skew

问题 I want to use a background for my buttons. But when I use a png it slows down the app. Therefore I want to use a xml shape but I do not know how to make the corner cut (like on the picture). By now I have the following shape which is just a rectangle: <?xml version="1.0" encoding="utf-8"?> <shape xmlns:android="http://schemas.android.com/apk/res/android"> <solid android:color="@color/blue_semi_transparent"/> <padding android:bottom="10dp" android:right="10dp" android:top="10dp" android:left=

问题 I have a small Problem. I am trying to make the structure from image. I have the CSS and HTML .mask-skew { transform: skewX(-10deg); /*width: 300px;*/ height: 390px; overflow: hidden; margin: 5px; /*border: 2px solid orange;*/ } .art-skew { transform: skewX(10deg); position: relative; left: -50%; } <div class="row flex--row advertising-row"> <div class="col-xs-12 col-sm-4 mask-skew"> <img class="art-skew" src="templates/Stordeur/themes/stordeur/images/BarbourSS18Banner_1140x392px.jpg" alt="">

问题 How to make a 3 div with distortion, as shown in the picture? I have made this: .cars { width: 100%; height: 500px; } .car { width: 33.33333333%; height: 100%; background: #3498db; position: relative; -webkit-transform: skewx(-10deg); -moz-transform: skewx(-10deg); -o-transform: skewx(-10deg); -ms-transform: skewx(-10deg); transform: skewx(-10deg); transform-origin: top left; float: left; display: inline; } .car:nth-child(2) { background: #000 } .car:nth-child(3) { background: #ff0000 } <div

问题 How to make a 3 div with distortion, as shown in the picture? I have made this: .cars { width: 100%; height: 500px; } .car { width: 33.33333333%; height: 100%; background: #3498db; position: relative; -webkit-transform: skewx(-10deg); -moz-transform: skewx(-10deg); -o-transform: skewx(-10deg); -ms-transform: skewx(-10deg); transform: skewx(-10deg); transform-origin: top left; float: left; display: inline; } .car:nth-child(2) { background: #000 } .car:nth-child(3) { background: #ff0000 } <div

问题 The code below maps the statistical moments (mean, variance, skewness, excess kurtosis) generated by corresponding parameters ( a , b , loc , scale ) of the Johnson-SU distribution ( johnsonsu ). For the range of loop values specified in my code below, no parameter configuration results in positive skewness, only negative skewness, even though it should be possible to parameterize the Johnson-SU distribution to be positively-skewed. import numpy as np import pandas as pd from scipy.stats

问题 I have a skewed data in a table which is then compared with other table that is small. I understood that salting works in case of joins- that is a random number is appended to keys in big table with skew data from a range of random data and the rows in small table with no skew data are duplicated with the same range of random numbers. Hence the the matching happens because there will be a hit in one among the duplicate values for particular slated key of skewed able I also read that salting

问题 I have a skewed data in a table which is then compared with other table that is small. I understood that salting works in case of joins- that is a random number is appended to keys in big table with skew data from a range of random data and the rows in small table with no skew data are duplicated with the same range of random numbers. Hence the the matching happens because there will be a hit in one among the duplicate values for particular slated key of skewed able I also read that salting

来源： https://stackoverflow.com/questions/63390864/how-to-use-zero-skewness-log-transform-in-python

问题 I really need to understand how skew(xdeg) function works all research does not seem to explain how the x angle affecting the other points and distorts it like that, I need to know if there any mathematical formula it or a way to be able to expect the result of using a specific degree. ps. I already read tons of docs which the best one of them was the DevDocs which say This transformation is a shear mapping (transvection) that distorts each point within an element by a certain angle in the

问题 Working with Spark Dataset/DataFrame joins, I faced long running and failed with OOM jobs. Here's input: ~10 datasets with different size, mostly huge(>1 TB) all left-joined to one base dataset some of join keys are null After some analysis, I found that failed and slow jobs reason is null skew key: when left side has millions of records with join key null . I made some brute force approach to solve this issue, and here's I want to share it. If you have better or any built-in solutions(for