For purpose of this logical operation, vector is tested for equality to 0. If it is, then its boolean value is defined as “false”, otherwise “true”. In the above example, the result is “true”. Be wary of the MONEY data type, it’s of limited precision. Say that you want to store financial amounts (which are numbers that may have a fractional part). First of all, you cannot use integers obviously (integers don’t have a fractional part).From a purely mathematical point of view, the natural tendency would be to use a float.
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The trick here is the chain of &&s will return the first falsey value it finds — and this can be fed to an if statement etc. So if b.foo is undefined, it will return undefined and skip the b.foo.bar statement, and we get no error. So the only place you’d really need to use “Boolean-cast variables” would be if you had a situation where you’re checking if both variables have the same truthiness, right? If you need to see if two variables are both truthy or both falsy (or not), that is, of equal (or not) truthiness. What we wish to determine in the comparison is the “truth” about the value of a reference, not the value of the reference itself.
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If you have a number with 15 decimal places and convert that to a double, then print it out with exactly 15 decimal places, you should get the same number. On the other hand, if you print out an arbitrary double with 15 decimal places and the convert it back to a double, you won’t necessarily get the same value back—you need 17 decimal places for that. And neither 15 nor 17 decimal places are enough to accurately display the exact decimal equivalent of an arbitrary double. In general, you need over 100 decimal places to do that precisely.
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(Think poor man’s way of “type-casting”.) Its intent is usually to convey to the reader that the code does not care what value is in the variable, but what its “truth” value is. Many (most?) debuggers actually look at the contents of the entire register. On an x86, that’s actually an 80-bit number.
Answers
- Many (most?) debuggers actually look at the contents of the entire register.
- For instance, False in JSX (React) has a special meaning that’s not triggered on simple falsiness.
- In essence, if you’re performing a calculation and the result is an irrational number or recurring decimal, then there will be rounding errors when that number is squashed into the finite size data structure you’re using.
- If it was falsy (e.g., 0, null, undefined, etc.), it would be false, otherwise, true.
, is one of the bitwise operators. So to answer the last two questions, I wouldn’t say there are any caveats besides “know the difference between the two operators.” They’re not interchangeable because they do two completely different things. This will check conditions 2 and 3, even if 1 is already true. As your conditions can be quite expensive functions, you can get a good performance boost by using them. Their intention is to set rafSupported to true or false based on the availability of function in requestAnimationFrame.
These data types typically go by the name of Decimal. Doubles always have 53 significant bits and floats always have 24 significant bits (except for denormals, infinities, and NaN values, but those are subjects for a different question). These are binary formats, and you can only speak clearly about the precision of their representations in terms of binary digits (bits). As the name implies, a double has 2x the precision of float1.
- Type float has good precision, which will often be good enough for whatever you’re doing.
- The long double has an exponent that just ridiculously huge and should have 19 digits precision.
- First of all, you cannot use integers obviously (integers don’t have a fractional part).From a purely mathematical point of view, the natural tendency would be to use a float.
- If requestAnimationFrame was assigned undefined then !
Literal floating point values used in expressions will be treated as doubles by default, and most of the math functions that return floating point values return doubles. You’ll save yourself many headaches and typecastings if you just use double. Consider a concise function which detects feature functionality how to double your money in a month (and in this case, platform compatibility) by way of dynamic typing (aka “duck typing”). We want to write a function that returns true if a user’s browser supports the HTML5 element, but we don’t want the function to throw an error if is undefined; and we don’t want to use try … To overcome this, computers offer specific datatypes that limit the binary rounding error in computers for decimal numbers. These are the data type thatshould absolutely be used to represent financial amounts.
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So if the precision of a float is enough to handle the needs, the program will execute some times faster with float then double. As noted on other answers, beware of accumulation errors. The upshot, which is not nearly as well known as it should be, is that you should almost always use type double. Unless you have some particularly special need, you should almost never use type float. In essence, if you’re performing a calculation and the result is an irrational number or recurring decimal, then there will be rounding errors when that number is squashed into the finite size data structure you’re using.
” (two pipes). One use is to lossily compress an object to its truth value, so that your code isn’t holding a reference to a big object and keeping it alive. Some_big_object to a variable instead of some_big_object lets go of it for the garbage collector. This is useful for cases that produce either an object or a false value such as null or the undefined value, such as browser feature detection. Converts the value to the right of it to its equivalent Boolean value.
Stack Exchange network consists of 183 Q&A communities including Stack Overflow, the largest, most trusted online community for developers to learn, share their knowledge, and build their careers. As everyone knows, “roundoff error” is often a problem when you’re doing floating-point work. Roundoff error can be subtle, and difficult to track down, and difficult to fix. Most programmers don’t have the time or expertise to track down and fix numerical errors in floating-point algorithms — because unfortunately, the details end up being different for every different algorithm. But type double has enough precision such that, much of the time, you don’t have to worry.You’ll get good results anyway. With type float, on the other hand, alarming-looking issues with roundoff crop up all the time.
I just ran into a error that took me forever to figure out and potentially can give you a good example of float precision. I would suggest having a look at the excellent What Every Computer Scientist Should Know About Floating-Point Arithmetic that covers the IEEE floating-point standard in depth. You’ll learn about the representation details and you’ll realize there is a tradeoff between magnitude and precision. The precision of the floating point representation increases as the magnitude decreases, hence floating point numbers between -1 and 1 are those with the most precision. The value representation of floating-point types is implementation-defined. Find centralized, trusted content and collaborate around the technologies you use most.
Which shows about 16 decimal digits of precision, as you’d expect. The built-in comparison operations differ as in when you compare 2 numbers with floating point, the difference in data type (i.e. float or double) may result in different outcomes. Type long double is nominally 80 bits, though a given compiler/OS pairing may store it as bytes for alignment purposes.