text stringlengths 0 1.25M | meta stringlengths 47 1.89k |
|---|---|
import copy
import cmath
import h5py
import math
import numpy
import scipy.linalg
import sys
import time
from pauxy.walkers.multi_ghf import MultiGHFWalker
from pauxy.walkers.single_det import SingleDetWalker
from pauxy.walkers.multi_det import MultiDetWalker
from pauxy.walkers.multi_coherent import MultiCoherentWalker... | {"hexsha": "fa078b878dbe30e91904cdecca0939654da23115", "size": 20669, "ext": "py", "lang": "Python", "max_stars_repo_path": "pauxy/walkers/handler.py", "max_stars_repo_name": "pauxy-qmc/pauxy", "max_stars_repo_head_hexsha": "1da80284284769b59361c73cfa3c2d914c74a73f", "max_stars_repo_licenses": ["Apache-2.0"], "max_star... |
[STATEMENT]
lemma agg_sum_commute:
fixes f :: "('a,'b::aggregation_order) square"
shows "(\<Sum>\<^sub>k \<Sum>\<^sub>l f (k,l)) = (\<Sum>\<^sub>l \<Sum>\<^sub>k f (k,l))"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. aggregation.sum_0 (\<lambda>k. aggregation.sum_0 (\<lambda>l. f (k, l)) {l. True}) {k. True} =... | {"llama_tokens": 197, "file": "Aggregation_Algebras_Matrix_Aggregation_Algebras", "length": 1} |
function slsharedisp_decindent(nsteps)
%SLSHAREDISP_DECINDENT Decreases the indent of the displayer
%
% $ Syntax $
% - slsharedisp_decindent()
% - slsharedisp_decindent(nsteps)
%
% $ Description $
% - slsharedisp_decindent() decreases the indent by one step.
%
% - slsharedisp_decindent(nsteps) decreases the ind... | {"author": "lmthang", "repo": "nmt.hybrid", "sha": "50d5c025f18ed280ff0fd2e2adce327f4170a2c3", "save_path": "github-repos/MATLAB/lmthang-nmt.hybrid", "path": "github-repos/MATLAB/lmthang-nmt.hybrid/nmt.hybrid-50d5c025f18ed280ff0fd2e2adce327f4170a2c3/code/wordsim/code/sltoolbox_r101/sltoolbox_r101/sltoolbox/utils/slshar... |
from fawkes.models import NetworkPoisson
import pandas as pd
import numpy as np
import h5py as h5
import sys
import os
"""Creates HDF5 datasets of estimates and stability from MCMC samples."""
def import_samples(path, name, date, burn):
print("Importing data for name {} and date {}...".format(name, date))
try... | {"hexsha": "68d17ad80c5bb0f20778af1e2525b149ecc39ba4", "size": 2356, "ext": "py", "lang": "Python", "max_stars_repo_path": "examples/continuous/postprocess_samples.py", "max_stars_repo_name": "cswaney/fawkes", "max_stars_repo_head_hexsha": "90c623476bf62b808947277840a2d5de3c95a7ce", "max_stars_repo_licenses": ["MIT"], ... |
#
# File:
# conmasklc.py
#
# Synopsis:
# Draws contours over a masked lambert conformal map.
#
# Category:
# Contouring over maps.
#
# Author:
# Mary Haley (based on a code by Fred Clare)
#
# Date of initial publication:
# December, 2009
#
# Description:
# This example produces two frames:
# ... | {"hexsha": "aa1e66dd3118d7f198cd6f67ce92070735153b21", "size": 3989, "ext": "py", "lang": "Python", "max_stars_repo_path": "examples/conmasklc.py", "max_stars_repo_name": "yang69can/pyngl", "max_stars_repo_head_hexsha": "78a7040ce9de4b7a442b0c3b5faecccab2f01426", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_co... |
///////////////////////////////////////////////////////////////////////////////
// importance_sampling::generate.hpp //
// //
// Copyright 2009 Erwann Rogard. Distributed under the Boost //
... | {"hexsha": "87f75b0dab49b72ac38c91c1468134f30dfdfaea", "size": 2447, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "importance_sampling/boost/statistics/detail/importance_sampling/random/generate.hpp", "max_stars_repo_name": "rogard/boost_sandbox_statistics", "max_stars_repo_head_hexsha": "16aacbc716a31a9f7bb6c53... |
import os
import numpy as np
from sklearn.naive_bayes import GaussianNB
import timeit
def nbClassifier(X_train, y_train, X_test):
clf = GaussianNB()
start_time = timeit.default_timer()
clf.fit(X_train, y_train)
elapsedTraining = (timeit.default_timer() - start_time) * 1000
start_time = timeit.defa... | {"hexsha": "0aa17d9a56d824bcd0abd5771968ec67537213fb", "size": 501, "ext": "py", "lang": "Python", "max_stars_repo_path": "Python/Classification Algorithms/NB.py", "max_stars_repo_name": "DrMoe/Evaluation-of-satellite-imagery-based-crop-classification", "max_stars_repo_head_hexsha": "ca7324ee6e5c399ea08d2c3ac11497e4ed9... |
#include "App/config.h"
#include <pwd.h>
#include <sys/types.h>
#include <boost/filesystem.hpp>
#include <boost/program_options.hpp>
#include <boost/property_tree/ini_parser.hpp>
#include <iostream>
using namespace std;
void tc::Config::parseConfigFile()
{
// parse the config files
boost::property_tree::ptree ... | {"hexsha": "eb110bd4e0257cbb4e4ca50f7458e45353a56b7d", "size": 2678, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "App/config.cpp", "max_stars_repo_name": "bl4ck5un/minimal-sgx-app", "max_stars_repo_head_hexsha": "970d368a02fc8ec37475cf8c1d42c3d48cbbdbdb", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1... |
//
// Created by calebcintary on 3/20/22.
//
#include <boost/test/unit_test.hpp>
#include "pyplot_cpp/Histogram.hpp"
#include "pyplot_cpp/plt/Properties.hpp"
BOOST_AUTO_TEST_SUITE(Histogram_Test)
BOOST_AUTO_TEST_CASE(Histogram_SimpleShow_Test) {
pyplot_cpp::Histogram hist;
hist.setData({1, 2, 3,... | {"hexsha": "3729e314522a261dae54991deafef1acde3fc166", "size": 760, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "test/Hist_Test.cpp", "max_stars_repo_name": "CalebCintary/pyplot_cpp", "max_stars_repo_head_hexsha": "de33c3e921229e5efd72a7fc4fdb17212edc9aa9", "max_stars_repo_licenses": ["MIT"], "max_stars_count":... |
hours : List Nat
hours = [1..12]
nats : Stream Nat
nats = [0,1..]
| {"hexsha": "869a0c471e7e63b10e60ccc57cf88f08ff00eb54", "size": 67, "ext": "idr", "lang": "Idris", "max_stars_repo_path": "tests/ideMode/ideMode005/Ranges.idr", "max_stars_repo_name": "ska80/idris-jvm", "max_stars_repo_head_hexsha": "66223d026d034578876b325e9fcd95874faa6052", "max_stars_repo_licenses": ["BSD-3-Clause"],... |
using Test
using Documenter
using ExperimentalDesign
tests = ["variance_predictions.jl"]
@testset "ExperimentalDesign" begin
for test in tests
include(test)
end
@testset "Doctests" begin
DocMeta.setdocmeta!(ExperimentalDesign,
:DocTestSetup,
... | {"hexsha": "4e8b2653a1710509512984828ff98d74ef59b831", "size": 583, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/runtests.jl", "max_stars_repo_name": "UnofficialJuliaMirrorSnapshots/ExperimentalDesign.jl-4babbea4-9e7d-11e9-116f-e1ada04bd296", "max_stars_repo_head_hexsha": "841a479c0f9a97ac88ae793703df7f90... |
# QuTiP Lecture: Particle emission from a photon cascade
D. Lukin, Stanford University
In this Jupyter notebook, we use QuTiP: The Quantum Toolbox in Python to study a cascaded three level system excited by a classical pulse. This model system captures the essense of the dynamics of a biexcitonic in a quantum dot [[1... | {"hexsha": "0e6c8f5c701bf5da234afac42cf3402ef49e331c", "size": 51083, "ext": "ipynb", "lang": "Jupyter Notebook", "max_stars_repo_path": "qutip-notebooks-master/examples/photon-emission.ipynb", "max_stars_repo_name": "OliverDudgeon/QSync", "max_stars_repo_head_hexsha": "34adbcf37d501b803aa000b0421ce22fb7934e9b", "max_s... |
import os
import numpy as np
from gym import spaces
import mujoco_py
from envs.gym_kuka_mujoco.envs.assets import kuka_asset_dir
from envs.gym_kuka_mujoco.utils.quaternion import identity_quat, subQuat, quatAdd, mat2Quat
from envs.mujoco.utils.kinematics import forwardKinSite, forwardKinJacobianSite, forwardVelKinSite... | {"hexsha": "18b0b1ca46b2cad84738680166609e91ca427f38", "size": 1699, "ext": "py", "lang": "Python", "max_stars_repo_path": "envs/envs_assistive/alg_test.py", "max_stars_repo_name": "hzm2016/assistive-gym-robosuite", "max_stars_repo_head_hexsha": "5c529f4444cc386383618bfa584341740a8468f9", "max_stars_repo_licenses": ["M... |
/-
Copyright (c) 2017 Johannes Hölzl. All rights reserved.
