Add statistics functions: sum, variance, mean.

src/base/statistics.rs

@@ -0,0 +1,163 @@
+use ::{Real, Dim, Matrix, VectorN, RowVectorN, DefaultAllocator, U1, VectorSliceN};
+use storage::Storage;
+use allocator::Allocator;
+
+impl<N: Real, R: Dim, C: Dim, S: Storage<N, R, C>> Matrix<N, R, C, S> {
+    #[inline]
+    pub fn compress_rows(&self, f: impl Fn(VectorSliceN<N, R, S::RStride, S::CStride>) -> N) -> RowVectorN<N, C>
+        where DefaultAllocator: Allocator<N, U1, C> {
+
+        let ncols = self.data.shape().1;
+        let mut res = unsafe { RowVectorN::new_uninitialized_generic(U1, ncols) };
+
+        for i in 0..ncols.value() {
+            // FIXME: avoid bound checking of column.
+            unsafe { *res.get_unchecked_mut(0, i) = f(self.column(i)); }
+        }
+
+        res
+    }
+
+    #[inline]
+    pub fn compress_rows_tr(&self, f: impl Fn(VectorSliceN<N, R, S::RStride, S::CStride>) -> N) -> VectorN<N, C>
+        where DefaultAllocator: Allocator<N, C> {
+
+        let ncols = self.data.shape().1;
+        let mut res = unsafe { VectorN::new_uninitialized_generic(ncols, U1) };
+
+        for i in 0..ncols.value() {
+            // FIXME: avoid bound checking of column.
+            unsafe { *res.vget_unchecked_mut(i) = f(self.column(i)); }
+        }
+
+        res
+    }
+
+    #[inline]
+    pub fn compress_columns(&self, init: VectorN<N, R>, f: impl Fn(&mut VectorN<N, R>, VectorSliceN<N, R, S::RStride, S::CStride>)) -> VectorN<N, R>
+        where DefaultAllocator: Allocator<N, R> {
+        let mut res = init;
+
+        for i in 0..self.ncols() {
+            f(&mut res, self.column(i))
+        }
+
+        res
+    }
+}
+
+impl<N: Real, R: Dim, C: Dim, S: Storage<N, R, C>> Matrix<N, R, C, S> {
+    /*
+     *
+     * Sum computation.
+     *
+     */
+    #[inline]
+    pub fn sum(&self) -> N {
+        self.iter().cloned().fold(N::zero(), |a, b| a + b)
+    }
+
+    #[inline]
+    pub fn row_sum(&self) -> RowVectorN<N, C>
+        where DefaultAllocator: Allocator<N, U1, C> {
+        self.compress_rows(|col| col.sum())
+    }
+
+    #[inline]
+    pub fn row_sum_tr(&self) -> VectorN<N, C>
+        where DefaultAllocator: Allocator<N, C> {
+        self.compress_rows_tr(|col| col.sum())
+    }
+
+    #[inline]
+    pub fn column_sum(&self) -> VectorN<N, R>
+        where DefaultAllocator: Allocator<N, R> {
+        let nrows = self.data.shape().0;
+        self.compress_columns(VectorN::zeros_generic(nrows, U1), |out, col| {
+            out.axpy(N::one(), &col, N::one())
+        })
+    }
+
+    /*
+     *
+     * Variance computation.
+     *
+     */
+    #[inline]
+    pub fn variance(&self) -> N {
+        if self.len() == 0 {
+            N::zero()
+        } else {
+            let val = self.iter().cloned().fold((N::zero(), N::zero()), |a, b| (a.0 + b * b, a.1 + b));
+            let denom = N::one() / ::convert::<_, N>(self.len() as f64);
+            val.0 * denom - (val.1 * denom) * (val.1 * denom)
+        }
+    }
+
+    #[inline]
+    pub fn row_variance(&self) -> RowVectorN<N, C>
+        where DefaultAllocator: Allocator<N, U1, C> {
+        self.compress_rows(|col| col.variance())
+    }
+
+    #[inline]
+    pub fn row_variance_tr(&self) -> VectorN<N, C>
+        where DefaultAllocator: Allocator<N, C> {
+        self.compress_rows_tr(|col| col.variance())
+    }
+
+    #[inline]
+    pub fn column_variance(&self) -> VectorN<N, R>
+        where DefaultAllocator: Allocator<N, R> {
+        let (nrows, ncols) = self.data.shape();
+
+        let mut mean = self.column_mean();
+        mean.apply(|e| -(e * e));
+
+        let denom = N::one() / ::convert::<_, N>(ncols.value() as f64);
+        self.compress_columns(mean, |out, col| {
+            for i in 0..nrows.value() {
+                unsafe {
+                    let val = col.vget_unchecked(i);
+                    *out.vget_unchecked_mut(i) += denom * *val * *val
+                }
+            }
+        })
+    }
+
+    /*
+     *
+     * Mean computation.
+     *
+     */
+    #[inline]
+    pub fn mean(&self) -> N {
+        if self.len() == 0 {
+            N::zero()
+        } else {
+            self.sum() / ::convert(self.len() as f64)
+        }
+    }
+
+    #[inline]
+    pub fn row_mean(&self) -> RowVectorN<N, C>
+        where DefaultAllocator: Allocator<N, U1, C> {
+        self.compress_rows(|col| col.mean())
+    }
+
+    #[inline]
+    pub fn row_mean_tr(&self) -> VectorN<N, C>
+        where DefaultAllocator: Allocator<N, C> {
+        self.compress_rows_tr(|col| col.mean())
+    }
+
+    #[inline]
+    pub fn column_mean(&self) -> VectorN<N, R>
+        where DefaultAllocator: Allocator<N, R> {
+        let (nrows, ncols) = self.data.shape();
+        let denom = N::one() / ::convert::<_, N>(ncols.value() as f64);
+        self.compress_columns(VectorN::zeros_generic(nrows, U1), |out, col| {
+            out.axpy(denom, &col, N::one())
+        })
+    }
+}