text stringlengths 0 1.25M | meta stringlengths 47 1.89k |
|---|---|
import geompy
if geompy.USE_PURE_SYMPY:
from symengine import Expr, Eq, sympify, nan
else:
from sympy import Expr, Eq, sympify, nan
from sympy.simplify import sqrtdenest
from sympy import simplify
from functools import lru_cache
from typing import Union
Expression = Union[Expr, str, int, float] # Anything ... | {"hexsha": "165b67405a906d1fa7552eaf6bd13685c3a1cb37", "size": 968, "ext": "py", "lang": "Python", "max_stars_repo_path": "geompy/cas/symengine_utils.py", "max_stars_repo_name": "qthequartermasterman/geometry", "max_stars_repo_head_hexsha": "eb1619002aa36585338e59d04aa9c9d2e69a7a02", "max_stars_repo_licenses": ["MIT"],... |
# Fix ambiguities on julia 0.4
*(a::ResElem{fmpz}, b::fmpz) = parent(a)(data(a) * b)
*(a::fmpz, b::ResElem{fmpz}) = b*a
+(a::ResElem{fmpz}, b::fmpz) = parent(a)(data(a) + b)
+(a::fmpz, b::ResElem{fmpz}) = b + a
-(a::ResElem{fmpz}, b::fmpz) = parent(a)(data(a) - b)
-(a::fmpz, b::ResElem{fmpz}) = parent(b)(a - data... | {"hexsha": "16f7186e49301e2a38087e47254f71da660b6bbc", "size": 2734, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "sources/src/ambiguities.jl", "max_stars_repo_name": "JeffreySarnoff/Nemo_v07_src.jl", "max_stars_repo_head_hexsha": "a8440b014212d6619dd5dd4ebfde74669f3c42e7", "max_stars_repo_licenses": ["MIT"], "... |
[GOAL]
p : ℕ
inst✝⁴ : Fact (Nat.Prime p)
k : Type u_1
inst✝³ : CommRing k
inst✝² : IsDomain k
inst✝¹ : CharP k p
inst✝ : PerfectRing k p
m : ℤ
x : StandardOneDimIsocrystal p k m
⊢ ↑Φ(p, k) x = ↑p ^ m • ↑φ(p, k) x
[PROOFSTEP]
erw [smul_eq_mul]
[GOAL]
p : ℕ
inst✝⁴ : Fact (Nat.Prime p)
k : Type u_1
inst✝³ : CommRing k
ins... | {"mathlib_filename": "Mathlib.RingTheory.WittVector.Isocrystal", "llama_tokens": 14360} |
using TransformVariables, Parameters, Statistics, StatsFuns, Optim
using NLSolversBase
function makeLoss(model)
t = getTransform(model)
fpre = @eval $(logdensity(model))
f(par, data) = Base.invokelatest(fpre, par, data)
loss(x, data) = -f(t(x), data)
(loss=loss, t=t)
end
export getMAP
functio... | {"hexsha": "aa7138e3dff5c3fc49054411b63bcbbf0480ca78", "size": 661, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/optim.jl", "max_stars_repo_name": "devmotion/Soss.jl", "max_stars_repo_head_hexsha": "0440210c71bdb6c69bf7b1930043e29bd19da3a4", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 388, "max_... |
import inspect
import warnings
import re
from typing import Union
import tensorflow as tf
import numpy as np
ArrayLike = Union[np.ndarray, tf.Tensor]
TfTensor = tf.Tensor
FreeRV = ArrayLike
def stabilize(K, shift=None):
r"""Add a diagonal shift to a covariance matrix."""
K = tf.convert_to_tensor(K)
dia... | {"hexsha": "5f616ae0ec26f5eea46f5a627dd246afcfa42336", "size": 1635, "ext": "py", "lang": "Python", "max_stars_repo_path": "pymc4/gp/util.py", "max_stars_repo_name": "mailology/pymc4", "max_stars_repo_head_hexsha": "42642efd89e71787e4d1af6ab8b6ca241b861f35", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count":... |
% BEGIN LICENSE BLOCK
% Version: CMPL 1.1
%
% The contents of this file are subject to the Cisco-style Mozilla Public
% License Version 1.1 (the "License"); you may not use this file except
% in compliance with the License. You may obtain a copy of the License
% at www.eclipse-clp.org/license.
%
% Software distribute... | {"hexsha": "23999fd39f9d00660800d812b63b5a59ae8be0a4", "size": 10167, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "usr/eclipseclp/documents/libman/introduction.tex", "max_stars_repo_name": "lambdaxymox/barrelfish", "max_stars_repo_head_hexsha": "06a9f54721a8d96874a8939d8973178a562c342f", "max_stars_repo_license... |
import numpy as np
from numpy.testing import assert_allclose
from cyvlfeat.quickshift.quickshift import quickshift
from cyvlfeat.test_util import lena
img = lena().astype(np.float32)
def test_quickshift_medoid_maps():
i = img.copy()
maps, gaps, estimate = quickshift(i, kernel_size=2, max_dist=10, medoid=True... | {"hexsha": "0f9c37296f2bb4e88f6aa5c6645a128b5179da40", "size": 1865, "ext": "py", "lang": "Python", "max_stars_repo_path": "cyvlfeat/quickshift/tests/quickshift_test.py", "max_stars_repo_name": "simmimourya1/cyvlfeat", "max_stars_repo_head_hexsha": "efc83dca40d335658620c49017ca0c814ad77132", "max_stars_repo_licenses": ... |
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
import json
import os
import torch.utils.data as data
class PANO(data.Dataset):
default_resolution = (512, 768)
num_classes = 1 # 1 or 32
def __init__(self, opt, split):
... | {"hexsha": "0f980b06551ee38c1c47fb58a904a4b82b8c6766", "size": 795, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/lib/datasets/dataset/pano.py", "max_stars_repo_name": "jscsmk/CenterNet", "max_stars_repo_head_hexsha": "d7c643bba2b373c15abfa3d25ffd5304a313fa49", "max_stars_repo_licenses": ["MIT"], "max_star... |
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
from covid_tools.calc import fill_missing_date, fill_missing_date_groups
sns.set()
def ts_plot_setup(dpi=100, x_rotation=60):
fig, ax = plt.subplots(dpi=dpi)
... | {"hexsha": "5521f68239bdbcb7902cb5c9e845fda04bd58de4", "size": 2233, "ext": "py", "lang": "Python", "max_stars_repo_path": "plot.py", "max_stars_repo_name": "amhirsch/covid_tools", "max_stars_repo_head_hexsha": "0073e8db0dc13ebcde3d86b78b53e982e34ecd5b", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max... |
/-
Copyright (c) 2018 Robert Y. Lewis. All rights reserved.
Released under Apache 2.0 license as described in the file LICENSE.
Authors: Robert Y. Lewis, Chris Hughes
-/
import algebra.associated
import algebra.big_operators.basic
import ring_theory.valuation.basic
/-!
# Multiplicity of a divisor
For a commutative mo... | {"author": "jjaassoonn", "repo": "projective_space", "sha": "11fe19fe9d7991a272e7a40be4b6ad9b0c10c7ce", "save_path": "github-repos/lean/jjaassoonn-projective_space", "path": "github-repos/lean/jjaassoonn-projective_space/projective_space-11fe19fe9d7991a272e7a40be4b6ad9b0c10c7ce/src/ring_theory/multiplicity.lean"} |
#! /usr/bin/env python
# -*- coding: utf-8 -*-
#
# Distributed under terms of the MIT license.
import argparse
import datetime
import cvxpy as cp
import numpy as np
import math
from sklearn.metrics import f1_score
from numpy.linalg import norm, eigh
from sklearn.model_selection import train_test_split
imp... | {"hexsha": "70261a8416ac5360de5d4b8b76ce02fe0b5fc487", "size": 8871, "ext": "py", "lang": "Python", "max_stars_repo_path": "product_space_svm.py", "max_stars_repo_name": "thupchnsky/product-space-linear-classifiers", "max_stars_repo_head_hexsha": "511a46be6061551ef6e279030505a1f0972de58b", "max_stars_repo_licenses": ["... |
