text stringlengths 0 1.25M | meta stringlengths 47 1.89k |
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import torch.optim.lr_scheduler as scheduler
import numpy as np
from vel.api import Callback, SchedulerFactory
class LadderScheduler(Callback):
""" Scheduler defined by a set of learning rates after reaching given number of iterations """
def __init__(self, optimizer, ladder, last_epoch):
self.sched... | {"hexsha": "0699c266722934fd86252e62b0a2b9f77ec115c6", "size": 1219, "ext": "py", "lang": "Python", "max_stars_repo_path": "vel/scheduler/ladder.py", "max_stars_repo_name": "galatolofederico/vel", "max_stars_repo_head_hexsha": "0473648cffb3f34fb784d12dbb25844ab58ffc3c", "max_stars_repo_licenses": ["MIT"], "max_stars_co... |
#https://codegolf.stackexchange.com/questions/10701/fastest-code-to-find-the-next-prime
import sys
import numpy as np
import tqdm
min_order = int(sys.argv[1])
max_order = int(sys.argv[2])
primes_order = int(sys.argv[3])
max_gap = int(sys.argv[4])
N_core = int(sys.argv[5])
N_order = max_order-min_order+1
N_primes = po... | {"hexsha": "849e623e729cb6dcb0030749c7e7cd1cdb808676", "size": 5699, "ext": "py", "lang": "Python", "max_stars_repo_path": "PrimeGaps/primegaps_sampling.py", "max_stars_repo_name": "DouglasBoubert/VisualisationEveryWeek", "max_stars_repo_head_hexsha": "aed332c3ae5706f9826a6e5460986a2b3df68b76", "max_stars_repo_licenses... |
(* Title: HOL/Auth/n_g2kAbsAfter_lemma_inv__28_on_rules.thy
Author: Yongjian Li and Kaiqiang Duan, State Key Lab of Computer Science, Institute of Software, Chinese Academy of Sciences
Copyright 2016 State Key Lab of Computer Science, Institute of Software, Chinese Academy of Sciences
*)
header{*T... | {"author": "lyj238Gmail", "repo": "newParaVerifier", "sha": "5c2d49bf8e6c46c60efa53c98b0ba5c577d59618", "save_path": "github-repos/isabelle/lyj238Gmail-newParaVerifier", "path": "github-repos/isabelle/lyj238Gmail-newParaVerifier/newParaVerifier-5c2d49bf8e6c46c60efa53c98b0ba5c577d59618/examples/n_g2kAbsAfter/n_g2kAbsAft... |
import csv
import pandas
import scipy.stats
class TimeDataSet:
sortTypes = ["BubbleSort", "InsertionSort", "MergeSort", "QuickSort", "SelectionSort"]
def __init__(self, fileName):
file = open(fileName)
reader = csv.DictReader(file, fieldnames=self.sortTypes)
self.sortTimes = {}
... | {"hexsha": "ff090498557bfa499634545d7df4577aea069b92", "size": 2194, "ext": "py", "lang": "Python", "max_stars_repo_path": "EvaluateData.py", "max_stars_repo_name": "SaurabhTotey/D-Language-Array-Measurements", "max_stars_repo_head_hexsha": "cc431df411adea1912ce4cd3deac159f030753f4", "max_stars_repo_licenses": ["Unlice... |
"""Unit tests for the `src.milannotations.datasets` submodule."""
import csv
import shutil
from tests import conftest
from src.milannotations import datasets
import numpy
import pytest
import torch
from PIL import Image
@pytest.fixture
def top_images():
"""Return TopImages for testing."""
return datasets.To... | {"hexsha": "b6e7d767f3f1c1742d5522f257b87c867deebcb9", "size": 15273, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/milannotations/datasets_test.py", "max_stars_repo_name": "ericotjo001/neuron-descriptions", "max_stars_repo_head_hexsha": "744fbf65c6538edd2fa423108eca7e2cd72f8b59", "max_stars_repo_license... |
# This code is based on: https://github.com/msmsajjadi/precision-recall-distributions/blob/master/prd_score.py
"""Precision and recall computation based on samples from two distributions.
Given a set of generated samples and samples from the test set, both embedded in some feature space (say, embeddings of
Inception N... | {"hexsha": "4c16fe10911b92edfc021251b091775065215eb7", "size": 8297, "ext": "py", "lang": "Python", "max_stars_repo_path": "eval/precision_recall.py", "max_stars_repo_name": "i-supermario/Cifar100_CL", "max_stars_repo_head_hexsha": "6c22151ea2c4c3014a569112fdf8a549331b27c4", "max_stars_repo_licenses": ["MIT"], "max_sta... |
using TerminalExtensions
function Base.display(disp::TerminalExtensions.iTerm2.InlineDisplay, p::PredictionFrame)
Base.display(disp, [p])
end
function Base.display(disp::TerminalExtensions.iTerm2.InlineDisplay, frames::Vector{PredictionFrame})
print_frame_table(image_display_callback, frames)
end
function im... | {"hexsha": "86b8e99c38de449ab8c484a477030a833cc1f820", "size": 1312, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/display/terminal_extensions.jl", "max_stars_repo_name": "UnofficialJuliaMirror/Metalhead.jl-dbeba491-748d-5e0e-a39e-b530a07fa0cc", "max_stars_repo_head_hexsha": "b61ddec642a6e9dfb88961c4ceb76ec... |
#include <stdio.h>
#include <stdlib.h>
#include <math.h>
#include <ndlinear/gsl_multifit_ndlinear.h>
#include <gsl/gsl_math.h>
#include <gsl/gsl_sf_laguerre.h>
#include <gsl/gsl_sf_legendre.h>
#include <gsl/gsl_matrix.h>
#include <gsl/gsl_vector.h>
#include <gsl/gsl_multifit.h>
#include <gsl/gsl_rng.h>
#include <gsl/... | {"hexsha": "a4872bf1a77cf2a2be99b4c83307d434f54fb98d", "size": 6164, "ext": "c", "lang": "C", "max_stars_repo_path": "src/BodyComponents/archive/ndlinear-1.0/doc/examples/harmosc.c", "max_stars_repo_name": "rennhak/Keyposes", "max_stars_repo_head_hexsha": "e5ffe4c849b0894f27d58985b41ec8edd3432be1", "max_stars_repo_lice... |
import unittest
from mltoolkit.mldp.steps.transformers.nlp import WindowSlider
from mltoolkit.mldp.steps.transformers.nlp.helpers import create_new_field_name
from mltoolkit.mldp.utils.tools import DataChunk
import numpy as np
class TestWindowSlider(unittest.TestCase):
def setUp(self):
self.field_name = "... | {"hexsha": "b91c61a752ad81bb3ad2ba5a7e0f42efd643c33d", "size": 4569, "ext": "py", "lang": "Python", "max_stars_repo_path": "mltoolkit/mldp/tests/transformers/test_window_slider.py", "max_stars_repo_name": "mancunian1792/FewSum", "max_stars_repo_head_hexsha": "c2f9ef0ae7445bdb188b6ceb28e998b3fd12b78e", "max_stars_repo_l... |
"""This module defines classes of the package."""
# Author: Henri Gérard <hgerard.proy@gmail.com>
# License: MIT
abstract type RegressionModel end
# Define an object to
type LinearRegression <: RegressionModel
function LinearRegression()
return new()
end
end
type LogisticRegression <: RegressionMod... | {"hexsha": "e1ebc908db23e84fe6397438d5a1168ad661d0f9", "size": 2879, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "tests/test_objects.jl", "max_stars_repo_name": "Henri-Gerard/robox", "max_stars_repo_head_hexsha": "8ad29add8641e3a1255a22185907124cb8afa050", "max_stars_repo_licenses": ["MIT"], "max_stars_count":... |
\subsection{Complexity Estimate} \label{complexity_estimate}
The Complexity Estimate (CE) is a complexity measure for time series and the authors of \cite{batista2011complexity}
introduced one possible approach of a CE implementation. Given is a time series $Q = (q_1, q_2, \dots, q_i, \dots, q_l)$
with length $l$ over ... | {"hexsha": "9e23a2af7f334bb0e83e00889ff261177e031f36", "size": 1020, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "bachelor-thesis/background_and_notation/complexity_estimate.tex", "max_stars_repo_name": "GordonLesti/SlidingWindowFilter", "max_stars_repo_head_hexsha": "22c11f2912a5c523ae8ad85a849e2d0b123536ec", ... |
# -*- coding: utf-8 -*-
"""
Created on Sun May 12 16:34:44 2019
@author: Alexandre
"""
###############################################################################
import numpy as np
###############################################################################
from pyro.control import robotcontrollers
from pyro... | {"hexsha": "da658709686ee2f9d2ecac853d71bf02af38f0c5", "size": 1239, "ext": "py", "lang": "Python", "max_stars_repo_path": "examples/robot_arms/twolinkrobot_effector_pid_controller.py", "max_stars_repo_name": "gabrielcabana21/pyro", "max_stars_repo_head_hexsha": "a3107d7b676a0fe1afb89a18a5a63d08fe9f0998", "max_stars_re... |
import numpy as np
from l5kit.data import ChunkedDataset, get_agents_slice_from_frames, get_tl_faces_slice_from_frames
def insert_agent(agent: np.ndarray, frame_idx: int, dataset: ChunkedDataset) -> None:
"""Insert an agent in one frame.
