Note that trailing format specifiers, specifiers that determine the type of a floating-point literal (1.0f is a float value; 1.0d is a double value), do not influence the results of this method. In other words, the numerical value of the input string is converted directly to the target floating-point type. The two-step sequence of conversions, string to float followed by float to double, is not equivalent to converting a string directly to double. For example, the float literal 0.1f is equal to the double value 0.10000000149011612; the float literal 0.1f represents a different numerical value than the double literal 0.1. (The numerical value 0.1 cannot be exactly represented in a binary floating-point number.)

In all cases, the result is a long integer that, when given to the longBitsToDouble(long) method, will produce a floating-point value the same as the argument to doubleToLongBits (except all NaN values are collapsed to a single "canonical" NaN value).

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If the argument is NaN, the result is the long integer representing the actual NaN value. Unlike the doubleToLongBits method, doubleToRawLongBits does not collapse all the bit patterns encoding a NaN to a single "canonical" NaN value.

If the argument is any value in the range 0x7ff0000000000001L through 0x7fffffffffffffffL or in the range 0xfff0000000000001L through 0xffffffffffffffffL, the result is a NaN. No IEEE 754 floating-point operation provided by Java can distinguish between two NaN values of the same type with different bit patterns. Distinct values of NaN are only distinguishable by use of the Double.doubleToRawLongBits method.

Note that this method may not be able to return a double NaN with exactly same bit pattern as the long argument. IEEE 754 distinguishes between two kinds of NaNs, quiet NaNs and signaling NaNs. The differences between the two kinds of NaN are generally not visible in Java. Arithmetic operations on signaling NaNs turn them into quiet NaNs with a different, but often similar, bit pattern. However, on some processors merely copying a signaling NaN also performs that conversion. In particular, copying a signaling NaN to return it to the calling method may perform this conversion. So longBitsToDouble may not be able to return a double with a signaling NaN bit pattern. Consequently, for some long values, doubleToRawLongBits(longBitsToDouble(start)) may not equal start. Moreover, which particular bit patterns represent signaling NaNs is platform dependent; although all NaN bit patterns, quiet or signaling, must be in the NaN range identified above.

The generic term he uses is a Test Double (think stuntdouble). Test Double is a generic term for any case where youreplace a production object for testing purposes. There are variouskinds of double that Gerard lists:

Because floats and doubles cannot accurately represent the base 10 multiples that we use for money. This issue isn't just for Java, it's for any programming language that uses base 2 floating-point types.

The problem with floats and doubles is that the vast majority of money-like numbers don't have an exact representation as an integer times a power of 2. In fact, the only multiples of 0.01 between 0 and 1 (which are significant when dealing with money because they're integer cents) that can be represented exactly as an IEEE-754 binary floating-point number are 0, 0.25, 0.5, 0.75 and 1. All the others are off by a small amount. As an analogy to the 0.333333 example, if you take the floating-point value for 0.01 and you multiply it by 10, you won't get 0.1. Instead you will get something like 0.099999999786...

Representing money as a double or float will probably look good at first as the software rounds off the tiny errors, but as you perform more additions, subtractions, multiplications and divisions on inexact numbers, errors will compound and you'll end up with values that are visibly not accurate. This makes floats and doubles inadequate for dealing with money, where perfect accuracy for multiples of base 10 powers is required.

This is not a matter of accuracy, nor is it a matter of precision. It is a matter of meeting the expectations of humans who use base 10 for calculations instead of base 2. For example, using doubles for financial calculations does not produce answers that are "wrong" in a mathematical sense, but it can produce answers that are not what is expected in a financial sense.

Using a calculator, or calculating results by hand, 1.40 * 165 = 231 exactly. However, internally using doubles, on my compiler / operating system environment, it is stored as a binary number close to 230.99999... so if you truncate the number, you get 230 instead of 231. You may reason that rounding instead of truncating would have given the desired result of 231. That is true, but rounding always involves truncation. Whatever rounding technique you use, there are still boundary conditions like this one that will round down when you expect it to round up. They are rare enough that they often will not be found through casual testing or observation. You may have to write some code to search for examples that illustrate outcomes that do not behave as expected.

