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use std::cmp::Ordering;
use array::dense::*;
pub trait SupervisedModel<T> {
fn fit(&mut self, X: T, y: &Array) -> Result<(), &'static str>;
fn decision_function(&self, X: T) -> Result<Array, &'static str>;
fn predict(&self, x: T) -> Result<Array, &'static str> {
let decision_func = try!(self.decision_function(x));
Ok(Array::from(decision_func.data()
.iter()
.map(|v| {
match v.partial_cmp(&0.5) {
Some(Ordering::Greater) => 1.0,
_ => 0.0,
}
})
.collect::<Vec<f32>>()))
}
}
pub trait ParallelPredict<T> {
fn decision_function_parallel(&self, X: T, num_threads: usize) -> Result<Array, &'static str>;
fn predict_parallel(&self, X: T, num_threads: usize) -> Result<Array, &'static str> {
let decision_func = try!(self.decision_function_parallel(X, num_threads));
Ok(Array::from(decision_func.data()
.iter()
.map(|v| {
match v.partial_cmp(&0.5) {
Some(Ordering::Greater) => 1.0,
_ => 0.0,
}
})
.collect::<Vec<f32>>()))
}
}
pub trait ParallelSupervisedModel<T> {
fn fit_parallel(&mut self, X: T, y: &Array, num_threads: usize) -> Result<(), &'static str>;
}