Released under Apache 2.0 license as described in the file LICENSE.
Authors: Johannes Hölzl
-/
import topology.instances.nnreal
import order.liminf_limsup
import topology.metric_space.lipschitz
/-!
# Extended non-negative reals
-/
noncomputable theory
open cl... | {"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/topology/instances/ennreal.lean"} |
# primitive collision detection helper
import math
import numpy as np
import basis.data_adapter as da
from panda3d.core import NodePath, CollisionNode, CollisionTraverser, CollisionHandlerQueue, BitMask32
from panda3d.core import CollisionBox, CollisionSphere, CollisionPolygon, GeomVertexReader
def gen_box_cdnp(pdnp,... | {"hexsha": "e1d4551754b71a8fffdb2514c120cbb640c0ad15", "size": 8860, "ext": "py", "lang": "Python", "max_stars_repo_path": "modeling/_panda_cdhelper.py", "max_stars_repo_name": "liang324/wrs", "max_stars_repo_head_hexsha": "46eadec355c61a9c7bac1fa0f3cf419b2aac19aa", "max_stars_repo_licenses": ["MIT"], "max_stars_count"... |
import Base.show
export show
function show{F}(io::IO, dr::DimRedux{F})
k,n = size(dr.Ξ)
print(io, "$(typeof(dr)): dimension reduction map over the field $F from $n to $k dimensions")
end
function show{F,DR}(io::IO, sk::Sketch{F,DR})
k,n = size(sk.X)
m,k = size(sk.Y)
s,s = size(sk.Z)
print(io, ... | {"hexsha": "89cdaec1a17cab29a0c2a21d6e55ff9320c94213", "size": 423, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/utilities.jl", "max_stars_repo_name": "udellgroup/LowRankSketch.jl", "max_stars_repo_head_hexsha": "041401ba5fbf1e6ac4e0abc4367b4111e877bcfa", "max_stars_repo_licenses": ["MIT"], "max_stars_coun... |
import os.path
import time
import re
# Core utilities
import SimpleCV
import random
import pickle
import numpy
import layer
def change_image_format(input_image):
r = numpy.array([input_image[:,:,0]])/255.
g = numpy.array([input_image[:,:,1]])/255.
b = numpy.array([input_image[:,:,2]])/255.
output_image... | {"hexsha": "19f021b7e8b479992886adcb30e23cb5ee70e744", "size": 14364, "ext": "py", "lang": "Python", "max_stars_repo_path": "train.py", "max_stars_repo_name": "unionsetde/ToyNN", "max_stars_repo_head_hexsha": "57dc642ee3500996514a86a129714d6cc38e0824", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_s... |
#include <boost/multi_index/composite_key.hpp>
| {"hexsha": "2570a53ba663a4d40415c53fc2d1081772265693", "size": 47, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "src/boost_multi_index_composite_key.hpp", "max_stars_repo_name": "miathedev/BoostForArduino", "max_stars_repo_head_hexsha": "919621dcd0c157094bed4df752b583ba6ea6409e", "max_stars_repo_licenses": ["BSL... |
[STATEMENT]
lemma reduced_row_echelon_form_def':
"reduced_row_echelon_form A =
(
(\<forall>i. is_zero_row i A \<longrightarrow> \<not> (\<exists>j. j>i \<and> \<not> is_zero_row j A)) \<and>
(\<forall>i. \<not> (is_zero_row i A) \<longrightarrow> A $ i $ (LEAST k. A $ i $ k \<noteq> 0) = 1) \<and>
(\<fo... | {"llama_tokens": 1291, "file": "Gauss_Jordan_Rref", "length": 2} |
[STATEMENT]
lemma remove_const_lv_mondaic_steps:
assumes lv: "lv \<R>" and fresh: "(c, 0) \<notin> funas_rel \<R>"
and mon: "monadic \<F>"
and steps: "(s \<cdot> const_subst c, t \<cdot> const_subst c) \<in> (srstep \<F> \<R>)\<^sup>+"
shows "(s, t) \<in> (srstep \<F> \<R>)\<^sup>+"
[PROOF STATE]
proof (pro... | {"llama_tokens": 328, "file": "Rewrite_Properties_Reduction_Rewriting_Rewriting_LLRG_LV_Mondaic", "length": 2} |
"""
python clean_generate_JSON.py
"""
import fnmatch
import cv2
import json
import numpy as np
import os
import base64
from copy import deepcopy
import imgaug as ia
import imgaug.augmenters as iaa
import imageio
import numpy as np
import imgaug as ia
import imgaug.augmenters as iaa
import shutil
# import matplotlib.p... | {"hexsha": "836266187a481112cc833492aba820056c49ada8", "size": 12090, "ext": "py", "lang": "Python", "max_stars_repo_path": "MLModel/LabelMeData/clean_generate_JSON.py", "max_stars_repo_name": "ShubhamKrSingh21/ObjectifyManthan", "max_stars_repo_head_hexsha": "e99a5e69b3bc8d0f8ca66aee61a2185f01bc5bd5", "max_stars_repo_... |
[STATEMENT]
lemma rel_sv[relator_props]: "single_valued R \<Longrightarrow> single_valued (\<langle>R\<rangle>rel)"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. single_valued R \<Longrightarrow> single_valued (\<langle>R\<rangle>rel)
[PROOF STEP]
unfolding rel_def
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. ... | {"llama_tokens": 167, "file": "Collections_ICF_ICF_Autoref", "length": 2} |
import os
from collections import OrderedDict
import numpy as np
np.set_printoptions(suppress=True)
import matplotlib as mpl
from matplotlib import cm
import matplotlib.pyplot as plt
from time import time
from copy import copy
class designer():
def __init__(self,ff,weight,method='D'):
'''
input:
... | {"hexsha": "8d72df36c7c8355ff60411363a5a9ff93d996ac2", "size": 3596, "ext": "py", "lang": "Python", "max_stars_repo_path": "design.py", "max_stars_repo_name": "zhul9311/optimal-design", "max_stars_repo_head_hexsha": "1bf6d3d1879030a679b23113a22d712abfb6fb4c", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null,... |
import os, sys, time
import numpy as np
from psychopy import visual, core, data, logging, event
from .task_base import Task
from .videogame import _onPygletKeyPress, _onPygletKeyRelease, _keyPressBuffer, _keyReleaseBuffer
from ..shared import config, utils
class ButtonPressTask(Task):
BUTTONS = {
'l': [(... | {"hexsha": "0452c40ee3c1b341e2a4140f2a08e5092c041299", "size": 8875, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/tasks/game_controller.py", "max_stars_repo_name": "eddyfortier/task_stimuli", "max_stars_repo_head_hexsha": "b3e0c477775d42b0efa4389531042a80a848fe86", "max_stars_repo_licenses": ["MIT"], "max... |
#!/usr/bin/env python
import os.path
import sys
import numpy as np
from mpl_toolkits.axes_grid1 import make_axes_locatable
from matplotlib.ticker import MultipleLocator, FormatStrFormatter
#import matplotlib.pyplot as plt
from matplotlib import cm
from matplotlib.colors import LinearSegmentedColormap, C... | {"hexsha": "862279c7c0efe902ef6a4402c3274469824cac6e", "size": 15739, "ext": "py", "lang": "Python", "max_stars_repo_path": "Visualization/Null-Space Shuttle/-2/plot_kernel_vp.py", "max_stars_repo_name": "qianchengliu0/Uncertainty_Quantification_Marmousi_Example", "max_stars_repo_head_hexsha": "501775f7ad2c6b83fc12ab7e... |
getlibraryfor{T<:Real}(::Type{T}) = SimplePolyhedraLibrary()
type SimplePolyhedraLibrary <: PolyhedraLibrary
end
type SimplePolyhedron{N, T} <: Polyhedron{N, T}
hrep::Nullable{HRepresentation{N, T}}
vrep::Nullable{HRepresentation{N, T}}
end
function polyhedron{N, T}(hrep::HRepresentation{N, T}, ::SimplePolyhedra... | {"hexsha": "f28780b54c1b6857729286c65e1a206d8ed0039a", "size": 1024, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/simplepolyhedron.jl", "max_stars_repo_name": "JuliaPackageMirrors/Polyhedra.jl", "max_stars_repo_head_hexsha": "a4489180581383b750b1af4e043650f66fa61e76", "max_stars_repo_licenses": ["MIT"], "m... |
import os
import numpy as np
import sklearn.metrics as metrics
from sklearn.externals import joblib
from utils.Results import ResultsSingleRun
class BaseOptionsClassifier:
def __init__(self, name, dir_models_base, options_filename_dataset_training, filename_options_clf):
self.name = name;
self.... | {"hexsha": "86f79ae1b13c43525fffd7a6182749ce9f5b06f2", "size": 3695, "ext": "py", "lang": "Python", "max_stars_repo_path": "learning/BaseClassifier.py", "max_stars_repo_name": "ASMDS/PATREC", "max_stars_repo_head_hexsha": "091df6ec20e0736340a2b2ff9a25ac81bec48259", "max_stars_repo_licenses": ["MIT"], "max_stars_count":... |
