1
  2
  3
  4
  5
  6
  7
  8
  9
 10
 11
 12
 13
 14
 15
 16
 17
 18
 19
 20
 21
 22
 23
 24
 25
 26
 27
 28
 29
 30
 31
 32
 33
 34
 35
 36
 37
 38
 39
 40
 41
 42
 43
 44
 45
 46
 47
 48
 49
 50
 51
 52
 53
 54
 55
 56
 57
 58
 59
 60
 61
 62
 63
 64
 65
 66
 67
 68
 69
 70
 71
 72
 73
 74
 75
 76
 77
 78
 79
 80
 81
 82
 83
 84
 85
 86
 87
 88
 89
 90
 91
 92
 93
 94
 95
 96
 97
 98
 99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200

//! A collection of (more or less) accurate floating point algorithms
//!
//! This crate implements several algorithms for floating point summation and dot product. The
//! algorithms are realized as types that implement the `SumAccumulator` and `DotAccumulator`
//! trait.
//!
//! # Basic usage
//!
//! Calculating a sum (or a dot product) begins by initializing an accumulator to zero:
//!
//! ```
//! use accurate::traits::*; // Most functionality is derived from traits in this module
//! use accurate::sum::NaiveSum; // Chose a specific algorithm to perform summation / dot product
//!
//! let s = NaiveSum::<f32>::zero();
//! ```
//!
//! The accumulator traits are generic over the type of the underlying floating point numbers and
//! the `zero()` constructor is supported if the number type implements the Zero trait.
//! Alternatively the accumulator traits imply that an accumulator can be constructed `from()` an
//! arbitrary value of the number type.
//!
//! ```
//! # use accurate::traits::*;
//! # use accurate::sum::NaiveSum;
//! let s = NaiveSum::from(42.0f64);
//! ```
//!
//! The actual calculation is performed via the `Add<F, Output = Self>` trait that is also implied
//! by the `SumAccumulator` trait, where `F` is the type of the floating point numbers.
//!
//! ```
//! # use accurate::traits::*;
//! use accurate::sum::Sum2;
//!
//! let s = Sum2::zero() + 1.0f64 + 2.0 + 3.0;
//! ```
//!
//! For dot products, the `DotAccumulator` trait implies `Add<(F, F), Output = Self>` to allow
//! accumulation of the products of pairs into the final result.
//!
//! ```
//! # use accurate::traits::*;
//! use accurate::dot::NaiveDot;
//!
//! let d = NaiveDot::zero() + (1.0f64, 1.0f64) + (2.0, 2.0) + (3.0, 3.0);
//! ```
//!
//! Once all of the terms have been accumulated, the result can be evaluated using the `sum()` and
//! `dot()` methods respectively.
//!
//! ```
//! # use accurate::traits::*;
//! # use accurate::sum::Sum2;
//! # use accurate::dot::NaiveDot;
//! let s = Sum2::zero() + 1.0f64 + 2.0 + 3.0;
//! assert_eq!(6.0, s.sum());
//!
//! let d = NaiveDot::zero() + (1.0f64, 1.0f64) + (2.0, 2.0) + (3.0, 3.0);
//! assert_eq!(14.0, d.dot());
//! ```
//!
//! Both `sum()` and `dot()` take their argument by value, because the evaluation of the final
//! result is in some cases a destructive operation on the internal state of the accumulator.
//! However, the evaluation of partial results is supported by `clone()`ing the accumulator.
//!
//! ```
//! # use accurate::traits::*;
//! # use accurate::sum::Sum2;
//! let s = Sum2::zero() + 1.0f32 + 2.0;
//! assert_eq!(3.0, s.clone().sum());
//! let s = s + 3.0;
//! assert_eq!(6.0, s.sum());
//! ```
//!
//! # Iterator consumption
//!
//! Accumulators can be used in `fold()` operations on iterators as one would expect.
//!
//! ```
//! # use accurate::traits::*;
//! # use accurate::sum::Sum2;
//! use accurate::dot::Dot2;
//!
//! let s = vec![1.0f32, 2.0, 3.0].into_iter().fold(Sum2::zero(), |acc, x| acc + x);
//! assert_eq!(6.0, s.sum());
//!
//! let d = vec![1.0f32, 2.0, 3.0].into_iter()
//!     .zip(vec![1.0, 2.0, 3.0].into_iter())
//!     .fold(Dot2::zero(), |acc, xy| acc + xy);
//! assert_eq!(14.0, d.dot());
//! ```
//!
//! For convenience, the accumulator traits also define `absorb()` methods to absorb values from
//! anything that implements `IntoIterator`.
//!
//! ```
//! # use accurate::traits::*;
//! # use accurate::sum::Sum2;
//! # use accurate::dot::Dot2;
//!
//! let s = Sum2::zero().absorb(vec![1.0f32, 2.0, 3.0]);
//! assert_eq!(6.0, s.sum());
//!
//! let d = Dot2::zero().absorb(vec![(1.0f32, 1.0), (2.0, 2.0), (3.0, 3.0)]);
//! assert_eq!(14.0, d.dot());
//! ```
//!
//! And for even more convenience, suitable iterators are extended by a `sum_with_accumulator()`
//! (and `dot_with_accumulator()`) method that directly evaluates to the result in the floating
//! point number type.
//!
//! ```
//! # use accurate::traits::*;
//! # use accurate::sum::Sum2;
//! # use accurate::dot::Dot2;
//!
//! let s = Sum2::zero().absorb(vec![1.0f32, 2.0, 3.0]);
//! assert_eq!(6.0f64, vec![1.0, 2.0, 3.0].into_iter().sum_with_accumulator::<Sum2<_>>());
//!
//! assert_eq!(14.0f64, vec![(1.0, 1.0), (2.0, 2.0), (3.0, 3.0)].into_iter()
//!     .dot_with_accumulator::<Dot2<_>>());
//! ```
//!
#![cfg_attr(feature = "parallel",
            doc = "
# Parallel computation

