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export DeterministicDistributionModel, get_states, get_actions
import ReinforcementLearningEnvironments: observation_space, action_space
"""
DeterministicDistributionModel(table::Array{Vector{NamedTuple{(:nextstate, :reward, :prob),Tuple{Int,Float64,Float64}}}, 2})
Store all the transformations in the `table` fi... | {"hexsha": "11ced4572d8fb6d2bb83f84498d1a189be634694", "size": 763, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/components/environment_models/deterministic_distribution_model.jl", "max_stars_repo_name": "UnofficialJuliaMirror/ReinforcementLearning.jl-158674fc-8238-5cab-b5ba-03dfc80d1318", "max_stars_repo_... |
# -*- coding: utf-8 -*-
"""
Created on Tue Mar 29 12:45:00 2022
Author: Adam Coxson, PhD student, University of Liverpool
Department of Chemistry, Materials Innovation Factory, Levershulme Research Centre
Project: Delta ML Zindo
Module: FNN.py
Dependancies: Sklearn library, Pandas, Scipy, all other libraries are... | {"hexsha": "3e5fe1ca87d093d0420845c2115ba57b4086eed6", "size": 9449, "ext": "py", "lang": "Python", "max_stars_repo_path": "FFNN.py", "max_stars_repo_name": "marcosdelcueto/DeltaML_excited_states", "max_stars_repo_head_hexsha": "aeb1f356e76ebf09334cba75f7d6da4b4020d4db", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
import math
import cmath
import warnings
#import scipy.constants as physical_constants # Container of physical constants which might be of use
import numpy as np
from Material import Material
class MaterialSolid(Material):
"""
Solid material class
"""
__name__ = 'solid'
name__ = 'Solid'
... | {"hexsha": "488f839167b1a579f7172043e8f0d1a29a5b9f63", "size": 6651, "ext": "py", "lang": "Python", "max_stars_repo_path": "Sea/model/materials/MaterialSolid.py", "max_stars_repo_name": "FRidh/Sea", "max_stars_repo_head_hexsha": "b474e93a449570a9ba3b915c4d80f814feee2545", "max_stars_repo_licenses": ["BSD-3-Clause"], "m... |
import numpy
from gensim import models, corpora
from gensim.similarities import MatrixSimilarity
from nltk.corpus import stopwords as pw
from nltk.tokenize import sent_tokenize
from nltk.tree import *
from spellchecker import SpellChecker
from stanfordcorenlp import StanfordCoreNLP
from src.config import STANFORDCOREN... | {"hexsha": "990e9df175ccc3f3bd481e0087e5aafdbb0b95e4", "size": 5888, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/feature/iku.py", "max_stars_repo_name": "wangqi1996/Essay_Scoring", "max_stars_repo_head_hexsha": "1194ad6841de3d95cd7e3733f7be152f02e4d93c", "max_stars_repo_licenses": ["MIT"], "max_stars_cou... |
'''
----------- Example_14 --------------
Load a turbine, tune a controller with open loop control commands
-------------------------------------
In this example:
- Load a turbine from OpenFAST
- Tune a controller
- Write open loop inputs
- Run simple simulation with open loop control
'''
# Python Modules
imp... | {"hexsha": "f91d4de5f847d8fcb09421395f3ad05e400a4092", "size": 5295, "ext": "py", "lang": "Python", "max_stars_repo_path": "Examples/example_14.py", "max_stars_repo_name": "ptrbortolotti/ROSCO", "max_stars_repo_head_hexsha": "5d201855f57d0773ad8304349257db19c6db6af2", "max_stars_repo_licenses": ["Apache-2.0"], "max_sta... |
## same as the analytic case but with the fft
import numpy as np
import matplotlib.pyplot as plt
from numpy.linalg import cond
import cmath;
from scipy import linalg as LA
from numpy.linalg import solve as bslash
import time
from convolution_matrices.convmat1D import *
from RCWA_1D_functions.grating_fft.gratin... | {"hexsha": "aa207cf485bf01dae3e9ad7dedc77d78268995c4", "size": 10672, "ext": "py", "lang": "Python", "max_stars_repo_path": "anisotropy_explorations/1D_Longitudinal_Anisotropy.py", "max_stars_repo_name": "zhaonat/RCWA", "max_stars_repo_head_hexsha": "a28fdf90b5b5fc0fedacc8bb44a0a0c2f2a02143", "max_stars_repo_licenses":... |
import collections
import time
import numpy as np
from evalRnn import test, testCaptionedImages
from ReadCOCOUtil import ReadCOCOUtil
import gc
import ipdb
COCO = ReadCOCOUtil()
trainingImageIds = COCO.imgIdsTrain
validationImageIds = COCO.imgIdsVal
def validateModel(validationX, validationY, model, epoch, loss_acc):... | {"hexsha": "9f135db6aef53638a05185821938ef2641aa50a0", "size": 1812, "ext": "py", "lang": "Python", "max_stars_repo_path": "training/trainOnCaptionedImages.py", "max_stars_repo_name": "dwright37/generative-concatenative-image-captioner", "max_stars_repo_head_hexsha": "2bb257d4791e362e42a30bf0e4ca32e84f80d942", "max_sta... |
import numpy as np
from bolero.wrapper import CppBLLoader
from bolero.environment import ContextualEnvironment
from bolero.utils import check_random_state
from time import sleep
class ThrowEnvironment(ContextualEnvironment):
"""Extract the relevant feedbacks from the SpaceBot environment."""
def __init__(self... | {"hexsha": "730869418fbda1901033c43426555ce71fae924f", "size": 2403, "ext": "py", "lang": "Python", "max_stars_repo_path": "evaluation/throw_environment.py", "max_stars_repo_name": "rock-learning/approxik", "max_stars_repo_head_hexsha": "877d50d4d045457593a2fafefd267339a11de20f", "max_stars_repo_licenses": ["BSD-3-Clau... |
module Lens
# Lens laws:
# get (put x y) == x
# put x (put y z) == put x z
# put (get x) y == y
export test_lens_put_get, test_lens_put_put, test_lens_get_put
function test_lens_put_get(get, put, containers, vals)
passed = true
for x in vals
for y in containers
passed &= get(put(x, y)) == x
end
... | {"hexsha": "82555bc1882372eb738e07d4916afc4135081ded", "size": 709, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/lens.jl", "max_stars_repo_name": "UnofficialJuliaMirror/Omega.jl-1af16e33-887a-59b3-8344-18f1671b3ade", "max_stars_repo_head_hexsha": "9dbaa559991a728e8239767d9627419e41037847", "max_stars_repo... |
"""
Code to extract a box-like region, typically for another modeler to use
as a boundary contition. In cases where it gets velocity in addition to
the rho-grid variables the grid limits mimic the standard ROMS organization,
with the outermost corners being on the rho-grid.
Job definitions are in LO_user/extract/box/... | {"hexsha": "5e03e280ee3d294abc4e24b53e89a4785246d493", "size": 12080, "ext": "py", "lang": "Python", "max_stars_repo_path": "extract/box/extract_box.py", "max_stars_repo_name": "parkermac/LO", "max_stars_repo_head_hexsha": "09e0197de7f2166bfa835ec62018b7a8fbfa7379", "max_stars_repo_licenses": ["MIT"], "max_stars_count"... |
[STATEMENT]
lemma smc_Funct_Comp_vsv[intro]: "vsv (smc_Funct \<alpha> \<AA> \<BB>\<lparr>Comp\<rparr>)"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. vsv (smc_Funct \<alpha> \<AA> \<BB>\<lparr>Comp\<rparr>)
[PROOF STEP]
unfolding smc_Funct_Comp
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. vsv (\<lambda>\<GG>\<... | {"llama_tokens": 258, "file": "CZH_Elementary_Categories_czh_ecategories_CZH_SMC_FUNCT", "length": 2} |
import logging
log = logging.getLogger(__name__)
from math import ceil
from numpy import linspace
import struct
from spacq.interface.resources import Resource
from spacq.tool.box import Synchronized
from ..abstract_device import AbstractDevice, AbstractSubdevice
from ..tools import str_to_bool, quantity_wrapped, qua... | {"hexsha": "45375e428bdc84b80771a9e6942484c260061e1c", "size": 10057, "ext": "py", "lang": "Python", "max_stars_repo_path": "spacq/devices/tektronix/dpo7104.py", "max_stars_repo_name": "zachparrott/SpanishAcquisitionIQC", "max_stars_repo_head_hexsha": "dd2e683c4cbc5fa420226d545077d94bf2dcb46b", "max_stars_repo_licenses... |
from misc import parallel, timing
import decomposition
from decomposition import NotDecomposableError
import networkx as nx
from datetime import datetime
# analysis of graphs on small numbe rof vertices
# we generate all possible graphs on n vertices
# and check how many are odd decomposable
def powerset(iterable, ch... | {"hexsha": "34cf12a66c11af42e9ec904d77db10e59cb4cbc2", "size": 2522, "ext": "py", "lang": "Python", "max_stars_repo_path": "decompositon/small_graphs.py", "max_stars_repo_name": "lodrantl/odd-decomposition", "max_stars_repo_head_hexsha": "11b51f72689d9912b6c31585c4d26aff3e384703", "max_stars_repo_licenses": ["Apache-2.... |
import numpy as np
from sklearn.linear_model import LinearRegression
def add_trend_feature(arr, abs_values=False):
idx = np.array(range(len(arr)))
if abs_values:
arr = np.abs(arr)
lr = LinearRegression()
lr.fit(idx.reshape(-1, 1), arr)
return lr.coef_[0]
def classic_sta_lta(x, length_sta... | {"hexsha": "552802e00e5363994eded3df59e25a9984439767", "size": 1133, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/features/feature_utils.py", "max_stars_repo_name": "sbussmann/earthquake-prediction", "max_stars_repo_head_hexsha": "ba1e0f1a29cab40c1e659ed372f097b78e8f8483", "max_stars_repo_licenses": ["MIT... |