Assumptions:
- the dataset has only 1 scene
- the dataset is in... | {"hexsha": "3660acac918f0a8864e6a8c083a30724c537cf53", "size": 5635, "ext": "py", "lang": "Python", "max_stars_repo_path": "l5kit/l5kit/simulation/utils.py", "max_stars_repo_name": "cdicle-motional/l5kit", "max_stars_repo_head_hexsha": "4dc4ee5391479bb71f0b373f39c316f9eef5a961", "max_stars_repo_licenses": ["Apache-2.0"... |
import pytest
import os
import cv2
import numpy as np
from plantcv.plantcv.transform import (get_color_matrix, get_matrix_m, calc_transformation_matrix, apply_transformation_matrix,
save_matrix, load_matrix, correct_color, create_color_card_mask, quick_color_check,
... | {"hexsha": "48a8adca689ae7b1e9f6c39f927c5822193a31cd", "size": 14190, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/plantcv/transform/test_color_correction.py", "max_stars_repo_name": "ygarrot/plantcv", "max_stars_repo_head_hexsha": "e934a891e0d1bf8987ca6a9f982a4ac1f420bfe7", "max_stars_repo_licenses": [... |
[STATEMENT]
lemma master1_automation:
assumes "g \<in> O(MASTER_BOUND'' p')" "1 < (\<Sum>i<k. as ! i * bs ! i powr p')"
"eventually (\<lambda>x. f x > 0) at_top"
shows "f \<in> \<Theta>(MASTER_BOUND p 0 0)"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. f \<in> \<Theta>(MASTER_BOUND p 0 0)
[PROOF ST... | {"llama_tokens": 1242, "file": "Akra_Bazzi_Akra_Bazzi_Method", "length": 11} |
'''Implementations of uniform distributions.'''
import numpy as np
from astropy import units
from astropy.coordinates import SkyCoord
TWO_PI = 2*np.pi
@units.quantity_input(area=units.sr)
def uniform_around(centre, area, size):
'''Uniform distribution of points around location.
Draws randomly distributed p... | {"hexsha": "661d15d4e2613694bb21ccad12a3f7ce431621c7", "size": 3703, "ext": "py", "lang": "Python", "max_stars_repo_path": "skypy/position/_uniform.py", "max_stars_repo_name": "ArthurTolley/skypy", "max_stars_repo_head_hexsha": "5621877ada75c667b1af7e665b02a91026f7ef0f", "max_stars_repo_licenses": ["BSD-3-Clause"], "ma... |
ouRevolution is a current publication of AS Papers which caters to the interest of AfricanAmericans African American students at UC Davis. Its Chief Editor is Alyssa Munson.
Mission Statement
ouRevolution Our Voice is an AS PAPERS publication. We place special emphasis on the unique needs of the African American an... | {"hexsha": "43561e430736c1f2ccab254379f45d0ef47d6c62", "size": 907, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "lab/davisWiki/ouRevolution.f", "max_stars_repo_name": "voflo/Search", "max_stars_repo_head_hexsha": "55088b2fe6a9d6c90590f090542e0c0e3c188c7d", "max_stars_repo_licenses": ["MIT"], "max_stars_count"... |
[STATEMENT]
lemma eval_fps_0 [simp]:
"eval_fps (0 :: 'a :: {banach, real_normed_div_algebra} fps) z = 0"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. eval_fps 0 z = (0::'a)
[PROOF STEP]
by (simp only: fps_const_0_eq_0 [symmetric] eval_fps_const) | {"llama_tokens": 122, "file": null, "length": 1} |
"""
Get candidate object boxes (both GT and candidates) to provide to detectron to compute appearance/object scores
The way we have implemented this :
1. Run the object detector to get candidate object (we used Detectron)
2. Create database object merging candidate detections and groundtruth
3. Forward the bounding box... | {"hexsha": "d464943e00cc7b0c6c797bb743cb55cee46a0682", "size": 1788, "ext": "py", "lang": "Python", "max_stars_repo_path": "utils/compute_candidate_boxes.py", "max_stars_repo_name": "doulemint/analogy", "max_stars_repo_head_hexsha": "75d17812080fde74c9032fb338ef0c6ab2667f44", "max_stars_repo_licenses": ["MIT"], "max_st... |
#Generacion de la Linea de Muerte para la UBA año 2020
#Solo necesita 32GB de memoria RAM , 8 vCPU y una hora para correr
#limpio la memoria
rm( list=ls() ) #remove all objects
gc() #garbage collection
require("data.table")
require("lightgbm")
require("DiceKriging")
require("mlrMBO")
#en estos archiv... | {"hexsha": "409183875a6811d8f62be8931ac1a03143c2d3b0", "size": 7561, "ext": "r", "lang": "R", "max_stars_repo_path": "scripts/lineademuerte.r", "max_stars_repo_name": "miglesias91/dmeyf", "max_stars_repo_head_hexsha": "6b73adacd2f23644b8a14efd784d038c5ec79157", "max_stars_repo_licenses": ["MIT"], "max_stars_count": nul... |
# from sklearn.tree import DecisionTreeClassifier
# import pickle
# import numpy as np
# def resume_clf(train_object):
# """
# :param train_object: List-like object with training data as values
# :return: 1 in case of any error, 0 otherwise.
# """
# try:
# X_train = np.ar... | {"hexsha": "d75189628cc3dba411a8c7341151f3b0bca0198b", "size": 682, "ext": "py", "lang": "Python", "max_stars_repo_path": "api/lib/algorithms.py", "max_stars_repo_name": "roshnet/peoplestat-api", "max_stars_repo_head_hexsha": "cf17a40def4dc2f094239870a09086c3f3a9eea5", "max_stars_repo_licenses": ["Apache-2.0"], "max_st... |
module Issue252 where
data I : Set where
zero : I
data D : I → Set where
c : ∀ i → D i → D i
id : I → I
id i = i
index : ∀ i → D i → I
index i _ = i
foo : ∀ i → D i → D zero
foo .i (c i d) with id i
foo ._ (c i d) | zero = d
bar : ∀ i → D i → D zero
bar .i (c i d) with index i d
bar ._ (c i d) | zero = d
-- ... | {"hexsha": "23f0bf5417b640cc0809fe15968f0279e8acb970", "size": 424, "ext": "agda", "lang": "Agda", "max_stars_repo_path": "test/succeed/Issue252.agda", "max_stars_repo_name": "larrytheliquid/agda", "max_stars_repo_head_hexsha": "477c8c37f948e6038b773409358fd8f38395f827", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
__author__ = 'INVESTIGACION'
import numpy as np
from copy import deepcopy
import math
def getHeuristic(matrix, pesos):
"""
Vamos a utilizar Cj/Pj donde Pi se obtiene por el numero de filas que cubre la columna
:param matrix:
:param pesos:
:return:
"""
lHeuristic = np.zeros((len(pesos),2)) #... | {"hexsha": "18381a125939e0115bbe4161a548efe8a644addc", "size": 9815, "ext": "py", "lang": "Python", "max_stars_repo_path": "problems/repairs/heuristic.py", "max_stars_repo_name": "m-arnao-molina/SCA-QL-SARSA", "max_stars_repo_head_hexsha": "65f859fce96bb8c11509238c2f7a5d8dd2ad042a", "max_stars_repo_licenses": ["MIT"], ... |
Address(Dali Place) is a residential Culdesacs culdesac in the Wildhorse section of East Davis.
Intersecting Streets
Hepworth Drive
| {"hexsha": "ffe4c79ad184183224f11917c2d9b95789a81344", "size": 139, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "lab/davisWiki/Dali_Place.f", "max_stars_repo_name": "voflo/Search", "max_stars_repo_head_hexsha": "55088b2fe6a9d6c90590f090542e0c0e3c188c7d", "max_stars_repo_licenses": ["MIT"], "max_stars_count": ... |
(*
This file is generated by Cogent
*)
theory U8rec_correctness_uabsfunsdeclfix
imports "build_u8rec/U8rec_uabsfunsdeclfix_AllRefine"
Cogent.ValueSemantics
begin
lemmas type_simps = U8rec_uabsfunsdeclfix_TypeProof.main_type_def
U8rec_uabsfunsdeclfix_TypeProof.abbreviatedType1_def
lemmas \<Xi>_simps = \<Xi>_def ... | {"author": "amblafont", "repo": "dargent-examples", "sha": "dbcfdd6573c088f65d4dade1b351b3bb2bc073e7", "save_path": "github-repos/isabelle/amblafont-dargent-examples", "path": "github-repos/isabelle/amblafont-dargent-examples/dargent-examples-dbcfdd6573c088f65d4dade1b351b3bb2bc073e7/correctness/U8rec_correctness_uabsfu... |
module SmoothDeltaDirectSumModule
use NumberKindsModule
use LoggerModule
use ParticlesModule
use EdgesModule, only : MaxEdgeLength
use PolyMesh2dModule
use FieldModule
use MPISetupModule
use SphereGeomModule, only : ChordDistance, SphereDistance, SphereProjection
implicit none
include 'mpif.h'
private
public Sphe... | {"hexsha": "07c0eb3ad1f5509f47af56a3a7f4202162a0e208", "size": 6316, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "SmoothDeltaDirectSum.f90", "max_stars_repo_name": "pbosler/LPPM", "max_stars_repo_head_hexsha": "33b9572120ceca28ee56630a1af54f3befbda672", "max_stars_repo_licenses": ["MIT"], "max_stars_count":... |
"""Module to read data analog data from NI-DAQ device
A simple high-level wrapper for NI-DAQmx functions
Requires: PyDAQmx, Numpy
See COPYING file distributed along with the pyForceDAQ copyright and license terms.
