text stringlengths 0 1.25M | meta stringlengths 47 1.89k |
|---|---|
"""
Remove images where cobj doesn't exist for easier coaddition
"""
import os
import numpy as np
ls_image=sorted(os.listdir('./image'))
ls_prod=sorted(os.listdir('./prod'))
# list all dates of images
dates=[]
for f in range(len(ls_image)):
dates.append(ls_image[f][0:6]) # indices 0:6 -> date string
dates=sorted(... | {"hexsha": "ef9cb6785c33463b24b4e74cbc64f5f1c8a3e7fc", "size": 1439, "ext": "py", "lang": "Python", "max_stars_repo_path": "py/rotseutil/rm_nocobj_ims.py", "max_stars_repo_name": "rotsehub/rosteutil", "max_stars_repo_head_hexsha": "33c62071c724ad3d1d08fa0289d7244cf716d66d", "max_stars_repo_licenses": ["MIT"], "max_star... |
function derivative(A::LinearInterpolation{<:AbstractVector}, t::Number)
idx = searchsortedfirst(A.t, t)
if A.t[idx] >= t
idx -= 1
end
idx == 0 ? idx += 1 : nothing
θ = 1 / (A.t[idx+1] - A.t[idx])
(A.u[idx+1] - A.u[idx]) / (A.t[idx+1] - A.t[idx])
end
function derivative(A::LinearInterpolation{<:Abstrac... | {"hexsha": "6335488cc6d161242e6298393d052f86fa264d96", "size": 5734, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/derivatives.jl", "max_stars_repo_name": "oxinabox/DataInterpolations.jl", "max_stars_repo_head_hexsha": "48dc13a270b29b49fde77e300a9e14db091ccbeb", "max_stars_repo_licenses": ["MIT"], "max_star... |
import numpy as np
from tqdm import tqdm
import tensorflow as tf
from keras.backend.tensorflow_backend import set_session
from keras.callbacks import Callback
from LeagueData.Database import Item
from LeagueData.DatabaseHandler import session
import operator
from Data.StaticChampionData import index_to_item_id, champio... | {"hexsha": "9193bf34fef2835e4107423ee1fea9e425e4f57d", "size": 4354, "ext": "py", "lang": "Python", "max_stars_repo_path": "tools.py", "max_stars_repo_name": "Plutokekz/LeagueItem", "max_stars_repo_head_hexsha": "23ed033b21857df777234ea8f43ade8daa8bf547", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "ma... |
import gym
import highway_env
from agent import Agent
import pandas as pd
import numpy as np
env = gym.make("highway-v0")
done = False
# Notes
# Action space between 0 and 4 inclusive
# 0 is merge left
# 1 is do nothing
# 2 is merge right
# 3 is speed up
# 4 is slow down
#
## Obs space is a 5x5 matrix with values be... | {"hexsha": "6d409d33559b4a6519981780018a2f54e1281d04", "size": 3487, "ext": "py", "lang": "Python", "max_stars_repo_path": "powerlaw.py", "max_stars_repo_name": "AlexanderDavid/Powerlaw-Highway-Env", "max_stars_repo_head_hexsha": "e3e3b6277e0a75e4dcbc7988a9cb144137328d22", "max_stars_repo_licenses": ["MIT"], "max_stars... |
const naivebayesstanmodel = "
// supervised naive Bayes
data {
// training data
int<lower=1> K; // num topics
int<lower=1> V; // num words
int<lower=0> M; // num docs
int<lower=0> N; // total word instances
int<lower=1,upper=K> z[M]; // topic for ... | {"hexsha": "fcab77e0d28e8553fe4eb5fa962db78faa0a6d73", "size": 888, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "models/stan-models/MoC-stan.model.jl", "max_stars_repo_name": "JuliaTagBot/ContinuousBenchmarks.jl", "max_stars_repo_head_hexsha": "000432d25acef05a11ea51dedfd841c761735e0a", "max_stars_repo_license... |
\documentclass[utf8x,xcolor=pdftex,dvipsnames,table]{beamer}
\usetheme{Malmoe} % Now it's a beamer presentation with the lisa theme!
\setbeamertemplate{footline}[page number]
\usecolortheme{beaver}
\usepackage[T1]{fontenc}
\usepackage{amsmath}
\usepackage[utf8x]{inputenc}
%\logo{\includegraphics[width=.8in]{UdeM_NoirB... | {"hexsha": "258bb582a7449d90db19d6123c84357cefbd89d5", "size": 34480, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "doc/nextml2015/presentation.tex", "max_stars_repo_name": "rpgoldman/Theano-PyMC", "max_stars_repo_head_hexsha": "4669be82a00da3bd78f6683c066c3e0073eecb52", "max_stars_repo_licenses": ["BSD-3-Clause... |
from __future__ import division, unicode_literals
import numpy as np
from traits.api import HasStrictTraits, Array, Float, Instance, Property, Tuple
from tvtk.api import tvtk
from tvtk.common import configure_input_data, is_old_pipeline
VolumeArray = Array(shape=(None, None, None))
# The point data scalars need a ... | {"hexsha": "c19553412ab2fa00003905a819824a80ae18f39f", "size": 4532, "ext": "py", "lang": "Python", "max_stars_repo_path": "ensemble/volren/volume_data.py", "max_stars_repo_name": "enthought/ensemble", "max_stars_repo_head_hexsha": "b63229224b1c0f1c18b04f0c1f2619e8e5e46428", "max_stars_repo_licenses": ["BSD-3-Clause"],... |
\documentclass[../../main.tex]{subfiles}
\begin{document}
\subsubsection{At Key}
The operation $atKey$ will return the Value $v$ at some specified Key $k$.
\begin{schema}{AtKey[KV, K]}
m? : KV \\
v! : V \\
k? : K \\
atKey~\_ : KV \cross K \surj V
\where
v! = atKey(m?, k?) @ \\
\t2 let ~~ coll == ((\seq m?... | {"hexsha": "81cd4fa15532c8f2813f7fdfb11dfe1a1af947d5", "size": 1516, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "docs/operations/kv/atKey.tex", "max_stars_repo_name": "yetanalytics/dave", "max_stars_repo_head_hexsha": "7a71c2017889862b2fb567edc8196b4382d01beb", "max_stars_repo_licenses": ["Apache-2.0"], "max_s... |
#!/usr/bin/env python
# coding: utf-8
# In[1]:
# train_ds = data['train'].map(lambda x,y: (resize(x),y)).shuffle(1024).cache().batch(config.batch_size).prefetch(-1)
def get_hardest_k_examples(test_dataset, model, k=32):
class_probs = model.predict(test_dataset)
predictions = np.argmax(class_probs, axis=1)
... | {"hexsha": "5c08feb8ea7129bd0a68677b11bf10c47edf7b11", "size": 37878, "ext": "py", "lang": "Python", "max_stars_repo_path": "Notebooks/generation_script.py", "max_stars_repo_name": "JacobARose/genetic_algorithm", "max_stars_repo_head_hexsha": "de4c52637f6b928b96c0306b7da59a054322b56c", "max_stars_repo_licenses": ["Apac... |
module MathFuns
export Eb, deltafun, deltadxfun, dabs
"""
Eb(x,m)
compute ``\\sqrt{(x^2+m)}``
"""
function Eb(x, m2)
sqrt(x^2 + m2)
end
"""
deltafun(x,dϵ=0.02)
compute ``\\delta`` function with the approximation ``\\frac{\\epsilon}{\\pi(\\epsilon^2+x^2)}``
and ``\\epsilon`` is set to be ``0.02`` by def... | {"hexsha": "de299fa9b99cd1efcb3f132e109f798fe64e4dc2", "size": 603, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/artifacts/math.jl", "max_stars_repo_name": "Yangyang-Tan/FRGRealTime.jl", "max_stars_repo_head_hexsha": "6581b783432a5d5d08d00c887b483f9596d12fe3", "max_stars_repo_licenses": ["MIT"], "max_stars... |
from statsmodels.tsa.seasonal import seasonal_decompose
from pyramid.arima import auto_arima
import pandas as pd
def DecomposeSeriesSeasonal(series_time_index,series, *frequency):
data = pd.DataFrame(series,index = series_time_index,columns=["Series"])
# use additive model if negative values in time series
... | {"hexsha": "609e6edec9925df5aea849f920fc5646cad05e83", "size": 2755, "ext": "py", "lang": "Python", "max_stars_repo_path": "Sloth/predict.py", "max_stars_repo_name": "NewKnowledge/sloth", "max_stars_repo_head_hexsha": "ed6c9011962f76a1ad1498a03af8b31ac75c6576", "max_stars_repo_licenses": ["MIT"], "max_stars_count": nul... |
import cv2
import numpy as np
from imread_from_url import imread_from_url
from mobileHumanPose import MobileHumanPose, YoloV5s
from mobileHumanPose.utils_pose_estimation import draw_skeleton, draw_heatmap, vis_3d_multiple_skeleton
if __name__ == '__main__':
draw_detections = False
# Camera parameters fo... | {"hexsha": "2af0adb0ad0247d429f3dcaef0b39d2670cd416b", "size": 2790, "ext": "py", "lang": "Python", "max_stars_repo_path": "image3DPoseEstimation.py", "max_stars_repo_name": "matthiasseibold/ONNX-Mobile-Human-Pose-3D", "max_stars_repo_head_hexsha": "fc69545981b8be3c5c7a2f13528fc58503887876", "max_stars_repo_licenses": ... |
[STATEMENT]
lemma solution_upd1:
"c \<noteq> 0 \<Longrightarrow> solution (A(p:=(\<lambda>j. A p j / c))) n x = solution A n x"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. c \<noteq> (0::'a) \<Longrightarrow> solution (A(p := \<lambda>j. A p j / c)) n x = solution A n x
