treez

A collection of useful data structures

segment tree

implementation: array based

todo: generic type

notes: for static use after initialization

let mut segments = vec![];
for i in 0..10 {
    let n = (i*5, 5*i+5, i); //(left_bound,right_bound,segment_id); inclusive bounds
    segments.push( n );
}

let t : treez::seg::TreeSeg = treez::seg::TreeSeg::init( segments.as_slice() );
let query_segs: HashSet<_> = t.get_segs_from_bound( (15,20) ).iter().cloned().collect();

let check: HashSet<_> = [ 2, 3, 4 ].iter().cloned().collect();
println!( "query segs: {:?}", query_segs );
assert!( check.intersection(&query_segs).count() == check.len() );

red black tree

implementation: array based, threshold compaction, minimal heap allocation

todo: optimize internal representation and operations, generic type

notes: comparable performance to BTreeMap

let mut t = treez::rb::TreeRb::new();
for i in 0..nums.len() {
    let r = nums[i];
    t.insert( r, i as isize );
}

for i in 0..nums.len() {
    let r = nums[i];
    let v = t.remove( &r ).expect( "remove unsuccessful" );
}

reverse automatic gradient differentiation

implementation: array based, scalar variable

todo: vectorize operations instead of scalars, add more test coverage, tweek to more ergonomic interface

let mut c : autograd::Context = Default::default();

//setup variables
let mut buf = {
    let mut x = autograd::init_var( & mut c, 6f64 );
    let mut y = autograd::init_var( & mut c, 7f64 );
    let mut z = autograd::init_op( & mut c, autograd::OpType::Mul, & mut [ & mut x, & mut y ] );
    let mut a = autograd::init_var( & mut c, 3f64 );
    let b = autograd::init_op( & mut c, autograd::OpType::Add, & mut [ & mut z, & mut a ] );
    vec![ x, y, z, a, b ]
};

let var_ids = autograd::fwd_pass( & mut c, & mut buf ).unwrap();

let mut var_map = HashMap::new();
for i in [ "x", "y", "z", "a", "b" ].iter().zip( var_ids ) {
    var_map.insert( i.0, i.1 );
}

//compute gradient of b with respect to every other variable
let mut var_grad = HashMap::new();
let b_index = *var_map.get(&"b").unwrap();
for i in var_map.iter() {
    let grad = autograd::compute_grad( & mut c, b_index, *i.1 ).unwrap();
    var_grad.insert( *i.0, grad );
}

assert!( *var_grad.get(&"b").unwrap() == 1f64 );
assert!( *var_grad.get(&"a").unwrap() == 1f64 );
assert!( *var_grad.get(&"z").unwrap() == 1f64 );
assert!( *var_grad.get(&"x").unwrap() == 7f64 );
assert!( *var_grad.get(&"y").unwrap() == 6f64 );