"""
__author__ = 'Oliver Lindemann'
import ctypes as ct
import numpy as np
import PyDAQmx
from ._con... | {"hexsha": "692c6c0836c43ccbe785e9023681afb7be1afd97", "size": 3580, "ext": "py", "lang": "Python", "max_stars_repo_path": "forceDAQ/daq/_daq_read_Analog_pydaqmx.py", "max_stars_repo_name": "raunaqbhirangi/pyForceDAQ", "max_stars_repo_head_hexsha": "a2a41cd7a4a4f0afd178bc5555ba4e0540902d30", "max_stars_repo_licenses": ... |
import numpy as np
import matplotlib.pyplot as plt
import torch, torchvision
from torch.utils.tensorboard import SummaryWriter
import os, argparse
from tqdm import tqdm
from degmo.gan.run_utils import config_model_test, generation, manifold, interpolation, helix_interpolation
from degmo.utils import setup_seed, selec... | {"hexsha": "c900c6e8f0ecca4bae7a44822b8d4fe299419990", "size": 2476, "ext": "py", "lang": "Python", "max_stars_repo_path": "degmo/test_gan.py", "max_stars_repo_name": "IcarusWizard/Deep-Generative-Models", "max_stars_repo_head_hexsha": "4117c11ad944bdeff106a80adbb3642a076af64e", "max_stars_repo_licenses": ["MIT"], "max... |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
#
# Author: Jayant Jain <jayantjain1992@gmail.com>
# Copyright (C) 2017 Radim Rehurek <me@radimrehurek.com>
# Licensed under the GNU LGPL v2.1 - http://www.gnu.org/licenses/lgpl.html
"""
Utilities for creating 2-D visualizations of Poincare models and Poincare distance he... | {"hexsha": "f20fd8ab2d1e45324895848ed8432cd1bfe110f1", "size": 6168, "ext": "py", "lang": "Python", "max_stars_repo_path": "bobo/Lib/site-packages/gensim/viz/poincare.py", "max_stars_repo_name": "nehiridil/MLDays_nlp", "max_stars_repo_head_hexsha": "20d29d01836c82361cb1b656f2e98d7435a93622", "max_stars_repo_licenses": ... |
# Import Python libraries
import cv2 # OpenCV
import numpy as np
# Mask for green objects in image using LAB (t = 115)
def colorMaskLAB(img: np.ndarray) -> np.ndarray:
# Convert image to LAB
LAB = cv2.cvtColor(img, cv2.COLOR_BGR2LAB) # BRG image to LAB color space
LAB = LAB[:, :, 1] # Extract 2. channe... | {"hexsha": "d83f82e3edd86cc767a98b0be18b89885aa7c685", "size": 1302, "ext": "py", "lang": "Python", "max_stars_repo_path": "softwareProgram/ColorMask.py", "max_stars_repo_name": "Sebastian-Whitehead/Medialogi-P3-02", "max_stars_repo_head_hexsha": "8fb144c17a10417aa2f5a01fcbc71b4d562d4d27", "max_stars_repo_licenses": ["... |
%Terminals
DollarSign ::= '$'
Percent ::= '%'
_
a b c d e f g h i j k l m n o p q r s t u v w x y z
A B C D E F G H I J K L M N O P Q R S T U V W X Y Z
%End
%Headers
/.
static readonly int[] tokenKind = new int[128];
static bool __b_init = init_block();
static bool init_... | {"hexsha": "77a23e259f58fa23803a2f0d4f700cd3dbf1cad3", "size": 3748, "ext": "gi", "lang": "GAP", "max_stars_repo_path": "lpg-generator-templates-2.1.00/include/csharp/KWLexerMapF.gi", "max_stars_repo_name": "kuafuwang/LPG2", "max_stars_repo_head_hexsha": "5cda43c109633d951facbeac361e060dd6d59dcd", "max_stars_repo_licen... |
# Many scipy.stats functions support `axis` and `nan_policy` parameters.
# When the two are combined, it can be tricky to get all the behavior just
# right. This file contains utility functions useful for scipy.stats functions
# that support `axis` and `nan_policy`, including a decorator that
# automatically adds `axis... | {"hexsha": "d9a93a66d4b569981603ef7acfead2535f4ab62f", "size": 21314, "ext": "py", "lang": "Python", "max_stars_repo_path": "scipy/stats/_axis_nan_policy.py", "max_stars_repo_name": "ChristinaCzaikowski/scipy", "max_stars_repo_head_hexsha": "544b938e06eba166b7e5bcc6298d9b3314f6cc33", "max_stars_repo_licenses": ["BSD-3-... |
fact=function(num)
{
if(num==1)
return(1)
else
{
return(num*fact(num-1))
}
print(fact)
}
fact(5)
| {"hexsha": "7076148f61a009f3226fbbcca83d143a799c3d1d", "size": 128, "ext": "r", "lang": "R", "max_stars_repo_path": "RecursionFact.r", "max_stars_repo_name": "ninadsumant/RProgramming", "max_stars_repo_head_hexsha": "62aaf73e33d8e65f0f17a89c358feecef973cd5e", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null,... |
import util
import json
import sys
import numpy as np
from metrics import CorefEvaluator, Evaluator
from collections import defaultdict
from eval_ontonotes import SEGMENT_EVAL
predictions = sys.argv[1:]
ALL = "__@ALL"
def read_file(path):
d = {}
with open(path, 'r') as f:
for line in f:
document_blob = ... | {"hexsha": "8c14d3a68659553d32ef5b34c87c522c466a18b3", "size": 7312, "ext": "py", "lang": "Python", "max_stars_repo_path": "eval_all.py", "max_stars_repo_name": "wgantt/incremental-coref", "max_stars_repo_head_hexsha": "fadc44b59456b67055bf25f3dc688dd982a083af", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_cou... |
__author__ = "Nikhil Mehta"
__copyright__ = "--"
import tensorflow as tf
import numpy as np
import os
import sys
import argparse
os.environ['CUDA_DEVICE_ORDER']='PCI_BUS_ID'
os.environ['CUDA_VISIBLE_DEVICES']="3"
from utils import load_train_data, load_validation_data, translate
from train_model import Model_Train
... | {"hexsha": "82cbcc2eab3ecd4ae8ef34a1247945c215cbd90c", "size": 1695, "ext": "py", "lang": "Python", "max_stars_repo_path": "main.py", "max_stars_repo_name": "nikhil-dce/Transforming-AutoEncoder-TF", "max_stars_repo_head_hexsha": "9475638e4c35342cdf71ba2bf5c2a6fa709f8e44", "max_stars_repo_licenses": ["MIT"], "max_stars_... |
[STATEMENT]
lemma permutation_edge_succ: "permutation (edge_succ M)"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. permutation (edge_succ M)
[PROOF STEP]
by (metis edge_succ_permutes finite_arcs permutation_permutes) | {"llama_tokens": 88, "file": "Planarity_Certificates_Planarity_Graph_Genus", "length": 1} |
import numpy as np
import numpy.testing as npt
import pytest
from pulse2percept.utils import (is_strictly_increasing, radial_mask, sample,
unique)
def test_is_strictly_increasing():
npt.assert_equal(is_strictly_increasing([1]), True)
npt.assert_equal(is_strictly_increasing([0... | {"hexsha": "ca6f3280451537d2a3ee1e8ee53980713b617f23", "size": 2549, "ext": "py", "lang": "Python", "max_stars_repo_path": "pulse2percept/utils/tests/test_array.py", "max_stars_repo_name": "narenberg/pulse2percept", "max_stars_repo_head_hexsha": "ca3aaf66672ccf3c9ee6a9a9d924184cdc6f031d", "max_stars_repo_licenses": ["B... |
// (C) Copyright 2015 by Autodesk, Inc.
