Crate iterative_methods[−][src]
Expand description
Iterative methods
Implements iterative methods and utilities for using and developing them as StreamingIterators. A series of blog posts provide a gentle introduction.
… but ok fine, here is a really quick example:
// Problem: minimize the convex parabola f(x) = x^2 + x let function = x x * x + x; // An iterative solution by gradient descent let derivative = x 2.0 * x + 1.0; let step_size = 0.2; let x_0 = 2.0; // Au naturale: let mut x = x_0; for i in 0..10 { x = step_size * derivative(x); println!("x_{} = {:.2}; f(x_{}) = {:.4}", i, x, i, x * x + x); } // Using replaceable components: let dd = DerivativeDescent::new(function, derivative, step_size, x_0); let dd = enumerate(dd); let mut dd = dd.take(10); while let Some(&Numbered{item: Some(ref curr), count}) = dd.next() { println!("x_{} = {:.2}; f(x_{}) = {:.4}", count, curr.x, count, curr.value()); }
Both produce the exact same output (below), and the first common approach is much easier to look at, the descent step is right there. The second separates the algorithm and every other concern into an easily reusable and composable components. If that sounds useful, have fun exploring.
x_0 = 1.00; f(x_0) = 2.0000 x_1 = 0.40; f(x_1) = 0.5600 x_2 = 0.04; f(x_2) = 0.0416 x_3 = 0.18; f(x_3) = 0.1450 x_4 = 0.31; f(x_4) = 0.2122 x_5 = 0.38; f(x_5) = 0.2364 x_6 = 0.43; f(x_6) = 0.2451 x_7 = 0.46; f(x_7) = 0.2482 x_8 = 0.47; f(x_8) = 0.2494 x_9 = 0.48; f(x_9) = 0.2498
Modules
algorithms  
conjugate_gradient  Implementation of conjugate gradient following lecture notes by Shen. Thanks Shen! 
derivative_descent  Library code for example from crate toplevel documentation 
utils 
Structs
Annotate  An adaptor that annotates every underlying item 
AnnotatedResult  Store a generic annotation next to the state. 
Enumerate  An adaptor that enumerates items. 
ExtractValue  An adaptor that converts items from 
Numbered  A struct that wraps an 
ReservoirSample  Adaptor to reservoir sample. 
StepBy  An iterator for stepping iterators by a custom amount. 
TakeUntil  An adaptor that returns initial elements until and including the first satisfying a predicate. 
Time  Adaptor that times every call to 
TimedResult  Wrapper for Time. 
Weight  Adaptor wrapping items with a computed weight. 
WeightedDatum  Wrapper for Weight. 
WeightedReservoirSample  Adaptor that reservoir samples with weights 
WriteYamlDocuments  Write items of StreamingIterator to a Yaml file. 
Enums
UntilState 
Traits
YamlDataType  Define a trait object for converting to YAML objects. 
Functions
assess  Annotate every underlying item with its score, as defined by 
enumerate  A constructor for Enumerate. 
extract_value  The constructor for ExtractValue. Apply it to a StreamingIterator with

inspect  Apply 
last  Get the item before the first None, assuming any exist. 
new_datum  Constructor for WeightedDatum. 
reservoir_sample  An adaptor for which the items are random samples of the underlying iterator up to the item processed. The constructor for ReservoirSample. 
step_by  Creates an iterator starting at the same point, but stepping by the given amount at each iteration. 
take_until  Creates an iterator which returns initial elements until and including the first satisfying a predicate. 
time  Wrap each value of a streaming iterator with the durations: 
wd_iterable  Annotates items of an iterable with a weight using a function 
weighted_reservoir_sample  Create a random sample of the underlying weighted stream. 
write_yaml_documents  Adaptor that writes each item to a YAML document. 
write_yaml_object  Function used by WriteYamlDocuments to specify how to write each item to file. 