# coding: utf-8
## @package pawpyseed.core.symmetry
# Utilities related to symmetry of the crystal structure,
# namely finding symmetrically identical k-points and the
# space group operators that map between them.
from pymatgen.symmetry.analyzer import SpacegroupAnalyzer
from pymatgen.core.operations import SymmOp
i... | {"hexsha": "4080d16c3954b838661c1814e20329602a2f0c4d", "size": 4491, "ext": "py", "lang": "Python", "max_stars_repo_path": "pawpyseed/core/symmetry.py", "max_stars_repo_name": "akashkumarsingh612/pawpyseed", "max_stars_repo_head_hexsha": "6f5aa0b8ca8c28a0221e5256afeb939c3344560b", "max_stars_repo_licenses": ["BSD-3-Cla... |
import string
import sys
import numpy as np
import io
from hashlib import md5
if sys.version_info < (3,):
maketrans = string.maketrans
else:
maketrans = str.maketrans
def np2csv(arr):
csv = io.BytesIO()
np.savetxt(csv, arr, delimiter=',', fmt='%g')
return csv.getvalue().decode().rstrip()
... | {"hexsha": "29412a466b9f1b07caede6c1b7f6fb5934d51a3d", "size": 5037, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/sms_spam_classifier_utilities.py", "max_stars_repo_name": "parampopat/cloud-project", "max_stars_repo_head_hexsha": "31e74cbf3f06b2be981be98d1cd3c832b8ca3a8c", "max_stars_repo_licenses": ["MIT... |
import numpy as np
from ineqpy import inequality
def test_gini_2d():
x = np.array([[57], [63], [81], [79], [88], [57], [42], [3], [77], [89]])
w = np.array([[2], [5], [2], [9], [5], [7], [4], [5], [9], [9]])
obtained = inequality.gini(income=x, weights=w)
expected = 0.2134389018024818
assert obtai... | {"hexsha": "468ca82afa5772d30ec1c4ef155f167f9e1a1359", "size": 2225, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/test_inequality.py", "max_stars_repo_name": "Grasia/IneqPy", "max_stars_repo_head_hexsha": "b6e06dcf60d1b08db03f9e055bf03510baa221c6", "max_stars_repo_licenses": ["MIT"], "max_stars_count": ... |
from typing import Any, List, Dict, Tuple, Optional, DefaultDict, Union
from torch.utils.tensorboard import SummaryWriter
from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset
from torch import cuda, nn, save, unsqueeze, sigmoid, stack, sum, no_grad
from transformers import BertConfig... | {"hexsha": "f067e34af63d2565fea89d1623a3dc917ac12279", "size": 11617, "ext": "py", "lang": "Python", "max_stars_repo_path": "ml/paraphrase/paraphraser_trainer.py", "max_stars_repo_name": "DavidThe4sian/marvin", "max_stars_repo_head_hexsha": "1dddd8cdb8be55d3b553df1b2cf89e8b6b0538b6", "max_stars_repo_licenses": ["MIT"],... |
#!/usr/bin/python
from sympy import isprime
## part 1
def xor_stack_with_palprimes(stack, palprimes):
result = []
if len(stack) != len(palprimes):
raise Exception("Args must have the same length. Length of stack={}, length of palprimes={}.")
for i in range(len(stack)):
a = stack[i]
... | {"hexsha": "310a665505659452518113824d1329b1f4218c69", "size": 5485, "ext": "py", "lang": "Python", "max_stars_repo_path": "posts/190727-googlectf-beginners-friendspacebookplusallaccessredpremium.com/solve.py", "max_stars_repo_name": "shiny-labs/shiny-labs.github.io", "max_stars_repo_head_hexsha": "c5a788e19e1260ccedfb... |
SUBROUTINE DT_DCHANT
IMPLICIT NONE
DOUBLE PRECISION hv , hwt , ONE , TINY10 , ZERO
INTEGER i , ik1 , ik2 , j
SAVE
INCLUDE 'inc/dtflka'
PARAMETER (TINY10=1.0D-10,ONE=1.0D0,ZERO=0.0D0)
C HADRIN: decay channel information
INCLUDE 'inc/hndech'
C particle properties ... | {"hexsha": "a85ed4ddeb30d919974546d9ca4d6be7a00470b3", "size": 1195, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "src/dpmjet/DT_DCHANT.f", "max_stars_repo_name": "pzhristov/DPMJET", "max_stars_repo_head_hexsha": "946e001290ca5ece608d7e5d1bfc7311cda7ebaa", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_star... |
/**
Copyright (c) 2016, Aumann Florian, Borella Jocelyn, Hutmacher Robin, Karrenbauer Oliver, Meißner Pascal, Schleicher Ralf, Stöckle Patrick, Trautmann Jeremias
All rights reserved.
Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions ... | {"hexsha": "b85cd2568dfe42b8aab48a5c24dd647e713ddbe3", "size": 6294, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "include/world_model/view_port_handler.hpp", "max_stars_repo_name": "asr-ros/asr_world_model", "max_stars_repo_head_hexsha": "ff4ab2e8f83212be0f3aeb0224ad892d4966befd", "max_stars_repo_licenses": ["B... |
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by appli... | {"hexsha": "98df62d6b1154453702880f59c5d3079ff815d0b", "size": 4529, "ext": "py", "lang": "Python", "max_stars_repo_path": "ppcls/data/preprocess/ops/randaugment.py", "max_stars_repo_name": "PaddlePaddle/PaddleImgClass", "max_stars_repo_head_hexsha": "f5265a1f2ab7aa113ae5245223f0528e3239a5e7", "max_stars_repo_licenses"... |
import os
import pandas as pd
import numpy as np
import SQLAlchemy
import sqlalchemy
from sqlalchemy.ext.automap import automap_base
from sqlalchemy.orm import Session
from sqlalchemy import create_engine
from flask import Flask, jsonify, render_template
from flask_sqlalchemy import SQLAlchemy
app = Flask(__name__)... | {"hexsha": "6548a99babb722ad70bf267a4a185c430185556d", "size": 1297, "ext": "py", "lang": "Python", "max_stars_repo_path": "app.py", "max_stars_repo_name": "BradleyHigdon/plotly", "max_stars_repo_head_hexsha": "c845c52a38cc53d922b7d1f9e53920c46337faab", "max_stars_repo_licenses": ["ADSL"], "max_stars_count": null, "max... |
This editor can edit this entry and tell us a bit about themselves by clicking the Edit icon.
20100712 17:40:52 nbsp Hi, Im Evan. Whats with the eNigma? Are you dressed in spandex, or are you four years old? Cmon youre acting like a totally antisocial, petty and juvenile child by introducing yourself as that. ... | {"hexsha": "600594ce48fc9f2e76202f79e5a02e6a5b94d8d9", "size": 723, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "lab/davisWiki/Mr.eNigma.f", "max_stars_repo_name": "voflo/Search", "max_stars_repo_head_hexsha": "55088b2fe6a9d6c90590f090542e0c0e3c188c7d", "max_stars_repo_licenses": ["MIT"], "max_stars_count": n... |
import numpy as np
import matplotlib.pyplot as plt
import qiskit.pulse.library as pulse_lib
from scipy.optimize import curve_fit
from scipy.signal import find_peaks
from qiskit import IBMQ
from qiskit import pulse
from qiskit.compiler import assemble
from qiskit.tools.monitor import job_monitor
IBMQ.enable_account("a... | {"hexsha": "feed37f052f8bf4f8da075a2707e67d0f2430f60", "size": 17784, "ext": "py", "lang": "Python", "max_stars_repo_path": "CosmiQ/ibmq/gate_constructor.py", "max_stars_repo_name": "stared/Hackathon2021", "max_stars_repo_head_hexsha": "69e2ba4345b311e62d09d02f6953b25614229e12", "max_stars_repo_licenses": ["MIT"], "max... |
from __future__ import absolute_import, division, print_function, unicode_literals
from keras import layers, models
from keras.models import Sequential
from keras import layers
import numpy as np
from tensorflow.keras.preprocessing.text import Tokenizer
from tensorflow.keras.utils import to_categorical
import random
fr... | {"hexsha": "64c57f031a7d5aef3af1b8abcab7b0dcac194bca", "size": 3546, "ext": "py", "lang": "Python", "max_stars_repo_path": "sign-prediction/train_utils.py", "max_stars_repo_name": "kurshakuz/krsl-recogniton", "max_stars_repo_head_hexsha": "2ffecd1a1bdaae1d9cee68a7d74ae8246719157e", "max_stars_repo_licenses": ["Apache-2... |
! .................................................
