@@ -768,7 +768,8 @@ mod prim_tuple {}
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///
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/// This type can represent a wide range of decimal numbers, like `3.5`, `27`,
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/// `-113.75`, `0.0078125`, `34359738368`, `0`, `-1`. So unlike integer types
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- /// (like `i32`), floating point types can represent non-integer numbers, too.
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+ /// (such as `i32`), floating point types can represent non-integer numbers,
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+ /// too.
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///
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/// However, being able to represent this wide range of numbers comes at the
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/// cost of precision: floats can only represent some of the real numbers and
@@ -779,15 +780,12 @@ mod prim_tuple {}
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/// often discard insignificant digits: `println!("{}", 1.0f32 / 5.0f32)` will
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/// print `0.2`.
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///
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- /// The precision is better for numbers near 0 and worse for large numbers. For
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- /// example, above 2<sup>24</sup>, not even all integers are representable.
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- ///
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/// Additionally, `f32` can represent a couple of special values:
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///
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/// - `-0`: this is just due to how floats are encoded. It is semantically
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/// equivalent to `0` and `-0.0 == 0.0` results in `true`.
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/// - [∞](#associatedconstant.INFINITY) and
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- /// [- ∞](#associatedconstant.NEG_INFINITY): these result from calculations
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+ /// [− ∞](#associatedconstant.NEG_INFINITY): these result from calculations
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/// like `1.0 / 0.0`.
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/// - [NaN (not a number)](#associatedconstant.NAN): this value results from
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/// calculations like `(-1.0).sqrt()`. NaN has some potentially unexpected
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