#!/usr/bin/env python3
#
# Copyright 2019 ROBOTIS CO., LTD.
#
# 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 applicabl... | {"hexsha": "2d7881c407cbe92a3730cf2ded3c74f3ca1572c4", "size": 15853, "ext": "py", "lang": "Python", "max_stars_repo_path": "pic4rl/pic4rl/trash/pic4rl_env_test.py", "max_stars_repo_name": "PIC4SeRCentre/pic4rl", "max_stars_repo_head_hexsha": "1a1a511042bf332c96750de084d9ac3a302efa12", "max_stars_repo_licenses": ["MIT"... |
import numpy as np
import tensorflow as tf
class TFGenerator(object):
def __init__(self, data, labels, idxs=None, batch_size=32, shuffle=True, prefetch=4, map_fn=None, one_shot=False, data_dtype=tf.float32, labels_dtype=tf.float32):
"""
Class to create a tf.data.Dataset given a set of data and ass... | {"hexsha": "acbb65688920776ac0fec75aafaf40257338ca89", "size": 4440, "ext": "py", "lang": "Python", "max_stars_repo_path": "tf_generator.py", "max_stars_repo_name": "geometrikal/IIC-1", "max_stars_repo_head_hexsha": "6b337670d58fcb5c34b6ec34236ea2ed472e1d92", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null,... |
import numpy as np
import pandas as pd
STDoSD = lambda std, n: std/(n**(1/2))
#print(STDoSD(1.36,50))
UCL_nsig = lambda x, nsig, std, n: x + nsig*(STDoSD(std,n))
LCL_nsig = lambda x, nsig, std, n: x - nsig*(STDoSD(std,n))
#print(UCL_nsig(420, 3, 30, 25))
#print(LCL_nsig(420, 3, 30, 25))
UCL_x = lambda MoSM, MF, ARo... | {"hexsha": "83ce2cab421be2e7eb244d2de2164642b60aada1", "size": 1107, "ext": "py", "lang": "Python", "max_stars_repo_path": "OM300Ch6.py", "max_stars_repo_name": "JoshChima/OM300_Mastered", "max_stars_repo_head_hexsha": "db17c8ca1eb1045b8b96fde34f193767d038629f", "max_stars_repo_licenses": ["MIT"], "max_stars_count": nu... |
[STATEMENT]
lemma not_cong_is_anga1:
assumes "\<not> A B C CongA A' B' C'" and
"A B C AngAcute a"
shows "\<not> A' B' C' AngAcute a"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. \<not> A' B' C' AngAcute a
[PROOF STEP]
using assms(1) assms(2) is_anga_conga
[PROOF STATE]
proof (prove)
using this:
\<not> A B ... | {"llama_tokens": 257, "file": "IsaGeoCoq_Tarski_Neutral", "length": 2} |
import numpy as np
from sklearn.linear_model import LinearRegression
from pyuplift import BaseModel
class Cadit(BaseModel):
"""The class which implements the cadit approach [1].
+----------------+-----------------------------------------------------------------------------------+
| **Parameters** | | **m... | {"hexsha": "f53fb24ff645da5051da99b9b66355b89f81b555", "size": 4289, "ext": "py", "lang": "Python", "max_stars_repo_path": "pyuplift/variable_selection/cadit.py", "max_stars_repo_name": "duketemon/pyuplift", "max_stars_repo_head_hexsha": "33daa0768ff333387cb8223ebfaedaffa57de335", "max_stars_repo_licenses": ["MIT"], "m... |
from __future__ import print_function, division
import sys
import os
import torch
import numpy as np
import random
import csv
from torch.utils.data import Dataset, DataLoader
from torchvision import transforms, utils
from torch.utils.data.sampler import Sampler
from future.utils import raise_from
from pycocotools.coco... | {"hexsha": "797cbb7f5ce496e3c52ee403e55492cec83bb1d0", "size": 26977, "ext": "py", "lang": "Python", "max_stars_repo_path": "MULTITASK_FILES/RETINANET_FILES/src/pytorch-retinanet/retinanet/dataloader.py", "max_stars_repo_name": "egoodman92/semi-supervised-surgery", "max_stars_repo_head_hexsha": "42f7af7e707e71ecd64b9f2... |
from ctapipe.instrument.optics import OpticsDescription
from astropy import units as u
import pytest
def test_guess_optics():
from ctapipe.instrument import guess_telescope
answer = guess_telescope(1855, 28.0 * u.m)
od = OpticsDescription.from_name(answer.name)
assert od.equivalent_focal_length.to_v... | {"hexsha": "d752dc2b4572a5fbbc8f667609c9bcfaba1f4e3c", "size": 827, "ext": "py", "lang": "Python", "max_stars_repo_path": "ctapipe/instrument/tests/test_optics.py", "max_stars_repo_name": "Pluto9th/ctapipe", "max_stars_repo_head_hexsha": "8c4faa674a1949210cbda8cb9e2413dd6362afea", "max_stars_repo_licenses": ["BSD-3-Cla... |
import cv2
from PIL import Image
import os
import sys
import torch
import argparse
import numpy as np
from modules import utils
from train import train
from data import VideoDataset
from torchvision import transforms
import data.transforms as vtf
from models.retinanet import build_retinanet
from gen_dets import gen_d... | {"hexsha": "ae110fe2c8a060989aff5d3e8e6785d0216768a1", "size": 22220, "ext": "py", "lang": "Python", "max_stars_repo_path": "inference_vis.py", "max_stars_repo_name": "salmank255/ROADSlowFast", "max_stars_repo_head_hexsha": "e939d8f79fe3eb6f3dd32e967a34530d00f45c8e", "max_stars_repo_licenses": ["Apache-2.0"], "max_star... |
import simpy
import sys
sys.path #sometimes need this to refresh the path
import matplotlib.pyplot as plt
import matplotlib.animation as animation
import torch
import numpy as np
from tabulate import tabulate
import pandas as pd
from pandas import DataFrame
import machine
import sequencing
import job_crea... | {"hexsha": "f628b0fec4facb2d3d6abdc0c632fe58c0b23e30", "size": 6699, "ext": "py", "lang": "Python", "max_stars_repo_path": "JSP/Thesis_tournament.py", "max_stars_repo_name": "Reallyhardtocreateaname/PhD-Thesis-Projects", "max_stars_repo_head_hexsha": "de0878f51ec66c9905227b2d260ffaa0b4946f1f", "max_stars_repo_licenses"... |
# coding: utf-8
# # Des dates qui font des nombres premiers ?
#
# Ce petit [notebook Jupyter](https://www.jupyter.org/), écrit en [Python](https://www.python.org/), a pour but de résoudre la question suivante :
#
# > *"En 2017, combien de jours ont leur date qui est un nombre premier ?"*
#
# Par exemple, en 2017, ... | {"hexsha": "77aa6f08f3870d645ac9f2b793f325e5189f5be5", "size": 12996, "ext": "py", "lang": "Python", "max_stars_repo_path": "simus/Des_dates_qui_font_des_nombres_premiers.py", "max_stars_repo_name": "IEWbgfnYDwHRoRRSKtkdyMDUzgdwuBYgDKtDJWd/narnt", "max_stars_repo_head_hexsha": "0eda13a7b8663e218b4fe2e06a974b99db9ff166"... |
[STATEMENT]
lemma verticesFrom_nth: "distinct (vertices f) \<Longrightarrow> d < length (vertices f) \<Longrightarrow>
v \<in> \<V> f \<Longrightarrow> (verticesFrom f v)!d = f\<^bsup>d\<^esup> \<bullet> v"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. \<lbrakk>distinct (vertices f); d < |vertices f|; v \<in> \<V... | {"llama_tokens": 8628, "file": "Flyspeck-Tame_FaceDivisionProps", "length": 46} |
import types
import numpy as np
import torch
from torch.nn.functional import mse_loss
from all.core import State
from ._agent import Agent
from .a2c import A2CTestAgent
from .utils import make_grads_observable, flatten_grads
class QMCPG(Agent):
"""
Quasi Monte Carlo Policy Gradient (QMCPG).