! ____ _ _ ____ _____ _
! | _ \| | |_| | _ \| ___| |_|
! | |_) | |___ _ | |_) | |___ _
! | _ /| _ | | | | _ /|___ | | |
! | | | | | | | | | | ... | {"hexsha": "56c1e8d0f991e45102f414913bbf8ca2a7e42aab", "size": 3846, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "Assemble_Stiffness_Matrix_FEM.f", "max_stars_repo_name": "PhiPsi-Software/PhiPsi_XFEM_Fortran_Codes_Early_Version", "max_stars_repo_head_hexsha": "1eae63a8f0dc968b9d5220397879645a83e5d083", "max_s... |
import time
import argparse
from datetime import datetime
import logging
import numpy as np
import os
import torch
import torch.nn.functional as F
import torch.multiprocessing as mp
from models import NavCnnModel, NavCnnRnnModel, NavCnnRnnMultModel, NavPlannerControllerModel
from data import EqaDataLoader
from metrics ... | {"hexsha": "206bdceed901242fca25b737a0e8e945f5ce902c", "size": 54328, "ext": "py", "lang": "Python", "max_stars_repo_path": "training/train_nav.py", "max_stars_repo_name": "catalina17/EmbodiedQA", "max_stars_repo_head_hexsha": "492c2e907697691899e7fe2102b0b859059d4efd", "max_stars_repo_licenses": ["BSD-3-Clause"], "max... |
import numpy as np
import pandas as pd
from . import ops
def make_chromarms(
chromsizes,
midpoints,
cols_chroms=("chrom", "length"),
cols_mids=("chrom", "mids"),
suffixes=("_p", "_q"),
):
"""
Split chromosomes into chromosome arms
Parameters
----------
chromsizes : pandas.Dat... | {"hexsha": "a61dddbf01ca2b002b91d211fcd2c9297d7b3248", "size": 8510, "ext": "py", "lang": "Python", "max_stars_repo_path": "bioframe/genomeops.py", "max_stars_repo_name": "itsameerkat/bioframe", "max_stars_repo_head_hexsha": "22675199efd1ee80304ed31a108505d84341d5ab", "max_stars_repo_licenses": ["MIT"], "max_stars_coun... |
#include "command.h"
#include "kits_cmd.h"
#include "genarchive.h"
#include "mergeruns.h"
#include "agglog.h"
#include "logcat.h"
#include "verifylog.h"
#include "truncatelog.h"
#include "propstats.h"
#include "logpagestats.h"
#include "loganalysis.h"
#include "dbscan.h"
#include "addbackup.h"
#include "xctlatency.h"
... | {"hexsha": "b0624f08a08b55155a8c0f64ff9dc67fdf6ce06e", "size": 20898, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/cmd/base/command.cpp", "max_stars_repo_name": "caetanosauer/zero", "max_stars_repo_head_hexsha": "2cf0c0e4420c8b87795300aeb71a729451d4f4c9", "max_stars_repo_licenses": ["Spencer-94"], "max_star... |
"""This scripts generates 3 examples where we regress HR real scans from multi-modal LR scans. Specifically we regress
HR T1 scans from LR T1 and T2 scans. We assume here that HR label maps are available with corresponding T1 scans. Thus
this script produces pairs of real HR T1 scans along with aligned HR synthetic sca... | {"hexsha": "5a083e899e7af222d090904c26d8623e9b324c79", "size": 7459, "ext": "py", "lang": "Python", "max_stars_repo_path": "scripts/tutorials/5-SR-synthesis_multimodal_real.py", "max_stars_repo_name": "hanzhiwangchn/SynthSR", "max_stars_repo_head_hexsha": "35c2483f1da272855bbeea3e76140845106b623d", "max_stars_repo_lice... |
import logging
from rlberry.agents.agent import AgentWithSimplePolicy
import numpy as np
import gym.spaces as spaces
from rlberry.agents.dynprog.utils import backward_induction
from rlberry.agents.dynprog.utils import backward_induction_in_place
from rlberry.agents.kernel_based.common import map_to_representative
log... | {"hexsha": "53b5a0fcf250efcac42449e630e22f079e1f8e6c", "size": 10979, "ext": "py", "lang": "Python", "max_stars_repo_path": "rlberry/agents/kernel_based/rs_ucbvi.py", "max_stars_repo_name": "akrouriad/rlberry", "max_stars_repo_head_hexsha": "dde4e2cbafca05fdef1df07646bb6368059eeadf", "max_stars_repo_licenses": ["MIT"],... |
"""
Created by Constantin Philippenko, 17th January 2022.
"""
import cmath
import matplotlib
import numpy as np
from matplotlib import pyplot as plt
from tqdm import tqdm
from src.CompressionModel import SQuantization, RandomSparsification, Sketching
from src.SyntheticDataset import SyntheticDataset
from src.Theoreti... | {"hexsha": "5700639e884924af5a85a37647ec59d457fc1021", "size": 4115, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/PlotEigenValues.py", "max_stars_repo_name": "philipco/structured_noise", "max_stars_repo_head_hexsha": "e7716abc5f90b2b5c3dccbb4c7e67708dab32a88", "max_stars_repo_licenses": ["MIT"], "max_star... |
from .distribution import Distribution
from .functions import choose, mascheroni
from scipy.integrate import quad
from math import factorial
from typing import Union
from dataclasses import dataclass
@dataclass
class Binomial(Distribution):
n: Union[int, float]
p: float
def __post__init__(self):
... | {"hexsha": "746d10fab24facd7460c4fb410b52a0a0761f059", "size": 1409, "ext": "py", "lang": "Python", "max_stars_repo_path": "noether/statistics/distcatalogue.py", "max_stars_repo_name": "yunruse/Noether", "max_stars_repo_head_hexsha": "7421a14000488819e52c1aeffa45833c27b8e3ef", "max_stars_repo_licenses": ["CC-BY-4.0"], ... |
#!/usr/bin/env python3.6
# -*- coding: utf-8 -*-
import linecache as lc
import numpy as np
import os
from sagar.io.vasp import read_vasp
import subprocess
class ExtractValue():
def __init__(self,data_folder='./',atomic_num=3):
self.data_folder = data_folder
self.atomic_num = atomic_num
def... | {"hexsha": "666b5d482ef163a35bc5fb3fe1fd700ece118fd7", "size": 6816, "ext": "py", "lang": "Python", "max_stars_repo_path": "pyvaspflow/io/vasp_out.py", "max_stars_repo_name": "Zhiwei-Lu/pyvaspflow", "max_stars_repo_head_hexsha": "b80eab3e8bfc52aed6a2459dd32655f1075d9058", "max_stars_repo_licenses": ["MIT"], "max_stars_... |
theory FixRestr
imports HOLCF
begin
find_consts name:funpow
term Nat.funpow
definition chainFrom :: "('a => 'a) => ('a :: cpo) => bool"
where "chainFrom F x = ((\<forall>n. (F^^n) x \<sqsubseteq> F ((F^^n) x)) \<and> (F (\<Squnion> i. ((F^^i) x)) = (\<Squnion> i. F ((F^^i) x))))"
lemma chainFrom_chain [simp]: ... | {"author": "Josh-Tilles", "repo": "AFP", "sha": "f4bf1d502bde2a3469d482b62c531f1c3af3e881", "save_path": "github-repos/isabelle/Josh-Tilles-AFP", "path": "github-repos/isabelle/Josh-Tilles-AFP/AFP-f4bf1d502bde2a3469d482b62c531f1c3af3e881/thys/Launchbury/FixRestr.thy"} |
[STATEMENT]
lemma partial_get_put: "\<rho> \<in> \<S> \<Longrightarrow> put \<sigma> (get \<rho>) = \<rho>"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. \<rho> \<in> \<S> \<Longrightarrow> put \<sigma> (get \<rho>) = \<rho>
[PROOF STEP]
by (metis put_det weak_get_put) | {"llama_tokens": 108, "file": "Optics_Lens_Laws", "length": 1} |
//
// Copyright (c) 2016-2017 Vinnie Falco (vinnie dot falco at gmail dot com)
//
// Distributed under the Boost Software License, Version 1.0. (See accompanying
// file LICENSE_1_0.txt or copy at http://www.boost.org/LICENSE_1_0.txt)
//
// Official repository: https://github.com/boostorg/beast
//
#ifndef BEAST_MULTI_... | {"hexsha": "44e19c7ea12d5fca165f4c205c1b7ccaf0569c62", "size": 8651, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "include/beast/core/multi_buffer.hpp", "max_stars_repo_name": "mandreyel/beast-asio-standalone", "max_stars_repo_head_hexsha": "627c633b0262a04b6dc71c7b946df63674274f0e", "max_stars_repo_licenses": [... |
################################################################################
# Fundamentos de la Ciencia de Datos - 78106 - R-PL6 #
# Grupo 4 - P6 #
# Authors: ... | {"hexsha": "33cc1ffea557509f663fa67539d4f8564d9a27e3", "size": 2791, "ext": "py", "lang": "Python", "max_stars_repo_path": "PL6/src/bidimensional.py", "max_stars_repo_name": "craciunescu/DataScience", "max_stars_repo_head_hexsha": "e246994974d817f48d6861162f2804ed4c9539ff", "max_stars_repo_licenses": ["MIT"], "max_star... |
# Copyright (c) 2015-2020 by the parties listed in the AUTHORS file.
# All rights reserved. Use of this source code is governed by
# a BSD-style license that can be found in the LICENSE file.
import sys
import types
import copy
import numbers
from collections.abc import MutableMapping, Sequence, Mapping
import n... | {"hexsha": "162a34997eef3cdfc946f244e3fada6e05d6d74c", "size": 23690, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/types/observation/__init__.py", "max_stars_repo_name": "nestordemeure/differentoast", "max_stars_repo_head_hexsha": "4f03f4d8732d18398930e4520b0807f299e11be3", "max_stars_repo_licenses": ["Ap... |
'''
Created on December 2019.