# MTRN4230 Robotics
# Group 6 Assignment
# Robot Motion Module
#
# Authors: Samir Mustavi & Matthew Bourke
# Date: 27.07.2020
# Description: ROS module for providing actuation functions to the UR5 robot arm in the simulated Gazebo environment.
# Desired x, y, z coordinates are received from the Image Proce... | {"hexsha": "10483409d6a3cf67bcf83d24a1a0e5e5808aefe6", "size": 10234, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/utils/kinematics.py", "max_stars_repo_name": "JimmeeX/ur5_t2_4230", "max_stars_repo_head_hexsha": "ae64c15a5c8040b5f3f5ba19710427c406607973", "max_stars_repo_licenses": ["MIT"], "max_stars_co... |
"""
TODO
----
More than one plot
"""
from string import Template
from os.path import join
import os
import pandas as pd
import numpy as np
def describe2latex(study_info, stats):
"""Function to translate the descriptions of the variables to latex.
TODO
----
- crete a plot folder
- get paths to s... | {"hexsha": "5f50840f37018377e238f96df6af5808875894a3", "size": 14276, "ext": "py", "lang": "Python", "max_stars_repo_path": "FirmsLocations/IO/output_to_latex.py", "max_stars_repo_name": "tgquintela/Firms_locations", "max_stars_repo_head_hexsha": "476680cbc3eb1308811633d24810049e215101a0", "max_stars_repo_licenses": ["... |
Require Export Iron.Language.SimpleData.Ty.
(* Data Constructors *)
Inductive datacon : Type :=
| DataCon : nat -> datacon.
Hint Constructors datacon.
Fixpoint datacon_beq t1 t2 :=
match t1, t2 with
| DataCon n1, DataCon n2 => beq_nat n1 n2
end.
(* Definitions.
Carries meta information about type a... | {"author": "discus-lang", "repo": "iron", "sha": "75c007375eb62e1c0be4b8b8eb17a0fe66880039", "save_path": "github-repos/coq/discus-lang-iron", "path": "github-repos/coq/discus-lang-iron/iron-75c007375eb62e1c0be4b8b8eb17a0fe66880039/done/Iron/Language/SimpleData/Def.v"} |
# -*- coding: utf-8 -*-
"""generate_attack_files.ipynb
Automatically generated by Colaboratory.
Original file is located at
https://colab.research.google.com/drive/1CDyCghmEMadl1NHbQvvXXFEsQHckKUtH
"""
# Commented out IPython magic to ensure Python compatibility.
# %cd /content/drive/MyDrive/attacks/
!ls
# load... | {"hexsha": "5fd5b95dc55cdd354765c1cba41fc59ead706757", "size": 13008, "ext": "py", "lang": "Python", "max_stars_repo_path": "generate_attack_files.py", "max_stars_repo_name": "sert121/shilling_a_and_d", "max_stars_repo_head_hexsha": "48ab1f2e48c1e13f3b19ab897b3372638d9d4eb3", "max_stars_repo_licenses": ["MIT"], "max_st... |
module class_Stack
type :: link
integer :: i
type (link), pointer :: previous
end type link
type, public :: StackIterator
integer, private :: index
type(link), pointer, private :: current
contains
procedure :: create => stack_iterator_create
procedure :: next => stack_iterator_nex... | {"hexsha": "61825ec248d8e888e8248cd09cf4a56712af8c35", "size": 4145, "ext": "f08", "lang": "FORTRAN", "max_stars_repo_path": "src/Stack.f08", "max_stars_repo_name": "ironmerchant/dfs_cycles", "max_stars_repo_head_hexsha": "ff2245934fab82f24d8d309d210d37f90f733c58", "max_stars_repo_licenses": ["BSD-2-Clause"], "max_star... |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
# File: Ampel-contrib-HU/ampel/contrib/hu/t0/XShooterFilter.py
# License: BSD-3-Clause
# Author: m. giomi <matteo.giomi@desy.de>
# Date: 28.08.2018
# Last Modified Date: 24.11.2021
# Last Modified By: jnordin... | {"hexsha": "e8b760f7e6323bfb4f58f8cce1f844bc6b5c59bf", "size": 4730, "ext": "py", "lang": "Python", "max_stars_repo_path": "ampel/contrib/hu/t0/XShooterFilter.py", "max_stars_repo_name": "mafn/Ampel-HU-astro", "max_stars_repo_head_hexsha": "93cf14874439c1f5d44622407fceff69eef7af2e", "max_stars_repo_licenses": ["BSD-3-C... |
(* Title: Environments.thy
Author: Florian Kammuller and Henry Sudhof, 2008
*)
theory Environments imports Main begin
subsection {* Type Environments*}
text{*Some basic properties of our variable environments.*}
(* We use a wrapped map and an error element *)
datatype 'a environment =
Env "(string ... | {"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/Locally-Nameless-Sigma/preliminary/Environments.thy"} |
Some local artists of renown include: (listed alphabetically)
Jed Alexander Artist and illustrator. You can see his work at http://jedalexander.com
http://www.natsoulas.com/html/artists/robertArneson/robertArneson.html Robert Arneson Former Art Department Art Faculty at UCD
http://www.verisimilitudo.com/arneson... | {"hexsha": "3e49db84a31432d784bf90e21bb801303862efc0", "size": 4958, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "lab/davisWiki/Local_Artists.f", "max_stars_repo_name": "voflo/Search", "max_stars_repo_head_hexsha": "55088b2fe6a9d6c90590f090542e0c0e3c188c7d", "max_stars_repo_licenses": ["MIT"], "max_stars_coun... |
# In order to understand how the code works, it is a good idea to check the
# final section of the file that starts with
# if __name__ == '__main__'
#
# Your task is essentially to replace all the parts marked as TODO or as
# instructed through comments in the functions, as well as the filenames
# and labels in the m... | {"hexsha": "06b82761abbad1af601d433894a876efe7428959", "size": 6988, "ext": "py", "lang": "Python", "max_stars_repo_path": "Exercice 2/properties_of_er_networks.py", "max_stars_repo_name": "Yanko96/CS-E5740-Complex-Networks", "max_stars_repo_head_hexsha": "708af24230218b77f1196c1a0ec5885165491a85", "max_stars_repo_lice... |
from deepface import DeepFace
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import os.path as osp
import os
import logging
logging.basicConfig(filename='example.log', level=logging.DEBUG)
df = pd.read_excel('second/sample_identity_gallery_probe.xlsx')
galleries = df[df['proposed Gallery/Prob... | {"hexsha": "fbcefad986174bedba148e89951ddf84b21bf42a", "size": 1182, "ext": "py", "lang": "Python", "max_stars_repo_path": "find.py", "max_stars_repo_name": "milad-4274/deepface", "max_stars_repo_head_hexsha": "bde16b2b79946f93c2934d2259daa6444defa248", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_... |
% !TEX root = ../thesis.tex
%%% Local Variables:
%%% mode: latex
%%% TeX-master: "thesis"
%%% End:
\chapter{Chapter Title}\label{chap:chapter_name}
Chapter introduction goes here.
\section{Section heading}\label{section:section_name}
Section description goes here.