Args:
... | {"hexsha": "bbaacbabddaf2886e366da08b67f00c20115f33c", "size": 6613, "ext": "py", "lang": "Python", "max_stars_repo_path": "all/agents/qmcpg.py", "max_stars_repo_name": "kstoneriv3/autonomous-learning-library-with-rrpg", "max_stars_repo_head_hexsha": "11f6dd4e72b4143944cf972a8f938406113d860f", "max_stars_repo_licenses"... |
[STATEMENT]
lemma pequiv_pr_trans[intro,trans]:
"\<lbrakk> a \<simeq> b; b \<sqsubseteq> c \<rbrakk> \<Longrightarrow> a \<sqsubseteq> c"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. \<lbrakk>a \<simeq> b; b \<sqsubseteq> c\<rbrakk> \<Longrightarrow> a \<sqsubseteq> c
[PROOF STEP]
unfolding pequiv_def refines_de... | {"llama_tokens": 239, "file": "pGCL_Algebra", "length": 2} |
(* Title: CTT/Arith.thy
Author: Lawrence C Paulson, Cambridge University Computer Laboratory
Copyright 1991 University of Cambridge
*)
section {* Elementary arithmetic *}
theory Arith
imports Bool
begin
subsection {* Arithmetic operators and their definitions *}
definition
add :: "[i,i]\<Righ... | {"author": "Josh-Tilles", "repo": "isabelle", "sha": "990accf749b8a6e037d25012258ecae20d59ca62", "save_path": "github-repos/isabelle/Josh-Tilles-isabelle", "path": "github-repos/isabelle/Josh-Tilles-isabelle/isabelle-990accf749b8a6e037d25012258ecae20d59ca62/src/CTT/Arith.thy"} |
import numpy as np
import open3d as o3d
import pybullet as p
# some codes are copied from https://github.com/ethz-asl/vgn.git
class CameraIntrinsic(object):
"""Intrinsic parameters of a pinhole camera model.
Attributes:
width (int): The width in pixels of the camera.
height(int): The height... | {"hexsha": "47d2e4b3718daee35dea4d034db16e639af039e1", "size": 4710, "ext": "py", "lang": "Python", "max_stars_repo_path": "utils/perception.py", "max_stars_repo_name": "guodashun/plug-in", "max_stars_repo_head_hexsha": "d805f57af12bbf94a17a52e518903a02c267f4df", "max_stars_repo_licenses": ["MIT"], "max_stars_count": n... |
import numpy as np
import numpy.testing as npt
from qspace.bases import spf
from qspace.sampling import sphere, space
from numpy.testing import (assert_, assert_equal, assert_almost_equal,
assert_array_almost_equal, run_module_suite,
assert_array_equal)
def test_s... | {"hexsha": "54d6b578742251789eac72c11d5ddca82346d226", "size": 2401, "ext": "py", "lang": "Python", "max_stars_repo_path": "qspace/bases/tests/test_spf.py", "max_stars_repo_name": "ecaruyer/qspace", "max_stars_repo_head_hexsha": "5aa6b714bb16e7e769c5dd4ecdd91591f46e04a8", "max_stars_repo_licenses": ["Unlicense"], "max_... |
@with_kw mutable struct MomentumParameters{T <: AbstractFloat} <: AbstractPolicyParameters
μ::T = 0.9
ϵ::T = 1e-3
end
abstract type MomentumTrait end
abstract type Classical <: MomentumTrait end
abstract type Nesterov <: MomentumTrait end
struct Momentum{T <: MomentumTrait} <: AbstractBoosting
params::Mom... | {"hexsha": "583a34c903141e3f8ab8560fb4158d2eb62bf0c4", "size": 1342, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/boosting/momentum.jl", "max_stars_repo_name": "pologrp/POLO.jl", "max_stars_repo_head_hexsha": "c866563d02d060c733e3e6da5ccd57a96f85db61", "max_stars_repo_licenses": ["MIT"], "max_stars_count":... |
"""
Created on Sun march 27 00:51:11 2020
@author: Gautam Pala
"""
import os
import numpy as np
import matplotlib.image as mpimg
import matplotlib.pyplot as plt
from PIL import Image
import glob
import time
import h5py
import pandas as pd
from keras.models import Sequential
from keras.layers import Conv2D, MaxPooling2... | {"hexsha": "1fee06ef550d5300ef45fbb645c6130e52707756", "size": 6903, "ext": "py", "lang": "Python", "max_stars_repo_path": "cnn_emotion.py", "max_stars_repo_name": "JayeshKriplani/Deep-Learning-based-Emotion_Detection", "max_stars_repo_head_hexsha": "6d77421a94ae73331e8da9adb3604bddf75e909a", "max_stars_repo_licenses":... |
# Copyright (c) Facebook, Inc. and its affiliates
# Copyright (c) MTRF authors
#!/usr/bin/env python
import os
# os.system('chmod +x sawyer_read_angles.py')
import rospy
from std_msgs.msg import String
from rospy_tutorials.msg import Floats
from rospy.numpy_msg import numpy_msg
from rospy_message_converter import mess... | {"hexsha": "af191b602de79e6874d1965ba9742161c52c012b", "size": 1438, "ext": "py", "lang": "Python", "max_stars_repo_path": "MTRF/r3l/r3l/sawyer_hardware/sawyer_read_angles.py", "max_stars_repo_name": "facebookresearch/MTRF", "max_stars_repo_head_hexsha": "2fee8f3f1c2150fcecc2db2fa9e122a664a72d72", "max_stars_repo_licen... |
/**
* @file tests/main_tests/emst_test.cpp
* @author Manish Kumar
*
* Test RUN_BINDING() of emst_main.cpp.
*
* mlpack is free software; you may redistribute it and/or modify it under the
* terms of the 3-clause BSD license. You should have received a copy of the
* 3-clause BSD license along with mlpack. If no... | {"hexsha": "3cf3a8802a46ce4ae6830886f424e26dae7be4ca", "size": 3432, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/mlpack/tests/main_tests/emst_test.cpp", "max_stars_repo_name": "oblanchet/mlpack", "max_stars_repo_head_hexsha": "e02ab3be544694294d2f73bd12a98d0d162ef3af", "max_stars_repo_licenses": ["BSD-3-Cl... |
[STATEMENT]
lemma absc_distr_self:
"MDP.MC.T (absc cfg) = distr (MDP.MC.T cfg) MDP.MC.S (smap absc)" if "cfg \<in> valid_cfg"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. MDP.MC.T (absc cfg) = distr (MDP.MC.T cfg) (stream_space (count_space UNIV)) (smap absc)
[PROOF STEP]
using \<open>cfg \<in> _\<close>
[PROOF ... | {"llama_tokens": 16430, "file": "Probabilistic_Timed_Automata_PTA", "length": 71} |
#!/usr/bin/python
import numpy as np
import deepSNP
import deepSNP_utils
def snp_pos_feature_matrix(read, window_start):
"""
Creates vector of zeros, except 1 at SNP position
:param read: pysam read
:param window_start: starting position of feature window
:return: (WINDOW_SIZE x 1) binary matrix... | {"hexsha": "c5164552d873550fea04536d0bb5515130986354", "size": 3180, "ext": "py", "lang": "Python", "max_stars_repo_path": "snp_pos_feature.py", "max_stars_repo_name": "brianhill11/deepSNP", "max_stars_repo_head_hexsha": "4979fbf84fe3e68d76b0054ba031e084d494411d", "max_stars_repo_licenses": ["MIT"], "max_stars_count": ... |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
#####
Tools
#####
*Created on Thu Jun 7 14:45 2017 by A. Pahl*
Helper Tools acting on individual data..