@author: Soroosh Tayebi Arasteh <soroosh.arasteh@fau.de>
https://github.com/tayebiarasteh/
'''
import numpy as np
from Layers import *
from Optimization import *
import copy
class NeuralNetwork:
'''
The Neural Network defines the whole architecture by containing all its layers f... | {"hexsha": "002faa0c6b874e21c080b62d5119562a0d86d47c", "size": 3144, "ext": "py", "lang": "Python", "max_stars_repo_path": "NeuralNetwork.py", "max_stars_repo_name": "starasteh/DeepLearning_from_scratch", "max_stars_repo_head_hexsha": "6ed4685e4da57ad5ea51edf84010f2cc9725a2ba", "max_stars_repo_licenses": ["MIT"], "max_... |
# https://en.wikipedia.org/wiki/Bradley%E2%80%93Terry_model
import numpy as np
import torch
import torch.nn.functional as F
from torch.autograd import grad
from scipy.optimize import minimize
def lossgrad(scores, outcomes):
# prior: player #0 is at 0 elo
scores[0] = 0.0
# gamma = 10^(elo/400)
# co... | {"hexsha": "7bd0e61e7c1412c9b9a19202dba7f0c0d398c2b2", "size": 1949, "ext": "py", "lang": "Python", "max_stars_repo_path": "azalea/ranking.py", "max_stars_repo_name": "jseppanen/azalea", "max_stars_repo_head_hexsha": "aea0e6f76f2d76e2c527163c88268c5a6e7dfd8b", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count... |
! { dg-do run }
! Short test program with a CASE statement that uses a range.
!
program select_4
integer i
do i = 1, 34, 4
select case(i)
case (:5)
if (i /= 1 .and. i /= 5) STOP 1
case (13:21)
if (i /= 13 .and. i /= 17 .and. i /= 21) STOP 2
case (29:)
if (i /= 29 .and. i /=... | {"hexsha": "90792a2f4337cf5274aea32d4366c2f82678b860", "size": 436, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "validation_tests/llvm/f18/gfortran.dg/select_3.f90", "max_stars_repo_name": "brugger1/testsuite", "max_stars_repo_head_hexsha": "9b504db668cdeaf7c561f15b76c95d05bfdd1517", "max_stars_repo_license... |
[STATEMENT]
lemma fmrestrict_set_insert_notin:
\<open>xa \<notin> fset (fmdom N) \<Longrightarrow>
fmrestrict_set (insert xa l1) N = fmrestrict_set l1 N\<close>
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. xa \<notin> fset (fmdom N) \<Longrightarrow> fmrestrict_set (insert xa l1) N = fmrestrict_set l1 N
[PR... | {"llama_tokens": 178, "file": "PAC_Checker_Finite_Map_Multiset", "length": 1} |
import matplotlib.pyplot as plt
import numpy as np
from individual import Individual
from main import *
from pynput.keyboard import Key, Controller
from random import sample, random, randrange
from operator import attrgetter
from kmeans import KMeans
from statistics import mean
from sklearn.decomposition import PCA
fro... | {"hexsha": "48f4885b55bff544ad2bc0bde3951d3e70f5e2e3", "size": 12723, "ext": "py", "lang": "Python", "max_stars_repo_path": "genetic.py", "max_stars_repo_name": "zanussbaum/Chrome-T-Rex-Rush-master", "max_stars_repo_head_hexsha": "d1b9fce102238be7e20c403fcdca7b671be482e6", "max_stars_repo_licenses": ["MIT"], "max_stars... |
# -*- coding: utf-8 -*-
"""
Created on Sun Nov 05 15:21:19 2017
@author: Administrator
"""
from PIL import Image
import os
import numpy as np
def mergeReport(files,img_name):
baseimg=Image.open(files[0])
sz = baseimg.size
basemat=np.atleast_2d(baseimg)
for file in files[1:]:
... | {"hexsha": "d727eceff25ba0739998d7710be6c09289cf2120", "size": 1978, "ext": "py", "lang": "Python", "max_stars_repo_path": "tools/montage.py", "max_stars_repo_name": "zhouxiaowei1120/practice", "max_stars_repo_head_hexsha": "95dd7ffa65f34a867578bea2f80404677cc5f5e5", "max_stars_repo_licenses": ["MIT"], "max_stars_count... |
import cv2 as cv
import numpy as np
url = '../Resources/Photos/cats.jpg'
img = cv.imread(url)
cv.imshow('Cat', img)
gray = cv.cvtColor(img, cv.COLOR_BGR2GRAY)
cv.imshow('gray', gray)
# BGR HSV
hsv = cv.cvtColor(img, cv.COLOR_BGR2HSV)
cv.imshow('HSV', hsv)
# BGR to L*A*B
lab = cv.cvtColor(img, cv.COLOR_BGR2Lab)
cv.... | {"hexsha": "505158dd7bd256d3100353f9222d85804440882f", "size": 502, "ext": "py", "lang": "Python", "max_stars_repo_path": "MS2-Advanced/spaces.py", "max_stars_repo_name": "PNightOwlY/opencv-course", "max_stars_repo_head_hexsha": "71f59327a9c2226144c16aaa42157d32bd392cca", "max_stars_repo_licenses": ["MIT"], "max_stars_... |
import tensorflow as tf
from baselines.common.process_manager import ProcessManager
import numpy as np
import yaml
import zmq
import os
from tqdm import tqdm
from rl_msg_pb2 import *
try:
from mpi4py import MPI
except ImportError:
MPI = None
class ProcessRunner(object):
"""
We use this object to mak... | {"hexsha": "50c8ee43a74442774f3a2f8ca0200f51f6ab5c29", "size": 11687, "ext": "py", "lang": "Python", "max_stars_repo_path": "baselines/ppo2/process_runner.py", "max_stars_repo_name": "junhyeokahn/baselines", "max_stars_repo_head_hexsha": "4d07893edd02a5a71d1c5026b6fef9dbbe0822b5", "max_stars_repo_licenses": ["MIT"], "m... |
function shortcutUtils = GetShortcutUtils()
%GETSHORTCUTUTILS Gets an instance of ShortcutUtils.
%
% SHORTCUTUTILS = GETSHORTCUTUTILS() gets an instance of ShortcutUtils.
%
% Examples:
%
% shortcutUtils = GetShortcutUtils();
% methods(shortcutUtils)
% methodsview(shortcutUtils)
% $Author: rcotton $ $Date: 2010/08... | {"author": "Sable", "repo": "mcbench-benchmarks", "sha": "ba13b2f0296ef49491b95e3f984c7c41fccdb6d8", "save_path": "github-repos/MATLAB/Sable-mcbench-benchmarks", "path": "github-repos/MATLAB/Sable-mcbench-benchmarks/mcbench-benchmarks-ba13b2f0296ef49491b95e3f984c7c41fccdb6d8/27567-shortcut-tools/shortcut_tools/GetShort... |
import pandas as pd
import os
import csv
from nltk.sentiment.vader import SentimentIntensityAnalyzer
import re
import matplotlib.pyplot as plt
import matplotlib
import numpy as np
matplotlib.style.use('ggplot')
mainMessageCorpus = pd.read_csv("fullText_Clean.csv",header=0, \
delimiter=",", skip_blank_lines = True)
m... | {"hexsha": "d3da5e5d0fa08152664d1e5e1ddf9fc316bf320e", "size": 2215, "ext": "py", "lang": "Python", "max_stars_repo_path": "MessageAnalysis.py", "max_stars_repo_name": "Beefstyles/FacebookMessageSentimentAnalysis", "max_stars_repo_head_hexsha": "c11959c54e09d1c804390b4b23aab0a2ac3f681b", "max_stars_repo_licenses": ["MI... |
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import glob
from plotly import graph_objects as go
import ppscore as pps
import seaborn as sns
# Data import
files = sorted(glob.glob("01_data/raw/*"))
# pokemon_index = pd.read_csv(files[0], sep="|")
# pokemon = pd.read_csv(files[1], sep="|")
batt... | {"hexsha": "34e74c270736e288e4c4f12c23e1718c7315d4e6", "size": 1887, "ext": "py", "lang": "Python", "max_stars_repo_path": "02_code/exploration/eda.py", "max_stars_repo_name": "franperic/pokehackathon", "max_stars_repo_head_hexsha": "c91977d630914d738de995ddddd1791b5b7b23b9", "max_stars_repo_licenses": ["MIT"], "max_st... |
import numpy as np
import frozen_lake
newletter = b"1"
if __name__ == '__main__':
pos = 14
print(pos // 5, pos % 5)
| {"hexsha": "f0464f79438b3b2d3e10522125e42fb7b97729d1", "size": 125, "ext": "py", "lang": "Python", "max_stars_repo_path": "Lux_Project_Env/test.py", "max_stars_repo_name": "WittyTheMighty/LUX_AI_Project", "max_stars_repo_head_hexsha": "39e302798ed6cdb98b098fd2d2bb02b3d5eda762", "max_stars_repo_licenses": ["MIT"], "max_... |
import numpy as np
import arcpy
import netCDF4
from netCDF4 import Dataset
##read the netCDF file and print metadata
test_file = "C:\\Users\\Lance\\Documents\\GitHub\\dnppy\\undeployed\\CDRs\\PERSIANN-CDR_v01r01_19890523_c20140523.nc"
fh = Dataset(test_file,'r')
print(fh) #this prints all of the metadata info
variab... | {"hexsha": "d5da33c606aac680016b062334f40a04857c49ec", "size": 3380, "ext": "py", "lang": "Python", "max_stars_repo_path": "undeployed/subjects/CDRs/NetCDF4_Numpy.py", "max_stars_repo_name": "NASA-DEVELOP/dnppy", "max_stars_repo_head_hexsha": "8f7ef6f0653f5a4ea730ee557c72a2c89c06ce0b", "max_stars_repo_licenses": ["NASA... |
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.optim import SGD, lr_scheduler
from sklearn.metrics.cluster import normalized_mutual_info_score as nmi_score
from sklearn.metrics import adjusted_rand_score as ari_score
from sklearn.cluster import KMeans, DBSCAN
from utils.util import BCE, P... | {"hexsha": "3308d28ed67c7ff99ff5ea19521d8667848fc118", "size": 18061, "ext": "py", "lang": "Python", "max_stars_repo_path": "ncl_cifar.py", "max_stars_repo_name": "JosephKJ/NCL", "max_stars_repo_head_hexsha": "e40bcbb6caf0f02764f46c1abc1e9597b6c96103", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": null,... |
\documentclass[a4paper, 12pt]{article}
\usepackage[english]{babel}
\usepackage[utf8]{inputenc}
\usepackage [autostyle, english = american]{csquotes}
\MakeOuterQuote{"}
\usepackage{url}
\usepackage{import}
\usepackage{tabularx}
\usepackage{booktabs}
\usepackage{amsmath}
\usepackage{amsfonts}
\usepackage{graphicx}
\usep... | {"hexsha": "d4c77275cec8db8d1552cff51b86dc53d33719c9", "size": 45790, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "mainNov27.tex", "max_stars_repo_name": "pisihara/ProfileRanking", "max_stars_repo_head_hexsha": "b8cda3d21bca0417d0786802e85f02a1faccc344", "max_stars_repo_licenses": ["BSD-2-Clause"], "max_stars_c... |
// Copyright Tom Westerhout 2017.