And if I want to include a graphic, I can use th... | {"hexsha": "56181e8f8457b2fa0acb259133c9c20d59e77ae4", "size": 1641, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "thesis-template/template/chapters/chapter1.tex", "max_stars_repo_name": "igorbrigadir/insight-templates", "max_stars_repo_head_hexsha": "8722fc7741181fae9fcfa45bce3fc28382729108", "max_stars_repo_li... |
#include <string>
#include <gtest/gtest.h>
#include "ros/ros.h"
#include "nav_msgs/Odometry.h"
#include "geometry_msgs/PoseWithCovarianceStamped.h"
#include <boost/thread.hpp>
using namespace ros;
int g_argc;
char** g_argv;
typedef boost::shared_ptr<geometry_msgs::PoseWithCovarianceStamped const> EkfConstPtr;
cla... | {"hexsha": "ba7866f69e65ebcc765f9a83758f995ee4255d2c", "size": 1331, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "robot_pose_ekf/test/test_robot_pose_ekf_zero_covariance.cpp", "max_stars_repo_name": "SNU-SF4/viwo", "max_stars_repo_head_hexsha": "8ce0757617b4204e1a367552be7fe6a98ff9363f", "max_stars_repo_license... |
# Copyright (c) SenseTime. All Rights Reserved.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
import torch as t
import torch.nn as nn
import torch.nn.functional as F
from pysot.core.config import cfg
from pysot.model... | {"hexsha": "9c635f05bc5375071108ee16f8720c395bc52567", "size": 5347, "ext": "py", "lang": "Python", "max_stars_repo_path": "pysot/models/utile/model_builder.py", "max_stars_repo_name": "vision4robotics/TCTrack", "max_stars_repo_head_hexsha": "1a094f108e09b40b84e6fa0fa06fc6ae0f53ae54", "max_stars_repo_licenses": ["Apach... |
##
## A kind of meta-loader to import data if not already in the workspace
## This lets you run each part of the analysis separately rather than all in a batch, should you prefer
##
if (!exists("dyads"))
source("init.r")
if (!exists("common_theme"))
source("init plots.r")
| {"hexsha": "7d2aab0273a46e2dc547fd7eee694460b788cc27", "size": 278, "ext": "r", "lang": "R", "max_stars_repo_path": "init if necessary.r", "max_stars_repo_name": "matthewgthomas/hierarchies-gifts", "max_stars_repo_head_hexsha": "7855e25e974b50c4c42d966ccd8ca75b3002f241", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
import argparse
import joblib
import json
import numpy as np
import os
import pandas as pd
import warnings
from itertools import chain
from scipy.io import mmread
from sklearn.pipeline import Pipeline
from sklearn.metrics._scorer import _check_multimetric_scoring
from sklearn.model_selection._validation import _score
f... | {"hexsha": "e0962b6c57e7fd2d8b3c35d12081ec1680b1b765", "size": 17734, "ext": "py", "lang": "Python", "max_stars_repo_path": "galaxy_ml/tools/keras_train_and_eval.py", "max_stars_repo_name": "bgruening/Galaxy-ML-1", "max_stars_repo_head_hexsha": "47514940c7ac39d6ca1d595b58b5d1311b3f3840", "max_stars_repo_licenses": ["MI... |
from Results import Results
from Channel import Channel
from Message import Image_message
from CorectionCodes import CorectionCodes
from Generator import Generator
import numpy as np
import komm as komm
test_image_file_name = "image.jpg"
saved_test_image = "save_image.jpeg"
results = Results()
results_file_name = 'ha... | {"hexsha": "2e7282ba60f28cf4921ffd16ef7556b5ef0de3fc", "size": 11003, "ext": "py", "lang": "Python", "max_stars_repo_path": "main.py", "max_stars_repo_name": "tomekrzymyszkiewicz/forward-error-correction", "max_stars_repo_head_hexsha": "fba72896b77cd4f5dee79648e3ecbcc1c827e95c", "max_stars_repo_licenses": ["MIT"], "max... |
from numpy import genfromtxt
import matplotlib.pyplot as plt
import mpl_finance
import numpy as np
import uuid
import matplotlib
# Input your csv file here with historical data
ad = genfromtxt("../financial_data/BPI-copy.csv", delimiter=",", dtype=str)
pd = ad
buy_dir = "../data/train/buy/"
sell_dir = "../data/train... | {"hexsha": "2b0ccf0474eece94bb0ca3f7dd102441ee1350a8", "size": 3479, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/backupSingle.py", "max_stars_repo_name": "accordinglyto/dferte", "max_stars_repo_head_hexsha": "d4b8449c1633973dc538c9e72aca5d37802a4ee4", "max_stars_repo_licenses": ["MIT"], "max_stars_count"... |
(* This program 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 option) any later version. *)
(* ... | {"author": "coq-contribs", "repo": "cats-in-zfc", "sha": "aa7067a8d0a243caec7288dffd1e0a86c65ece0e", "save_path": "github-repos/coq/coq-contribs-cats-in-zfc", "path": "github-repos/coq/coq-contribs-cats-in-zfc/cats-in-zfc-aa7067a8d0a243caec7288dffd1e0a86c65ece0e/fiprod.v"} |
#!/usr/bin/env python
import matplotlib.pyplot as plt
import matplotlib.animation as animation
import math
from matplotlib import style
from Points import BuildPath
from std_msgs.msg import String
import rospy
import tf
import time
import datetime ##
import numpy as np
from numpy.linalg import inv,pinv
from threading i... | {"hexsha": "7cda57294d9f20248bceb65d906c2f537eae0803", "size": 11144, "ext": "py", "lang": "Python", "max_stars_repo_path": "Roundabouts/DynamicMerging.py", "max_stars_repo_name": "NKdeveloper/Autonomous-Vehicle-Research", "max_stars_repo_head_hexsha": "93c262a9ae9e6246b7f0d74023cfaadb4ee71eb7", "max_stars_repo_license... |
import os
import sys
from statsmodels.datasets import get_rdataset
from numpy.testing import assert_
cur_dir = os.path.dirname(os.path.abspath(__file__))
def test_get_rdataset():
# smoke test
if sys.version_info[0] >= 3:
#NOTE: there's no way to test both since the cached files were
#created w... | {"hexsha": "f3e13fbd2429cb132d89ef667c1533479840b149", "size": 655, "ext": "py", "lang": "Python", "max_stars_repo_path": "statsmodels/datasets/tests/test_utils.py", "max_stars_repo_name": "toobaz/statsmodels", "max_stars_repo_head_hexsha": "5286dd713a809b0630232508bf9ad5104aae1980", "max_stars_repo_licenses": ["BSD-3-... |
# -*- coding: utf-8 -*-
'''
A Convolutional Neural Network implementation example using Tensorflow Library.
The example uses the mnist data in kaggle
https://www.kaggle.com/c/digit-recognizer
Author:sfailsthy
'''
# Libraries
import tensorflow as tf
import numpy as np
import csv
# setting parameters
learning_rate=1e-4... | {"hexsha": "566fdbe5189bccef6c52203fb6ecceb50215e0f2", "size": 7036, "ext": "py", "lang": "Python", "max_stars_repo_path": "mnist/mnist_with_cnn.py", "max_stars_repo_name": "sfailsthy/kaggle", "max_stars_repo_head_hexsha": "4e03042278e891a99e774a0e0c5a404d62cdfc13", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars... |
# Test script to test solver with JuMP on a closest correlation matrix problem
using COSMO, JuMP, LinearAlgebra, SparseArrays, Test, Random
rng = Random.MersenneTwister(12345);
# Original problem has the following format:
# min_X 1/2 ||X-C||^2
# s.t. Xii = 1
# X ⪴ 0
# create a random test matrix C
n = 8
... | {"hexsha": "e296832d7de356c125a9ee029226a13454bae3d4", "size": 1739, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "examples/closest_correlation_matrix.jl", "max_stars_repo_name": "UnofficialJuliaMirror/COSMO.jl-1e616198-aa4e-51ec-90a2-23f7fbd31d8d", "max_stars_repo_head_hexsha": "f90cc6218d86db2fcd47b7ca533df2a... |
using OpenVR
# include(joinpath((@__DIR__),"..","src","OpenVR_C.jl"))
# using Main.OpenVR_C
# const OpenVR = OpenVR_C
# unfortunatley the C-API is not maintained by Valve … and the currently distributed version does not provide all necessary function pointers in the C-function-tables
# https://github.com/ValveSoftw... | {"hexsha": "781623a02822a973ce7dbfa8a54606673866023f", "size": 79804, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/hellovr_opengl_julia.jl", "max_stars_repo_name": "mchristianl/OpenVR.jl", "max_stars_repo_head_hexsha": "55c1a115e518d1176ab885478d20e3dbb324a199", "max_stars_repo_licenses": ["MIT"], "max_st... |
import torchvision
import torch
import numpy as np
def get_mnist_batcher(batch_size):
"""Downloads MNIST and stores in the data folder in case it is not available, and
builds a data loader based batcher of the specified size
Args:
batch_size (int): size of the minibatch
Returns:
torc... | {"hexsha": "bca06256f758a8ba579feb9b3d8f79516f70bbc7", "size": 723, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/data.py", "max_stars_repo_name": "ivallesp/VAE", "max_stars_repo_head_hexsha": "57340b02ee7b07e7e1e236c8a48aa7bd7fab364b", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_star... |
# coding: utf-8
from __future__ import division, print_function, unicode_literals, absolute_import
"""
This module is a wrapper for AntechamberRunner which generates force field files
or a specified molecule using gaussian output file as input. Currently, the AntechamberRunner
class does not work properly.