[PROOF STEP]
apply(cases "p<n")
[PROOF ST... | {"llama_tokens": 2392, "file": "Gauss-Jordan-Elim-Fun_Gauss_Jordan_Elim_Fun", "length": 11} |
# encoding: utf-8
# https://github.com/charlesCXK/TorchSSC/blob/master/model/sketch.nyu/network.py
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
from functools import partial
from collections import OrderedDict
from models.config_sketch import config
from models.resnet_sketch i... | {"hexsha": "44f6dabf3d3fc6a47dbc5c9bb4615a3778f4c66b", "size": 34321, "ext": "py", "lang": "Python", "max_stars_repo_path": "xmuda/models/sketch.py", "max_stars_repo_name": "anhquancao/xmuda-extend", "max_stars_repo_head_hexsha": "4b670ec2f6766e3a624e81dbe5d97b209c1c4f76", "max_stars_repo_licenses": ["Apache-2.0"], "ma... |
subroutine residual_func(snes_in, stateVector, residualVector, ctx, localerr)
#include <petsc/finclude/petscsnes.h>
use petscsnes
use contexts
use grids
use input
use geometry
use mp
use diffusion
use source
implicit none
SNES:: snes_in
PetscScalar,dimension(:),intent(in):: stateVector
... | {"hexsha": "12ed3e07ab7ebd5674a6e5a28b2bb50c6da09e9c", "size": 13438, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "residual.f90", "max_stars_repo_name": "gjwilkie/nlfp", "max_stars_repo_head_hexsha": "9e566c4566d0657ea50686d79c3ad353a6c82975", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "ma... |
import numpy as np
import flopter.core.constants as c
from flopter.core import normalise as nrm
from abc import ABC, abstractmethod
class LangmuirProbe(ABC):
def __init__(self, g, d_perp):
self.g = g
self.d_perp = d_perp
@abstractmethod
def is_angled(self):
pass
@abstractmet... | {"hexsha": "0b77e501578e897f3307196e52c8042a0387c3ff", "size": 14496, "ext": "py", "lang": "Python", "max_stars_repo_path": "flopter/core/lputils.py", "max_stars_repo_name": "jackleland/flopter", "max_stars_repo_head_hexsha": "8f18f81470b456884108dc33baee836a672409c4", "max_stars_repo_licenses": ["MIT"], "max_stars_cou... |
# Train neural network to map RGB images to RAM output
import os
import utils
import numpy as np
from models import *
import argparse
from keras import optimizers
description = "Train RGB2RAM models"
parser = argparse.ArgumentParser(description)
parser.add_argument('--game_name', type=str, default='Breakout')
parser... | {"hexsha": "73d00fb4407336e61d4afd06f0372b3b8b950f5e", "size": 2789, "ext": "py", "lang": "Python", "max_stars_repo_path": "rgb2ram/train.py", "max_stars_repo_name": "Nidhi-K/keras-rl", "max_stars_repo_head_hexsha": "535662d41dc51a56ead22a612b67a9e2dcb7b532", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "m... |
# SPDX-FileCopyrightText: 2022 Daniel Laidig <daniel@laidig.info>
#
# SPDX-License-Identifier: MIT
import copy
import numpy as np
def shuffled(items, seed=None, prefixHead=None, prefixTail=None):
if seed is not None:
r = np.random.RandomState(seed)
else:
r = np.random
itemsCopy = copy.co... | {"hexsha": "5d12fc71548656987670fb1dcb30b097a8d8db5d", "size": 2858, "ext": "py", "lang": "Python", "max_stars_repo_path": "textemplate/filters.py", "max_stars_repo_name": "dlaidig/textemplate", "max_stars_repo_head_hexsha": "cfd9781801bbdf6b3a3cc902f0a2eb51228072c3", "max_stars_repo_licenses": ["MIT"], "max_stars_coun... |
import numpy as np
import pandas as pd
from players_data import *
from players_query import *
from players_func import *
| {"hexsha": "5496d9d1d2e1fd99f2575eb4aa9f3b66390bdae1", "size": 122, "ext": "py", "lang": "Python", "max_stars_repo_path": "source/players/players.py", "max_stars_repo_name": "PhyProg/NBA-League-Data-Analysis", "max_stars_repo_head_hexsha": "3be282101da64d194949fb94ca6491559cd6d7ef", "max_stars_repo_licenses": ["MIT"], ... |
#include <stdlib.h>
#include <stdio.h>
#include <vector>
#include <string>
#include <sstream>
#include <iostream>
#include <fstream>
#include <iomanip>
#include <stdexcept>
#include <functional>
#include <random>
#include <Eigen/Geometry>
#include <visualization_msgs/Marker.h>
#include "arc_utilities/eigen_helpers.hpp"... | {"hexsha": "44466a4615d8f2e5bcadddbb4e95d243127c702c", "size": 7651, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "include/uncertainty_planning_examples/se2_common_config.hpp", "max_stars_repo_name": "UM-ARM-Lab/uncertainty_planning_examples", "max_stars_repo_head_hexsha": "0be4bf50db1539e8f79d9225d387270e67bcc2... |
[STATEMENT]
lemma matching_sel_symm:
assumes "matching_sel f"
shows "sel_symm f"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. sel_symm f
[PROOF STEP]
unfolding sel_symm_def
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. \<forall>x y. f x y = f y x
[PROOF STEP]
proof (standard, standard)
[PROOF STATE]
proof ... | {"llama_tokens": 1620, "file": "Query_Optimization_QueryGraph", "length": 18} |
function [mu, sigma, map] = correction_step(mu, sigma, z, map);
% Updates the belief, i.e., mu and sigma after observing landmarks,
% and augments the map with newly observed landmarks.
% The employed sensor model measures the range and bearing of a landmark
% mu: state vector containing robot pose and poses of landmar... | {"author": "kiran-mohan", "repo": "SLAM-Algorithms-Octave", "sha": "e0254ad38cfca2170b2af68c96c183df77c76252", "save_path": "github-repos/MATLAB/kiran-mohan-SLAM-Algorithms-Octave", "path": "github-repos/MATLAB/kiran-mohan-SLAM-Algorithms-Octave/SLAM-Algorithms-Octave-e0254ad38cfca2170b2af68c96c183df77c76252/3_UKF_SLAM... |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
from datetime import datetime, timedelta
from models import JobDescription
from models.runner import goes
from populartwitterbot import Bot
from grapher import draw
import time
import random
from StringIO import StringIO
from netcdf import netcdf as nc
import numpy as np
i... | {"hexsha": "e938abfca370fdc21dc6dc8c958fa02dda427f39", "size": 8600, "ext": "py", "lang": "Python", "max_stars_repo_path": "solarbot/bot.py", "max_stars_repo_name": "limiear/solarbot", "max_stars_repo_head_hexsha": "2596f0b2bddceacf9d04bae134e30cec5c6fa965", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, ... |
@testset "481.magical-string.jl" begin
let
res = [1, 1, 1, 2, 3, 3, 4, 4, 4, 5]
for i in 1:10
@test magical_string(i) == res[i]
end
end
end | {"hexsha": "f4fa5429c8ba5f50aadc726c5bc42d704f237cb4", "size": 192, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/problems/481.magical-string.jl", "max_stars_repo_name": "jmmshn/LeetCode.jl", "max_stars_repo_head_hexsha": "dd2f34af8d253b071e8a36823d390e52ad07ab2e", "max_stars_repo_licenses": ["MIT"], "max_... |
"""
Use the basinhopping algorithm to find best alpha, speed, and frequency
that produces the best spatial correlation for a given canonical network
"""
# number stuff imports
import h5py
import numpy as np
import pandas as pd
from scipy.optimize import basinhopping
from scipy.stats import pearsonr
from sklearn.linear... | {"hexsha": "79243e463e8170e6acfb7c93d438d57b861978dd", "size": 6209, "ext": "py", "lang": "Python", "max_stars_repo_path": "scripts/laplacian_pearson_basinhopping.py", "max_stars_repo_name": "axiezai/complex_laplacian", "max_stars_repo_head_hexsha": "e84574a7d9c051a95b5d37aa398765aeb5f85fa4", "max_stars_repo_licenses":... |
# coding=utf-8
import cv2
import numpy as np
import os
import pickle
from sklearn.decomposition import PCA
from sklearn.preprocessing import StandardScaler
import os
from fnmatch import fnmatch
def main():
S = []
project_path = os.path.dirname(os.path.realpath(__file__))
if os.path.exists(project_path + r... | {"hexsha": "6c6d799ea4afef1fa95d9b8579308cce202ea457", "size": 12018, "ext": "py", "lang": "Python", "max_stars_repo_path": "AbnormalEventDetection/src/project_train_test.py", "max_stars_repo_name": "Scott1123/ComputerVisionLab", "max_stars_repo_head_hexsha": "a588d328a6c9a4519bdaa81382968cd07711d648", "max_stars_repo_... |