"""
import os
import os.path as op
import sys
import glob
from collections import Counter, namedtuple
import yaml
import pandas as pd
import numpy as np
import ... | {"hexsha": "acc3fc6c2acc0495d8d12f612f8db50f46203a8d", "size": 11059, "ext": "py", "lang": "Python", "max_stars_repo_path": "cellpainting2/tools.py", "max_stars_repo_name": "apahl/cellpainting2", "max_stars_repo_head_hexsha": "b0fd4adbca804b136e3dca9c1b466a6efb49cbd3", "max_stars_repo_licenses": ["MIT"], "max_stars_cou... |
# example of neural net functions in Econometrics.jl
using Econometrics, Glob
# generate draws from linear regression model, and
# fitted coefficients from correct model, plus
# quadratic and cubic models (irrelevant regressors)
# and 5 pure noise statistics
function make_simdata(reps=100000)
n = 30
simdata = ... | {"hexsha": "a55d0937f546ccb0da670c2679c4fa508ce932f1", "size": 1895, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "examples/neural_net_example.jl", "max_stars_repo_name": "UserQuestions/Econometrics.jl", "max_stars_repo_head_hexsha": "f9db0ca3046e7c5328f085581a12b1c733cf9bcf", "max_stars_repo_licenses": ["MIT"]... |
# Copyright 2017 Amir Hossein Delgoshaie, amirdel@stanford.edu
#
# Permission to use, copy, modify, and/or distribute this software for any purpose with or without fee
# is hereby granted, provided that the above copyright notice and this permission notice appear in all
# copies.
#
# THE SOFTWARE IS PROVIDED "AS IS" AN... | {"hexsha": "bb957ed6d82e594a1e056c15581a165ccbae810e", "size": 3464, "ext": "py", "lang": "Python", "max_stars_repo_path": "plot_scripts/plot_test_uncorrelated_cees.py", "max_stars_repo_name": "amirdel/dispersion-continua", "max_stars_repo_head_hexsha": "2e1f7a3fbfcdc0b27c546cb0ae51a628a926ad60", "max_stars_repo_licens... |
% !TeX spellcheck = en_GB
\chapter{Results}
\section{LWC and LWP from MEPS}%\hfill}
\label{app:LWP_MEPS}
%%% image LWC Retrieval MEPS comparison %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\begin{figure}[t]%\ContinuedFloat
\centering
% 21/12
\begin{subfigure}[t]{0.85\textwidth}
\includegraphics[trim={.5cm 0.5cm 27cm .... | {"hexsha": "5c4e943779929119f4481b28e4bc05b4c33b918f", "size": 1934, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "thesis_full/Appendix/LWC_MEPS.tex", "max_stars_repo_name": "franzihe/Latex_thesis", "max_stars_repo_head_hexsha": "128284a01155bdc28b3e9374e538a07a1e5722c5", "max_stars_repo_licenses": ["MIT"], "max... |
\documentclass[11pt,addpoints,answers]{exam}
%\documentclass[11pt]{article}
\usepackage[margin=1in]{geometry}
\usepackage{amsmath, amsfonts}
\usepackage{enumerate}
\usepackage{graphicx}
\usepackage{titling}
\usepackage{url}
\usepackage{xfrac}
% \usepackage{fancyhdr} % CONFLICTS with the exam class
\usepackage{geometry}... | {"hexsha": "415cdfa8d4f4dcd2585a9c772ae07de4517669a5", "size": 61208, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "assignments/solutions/hw1/problems/latex_template/hw1.tex", "max_stars_repo_name": "punit-bhatt/cmu-10601-intro-to-ml", "max_stars_repo_head_hexsha": "a4b7bdb27a388a2996e3f4dac99f34156ff9f01e", "ma... |
# author: Fernando Paolo;
# modify: xin luo, 2021.8.10.
"""
des: merges several HDF5 files into a single file or multiple larger files.
example
merge.py ifiles_*.h5 -o ofile.h5
merge.py ifiles_*.h5 -o ofile.h5 -m 5 -n 5
notes
- The parallel option (-n) only works for multiple outputs (-m)!
- If no 'k... | {"hexsha": "d4647cf450266c851dd68cf8135af3cd1c89201d", "size": 6883, "ext": "py", "lang": "Python", "max_stars_repo_path": "utils/merge_files.py", "max_stars_repo_name": "xinluo2018/Glacier-Change-for-RGI1305", "max_stars_repo_head_hexsha": "c0850d7c681181a2046d87c7ede566050cc627b6", "max_stars_repo_licenses": ["MIT"],... |
[STATEMENT]
lemma "\<not> no_spoofing_iface
(Iface ''eth0'')
[Iface ''eth0'' \<mapsto> [(ipv4addr_of_dotdecimal (192,168,0,0), 24)]]
[Rule (MatchAnd (Match (Src (IpAddrNetmask (ipv4addr_of_dotdecimal (192,168,0,0)) 24))) (MatchNot (Match (IIface (Iface ''eth0''))))) action.Drop,
... | {"llama_tokens": 317, "file": "Iptables_Semantics_Primitive_Matchers_No_Spoof", "length": 1} |
@testset "TinayHanabiEnv" begin
env = TinyHanabiEnv()
RLBase.test_interfaces!(env)
RLBase.test_runnable!(env)
end
| {"hexsha": "23c1ae2043b8605ee60b06807135dbcf6e121629", "size": 129, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/ReinforcementLearningEnvironments/test/environments/examples/tiny_hanabi.jl", "max_stars_repo_name": "LaarsOman/ReinforcementLearning.jl", "max_stars_repo_head_hexsha": "b04e3f192e71418dbca49633... |
# (c) 2016 Gregor Mitscha-Baude
import nanopores
from matplotlib import pyplot as plt
from itertools import product
from folders import fields, FIGDIR
#fields.update()
r = 0.11
#D2D = fields.get_field("pugh_diff2D_test", "D")[0]
#data = fields.get_fields("pugh_diff3D_cross", bulkbc=True, rMolecule=2.0779)
data = fields... | {"hexsha": "9cc4c0f46284a757ad732ad85356efd15515c397", "size": 3713, "ext": "py", "lang": "Python", "max_stars_repo_path": "scripts/pughpore/plot_diff3D.py", "max_stars_repo_name": "jhwnkim/nanopores", "max_stars_repo_head_hexsha": "98b3dbb5d36464fbdc03f59d224d38e4255324ce", "max_stars_repo_licenses": ["MIT"], "max_sta... |
from typing import *
import torch
import torch.optim as optim
import numpy as np
from allennlp.data import Instance
from allennlp.data.fields import TextField, SequenceLabelField
from allennlp.data.dataset_readers import DatasetReader
from allennlp.common.file_utils import cached_path
from allennlp.data.token_indexers ... | {"hexsha": "08882aebfbcb2cdbe7c185f3415957abeeb27d13", "size": 7036, "ext": "py", "lang": "Python", "max_stars_repo_path": "model/hybrid_model/predicator.py", "max_stars_repo_name": "ShawnLYU/A-Hybrid-Approach-of-Insincere-Questions-Detection", "max_stars_repo_head_hexsha": "0bc76c3fc186245f83e665732dac53a1af3f3fbf", "... |
import numpy as np
import matplotlib.pyplot as pl
import healpy as hp
import pickle
def graticule(ax):
ax.axhline(0.0, color='k')
for i in range(2):
ax.axhline(30+30*i, color='k', linestyle=':')
ax.axhline(-30-30*i, color='k', linestyle=':')
for i in range(12):
ax.axvline(-180+30*i,... | {"hexsha": "5f3478e35fe26b3122c6d10e04895ba6622e0946", "size": 12879, "ext": "py", "lang": "Python", "max_stars_repo_path": "doplot_iipeg.py", "max_stars_repo_name": "aasensio/bayesDI", "max_stars_repo_head_hexsha": "4ddad57d89c3512b4c4ee5684ddc5608060ebdec", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2, "m... |
# -*- coding: utf-8 -*-
from __future__ import absolute_import
from pmdarima.arima._arima import C_is_not_finite
import numpy as np
def test_not_finite():
assert C_is_not_finite(np.nan)
assert C_is_not_finite(np.inf)
assert not C_is_not_finite(5.)
| {"hexsha": "213285dcd8e57b152df4d8650dffd9668d12aa4b", "size": 265, "ext": "py", "lang": "Python", "max_stars_repo_path": "pmdarima/arima/tests/test_c_arima.py", "max_stars_repo_name": "Saravji/pmdarima", "max_stars_repo_head_hexsha": "7f42e36beb888d9e1e7e41b0d9c9f7419c730a3a", "max_stars_repo_licenses": ["MIT"], "max_... |
# Authors: Alexandre Gramfort <alexandre.gramfort@inria.fr>
# Mathieu Blondel <mathieu@mblondel.org>
# Olivier Grisel <olivier.grisel@ensta.org>
# Andreas Mueller <amueller@ais.uni-bonn.de>
# Joel Nothman <joel.nothman@gmail.com>
# Hamzeh Alsalhi <ha258@cornell.edu>
# Licens... | {"hexsha": "43ab31d5782ecde55cfe379656b20dd60a7ff259", "size": 29644, "ext": "py", "lang": "Python", "max_stars_repo_path": "preprocessing/_label.py", "max_stars_repo_name": "jessica-tu/jupyter", "max_stars_repo_head_hexsha": "917e02bc29e0fa06bd8adb25fe5388ac381ec829", "max_stars_repo_licenses": ["PSF-2.0", "Apache-2.0... |
'''
Recurrent Deterministic Policy Gradient (DDPG with LSTM network)
Update with batch of episodes for each time, so requires each episode has the same length.
