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Shown here is another possible encoding; XML schema does not define an encoding for this datatype. ^ The RFC CSV specification only deals with delimiters, newlines, and quote characters; it does not directly deal with serializing programming data structures .
load a double from an array dastore 52 0101 0010 arrayref, index, value → store a double into an array dcmpg 98 1001 1000 value1, value2 → result compare two doubles, 1 on NaN dcmpl 97 1001 0111 value1, value2 → result compare two doubles, -1 on NaN dconst_0 0e 0000 1110 → 0.0 push the constant 0.0 (a double) onto the stack dconst_1 0f
In particular, IEEE 754 already uses "canonical NaN" with the meaning of "canonical encoding of a NaN" (e.g. "isCanonical(x) is true if and only if x is a finite number, infinity, or NaN that is canonical." page 38, but also for totalOrder page 42), thus a different meaning from what is used here. Please help clarify the section.
Single precision is termed REAL in Fortran; [1] SINGLE-FLOAT in Common Lisp; [2] float in C, C++, C# and Java; [3] Float in Haskell [4] and Swift; [5] and Single in Object Pascal , Visual Basic, and MATLAB. However, float in Python, Ruby, PHP, and OCaml and single in versions of Octave before 3.2 refer to double-precision numbers.
The leading bit (s in the above) is a sign bit, and the following bits (xxx in the above) encode the additional exponent bits and the remainder of the most significant digit, but the details vary depending on the encoding alternative used. The final combinations are used for infinities and NaNs, and are the same for both alternative encodings:
The remaining combinations encode infinities and NaNs. BID and DPD use different bits of the combination field for that. In the cases of Infinity and NaN, all other bits of the encoding are ignored. Thus, it is possible to initialize an array to Infinities or NaNs by filling it with a single byte value.
Java bytecode is used at runtime either interpreted by a JVM or compiled to machine code via just-in-time (JIT) compilation and run as a native application. As Java bytecode is designed for a cross-platform compatibility and security, a Java bytecode application tends to run consistently across various hardware and software configurations. [3]
MessagePack is more compact than JSON, but imposes limitations on array and integer sizes.On the other hand, it allows binary data and non-UTF-8 encoded strings. In JSON, map keys have to be strings, but in MessagePack there is no such limitation and any type can be a map key, including types like maps and arrays, and, like YAML, numbers.