我正在从 ziglings.org 练习的评论中阅读有关浮点实现的内容，然后我偶然发现了有关它的信息。
// Floating further:
//
// As an example, Zig's f16 is a IEEE 754 "half-precision" binary
// floating-point format ("binary16"), which is stored in memory
// like so:
//
// 0 1 0 0 0 0 1 0 0 1 0 0 1 0 0 0
// | |-------| |-----------------|
// | exponent significand
// |
// sign
//
// This example is the decimal number 3.140625, which happens to
// be the closest representation of Pi we can make with an f16
// due to the way IEEE-754 floating points store digits:
//
// * Sign bit 0 makes the number positive.
// * Exponent bits 10000 are a scale of 16.
// * Significand bits 1001001000 are the decimal value 584.
//
// IEEE-754 saves space by modifying these values: the value
// 01111 is always subtracted from the exponent bits (in our
// case, 10000 - 01111 = 1, so our exponent is 2^1) and our
// significand digits become the decimal value _after_ an
// implicit 1 (so 1.1001001000 or 1.5703125 in decimal)! This
// gives us:
//
// 2^1 * 1.5703125 = 3.140625
这里有些地方我不太明白。为什么 1.1001001000 不是 1.584？为什么 1001001000 是 584 时会变成 1.5703125？这里的 . 有什么作用吗？
基本上，为什么隐式的会改变小数部分的值？