yongjun823

import csv
import requests
import shutil
import os
import argparse
import numpy as np
import time
from tqdm import tqdm
from PIL import Image, ImageOps
from concurrent.futures import ThreadPoolExecutor

yongjun823

function Firebase1() {
    return new Promise(resolve => {
        db.collection("ReceivedTransferFinAccount").get().then((querySnapshot) => {

            const data = []

            querySnapshot.forEach((doc) => {
                data.push(doc.data())
            });

yongjun823

# reference
# https://github.com/c0decracker/video-splitter

import math
import re
import subprocess
import os

length_regexp = 'Duration: (\d{2}):(\d{2}):(\d{2})\.\d+,'
re_length = re.compile(length_regexp)

yongjun823

import torch
from torch.autograd import Variable
from udacity_car.data_loader import img_road_path

model = torch.load('model.pkl')

img_tensor = img_road_path(['./img/temp.jpg'])
img_tensor.unsqueeze_(0)
tt = Variable(img_tensor)

yongjun823

LRESULT CALLBACK WndProc(HWND hWnd, UINT message, WPARAM wParam, LPARAM lParam)
{
	int wmId, wmEvent;
	PAINTSTRUCT ps;
	HDC hdc;
    POINTS pt;

	static int x;
	static int y;
	static bool isDraw = false;

yongjun823

using System;
using System.Collections.Generic;

namespace PeopleManagement
{
    class Person
    {
        public int Number;
        public string Name;
        public string Phone, Email, year;

yongjun823

from __future__ import print_function  # for compatibility with both python 2 and 3
from subprocess import call  # for calling mplayer and lame
import os  # help with file handling


def check_file_exists(directory, filename, extension):
    path = directory + "/" + filename + extension
    return os.path.isfile(path)

yongjun823

  Iterable<DataSnapshot> dataSnapshots = dataSnapshot.getChildren();

for (DataSnapshot tempSnapshot : dataSnapshots) {
    String value = tempSnapshot.getValue().toString();
    String key = tempSnapshot.getKey();

    if (key.equals("num")) {
        Log.e(TAG, value);
    } else if (key.equals("value")) {
        Log.e(TAG, value);

yongjun823

import com.google.gson.Gson;
import com.google.gson.GsonBuilder;
import com.google.maps.DirectionsApi;
import com.google.maps.GeoApiContext;
import com.google.maps.GeocodingApi;
import com.google.maps.errors.ApiException;
import com.google.maps.model.DirectionsResult;
import com.google.maps.model.GeocodingResult;
import com.google.maps.model.TravelMode;

yongjun823

import numpy as np
import pandas as pd
import tensorflow as tf

train = pd.read_csv("data_bike/train.csv", parse_dates=["datetime"])

test = train[:2000]
train= train[2000:]

feature=['temp', 'atemp', 'humidity', 'windspeed', 'casual', 'registered']