# Copyright (c) 2020 fortiss GmbH
#
# Authors: Patrick Hart, Julian Bernhard, Klemens Esterle, and
# Tobias Kessler
#
# This software is released under the MIT License.
# https://opensource.org/licenses/MIT
import numpy as np
from bark.core.models.behavior import BehaviorModel, BehaviorMPContinuousActions
from bark.c... | {"hexsha": "3864d48073624ab1cf7374ffbd7cbac671dc282e", "size": 1429, "ext": "py", "lang": "Python", "max_stars_repo_path": "bark_ml/behaviors/discrete_behavior.py", "max_stars_repo_name": "GAIL-4-BARK/bark-ml", "max_stars_repo_head_hexsha": "c61c897842c2184ee842428e451bae3be2cd7242", "max_stars_repo_licenses": ["MIT"],... |
# -*- coding: utf-8 -*-
"""
decode
======
"""
# import standard libraries
import os
import subprocess
from pathlib import Path
# import third-party libraries
import matplotlib.pyplot as plt
import numpy as np
# import my libraries
import test_pattern_generator2 as tpg
import plot_utility as pu
# information
__auth... | {"hexsha": "02cbde70055dc77cf2d5cee65a78db42d6df007b", "size": 6603, "ext": "py", "lang": "Python", "max_stars_repo_path": "2021/03_investigate_avif/decode_captured_mp4.py", "max_stars_repo_name": "toru-ver4/sample_code", "max_stars_repo_head_hexsha": "9165b4cb07a3cb1b3b5a7f6b3a329be081bddabe", "max_stars_repo_licenses... |
"""
This is only meant to demonstrate the agreement of the `sweep` implementation
with `scipy.signal.chirp`. Can be used for unit testing of our sine sweep
implementation down the road, but it also shows that the current `sine_sweep`
function could be reworked to only call `scipy.signal.sweep` with minimal effort.
"... | {"hexsha": "51ac30767695e86c679e82bac29b397415ac4c95", "size": 2016, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/test_scipy_chirp.py", "max_stars_repo_name": "nilswagner/pyExSi", "max_stars_repo_head_hexsha": "6fc0117eb2dbc9f8e2d390bdcf31987a939b25c3", "max_stars_repo_licenses": ["MIT"], "max_stars_cou... |
/-
Take funtion as argument and use it in implementation.
-/
def apply_nat_to_nat (f : ℕ → ℕ) (n : ℕ) : ℕ :=
f n
#eval apply_nat_to_nat nat.succ 1
#eval apply_nat_to_nat nat.pred 1
/-
Make idea completely general using polymorphism
-/
def apply {α β : Type} (f : α → β) (a : α) : β :=
f a
#eval apply nat.... | {"author": "kevinsullivan", "repo": "dm.s20", "sha": "6f90ecb3881c602cdd1e3f12aad458bcdabd250a", "save_path": "github-repos/lean/kevinsullivan-dm.s20", "path": "github-repos/lean/kevinsullivan-dm.s20/dm.s20-6f90ecb3881c602cdd1e3f12aad458bcdabd250a/instructor/higher_order_funcs/higher_order_intro.lean"} |
from gensim.models.word2vec import Word2Vec
import fileDispose
import numpy as np
def get_word2vec(corpus,size=500,window=3,sg=1,epochs=50,save_flag=False):
"""
获取word2vec词向量
:param corpus: 输入词库，如glove
:param size: 生成词向量维度
:param window: 词窗大小如glove
:param sg: 是否使用skip-grams模式，为0时使用CBOW模式
:... | {"hexsha": "e455d8c39dc17e40eacdd4fe5933ba6bb94386c1", "size": 1832, "ext": "py", "lang": "Python", "max_stars_repo_path": "word2vec_embedding.py", "max_stars_repo_name": "fanglala997/test", "max_stars_repo_head_hexsha": "a6268ba9af517342c719dc99384ab6f7e42ef584", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_c... |
from ppadb.client import Client
from PIL import Image
from numpy import array, uint8
from time import sleep
adb = Client(host='127.0.0.1', port=5037)
devices = adb.devices()
if len(devices) == 0:
print('no device attached')
quit()
device = devices[0]
sleep(30)
# device.shell('input touchscreen swipe ... | {"hexsha": "4bc15ba04a229b3e3a473a78ebfac65ae2f23aaf", "size": 2095, "ext": "py", "lang": "Python", "max_stars_repo_path": "StickHeroBot.py", "max_stars_repo_name": "Ben-Donnelly/ADBAutomation", "max_stars_repo_head_hexsha": "8f1305c4128cc560b22480a221bf788837094886", "max_stars_repo_licenses": ["MIT"], "max_stars_coun... |
# input ---------------------------------------------------------------
input_data_tab<-function(){
tabItem(tabName = "input_data_tab",
fluidRow(
box(width=12,title="",
includeMarkdown("save_data.md"))
),
fluidRow(
box(width=12,title="",
numericInput(inputId="id_n",label="id_n",value=1, min = NA, max = N... | {"hexsha": "90982073806ab12064ede89dc2c1375bf38ed40e", "size": 4245, "ext": "r", "lang": "R", "max_stars_repo_path": "ui.r", "max_stars_repo_name": "qualitylimpieza/qualitylimpieza", "max_stars_repo_head_hexsha": "8394aed84cee434664da2a5941eb773cbfc1412a", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "m... |
import csv
import os
import numpy as np
import torch
from torch.utils.data import Dataset, DataLoader, SubsetRandomSampler
from torchtext import data
import pandas as pd
import re
import nltk
# word tokenization
#nltk.download('punkt')
from nltk.tokenize import word_tokenize
# Stemming
from nltk.stem import PorterS... | {"hexsha": "36f1fb5ea60408eb0eaf4d4710439d602ae57124", "size": 2108, "ext": "py", "lang": "Python", "max_stars_repo_path": "a4/clean_data.py", "max_stars_repo_name": "zhangtravis/IntSys-Education", "max_stars_repo_head_hexsha": "407e0e1f60b57a922b84f6813378a0bddc178c3a", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
from enum import unique
from lib2to3.pytree import convert
import numpy as np
import mpmath as mp
from scipy.fftpack import diff
mp.mp.dps = 300
mp.mp.pretty = False
def enumerate_classes(inverse_hash,limit):
letters = list(inverse_hash.keys())
unique_letters = []
for letter in letters:
i... | {"hexsha": "85452c9f4b0b624193e60ba82e45e6cff9d81222", "size": 10208, "ext": "py", "lang": "Python", "max_stars_repo_path": "helper_functions/add_new_triangle_functions.py", "max_stars_repo_name": "sepehrsaryazdi/cpsvis", "max_stars_repo_head_hexsha": "fe57797091a9bf5c116da9827a19cf7e48b45e98", "max_stars_repo_licenses... |
import numpy as np
from collections import namedtuple
INPUT = open("advent2018_day18_input.txt", "r").read().split("\n")
OPEN_GROUND = 0
TREE = 1
LUMBERYARD = 2
PRINT_DICT = {OPEN_GROUND: '.',
TREE: '|',
LUMBERYARD: '#'}
READ_DICT = {v: k for k, v in PRINT_DICT.iteritems()}
np.... | {"hexsha": "7e71900c6e39c3820f5752afbac504175b0176b0", "size": 2816, "ext": "py", "lang": "Python", "max_stars_repo_path": "advent2018_day18.py", "max_stars_repo_name": "coandco/advent2018", "max_stars_repo_head_hexsha": "5d51780cbcf425857f99c1f6b2c648a3e5852581", "max_stars_repo_licenses": ["MIT"], "max_stars_count": ... |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
'''
Author : windz
Date : 2021-10-13 14:18:24
LastEditTime : 2021-10-13 16:28:30
LastEditors : windz
FilePath : /flair/script/get_unique_isoform.py
Description : get isoform unique to a_bed
'''
import concurrent.futures
import pyranges as pr
import num... | {"hexsha": "d578a2866109598ce51542aff1d82ecbc8f629d3", "size": 4550, "ext": "py", "lang": "Python", "max_stars_repo_path": "script/get_unique_isoform.py", "max_stars_repo_name": "WeipengMO/flair", "max_stars_repo_head_hexsha": "e6c9990bcfdd1d2e585bab1f45b7f8dc68b21fbc", "max_stars_repo_licenses": ["BSD-3-Clause"], "max... |
//==================================================================================================
/*!
