Is there any efficient way (without converting the float into a string) to obtain the number of digits a floating-point number consists of (independent of its length and precision) ?
是否有任何有效的方法(不将浮点数转换成字符串)来获得浮点数由(独立于其长度和精度)所组成的数字的位数?
On that way I can implement a fairly good, portable, problematic-less function for comparison/conditioning by multiplying the float by the number of the digits it consists of.
通过这种方式，我可以实现一个相当好的、可移植的、没有问题的功能，通过将浮点数乘以它所包含的数字的数目来进行比较/调节。
1 个解决方案
#1
3
Q: Is there any efficient way to obtain the number of digits a floating-point number?
A: I doubt it.
问:有没有有效的方法来获得浮点数的位数?我对此表示怀疑。
Every finite FP number is exact, but maybe not the exact value one thinks.
每个有限的FP数字都是精确的，但可能不是一个人想的精确值。
Due to typical binary64 implementation of a double,double x = 0.53, x value: .5300000000000000266453525910037569701671600341796875, 52 digits.double x = 0.1, x value:.1000000000000000055511151231257827021181583404541015625 55 digits.
The next closest double to mathematical 0.1 is .09999999999999999167332731531132594682276248931884765625 with 56 digits.
由于典型的binary64实现的双，双x = 0.53, x值:. 530000000000000000000000266453525910037569701600341796875,52位数字。双x = 0.1, x值:。1000000000000000055511151231257827021181583404541015625 55位。下一个最接近数学0.1的是。09999999999999999999999991673153153113259468476248931884765625和56位数字。
DBL_MAX: in decimal, typically about 300 digits 17976931348623158... ending with 6728515625..
DBL_MAX:在十进制中，通常约为300位数字17976931348623158……以6728515625 . .
DBL_MIN: typically 0.000000(~300 zeros) 22250738585072014... (maybe about 700 more digits).
DBL_MIN:典型的0.000000(~300个零)22250738585072014…(可能多出700个数字)。
Comparison of FP numbers need not determine the number of digitis in its decimal representation. To compare FP numbers, use the usual relationship operators >, >=, ==, etc.
FP数字的比较不需要用十进制数来确定数字的数量。为了比较FP数字，使用通常的关系运算符>，>=，==，等等。
Theses values are illustrative. YMMV.
这些值是说明性的。YMMV。
#1
3
Q: Is there any efficient way to obtain the number of digits a floating-point number?
A: I doubt it.
问:有没有有效的方法来获得浮点数的位数?我对此表示怀疑。
Every finite FP number is exact, but maybe not the exact value one thinks.
每个有限的FP数字都是精确的，但可能不是一个人想的精确值。
Due to typical binary64 implementation of a double,double x = 0.53, x value: .5300000000000000266453525910037569701671600341796875, 52 digits.double x = 0.1, x value:.1000000000000000055511151231257827021181583404541015625 55 digits.
The next closest double to mathematical 0.1 is .09999999999999999167332731531132594682276248931884765625 with 56 digits.
由于典型的binary64实现的双，双x = 0.53, x值:. 530000000000000000000000266453525910037569701600341796875,52位数字。双x = 0.1, x值:。1000000000000000055511151231257827021181583404541015625 55位。下一个最接近数学0.1的是。09999999999999999999999991673153153113259468476248931884765625和56位数字。
DBL_MAX: in decimal, typically about 300 digits 17976931348623158... ending with 6728515625..
DBL_MAX:在十进制中，通常约为300位数字17976931348623158……以6728515625 . .
DBL_MIN: typically 0.000000(~300 zeros) 22250738585072014... (maybe about 700 more digits).
DBL_MIN:典型的0.000000(~300个零)22250738585072014…(可能多出700个数字)。
Comparison of FP numbers need not determine the number of digitis in its decimal representation. To compare FP numbers, use the usual relationship operators >, >=, ==, etc.
FP数字的比较不需要用十进制数来确定数字的数量。为了比较FP数字，使用通常的关系运算符>，>=，==，等等。
Theses values are illustrative. YMMV.
这些值是说明性的。YMMV。