//== INCLUDES =================================================================
//== COMPILE-TIME PACKAGE REQUIREMENTS ========================================
#include <CoMISo/Config/config.hh>
#if COMISO_DOCLOUD_AVAILABLE
//===================================================... | {"hexsha": "58c1cab14ae847779a3980315b0818bc6236f140", "size": 7260, "ext": "cc", "lang": "C++", "max_stars_repo_path": "ACAP_linux/3rd/CoMISo/NSolver/DOCloudCache.cc", "max_stars_repo_name": "shubhMaheshwari/Automatic-Unpaired-Shape-Deformation-Transfer", "max_stars_repo_head_hexsha": "8c9afe017769f9554706bcd267b6861c... |
import unittest
import numpy as np
from smt.utils.sm_test_case import SMTestCase
from smt.utils.kriging_utils import standardization
class Test(SMTestCase):
def test_standardization(self):
d, n = (10, 100)
X = np.random.normal(size=(n, d))
y = np.random.normal(size=(n, 1))
X_norm,... | {"hexsha": "f18fba92984333efeb4b825bf4887fb82c49e799", "size": 526, "ext": "py", "lang": "Python", "max_stars_repo_path": "smt/utils/test/test_kriging_utils.py", "max_stars_repo_name": "joshuauk1026/smt", "max_stars_repo_head_hexsha": "ec6aa20643b1e4fa772c6f470281c58df113c3a6", "max_stars_repo_licenses": ["BSD-3-Clause... |
"""Modification CLAHE (Contrast Limited Adaptive Histogram Equalization)."""
import cv2 as cv
import numpy as np
from dfd.datasets.modifications.interfaces import ModificationInterface
class CLAHEModification(ModificationInterface):
"""Modification CLAHE (Contrast Limited Adaptive Histogram Equalization)"""
... | {"hexsha": "3ea8787fe392273afaaf0e54603353732a05a5a2", "size": 1745, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/dfd/datasets/modifications/definitions/clahe.py", "max_stars_repo_name": "cicheck/dfd", "max_stars_repo_head_hexsha": "b02752f958cfea2f85222e2b4b3ba7e265a6152d", "max_stars_repo_licenses": ["M... |
#ifndef YQVMC_EXTERNAL_LIBRARY_ADAPTOR_EIGEN3_HPP
#define YQVMC_EXTERNAL_LIBRARY_ADAPTOR_EIGEN3_HPP
#include <Eigen/Core>
#include "../impl_/mae_traits.hpp"
namespace yqvmc {
namespace impl_ {
template <typename S_, int R_, int C_, int O_, int MR_, int MC_>
struct MeanAndErrorTraits<Eigen::Array<S_, R_, C_,... | {"hexsha": "c1ca65d58c643f92c3c1a6f23a72a36ca5fac381", "size": 1277, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "src/yqvmc/libadapt/eigen3_adaptor.hpp", "max_stars_repo_name": "yangqi137/yqvmc", "max_stars_repo_head_hexsha": "73b7367f6d4b01ea61612ea0888b285c8dac2fad", "max_stars_repo_licenses": ["MIT"], "max_s... |
# script for extracting patches from video frames suitable for neural network
# training
from keras.preprocessing.image import ImageDataGenerator, load_img, img_to_array, array_to_img
from PIL import Image
import sys
import os
import glob
from PIL import Image
from os.path import basename, splitext
import numpy as n... | {"hexsha": "118bb9cc42104087368b917dcb655edae791e512", "size": 2624, "ext": "py", "lang": "Python", "max_stars_repo_path": "extract-subimages-videos.py", "max_stars_repo_name": "rzaluska/fcnn-conferences", "max_stars_repo_head_hexsha": "509946a4d342451f29e7b8706b6ff46b0af20f36", "max_stars_repo_licenses": ["MIT"], "max... |
"""
Modified from: http://hubpages.com/technology/Simplex-Algorithm-in-Python
"""
from __future__ import division
from numpy import *
# Ref: http://stackoverflow.com/questions/23344185/how-to-convert-a-decimal-number-into-fraction
from fractions import Fraction
class Tableau:
def __init__(self, obj):
... | {"hexsha": "f19a5864b7030d2e7e0bf7d50ec0cd87efe0be2b", "size": 4860, "ext": "py", "lang": "Python", "max_stars_repo_path": "tableau.py", "max_stars_repo_name": "infimath/optimization-taha", "max_stars_repo_head_hexsha": "5f2c02429710614cf7ec5993cddb5e15afcb8103", "max_stars_repo_licenses": ["MIT"], "max_stars_count": n... |
# -*- coding: utf-8 -*-
# Dependencies:
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from statsmodels.tsa.seasonal import seasonal_decompose
from src import (
PATH_COVID_IMPACT_GRAPH,
PATH_HISTOGRAM_BOOKINGS,
PATH_PLOT_REVENUE_PER_DATE,
PATH_PREPROCESSOR_COVID_... | {"hexsha": "027e24bd44347785f1e9da6dc0fe1630584af266", "size": 8812, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/visualization/visualize.py", "max_stars_repo_name": "carolmoraescruz/case_seazone", "max_stars_repo_head_hexsha": "76b44a64272685681442929c04ea9e4fd21a147e", "max_stars_repo_licenses": ["MIT"]... |
module MeshingBenchmarks
using FileIO
using NRRD
using Meshing
using MeshIO
using GeometryTypes
using BenchmarkTools
function benchmark()
here = dirname(@__FILE__)
println(pwd())
println("CTA-cardio.nrrd loading...")
ctacardio = @btime load($here*"/../data/CTA-cardio.nrrd")
q = 100
samples =... | {"hexsha": "c4fc6b88f75f70c7ddca98c3b4c1b15bb4cc0c57", "size": 2043, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/MeshingBenchmarks.jl", "max_stars_repo_name": "sjkelly/MeshingBenchmarks.jl", "max_stars_repo_head_hexsha": "3184b00206e0b8be8626afa63799f7991a21e032", "max_stars_repo_licenses": ["MIT"], "max_... |
[STATEMENT]
lemma \<rho>'_ide_simp:
assumes "ide a"
shows "\<rho>'.map a = \<r>\<^sup>-\<^sup>1[a]"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. \<rho>' a = \<r>\<^sup>-\<^sup>1[a]
[PROOF STEP]
using assms \<rho>'.inverts_components \<rho>_ide_simp inverse_unique
[PROOF STATE]
proof (prove)
using this:
ide... | {"llama_tokens": 244, "file": "MonoidalCategory_MonoidalCategory", "length": 2} |
"""Class for reading, parsing, and downloading data from the Harmonizome API.
"""