"""
import... | {"hexsha": "22dc7c7f0b08a9282f0e2a7cc968ae8b17e75583", "size": 29914, "ext": "py", "lang": "Python", "max_stars_repo_path": "pymatgen/io/ambertools.py", "max_stars_repo_name": "mmbliss/pymatgen", "max_stars_repo_head_hexsha": "0d2e39bb6406d934c03e08919f2cd4dedb41bc22", "max_stars_repo_licenses": ["MIT"], "max_stars_cou... |
[STATEMENT]
lemma swap_rule [hoare_triple]:
"i < length xs \<Longrightarrow> j < length xs \<Longrightarrow>
<p \<mapsto>\<^sub>a xs>
swap p i j
<\<lambda>_. p \<mapsto>\<^sub>a list_swap xs i j>"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. \<lbrakk>i < length xs; j < length xs\<rbrakk> \<Longrightarro... | {"llama_tokens": 163, "file": "Auto2_Imperative_HOL_Imperative_Arrays_Impl", "length": 1} |
# -*- coding: utf-8 -*-
"""
Created on Wed Nov 11 13:48:41 2020
@author: Amir Moradi
"""
import cv2
import numpy as np
def undistortion(img_1, img_2):
h, w = img_1.shape[:2]
Camera_L_Matrix = np.load("SmartCar/Calibration/matrices/matrix/Camera_L_Matrix.npy")
Camera_R_Matrix = np.load("Smart... | {"hexsha": "f3ea4be195565fa932710c2775cd60dc9db1b2ef", "size": 1346, "ext": "py", "lang": "Python", "max_stars_repo_path": "Utils/undistortion.py", "max_stars_repo_name": "Amirmoradi94/SmartCar", "max_stars_repo_head_hexsha": "4c0f17a6a98e6db46769787dc95d11e48b335488", "max_stars_repo_licenses": ["MIT"], "max_stars_cou... |
import inspect
import numpy as np
from numpy import testing
# def PrintFrame():
# callerframerecord = inspect.stack() #[1] # 0 represents this line
# print(callerframerecord[1]) # 1 represents line at caller
# frame = callerframerecord[1][0]
# print(frame)
# info = ... | {"hexsha": "3c70f63edfd8e3e9bde13db6243a84cde60a9f04", "size": 1068, "ext": "py", "lang": "Python", "max_stars_repo_path": "ds_clean/test.py", "max_stars_repo_name": "swjz/DSClean", "max_stars_repo_head_hexsha": "1311ea373049f089b0b96e9913c02fe0c339dee4", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "ma... |
import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
from scipy.constants import golden
mpl.rc("text", usetex=True)
mpl.rc("font", family="serif")
x = np.array([-1, -0.8, -0.6, -0.4, -0.2, 0, 0.2, 0.4, 0.6, 0.8, 1])
t = np.array([-4.9, -3.5, -2.8, 0.8, 0.3, -1.6, -1.3, 0.5, 2.1, 2.9, 5.6])
def... | {"hexsha": "8d7be49f3795ab46f2fc0c42edbf5728487d9d6f", "size": 1339, "ext": "py", "lang": "Python", "max_stars_repo_path": "extra/bsmalea-notes-1a/polynomial_regression.py", "max_stars_repo_name": "cookieblues/cookieblues.github.io", "max_stars_repo_head_hexsha": "9b570d83887eb2d6f92cfaa927a1adf136124a90", "max_stars_r... |
/*
* Copyright (c) Meta Platforms, Inc. and affiliates.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by appl... | {"hexsha": "a43e9c36bcf1b21dc3533c4ca7443e9c3f941d43", "size": 4915, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "thrift/test/reflection/FieldsTest.cpp", "max_stars_repo_name": "ahornby/fbthrift", "max_stars_repo_head_hexsha": "59dd614960da745e6a7b89c69c7aac77e0adf9b5", "max_stars_repo_licenses": ["Apache-2.0"]... |
from quchem_ibm.IBM_experiment_functions import *
import pickle
import os
import argparse
import numpy as np
def main(method_name):
molecule_name='H2'
## Load input data
base_dir = os.getcwd()
data_dir = os.path.join(base_dir, 'Input_data')
input_file = os.path.join(data_dir, 'H2_bravyi_kitaev_2_... | {"hexsha": "67251399861fd603b6553705254c7783c93c3540", "size": 2637, "ext": "py", "lang": "Python", "max_stars_repo_path": "old_projects/quchem_ibm/Experiments/H2_2_qubit_exp.py", "max_stars_repo_name": "AlexisRalli/VQE-code", "max_stars_repo_head_hexsha": "4112d2bba4c327360e95dfd7cb6120b2ce67bf29", "max_stars_repo_lic... |
import os
import csv
import cv2
import numpy as np
from sklearn.utils import shuffle
def load_csv_data(log_file, data_dir, steering_correction = 0.25):
image_filepaths = []
measurements = []
with open(log_file, 'r') as f:
r = csv.reader(f)
next(r) # skip the header
... | {"hexsha": "2b1748f4d2509c2159bcc7932408343a6e367c96", "size": 2143, "ext": "py", "lang": "Python", "max_stars_repo_path": "project/data.py", "max_stars_repo_name": "tnweiss/CarND-Behavioral-Cloning-P3", "max_stars_repo_head_hexsha": "dafff3f62f691954012a4a84364dee3a0706414a", "max_stars_repo_licenses": ["MIT"], "max_s... |
module NeuroMetadata
using NeuroCore.AnatomicalAPI
using FieldProperties
export
EncodingDirection,
encoding_names,
freqdim,
freqdim!,
phasedim,
phasedim!,
slice_start,
slice_start!,
slice_end,
slice_end!,
slicedim,
slicedim!,
slice_duration,
slice_duration!,
... | {"hexsha": "ae44195abbfe6601a232c9a16b2d745e3dbe3b65", "size": 1615, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/NeuroMetadata/NeuroMetadata.jl", "max_stars_repo_name": "SimonDanisch/NeuroCore.jl", "max_stars_repo_head_hexsha": "b5d9a85eec4817732bda9bfff87910fae6c7049b", "max_stars_repo_licenses": ["MIT"]... |
#include "VMController.h"
#include "CollabVM.h"
#include "Database/VMSettings.h"
#include <boost/asio.hpp>
VMController::VMController(CollabVMServer& server, boost::asio::io_service& service, const std::shared_ptr<VMSettings>& settings) :
server_(server),
io_service_(service),
settings_(settings),
turn_timer_(serv... | {"hexsha": "64956660c24a1dfda6656bb32a8c67e6c6884bce", "size": 13779, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/VMControllers/VMController.cpp", "max_stars_repo_name": "FurryFan2003/collab-vm-server", "max_stars_repo_head_hexsha": "1b4b2e602dfe61a4502ab3cadcecc4106b5e766e", "max_stars_repo_licenses": ["A... |
import isopy
import pytest
import isopy.toolbox as toolbox
import numpy as np
class Test_Inversion:
def test_one(self):
spike = isopy.array(pd104=1, pd106=0, pd108=1, pd110=0)
spike = spike.normalise(1)
self.compare_rudge_siebert('pd', spike, 1.6, 0.1, 0.5)
self.compare_rudge_siebe... | {"hexsha": "b756ad875f33a35b83ff6ca0ea7fc5ed870f249c", "size": 16629, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/test_tb_doublespike.py", "max_stars_repo_name": "mattias-ek/isopy", "max_stars_repo_head_hexsha": "96d5530034655c7f9559568ab9b0879b978ef566", "max_stars_repo_licenses": ["MIT"], "max_stars_... |
using Documenter
using StatsBase
using MixedModels
makedocs(
root = joinpath(dirname(pathof(MixedModels)), "..", "docs"),
sitename = "MixedModels",
pages = [
"index.md",
"constructors.md",
"optimization.md",
"GaussHermite.md",
"bootstrap.md",
# "SimpleLMM.md"... | {"hexsha": "24197b4f39899c285bcfa383cf12be422231c5fc", "size": 534, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "docs/make.jl", "max_stars_repo_name": "Nosferican/MixedModels.jl", "max_stars_repo_head_hexsha": "dec030a95103158aa738616f9dc72b9d0563fb26", "max_stars_repo_licenses": ["MIT"], "max_stars_count": nu... |
"""
Evaluate co-ocurrence between `regions` and `query`
"""
from __future__ import print_function
import sys
from argparse import ArgumentParser
from collections import defaultdict
from concurrent.futures import ProcessPoolExecutor
import random
import copy
from interlap import InterLap
from peddy import Ped
import t... | {"hexsha": "962665e1bb23ab5f88c149a661b4e3eb29c555a1", "size": 5127, "ext": "py", "lang": "Python", "max_stars_repo_path": "recombinator/enrichment.py", "max_stars_repo_name": "quinlan-lab/recombinator", "max_stars_repo_head_hexsha": "a164c2ea5e91debacbe658e85fa38e89ebafad05", "max_stars_repo_licenses": ["MIT"], "max_s... |
from __future__ import annotations
from dataclasses import dataclass
from typing import Any
import numpy as np
from edutorch.typing import NPArray
from ..nn.module import Module
from .optimizer import Optimizer
@dataclass
class RMSProp(Optimizer):
"""
Uses the RMSProp update rule, which uses a moving aver... | {"hexsha": "ed20393995b03c32cd0e561458b0de4d96911689", "size": 1250, "ext": "py", "lang": "Python", "max_stars_repo_path": "edutorch/optim/rmsprop.py", "max_stars_repo_name": "TylerYep/edutorch", "max_stars_repo_head_hexsha": "6a4a425cbfd7fcdcd851b010816d29c3b5bae8bd", "max_stars_repo_licenses": ["MIT"], "max_stars_cou... |
using LinearAlgebra
function loss(w, s=4.0)
return (w[1]*w[2] - s)^2/2
end
function grad_loss(w, s=4.0)
term = w[1]*w[2] - s
return term*[w[2], w[1]]
end
function hess_loss(w, s=4.0)
term = w[1]*w[2] - s
dterm = [w[2] w[1]]
return term*[0 1;1 0] .+
[w[2]*dterm; w[1]*dterm]
end
next(w, η, s=4.0) = w - η*grad_l... | {"hexsha": "356878cdaa964160f932040f309427205b1d74a8", "size": 620, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "examples/nonconvex_smooth_noncompact.jl", "max_stars_repo_name": "nishaChandramoorthy/neuralOMET", "max_stars_repo_head_hexsha": "4be74e94b6ebe3c103e7dee7daada94b8252984c", "max_stars_repo_licenses"... |
classdef SOP_F21 < PROBLEM
% <single> <real> <expensive/none>
% Shekel's family
%------------------------------- Reference --------------------------------
% X. Yao, Y. Liu, and G. Lin, Evolutionary programming made faster, IEEE
% Transactions on Evolutionary Computation, 1999, 3(2): 82-102.
