language-agnostic

问题 While looking at various methods for point-in-triangle testing (2D case), I found that the method which uses barycentric coordinates is the most used one. Here is a StackOverflow answer which explains it. Why is this method the most preferred one? It probably has to do with doing less calculations, but what about numerical stability? Is this algorithm better suited than say, the "same side" technique, for cases in which the point is particularly near the border? 回答1: If you solve it: p = p0 +

问题 While looking at various methods for point-in-triangle testing (2D case), I found that the method which uses barycentric coordinates is the most used one. Here is a StackOverflow answer which explains it. Why is this method the most preferred one? It probably has to do with doing less calculations, but what about numerical stability? Is this algorithm better suited than say, the "same side" technique, for cases in which the point is particularly near the border? 回答1: If you solve it: p = p0 +

问题 I know that I can get the Kth order statistic (i.e. the kth smallest number in an array) by using quickselect in almost linear time, but what if I needed the k smallest elements of an array? The wikipedia link has a pseudocode for the single-element lookup, but not for the k smallest element s lookup. How should quickselect be modified to attain it in linear time (if possible) ? 回答1: Actually modifying quickselect is not needed. If I had an array (called arrayToSearch in this example) and I

问题 I know that I can get the Kth order statistic (i.e. the kth smallest number in an array) by using quickselect in almost linear time, but what if I needed the k smallest elements of an array? The wikipedia link has a pseudocode for the single-element lookup, but not for the k smallest element s lookup. How should quickselect be modified to attain it in linear time (if possible) ? 回答1: Actually modifying quickselect is not needed. If I had an array (called arrayToSearch in this example) and I

问题 I know that I can get the Kth order statistic (i.e. the kth smallest number in an array) by using quickselect in almost linear time, but what if I needed the k smallest elements of an array? The wikipedia link has a pseudocode for the single-element lookup, but not for the k smallest element s lookup. How should quickselect be modified to attain it in linear time (if possible) ? 回答1: Actually modifying quickselect is not needed. If I had an array (called arrayToSearch in this example) and I

问题 I want to .... create a html-email containing several images (company header, etc). The images shall be displayed as email content, not as an attachement. So how is the best way to do this? I'm aware of two possibilities : embedded images send as attachment ( <IMG src="cid:321353119@02062010-119E"> ) images are placed on the server ( <img src="http://myserver.com/mypicture.gif"> ) 回答1: Many mail clients will block access to external images (your second method) by default. However, most will

问题 I'm currently doing distance calculations between coordinates and have been getting slightly different results depending on the language used. Part of the calculation is taking calculating the cosine of a given radian . I get the following results // cos(0.8941658257446736) // 0.6261694290123146 node // 0.6261694290123146 rust // 0.6261694290123148 go // 0.6261694290123148 python // 0.6261694290123148 swift // 0.6261694290123146 c++ // 0.6261694290123146 java // 0.6261694290123147 c I would

问题 Consider the following code: 0.1 + 0.2 == 0.3 -> false 0.1 + 0.2 -> 0.30000000000000004 Why do these inaccuracies happen? 回答1: Binary floating point math is like this. In most programming languages, it is based on the IEEE 754 standard. The crux of the problem is that numbers are represented in this format as a whole number times a power of two; rational numbers (such as 0.1 , which is 1/10 ) whose denominator is not a power of two cannot be exactly represented. For 0.1 in the standard

问题 Consider the following code: 0.1 + 0.2 == 0.3 -> false 0.1 + 0.2 -> 0.30000000000000004 Why do these inaccuracies happen? 回答1: Binary floating point math is like this. In most programming languages, it is based on the IEEE 754 standard. The crux of the problem is that numbers are represented in this format as a whole number times a power of two; rational numbers (such as 0.1 , which is 1/10 ) whose denominator is not a power of two cannot be exactly represented. For 0.1 in the standard

问题 I am working in large code base in C++, totaling approximately 8 million lines of code. In my application I have seen thousands of unused variables, which were reported by g++ but were ignored by my team. I want to take initiative for cleaning these variables but I need some info before working this issue. Will there be any issues or disadvantages of having thousands of unused variables? The compiler by default treats this as an ignored warning, but I believe we should treat warnings as