import gzip
import json
import os
import logging
# Support for both Python2.X and 3.X.
# -----------------------------------------------------------------------------
try:
import io
from urllib.request import urlopen
from ur... | {"hexsha": "230d68bf47bd6dd7857e8eeb2f114fa3030c4a44", "size": 11967, "ext": "py", "lang": "Python", "max_stars_repo_path": "appyters/harmonizome_ml/harmonizome.py", "max_stars_repo_name": "shui02/appyter-catalog", "max_stars_repo_head_hexsha": "dfa15946d151daeb7d7b1bc9af9e48428474f012", "max_stars_repo_licenses": ["CC... |
import numpy as np
from alphaomega.cv.channel.channel_split import channel_splitter_apply
from alphaomega.cv.channel.channel_merge import channel_merger_apply
from alphaomega.utils.exceptions import WrongAttribute, WrongDimension
class BorderIntropolation:
"""
Usage: Use this class to intropolate the b... | {"hexsha": "bc9805d95f78fc3bef75e0c26e17d0dc4f416a86", "size": 10554, "ext": "py", "lang": "Python", "max_stars_repo_path": "alphaomega/cv/border/border_intropolation.py", "max_stars_repo_name": "heidariarash/Alpha-Omega", "max_stars_repo_head_hexsha": "123be3c90cfb0e382845a1243923613d5475b529", "max_stars_repo_license... |
from ShazamAPI import Shazam
import subprocess
import os
import time
import urllib.request
from PIL import Image, ImageOps
from pathlib import Path
from selenium.common.exceptions import WebDriverException
from selenium import webdriver
from selenium.webdriver.chrome.options import Options
import sys
import pylast
from... | {"hexsha": "60a5e2d62a66c3318d6b3f4bec8e06b083322a27", "size": 7888, "ext": "py", "lang": "Python", "max_stars_repo_path": "listen-show-scrobble-setlamps.py", "max_stars_repo_name": "jbrepogmailcom/listen-show-scrobble", "max_stars_repo_head_hexsha": "a8a4313570f19a5fd5ba195f13da0c72826c6800", "max_stars_repo_licenses"... |
import tensorflow as tf
import tensorflow_compression as tfc
from focal_loss import focal_loss
import os
import numpy as np
from collections import namedtuple
def pc_to_tf(points, dense_tensor_shape):
x = points
x = tf.pad(x, [[0, 0], [1, 0]])
st = tf.sparse.SparseTensor(x, tf.ones_like(x[:,0]), dense_tens... | {"hexsha": "e036813680e0eba75b5313f6484865fa99112496", "size": 9268, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/compression_model.py", "max_stars_repo_name": "mauriceqch/pcc_geo_cnn", "max_stars_repo_head_hexsha": "22bbf081ffe7b77c9308f54c15490da60e78803c", "max_stars_repo_licenses": ["MIT"], "max_stars... |
#include <boost/graph/graph_traits.hpp>
| {"hexsha": "3a4eedb92bee9f756d0b598ff7a5ae070438a37c", "size": 40, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "src/boost_graph_graph_traits.hpp", "max_stars_repo_name": "miathedev/BoostForArduino", "max_stars_repo_head_hexsha": "919621dcd0c157094bed4df752b583ba6ea6409e", "max_stars_repo_licenses": ["BSL-1.0"],... |
import network3
import numpy as np
import scipy
import matplotlib.pyplot as plt
import PIL
training_data, _, _ = network3.load_data_shared()
training_x, training_y = training_data
x = training_x.get_value()[0] # vector corresponding to a 5
x_img = np.reshape(x, (-1, 28)) # recognizable 5
y = training_y.eval()[0] # ... | {"hexsha": "37ca91916338b4baf1a396480a5e34dd50c5c163", "size": 2121, "ext": "py", "lang": "Python", "max_stars_repo_path": "Tutorials/Intro_To_NN/NNDL-solutions/code/chap6p4-9/display_transformed_image.py", "max_stars_repo_name": "lev1khachatryan/ASDS_CV", "max_stars_repo_head_hexsha": "c9f0c0412002e929bcb7cc2fc6e53929... |
\documentclass{my_cv}
\usepackage[skins]{tcolorbox}
\usepackage{hyperref}
\usepackage{parskip}
\usepackage{parskip}
\begin{document}
\begin{multicols}{2}[
\titletext{Ankit}%
{Devri}%
{Hno.377/5,Mohan Mikins Sociey,Vasundhara,GZB,UP,201012}%
{\href{mailto:first.last@mail.com}{vivekdevri@gmail... | {"hexsha": "3c4dca35304bb8488d785d8e798a27d19309d4c6", "size": 5063, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "resume.tex", "max_stars_repo_name": "AnkitDevri/Resume", "max_stars_repo_head_hexsha": "45e97c151a57a8dba6e41b45ffbd4e9f8c1c6acf", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_s... |
import jax.numpy as np
def adam_ops_init(flat_params):
adam_ops = {
"b1": 0.9,
"b2": 0.999,
"step_size": 0.001,
"eps": 1e-8,
"wd": 0.001,
}
adam_ops.update({"m": np.zeros(len(flat_params))})
adam_ops.update({"v": np.zeros(len(flat_params))})
return adam_ops
... | {"hexsha": "ad65727e45726a1d52b61f393d5596944f34dbb4", "size": 1466, "ext": "py", "lang": "Python", "max_stars_repo_path": "fundl/optimizers/step.py", "max_stars_repo_name": "ElArkk/fundl", "max_stars_repo_head_hexsha": "04b126d484f77e480196a24849683df93a0eabd8", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1... |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import pandas as pd
from datetime import datetime, timedelta
import numpy as np
from scipy.stats import pearsonr
# from mpl_toolkits.axes_grid1 import host_subplot
# import mpl_toolkits.axisartist as AA
# import matplotlib
import matplotlib.pyplot as plt
import matplotlib.t... | {"hexsha": "cbbb4ca34fa8722dd42e1e6ca687460874a08b2d", "size": 61000, "ext": "py", "lang": "Python", "max_stars_repo_path": "Tesis_Eficiencia_Teorica.py", "max_stars_repo_name": "cmcuervol/Estefania", "max_stars_repo_head_hexsha": "13b564261dfc786b93c77fbc442a568018f87cc9", "max_stars_repo_licenses": ["MIT"], "max_star... |
import sys
import numpy as np
import wave
import pyaudio
import os
import os.path
class Distribution(dict):
def __missing__(self, key):
# if missing, return 0
return 0
def renormalize(self):
normalization_constant = sum(self.values())
assert normalization_constant > 0, "Sum of... | {"hexsha": "b57b52c33d88d0288953db4d53a68e049ef3dd6b", "size": 4015, "ext": "py", "lang": "Python", "max_stars_repo_path": "util.py", "max_stars_repo_name": "jhell96/music-perception-mcmc", "max_stars_repo_head_hexsha": "327ad9d15ccfda72c25efd370041fedf28686141", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2... |
[STATEMENT]
lemma authKeysI:
"Says Kas A \<lbrace>Crypt (shrK A) \<lbrace>Key K, Agent Tgs, Number Ta\<rbrace>,
Crypt (shrK Tgs) \<lbrace>Agent A, Agent Tgs, Key K, Number Ta\<rbrace> \<rbrace> \<in> set evs
\<Longrightarrow> K \<in> authKeys evs"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. Sa... | {"llama_tokens": 227, "file": null, "length": 1} |
# Autogenerated wrapper script for Zellij_jll for i686-linux-gnu
export zellij
JLLWrappers.@generate_wrapper_header("Zellij")
JLLWrappers.@declare_executable_product(zellij)
function __init__()
JLLWrappers.@generate_init_header()
JLLWrappers.@init_executable_product(
zellij,
"bin/zellij",
)... | {"hexsha": "28761b1f615726d82097573b4c02a60e56f24a24", "size": 380, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/wrappers/i686-linux-gnu.jl", "max_stars_repo_name": "JuliaBinaryWrappers/Zellij_jll.jl", "max_stars_repo_head_hexsha": "0730f1730fc6707c6a401a9968b2d46cdb24be7e", "max_stars_repo_licenses": ["MI... |
# implementation of iWare-E for PAWS
# Lily Xu
# May 2019
import sys
import time
import pickle
import pandas as pd
import numpy as np
from scipy.optimize import minimize
from sklearn import metrics
from sklearn.model_selection import train_test_split, StratifiedKFold
from sklearn.preprocessing import StandardScaler... | {"hexsha": "fd924bc61dfe40c5d9c56a1cc38c70fdec605c48", "size": 45033, "ext": "py", "lang": "Python", "max_stars_repo_path": "iware/iware.py", "max_stars_repo_name": "lily-x/PAWS-public", "max_stars_repo_head_hexsha": "32f79d10a1187686f301be447de9f4d0e83cf127", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 3, "... |
SUBROUTINE DHC (P,PA,PB,XI,NAT,IF,IM,IL,JF,JM,JL,
1NORBS,DENER)
IMPLICIT DOUBLE PRECISION (A-H,O-Z)
DIMENSION P(*), PA(*), PB(*)
DIMENSION XI(3,*),NFIRST(2),NMIDLE(2),NLAST(2),NAT(*)
C***********************************************************************
C
C DHC CALCULATES THE ENERGY CONT... | {"hexsha": "ff9e739df7bc6bdd8e0004ff235b568b1af42bb0", "size": 3159, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "1989_MOPAC5/dhc.f", "max_stars_repo_name": "openmopac/MOPAC-archive", "max_stars_repo_head_hexsha": "01510e44246de34a991529297a10bcf831336038", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_st... |
from os import system
import numpy as np
import scipy.optimize as op
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
####################################################################
def initTheta(X,degree):
size=getThetaSizeFromDegree(X,degree)
return np.zeros((size, 1))
##########... | {"hexsha": "ea86414736b62f298a610f507789bb655fd57eaa", "size": 5481, "ext": "py", "lang": "Python", "max_stars_repo_path": "06_LinearRegression_Line3d/linearRegressionPlane.py", "max_stars_repo_name": "ManMohan291/PyProgram", "max_stars_repo_head_hexsha": "edcaa927bd70676bd14355acad7262ae2d32b8e5", "max_stars_repo_lice... |
import numpy as np
import pandas as pd
from plotnine import ggplot, aes, geom_bar, geom_col, geom_histogram
from plotnine import after_stat, theme, scale_x_sqrt, geom_text
from plotnine.tests import layer_data
n = 10 # Some even number greater than 2
# ladder: 0 1 times, 1 2 times, 2 3 times, ...
df = pd.DataFrame... | {"hexsha": "0ed2a64289678f47000780443817cbebdd35ba99", "size": 1950, "ext": "py", "lang": "Python", "max_stars_repo_path": "venv/Lib/site-packages/plotnine/tests/test_geom_bar_col_histogram.py", "max_stars_repo_name": "EkremBayar/bayar", "max_stars_repo_head_hexsha": "aad1a32044da671d0b4f11908416044753360b39", "max_sta... |
# GUI改善版（画像でyolo実行可能）
from tkinter import *
import tkinter.ttk as ttk
import tkinter.filedialog
import os
from PIL import Image, ImageTk
from key_frame import get_keyframe
from key_frame import detect_cloth_by_yolo
import numpy as np
import cv2
class Tab1(ttk.Frame):
def __init__(self,mode, master=None... | {"hexsha": "c0c8857b1ffbf7555cbea4121f087c5d66af6d96", "size": 14347, "ext": "py", "lang": "Python", "max_stars_repo_path": "FIRS/gui.py", "max_stars_repo_name": "yuichikano/Fashion-Image-Retrieval-System", "max_stars_repo_head_hexsha": "5d712a4e400716e84337defe08f51c2165d44ade", "max_stars_repo_licenses": ["Apache-2.0... |
# File: test_bayesian_optimization.py
# File Created: Tuesday, 5th November 2019 10:00:04 am
# Author: Steven Atkinson (212726320@ge.com)
import os
import sys
import numpy as np
import torch
import pytest
base_path = os.path.join(os.path.dirname(__file__), "..")