'''
import math
import random
import gym
import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
f... | {"hexsha": "c324c1b90ad31344c6d8cdb5cfcc631c37a8eb00", "size": 11370, "ext": "py", "lang": "Python", "max_stars_repo_path": "rdpg.py", "max_stars_repo_name": "chagri/SOTA-RL-Algorithms", "max_stars_repo_head_hexsha": "58b416e7c706d8426dc402482e72ca7283568e71", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count... |
(*
* Copyright 2014, General Dynamics C4 Systems
*
* This software may be distributed and modified according to the terms of
* the GNU General Public License version 2. Note that NO WARRANTY is provided.
* See "LICENSE_GPLv2.txt" for details.
*
* @TAG(GD_GPL)
*)
theory Fastpath_C
imports
SyscallArgs_C
Del... | {"author": "SEL4PROJ", "repo": "jormungand", "sha": "bad97f9817b4034cd705cd295a1f86af880a7631", "save_path": "github-repos/isabelle/SEL4PROJ-jormungand", "path": "github-repos/isabelle/SEL4PROJ-jormungand/jormungand-bad97f9817b4034cd705cd295a1f86af880a7631/case_study/l4v/proof/crefine/Fastpath_C.thy"} |
Require Import FunctionalExtensionality.
Require Import ProofIrrelevance.
Require Import Program.
Require Import Category.
Require Import Isomorphism.
Require Import Coq.
Require Import Co.
Set Universe Polymorphism.
Class Terminal `{Category} := {
terminal : object;
receivesAll o : o → terminal;
receivesAllUni... | {"author": "konne88", "repo": "category-theory", "sha": "883c4edd35ad47c82300315d1cd5c7f9238bede6", "save_path": "github-repos/coq/konne88-category-theory", "path": "github-repos/coq/konne88-category-theory/category-theory-883c4edd35ad47c82300315d1cd5c7f9238bede6/Construction/Terminal.v"} |
import numpy as np
from collections import defaultdict
import matplotlib.pyplot as plt
from multiprocessing.dummy import Pool as ThreadPool
import multiprocessing
from time import sleep
import pickle
import utils
import search.csp as csp
import search.viz as viz
#LEFT = 0
#RIGHT = 1
#UP = 2
#DOWN = 3
#ANY = -1
LEFT =... | {"hexsha": "d8949458bd93e20c4665d5eb27f0c488da5c9513", "size": 13278, "ext": "py", "lang": "Python", "max_stars_repo_path": "practice/attempt3.py", "max_stars_repo_name": "bshishov/HashCode2019", "max_stars_repo_head_hexsha": "026ab14fd22d269deec6d809d4426e78a9417677", "max_stars_repo_licenses": ["MIT"], "max_stars_cou... |
"""
require(package::Symbol; [fun_name]::String="", [explanation]::String="")
Helper method to check for optional packages and print an error message.
### Input
- `package` -- symbol (the package name)
- `fun_name` -- (optional; default: `""`) name of the function that requires
the pack... | {"hexsha": "93d57018579e0979709ad83f6eb3dcb8ba0e2e92", "size": 2180, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/Utils/require.jl", "max_stars_repo_name": "nablabits/LazySets.jl", "max_stars_repo_head_hexsha": "e839322ae970e5b61271b709f8a865184b32c8e5", "max_stars_repo_licenses": ["MIT"], "max_stars_count... |
from pythonequipmentdrivers import Scpi_Instrument
import numpy as np
from typing import Union, Tuple
from pathlib import Path
class Lecroy_WR8xxx(Scpi_Instrument):
"""
Lecroy_WR8xxx(address)
address : str, address of the connected oscilloscope
object for accessing basic functionallity of the Lecroy... | {"hexsha": "689ce5191c667ba849a3126c97144e9a0ed98e83", "size": 30083, "ext": "py", "lang": "Python", "max_stars_repo_path": "pythonequipmentdrivers/oscilloscope/Lecroy_WR8xxx.py", "max_stars_repo_name": "admleman/PythonEquipmentDrivers", "max_stars_repo_head_hexsha": "1e1fbf96ae372757ad90339af5863ab64daef2a0", "max_sta... |
import numpy as np
import collections
from .penalized_regression import PenalizedRegression as PLR
from . import elbo as elbo_py
from ..models.normal_means_ash_scaled import NormalMeansASHScaled
RES_FIELDS = ['theta', 'coef', 'prior', 'residual_var', 'intercept', 'elbo_path', 'outer_elbo_path', 'obj_path', 'niter']
c... | {"hexsha": "b2c088fdab7b05823959ad5d81a8b1ff896bfc4a", "size": 6280, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/mrashpen/inference/ebfit.py", "max_stars_repo_name": "banskt/mr-ash-pen", "max_stars_repo_head_hexsha": "a9e574f66ce64265bff22cf0661d23a5706e4515", "max_stars_repo_licenses": ["MIT"], "max_sta... |
"""
Class and function definitions for word-based modifications
"""
import json
import torch
import numpy as np
from tqdm import tqdm
class WordBasedModifications():
def __init__(self, data_args):
self.data_args = data_args
# Function for modifying string json to integer json
# https://st... | {"hexsha": "2c2f05400ce28fbd8a5ea58be8fe612e04ae65e1", "size": 2477, "ext": "py", "lang": "Python", "max_stars_repo_path": "archive/synthetic_language_modifications_utils.py", "max_stars_repo_name": "princeton-nlp/MultilingualAnalysis", "max_stars_repo_head_hexsha": "b0d61c93c0c020a698a06264897dde14c9db471c", "max_star... |
# -*- coding: UTF-8 -*-
# !/usr/bin/python3
"""
Model for SEMI-CLDC task
"""
# ************************************************************
# Imported Libraries
# ************************************************************
import math
import numpy as np
import sympy
from collections import defaultdict
import torch
i... | {"hexsha": "58e791d5e78b4286e54417a7c1c77c5ef1361621", "size": 31648, "ext": "py", "lang": "Python", "max_stars_repo_path": "nn_model/semicldc_model.py", "max_stars_repo_name": "onlyrico/mling_sdgms", "max_stars_repo_head_hexsha": "ef6015d1a815a317f16fa1e42cbb048e4fe443f7", "max_stars_repo_licenses": ["MIT"], "max_star... |
from __future__ import division
from .checks import *
from pymc3 import *
from numpy import array, inf
from nose.tools import raises
class DistTest(Continuous):
def __init__(self, a, b, *args, **kwargs):
super(DistTest, self).__init__(*args, **kwargs)
self.a = a
self.b = b
def logp(s... | {"hexsha": "b3cd04d244f633c5515da3c26c97372bcaf75764", "size": 1412, "ext": "py", "lang": "Python", "max_stars_repo_path": "pymc3/tests/test_distribution_defaults.py", "max_stars_repo_name": "MichielCottaar/pymc3", "max_stars_repo_head_hexsha": "f37198653e7d09881e7bc411cbd10fffbab442c2", "max_stars_repo_licenses": ["Ap... |
[STATEMENT]
lemma F_base_aux: "{l. length l=n \<and> valid l} = {replicate n B}" if "n > 0" "n < m"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. {l. length l = n \<and> local.valid l} = {replicate n B}
[PROOF STEP]
using that
[PROOF STATE]
proof (prove)
using this:
0 < n
n < m
goal (1 subgoal):
1. {l. length l =... | {"llama_tokens": 3541, "file": "Monad_Memo_DP_example_Counting_Tiles", "length": 26} |
program add_real
implicit none
INTEGER, PARAMETER :: np=100
REAL, DIMENSION(np) :: A, B
INTEGER:: i
DO i=1, np
A(i)=1
B(i)=1
END DO
!$OMP PARALLEL DO REDUCTION(+:A)
DO i=1, np
A(i)=A(i)+B(i)
END DO
!$OMP END PARALLEL DO
... | {"hexsha": "762cfbf6ff384379590eae3c091c840359c63e69", "size": 490, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "test/smoke/flang-272285/flang-272285.f90", "max_stars_repo_name": "raramakr/aomp", "max_stars_repo_head_hexsha": "9a224fe01ca8eff4209b8b79aa1fa15a18da65db", "max_stars_repo_licenses": ["Apache-2.... |
import os
import json
import numpy as np
import torch
import cv2
from PIL import Image
import torch.utils.data as data
from torch.utils import data
from matplotlib.image import imread
from pycocotools.coco import COCO
from effdet.data.parsers import create_parser
class VdotDataset(data.Dataset):
def __init__(self, ... | {"hexsha": "757232ac11cb525f233d0c04568bde8d3f9167a6", "size": 4586, "ext": "py", "lang": "Python", "max_stars_repo_path": "vdot.py", "max_stars_repo_name": "Ekta246/efficientdet-pytorch", "max_stars_repo_head_hexsha": "f284a465a9050c26723c268de71fb31bf080048a", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_cou... |
import unittest
import numpy
import chainer
from chainer import cuda
from chainer.functions import convolution_2d
from chainer.functions import deformable_convolution_2d_sampler
from chainer import utils
from chainer import testing
from chainer.testing import attr
@testing.parameterize(*testing.product({
'para... | {"hexsha": "a3147c76d61ed100a0f25d5ceb95e8daee484906", "size": 4512, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/chainer_tests/functions_tests/connection_tests/test_deformable_convolution_2d_sampler.py", "max_stars_repo_name": "zaltoprofen/chainer", "max_stars_repo_head_hexsha": "3b03f9afc80fd67f65d5e0... |
# -*- coding: utf-8 -*-
"""
Transport example using GSTools.