%-------------------------... | {"author": "BIMK", "repo": "PlatEMO", "sha": "c5b5b7c37a9bb42689a5ac2a0d638d9c4f5693d5", "save_path": "github-repos/MATLAB/BIMK-PlatEMO", "path": "github-repos/MATLAB/BIMK-PlatEMO/PlatEMO-c5b5b7c37a9bb42689a5ac2a0d638d9c4f5693d5/PlatEMO/Problems/Single-objective optimization/Simple SOPs/SOP_F21.m"} |
[STATEMENT]
lemma Trans: assumes "H \<turnstile> x EQ y" "H \<turnstile> y EQ z" shows "H \<turnstile> x EQ z"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. H \<turnstile> x EQ z
[PROOF STEP]
proof -
[PROOF STATE]
proof (state)
goal (1 subgoal):
1. H \<turnstile> x EQ z
[PROOF STEP]
have "\<And>H. H \<turnstile> (... | {"llama_tokens": 1000, "file": "Robinson_Arithmetic_Robinson_Arithmetic", "length": 12} |
import numpy as np
from utils import *
def sort_pixels(dnn, layer_functions, image, nc_layer, pos, gran=2):
sort_list=np.linspace(0, 1, gran)
image_batch = np.kron(np.ones((gran, 1, 1, 1)), image)
images=[]
(row, col, chl) = image.shape
dim_row, dim_col=row, col
if row>DIM: dim_row=DIM
if col>DIM: dim_c... | {"hexsha": "b5cf0ef05b7d4a54460d6ba20b1ae32ce7027cd7", "size": 4326, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/l0_encoding.py", "max_stars_repo_name": "853108389/DeepConcolic", "max_stars_repo_head_hexsha": "2fb4bee11a07dcf39d9df9b2534f377336257def", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_st... |
#!/usr/bin/env python
# -*- coding:utf-8 -*-
# Author: shirui <shirui816@gmail.com>
import numpy as np
class Evaluation(object):
r"""Evaluation of model."""
def __init__(self, model):
r"""Initialize with model.
Arguments:
model: a fit object
"""
self.model = model
... | {"hexsha": "603abed99b992abc7946158668d8ec5e715dc3bb", "size": 2904, "ext": "py", "lang": "Python", "max_stars_repo_path": "utils/Evaluate.py", "max_stars_repo_name": "Shirui816/MultipleDistributionFitting", "max_stars_repo_head_hexsha": "5d3a51383fb8057f725468a5da6bdbc75dc40b99", "max_stars_repo_licenses": ["MIT"], "m... |
# Data Preprocessing
# Importing the libraries
# import matplotlib.pyplot as plt
# library pandas offers data structures and operations for manipulating numerical tables and time series
import numpy as np
import pandas as pd
# Importing the dataset
df = pd.read_csv('Data.csv')
X = df.iloc[:, :-1].values
y = df.iloc... | {"hexsha": "ee4e13d85aa42d8c27a08cf542b2cf0cd58d1690", "size": 648, "ext": "py", "lang": "Python", "max_stars_repo_path": "Machine Learning A-Z/Part 1 - Data Preprocessing/missing_data.py", "max_stars_repo_name": "SenonLi/LearnPython", "max_stars_repo_head_hexsha": "0d37ed625c623a79daa9c4407751050e683fa3ed", "max_stars... |
from unittest import TestCase, SkipTest
import sys
from parameterized import parameterized
import numpy as np
import pandas as pd
from holoviews.core import GridMatrix, NdOverlay
from holoviews.element import (
Bivariate,
Distribution,
HexTiles,
Histogram,
Scatter,
)
from hvplot import scatter_mat... | {"hexsha": "e11854296f5437afb6c864388ab500aa8ec27cd4", "size": 5153, "ext": "py", "lang": "Python", "max_stars_repo_path": "hvplot/tests/plotting/testscattermatrix.py", "max_stars_repo_name": "vishalbelsare/hvplot", "max_stars_repo_head_hexsha": "e0767f2533daf0ba8ed5b6ea2f28000803d99b91", "max_stars_repo_licenses": ["B... |
import numpy as np
import pandas as pd
import datetime as dt
import sqlalchemy
from sqlalchemy.ext.automap import automap_base
from sqlalchemy.orm import Session
from sqlalchemy import create_engine, func
from dateutil.relativedelta import relativedelta
from flask import Flask, jsonify
engine = create_engine("sqlite:/... | {"hexsha": "6f06a440dd502c3d59e1cd2921303cdcb6a1b0dd", "size": 3733, "ext": "py", "lang": "Python", "max_stars_repo_path": "appHW.py", "max_stars_repo_name": "kristine848/SQL-Alchemy-challenge", "max_stars_repo_head_hexsha": "3cca93b8c46532c3900da6c8013902fb451dae99", "max_stars_repo_licenses": ["ADSL"], "max_stars_cou... |
(******************************************************************************)
(* Project: The Isabelle/UTP Proof System *)
(* File: Time.thy *)
(* Authors: Frank Zeyda and Simon Foster (University of York, UK) ... | {"author": "isabelle-utp", "repo": "utp-main", "sha": "27bdf3aee6d4fc00c8fe4d53283d0101857e0d41", "save_path": "github-repos/isabelle/isabelle-utp-utp-main", "path": "github-repos/isabelle/isabelle-utp-utp-main/utp-main-27bdf3aee6d4fc00c8fe4d53283d0101857e0d41/fmi/Time.thy"} |
import numpy as np
from rapt import Re, B0
from scipy.interpolate import RegularGridInterpolator
class _Field:
"""
The superclass for fields. Not used directly, but subclassed. All field-
related data and methods are defined in field objects.
Attributes
----------
gradientstepsize : float
... | {"hexsha": "79eff2c7fcd843cee60c244c17ac5535481bd75e", "size": 27621, "ext": "py", "lang": "Python", "max_stars_repo_path": "rapt/fields.py", "max_stars_repo_name": "mkozturk/rapt", "max_stars_repo_head_hexsha": "cb293ac98d2d7707baf822b4e0efe18b2355f35c", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_s... |
SUBROUTINE ECCMOD(I,ITERM)
*
*
* Eccentricity modulation of hierarchical binary.