@file
@copyright 2016 NumScale SAS
Distributed under the Boost Software License, Version 1.0.
(See accompanying file LICENSE.md or copy at http://boost.org/LICENSE_1_0.txt)
*/
//===========================... | {"hexsha": "929e4e0999a49f0d65496945ca163797411e51d4", "size": 2733, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "include/boost/simd/function/atan2.hpp", "max_stars_repo_name": "nickporubsky/boost-simd-clone", "max_stars_repo_head_hexsha": "b81dfcd9d6524a131ea714f1eebb5bb75adddcc7", "max_stars_repo_licenses": [... |
import glob
import pandas as pd
import geopandas as gpd
import shared
import numpy as np
xwalk = pd.read_csv('data/GeogXWalk2010_Blocks_MAZ_TAZ.csv')
maz_controls = pd.read_csv("data/maz_controls.csv")
buildings = glob.glob("cache/*buildings_match_controls.csv")
juris_names = [b.replace("_buildings_match_controls.csv... | {"hexsha": "a743dc0f41e1a13d517eb62ed694002138f8f53e", "size": 3712, "ext": "py", "lang": "Python", "max_stars_repo_path": "scripts/merge_cities.py", "max_stars_repo_name": "oaklandanalytics/parcel_cutting_board", "max_stars_repo_head_hexsha": "c134ab3c239090e7acb04d1257186763bf437640", "max_stars_repo_licenses": ["BSD... |
import os
import pathlib
import tempfile
import numpy
import pytest
from pandas.util.testing import assert_frame_equal, assert_dict_equal, assert_index_equal
from pandas import DataFrame
from tfs import read_tfs, write_tfs, TfsDataFrame
from tfs.handler import TfsFormatError
CURRENT_DIR = pathlib.Path(__file__).pare... | {"hexsha": "e46cb77e03e95ce3322247893b08e4655e38879d", "size": 6260, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/test_handler.py", "max_stars_repo_name": "nikitakuklev/tfs", "max_stars_repo_head_hexsha": "7c515aa3c4dd311622380efb48ea98b557d04c01", "max_stars_repo_licenses": ["MIT"], "max_stars_count": ... |
# -*- coding: utf-8 -*-
from __future__ import division, unicode_literals, print_function, absolute_import
from pyvisa.testsuite import BaseTestCase
from pyvisa import util
try:
# noinspection PyPackageRequirements
import numpy as np
except ImportError:
np = None
class TestParser(BaseTestCase):
de... | {"hexsha": "85ff8c70c74e1a33446aae581434ed0b041e3594", "size": 2451, "ext": "py", "lang": "Python", "max_stars_repo_path": "pyvisa/testsuite/test_util.py", "max_stars_repo_name": "ap98nb26u/pyvisa", "max_stars_repo_head_hexsha": "6c36592c1bc26fc49785a43160cd6f27623a50fc", "max_stars_repo_licenses": ["MIT"], "max_stars_... |
import numpy as np
import cv2
# https://stackoverflow.com/questions/22937589/how-to-add-noise-gaussian-salt-and-pepper-etc-to-image-in-python-with-opencv
class Noiser(object):
def __init__(self, cfg):
self.cfg = cfg
def apply(self, img):
"""
:param img: word image with big background... | {"hexsha": "d4c1650103c160474be62086a7944c599d6a9d66", "size": 2788, "ext": "py", "lang": "Python", "max_stars_repo_path": "textrenderer/noiser.py", "max_stars_repo_name": "light1003/text_renderer", "max_stars_repo_head_hexsha": "cd727dee9538067fc8f9f2e05265938007b71bc1", "max_stars_repo_licenses": ["MIT"], "max_stars_... |
import copy
import numpy as np
from .util import is_ccw
from .. import util
from .. import grouping
from .. import constants
try:
import networkx as nx
except BaseException as E:
# create a dummy module which will raise the ImportError
# or other exception only when someone tries to use networkx
from... | {"hexsha": "f4417b85cccfdf3a6ec10f73179b1f5574129d65", "size": 14481, "ext": "py", "lang": "Python", "max_stars_repo_path": "trimesh/path/traversal.py", "max_stars_repo_name": "jpmaterial/trimesh", "max_stars_repo_head_hexsha": "4f493ff0a96a14e62eb7c748964fd8f4e44064c5", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
# -*- coding:utf-8 -*-
from preprocessing import Tokenizer
import random
import csv
import json
import numpy as np
import sentencepiece as spm
from konlpy.tag import Okt
import torch
from torch.utils.data import Dataset, DataLoader
class BertLMDataset(Dataset):
def __init__(self, dataset, token... | {"hexsha": "fe3df81cb837bc5953fd3a5d8a012b03a26b4a30", "size": 4993, "ext": "py", "lang": "Python", "max_stars_repo_path": "datasets.py", "max_stars_repo_name": "sdh9446/document-classification-BERT", "max_stars_repo_head_hexsha": "1b5a0eb72bbd9b67693209e6735af71ab382516a", "max_stars_repo_licenses": ["Apache-2.0"], "m... |
[STATEMENT]
lemma intro_bind_refine_id:
assumes "m \<le> (SPEC ((=) m'))"
assumes "f m' \<le> \<Down>R m''"
shows "bind m f \<le> \<Down>R m''"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. m \<bind> f \<le> \<Down> R m''
[PROOF STEP]
using assms
[PROOF STATE]
proof (prove)
using this:
m \<le> SPEC ((=) m')
f... | {"llama_tokens": 451, "file": "Refine_Monadic_Refine_Basic", "length": 4} |
import torch
import torch.nn as nn
import numpy as np
import torch.nn.functional as F
class Conv1dSame(nn.Module):
"""
Add PyTorch compatible support for Tensorflow/Keras padding option: padding='same'.
Discussions regarding feature implementation:
https://discuss.pytorch.org/t/converting-tensorflow-m... | {"hexsha": "8efb51c0c256eddc50181aa8198f0f1edee52dc8", "size": 4648, "ext": "py", "lang": "Python", "max_stars_repo_path": "project/utils.py", "max_stars_repo_name": "asifreal/Magnet", "max_stars_repo_head_hexsha": "b71302997fcf71b2e7b5ed7ec6d714babf35cb0e", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count":... |
import torch
import torch.nn.functional as F
import numpy as np
import utils
# U update
def update_U(model, eval_loader, z_dim, device):
model.eval()
FF = []
with torch.no_grad():
for batch_idx, (x, y, _) in enumerate(eval_loader):
x = x.to(device)
y = y.to(device)
... | {"hexsha": "c881bd0e77c0261cf35b020e4ccb27bbe85c14b1", "size": 4862, "ext": "py", "lang": "Python", "max_stars_repo_path": "train.py", "max_stars_repo_name": "llvqi/multiview_and_self-supervision", "max_stars_repo_head_hexsha": "1ae8ba14a250fd4a235b64cdd535e93c0ac06608", "max_stars_repo_licenses": ["Apache-2.0"], "max_... |
using DifferentiableStateSpaceModels
using Test, LinearAlgebra
# The BLAS threads is still an issue in Julia 1.7
# This has no effect with MKL
DifferentiableStateSpaceModels.set_blas_threads()
println("Running Testsuite with Threads.nthreads() = $(Threads.nthreads()) BLAS.vendor = $(BLAS.vendor()), and BLAS.num_threa... | {"hexsha": "054f2d501a999b02de49f5834ad3b328dc4f6bd8", "size": 919, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/runtests.jl", "max_stars_repo_name": "HighDimensionalEconLab/DifferentiableStateSpaceModels.jl", "max_stars_repo_head_hexsha": "da7700a84fdabc07edc73728d09905e5d7cb31be", "max_stars_repo_licens... |
# Copyright 2019 Google LLC
#
# 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
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,... | {"hexsha": "9c415eb9aa2aff6c9688bc508233b096fc8bf06a", "size": 7003, "ext": "py", "lang": "Python", "max_stars_repo_path": "opencv/[video]person_count_rev0.1.py", "max_stars_repo_name": "sity0825/examples-camera", "max_stars_repo_head_hexsha": "63d7844211d7141eee75de15043b895248bf2d91", "max_stars_repo_licenses": ["Apa... |
/**
* @project zapdos
* @file include/http/AsioCompat.hpp
* @author S Roychowdhury < sroycode at gmail dot com >
* @version 1.0.0
*
* @section LICENSE
*
* Copyright (c) 2018-2020 S Roychowdhury
*
* Permission is hereby granted, free of charge, to any person obtaining a copy of
* this software and associated... | {"hexsha": "fa9b98c7e865321b4f16a8dad924d547e9de3cb6", "size": 4437, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "include/http/AsioCompat.hpp", "max_stars_repo_name": "sroycode/zapdos", "max_stars_repo_head_hexsha": "8818ef109e072dcbe990914d9a2a6d70ef190d3e", "max_stars_repo_licenses": ["MIT"], "max_stars_count... |
import numpy as np
from otk import sdb
from otk.sdb import demoscenes, webex
scene = demoscenes.make_primitives()
eye_to_world = sdb.lookat(scene.eye, scene.center)
projection = sdb.Perspective(np.pi/3, scene.z_near, scene.z_far)
#projection = sdb.Orthographic(scene.z_far*np.tan(np.pi/6), scene.z_far)
webex.gen_html('p... | {"hexsha": "53b4aaf29f8862c988a0e9dee49c23cb6407b764", "size": 404, "ext": "py", "lang": "Python", "max_stars_repo_path": "examples/sdb/make_primitives_html.py", "max_stars_repo_name": "draustin/otk", "max_stars_repo_head_hexsha": "c6e91423ec79b85b380ee9385f6d27c91f92503d", "max_stars_repo_licenses": ["MIT"], "max_star... |
import cv2
import numpy as np
class pySaliencyImage:
def __init__(self):
return None
#--------------------Extraccion de colores--------------------
def SMExtractRGBI(self, inputImage):
#Convierte la imagen en un array
src = np.float32(inputImage) * 1./255
#Regresa una lista de acuerdo al separador indicado ... | {"hexsha": "026db44797ff0cd338994ef018ea7f0925eaf17d", "size": 2061, "ext": "py", "lang": "Python", "max_stars_repo_path": "pySaliencyImage.py", "max_stars_repo_name": "ErickJuarez/SaliencyMaps", "max_stars_repo_head_hexsha": "2d41af2c2a95e81c3fccceebc3ffd292760b185a", "max_stars_repo_licenses": ["MIT"], "max_stars_cou... |
Load LFindLoad.
