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What forces layout / reflow

All of the below properties or methods, when requested/called in JavaScript, will trigger the browser to synchronously calculate the style and layout*. This is also called reflow or layout thrashing, and is common performance bottleneck.

Element

Box metrics

• elem.offsetLeft, elem.offsetTop, elem.offsetWidth, elem.offsetHeight, elem.offsetParent
• elem.clientLeft, elem.clientTop, elem.clientWidth, elem.clientHeight
• elem.getClientRects(), elem.getBoundingClientRect()

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// transform cropper dataURI output to a Blob which Dropzone accepts
function dataURItoBlob(dataURI) {
    var byteString = atob(dataURI.split(',')[1]);
    var ab = new ArrayBuffer(byteString.length);
    var ia = new Uint8Array(ab);
    for (var i = 0; i < byteString.length; i++) {
        ia[i] = byteString.charCodeAt(i);
    }
    return new Blob([ab], { type: 'image/jpeg' });
}

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DISCLAIMER: Follow these instructions entirely at your own risk. I, nor my company (Digital Ashes PTY LTD) take any responsibility whatsoever for any damage done to any device/property that occurs as a result of following these instructions.

The following steps describe how to install Action Launcher 3 on a rooted device as a system app, so that you will be able to enable Action Launcher 3's Google Now integration.

Prerequisites

1. A rooted device (Google if you need help here).
2. A computer with [ADB][0] installed, and a working knowledge of ADB.
3. A copy of an Action Launcher 3 APK on your computer. [APKMirror.com][1] is a good place to grab a copy from. All you're doing here is setting Action 3 as a system app, so any version of the app will work. From there, the Action 3 will update via the Play Store like a typical pre-installed app (such as Gmail or YouTube).

thm1118

/**
 * ==================  angular-ios9-uiwebview.patch.js v1.1.1 ==================
 *
 * This patch works around iOS9 UIWebView regression that causes infinite digest
 * errors in Angular.
 *
 * The patch can be applied to Angular 1.2.0 – 1.4.5. Newer versions of Angular
 * have the workaround baked in.
 *
 * To apply this patch load/bundle this file with your application and add a

thm1118

# usage: `python ~/Desktop/contours.py 1994-654-12_v02.tif`
# output is to a squareless.txt file and the directory "out"
# Working well with thumbnails with 400px as their longest side - untested with other dimensions

# for i in $(ls -1 | grep tif); do python /Users/artsyinc/Documents/resistance/experiments/artwork_image_cropping/contours.py $i; done

import cv2
import numpy as np
from matplotlib import pyplot as plt
import sys

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from time import time
import logging
import numpy as np
from sklearn.datasets import fetch_20newsgroups
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.decomposition import LatentDirichletAllocation

from gensim.matutils import Sparse2Corpus
#from gensim.models.ldamodel import LdaModel
from gensim.models.ldamulticore import LdaMulticore

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""" Example using GenSim's LDA and sklearn. """

import numpy as np

from gensim import matutils
from gensim.models.ldamodel import LdaModel
from sklearn import linear_model
from sklearn.datasets import fetch_20newsgroups
from sklearn.feature_extraction.text import CountVectorizer

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# -*- coding: utf-8 -*-
"""
    使用sklearn 提供的距离函数测试,对 'euclidean', 'l2', 'l1', 'manhattan', 'cityblock',
                  'braycurtis', 'canberra', 'chebyshev', 'correlation',
                  'cosine', 'dice', 'hamming', 'jaccard', 'kulsinski',
                  'mahalanobis', 'matching', 'minkowski', 'rogerstanimoto',
                  'russellrao', 'seuclidean', 'sokalmichener',
                  'sokalsneath', 'sqeuclidean', 'yule', "wminkowski"
        pearson 等20几个度量距离算法进行逐一测试。
        向量空间模型主要使用tfidf

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from pylab import *
from pprint import pprint


def arrayToList(arr):
    if type(arr) == type(array([])):
        return arrayToList(arr.tolist())
    elif type(arr) == type([]):
        return [arrayToList(a) for a in arr]
    else:

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# coding: utf-8

import sys
import jieba
import numpy
from sklearn import metrics
from sklearn.feature_extraction.text import HashingVectorizer
from sklearn.naive_bayes import MultinomialNB