* -----------------------------------------------
*
INCLUDE 'common6.h'
COMMON/BINARY/ CM(4,MMAX),XREL(3,MMAX),VREL(3,MMAX),
& HM(MMAX),UM(4,MMAX),UMDOT(4,MMAX),TMDIS(MMAX),
& ... | {"hexsha": "6d9aea6f43a8754d4077ea81665bfd6cb3ca5c7e", "size": 4471, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "src/amuse/community/nbody6xx/src/eccmod.f", "max_stars_repo_name": "rknop/amuse", "max_stars_repo_head_hexsha": "85d5bdcc29cfc87dc69d91c264101fafd6658aec", "max_stars_repo_licenses": ["Apache-2.0"... |
import numpy as np
import pytest
import tinynn as tn
tn.seeder.random_seed(31)
@pytest.fixture(name="mock_dataset")
def fixture_mock_dataset():
X = np.random.normal(size=(100, 5))
y = np.random.uniform(size=(100, 1))
return X, y
@pytest.fixture(name="mock_img_dataset")
def fixture_mock_img_dataset():
... | {"hexsha": "8d117c26d6ddf50bc882f2e094b9562629305b38", "size": 2246, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/others/test_functionality.py", "max_stars_repo_name": "lx120/tinynn", "max_stars_repo_head_hexsha": "88b941a706700ca7f6b1cc4ae7f271df7049348c", "max_stars_repo_licenses": ["MIT"], "max_stars... |
# Use the Dask executor scheduler with a threadpool executor
import dask.array as da
import numpy as np
from dask_executor_scheduler import executor_scheduler
if __name__ == '__main__':
x = da.random.random((10000, 1000), chunks=(1000, 1000))
y = np.sum(x, axis=1)
z = y.compute(scheduler=executor_schedu... | {"hexsha": "59229956eab17f48f99e5bd917f53c715d242643", "size": 357, "ext": "py", "lang": "Python", "max_stars_repo_path": "examples/threadpool_executor.py", "max_stars_repo_name": "arontsang/dask-executor-scheduler", "max_stars_repo_head_hexsha": "c502f128cbad7421493b1dc2a70ee2ce723afaa9", "max_stars_repo_licenses": ["... |
"""flat.py
Provides alternative functions to hdbscan.HDBSCAN and others to
1. Allow prediction on a flat clustering by specifying 'n_clusters'.
This is done by choosing the best cluster_selection_epsilon that produces
the required number of clusters without adding unnecessary outliers.
2. Makes approximate_pre... | {"hexsha": "1bbbb5acfb11a629736d380607967ae74a621771", "size": 38864, "ext": "py", "lang": "Python", "max_stars_repo_path": "hdbscan/flat.py", "max_stars_repo_name": "ainkov/hdbscan", "max_stars_repo_head_hexsha": "29dbaedfd281addc86cdae69a65798b9230a2c6e", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count"... |
function boys_function(n::Int, x::Float64)::Float64
x < 1e-6 && return 1/(2n+1)
n == 0 && return 0.5*√(π/x)*erf(√x)
return ((2n-1)*boys_function(n-1, x) - exp(-x))/(2x)
end
"S(gs, gs)"
function overlap_integral(g1::Gaussian_s, g2::Gaussian_s)::Float64
p = (g1.α + g2.α)
μ = (g1.α * g2.α) / p
γ =... | {"hexsha": "cde3a813e5be713001edaa82c8cd80b44521cde1", "size": 5278, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/gto_integral.jl", "max_stars_repo_name": "0382/HartreeFock.jl", "max_stars_repo_head_hexsha": "1cf2c3eb52c84a23ada62196ae5e8739d02027a2", "max_stars_repo_licenses": ["MIT"], "max_stars_count": ... |
```python
"""Purely numerical 1D finite element program."""
import sys, os, time
sys.path.insert(
0, os.path.join(os.pardir, os.pardir, 'approx', 'src-approx'))
from numint import GaussLegendre, NewtonCotes
#from fe_approx1D_numint import u_glob
import sympy as sym
import numpy as np
def Lagrange_polynomial(x, i,... | {"hexsha": "339cdbe704d161afe5e7a1c14307646a0a8c1bf1", "size": 19576, "ext": "ipynb", "lang": "Jupyter Notebook", "max_stars_repo_path": "Data Science and Machine Learning/Machine-Learning-In-Python-THOROUGH/EXAMPLES/FINITE_ELEMENTS/INTRO/SRC/19_FE1D.ipynb", "max_stars_repo_name": "okara83/Becoming-a-Data-Scientist", "... |
import os
from collections import OrderedDict
import numpy as np
import scipy.io
try:
import pandas as pd
HAS_PANDAS = True
except ImportError:
HAS_PANDAS = False
class GenericSpikeExporter:
def __call__(self,spikes, catalogue, seg_num, chan_grp, export_path,
split_by_cluster... | {"hexsha": "b48bae98bb56abf765c039c658f5e90dccfa7b28", "size": 3407, "ext": "py", "lang": "Python", "max_stars_repo_path": "tridesclous/export.py", "max_stars_repo_name": "remi-pr/tridesclous", "max_stars_repo_head_hexsha": "074f425fd40f1fb76f619f74cc024dd9817b7ee7", "max_stars_repo_licenses": ["MIT"], "max_stars_count... |
#! /usr/bin/env python
# -*- coding: utf-8 -*-
#
# Copyright 2020-2021 Alibaba Group Holding Limited.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LI... | {"hexsha": "17f6d65a930270abe03f3a20dceced43683cb5ee", "size": 4285, "ext": "py", "lang": "Python", "max_stars_repo_path": "python/vineyard/deploy/tests/test_migration.py", "max_stars_repo_name": "v6d-io/v6d", "max_stars_repo_head_hexsha": "8f692c9bd95dad06c304a0020d4f946a5756c1e0", "max_stars_repo_licenses": ["Apache-... |
[STATEMENT]
lemma step_induction[consumes 2, case_names app\<^sub>1 app\<^sub>2 thunk lamvar var\<^sub>2 let\<^sub>1 if\<^sub>1 if\<^sub>2 refl trans]:
assumes "c \<Rightarrow>\<^sup>* c'"
assumes "\<not> boring_step c'"
assumes app\<^sub>1: "\<And> \<Gamma> e x S . P (\<Gamma>, App e x, S) (\<Gamma>, e , Arg x... | {"llama_tokens": 686, "file": "Call_Arity_Sestoft", "length": 1} |
c.......subroutine bfilter
c
c Written by: David R. Russell, AFTAC/TT 10 December 2004
c
c Subroutine bfilter executes a fast, stable zero phase butterworth
c bandpass filter of order (m), which is optimized for narrow band
c applications. The method produces a complex time series output,
c of... | {"hexsha": "ebb29972c24598ceecba0f5d7e9aec9fceb1041b", "size": 4541, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "ni/src/lib/nfpfort/bfilter1.f", "max_stars_repo_name": "jlost/ncl_ncarg", "max_stars_repo_head_hexsha": "2206367f1887732bc7745bfb5ca56f6543f77948", "max_stars_repo_licenses": ["Apache-2.0"], "max_... |
[STATEMENT]
lemma (in dist_execution) recv_insert_once:
"event_at (i,j) (Receive s (Insert m)) \<Longrightarrow> event_at (i,k) (Receive t (Insert m)) \<Longrightarrow> j = k"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. \<lbrakk>event_at (i, j) (Receive s (Insert m)); event_at (i, k) (Receive t (Insert m))\<rbr... | {"llama_tokens": 379, "file": "WOOT_Strong_Eventual_Consistency_StrongConvergence", "length": 2} |
[STATEMENT]
lemma strategy_attracts_irrelevant_override:
assumes "strategy_attracts p \<sigma> A W" "strategy p \<sigma>" "strategy p \<sigma>'"
shows "strategy_attracts p (override_on \<sigma>' \<sigma> (A - W)) A W"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. strategy_attracts p (override_on \<sigma>' \<sig... | {"llama_tokens": 7579, "file": "Parity_Game_AttractingStrategy", "length": 74} |
import random
from enum import Enum
import numpy as np
class LocalSearch(Enum):
PER_VARIABLE_LOCAL_SEARCH = 1
def local_search_gene(population, fitness_function, method, config):
new_population = None
if (method == LocalSearch.PER_VARIABLE_LOCAL_SEARCH):
new_population = _per_variable_local_se... | {"hexsha": "eb7bec4bad80bfd22abd0bd13810b0e1543a3177", "size": 1548, "ext": "py", "lang": "Python", "max_stars_repo_path": "ga/local_search.py", "max_stars_repo_name": "YannHyaric/evolutionary-computation", "max_stars_repo_head_hexsha": "af7778fd1b5d60a1e5630b483b55257adac0bbc6", "max_stars_repo_licenses": ["MIT"], "ma... |
using DashBootstrapComponents, DashHtmlComponents
toast = dbc_toast(
[html_p("This is the content of the toast", className = "mb-0")],
header = "This is the header",
);
| {"hexsha": "f1969c81c018f8d211dce4d4c3c38f847e73ce25", "size": 178, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "docs/components_page/components/toast/simple.jl", "max_stars_repo_name": "glsdown/dash-bootstrap-components", "max_stars_repo_head_hexsha": "0ebea4f7de43975f6e3a2958359c4480ae1d4927", "max_stars_rep... |
import pandas as pd
import json
import scipy.stats
import random
import numpy as np
from yattag import Doc
import itertools
from collections import defaultdict
import argparse
import os
random.seed(1)
import hashlib
def hashhex(s):
"""Returns a heximal formated SHA1 hash of the input string."""