From lfind Require Import LFind.
From QuickChick Require Import QuickChick.
From adtind Require Import goal35.
Derive Show for natural.
Derive Arbitrary for natural.
Instance Dec_Eq_natural : Dec_Eq natural.
Proof. dec_eq. Qed.
Lemma lfind_hyp_test : (@eq n... | {"author": "yalhessi", "repo": "lemmaranker", "sha": "53bc2ad63ad7faba0d7fc9af4e1e34216173574a", "save_path": "github-repos/coq/yalhessi-lemmaranker", "path": "github-repos/coq/yalhessi-lemmaranker/lemmaranker-53bc2ad63ad7faba0d7fc9af4e1e34216173574a/benchmark/clam/_lfind_clam_lf_goal35_mult_succ_78_plus_commut/lfindlf... |
__author__ = 'lucabasa'
__version__ = '1.0.0'
__status__ = 'development'
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.metrics import accuracy_score, roc_auc_score, f1_score, classification_report
def report_oof(df_train, oof):
acc = accuracy_score... | {"hexsha": "d68e449fcba69fbeeee42fc86964731b68fa5985", "size": 2300, "ext": "py", "lang": "Python", "max_stars_repo_path": "instantgratification/utility.py", "max_stars_repo_name": "lucabasa/kaggle_competitions", "max_stars_repo_head_hexsha": "15296375dc303218093aa576533fb809a4540bb8", "max_stars_repo_licenses": ["Apac... |
c subroutine qrbd (ipass,q,e,nn,v,mdv,nrv,c,mdc,ncc)
c c.l.lawson and r.j.hanson, jet propulsion laboratory, 1973 jun 12
c to appear in 'solving least squares problems', prentice-hall, 1974
c qr algorithm for singular values of a bidiagonal matrix.
c
c the bidiagonal matrix
c
c (q1,e2,0... )
c ... | {"hexsha": "d9a12a24413e7ac0623abde98f4656a09535cb78", "size": 4634, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "iraf.v2161/math/llsq/original_f/qrbd.f", "max_stars_repo_name": "ysBach/irafdocgen", "max_stars_repo_head_hexsha": "b11fcd75cc44b01ae69c9c399e650ec100167a54", "max_stars_repo_licenses": ["MIT"], "... |
"""K nearest neighbors.
Probably should be an odd number of K so that there cannot be a tie
Groups applications by distance to other points."""
#we will do Euclidian distance, which is a super slow algorithm for large data
#can be threaded decently but still is slow
#uses breast cancer data from https://archive.... | {"hexsha": "3e437362b4a577c91f3882189a67182ef125a360", "size": 1219, "ext": "py", "lang": "Python", "max_stars_repo_path": "ML_Tutorials/Breast_cancer_kneighbors.py", "max_stars_repo_name": "Ryandry1st/Machine-Learning", "max_stars_repo_head_hexsha": "f5de4c699edb350c8996bf6b14aae28e20cbccf3", "max_stars_repo_licenses"... |
import math
import numpy as np
def calc_dist(p1, p2):
'''
Calculate distance between point 1 and point 2
'''
return np.sqrt((p2[0] - p1[0]) ** 2 +
(p2[1] - p1[1]) ** 2)
def phi(x):
'Cumulative distribution function for the standard normal distribution'
return (1.0 + math.e... | {"hexsha": "a3b6751ac70de2fbbba96c61d1c10206440db1a9", "size": 10392, "ext": "py", "lang": "Python", "max_stars_repo_path": "gym_invaders/gym_game/envs/classes/Game/Sprites/statemachine.py", "max_stars_repo_name": "Jh123x/Orbital", "max_stars_repo_head_hexsha": "6f8f2da4fd26ef1d77c0c6183230c3a5e6bf0bb9", "max_stars_rep... |
import math
import numpy as np
from multiprocessing import Pool
class paramAdapter(object):
"""This object stores the variables required to implement an adaptive
step size and number of leapfrog steps as detailed in "Adaptive Hamiltonian
and Riemann Manifold Monte Carlo Samplers" by Wang, Mohamed, and de ... | {"hexsha": "072c295edd78600cd6927fd83dcb0fdd3a182bd1", "size": 8405, "ext": "py", "lang": "Python", "max_stars_repo_path": "bayesianNetwork2.0/paramAdapter.py", "max_stars_repo_name": "brkronheim/BNNs-for-SUSY", "max_stars_repo_head_hexsha": "1f845e7cd5437970cfd6b2bd0b4af6c26354ce78", "max_stars_repo_licenses": ["MIT"]... |
#coverage:ignore
import os
from uuid import uuid4
import scipy.optimize
import jax.numpy as jnp
from jax.config import config
from jax import jit, grad
import h5py
import numpy
import numpy.random
import numpy.linalg
from scipy.optimize import minimize
from .adagrad import adagrad
# set mkl thread count for numpy eins... | {"hexsha": "ecb0af3217d5cb5652376ff498589d53c67fba8e", "size": 11686, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/openfermion/resource_estimates/thc/utils/thc_objectives.py", "max_stars_repo_name": "cvmxn1/OpenFermion", "max_stars_repo_head_hexsha": "cf53c063d0f124a02ff8776bb7f8afb110d4bde6", "max_stars_... |
import os
import shutil
import tempfile
import zipfile
import h5py
import numpy
import six
from PIL import Image
from numpy.testing import assert_raises
from fuel import config
from fuel.converters.dogs_vs_cats import convert_dogs_vs_cats
from fuel.datasets.dogs_vs_cats import DogsVsCats
from fuel.streams import Data... | {"hexsha": "8ccee6169137d3e6cc819ac97b167bc6b7aa3c54", "size": 2509, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/test_dogs_vs_cats.py", "max_stars_repo_name": "zaimusho/fuel", "max_stars_repo_head_hexsha": "a5ae89c2c77c7865544e2c68dff3207a62085dd6", "max_stars_repo_licenses": ["MIT"], "max_stars_count"... |
import sys
from pathlib import Path
import numpy as np
import pandas as pd
sys.path.append("./")
from brainrender import Scene, settings
from brainrender.actors import Points
from data.dbase.db_tables import Probe
from myterial import blue_grey, grey_darker
settings.SHOW_AXES = False
CONFIGURATION = "longcolumn"
... | {"hexsha": "a2ef216168c9fd7046f92c094443872b44a37486", "size": 2161, "ext": "py", "lang": "Python", "max_stars_repo_path": "analysis/ephys/probe_3d_probes.py", "max_stars_repo_name": "FedeClaudi/LocomotionControl", "max_stars_repo_head_hexsha": "1281f7894825096ad212407351463a2105c5152a", "max_stars_repo_licenses": ["MI... |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import logging
import os
import contextlib
import numpy as np
import torch
from fairseq.data import FairseqDataset, data_utils
logger = l... | {"hexsha": "22071509f43bfe32d32f4745a69a9fa43744705d", "size": 4274, "ext": "py", "lang": "Python", "max_stars_repo_path": "examples/wav2vec/unsupervised/data/extracted_features_dataset.py", "max_stars_repo_name": "Matrix-Zheng/fairseq", "max_stars_repo_head_hexsha": "4b5267aaedc2d1e05111bd7a9bfc947fba74e2b4", "max_sta... |
%%%%%%%%%%%%%%%%%%%% author.tex %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%
% sample root file for your "contribution" to a contributed volume
%
% Use this file as a template for your own input.