if not base_path in sys.path:
sys.path.append(bas... | {"hexsha": "e2fb843b920235d6ce31be21d450adfe7711f417", "size": 2747, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/test_bayesian_optimization.py", "max_stars_repo_name": "212726320/BEBO-1", "max_stars_repo_head_hexsha": "2909b3a00161b2e29fad667add30392abc11a968", "max_stars_repo_licenses": ["MIT"], "max_... |
import numpy as np
import pytest
from pandas._libs import join as libjoin
from pandas._libs.join import inner_join, left_outer_join
import pandas._testing as tm
class TestIndexer:
@pytest.mark.parametrize(
"dtype", ["int32", "int64", "float32", "float64", "object"]
)
def test_outer_... | {"hexsha": "37e1cf4dbc733f7bf1c967e42cf3a83a9892acc0", "size": 11296, "ext": "py", "lang": "Python", "max_stars_repo_path": "mypython/Lib/site-packages/pandas/tests/libs/test_join.py", "max_stars_repo_name": "lilianatang/data-modelling-with-postgresql", "max_stars_repo_head_hexsha": "4b5d057d23c346cc36695dc0548f11908ae... |
[STATEMENT]
lemma knows_Spy_Inputs_secureM_sr:
"\<lbrakk> A \<noteq> Spy; evs \<in>sr \<rbrakk> \<Longrightarrow> knows Spy (Inputs A C X # evs) = knows Spy evs"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. \<lbrakk>A \<noteq> Spy; evs \<in> sr\<rbrakk> \<Longrightarrow> knows Spy (Inputs A C X # evs) = know... | {"llama_tokens": 191, "file": null, "length": 2} |
[STATEMENT]
lemma UGroupHomI:
assumes "\<And>g g'. T (g + g') = T g + T g'"
shows "UGroupHom T"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. UGroupHom T
[PROOF STEP]
using assms
[PROOF STATE]
proof (prove)
using this:
T (?g + ?g') = T ?g + T ?g'
goal (1 subgoal):
1. UGroupHom T
[PROOF STEP]
by unfol... | {"llama_tokens": 150, "file": "Buildings_Algebra", "length": 2} |
from swarms.lib.agent import Agent
# from swarms.objects import Sites
from swarms.lib.model import Model
from swarms.lib.time import SimultaneousActivation
from swarms.lib.space import Grid
from unittest import TestCase
from swarms.utils.bt import BTConstruct
import py_trees
from py_trees import Blackboard
import numpy... | {"hexsha": "cd66d75e56e445f8bb285108337d374765a279c2", "size": 6782, "ext": "py", "lang": "Python", "max_stars_repo_path": "test/test_full_bt.py", "max_stars_repo_name": "aadeshnpn/swarm", "max_stars_repo_head_hexsha": "873e5d90de4a3b3f69d4edc8de55eb9311226c2e", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 9,... |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Mon May 7 14:57:18 2018
@author: root
"""
import numpy as np
from matplotlib import pyplot as plt
def load_band(file:str):
band=np.loadtxt(file, skiprows=1,delimiter=",")
return band
def open_figure(rows,cols):
fig,ax=plt.subplots(rows,cols)
... | {"hexsha": "6b388da8c6235c2f6b3e4ae921f729a0d7eb053b", "size": 2060, "ext": "py", "lang": "Python", "max_stars_repo_path": "methods.py", "max_stars_repo_name": "Massetting/Spectral_Convolution", "max_stars_repo_head_hexsha": "f0e86d707d3ab64f39a24ab0181d7c280356da60", "max_stars_repo_licenses": ["MIT"], "max_stars_coun... |
"""
Class for the gravity and magnetic field 'gradient' tensors.
"""
import numpy as _np
import matplotlib as _mpl
import matplotlib.pyplot as _plt
import copy as _copy
from scipy.linalg import eigvalsh as _eigvalsh
import xarray as _xr
from .shgrid import SHGrid as _SHGrid
class Tensor(object):
"""
Gene... | {"hexsha": "167c9f2314570c1155e94a618557e4d5e0a04fe7", "size": 176595, "ext": "py", "lang": "Python", "max_stars_repo_path": "pyshtools/shclasses/shtensor.py", "max_stars_repo_name": "nephanth/SHTOOLS", "max_stars_repo_head_hexsha": "663d267715639de65f244b1e5ff8826cda0e9c8d", "max_stars_repo_licenses": ["BSD-3-Clause"]... |
import numpy as np
import pandas as pd
import pytest
import tempfile
import calliope
from . import common
from .common import assert_almost_equal
class TestInitialization:
def test_model_initialization_default(self):
model = calliope.Model()
assert hasattr(model, 'data')
assert hasattr(m... | {"hexsha": "3b4462d1e784593c20888c2c3da1784cc6cffbb3", "size": 13086, "ext": "py", "lang": "Python", "max_stars_repo_path": "calliope/test/test_core.py", "max_stars_repo_name": "sjpfenninger/calliope", "max_stars_repo_head_hexsha": "a4e49c3b7d37f908bafc84543510eec0b4cf5d9f", "max_stars_repo_licenses": ["Apache-2.0"], "... |
#include "functions/transpose.hh"
#include <boost/test/unit_test.hpp>
#include "data/matrix.hh"
BOOST_AUTO_TEST_CASE(tranpose_test) {
using namespace manifolds;
auto m1 = GetRowMatrix<3, 2>(1, 2, 3, 4, 5, 6);
auto m2 = GetColMatrix<2, 3>(1, 2, 3, 4, 5, 6);
auto check = GetMatrix<2, 3>(1, 3, 5, 2, 4, 6);
BOOS... | {"hexsha": "578ce7edc5f07a450cc6ee21c21605a1dac37fb6", "size": 388, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "functions/tests/test_transpose.cpp", "max_stars_repo_name": "GuylainGreer/manifolds", "max_stars_repo_head_hexsha": "96f996f67fc523c726f2edbc9705125c212bedae", "max_stars_repo_licenses": ["MIT"], "ma... |
#!/usr/bin/env python
# coding: utf-8
# In[1]:
from keras.layers import Input, Dense, concatenate
from keras.layers.recurrent import GRU
from keras.utils import plot_model
from keras.models import Model, load_model
from keras.callbacks import ModelCheckpoint
import keras
import pandas as pd
import numpy as np
import... | {"hexsha": "cdb6ef3090bfc1b9dd21862cda58aa974b9762c7", "size": 17890, "ext": "py", "lang": "Python", "max_stars_repo_path": "ipython/3_Training_Predicting/prnn_cb12_pred_hyp.py", "max_stars_repo_name": "samuelru/session-knn-ae", "max_stars_repo_head_hexsha": "c6232667dbe57f82391d487875b52f651ca08a21", "max_stars_repo_l... |
"""
This contains classes used for analyzing the sentiments of input texts
"""
import re
import pprint
import shelve
# import IOMDataService as DS
# from TextFiltration import Sentences, Words, Lemmatized, Bigrams, Trigrams
import numpy as np
from senti_classifier import senti_classifier
import nltk
from nltk.corp... | {"hexsha": "1a6dabe5888941644a552c39ff6e5464a6927692", "size": 8919, "ext": "py", "lang": "Python", "max_stars_repo_path": "SentimentTools/SentimentAnalysis.py", "max_stars_repo_name": "AdamSwenson/TwitterProject", "max_stars_repo_head_hexsha": "8c5dc7a57eac611b555058736d609f2f204cb836", "max_stars_repo_licenses": ["MI... |
#include <mpi.h>
#include <sys/time.h>
#include <iostream>
#include <functional>
#include <algorithm>
#include <vector>
#include <string>
#include <sstream>
#ifdef THREADED
#ifndef _OPENMP
#define _OPENMP
#endif
#include <omp.h>
#endif
// These macros should be defined before stdint.h is included
#ifndef __STDC_C... | {"hexsha": "e731815b16b8bb60398f3713628854aabadda096", "size": 19522, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "CombBLAS/Applications/Graph500.cpp", "max_stars_repo_name": "shoaibkamil/OLD-kdt-specializer", "max_stars_repo_head_hexsha": "85074ec1990df980d25096ea8c55dd81350e531e", "max_stars_repo_licenses": [... |
from setuptools import Extension, setup
from Cython.Build import cythonize
import numpy
setup(
ext_modules=cythonize(
[Extension('match', ['match.pyx'], include_dirs=[numpy.get_include()])]
)
)
| {"hexsha": "75e2cc86975d1e02e1f220515026cccc59ceee5f", "size": 212, "ext": "py", "lang": "Python", "max_stars_repo_path": "pose_util/setup.py", "max_stars_repo_name": "SelvamArul/MOTR", "max_stars_repo_head_hexsha": "2a0b70288feaca665d460096159100d5077e9312", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null,... |