h = hashlib.s... | {"hexsha": "9ea60931f32fbe346e8f347edcb80e6b554e2979", "size": 1747, "ext": "py", "lang": "Python", "max_stars_repo_path": "scripts/generate_html_from_outputs.py", "max_stars_repo_name": "oja/qfsumm", "max_stars_repo_head_hexsha": "dfa3541cfad928df412c86888ef0354ea97e8382", "max_stars_repo_licenses": ["MIT"], "max_star... |
# !/usr/bin/python
# -*- coding:utf-8 -*-
# Author: Shengjia Yan
# Date: 2017-10-26
# Email: i@yanshengjia.com
import sys
reload(sys)
sys.setdefaultencoding('utf8')
import logging
import numpy as np
import itertools
import matplotlib.pyplot as plt
from sklearn.metrics import confusion_matrix
def load_confusion_ma... | {"hexsha": "04c463c4e66719daeda64853ef761423a9ff054f", "size": 3128, "ext": "py", "lang": "Python", "max_stars_repo_path": "utils/plot/plot_confusion_matrix.py", "max_stars_repo_name": "yanshengjia/nlp", "max_stars_repo_head_hexsha": "43398652b2cab9b85fd042f60e6f68c7b48697bc", "max_stars_repo_licenses": ["MIT"], "max_s... |
#todo : just for test ===================
import numpy
from PIL import Image
import torch
'''
a= numpy.array(Image.open('/home/leejeyeol/Datasets/Avenue/training_videos/15/output_00118.png'),dtype=numpy.float)
a2 =Image.fromarray(a)
a2.show()
print(a)
b= numpy.array(Image.open('/home/leejeyeol/Datasets/Avenue/mean_ima... | {"hexsha": "ef77b90621ee2b166cbfc39f6553617928c719a8", "size": 821, "ext": "py", "lang": "Python", "max_stars_repo_path": "legacy/data_generation/NIPS2017_evaluation.py", "max_stars_repo_name": "neohanju/AutoencodingTheWorld", "max_stars_repo_head_hexsha": "23f8a89bb7399df63cd7a0cb1b5a750214a44072", "max_stars_repo_lic... |
import numpy as np
class Features:
def __init__(self):
self._horest_features = []
self._texture_feature = []
self._sift_SDS = []
self._sift_SOH = []
@property
def horest_features(self):
return self._horest_features
@property
def texture_feature(self):
... | {"hexsha": "2f1158442a599aa1246e5addf62f2e6ec608158f", "size": 866, "ext": "py", "lang": "Python", "max_stars_repo_path": "server/models/features.py", "max_stars_repo_name": "Shaalan31/LIWI", "max_stars_repo_head_hexsha": "b4d615e0951b7c28c9258d0d7a8ff86c73c4ebe2", "max_stars_repo_licenses": ["MIT"], "max_stars_count":... |
import numpy as np
import matplotlib.pyplot as plt
import imageio
import scipy, scipy.misc, scipy.signal
import cv2
import sys
import PIL
from PIL import Image
windowName = ''
threshold = 11
size = 5
# path to input image is specified and
# image is loaded with imread command
image1 = cv2.imread('0136ns.png')
# ... | {"hexsha": "49a2ab22e8192297490d8fa6636222f77a77ec93", "size": 2229, "ext": "py", "lang": "Python", "max_stars_repo_path": "adaptative_threshold.py", "max_stars_repo_name": "fthernan/interferometry-processing-tools", "max_stars_repo_head_hexsha": "84420990410e117af08675247078734050079a22", "max_stars_repo_licenses": ["... |
import argparse
from numpy.random import default_rng
NUMBER_OF_SAMPLES = 1
def generate_normal_random_numbers(samples=1):
rng = default_rng()
return rng.standard_normal(samples)
if __name__ == '__main__':
# parse arguments
parser = argparse.ArgumentParser(
description="Generate random num... | {"hexsha": "c13616ee0118d701f5ff3c365755a4bd428ecf94", "size": 770, "ext": "py", "lang": "Python", "max_stars_repo_path": "python/normal_numbers.py", "max_stars_repo_name": "sernamar/random-numbers", "max_stars_repo_head_hexsha": "9117a59b246ced3d82803cfd7b82b05f8a456d97", "max_stars_repo_licenses": ["MIT"], "max_stars... |
import sys
sys.path.insert(1, '..')
import pickle
import requests
from bs4 import BeautifulSoup
from tqdm import tqdm
import datetime as dt
from collections import defaultdict
import numpy as np
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
import tensorflow as tf
from tensorflow.keras.models impo... | {"hexsha": "44eb4904e210a7a3531ee94621a96fc75ddcc273", "size": 6780, "ext": "py", "lang": "Python", "max_stars_repo_path": "model/offline_predict_q.py", "max_stars_repo_name": "penguinwang96825/Intelligent-Asset-Allocation", "max_stars_repo_head_hexsha": "62aa4e70dae50c60c4dae7acfe0388028be242a2", "max_stars_repo_licen... |
import scipy.fft
import matplotlib.pyplot as plt
import numpy as np
x1=([0,4,2,0])
dft=scipy.fft.fft(x1)
plt.figure(figsize=(8,9))
plt.subplot(2, 1, 1)
plt.stem(dft.real, use_line_collection = True)
plt.xlabel('k')
plt.ylabel('Real{x[k]}')
plt.title('Real part of DFT')
plt.subplot(2, 1, 2)
plt.s... | {"hexsha": "fc10120e6149f6e7f0da26e9706dbcb0eee7c372", "size": 486, "ext": "py", "lang": "Python", "max_stars_repo_path": "Py_lab/Lab 4/scipy_one.py", "max_stars_repo_name": "veterinarian-5300/Genious-Python-Code-Generator", "max_stars_repo_head_hexsha": "d78cd5f4b64221e8e4dc80d6e1f5ba0a4c613bcd", "max_stars_repo_licen... |
import autograd.numpy as np
import numpy.random as npr
from trajopt import core
npr.seed(1337)
if __name__ == '__main__':
Q = np.eye(2)
q = np.zeros((2, ))
q0 = 0.0
mu = np.zeros((2, ))
sigma = np.eye(2)
# expectation of quadratic under gaussian
print(core.quad_expectation(mu, sigma, Q,... | {"hexsha": "61eeb54c3ff8e1d4bf4c04a4cc190166f9b9822a", "size": 329, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/arma_test.py", "max_stars_repo_name": "JoeMWatson/trajopt", "max_stars_repo_head_hexsha": "8b98718721e0c373cd7dc01a35f42447c1134713", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1,... |
import os, sys, time
import argparse
import learn_mtfixbmodel
import mtfixb_model
import parseopts
import torch
import torch.optim as optim
import numpy as np
def parse_args(args=None):
parser = argparse.ArgumentParser(description='Optimise Z on new data')
parser.add_argument('--style_ix', dest='style_ix',
... | {"hexsha": "f2fc35440a5f735f238cbfec1e95b6f216b3caca", "size": 11521, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/optim_z.py", "max_stars_repo_name": "ornithos/pytorch-mtds-mocap", "max_stars_repo_head_hexsha": "3ec10387d3d897e9a20d789bd4a3782a047519f7", "max_stars_repo_licenses": ["MIT"], "max_stars_cou... |
# created by Dmitrey
#PythonAll = all
from numpy import asarray, empty, inf, any, array, \
asfarray, isscalar, ndarray, int16, int32, int64, float64, tile, vstack, searchsorted, \
logical_or, where, asanyarray, arange, log2, logical_and, ceil, string_, atleast_1d
import numpy as np
from FDmisc import FuncDesignerExcep... | {"hexsha": "ca4193180bfc9518d393815530f27c1a7d22f9bf", "size": 16356, "ext": "py", "lang": "Python", "max_stars_repo_path": "lib/python2.7/site-packages/FuncDesigner/ooVar.py", "max_stars_repo_name": "wangyum/anaconda", "max_stars_repo_head_hexsha": "6e5a0dbead3327661d73a61e85414cf92aa52be6", "max_stars_repo_licenses":... |
import argparse
import calendar
import copy
import glob
import shutil
import subprocess
import numpy as np
import pandas as pd
from sqlalchemy.exc import IntegrityError
from datetime import date
from urllib2 import urlopen
from httplib import BadStatusLine
from time import sleep
import bom_data_parser as bdp
from p... | {"hexsha": "2d9c5a66b7850081772c0dca622f61f827b04ba8", "size": 2363, "ext": "py", "lang": "Python", "max_stars_repo_path": "load_storages.py", "max_stars_repo_name": "amacd31/hydromet-toolkit", "max_stars_repo_head_hexsha": "d39edc6d3e02adeb3cd89ca13fdb9660be3247b4", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_st... |
/*******************************************************************************
*
* MIT License
*
* Copyright (c) 2019 Advanced Micro Devices, Inc.