%
%%%%%%%%%%%%%%%% Springer %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% RECOMMENDED %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
... | {"hexsha": "65a5387a26bfaeaee1be1837ac4895847d262da7", "size": 15557, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "IMR_18/engpar_imr18.tex", "max_stars_repo_name": "SCOREC/EnGPar-Docs", "max_stars_repo_head_hexsha": "e99ac24b81842e2638f34420abce7cf981efbca1", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_st... |
import cv2
import numpy as np
import time
import packages.dcci as dcci
import img_resources as imr
import timeit
# Init
window_name = "UpScaling"
# img = cv2.imread(img_files.pixel_art[0], cv2.IMREAD_GRAYSCALE)
# def time_results(fn):
# time_start = time.clock()
# output = fn
# time_stop = time.clock()
#... | {"hexsha": "e3620b63453632bc8341278827732aed80d72d2b", "size": 1580, "ext": "py", "lang": "Python", "max_stars_repo_path": "test.py", "max_stars_repo_name": "revent-studio/organa_hqx", "max_stars_repo_head_hexsha": "81a4027da856c6484b78c53e2478d73a010ce798", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 7, "ma... |
import abc
import numpy as np
import mlalgorithms.checks as checks
class IModel(abc.ABC):
@abc.abstractmethod
def train(self, train_samples, train_labels, **kwargs):
"""
Train current model.
:param train_samples: array-like, sparse matrix.
Training data.
:param... | {"hexsha": "22c62d9a43099bc91d8597b106b8663bbb162f00", "size": 2830, "ext": "py", "lang": "Python", "max_stars_repo_path": "mlalgorithms/models/model.py", "max_stars_repo_name": "danila19991/tinkoff-web-service", "max_stars_repo_head_hexsha": "ccc8ac4e8dae6aae5e1c843ddf8730f6216e8450", "max_stars_repo_licenses": ["Apac... |
module CPUTime
export
CPUtime_us,
CPUtic,
CPUtoq,
CPUtoc,
@CPUtime,
@CPUelapsed
function CPUtime_us()
rusage = Libc.malloc(4*sizeof(Clong) + 14*sizeof(UInt64)) # sizeof(uv_rusage_t); this is different from sizeof(rusage)
ccall(:uv_getrusage, Cint, (Ptr{Nothing},), rusage)
utime = ... | {"hexsha": "4a843750088e960572638276f911332e4027c22a", "size": 1746, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/CPUTime.jl", "max_stars_repo_name": "JuliaTagBot/CPUTime.jl", "max_stars_repo_head_hexsha": "b2968b0e90ded508273c93da89ec0341ed5f3334", "max_stars_repo_licenses": ["MIT"], "max_stars_count": nu... |
// Copyright Nick Thompson, 2017
// Use, modification and distribution are subject to the
// Boost Software License, Version 1.0.
// (See accompanying file LICENSE_1_0.txt
// or copy at http://www.boost.org/LICENSE_1_0.txt)
#define BOOST_TEST_MODULE Gauss Kronrod_quadrature_test
#include <complex>
#include <boost/con... | {"hexsha": "c80ffe85405d58d41da7d27567c1104b225e7266", "size": 18332, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "test/gauss_kronrod_quadrature_test.cpp", "max_stars_repo_name": "oleg-alexandrov/math", "max_stars_repo_head_hexsha": "2137c31eb8e52129d997a76b893f71c1da0ccc5f", "max_stars_repo_licenses": ["BSL-1.... |
# coding: utf-8
# !/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue Sep 19 11:05:23 2017
@author: zhangji
"""
# from matplotlib import pyplot as plt
# plt.rcParams['figure.figsize'] = (18.5, 10.5)
# fontsize = 40
import codeStore.support_fun as spf
# import os
import glob
import numpy as np
# import ma... | {"hexsha": "ecbbe19950fa930f1c058be70ba121e211a5b98b", "size": 2849, "ext": "py", "lang": "Python", "max_stars_repo_path": "codeStore/post_support_fun.py", "max_stars_repo_name": "pcmagic/stokes_flow", "max_stars_repo_head_hexsha": "464d512d3739eee77b33d1ebf2f27dae6cfa0423", "max_stars_repo_licenses": ["MIT"], "max_sta... |
# THEANO_FLAGS=device=gpu,floatX=float32 python train.py
# bug: training length should be larger than batch size
from keras.models import Sequential
from keras.layers import Dense, Activation, Dropout
from keras.layers import LSTM
from keras.utils.data_utils import get_file
from keras.models import load_model
im... | {"hexsha": "9fa4a99d4c29099f8d097cee4d6d0fbce6b8b3de", "size": 4437, "ext": "py", "lang": "Python", "max_stars_repo_path": "train.py", "max_stars_repo_name": "luseiee/project1", "max_stars_repo_head_hexsha": "5964738b095cee7d18715e057b1b93445d3e1a73", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 4, "max_stars... |
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by app... | {"hexsha": "4ddcfbac8791f8324a875c62e469997330a1e273", "size": 9777, "ext": "py", "lang": "Python", "max_stars_repo_path": "python/paddle/incubate/operators/resnet_unit.py", "max_stars_repo_name": "RangeKing/Paddle", "max_stars_repo_head_hexsha": "2d87300809ae75d76f5b0b457d8112cb88dc3e27", "max_stars_repo_licenses": ["... |
import pytest
import numpy as np
from symbolic_pymc.utils import HashableNDArray
from symbolic_pymc.meta import MetaSymbol, MetaOp, metatize
class SomeOp(object):
def __repr__(self):
return "<SomeOp>"
class SomeType(object):
def __init__(self, field1, field2):
self.field1 = field1
... | {"hexsha": "632432817bef3ea5f3c4dff10cadf74499e5f238", "size": 4701, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/test_meta.py", "max_stars_repo_name": "josephwillard/symbolic-pymc", "max_stars_repo_head_hexsha": "7bef08dd572c3ddc32ddc8e8e3c0b1809b4ce654", "max_stars_repo_licenses": ["Apache-2.0"], "max... |
function not_disappearing(data)
idx=[]
for s in 1:data.S
flag = true
for t in 1:data.T-1
if data.counts[s,1,t]==0 && data.counts[s,1,t+1]>0
flag = false
break
end
end
if flag
push!(idx,s)
end
end
... | {"hexsha": "85f7852edb84369b010fc54b3f5e12d070a1306a", "size": 845, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/preprocess_utils.jl", "max_stars_repo_name": "matteodeleonardis/UAF2.jl", "max_stars_repo_head_hexsha": "83af0afd03a6e9bacdc809c7fcbcdd2760bbb98d", "max_stars_repo_licenses": ["MIT"], "max_stars... |
cxx"""
static cv::UMat image;
static bool backprojMode = false;
static bool selectObject = false;
static int trackObject = 0;
static bool showHist = true;
static cv::Rect selection;
static int vmin = 10, vmax = 256, smin = 30;
//int argc = 0;
//char** argv;
//cv::String keys;
//cv::CommandLineParser parser(int argc, co... | {"hexsha": "15241967ef1fe3f61413c0c21500e3e982ddf89c", "size": 7323, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/cxx/ocl_camshift.jl", "max_stars_repo_name": "vernwalrahul/opencv.jl", "max_stars_repo_head_hexsha": "d2851b46b6226a547706aaf256507a1a17cccd1f", "max_stars_repo_licenses": ["MIT"], "max_stars_... |
[STATEMENT]
lemma stc_mult:
"\<lbrakk>x \<in> HFinite; y \<in> HFinite\<rbrakk>
\<Longrightarrow> stc (x * y) = stc(x) * stc(y)"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. \<lbrakk>x \<in> HFinite; y \<in> HFinite\<rbrakk> \<Longrightarrow> stc (x * y) = stc x * stc y
[PROOF STEP]
by (simp add... | {"llama_tokens": 169, "file": null, "length": 1} |
#!/usr/bin/env python3
import time
import os
import tempfile
import shutil
import logging
from argparse import ArgumentParser, Namespace
from netCDF4 import Dataset, MFDataset
import geopandas as gpd
import numpy as np
domain_nodes_shp = "gis/ssm domain nodes.shp"
def get_node_ids(shp):
domain_nodes = gpd.read_f... | {"hexsha": "3b1f91e68aad4b98a908395abd289bf83c53d91e", "size": 5161, "ext": "py", "lang": "Python", "max_stars_repo_path": "do_rawcdf_extraction.py", "max_stars_repo_name": "bedaro/ssm-analysis", "max_stars_repo_head_hexsha": "09880dbfa5733d6301b84accc8f42a5ee320d698", "max_stars_repo_licenses": ["MIT"], "max_stars_cou... |
#!/usr/bin/env python3
# coding: utf-8
import time
import wiringpi as wi
import numpy as np
# from scipy.optimize import least_squares
class MPU9250(object):
wi.wiringPiSetup()
i2c = wi.I2C()
_address = 0x68 # addresses of gyroscope and accelerometer
_addr_AK8963 = 0x0C # a address of magnetome... | {"hexsha": "dec481cfc2c8fff73bad31eb4b2d4fc49b6531bf", "size": 14205, "ext": "py", "lang": "Python", "max_stars_repo_path": "client/attiude-est/mpu9250/mpu9250.py", "max_stars_repo_name": "tetsuzawa/rpi_ahrs", "max_stars_repo_head_hexsha": "5db341073b7c711c33d8854a22535655170d82c0", "max_stars_repo_licenses": ["MIT"], ... |
"""SK-learn grid-search and test scripts for Logistic Regression models"""
__author__ = "Gabriel Urbain"
__copyright__ = "Copyright 2017, Gabriel Urbain"
__license__ = "MIT"
__version__ = "0.2"
__maintainer__ = "Gabriel Urbain"
__email__ = "gabriel.urbain@ugent.be"
__status__ = "Research"
__date__ = "September 1st, 2... | {"hexsha": "776e1bbd5fcaa0c47efdad01f74ab617d6b14a6b", "size": 9794, "ext": "py", "lang": "Python", "max_stars_repo_path": "linear.py", "max_stars_repo_name": "Gabs48/ML_competition", "max_stars_repo_head_hexsha": "e294b31036f11f2ec65f9971f750855f39167aaf", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "... |
[STATEMENT]
lemma loose_bvar1_subst_bvs1'_closeds: "\<not> loose_bvar1 t lev \<Longrightarrow> lev < k \<Longrightarrow> \<forall>x\<in>set us . is_closed x
\<Longrightarrow> \<not> loose_bvar1 (subst_bvs1' t k us) lev"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. \<lbrakk>\<not> loose_bvar1 t lev; lev < k; \<fo... | {"llama_tokens": 235, "file": "Metalogic_ProofChecker_BetaNorm", "length": 1} |
#!/usr/bin/env python
"""The setup script."""