[STATEMENT]
lemma atU_union_cases[case_names left right, consumes 1]: "\<lbrakk>
atU U (c1+c2);
atU U c1 \<Longrightarrow> P;
atU U c2 \<Longrightarrow> P
\<rbrakk> \<Longrightarrow> P"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. \<lbrakk>atU U (c1 + c2); atU U c1 \<Longrightarrow> P; atU U c2 \... | {"llama_tokens": 176, "file": "Program-Conflict-Analysis_Semantics", "length": 1} |
from os import stat
import numpy as np
from matplotlib import pyplot as plt
import random
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import ReferenceModification.LibFunctions as lib
MEMORY_SIZE = 100000
# hyper parameters
BATCH_SIZE = 100
GAMMA = 0.99
tau = 0.00... | {"hexsha": "d5fd0d2a28d8b882326baef072a80dbb786e44e1", "size": 9875, "ext": "py", "lang": "Python", "max_stars_repo_path": "ReferenceModification/NavUtils/TD3.py", "max_stars_repo_name": "BDEvan5/ReferenceModification", "max_stars_repo_head_hexsha": "8d9d13c8f563cc331809836d148b3dc83dd5d9ac", "max_stars_repo_licenses":... |
import sys
hoomd_path = str(sys.argv[4])
gsd_path = str(sys.argv[5])
# need to extract values from filename (pa, pb, xa) for naming
part_perc_a = int(sys.argv[3])
part_frac_a = float(part_perc_a) / 100.0
pe_a = int(sys.argv[1])
pe_b = int(sys.argv[2])
sys.path.append(hoomd_path)
import hoomd
from hoomd import md
fr... | {"hexsha": "4906fc5e3de0aa1ab6963469f1f986091d27ebbb", "size": 15968, "ext": "py", "lang": "Python", "max_stars_repo_path": "deprecated/deprecated_post_proc_msd.py", "max_stars_repo_name": "kolbt/whingdingdilly", "max_stars_repo_head_hexsha": "4c17b594ebc583750fe7565d6414f08678ea7882", "max_stars_repo_licenses": ["BSD-... |
[STATEMENT]
lemma bindU_lifted_strict [simp]: "bindU\<cdot>\<bottom>\<cdot>k = (\<bottom>::udom\<cdot>lifted)"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. bindU\<cdot>\<bottom>\<cdot>k = \<bottom>
[PROOF STEP]
by fixrec_simp | {"llama_tokens": 94, "file": "Tycon_Lift_Monad", "length": 1} |
__author__ = 'mangalbhaskar'
__version__ = '1.0'
"""
## Description:
# --------------------------------------------------------
# Annotation Parser Interface for Annotation work flow.
# It uses the annotations created by VGG VIA tool v2.03 (not tested), v2.05 (tested).
#
## References
* https://datascience.stackexchang... | {"hexsha": "e9f04c713c207213f23229eaa3cfb465dfab30ea", "size": 6876, "ext": "py", "lang": "Python", "max_stars_repo_path": "apps/annon/dataset/hmd_to_aids.py", "max_stars_repo_name": "Roy-Tuhin/maskrcnn_sophisticate-", "max_stars_repo_head_hexsha": "a5a2300abbe2633d66847cdbfa7ed2bc2f901ec3", "max_stars_repo_licenses": ... |
import numpy as np
import copy
import torch
import pickle
from tqdm import tqdm
import cv2
def detach_single(state):
return state.detach().cuda()
def visualize_text_attention_weights(model_obj, test_loader, device, reverse_word_map, num_layers = 2, batch_size =24, rnn_weights = (512, 1024], max_num_words = 48):
... | {"hexsha": "370f87c8809ef799c27766b6df5b43fc756c2253", "size": 2625, "ext": "py", "lang": "Python", "max_stars_repo_path": "Utils/attention_visualization.py", "max_stars_repo_name": "tjvsonsbeek/Multi-modal-automated-diagnosis-with-chestXray-and-EHR", "max_stars_repo_head_hexsha": "2ffa98b88708ca19475e09b31aac7b6569c37... |
from tqdm.auto import tqdm
import numpy as np
from pyhopper.callbacks import Callback
from pyhopper.utils import (
ParamInfo,
CandidateType,
steps_to_pretty_str,
time_to_pretty_str,
parse_timeout,
)
import time
class ScheduledRun:
def __init__(
self,
max_steps=None,
tim... | {"hexsha": "e22595379d10a4fb27cc8ae82fb1a1fff6d3fb01", "size": 17151, "ext": "py", "lang": "Python", "max_stars_repo_path": "pyhopper/run_context.py", "max_stars_repo_name": "pyhopper/pyhopper", "max_stars_repo_head_hexsha": "3a5a449ba36c03ba365d33f900c3ecbb2d107e6b", "max_stars_repo_licenses": ["Apache-2.0"], "max_sta... |
import numpy as np
import string
import time
import matplotlib
matplotlib.use('TkAgg')
import matplotlib.pyplot as plt
import os,shutil
arrr = [1,2,3,4]
print(arrr[-1:0:-1])
arr = np.array([1,2,3])
arr = np.append(arr,[4,5,6])
print(arr.reshape((-1,1)))
def m2():
labels = ['ellipse','rectangle','line']
... | {"hexsha": "b78a495cd767ab6f8a902ff6f31b2920bcda2c79", "size": 936, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/study/testbox.py", "max_stars_repo_name": "sushanted/NNDL-forked", "max_stars_repo_head_hexsha": "3d8675f3e9258d17226b5a6da29854d75ee3315e", "max_stars_repo_licenses": ["Unlicense"], "max_stars... |
"""
Scitail: A textual entailment dataset from science question answering
https://arxiv.org/pdf/1910.14599.pdf
The SciTail dataset is an entailment dataset created from multiple-choice science exams and web sentences. Each question and the correct answer choice are converted into an assertive statement to form the hyp... | {"hexsha": "0359f1d7c32d82a229619f287ed26ba8383536eb", "size": 1482, "ext": "py", "lang": "Python", "max_stars_repo_path": "lm_eval/tasks/scitail.py", "max_stars_repo_name": "bigscience-workshop/lm-evaluation-harness", "max_stars_repo_head_hexsha": "c639c81974d6d0efea2e471f6292cf3c6ae67e4c", "max_stars_repo_licenses": ... |
!=======================================================================
!
! Check for convergence in the relative abundances of all species
! of each particle. Set the relevant convergence flags. Calculate
! the percentage of particles that have converged.
!
!--------------------------------------------------------... | {"hexsha": "edde42fe54f002536e1c2a958d9c11214465f04b", "size": 2072, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "Source/check_chemistry_convergence.f90", "max_stars_repo_name": "uclchem/uclpdr", "max_stars_repo_head_hexsha": "a1c5ece6f21852af040ddf0af463cff26757d208", "max_stars_repo_licenses": ["MIT"], "m... |
# -*- coding: utf-8 -*-
"""
consciousness figure - save data
"""
from __future__ import division
from brian2 import *
##from brian2.only import *
prefs.codegen.target = 'auto'
import matplotlib.pyplot as plt
import scipy.io
import numpy as np
import numpy.random
import random as pyrand
from brian2 import defaultclock... | {"hexsha": "e258f83af50db2eb914baa5a89e8e1f01f18f301", "size": 7696, "ext": "py", "lang": "Python", "max_stars_repo_path": "consciousness.py", "max_stars_repo_name": "xjwanglab/JoglekarEtAl2018_Neuron", "max_stars_repo_head_hexsha": "42c21da0df79611f62b8aa549b2bacd921c79d61", "max_stars_repo_licenses": ["MIT"], "max_st... |
NAME Ackermann
MODE REDUCTION
SORTS NAT
SIGNATURE
0 : -> NAT
s : NAT -> NAT
+ : NAT NAT -> NAT
* : NAT NAT -> NAT
fac : NAT -> NAT
ack : NAT NAT -> NAT
ORDERING KBO
ack = 1, fac = 1, * = 1, + = 1, s = 1, 0 = 1
ack > fac > * > + > s > 0
VARIABLES
x,y : NAT
EQUATIONS
+(x,0) = x
+(x,s(y)) = s(+(x... | {"hexsha": "a0b646785b5d801b83d50a81a88ed3e88dbb0adf", "size": 589, "ext": "rd", "lang": "R", "max_stars_repo_path": "src/test/resources/specs/Ackermann.rd", "max_stars_repo_name": "falsewasnottrue/forstmeister", "max_stars_repo_head_hexsha": "a6402a479d6218b71b12369a97dab8f61e3f9717", "max_stars_repo_licenses": ["Apac... |
import numpy as np
import cv2
kNearest = cv2.ml.KNearest_create()