*
* Permission is hereby granted, free of charge, to any person obtaining a copy
* of this software and associated documentation files (the "Software"), to deal
* ... | {"hexsha": "b938461d4ee74f45aab6ba355de52c8a16bae59a", "size": 6882, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "src/include/miopen/find_db.hpp", "max_stars_repo_name": "j4yan/MIOpen", "max_stars_repo_head_hexsha": "dc38f79bee97e047d866d9c1e25289cba86fab56", "max_stars_repo_licenses": ["MIT"], "max_stars_count... |
#include "driver-test.h"
#include <boost/test/unit_test.hpp>
BOOST_AUTO_TEST_SUITE(BankcardTestSuite)
BOOST_AUTO_TEST_CASE(bankcard_test)
{
Driver driver;
DriverTest dt;
boost::filesystem::path directory("TestFiles");
std::ifstream metadataFile((directory / "bankcardExample" / "zone_files" / "metadata... | {"hexsha": "fd27c6a404430ab6ff84f515b0e82a71ad616f2f", "size": 1395, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "test/bankcardTest.cpp", "max_stars_repo_name": "dns-groot/groot", "max_stars_repo_head_hexsha": "995b1bb64bfe4a1407dcf0c5a6910dfe1d60e427", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 58.... |
from os.path import splitext,basename
import os
import tensorflow as tf
from keras.models import model_from_json
from sklearn.preprocessing import LabelEncoder
import glob
import numpy as np
def load_models(wpod_net_path, mobile_net_path):
try:
#open wpod-net model json file and make model using it
... | {"hexsha": "f407ee047644a13c68908214a11c93852c88aeed", "size": 1639, "ext": "py", "lang": "Python", "max_stars_repo_path": "server/number_plate_detection_and_recognition/load_models.py", "max_stars_repo_name": "CS305-software-Engineering/vehicle-attendance-system", "max_stars_repo_head_hexsha": "b33a583f923d92be669ee89... |
(* ** Imports and settings *)
From mathcomp Require Import all_ssreflect all_algebra.
From mathcomp Require Import word_ssrZ.
Require Import strings word utils type var expr.
Require Import compiler_util byteset.
Require Import ZArith.
Set Implicit Arguments.
Unset Strict Implicit.
Unset Printing Implicit Defensive.
... | {"author": "jasmin-lang", "repo": "jasmin", "sha": "3c783b662000c371ba924a953d444fd80b860d9f", "save_path": "github-repos/coq/jasmin-lang-jasmin", "path": "github-repos/coq/jasmin-lang-jasmin/jasmin-3c783b662000c371ba924a953d444fd80b860d9f/proofs/compiler/stack_alloc.v"} |
import numpy as np
import torch
from baselines.common.vec_env import VecEnvWrapper
from gym import spaces, ActionWrapper
from envs.ImageObsVecEnvWrapper import get_image_obs_wrapper
from envs.ResidualVecEnvWrapper import get_residual_layers
from pose_estimator.utils import unnormalise_y
class PoseEstimatorVecEnvWrap... | {"hexsha": "66b21eabe50e9934a4811aa461c0814373767b70", "size": 6412, "ext": "py", "lang": "Python", "max_stars_repo_path": "envs/wrappers.py", "max_stars_repo_name": "harry-uglow/Curriculum-Reinforcement-Learning", "max_stars_repo_head_hexsha": "cb050556e1fdc7b7de8d63ad932fc712a35ac144", "max_stars_repo_licenses": ["MI... |
'''
Author: S.T. Castle
Created: 2015-03-15
'''
#import math
import numpy as np
from scipy import ndimage
from scipy import stats
import scipy.ndimage.filters
import scipy.linalg
#import skimage.feature
import cv2
from matplotlib import pyplot as plt
def main():
'''
Run the explicit coherence enhancing filt... | {"hexsha": "a78080861ab4e14c3531c836736769622a919864", "size": 8806, "ext": "py", "lang": "Python", "max_stars_repo_path": "coherence-elliptical-kernel/main-iter.py", "max_stars_repo_name": "stcastle/shell-detection", "max_stars_repo_head_hexsha": "cdc49190deae7310db66e56574b6737771821f31", "max_stars_repo_licenses": [... |
#!/usr/bin/python
#-*- coding: utf-8 -*-
#===========================================================
# File Name: layers.py
# Author: Xu Zhang, Columbia University
# Creation Date: 09-07-2018
# Last Modified: Fri Sep 7 21:07:05 2018
#
# Usage:
# Description:
#
# Copyright (C) 2018 Xu Zhang
# All rights rese... | {"hexsha": "a4bd4ff95b6323aac18e6acfab16e91f7134ffe1", "size": 2756, "ext": "py", "lang": "Python", "max_stars_repo_path": "tensorflow/layers.py", "max_stars_repo_name": "ColumbiaDVMM/Heated_Up_Softmax_Embedding", "max_stars_repo_head_hexsha": "cb62d28e5faaf7fdb134b31c461125e3fef50d06", "max_stars_repo_licenses": ["BSD... |
// Copyright 2018 Your Name <your_email>
#ifndef INCLUDE_HEADER_HPP_
#define INCLUDE_HEADER_HPP_
#include <iostream>
#include <boost/filesystem.hpp>
#include <vector>
#include <string>
#define DIRECTORY 3
#define COM_FILE 2
using boost::filesystem::path;
using std::cout;
using std::endl;
using std::vector;
using st... | {"hexsha": "db03c42c2478800918ba605028a0f49b0ce1063b", "size": 4576, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "include/header.hpp", "max_stars_repo_name": "Darioshka/lab_04", "max_stars_repo_head_hexsha": "91cdb16431394359c2afa42c71b321acbd3a80ee", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null,... |
import numpy as np
import matplotlib.pyplot as plt
rau = np.logspace(-1, 3, 1000)
# Isella et al. 2009
Rt = [55., 21., 86., 28., 21., 110., 60., 25., 43., 66., 20.]
St = [10., 608., 1.5, 13., 80., 4., 31., 58., 12., 4.7, 50.]
gam = [-0.3, -0.5, 0.8, 0.0, -0.3, 0.7, -0.8, -0.1, 0.8, 0.5, 0.1]
bmaj = np.array([1.05, 0.... | {"hexsha": "619d39c8d4e4ebc0cf8b29fad3b36f1880d5dbbf", "size": 912, "ext": "py", "lang": "Python", "max_stars_repo_path": "sigmas_profiles.py", "max_stars_repo_name": "seanandrews/ARAA", "max_stars_repo_head_hexsha": "6c95f88f5619642b6914c611ba6c902b5412ab29", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null... |
#!/usr/bin/python
#
# Determine signal strength upper limit for Dark Matter Z' mediator simplified model
# based on the ATLAS Run 2 dilepton resonance search
#
import sys, argparse, os, ROOT
import numpy as np
def getMuLimit(x = np.empty(0), mediator_type = "V"):
argParser = argparse.ArgumentParser( descrip... | {"hexsha": "aabeef609b57305da42cb5c84a181a2f45771369", "size": 4993, "ext": "py", "lang": "Python", "max_stars_repo_path": "excursion/testcases/madgraph5atlasval/getMuLimit.py", "max_stars_repo_name": "irinaespejo/excursion", "max_stars_repo_head_hexsha": "c5a5c6d882b8dd1008fbabf1a3b81eaba382bef6", "max_stars_repo_lice... |
import numpy as np
def lanc(numwt, haf):
"""
Generates a numwt + 1 + numwt lanczos cosine low pass filter with -6dB
(1/4 power, 1/2 amplitude) point at haf
Parameters
----------
numwt : int
number of points
haf : float
frequency (in 'cpi' of -6dB point, 'cpi' is cycl... | {"hexsha": "5e5915684ffcce5d999eda9fbdd61b43e78f8c36", "size": 19922, "ext": "py", "lang": "Python", "max_stars_repo_path": "oceans/filters.py", "max_stars_repo_name": "Michelly-GC/python-oceans", "max_stars_repo_head_hexsha": "7b0e12a00cd125683e7b89acea0cdd67f7729a43", "max_stars_repo_licenses": ["BSD-3-Clause"], "max... |
import numpy as np
import os
from sklearn import ensemble, feature_extraction, preprocessing
from otto_utils import consts, utils
MODEL_NAME = 'model_02_random_forest'
MODE = 'holdout' # cv|submission|holdout
# import data
train, labels, test, _, _ = utils.load_data()
# transform counts to TFIDF features
tfidf =... | {"hexsha": "52339a2d7c8253906fd34a61bc45232f215bc9a0", "size": 1446, "ext": "py", "lang": "Python", "max_stars_repo_path": "data/external/repositories/156296/kaggle_otto-master/otto/model/model_02_random_forest/random_forest.py", "max_stars_repo_name": "Keesiu/meta-kaggle", "max_stars_repo_head_hexsha": "87de739aba2399... |
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