// Distributed under the Boost Software License, Version 1.0.
// (See accompanying file LICENSE_1_0.txt or copy at
// http://www.boost.org/LICENSE_1_0.txt)
#include "testing.hpp"
#include <boost/static_views/raw_view.hpp>
#include <boost/static_views/view_concept.... | {"hexsha": "8befe984fb0f4058920b5aaebee478eb2381c22b", "size": 4069, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "test/correctness/raw_view_pass.cpp", "max_stars_repo_name": "BoostGSoC17/static-map", "max_stars_repo_head_hexsha": "32537a69dbf693697577816ee06450fc4ec2a6fb", "max_stars_repo_licenses": ["BSL-1.0"]... |
from gym import spaces
import numpy as np
import random
from itertools import groupby
from itertools import product
class TicTacToe():
def __init__(self):
"""initialise the board"""
# initialise state as an array
self.state = [np.nan for _ in range(9)] # initialises the board po... | {"hexsha": "e15af54ad79308ec4bc2b4d78ca449ae95decd00", "size": 4815, "ext": "py", "lang": "Python", "max_stars_repo_path": "TCGame_Env.py", "max_stars_repo_name": "sameerg07/TicTacToe-RL", "max_stars_repo_head_hexsha": "cfad2719c043e90a25e344866ab5c505ffb83ec6", "max_stars_repo_licenses": ["MIT"], "max_stars_count": nu... |
struct DeivVecTag end
# J(f(x))*v
function auto_jacvec!(du, f, x, v,
cache1 = ForwardDiff.Dual{DeivVecTag}.(x, v),
cache2 = ForwardDiff.Dual{DeivVecTag}.(x, v))
cache1 .= Dual{DeivVecTag}.(x, v)
f(cache2,cache1)
du .= partials.(cache2, 1)
end
function auto_jacvec... | {"hexsha": "ea3815447503e5bd88534335a73047025f864027", "size": 6746, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/differentiation/jaches_products.jl", "max_stars_repo_name": "devmotion/SparseDiffTools.jl", "max_stars_repo_head_hexsha": "7dc407ad10b0a0e82627ea7d9dfcd2490fdf45a3", "max_stars_repo_licenses": ... |
[STATEMENT]
lemma check_addition_l_check_add:
assumes \<open>(A, B) \<in> fmap_polys_rel\<close> and \<open>(r, r') \<in> sorted_poly_rel O mset_poly_rel\<close>
\<open>(p, p') \<in> Id\<close> \<open>(q, q') \<in> Id\<close> \<open>(i, i') \<in> nat_rel\<close>
\<open>(\<V>', \<V>) \<in> \<langle>var_rel\<ra... | {"llama_tokens": 20589, "file": "PAC_Checker_PAC_Checker", "length": 31} |
"""
Basic docstring explaining example
"""
from __future__ import print_function
#********************
#sf3dmodels libraries
#********************
from sf3dmodels.outflow import OutflowModel #Model functions
import sf3dmodels.utils.units as u #Units
import sf3dmodels.rt as rt #Writing fu... | {"hexsha": "b9b98b9811c9855875edffd32af460e7788db7c2", "size": 3387, "ext": "py", "lang": "Python", "max_stars_repo_path": "examples/disc+outflow/overlap_models.py", "max_stars_repo_name": "andizq/star-forming-regions", "max_stars_repo_head_hexsha": "1105b856c5814635692704b41da66dc6fe38092b", "max_stars_repo_licenses":... |
from contextlib import contextmanager
import copy
import torch
import numpy as np
from base import BaseTrainer
from utils import memory_summary
from model.metric import APMeter, APMeterChallenge
def verbose(epoch, metrics, mode, name="TEST"):
r1, r5, r10, r50 = metrics["R1"], metrics["R5"], metrics["R10"], metr... | {"hexsha": "2799298a8339b52156e6eb97a89c497fa6c95b28", "size": 13481, "ext": "py", "lang": "Python", "max_stars_repo_path": "trainer/trainer.py", "max_stars_repo_name": "dendisuhubdy/collaborative-experts", "max_stars_repo_head_hexsha": "e6db63837537c054723ce00b73264101acc29d39", "max_stars_repo_licenses": ["MIT"], "ma... |
# Copyright 2018 Amazon.com, Inc. or its affiliates. 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.
# A copy of the License is located at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# or in the "license... | {"hexsha": "b16738d1d01ff07be66160901d189bc7da78fd4d", "size": 1664, "ext": "py", "lang": "Python", "max_stars_repo_path": "test/model/deepvar_hierarchical/test_reconciliation_error.py", "max_stars_repo_name": "Xiaoxiong-Liu/gluon-ts", "max_stars_repo_head_hexsha": "097c492769258dd70b7f223f826b17b0051ceee9", "max_stars... |
subroutine chabs (sDCM, l, ine, ne1, ne2, a, z, r1)
! ======================================================================
!
! Determines isospin (p or n) of nucleons after pion absorption.
! This modified version keeps track of the isospin of the original
! first nucleon partner.
!
! Called by: ABS... | {"hexsha": "ae63d51e98839f0579868ec2a8f9f192687f0668", "size": 3692, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "src/StandardDCM/chabs.f90", "max_stars_repo_name": "lanl/generalized-spallation-model", "max_stars_repo_head_hexsha": "4a2f01a873d2e8f2304b8fd1474d43d1ce8d744d", "max_stars_repo_licenses": ["Int... |
/**
* Copyright (C) 2020-present MongoDB, Inc.
*
* This program is free software: you can redistribute it and/or modify
* it under the terms of the Server Side Public License, version 1,
* as published by MongoDB, Inc.
*
* This program is distributed in the hope that it will be useful,
* but W... | {"hexsha": "98fbea63def99abf3b9a201e218e7ee21b991094", "size": 4548, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/mongo/util/ctype_test.cpp", "max_stars_repo_name": "benety/mongo", "max_stars_repo_head_hexsha": "203430ac9559f82ca01e3cbb3b0e09149fec0835", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars... |
# Import Packages
import os, sys, csv, random
import matplotlib.pyplot as plt
import numpy as np
from skimage import io
import seaborn as sns
import pandas as pd
from PIL import Image
from ipywidgets import widgets
from IPython.display import display
# Load in the dataset
path_to_orig_csv = 'https://raw.githubusercon... | {"hexsha": "cfd4b3eb0ede44f3cb31fda4f185c73d21864057", "size": 13848, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/myproject/PlantProject_run.py", "max_stars_repo_name": "jacksonsn5/spring2019-group-project-team_plant", "max_stars_repo_head_hexsha": "a2d720c8e8deef8cda0b7995f3aaecf0f3393aa8", "max_stars_r... |
Welcome to the DavisWiki. Im not sure if you are clear on what this wiki is. It is for Davis, California. So pages about businesses that arent anywhere near here are likely to be removed.
| {"hexsha": "9513db835191421e87ac46e8955f8a16d3c8533c", "size": 188, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "lab/davisWiki/caesarcunaccia.f", "max_stars_repo_name": "voflo/Search", "max_stars_repo_head_hexsha": "55088b2fe6a9d6c90590f090542e0c0e3c188c7d", "max_stars_repo_licenses": ["MIT"], "max_stars_coun... |
"""# Conditional distribution
A `ConditionalDistribution` estimates the conditional distribution p(y|x) for
any x using known conditional distributions for a sample of x's.