from setuptools import setup, find_packages
from setuptools.extension import Extension
from setuptools import dist
dist.Distribution().fetch_build_eggs(["Cython>=0.29", "numpy>=1.18"])
import numpy as np
from Cython.Build import cythonize
with open("README.md") as rea... | {"hexsha": "8a682143f91ba603e0969cf808d7e2be64c77481", "size": 2362, "ext": "py", "lang": "Python", "max_stars_repo_path": "setup.py", "max_stars_repo_name": "Heuro-labs/pyssa", "max_stars_repo_head_hexsha": "d2f368787eeb90e3459d405a0cf769035103433f", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": 9, "ma... |
\documentclass[a4paper, 11 pt, article, accentcolor=tud7b]{tudreport}
\usepackage[utf8]{inputenc}
\usepackage{amsmath}
\usepackage{placeins}
\title{CNuVS Exercise 4}
\author{Nils Rollshausen, Daniel Drodt}
\subtitle{Nils Rollshausen, Daniel Drodt}
\begin{document}
\maketitle
\section{Dynamic Source Routing}
\subs... | {"hexsha": "077f94b9532da6f2cf2f677c133a0a344ea6fba7", "size": 5122, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "ex4/ex4.tex", "max_stars_repo_name": "rec0de/CNuVS19", "max_stars_repo_head_hexsha": "52d07fe5c4380af707c63f718aa9533044224a3e", "max_stars_repo_licenses": ["Unlicense"], "max_stars_count": null, "m... |
(* Author: Tobias Nipkow *)
theory Hoare_Examples imports Hoare begin
text{* Summing up the first @{text x} natural numbers in variable @{text y}. *}
fun sum :: "int \<Rightarrow> int" where
"sum i = (if i \<le> 0 then 0 else sum (i - 1) + i)"
lemma sum_simps[simp]:
"0 < i \<Longrightarrow> sum i = sum (i - 1) + ... | {"author": "SEL4PROJ", "repo": "jormungand", "sha": "bad97f9817b4034cd705cd295a1f86af880a7631", "save_path": "github-repos/isabelle/SEL4PROJ-jormungand", "path": "github-repos/isabelle/SEL4PROJ-jormungand/jormungand-bad97f9817b4034cd705cd295a1f86af880a7631/case_study/isabelle/src/HOL/IMP/Hoare_Examples.thy"} |
import os
import sys
sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
import fxpmath as fxp
from fxpmath.objects import Fxp
from fxpmath import utils
import numpy as np
def test_shift_bitwise():
# integer val
x = Fxp(32, True, 8, 0)
# left
assert (x << 1)() == 64
... | {"hexsha": "a0d6f0683b2ef6c0c1059b0213f13a62e8f01e67", "size": 12423, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/test_operators.py", "max_stars_repo_name": "NJDFan/fxpmath", "max_stars_repo_head_hexsha": "a4d67e421c351c3901d62e22c60a5c81d427811d", "max_stars_repo_licenses": ["MIT"], "max_stars_count":... |
#-------------------------------------------------------------------------------
#
# Project: EOxServer <http://eoxserver.org>
# Authors: Martin Paces <martin.paces@eox.at>
#
#-------------------------------------------------------------------------------
# Copyright (C) 2014 EOX IT Services GmbH
#
# Permission is here... | {"hexsha": "aebf213d07dfeeb062ead02469991706dd25f64d", "size": 6954, "ext": "py", "lang": "Python", "max_stars_repo_path": "autotest/autotest_services/processes/test03_binary_complex.py", "max_stars_repo_name": "kalxas/eoxserver", "max_stars_repo_head_hexsha": "8073447d926f3833923bde7b7061e8a1658dee06", "max_stars_repo... |
MODULE fockd2_I
INTERFACE
!...Generated by Pacific-Sierra Research 77to90 4.4G 12:41:19 03/10/06
SUBROUTINE fockd2 (F, PTOT, P, W, LMW, WJ, WK, NUMAT, NFIRST, NLAST, NW)
USE vast_kind_param,ONLY: DOUBLE
integer, INTENT(IN) :: LMW, NUMAT
real(DOUBLE), DIMENSION(*), IN... | {"hexsha": "331c09d621a5d7912fccbd9c609d5956e5bbaaff", "size": 844, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "2006_MOPAC7.1/src_interfaces/fockd2_I.f90", "max_stars_repo_name": "openmopac/MOPAC-archive", "max_stars_repo_head_hexsha": "01510e44246de34a991529297a10bcf831336038", "max_stars_repo_licenses": ... |
"""
The code is taken and changed from Deep Reinforcement Learning Hands on book author Max Lapan
link: https://github.com/PacktPublishing/Deep-Reinforcement-Learning-Hands-On
"""
import enum
import numpy as np
import globalvars
from utils import utils
np.random.seed(globalvars.GLOBAL_SEED)
class Actions(enum.Enum)... | {"hexsha": "5f3f52b61b1d17f4217865929ba44e7eececfa65", "size": 7750, "ext": "py", "lang": "Python", "max_stars_repo_path": "envs/states.py", "max_stars_repo_name": "doandongnguyen/autoscaling", "max_stars_repo_head_hexsha": "f5e604bbacedbf352bd301038276c49767c18dd6", "max_stars_repo_licenses": ["Unlicense"], "max_stars... |
%------------------------------------------%
% Cannabis Data Science
% Date: 3/9/2022
%------------------------------------------%
\documentclass[xcolor={dvipsnames}]{beamer}
\hypersetup{pdfpagemode = FullScreen}
\mode<presentation>{
\usetheme{Boadilla}
\usecolortheme{orchid}
\usefonttheme{default}
\setbeamerte... | {"hexsha": "0919d3811adc54397f508b5e6a92c735578773e8", "size": 7458, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "2022-03-09/presentation/presentation.tex", "max_stars_repo_name": "GrahamAnto/cannabis-data-science", "max_stars_repo_head_hexsha": "1d5f3085e7b2858b6791840b90335be4669268b3", "max_stars_repo_licens... |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Module to run a model of a parsec application.