If compiled with the `parallel` feature enabled (which is the default) the `rayon` parallel
iterator facilities are used to perform large calculations in parallel. Parallel calculations are
performed through the `parallel_sum_with_accumulator()` and `parallel_dot_with_accumulator()`
extension methods on parallel iterators.

```
# extern crate accurate;
extern crate rayon;

use rayon::prelude::*;

# use accurate::traits::*;
# use accurate::sum::Sum2;
# fn main() {
let xs = vec![1.0f64; 100_000];
let s = xs.par_iter().map(|&x| x).parallel_sum_with_accumulator::<Sum2<_>>();
assert_eq!(100_000.0, s);
# }
```
")]
#![deny(missing_docs)]
#![warn(missing_copy_implementations)]
#![warn(missing_debug_implementations)]
#![warn(trivial_casts)]
#![warn(trivial_numeric_casts)]
// This has false positives on #[macro_use],
// see https://github.com/rust-lang/rust/issues/30849
// #![warn(unused_extern_crates)]
#![warn(unused_import_braces)]
#![warn(unused_qualifications)]
#![warn(unused_results)]
#![deny(warnings)]
#![cfg_attr(feature = "cargo-clippy", warn(cast_possible_truncation))]
#![cfg_attr(feature = "cargo-clippy", warn(cast_possible_wrap))]
#![cfg_attr(feature = "cargo-clippy", warn(cast_precision_loss))]
#![cfg_attr(feature = "cargo-clippy", warn(cast_sign_loss))]
#![cfg_attr(feature = "cargo-clippy", allow(doc_markdown))]
#![cfg_attr(feature = "cargo-clippy", allow(many_single_char_names))]
#![cfg_attr(feature = "cargo-clippy", warn(mut_mut))]
#![cfg_attr(feature = "cargo-clippy", warn(mutex_integer))]
#![cfg_attr(feature = "cargo-clippy", warn(non_ascii_literal))]
#![cfg_attr(feature = "cargo-clippy", warn(option_unwrap_used))]
#![cfg_attr(feature = "cargo-clippy", warn(print_stdout))]
#![cfg_attr(feature = "cargo-clippy", warn(result_unwrap_used))]
#![cfg_attr(feature = "cargo-clippy", warn(single_match_else))]
#![cfg_attr(feature = "cargo-clippy", warn(string_add))]
#![cfg_attr(feature = "cargo-clippy", warn(string_add_assign))]
#![cfg_attr(feature = "cargo-clippy", warn(unicode_not_nfc))]
#![cfg_attr(feature = "cargo-clippy", warn(wrong_pub_self_convention))]
#![cfg_attr(feature = "cargo-clippy", allow(suspicious_op_assign_impl))]

#[macro_use]
extern crate cfg_if;
extern crate ieee754;
extern crate num;

#[cfg(feature = "parallel")]
extern crate rayon;

pub mod dot;
pub mod sum;
pub mod util;

/// Includes all traits of this crate
pub mod traits {
    #[doc(inline)]
    pub use dot::traits::*;
    #[doc(inline)]
    pub use sum::traits::*;
    #[doc(inline)]
    pub use util::traits::*;
}