The known conditional distributions are objects with the following methods, as
defined in `scipy.stats`:
- `pdf`
- `cdf`
- `ppf`
"""
from .dist... | {"hexsha": "698cd2b50cdbd6dad08a244f6c36c8cc52595ca8", "size": 7596, "ext": "py", "lang": "Python", "max_stars_repo_path": "smoother/conditional.py", "max_stars_repo_name": "dsbowen/smoother", "max_stars_repo_head_hexsha": "cc2dadb34e7452d93226b70e7f9d1db421ee19e4", "max_stars_repo_licenses": ["MIT"], "max_stars_count"... |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
import dataclasses
import logging
import random
from typing import Optional
import numpy as np
import torch
from ml.rl.test.gym.open_ai_gym_environment import ModelType
from ml.rl.torch_utils import stack
from ml.rl.trainin... | {"hexsha": "29634980a64c13e581adc00f25bb2f6c07de5b9b", "size": 11375, "ext": "py", "lang": "Python", "max_stars_repo_path": "ml/rl/test/gym/open_ai_gym_memory_pool.py", "max_stars_repo_name": "sdaulton/ReAgent", "max_stars_repo_head_hexsha": "426d4915dcd90beb3c3781d030c64e748e336351", "max_stars_repo_licenses": ["BSD-3... |
# Class for experimenting various single input DL models at word level
#
# model_type type of the model to be used into ['lstm', 'bidLstm', 'cnn', 'cudnngru', 'cudnnlstm']
# fold_count number of folds for k-fold training (default is 1)
#
import pandas as pd
import numpy as np
import pandas as pd
import s... | {"hexsha": "e3a172b7dd5fc9ea33b0b98ed8e98ae7ff7903d9", "size": 55839, "ext": "py", "lang": "Python", "max_stars_repo_path": "delft/textClassification/models.py", "max_stars_repo_name": "tantikristanti/delft", "max_stars_repo_head_hexsha": "620ddf9e55e13213d2fc9af25b9d01331256d698", "max_stars_repo_licenses": ["Apache-2... |
import cv2
import numpy as np
image = cv2.imread('apple.jpg')
cv2.imshow('Original Image', image)
cv2.waitKey(0)
#Guassian Blurr
Gaussian = cv2.GaussianBlur(image,(7,7),0)
cv2.imshow('Gaussian Blurring', Gaussian)
cv2.imwrite('GaussianResult.jpg',Gaussian)
cv2.waitKey(0)
#Median Blur
median = cv2.medianBlur(image,5... | {"hexsha": "90204ead5b1f6a0040bca0f09bf50c82334e5052", "size": 581, "ext": "py", "lang": "Python", "max_stars_repo_path": "SingleCORE/Smoothing Algorithm/SmoothingAlgorithm.py", "max_stars_repo_name": "RjPatil27/ACA-Project", "max_stars_repo_head_hexsha": "96c60c4e93633a2362cd032dbd555396ded18081", "max_stars_repo_lice... |
# slc_prj.py
import os
import os.path as osp
import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
from matplotlib.colors import Normalize, LogNorm
import astropy.units as au
import astropy.constants as ac
from ..load_sim import LoadSim
from ..io.read_starpar_vtk import read_starpar_vtk
from ..p... | {"hexsha": "c617b567d7c581cbc3430dffa0b3511a435efc33", "size": 7286, "ext": "py", "lang": "Python", "max_stars_repo_path": "pyathena/sf_cloud_rad/slc_prj.py", "max_stars_repo_name": "changgoo/pyathena-1", "max_stars_repo_head_hexsha": "c461ac3390d773537ce52393e3ebf68a3282aa46", "max_stars_repo_licenses": ["MIT"], "max_... |
import cv2
import pandas as pd
import numpy as np
import argparse
import time
from keypoint_utils import KeypointMapper
def make_args():
parser = argparse.ArgumentParser()
parser.add_argument('--video', '-v', default='', help='Enter path to video')
parser.add_argument('--csv_file', '-cf', default='', hel... | {"hexsha": "8b629c35765454aad934249a88287f0406bf70fa", "size": 2934, "ext": "py", "lang": "Python", "max_stars_repo_path": "pose_estimation/visualise/keypoint_signature_mapping.py", "max_stars_repo_name": "Rohansjamadagni/topspin-tracker", "max_stars_repo_head_hexsha": "301abdb4b7eda1596c9e9647cd5f3aa22fa36aea", "max_s... |
import os
import random
import time
import gym
import numpy as np
# use following command to install required package and all the dependencies:
# pip install gym[box2d,atari]
# for windows replace one of the atari files:
# pip install -f https://github.com/Kojoley/atari-py/releases atari_py
def ex_01():
env = ... | {"hexsha": "8c772ac843e0114803a92319eb990b556382f380", "size": 3493, "ext": "py", "lang": "Python", "max_stars_repo_path": "machine_learning_course/lab_s01e09_reinforcement_learning.py", "max_stars_repo_name": "PUTvision/MachineLearningCourse", "max_stars_repo_head_hexsha": "0958615e096bed8b7837c0840e7cc8c1df001cff", "... |
from pylsl import StreamInlet, resolve_stream
import sys
import time
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
from scipy.integrate import simps
from scipy import signal
import eegspectrum
def main(epochTime,fileNumber):
i=0
# first resolve an EEG stream o... | {"hexsha": "4ad84d45f0755b180f8318bb82ff7d6e27999777", "size": 4096, "ext": "py", "lang": "Python", "max_stars_repo_path": "python/pyScript/liblsl-Python-master/Package/Work_dir/ReceiveData.py", "max_stars_repo_name": "MScResearch/EEGprocessing", "max_stars_repo_head_hexsha": "6ad37e80baadc462723fc4c3c7c3cec0c1c88dac",... |
using BandedMatrices, MatrixFactorizations, LinearAlgebra, Test, Random
Random.seed!(0)
@testset "QR tests" begin
for T in (Float64,ComplexF64,Float32,ComplexF32)
A=brand(T,10,10,3,2)
Q,R=qr(A)
@test Matrix(Q)*Matrix(R) ≈ A
b=rand(T,10)
@test mul!(similar(b),Q,mul!(similar... | {"hexsha": "25b7beac5dc4ae253d92163dd10ba30b02c8782b", "size": 6602, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/test_bandedqr.jl", "max_stars_repo_name": "kshyatt/BandedMatrices.jl", "max_stars_repo_head_hexsha": "ecfc1957397064740534d1c17b1752c654796544", "max_stars_repo_licenses": ["BSD-3-Clause-Open-... |
//==================================================================================================
/*!
@file
@copyright 2016 NumScale SAS
Distributed under the Boost Software License, Version 1.0.
