lsph 0.1.9

Learned Spatial HashMap
Documentation 
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use crate::models::Model;
use num_traits::cast::{AsPrimitive, FromPrimitive};
use num_traits::float::Float;

/// LearnedHasher takes a model and produces hash from the model
#[derive(Debug, Clone)]
pub struct LearnedHasher<M> {
    state: u64,
    pub model: M,
    sort_by_x: bool,
}

impl<M, F> Default for LearnedHasher<M>
where
    F: Float,
    M: Model<F = F> + Default,
{
    #[inline]
    fn default() -> Self {
        Self {
            state: 0,
            model: Default::default(),
            sort_by_x: true,
        }
    }
}
impl<M, F> LearnedHasher<M>
where
    F: Float,
    M: Model<F = F>,
{
    /// Returns a default LearnedHasher with Model and Float type.
    ///
    /// # Arguments
    /// * `model` - A model that implements Model trait
    ///
    /// # Examples
    ///
    /// ```
    /// use lsph::{LearnedHashMap, LinearModel, LearnedHasher};
    /// let hasher = LearnedHasher::with_model(LinearModel::<f32>::new());
    /// ```
    #[inline]
    pub fn with_model(model: M) -> Self {
        Self {
            state: 0,
            model,
            sort_by_x: true,
        }
    }

    /// Returns a current Hasher state.
    #[inline]
    fn finish(&self) -> u64 {
        self.state
    }

    /// Returns the sorted index base on parameter self.sort_by_x.
    #[inline]
    pub fn sort_by_x(&self) -> bool {
        self.sort_by_x
    }

    /// Sets self.sort_by_x to a given boolean value.
    #[inline]
    pub fn set_sort_by_x(&mut self, x: bool) {
        self.sort_by_x = x;
    }
}

impl<M, F> LearnedHasher<M>
where
    F: Float,
    M: Model<F = F> + Default,
{
    /// Returns a default LearnedHasher.
    #[inline]
    pub fn new() -> Self {
        Self::default()
    }
}

impl<M, F> LearnedHasher<M>
where
    F: Float + AsPrimitive<u64>,
    M: Model<F = F>,
{
    /// Writes a data into self.data by inferencing the input data into the trained model.
    #[inline]
    fn write(&mut self, data: &F) {
        self.state = self.model.predict(*data).floor().as_();
    }
}

impl<M, F> LearnedHasher<M>
where
    F: Float + FromPrimitive,
    M: Model<F = F> + Default,
{
    /// Unwrite takes a hash value, and unpredict the hash value to estimate the approximate input
    /// data from the target data(e.g. in linear regression, to get the x value for given y).
    ///
    /// # Arguments
    /// * `hash` - An usize hash value
    #[inline]
    fn unwrite(&mut self, hash: u64) -> F {
        let hash = FromPrimitive::from_u64(hash).unwrap();
        self.model.unpredict(hash)
    }
}
/// Make hash value from a given hasher, returns a u64 hash value.
///
/// # Arguments
/// * `hasher` - A LearnedHasher type
#[inline]
pub fn make_hash<M, F>(hasher: &mut LearnedHasher<M>, p: &F) -> u64
where
    F: Float + FromPrimitive + AsPrimitive<u64>,
    M: Model<F = F> + Default,
{
    hasher.write(p);
    hasher.finish()
}

/// Make hash value from a given hasher, and 2 item array with float data.
///
/// # Arguments
/// * `hasher` - A LearnedHasher type
/// * `p` - Point data
#[inline]
pub fn make_hash_point<M, F>(hasher: &mut LearnedHasher<M>, p: &[F; 2]) -> u64
where
    F: Float + FromPrimitive + AsPrimitive<u64>,
    M: Model<F = F> + Default,
{
    if hasher.sort_by_x {
        make_hash(hasher, &p[0])
    } else {
        make_hash(hasher, &p[1])
    }
}

/// Unmake hash value from a given hasher, and a u64 hash value.
/// Reverse the hash function, which it takes a hash and returns float
///
/// # Arguments
/// * `hasher` - A LearnedHasher type
/// * `p` - Point data
#[inline]
pub fn unhash<M, F>(hasher: &mut LearnedHasher<M>, hash: u64) -> F
where
    F: Float + FromPrimitive + AsPrimitive<u64>,
    M: Model<F = F> + Default,
{
    hasher.unwrite(hash)
}

#[cfg(test)]
mod tests {
    use super::LearnedHasher;
    use crate::models::LinearModel;

    #[test]
    fn hasher_with_empty_model() {
        let mut hasher: LearnedHasher<LinearModel<f64>> = LearnedHasher::new();
        hasher.write(&10f64);
        assert_eq!(0u64, hasher.finish());
    }

    #[test]
    fn unhash() {
        let mut hasher: LearnedHasher<LinearModel<f64>> = LearnedHasher::with_model(LinearModel {
            coefficient: 3.,
            intercept: 2.,
        });
        hasher.write(&10.5);
        assert_eq!(33u64, hasher.finish());
        assert_delta!(10.33f64, hasher.unwrite(33u64), 0.01);
    }
}