""
@author: M. Grajewski, E. Bertelsmann, FH Aachen University of Applied Sciences
"""
import numpy as np
import pypoman as pp
from scipy.optimize import minimize
from numba import jit, float64, int32, boolean
def hrep_from_ranking(P, ranking):
"""
We assume a decision model m of th... | {"hexsha": "d2372819652f2cf1a2c8c56e4275307f87d9a5d2", "size": 37194, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/weights_from_rankings.py", "max_stars_repo_name": "mgrajewski/pysmaa", "max_stars_repo_head_hexsha": "7034ed98f587c30f3649c780b0c14d5033b53756", "max_stars_repo_licenses": ["BSD-3-Clause"], "... |
import tensorflow as tf
gpus = tf.config.experimental.list_physical_devices(device_type='GPU')
for gpu in gpus:
tf.config.experimental.set_memory_growth(gpu, True)
'''
tensorflow drop layer in training phase:
output = input / survival prob
survival prob = 1 - drop rate
i.e., 全1的輸入進入drop layer(假設drop rate = 0.4)
則... | {"hexsha": "57ac8bf87eba9b1bf17523ee0c79926995db41c9", "size": 2998, "ext": "py", "lang": "Python", "max_stars_repo_path": "dropblock.py", "max_stars_repo_name": "qwerasdf887/TF2_DropBlock", "max_stars_repo_head_hexsha": "13e04cfab605ac32dd3e72685dd3c8c30fcdd7ef", "max_stars_repo_licenses": ["MIT"], "max_stars_count": ... |
import logging
import multiprocessing as mp
from dataclasses import dataclass
from typing import Dict, Tuple
import gym
import jax
import numpy as np
import rjax.networks.policies as policies
from rjax.agents.learner import Learner
from rjax.agents.model import Model
from rjax.common import PRNGKey
def _sample_acti... | {"hexsha": "8c6fb22f6fc72b670f919c4facb8e5ced7aae426", "size": 1939, "ext": "py", "lang": "Python", "max_stars_repo_path": "rjax/experiment/evaluation.py", "max_stars_repo_name": "charlesjsun/rjax", "max_stars_repo_head_hexsha": "8b56f78b34f593f442db3cc0315a3b6f22191442", "max_stars_repo_licenses": ["MIT"], "max_stars_... |
[STATEMENT]
lemma finite_divisor_set [simp]: "finite(divisor_set n)"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. finite (divisor_set n)
[PROOF STEP]
using divisor_def divisor_set
[PROOF STATE]
proof (prove)
using this:
?n divisor ?m \<equiv> 1 \<le> ?n \<and> ?n \<le> ?m \<and> ?n dvd ?m
divisor_set ?m = {n. n di... | {"llama_tokens": 180, "file": "Amicable_Numbers_Amicable_Numbers", "length": 2} |
import time
import numbers
import threading
import argparse
import numpy as np
try:
from p5 import * # pip install p5
except Exception as e:
print(str(e))
print("Failed to import p5")
import matplotlib.pyplot as plt
import tqdm
import pickle
from policy import Policy
from policy_simplified_boids import P... | {"hexsha": "ec9aac5b08c32f1a49416eea3a3b0c4d5399111b", "size": 8522, "ext": "py", "lang": "Python", "max_stars_repo_path": "prototype_0/main.py", "max_stars_repo_name": "liusida/pocs", "max_stars_repo_head_hexsha": "c5e0f5bbae5298fe6e78353661790094e4ca03f9", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "ma... |
"""
sunbeam.py
-----------
(S)pectral (N)on-(B)acktracking (E)mbedding (A)nd Pseudo-(M)etric. This
module contains functions related to using the eigenvalues of the
non-backtracking matrix to perform graph mining tasks such as graph
distance and graph embedding. The functions found here are grouped under
the following... | {"hexsha": "efcff02f66075975d123f1ebe88112a663d91788", "size": 19738, "ext": "py", "lang": "Python", "max_stars_repo_path": "sunbeam.py", "max_stars_repo_name": "psuarezserrato/sunbeam", "max_stars_repo_head_hexsha": "22233f84258f23b4c0ea3c78f0e031506e6c105c", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 13, ... |
######################################################################
## ################################################################ ##
## ## SCRIPT FROM ../elecReturns/code/incumbents.r *STARTS* HERE ## ##
## ## 1aug2020 ## ##
## #################################... | {"hexsha": "f656138dd13f10a2233d0b946191a037167b54cb", "size": 151541, "ext": "r", "lang": "R", "max_stars_repo_path": "code/incumbent-reelection.r", "max_stars_repo_name": "emagar/reelec", "max_stars_repo_head_hexsha": "7f2793f55e5dd705e1a7ca0b953797de50929b2a", "max_stars_repo_licenses": ["MIT"], "max_stars_count": n... |
%===================================== CHAP 2 =================================
