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extern crate num;

use matrix::Matrix;
use ops_inplace::VectorVectorOpsInPlace;
use math::Dimension;
use math::{Sum, SumVec};

// ----------------------------------------------------------------------------

pub trait MeanVec<T> {
    fn mean(&self) -> T;
}

macro_rules! mean_vec_impl {
    ($($t:ty)*) => ($(
        impl MeanVec<$t> for Vec<$t> {

            fn mean(&self) -> $t {
                (&self[..]).mean()
            }
        }

        impl MeanVec<$t> for [$t] {

            fn mean(&self) -> $t {
                let n = if self.len() == 0 { 1 as $t } else { self.len() as $t };
                self.sum() / n
            }
        }
    )*)
}

mean_vec_impl!{ usize u8 u16 u32 u64 isize i8 i16 i32 i64 f32 f64 }

// ----------------------------------------------------------------------------

/// Trait to compute the mean of values.
pub trait Mean<T> {
    /// Computes the mean over all elements for the specified dimension.
    ///
    /// When computing the mean of a vector or a slice both
    /// are interpreted as a row vector. Thus,
    /// the only valid dimension for vectors or slices
    /// is `Dimension::Row`. For other dimensions or if the vector or
    /// slice is empty the function will always return zero. 
    ///
    /// # Examples
    ///
    /// ```
    /// use rustml::*;
    ///
    /// let v = vec![1.0, 5.0, 9.0];
    /// assert_eq!(v.mean(), 5.0);
    /// ```
    ///
    /// # Implementation details
    ///
    /// Uses the [Sum](trait.Sum.html) trait to compute the sum which is then
    /// divided by the number of values.
    fn mean(&self, dim: Dimension) -> T;
    // TODO document matrix
}

macro_rules! mean_impl {
    ($($t:ty)*) => ($(
        impl Mean<Vec<$t>> for Matrix<$t> {

            fn mean(&self, dim: Dimension) -> Vec<$t> {

                if self.rows() == 0 || self.cols() == 0 {
                    return vec![];
                }

                match dim {
                    Dimension::Column => {
                        // TODO reimplement when Sum for Matrix is implemented
                        let mut r: Vec<$t> = self.iter().take(self.cols()).cloned().collect();
                        for row in self.row_iter_at(1) {
                            r.iadd(row);
                        }
                        let n = self.rows() as $t;
                        for i in r.iter_mut() {
                            *i = *i / n;
                        }
                        r
                    }
                    Dimension::Row => {
                        // TODO reimplement when Sum for Matrix is implemented
                        let n = self.cols() as $t;
                        self.row_iter().map(|row| row.sum() / n).collect()
                    }
                }
            }
        }
    )*)
}

mean_impl!{ f32 f64 }

// ----------------------------------------------------------------------------

#[cfg(test)]
mod tests {
    use super::*;
    use matrix::*;
    use math::Dimension;

    #[test]
    fn test_mean_vec_f32() {

        let x: Vec<f32> = vec![1.0, 2.0, 3.0, 4.0];
        assert_eq!(x.mean(), 2.5);

        let a: Vec<f32> = Vec::new();
        assert_eq!(a.mean(), 0.0);

        let b = [1.0f32, 2.0, 3.0, 4.0];
        assert_eq!(b.mean(), 2.5);
    }

    #[test]
    fn test_mean_mat_f32() {

        let a = Matrix::<f32>::new();
        assert_eq!(a.mean(Dimension::Column), vec![]);
        assert_eq!(a.mean(Dimension::Row), vec![]);

        let b = mat![5.0, 6.0];
        assert_eq!(b.mean(Dimension::Column), vec![5.0, 6.0]);
        assert_eq!(b.mean(Dimension::Row), vec![5.5]);

        let x = mat![
            1.0, 2.0;
            3.0, 4.0
        ];
        assert_eq!(x.mean(Dimension::Column), vec![2.0, 3.0]);
        assert_eq!(x.mean(Dimension::Row), vec![1.5, 3.5]);
    }
}