Its possible define the number of threads to execute a model
on a fast way; The modelfunc to represent the application should be
provided by user on a python module file. Its possible, also, provide a
... | {"hexsha": "190f218eba7ca878f5ed3bf745f5647bbc17c479", "size": 19695, "ext": "py", "lang": "Python", "max_stars_repo_path": "parsecpy/runmodel.py", "max_stars_repo_name": "alexfurtunatoifrn/parsecpy", "max_stars_repo_head_hexsha": "8c77f8dc02c6c4f28bcf2da1dec1b99ff5cd5516", "max_stars_repo_licenses": ["MIT"], "max_star... |
import autodiff as ad
import numpy as np
if __name__ == '__main__':
print('Linear regression using SGD and self made autodiff')
N = 1500
D = 100.0
alpha = -1.45
beta = 2.2
xx = np.arange(N) / float(N) * D
yy = alpha * xx + beta + np.random.normal(loc=0, scale=0.125, size=N)
# Model
... | {"hexsha": "eaa8461d33aebf17672cb0c4fcf1954abfcd5e65", "size": 791, "ext": "py", "lang": "Python", "max_stars_repo_path": "main.py", "max_stars_repo_name": "lutsker/simple-autodiff", "max_stars_repo_head_hexsha": "3eaecbae7e46566a51f12c923d72af305d39b4ee", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "m... |
# -*- coding: utf-8 -*-
import numpy as np
import time
import sys
import theano
import theano.tensor as T
import theano.printing as P
from theano.tensor.signal import downsample
from theano.tensor.nnet import conv
rng = np.random.RandomState(23455)
class LogisticRegression(object):
def __init__(self, input, n_in,... | {"hexsha": "afef3ac427e216360ef9e78654c5104994cd02e5", "size": 12291, "ext": "py", "lang": "Python", "max_stars_repo_path": "lenet5.py", "max_stars_repo_name": "allchemist/mnist-handwritten-task", "max_stars_repo_head_hexsha": "e79fceae95adfaf92349bf01b75b1905142dfbd1", "max_stars_repo_licenses": ["MIT"], "max_stars_co... |
import numpy as np
import cv2
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import pickle
# Read in an image
image = mpimg.imread('resources/signs_vehicles_xygrad.png')
# Define a function that applies Sobel x or y,
# then takes an absolute value and applies a threshold.
# Note: calling your func... | {"hexsha": "c854a4171e14f780b0832694b91d36608c513c68", "size": 4722, "ext": "py", "lang": "Python", "max_stars_repo_path": "lesson_quizzes/gradiant_color_space/combined_thresholds.py", "max_stars_repo_name": "WbHappy/advancedLaneLinesDetection", "max_stars_repo_head_hexsha": "04cce9d2a6fac62ca6322a21c7800eb95c3d4f95", ... |
import numpy as np
import pydicom
import glob
from read_roi import read_roi_file
import matplotlib.pyplot as plt
import cv2
__author__ = ['Giuseppe Filitto']
__email__ = ['giuseppe.filitto@studio.unibo.it']
def rescale(im, max_value, min_value):
'''
Rescale image in range (0,255)
Parameters
-------... | {"hexsha": "20d501bdd4143fa13c519519c58685a3cf367641", "size": 9550, "ext": "py", "lang": "Python", "max_stars_repo_path": "MRIsegm/utils.py", "max_stars_repo_name": "giuseppefilitto/img-segm", "max_stars_repo_head_hexsha": "27744083b412c8470ad58b484d20acfb4be91271", "max_stars_repo_licenses": ["MIT"], "max_stars_count... |
# CompilerInvocation
CompilerInvocation() = CompilerInvocation(create_compiler_invocation())
"""
create_compiler_invocation() -> CXCompilerInvocation
Return a pointer to a `clang::CompilerInvocation` object.
"""
function create_compiler_invocation()
status = Ref{CXInit_Error}(CXInit_NoError)
invocation = c... | {"hexsha": "2a296d453cb88da3276ef1240bf6a6de21faeb53", "size": 2132, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/clang/api/Frontend/CompilerInvocation.jl", "max_stars_repo_name": "vchuravy/ClangCompiler.jl", "max_stars_repo_head_hexsha": "47080072b059465f8176349c6e67bc678fa238d2", "max_stars_repo_licenses... |
"""Operations for [N, 2] numpy arrays or torch tensors representing segments.
Example segment operations that are supported:
* length: compute bounding box areas
* IOU: pairwise intersection-over-union scores
* intersection: pairwise intersection-over-union scores
TODO (refactor): rename module to segments_ops
"... | {"hexsha": "9c4bf93813d605bfe17e7a74e765a820ec974d82", "size": 5843, "ext": "py", "lang": "Python", "max_stars_repo_path": "np_segments_ops.py", "max_stars_repo_name": "escorciav/moments-retrieval-page", "max_stars_repo_head_hexsha": "84c31150246797e2db1a63159cceded30998e2be", "max_stars_repo_licenses": ["MIT"], "max_s... |
# -*- coding: utf-8 -*-
"""
Description: A module to define resilience models and simulations.
- :class:`Common`: Class defining common methods accessible by Function/Flow/Component Classes
- :class:`FxnBlock`: Class defining Model Functions and their attributes
- :class:`Flow`: Class defini... | {"hexsha": "f97f9e0b00ebadb7288dde168e1e1c5a9595ffcf", "size": 136642, "ext": "py", "lang": "Python", "max_stars_repo_path": "fmdtools/modeldef.py", "max_stars_repo_name": "nasa/fmdtools", "max_stars_repo_head_hexsha": "7415068776998ff05eb199c78531ee7f9c2422e7", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 3,... |
import torch
import numpy as np
from torch import nn
from util_layers import *
class NaiveNet(nn.Module):
"""
CNN only
"""
def __init__(self,input_size=None,num_task=None):
self.num_task = num_task
super(NaiveNet, self).__init__()
self.NaiveCNN = nn.Sequential(
... | {"hexsha": "6b2361ada0c28f54c5856499134030e6b2c51d30", "size": 9274, "ext": "py", "lang": "Python", "max_stars_repo_path": "Scripts/models.py", "max_stars_repo_name": "Tsedao/MultiRM", "max_stars_repo_head_hexsha": "3947d0d74b8c6d6c7deeb534ef51fc1c85c27309", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 12, "m... |
/// @file
/// @copyright The code is licensed under the BSD License
/// <http://opensource.org/licenses/BSD-2-Clause>,
/// Copyright (c) 2013-2015 Alexandre Hamez.
/// @author Alexandre Hamez
#pragma once
#include <boost/filesystem.hpp>
namespace pnmc { namespace util {
/*---------------------... | {"hexsha": "ed67c1332e5d791d5f6ae6c4625209faff79d917", "size": 886, "ext": "hh", "lang": "C++", "max_stars_repo_path": "support/util/paths.hh", "max_stars_repo_name": "ahamez/pnmc", "max_stars_repo_head_hexsha": "cee5f2e01edc2130278ebfc13f0f859230d65680", "max_stars_repo_licenses": ["BSD-2-Clause"], "max_stars_count": ... |
import os
import sys
import numpy as np
import tensorflow as tf
class Model(object):
def __init__(self,
images,
labels,
cutout_size=None,
batch_size=32,
eval_batch_size=100,
clip_mode=None,
grad... | {"hexsha": "089fe846a6e1c0bce909ee7fdd1737130f6206e1", "size": 7331, "ext": "py", "lang": "Python", "max_stars_repo_path": "examples/trials/nas_cifar10/src/cifar10/models.py", "max_stars_repo_name": "runauto/nni", "max_stars_repo_head_hexsha": "30152b04c4739f5b4f95087dee5f1e66ee893078", "max_stars_repo_licenses": ["MIT... |
# -*- coding:utf-8 -*-
# author: Huang Zilong
# 将wav文件随机打乱，并打标签
import numpy as np
from DCASE2018_1 import read_file
def file_path_shuffle(feature, label):
train_f, train_l = np.array(feature), np.array(label)
np.random.seed(1)
shuffle_indices = np.random.permutation(np.arange(len(train_f)))
train_f = ... | {"hexsha": "b3570e64af7a4735f178700b13a7d405778846f6", "size": 1823, "ext": "py", "lang": "Python", "max_stars_repo_path": "file_shuffle.py", "max_stars_repo_name": "zw76859420/Voiceprint_Recognition", "max_stars_repo_head_hexsha": "b883297fe8c61858a2a5ad83064ed0da0456c7b7", "max_stars_repo_licenses": ["Apache-2.0"], "... |
#!/usr/bin/env python
'''
Script for predicting PHOCs for a number of images residing in a folder on
disk.
'''
import argparse
import logging
import os
import caffe
import numpy as np
import cv2
from phocnet.evaluation.cnn import net_output_for_word_image_list
def load_siamese_model(siamese_model, siamese_proto, ph... | {"hexsha": "0d0798500476770b7e38666acf461e14cde25346", "size": 5075, "ext": "py", "lang": "Python", "max_stars_repo_path": "tools/predict_phocs.py", "max_stars_repo_name": "FlorianWestphal/phocnet", "max_stars_repo_head_hexsha": "737b7bdd58441fc0a1fa35013db885bfa2cfdfe0", "max_stars_repo_licenses": ["BSD-3-Clause"], "m... |
#!/usr/bin/env python3
from visualization import Scenes3DVisualizer
from visualization import set_pointcloud_obj
from visualization import set_camera_view
from odom import SiftOdom, OrbOdom
from camera import Camera
import open3d as o3d
import numpy as np
import argparse
import pykitti
import cv2
parser = argparse.Ar... | {"hexsha": "d31b5d3761dd4cf3c71e1abeee359f25246d94b9", "size": 3112, "ext": "py", "lang": "Python", "max_stars_repo_path": "kitti_test.py", "max_stars_repo_name": "loaywael/VisualOdom", "max_stars_repo_head_hexsha": "c090a78d7166ce12e0b526df0219015949c3de79", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count"... |
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