\chapter{Literature Review}
\section{Reproducibility Terminology}
### Fehr 2016 Best Practices for Replicability, Reproducibility and Reusability of Computer-Based Experiments Exemplified by Model Reduction Software: https://arxiv.org/ab... | {"hexsha": "8f7d6ec73126a2ecf323e2e60d6f2a9d3754e38a", "size": 5055, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "doc/report/chapters/chapter2.tex", "max_stars_repo_name": "sidgek/msoppgave", "max_stars_repo_head_hexsha": "b67192b0d57703e8205a48dda2632d56c9872fba", "max_stars_repo_licenses": ["MIT"], "max_stars... |
import numpy as np
import os
import matplotlib.pyplot as plt
def preprocess_save_data(file):
path = "/atlas/u/jiaxuan/data/google_drive/img_output/"
if file.endswith(".npy"):
path_current=os.path.join(path, file)
image_temp = np.load(path_current)
image_temp=np.reshape(image_temp,(imag... | {"hexsha": "edc29e77f0ae154d6aa44aea02778c8cf52b353c", "size": 1632, "ext": "py", "lang": "Python", "max_stars_repo_path": "6_result_analysis/corr.py", "max_stars_repo_name": "tommylees112/crop_yield_prediction", "max_stars_repo_head_hexsha": "43299b6d6a1f22e4431e23bf92f9cff87c6f5073", "max_stars_repo_licenses": ["MIT"... |
\section{Introduction}
\label{sec:intro}
\paragraph{Domain of application.}
`Simple' robots which can easily be manufactured using DYI tools and off-the-shelf electronic.
\paragraph{Target audience.}
Students or prosumer hobbyists wanting to learn robotic.
We assume some basic technical/programming knowledge.
The use... | {"hexsha": "5518f64a397389f6b2402f47a2e59a8c66882f5f", "size": 3092, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "doc/draft/intro.tex", "max_stars_repo_name": "aleksandarmilicevic/react-lang", "max_stars_repo_head_hexsha": "18041bbf1f43668b3f600c2d6daa994264915881", "max_stars_repo_licenses": ["Apache-2.0"], "m... |
"""
qtools.py - some unassociated random tools for working with QuTiP
"""
import numpy as np
import qutip as qt
import math
def ketify(ket, M, LaTeX=False):
"""
Given a state vector of N dimensions and composed of M subspaces, transform
into an expression of bra, kets
:param M: number of subspaces to ... | {"hexsha": "b6199a13501122aa2cf24ca9ee0ce57e99241cb5", "size": 3522, "ext": "py", "lang": "Python", "max_stars_repo_path": "qtools.py", "max_stars_repo_name": "peterse/transmon_simulations", "max_stars_repo_head_hexsha": "364e898de96c10987bd4a8588bdecf54ea28f9ac", "max_stars_repo_licenses": ["MIT"], "max_stars_count": ... |
import numpy as np
import math
from collections import defaultdict
class Agent:
def __init__(self, nA=6, epsilon=0.08926, gamma=0.8597, epsilon_divisor = 17.87):
""" Initialize agent.
Params
======
- nA: number of actions available to the agent
"""
self.nA = nA
... | {"hexsha": "49710eadcb212dc3e51c66e1f9d0d032fb087c3e", "size": 1923, "ext": "py", "lang": "Python", "max_stars_repo_path": "lab-taxi/agent.py", "max_stars_repo_name": "faisman/deep-reinforcement-learning", "max_stars_repo_head_hexsha": "1e8fd9ed9e155b1939384ccb6dbba20624836d0d", "max_stars_repo_licenses": ["MIT"], "max... |
# -*- coding: utf-8 -*-
from __future__ import print_function
from acq4.util import Qt
from acq4.analysis.AnalysisModule import AnalysisModule
from collections import OrderedDict
import pyqtgraph as pg
from pyqtgraph.metaarray import MetaArray
import numpy as np
from six.moves import range
class ImageAnalysis(Analysis... | {"hexsha": "1bec0c5745a141c381b2e19bc6051d1917f19695", "size": 8627, "ext": "py", "lang": "Python", "max_stars_repo_path": "acq4/analysis/modules/ImageAnalysis/ImageAnalysis.py", "max_stars_repo_name": "campagnola/acq4", "max_stars_repo_head_hexsha": "09699c07d8949950f6df149cf17892aaa3a37402", "max_stars_repo_licenses"... |
Add LoadPath "..".