Plotting the plumes at t=15d and calculating the breakthrough curves at
the observation wells.
Authors: Alraune Zech and Sebastian Müller
"""
import os
import numpy as np
from ogs5py.reader import readtec_polyline
import meshio as mio
import matplotlib.pypl... | {"hexsha": "748d72d3dab4459993a2e18ddf0fd2aae919b7d1", "size": 4745, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/01_plot.py", "max_stars_repo_name": "GeoStat-Examples/gstools-connectivity-and-transport", "max_stars_repo_head_hexsha": "64229f989ff04ad1b822db1369f334353df206ab", "max_stars_repo_licenses": ... |
import numpy as np
import loupe
def test_expc():
a = loupe.rand(size=(10,10))
res = loupe.expc(a)
assert np.array_equal(res, np.exp(a.data*1j))
def test_expc_backward():
a = loupe.rand(size=(10,10), requires_grad=True)
res = loupe.expc(a)
res.backward(grad=np.ones((10,10)))
assert np.allcl... | {"hexsha": "d6e6f8ad1994d7ffff4f05c2194a4b492359690b", "size": 355, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/numeric/test_expc.py", "max_stars_repo_name": "andykee/loupe", "max_stars_repo_head_hexsha": "8b10781598973aac7c129e190209acad7e5a9559", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_star... |
from __future__ import division
from __future__ import absolute_import
from builtins import range
from past.utils import old_div
import numpy as np
import cv2
import matplotlib.pyplot as plt
from .tesisfunctions import hist_cdf,findminima,threshold
import glob
def brightness(img):
### LESS BRIGHT http://alienryde... | {"hexsha": "caec59851d589e9da5017946dfce466afe58ef4c", "size": 5066, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/hypothesis2.py", "max_stars_repo_name": "davtoh/RRTools", "max_stars_repo_head_hexsha": "6dde2d4622719d9031bf21ffbf7723231a0e2003", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_co... |
import time
import numpy as np
import pandas as pd
import os
class SpotifyDataExtractor:
def __init__(self, sp, artist_uri, artist_name, conn):
self.sp = sp
self.artist_uri = artist_uri
self.artist_name = artist_name
self.conn = conn
self.sp_albums = self.sp.artist_albums(se... | {"hexsha": "ab318e8aed17367900e3d71f8c678b4aa15554e4", "size": 5559, "ext": "py", "lang": "Python", "max_stars_repo_path": "AI/spotifydataextractor.py", "max_stars_repo_name": "pradeepsalunke/muser-data-analysis", "max_stars_repo_head_hexsha": "08ea051d422431b29e6e32841d8e349e136c6f14", "max_stars_repo_licenses": ["Apa... |
#importing the libraries
import tensorflow as tf
import numpy as np
import pandas as pd
import json
import nltk
from tensorflow.keras.preprocessing.text import Tokenizer
from tensorflow.keras.layers import Input, Embedding, LSTM , Dense,GlobalMaxPooling1D,Flatten
from tensorflow.keras.models import Model
import matp... | {"hexsha": "33bdb85416dc22da20f39bdeca277af1fe2141e2", "size": 3092, "ext": "py", "lang": "Python", "max_stars_repo_path": "DEW.py", "max_stars_repo_name": "Eeman1113/DEW", "max_stars_repo_head_hexsha": "1b970bf0c8cf83b79d483c3836a6a50349d9ab41", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo... |
# Copyright (c) 2020 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": "6a56ff75465b9609e3452617eb4fc91b8dc6263a", "size": 12061, "ext": "py", "lang": "Python", "max_stars_repo_path": "paddlenlp/data/collate.py", "max_stars_repo_name": "JunnYu/ConvBERT-Prod", "max_stars_repo_head_hexsha": "a1351e1e7f9400cb8c71d0a15d23629b4cb055d4", "max_stars_repo_licenses": ["Apache-2.0"], "ma... |
#!/usr/bin/env python3
#
# (C) 2014-2020 Ed Bueler
import sys, argparse
import numpy as np
import PetscBinaryIO # may use link
petsc = PetscBinaryIO.PetscBinaryIO()
parser = argparse.ArgumentParser(description='Generate a structured grid on the unit square in PETSc binary format (.vec,.is), readable by ch9/unfem.')... | {"hexsha": "ff98442a7130057e2c720294822b270354018dd9", "size": 2462, "ext": "py", "lang": "Python", "max_stars_repo_path": "c/ch10/genstructured.py", "max_stars_repo_name": "thw1021/p4pdes", "max_stars_repo_head_hexsha": "421fd3d809b1e23e5a6f3c3e51252cb275a76140", "max_stars_repo_licenses": ["MIT"], "max_stars_count": ... |
import os
import sys
from glob import glob
import numpy as np
from setuptools import Extension, find_packages, setup
here = os.path.abspath(os.path.dirname(__file__))
with open(os.path.join(here, "README.md"), encoding="utf-8") as f:
long_description = f.read()
# MSVC compiler has different flags; assume that'... | {"hexsha": "7548e10dee63bb8dce13b37a12d169bb829b12e3", "size": 2044, "ext": "py", "lang": "Python", "max_stars_repo_path": "setup.py", "max_stars_repo_name": "duynguyenhoang/pantab", "max_stars_repo_head_hexsha": "d6d44a1a03ab50adfb8f8d850fc6ba98195b6056", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count":... |
import numpy as np
from .transform import sph2vec, vec2sph
def angle_between(ang1, ang2, sign=True):
d = (ang1 - ang2 + np.pi) % (2 * np.pi) - np.pi
if not sign:
d = np.abs(d)
return d
def angdist(v1, v2, zenith=True):
if v1.shape[0] == 2:
v1 = sph2vec(v1, zenith=zenith)
if v2.sh... | {"hexsha": "790ec48bd68710ab73361d201cd9d2a9e9505382", "size": 1142, "ext": "py", "lang": "Python", "max_stars_repo_path": "sphere/distance.py", "max_stars_repo_name": "jannsta1/insectvision", "max_stars_repo_head_hexsha": "d98a7acbcde1d5faf00131485fa85c706f313814", "max_stars_repo_licenses": ["MIT"], "max_stars_count"... |
import unittest
import numpy as np
import scipy.sparse
from sklearn.datasets import load_boston, load_iris, load_wine
from flaml import AutoML
from flaml.data import get_output_from_log
from flaml.model import SKLearnEstimator
from rgf.sklearn import RGFClassifier, RGFRegressor
from flaml import tune
class MyRegul... | {"hexsha": "d502cb056ce0fb28434fa8fe07f4b0924470faf1", "size": 13120, "ext": "py", "lang": "Python", "max_stars_repo_path": "test/test_automl.py", "max_stars_repo_name": "dan0nchik/FLAML", "max_stars_repo_head_hexsha": "9d661759b49de6e403d9288af7a015606528fe7e", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_cou... |
## Open a dataset
```python
import pandas as pd
fn = "../data/benchmarks/diffeq/predpreyfrac_clean.csv"
df = pd.read_csv(fn, skipinitialspace=True)
print df.columns
```
Index([u'T', u'x', u'y', u'dx', u'dy'], dtype='object')
## Graph the data
```python
# visualization libraries
import matplotlib.pyplot as p... | {"hexsha": "af5e0ed93e10ec79c93769dcb0ed86c18aa5544b", "size": 68956, "ext": "ipynb", "lang": "Jupyter Notebook", "max_stars_repo_path": "notebooks/Dissertation/experiments/diffeq_clean.ipynb", "max_stars_repo_name": "verdverm/pypge", "max_stars_repo_head_hexsha": "7f94595735c08e147bd17056f15d944da61eec6d", "max_stars_... |
[STATEMENT]
lemma split_two_block_non_interfering:
assumes "split_block (two_block_non_interfering A B) (dim_row A) (dim_col A) = (Q1, Q2, Q3, Q4)"
shows "Q1 = A" "Q4 = B"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. Q1 = A &&& Q4 = B
[PROOF STEP]
using split_four_block_dual_fst_lst[of A _ _ B Q1 Q2 Q3 Q4]
... | {"llama_tokens": 349, "file": "Linear_Programming_LP_Preliminaries", "length": 2} |
#' Unit Testing script for NMF package: NMF utility functions.