Assume you want to round something to the nearest penny. So you take your final result, multiply by 100, add 0.5, truncate, then divide the result by 100 to get back to pennies. If the internal number you stored was 3.46499999.... instead of 3.465, you are going to get 3.46 instead 3.47 when you round the number to the nearest penny. But your base 10 calculations may have indicated that the answer should be 3.465 exactly, which clearly should round up to 3.47, not down to 3.46. These kinds of things happen occasionally in real life when you use doubles for financial calculations. It is rare, so it often goes unnoticed as an issue, but it happens.

I'm troubled by some of these responses. I think doubles and floats have a place in financial calculations. Certainly, when adding and subtracting non-fractional monetary amounts there will be no loss of precision when using integer classes or BigDecimal classes. But when performing more complex operations, you often end up with results that go out several or many decimal places, no matter how you store the numbers. The issue is how you present the result.

Of course, you have to stay within reason; e.g. a simple webshop would probably never experience any problem with double precision floats, but if you do e.g. accounting or anything else that requires adding a large (unrestricted) amount of numbers, you wouldn't want to touch floating point numbers with a ten foot pole.

Coming from a non-computer science background (physics and engineering), I tend to look at problems from a different perspective. For me, the reason why I wouldn't use a double or float in a mathematical calculation is that I would lose too much information.

Completely agree with double opt-in and not CCing everyone.Another polite thing that people that request intros seldom do is to send a quick follow-up a few days later letting you know if the intro that you made was helpful or not. Take care of your network and your network will take care of you.

Compares this instance to a specified double-precision floating-point number and returns an integer that indicates whether the value of this instance is less than, equal to, or greater than the value of the specified double-precision floating-point number.

Converts the span representation of a number in a specified style and culture-specific format to its double-precision floating-point number equivalent. A return value indicates whether the conversion succeeded or failed.

Converts a character span containing the string representation of a number in a specified style and culture-specific format to its double-precision floating-point number equivalent. A return value indicates whether the conversion succeeded or failed.

Converts the string representation of a number in a specified style and culture-specific format to its double-precision floating-point number equivalent. A return value indicates whether the conversion succeeded or failed.

Having issues with my trackball mouse. Most of the time when I single click in Microsoft edge or windows, I get an annoying double click. makes it tough when selecting files or text. Have tried several different mice/trackballs with the same results. went into mouse settings and slowed double click all the way down to no avail. Any ideas. BTW: I don't use any software for the trackball other than what windows installs and never did. TIA

Handling a double click mouse event depends on the context of your application and the programming framework you're using. Below is a general example using C# and the Windows Presentation Foundation (WPF) framework, but keep in mind that the details may vary depending on your specific technology stack.

Double-duty actions include interventions, programmes and policies that have the potential to simultaneously reduce the risk or burden of both undernutrition (including wasting, stunting and micronutrient deficiency or insufficiency) and overweight, obesity or diet-related NCDs. Reflecting the shared drivers and platforms of contrasting forms of malnutrition, double duty can be achieved at three levels: through doing no harm with regard to existing actions on malnutrition; by retrofitting existing nutrition actions to address or improve new or other forms of malnutrition; and through the development of de-novo, integrated actions aimed at the double burden of malnutrition.

The coexistence of contrasting forms of malnutrition is known as the double burden of malnutrition. A global challenge, this double burden is united by shared drivers and solutions and therefore offers a unique opportunity for integrated nutrition action. This policy brief sets out the potential for double-duty actions to contribute to this intensified effort by addressing both sides of malnutrition through common interventions.

In the IEEE 754-2008 standard, the 64-bit base-2 format is officially referred to as binary64; it was called double in IEEE 754-1985. IEEE 754 specifies additional floating-point formats, including 32-bit base-2 single precision and, more recently, base-10 representations (decimal floating point). 0852c4b9a8

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