Require Import L_Substitution.
Require Import PermutLib.
Require Import LibTactics.
Require Import L_OkLib.
Require Import Relations.
Open Scope is5_scope.
Open Scope labeled_is5_scope.
Open Scope permut_scope.
Global Reserved Notation " Omega ';' Gamma '|-' M ':::' A '@' w " (at level 70).
Induct... | {"author": "Ayertienna", "repo": "IS5", "sha": "3bfd1b8510f269071d59d77818f8936d194364bc", "save_path": "github-repos/coq/Ayertienna-IS5", "path": "github-repos/coq/Ayertienna-IS5/IS5-3bfd1b8510f269071d59d77818f8936d194364bc/src/Labeled/L_Semantics.v"} |
import cv2
import numpy as np
import colors
import position
from twophase import solve
import threading
from cube3d import RubiksCube
def show_scramble_2d(text=None, img=None):
cv2.imshow(text, img)
cv2.waitKey(0)
def create_cube(solution, start_position):
game = RubiksCube(solution, start_position)
... | {"hexsha": "6a99097fc2a134fd9abece89a76bc33570932e02", "size": 8064, "ext": "py", "lang": "Python", "max_stars_repo_path": "main.py", "max_stars_repo_name": "Sheshkon/CubeRecognition", "max_stars_repo_head_hexsha": "0d65831a9c43ce7231f8758bf57a75f49ea2aca2", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2, "ma... |
#!/usr/bin/python
import sys
import os
import numpy
import pickle as pkl
import re
from collections import OrderedDict
sys.setrecursionlimit(1000)
def build_dictionary_wordnet(filepaths, dst_path=None, lowercase=False, remove_phrase=True):
word_id_num = OrderedDict()
id_word = OrderedDict()
id_num_word = ... | {"hexsha": "144676f06b57c8feba2b515bcb358bcbc781517b", "size": 13671, "ext": "py", "lang": "Python", "max_stars_repo_path": "data/preprocess_data.py", "max_stars_repo_name": "ksboy/NLP_External_knowledge", "max_stars_repo_head_hexsha": "989cf4debce2bc1040c0d95a46e4307336624917", "max_stars_repo_licenses": ["Apache-2.0"... |
import numpy as np
import torch
from torch.autograd import Variable
from neural_net import HVVNet
class Predictor():
def __init__(self, insize, outsize, model_path):
self.model = HVVNet(insize, outsize)
self.model.load_state_dict(torch.load(model_path))
self.model.eval()
def predic... | {"hexsha": "08c7c2cbacbec068ef2cfc896ec4ad1087e74443", "size": 1146, "ext": "py", "lang": "Python", "max_stars_repo_path": "predict.py", "max_stars_repo_name": "BlueHC/TTHack-2018--Traffic-Guide-1", "max_stars_repo_head_hexsha": "6a720e268d18c8e090c12b874336d17e89a183bb", "max_stars_repo_licenses": ["MIT"], "max_stars_... |
import numpy as np
a = np.ndarray((2,6,7,3))
copy01 = np.abs(a)
copy02 = np.arcos(a)
copy03 = np.arcosh(a)
copy04 = np.arcsin(a)
copy05 = np.arcsinh(a)
copy06 = np.arctan(a)
copy07 = np.arctanh(a)
copy08 = np.cos(a)
copy09 = np.floor(a)
copy10 = np.zeros_like(a)
# show_store()
| {"hexsha": "a01b5212a2962fd6fa3c0d0cc18919a058157d41", "size": 280, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/inputs/numpy-lib/52-np-functions-that-dont-modify-array.py", "max_stars_repo_name": "helq/pytropos", "max_stars_repo_head_hexsha": "497ed5902e6e4912249ca0a46b477f9bfa6ae80a", "max_stars_repo_... |
## simple interactive edge finding using locator()
pupil.pars <- function(im=NULL, obstructed=FALSE) {
if (!is.null(im))
image(1:nrow(im), 1:ncol(im), im, col=grey256, asp=1, xlab="", ylab="", useRaster=TRUE)
cat("click on edge of pupil; right click to exit\n")
flush.console()
edge <- locator(type="p", col="gree... | {"hexsha": "bd628bba01165c38aba71b3d64750f0dd1d8ee98", "size": 5186, "ext": "r", "lang": "R", "max_stars_repo_path": "R/circle.pars.r", "max_stars_repo_name": "gmke/zernike", "max_stars_repo_head_hexsha": "0880b0ae43cbb051afb54aa5decc246252467a8d", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 4, "max_stars_re... |
import os
import pandas as pd
import numpy as np
from Utility import countLabelCorrespondMovieNum, countSingleLabelCorrespondMovieNum
strProjectFolder = os.path.dirname(os.path.dirname(__file__))
DataUser = pd.read_csv(os.path.join(strProjectFolder, "01-Data/users.csv"), sep="::", engine="python", usecols=["UserID",... | {"hexsha": "32f1131f400a7d8e8ac8801131feec62681600e5", "size": 1517, "ext": "py", "lang": "Python", "max_stars_repo_path": "Homework/HW5/Base/DataProcessing.py", "max_stars_repo_name": "zhufyaxel/ML_SaltyFish", "max_stars_repo_head_hexsha": "84b839fa236c471e1fa8600093f0096ff79e4097", "max_stars_repo_licenses": ["MIT"],... |