(See accompanying file LICENSE.md or copy at http://boost.org/LICENSE_1_0.txt)
*/
//=========================... | {"hexsha": "a56a23ecc9af8c648e3b57d4dd54df759751cc2e", "size": 1160, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "third_party/boost/simd/function/is_less.hpp", "max_stars_repo_name": "SylvainCorlay/pythran", "max_stars_repo_head_hexsha": "908ec070d837baf77d828d01c3e35e2f4bfa2bfa", "max_stars_repo_licenses": ["B... |
import unittest
import numpy
from csep.utils.calc import bin1d_vec, cleaner_range
class TestCleanerRange(unittest.TestCase):
def setUp(self):
self.start = 0.0
self.end = 0.9
self.dh = 0.1
self.truth = [0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]
def test_discrepancy_wi... | {"hexsha": "0dea97039592e245ff2c69a5358a6cb501d3277b", "size": 4693, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/test_calc.py", "max_stars_repo_name": "mherrmann3/pycsep", "max_stars_repo_head_hexsha": "17a1ba2abd7c729f0eee20c08c94b9d3c7bfdc6f", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_c... |
"""
python module for calculating microlensing magnification with finite source size effect
by Sunao Sugiyama
Jan 19, 2022
"""
import numpy as np
from. import fftlog
from scipy.special import j0, j1, jn, gamma
from scipy.special import ellipk as spellipk
from scipy.special import ellipe as spellipe
from scipy.interpol... | {"hexsha": "15b39eb20a19952328d40ce8816690df512e6b29", "size": 10139, "ext": "py", "lang": "Python", "max_stars_repo_path": "magnification/mag_fft.py", "max_stars_repo_name": "git-sunao/fft-extended-source", "max_stars_repo_head_hexsha": "f10ff7a8f55cb899f383cd741ec9e48b148a762f", "max_stars_repo_licenses": ["MIT"], "m... |
import numpy as np
import tensorflow as tf
import tensorflow.keras as tfk
import u_net3
INPUT_DIM = [132, 132, 116]
OUTPUT_DIM = [44, 44, 28]
NO_CHANNELS = 3
NO_CLASSES = 3
NO_FILTERS = 32
unet_model = u_net3.UNet3D(in_channels=NO_CHANNELS, out_classes=NO_CLASSES, img_shape = [INPUT_DIM[0], INPUT_DIM[1], INPUT_DIM[... | {"hexsha": "4d7828ba2409551d3ee264e4577057b1ecfed8f1", "size": 480, "ext": "py", "lang": "Python", "max_stars_repo_path": "main.py", "max_stars_repo_name": "cianmscannell/3DU-Net", "max_stars_repo_head_hexsha": "222382366ab7bdd06f9c6decdd32a468ffca2b29", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_st... |
"""Find naked singles"""
import numpy as np
from typing import List
from ..core.types import Cell, Placement
def find_placements(
grid: np.ndarray,
candidates: np.ndarray,
cells: List[Cell],
) -> List[Placement]:
return [
Placement(cell, digit)
for cell in cells
if len(candida... | {"hexsha": "0ea6fa18c6153596b8892d3f1535c004ee0bfdb4", "size": 400, "ext": "py", "lang": "Python", "max_stars_repo_path": "pyslab/strategies/naked_single.py", "max_stars_repo_name": "benhorsburgh/pyslab", "max_stars_repo_head_hexsha": "d495ca87110f1ac0fb1b49df49024f590bbb6c5c", "max_stars_repo_licenses": ["MIT"], "max_... |
from qunetsim.components import Host
from .computing_host import ComputingHost
from .clock import Clock
from ..utils import DefaultOperationTime
from ..utils.constants import Constants
from ..objects import Operation, Circuit, Layer
import numpy as np
import uuid
import json
from typing import List, Optional, Dict, ... | {"hexsha": "1f7fe8a4993c7501e30af3acbd835a8872e6f0cc", "size": 17958, "ext": "py", "lang": "Python", "max_stars_repo_path": "interlinq/components/controller_host.py", "max_stars_repo_name": "rheaparekh/Distributed_Quantum_Phase_Estimation", "max_stars_repo_head_hexsha": "3441a023fedd113b8b3e68e6c6c07f6642c0344a", "max_... |
"""Noise model reference
Model of a generic piecewise noise, LVDT noise, and Geophone noise are avaliable.
"""
import numpy as np
import scipy.optimize
def piecewise_noise(f, n0, exp=[0], fc=[0]):
"""Piecewise noise specified corner frequencies and exponents
Parameters
----------
f:... | {"hexsha": "9cac00e2eb1d0bdde674bf2d498158386ab32c2f", "size": 3059, "ext": "py", "lang": "Python", "max_stars_repo_path": "archive/kontrol_v1/model/noise.py", "max_stars_repo_name": "terrencetec/kontrol", "max_stars_repo_head_hexsha": "ba6461784e38d01399efeb7a42911259f9254db0", "max_stars_repo_licenses": ["MIT"], "max... |
# -*- coding: utf-8 -*-
#
# k平均法による画像の減色処理
#
# 2015/04/24 ver1.0
#
import numpy as np
from numpy.random import randint
from PIL import Image
# ------------#
# Parameters #
# ------------#
Colors = [2, 3, 5, 16] # 減色後の色数（任意の個数の色数を指定できます）
# k平均法による減色処理
def run_kmeans(pixels, k):
cls = [0] * le... | {"hexsha": "762294ed052c1860d233fceddf13df5e939093c2", "size": 2221, "ext": "py", "lang": "Python", "max_stars_repo_path": "k_means.py", "max_stars_repo_name": "Sabaniki/Evin", "max_stars_repo_head_hexsha": "b68e1ec16505169b7073c274e72d7f8b227051e3", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_sta... |
(**************************************************************************)
(* *)
(* This file is part of octant-proof. *)
(* *)
(* Copyrigh... | {"author": "Orange-OpenSource", "repo": "octant-proof", "sha": "ac920f5d906b7822ec585bc1bf3ec55ee74acddf", "save_path": "github-repos/coq/Orange-OpenSource-octant-proof", "path": "github-repos/coq/Orange-OpenSource-octant-proof/octant-proof-ac920f5d906b7822ec585bc1bf3ec55ee74acddf/octalgo/tSemantics.v"} |
import sympy
import sympy.functions.elementary.exponential as symExp
constant = sympy.symbols('constant')
def get_constant(): return constant
def integrateFunc(func, variable, bounds=None, paramsToSub = {}, conds='none'):
if bounds == None:
func_int = sympy.integrate(func.subs(paramsToSub), variable, conds=conds)... | {"hexsha": "ba41a4e9862a03ff3ef4f16d28c0c8ab940fcbe7", "size": 442, "ext": "py", "lang": "Python", "max_stars_repo_path": "lib/auxlib.py", "max_stars_repo_name": "carlos-faria/Stochastic-Processes", "max_stars_repo_head_hexsha": "2ee57a1029566b606af781ec5d307eb33434fb79", "max_stars_repo_licenses": ["MIT"], "max_stars_... |
# This file was generated by the Julia Swagger Code Generator
# Do not modify this file directly. Modify the swagger specification instead.
mutable struct NetworkWatcherPropertiesFormat <: SwaggerModel
provisioningState::Any # spec type: Union{ Nothing, String } # spec name: provisioningState
function Netwo... | {"hexsha": "ed11b38cac532637ae4253fd9a205d987715b1f1", "size": 1784, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/Network/NetworkWatcherClient/model_NetworkWatcherPropertiesFormat.jl", "max_stars_repo_name": "UnofficialJuliaMirrorSnapshots/Azure.jl-34b51195-c7f2-5807-8107-6ca017e2682c", "max_stars_repo_hea... |
import matplotlib.pyplot as plt
import numpy as np
def Bisection(func, x, y, n):
## func= function,
## x,y = teo guess points
## n= number of iterations
if func(x) * func(y) >= 0:
print("Wrong Input")
return
a = x ### First point
b = y ## second point
for... | {"hexsha": "6d209d90249ac8b62c35c71f9d2fdbd6b6a74073", "size": 3095, "ext": "py", "lang": "Python", "max_stars_repo_path": "Numerical_Methods_Physics/Bisection.py", "max_stars_repo_name": "Simba2805/Computational_Physics_Python", "max_stars_repo_head_hexsha": "be687939c16a1d08066939830ac31ba666a3e1bb", "max_stars_repo_... |
import os
import sys
import numpy as np
import keras
import kaldi_io
import tensorflow as tf
from keras.models import Model
from keras.layers import Input
from learning_to_adapt.model import load_model, create_maml, create_model, create_adapter, create_model_wrapper, set_model_weights
def converted_models_produce_c... | {"hexsha": "773df43ecd7bf32df0a602d8e854a82578c4c9f8", "size": 2488, "ext": "py", "lang": "Python", "max_stars_repo_path": "steps/meta/convert_maml_model.py", "max_stars_repo_name": "ondrejklejch/learning_to_adapt", "max_stars_repo_head_hexsha": "6de0b98370769596da16a1688582925ea2e1fa29", "max_stars_repo_licenses": ["A... |
import cv2
import numpy as np
# -----------------------读取原始图像--------------------------
o = cv2.imread("cc.bmp")
cv2.imshow("original", o)
# 读取轮廓
gray = cv2.cvtColor(o, cv2.COLOR_BGR2GRAY)
ret, binary = cv2.threshold(gray, 127, 255, cv2.THRESH_BINARY)
contours, hierarchy = cv2.findContours(binary, cv2.RETR_LIST, cv2.... | {"hexsha": "f8f45cf04c3d9ed000ed0ddb20867a031d84cd1b", "size": 887, "ext": "py", "lang": "Python", "max_stars_repo_path": "opencv_learn/charpter12/demo_12.36.py", "max_stars_repo_name": "zhangxinzhou/play_game", "max_stars_repo_head_hexsha": "854448f8416b2d3f98bb2c3ed0f7d834a61593de", "max_stars_repo_licenses": ["Apach... |
import numpy as np
from shapely import affinity
from shapely.geometry import Point
from shapely.geometry.base import BaseGeometry
import problem_solution
# resolution of the polygon approximating a circle then scaled to approximate the ellipsis; according to Shapely documentation, a resolution of 16 allows to cover 99... | {"hexsha": "9a34811a6cc4888beeb48b972c8e5a794449914a", "size": 2422, "ext": "py", "lang": "Python", "max_stars_repo_path": "ellipse.py", "max_stars_repo_name": "uuanuo/KnapsackPacking", "max_stars_repo_head_hexsha": "1188d13f4f3071c3ca29b33cceafe4f8f5666691", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null,... |
from matplotlib import pyplot as plt
import numpy as np
from abmarl.sim.components.agent import \
AttackingAgent, BroadcastingAgent, GridMovementAgent, \
PositionObservingAgent, LifeObservingAgent, TeamObservingAgent, AgentObservingAgent
from abmarl.sim.components.state import GridPositionState, BroadcastState... | {"hexsha": "d7824396db0dfa3d30f820e9447b1f9f429c6e76", "size": 6373, "ext": "py", "lang": "Python", "max_stars_repo_path": "abmarl/sim/components/examples/comms_team_battle.py", "max_stars_repo_name": "Leonardo767/Abmarl", "max_stars_repo_head_hexsha": "9fada5447b09174c6a70b6032b4a8d08b66c4589", "max_stars_repo_license... |
[STATEMENT]
lemma the_cat_sspan_Comp_app_\<oo>\<oo>[cat_ss_cs_simps]:
assumes "g = \<oo>\<^sub>S\<^sub>S" and "f = \<oo>\<^sub>S\<^sub>S"
shows "g \<circ>\<^sub>A\<^bsub>\<leftarrow>\<bullet>\<rightarrow>\<^sub>C\<^esub> f = g" "g \<circ>\<^sub>A\<^bsub>\<leftarrow>\<bullet>\<rightarrow>\<^sub>C\<^esub> f = f"
[PRO... | {"llama_tokens": 1406, "file": "CZH_Elementary_Categories_czh_ecategories_CZH_ECAT_SS", "length": 9} |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created Nov 2020
@author: hassi
"""