#'
#' @author Renaud Gaujoux
#' @creation 10 Aug 2010
#' Unit test for rmatrix: random matrix generation
test.rmatrix <- function(){
n <- 100; p <- 20
A <- matrix(1, n, p)
# square matrix if y is missing
set.seed(123456); M <- matrix(runif(n*n), n... | {"hexsha": "d393e2ce559b5da3957050af16034fe19dee8020", "size": 5168, "ext": "r", "lang": "R", "max_stars_repo_path": "packrat/lib/x86_64-pc-linux-gnu/3.2.5/NMF/tests/runit.utils.r", "max_stars_repo_name": "Chicago-R-User-Group/2017-n3-Meetup-RStudio", "max_stars_repo_head_hexsha": "71a3204412c7573af2d233208147780d31343... |
# ******************************************************************************
# Copyright 2017-2018 Intel Corporation
#
# 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.apa... | {"hexsha": "8bc717b875565a49b08f5346c4077b04d6a70e03", "size": 3491, "ext": "py", "lang": "Python", "max_stars_repo_path": "nlp_architect/data/cdc_resources/embedding/embed_elmo.py", "max_stars_repo_name": "maheshwarigagan/nlp-architect", "max_stars_repo_head_hexsha": "f6466edfd3ec6fe7d3682ec54306a1c65980d288", "max_st... |
import investor_simulator as invsim
import os
import pandas as pd
import seaborn as sns
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.dates import DateFormatter
from datetime import datetime
# Handle date time conversions between pandas and matplotlib
from pandas.plotting import register_matplotli... | {"hexsha": "b89c02337b7151aee7f608cfc0eb1814497204ea", "size": 3532, "ext": "py", "lang": "Python", "max_stars_repo_path": "Code/Simulation_bonus.py", "max_stars_repo_name": "caiomts/financial-programming", "max_stars_repo_head_hexsha": "ad23c091b6d7238e3dffdf748eedd0b8a2e41874", "max_stars_repo_licenses": ["MIT"], "ma... |
import os
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.nn as nn
from torch.autograd import Variable
from torch.optim.lr_scheduler import ExponentialLR
from probabilistic_forecast.utils.torch_utils import get_device
from probabilistic_forecast.utils.plot_utils impor... | {"hexsha": "a8b18f8791797976a6ecc092baf95976ee4389ab", "size": 10017, "ext": "py", "lang": "Python", "max_stars_repo_path": "probabilistic_forecast/lstm_mc.py", "max_stars_repo_name": "Abdulmajid-Murad/deep_probabilistic_forecast", "max_stars_repo_head_hexsha": "399846381af4bb789021c9f63f121dd69fa0125d", "max_stars_rep... |
import pandas as pd
import numpy as np
from os import listdir, makedirs
from os.path import join, exists
import gc
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('-index_batch', type=int)
parser.add_argument('--data_split', default='temporal_5')
args = parser.parse_args()
index_batch = args.... | {"hexsha": "1603b805350c2810080360348310bd5dd221bb11", "size": 5115, "ext": "py", "lang": "Python", "max_stars_repo_path": "lstm/data_processing/signal_extraction.py", "max_stars_repo_name": "ratschlab/circEWS", "max_stars_repo_head_hexsha": "b2b1f00dac4f5d46856a2c7abe2ca4f12d4c612d", "max_stars_repo_licenses": ["MIT"]... |
import pytest
import numpy as np
import tensorflow as tf
from tensorflow.keras.optimizers import Adam
from flowket.callbacks.exact import ExactLocalEnergy
from flowket.callbacks.monte_carlo import LocalEnergyStats
from flowket.evaluation import evaluate, exact_evaluate
from flowket.operators import Heisenberg, NetketO... | {"hexsha": "b78b499c69dccf3702024011451e3fdd24f47744", "size": 5578, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/test_variational.py", "max_stars_repo_name": "vigsterkr/FlowKet", "max_stars_repo_head_hexsha": "0d8f301b5f51a1bab83021f10f65cfb5f2751079", "max_stars_repo_licenses": ["MIT"], "max_stars_cou... |
"""Created on 25 mars 2019.
@author: Aurele Durand
"""
import datetime, decimal
import pandas as pd
import numpy as np
from _collections_abc import dict_keys
from flask.json import JSONEncoder
from sqlalchemy.exc import OperationalError
class AlphaJSONEncoder(JSONEncoder):
rules = {}
def __init__(self, *a... | {"hexsha": "9a7fc7c87c940cadec790a63805b1cb3a1c432db", "size": 2255, "ext": "py", "lang": "Python", "max_stars_repo_path": "models/json/_converters.py", "max_stars_repo_name": "ZAurele/alpha-py", "max_stars_repo_head_hexsha": "b6330f1e714d07a2010ebe500d5ccdf4cc637998", "max_stars_repo_licenses": ["MIT"], "max_stars_cou... |
[STATEMENT]
lemma ListAif1: "bval b s \<Longrightarrow> preList upds (IF b THEN C1 ELSE C2) l s = preList upds C1 l s"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. bval b s \<Longrightarrow> preList upds (IF b THEN C1 ELSE C2) l s = preList upds C1 l s
[PROOF STEP]
apply(induct upds)
[PROOF STATE]
proof (prove)
go... | {"llama_tokens": 404, "file": "Hoare_Time_Nielson_VCG", "length": 3} |
# ======================================================================
# Copyright (c) 2010, G. Fiori, University of Pisa
#
# This file is released under the BSD license.
# See the file "license.txt" for information on usage and
# redistribution of this file, and for a DISCLAIMER OF ALL WARRANTIES.
# ==========... | {"hexsha": "e7ed367b26169c48713f315052f85ad6fa10092f", "size": 4370, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/section.py", "max_stars_repo_name": "aravindhk/Vides", "max_stars_repo_head_hexsha": "65d9ea9764ddf5f6ef40e869bd31387d0e3e378f", "max_stars_repo_licenses": ["BSD-4-Clause"], "max_stars_count":... |
import numpy as np
import random
from rl.core import Env
class MultiInputTestEnv(Env):
def __init__(self, observation_shape):
self.observation_shape = observation_shape
def step(self, action):
return self._get_obs(), random.choice([0, 1]), random.choice([True, False]), {}
def reset(self... | {"hexsha": "5d8cee49375c0a894cb8c20f0732e6ec340536ae", "size": 612, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/rl/util.py", "max_stars_repo_name": "stefanbschneider/keras-rl", "max_stars_repo_head_hexsha": "216c3145f3dc4d17877be26ca2185ce7db462bad", "max_stars_repo_licenses": ["MIT"], "max_stars_count... |
###### Content provided under a Creative Commons Attribution license, CC-BY 4.0; code under MIT License. (c)2014 [David I. Ketcheson](http://davidketcheson.info)
#An illustrated guide to limiters
## Or: how to interpolate non-smooth data without creating wiggles
Many interesting wave phenomena -- like fluid dynamics,... | {"hexsha": "92f8bbeaeafef5c456f0b31a54f5e1ff1807b234", "size": 39615, "ext": "ipynb", "lang": "Jupyter Notebook", "max_stars_repo_path": "Guide_to_limiters.ipynb", "max_stars_repo_name": "nemethedr/HyperPython", "max_stars_repo_head_hexsha": "ce3d8ccd898fcb3d54f04af283d92b2436ba3eaa", "max_stars_repo_licenses": ["CC-BY... |
################################################################################
##
## This library is free software; you can redistribute it and/or
## modify it under the terms of the GNU Lesser General Public
## License as published by the Free Software Foundation; either
## version 2.1 of the License, or (at your op... | {"hexsha": "4590c0b79bd42361d4a03a813d3960db493b7918", "size": 17719, "ext": "py", "lang": "Python", "max_stars_repo_path": "bin/ADFRsuite/CCSBpckgs/DejaVu2/Qt/Viewer.py", "max_stars_repo_name": "AngelRuizMoreno/Jupyter_Dock_devel", "max_stars_repo_head_hexsha": "6d23bc174d5294d1e9909a0a1f9da0713042339e", "max_stars_re... |
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