using ReversibleJumpMCMC
using Test
@testset "ReversibleJumpMCMC.jl" begin
# Write your tests here.
@test 1==1
end
| {"hexsha": "9b3bb27ce0dab32c13c3d9a4de91fd492d4026c9", "size": 124, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/runtests.jl", "max_stars_repo_name": "LidkeLab/RJMCMC.jl", "max_stars_repo_head_hexsha": "2b430726f061332a06f44399471abbb7910ed5ba", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "m... |
import os, sys
import pickle
import inspect
import numpy as np
import time
from copy import copy
from psana import dgram
from psana.dgrammanager import DgramManager
from psana.detector.detector_impl import MissingDet
from psana.event import Event
from psana.psexp import *
class DetectorNameError(Exception): pass
de... | {"hexsha": "86db65a0c012a2cb73e6fbfbd7b91043b11c079d", "size": 12203, "ext": "py", "lang": "Python", "max_stars_repo_path": "psana/psana/psexp/run.py", "max_stars_repo_name": "ZhenghengLi/lcls2", "max_stars_repo_head_hexsha": "94e75c6536954a58c8937595dcac295163aa1cdf", "max_stars_repo_licenses": ["BSD-3-Clause-LBNL"], ... |
[STATEMENT]
lemma map_onws_sb_owned:"\<And>j. j < length ts \<Longrightarrow> map \<O>_sb ts ! j = (\<O>\<^sub>j,sb\<^sub>j) \<Longrightarrow> map owned ts ! j = \<O>\<^sub>j"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. \<And>j. \<lbrakk>j < length ts; map \<O>_sb ts ! j = (\<O>\<^sub>j, sb\<^sub>j)\<rbrakk> \<Lo... | {"llama_tokens": 1416, "file": "Store_Buffer_Reduction_ReduceStoreBufferSimulation", "length": 8} |
import os
import torch
import numpy as np
import torch.optim as optim
from torch.autograd import Variable
from utils.meta import META
# we build here on premmise, that batch contains multi-task experience!!
# therefore we will take few grad steps in 'inner-loop' and then one FOMAML step in here
class FOML(META):
... | {"hexsha": "bf0cbb2b0572c045bfe06186d0875762044f00d0", "size": 1001, "ext": "py", "lang": "Python", "max_stars_repo_path": "utils/foml.py", "max_stars_repo_name": "rezer0dai/bnpo", "max_stars_repo_head_hexsha": "e32f2b013d28714530c46c2f1084a14385af1ce9", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max... |
import numpy as np
import matplotlib.pyplot as plt
def moving_average(interval, window_size):
window= np.ones(int(window_size))/float(window_size)
return np.apply_along_axis(lambda m: np.convolve(m, window, 'valid'),
axis=0, arr=interval)
def plot_moving_average_scores(scores, ... | {"hexsha": "cfd84ab299d8171e6098afb9b51902c5e57c4ccd", "size": 430, "ext": "py", "lang": "Python", "max_stars_repo_path": "plotting.py", "max_stars_repo_name": "mvelax/wizard-python", "max_stars_repo_head_hexsha": "a2695861eee07746a9b3d7dbbee4b471e2675b20", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max... |
"""NOTE: This file may be difficult to run from the railrl-private environment!
I started with a fresh python2 env (bairdataset) and installed
tensorflow 0.11.2 off a random stack overflow post, adjusted TF calls to
match that API
https://stackoverflow.com/questions/41626830/pip-only-install-cpu-tensorflow-of-tensorfl... | {"hexsha": "81df97fb6664b4a9b2c4268440e9577d83815870", "size": 18953, "ext": "py", "lang": "Python", "max_stars_repo_path": "rlkit/data_management/external/bair_dataset/bair_dataset_tensorflow.py", "max_stars_repo_name": "Asap7772/railrl_evalsawyer", "max_stars_repo_head_hexsha": "baba8ce634d32a48c7dfe4dc03b123e18e96e0... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.