# Let's start by importing all we need.
import numpy as np
from math import sqrt
print("Ch 2: Quantum gates")
print("-------------------")
# Set up the basic matrices
print("Vector representations of our qubits:")
print("--------... | {"hexsha": "1edf7eb14fd6a5eab2373893232e2d83985cbc43", "size": 2270, "ext": "py", "lang": "Python", "max_stars_repo_path": "Chapter02/ch2_r3_qubit_gates.py", "max_stars_repo_name": "PacktPublishing/Quantum-Computing-in-Practice-with-IBM-Q-Experience", "max_stars_repo_head_hexsha": "91423f8ff1d039b5eb3fd18fc64bbb5967fdd... |
module mod_rmbv_dia
! **********************************************************************
! Author : C. Voemel
! Date of last modification : 7.7.00
! Description : PERFORMS MV MULT. WITH MATRIX IN 'DIA'-STORAGE
! rmbv = Right Multiplication By Vector: y=Ax
! **********************... | {"hexsha": "96fe9d5dddb31cb8912642c0cd7b94b4809e72d7", "size": 29998, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "ext/SOFTWARE/rmbv_dia.f90", "max_stars_repo_name": "ittnas/qsc", "max_stars_repo_head_hexsha": "5e23c7f0c2985d79b966f8301a17c2679a5f2c8d", "max_stars_repo_licenses": ["MIT"], "max_stars_count":... |
from __future__ import print_function, division
import numpy as np
import Nio
fn = "MSG3-SEVI-MSG15-0100-NA-20130521001244.164000000Z-1074164.h5"
opt = Nio.options()
opt.FileStructure = 'advanced'
f = Nio.open_file(fn, "r", options=opt)
#f = Nio.open_file(fn)
print(list(f.variables.keys()))
#print f.groups
#n = 0
#fo... | {"hexsha": "10b657712a5eb9a7ce1bfaaeff859e273f6970bc", "size": 1637, "ext": "py", "lang": "Python", "max_stars_repo_path": "examples/read_h5_compound.py", "max_stars_repo_name": "briandobbins/pynio", "max_stars_repo_head_hexsha": "1dd5fc0fc133f2b8d329ae68929bd3c6c1c5fa7c", "max_stars_repo_licenses": ["Apache-2.0"], "ma... |
% Options for packages loaded elsewhere
\PassOptionsToPackage{unicode}{hyperref}
\PassOptionsToPackage{hyphens}{url}
%
\documentclass[
]{article}
\usepackage{lmodern}
\usepackage{amssymb,amsmath}
\usepackage{ifxetex,ifluatex}
\ifnum 0\ifxetex 1\fi\ifluatex 1\fi=0 % if pdftex
\usepackage[T1]{fontenc}
\usepackage[utf... | {"hexsha": "d1c280823c37eff753ab576139fba2740175efdd", "size": 8901, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "docs/syllabus.tex", "max_stars_repo_name": "acs14007/Math-3094-Spring-2021", "max_stars_repo_head_hexsha": "0a16c4256f8fe08eda841b740d0c02b75005d049", "max_stars_repo_licenses": ["CC-BY-4.0"], "max_... |
subroutine pgenhr(jj)
!! ~ ~ ~ PURPOSE ~ ~ ~
!! this subroutine distributes daily rainfall exponentially within the day
!! ~ ~ ~ INCOMING VARIABLES ~ ~ ~
!! name |units |definition
!! ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~
!! amp_r... | {"hexsha": "def25d6a09ecec6b2d5151fbc9e90f84ce6fb514", "size": 6853, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "src/pgenhr.f", "max_stars_repo_name": "allixender/swatmodel-trusty64", "max_stars_repo_head_hexsha": "dd797671a5b7bd2c9c00f89dc62541a72450be4b", "max_stars_repo_licenses": ["CC0-1.0"], "max_stars_... |
"""PyMC3-ArviZ conversion code."""
import logging
import warnings
from typing import ( # pylint: disable=unused-import
TYPE_CHECKING,
Any,
Dict,
Iterable,
List,
Mapping,
Optional,
Tuple,
Union,
)
import numpy as np
import xarray as xr
from aesara.graph.basic import Constant
from ... | {"hexsha": "8a3f7b46cc7661169b766db44be9149c385a4836", "size": 26498, "ext": "py", "lang": "Python", "max_stars_repo_path": "pymc3/backends/arviz.py", "max_stars_repo_name": "akoscsiszar/pymc3", "max_stars_repo_head_hexsha": "52c842be508eb25a3a9c6df657b92c31f3b561a7", "max_stars_repo_licenses": ["Apache-2.0"], "max_sta... |
try:
import numpy as np
except ImportError:
np = None
try:
import pandas as pd
except ImportError:
pd = None
from tests.numpy.testcase import NumpyBaseTestCase
from clickhouse_driver import errors
ErrorCodes = errors.ErrorCodes
class NullableTestCase(NumpyBaseTestCase):
def test_simple(self):
... | {"hexsha": "3aa3cf2c8619640596a0f410f6b96ea09dcb67d6", "size": 1866, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/numpy/columns/test_nullable.py", "max_stars_repo_name": "fasttrack-solutions/clickhouse-driver", "max_stars_repo_head_hexsha": "676dfb09f74b8b55bfecaedbe70ddc971e1badd7", "max_stars_repo_lic... |
# coding=utf-8
# Copyright 2018 The Dopamine Authors.
#
# 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... | {"hexsha": "0c131475d2a9e91bc0ae6b05e3c5c1cffa1476c6", "size": 19708, "ext": "py", "lang": "Python", "max_stars_repo_path": "dopamine/jax/agents/implicit_quantile/implicit_quantile_agent.py", "max_stars_repo_name": "crawlingcub/dopamine", "max_stars_repo_head_hexsha": "0d155c12f96606188a97a001e02189bdd3723d4d", "max_st... |
import torch
import numpy as np
from torch.autograd import Variable
import torch.nn as nn
import torch.optim
import json
import torch.utils.data.sampler
import os
import glob
import random
import time
from tqdm import tqdm
import configs
import backbone
import data.feature_loader as feat_loader
from data.datamgr impor... | {"hexsha": "ca4d5b5681825330234becdedbe16793e3829df9", "size": 7683, "ext": "py", "lang": "Python", "max_stars_repo_path": "omni-mini/prelinear/test.py", "max_stars_repo_name": "indy-lab/ProtoTransfer", "max_stars_repo_head_hexsha": "90a526bb209160e376b2b8290e99b0f62b240052", "max_stars_repo_licenses": ["MIT"], "max_st... |
import jax
import jax.numpy as jnp
import numpy as onp
from flax import struct
from flax.optim.adam import _AdamParamState
from ..hessian_computation import average_magnitude
from .second_order_optimizer_builder import SecondOrderOptimizerDef
@struct.dataclass
class _AdahessianHyperParams:
learning_rate: onp.ndar... | {"hexsha": "eec1d6241aa7f2bd8fe1d688c61be46c76e8fbb9", "size": 2611, "ext": "py", "lang": "Python", "max_stars_repo_path": "adahessianJax/flaxOptimizer/adahessian.py", "max_stars_repo_name": "nestordemeure/AdaHessianJax", "max_stars_repo_head_hexsha": "20f789093141fdfe998753f85bca6e8b3f5acb81", "max_stars_repo_licenses... |
##################################################################################################
# Integration with UncertainData.jl (sampling from full supports of the furnishing distributions)
#################################################################################################
uvals_x = [UncertainVal... | {"hexsha": "29b6e52fbf6475361aa7792281850c534e5e94c1", "size": 3963, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/causality_tests/integration_uncertaindata/test_uncertaindata_integration_VisitationFrequencyTest.jl", "max_stars_repo_name": "UnofficialJuliaMirrorSnapshots/CausalityTools.jl-5520caf5-2dd7-5c5... |
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