Merge pull request #2 from alecmocatta/bump

bump .travis.yml

README.md

@@ -22,7 +22,7 @@ A goal of this library is to enable composition of these algorithms; for example
 Run your application with `RUSTFLAGS="-C target-cpu=native"` to benefit from the SIMD-acceleration like so:
 
 ```bash
-	RUSTFLAGS="-C target-cpu=native" cargo run --release
+RUSTFLAGS="-C target-cpu=native" cargo run --release
 ```
 
 See [this gist](https://gist.github.com/debasishg/8172796) for a good list of further algorithms to be implemented. Other resources are [Probabilistic data structures – Wikipedia](https://en.wikipedia.org/wiki/Category:Probabilistic_data_structures), [DataSketches – A similar Java library originating at Yahoo](https://datasketches.github.io/), and [Algebird  – A similar Java library originating at Twitter](https://github.com/twitter/algebird).

src/count_min.rs

@@ -89,7 +89,8 @@ where
 					.iter()
 					.enumerate()
 					.map(|(k_i, &offset)| &self.counters[k_i][offset]),
-			).unwrap();
+			)
+			.unwrap();
 			lowest += value;
 			self.counters
 				.iter_mut()
@@ -108,7 +109,8 @@ where
 						counters[offset] += value;
 						&counters[offset]
 					}),
-			).unwrap()
+			)
+			.unwrap()
 		}
 	}
 
@@ -138,7 +140,8 @@ where
 				.iter()
 				.zip(self.offsets(key))
 				.map(|(counters, offset)| &counters[offset]),
-		).unwrap()
+		)
+		.unwrap()
 	}
 
 	// pub fn estimate_memory(

src/lib.rs

@@ -14,23 +14,15 @@
 //! Run your application with `RUSTFLAGS="-C target-cpu=native"` to benefit from the SIMD-acceleration like so:
 //!
 //! ```bash
-//! 	RUSTFLAGS="-C target-cpu=native" cargo run --release
+//! RUSTFLAGS="-C target-cpu=native" cargo run --release
 //! ```
 //!
 //! See [this gist](https://gist.github.com/debasishg/8172796) for a good list of further algorithms to be implemented. Other resources are [Probabilistic data structures – Wikipedia](https://en.wikipedia.org/wiki/Category:Probabilistic_data_structures), [DataSketches – A similar Java library originating at Yahoo](https://datasketches.github.io/), and [Algebird  – A similar Java library originating at Twitter](https://github.com/twitter/algebird).
 //!
 //! As these implementations are often in hot code paths, unsafe is used, albeit only when necessary to a) achieve the asymptotically optimal algorithm or b) mitigate an observed bottleneck.
 
 #![doc(html_root_url = "https://docs.rs/streaming_algorithms/0.1.0")]
-#![feature(
-	nll,
-	tool_lints,
-	non_modrs_mods,
-	specialization,
-	stdsimd,
-	mmx_target_feature,
-	convert_id
-)]
+#![feature(nll, specialization, convert_id)]
 #![warn(
 	missing_copy_implementations,
 	missing_debug_implementations,
@@ -40,10 +32,11 @@
 	unused_import_braces,
 	unused_qualifications,
 	unused_results,
-)] // from https://github.com/rust-unofficial/patterns/blob/master/anti_patterns/deny-warnings.md
-#![allow(dead_code, stable_features)]
-#![warn(clippy::pedantic)]
+	clippy::pedantic
+)]
+// from https://github.com/rust-unofficial/patterns/blob/master/anti_patterns/deny-warnings.md
 #![allow(
+	dead_code,
 	clippy::doc_markdown,
 	clippy::inline_always,
 	clippy::stutter,
@@ -54,7 +47,7 @@
 	clippy::cast_sign_loss,
 	clippy::cast_precision_loss,
 	clippy::cast_lossless,
-	clippy::float_cmp,
+	clippy::float_cmp
 )]
 
 extern crate twox_hash;

src/linked_list.rs

@@ -28,7 +28,8 @@ impl<T> LinkedList<T> {
 			(0..cap)
 				.map(|_| (usize::max_value(), usize::max_value(), None))
 				.collect::<Vec<_>>()
-		}.into_boxed_slice();
+		}
+		.into_boxed_slice();
 		let ret = Self {
 			vec,
 			head: usize::max_value(),