# The size of license plate in Poland is 520 x 114 mm.
# choose smaller ratio to accept bigger contours
# Width to height ratio of license plates in Poland.
PLATE_HEIGHT_TO_WIDTH_RATIO = 90 / 520
# Width and height ratio of character
CHAR_RATIO_MIN = ... | {"hexsha": "7885a182f7e5342a220573bac1cc9240bebf57b4", "size": 22029, "ext": "py", "lang": "Python", "max_stars_repo_path": "license_plate_processing/license_plate_recognizer.py", "max_stars_repo_name": "arekmula/license_plate_recognition", "max_stars_repo_head_hexsha": "62e5374fc56a0709d6d951629449aed347e101d2", "max_... |
import glob
import os
from PIL import Image
import numpy as np
import h5py
import IPython
path = '/home/ivanwilliam/Documents/Full_images/5.0/'
all_dirs = os.listdir(path)
dir_it=0
for dir_it in range(len(all_dirs)):
file_path = '/home/ivanwilliam/Documents/Full_images/5.0/'+str(all_dirs[dir_it])
# import IPython;... | {"hexsha": "aff2aa7e024a900b3aa17147a3767311837d4643", "size": 6591, "ext": "py", "lang": "Python", "max_stars_repo_path": "Original_size/HDF5_converter_5.0.py", "max_stars_repo_name": "ivanwilliammd/32images_hdf5converter", "max_stars_repo_head_hexsha": "2956c163b790d1fc1c3248e46d17894dde52eeb9", "max_stars_repo_licen... |
# -*- coding: utf-8 -*-
"""Copy of rnn.ipynb
Automatically generated by Colaboratory.
Original file is located at
https://colab.research.google.com/drive/1hw5VX0w03qnA-pD4YmOck-HAmzP9_fO8
# Recurrent Neural Network
## Part 1 - Data Preprocessing
### Importing the libraries
"""
import numpy as np
import matplo... | {"hexsha": "04882599488e47e956405e21234df8e7bc1e87d4", "size": 4747, "ext": "py", "lang": "Python", "max_stars_repo_path": "Auto_Encoders_Materials/RNN/copy_of_rnn.py", "max_stars_repo_name": "mithiljoshi/Classification_Denoising_GWS", "max_stars_repo_head_hexsha": "6840cd58041dcf12e4fa88fce935977ddae205d6", "max_stars... |
import json
import numpy as np
import tensorflow.keras as keras
data_path = "/Users/talen/Desktop/Audio_features.json"
#Loading the desired data from the json file
def data_loading(data_path, session_num):
#Read data from the json file
with open(data_path, "r") as file:
data = json.load(file)
#S... | {"hexsha": "5a447180cbdfb48be040398805dd8595af093e48", "size": 4564, "ext": "py", "lang": "Python", "max_stars_repo_path": "Classify_CNN.py", "max_stars_repo_name": "chentalen2021/CNN-model", "max_stars_repo_head_hexsha": "44de28e590eeea1287fd1a7b8c2df7f740727bde", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_... |
import torch
import numpy as np
class ScheduledOptim:
""" A simple wrapper class for learning rate scheduling """
def __init__(self, model, train_config, current_step):
self._optimizer = torch.optim.Adam(
model.parameters(),
betas=train_config["optimizer"]["betas"]... | {"hexsha": "aa285c4838e3f5d876395e437075fe889c24ae40", "size": 1493, "ext": "py", "lang": "Python", "max_stars_repo_path": "model/optimizer.py", "max_stars_repo_name": "shaun95/WaveGrad2", "max_stars_repo_head_hexsha": "167d5d6e98072f34a30296ff767c70a5696a4051", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 45... |
"""
The structure provides convenient computation of
a Groebner basis of given ideal at a point.
See evaluate function for more details
"""
### Probably we want to dispatch on coefficients type
mutable struct GroebnerEvaluator
ideal::IdealContext
end
"""
Convenience ctor 1
?? conv... | {"hexsha": "d7b58b02bcf91a1f75a9509a28df9ea012eb16e0", "size": 3307, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/groebner.jl", "max_stars_repo_name": "sumiya11/RationalFunctionFields", "max_stars_repo_head_hexsha": "648db6a3ca01fd087b9eeba4e72930f73210e765", "max_stars_repo_licenses": ["MIT"], "max_stars_... |
'''
part dataset. Support ModelNet40, ModelNet10, XYZ and normal channels. Up to 10000 points.
'''
import os
import os.path
import json
import numpy as np
import sys
import torch
from torch.utils.data import Dataset
from torch.utils.data import DataLoader
BASE_DIR = os.path.dirname(os.path.abspath(__file__))
ROOT... | {"hexsha": "13eb2d3d20114a14e1cc5f0cd6f388b1db76bb8c", "size": 5326, "ext": "py", "lang": "Python", "max_stars_repo_path": "DataLoader/part_dataset_seg.py", "max_stars_repo_name": "meihuaz/PCUNet", "max_stars_repo_head_hexsha": "c3aafd456800a1dd4e83e8d60e2606830d3e3ffc", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
\filetitle{freq}{Frequency of a tseries object}{tseries/freq}
\paragraph{Syntax}\label{syntax}
\begin{verbatim}
f = freq(x)
\end{verbatim}
\paragraph{Input arguments}\label{input-arguments}
\begin{itemize}
\itemsep1pt\parskip0pt\parsep0pt
\item
\texttt{x} {[} tseries {]} - Tseries object.
\end{itemize}
\p... | {"hexsha": "d5a331f862b39a2c6f7cb9c949d3096108268ca3", "size": 825, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "-help/tseries/freq.tex", "max_stars_repo_name": "OGResearch/IRIS-Toolbox-For-Octave", "max_stars_repo_head_hexsha": "682ea1960229dc701e446137623b120688953cef", "max_stars_repo_licenses": ["BSD-3-Clau... |
import numpy as np
import socket
import asyncio
from matplotlib import pyplot as plt
from time import sleep
from watchdog.observers import Observer
from watchdog.events import FileSystemEventHandler, LoggingEventHandler
import logging
HOST = "NUS15128-11-albhuan.local" # Needs to be constantly updated except 127.0.0.1... | {"hexsha": "916b277d40eb004eb1c77b2ca1edf0f6dc867ccd", "size": 2414, "ext": "py", "lang": "Python", "max_stars_repo_path": "server.py", "max_stars_repo_name": "RoboticsTeam4904/2022-camera-server", "max_stars_repo_head_hexsha": "8ba2d4b5b51f2e92b468212e73dba5af419e0a49", "max_stars_repo_licenses": ["Unlicense"], "max_s... |
# # Mauna Loa time series example
#
# In this notebook, we apply Gaussian process regression to the Mauna Loa CO₂
# dataset. This showcases a rich combination of kernels, and how to handle and
# optimize all their parameters.
# ## Setup
#
# We make use of the following packages:
using CSV, DataFrames # data loading
... | {"hexsha": "50f47e80c809d04e82ef81f7ba0c176e405e7f48", "size": 11222, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "examples/1-mauna-loa/script.jl", "max_stars_repo_name": "JuliaGaussianProcesses/AbstractGP", "max_stars_repo_head_hexsha": "6ee8549f536c6037a02a1cc445fd35c0811425ef", "max_stars_repo_licenses": ["... |
#!/usr/bin/env python3
import json
import os.path
import multiprocessing as mp
import timeit
import numpy as np
import _init_path
from skimage import io
from spacenet7_model.configs import load_config
from spacenet7_model.utils import (dump_prediction_to_png, ensemble_subdir,
exper... | {"hexsha": "dbc4264ef416b43749b313e706f7553f0e13e05f", "size": 3793, "ext": "py", "lang": "Python", "max_stars_repo_path": "4-motokimura/code/tools/ensemble_models.py", "max_stars_repo_name": "remtav/SpaceNet7_Multi-Temporal_Solutions", "max_stars_repo_head_hexsha": "ee535c61fc22bffa45331519239c6d1b044b1514", "max_star... |
import networkx as nw
import random as rand
import math
import numpy as np
from matplotlib import pyplot as plt
point_dict = {}
neighbor_dict = {}
R = 50
infinity = 10000
X_MAX = 500
Y_MAX = 500
def check_if_same_point(x, y):
global point_dict
for item in point_dict:
if item is not... | {"hexsha": "20ebdfa158abb9e53a21cf3892bf78139950bbba", "size": 5042, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/drawNetwork.py", "max_stars_repo_name": "SaltyFish6952/RoutingAlgorithmQt", "max_stars_repo_head_hexsha": "8020cf034c886e3cb401ed151b78575508c72f4b", "max_stars_repo_licenses": ["MIT"], "max_s... |
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