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feat: pre-release

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This commit is contained in:
Volodymyr Orlov
2020-09-25 17:52:21 -07:00
parent 4067b20ed3
commit 2aca488553
4 changed files with 8 additions and 33 deletions
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src/algorithm/neighbour/bbd_tree.rs
-15
View File
@@ -101,7 +101,6 @@ impl<T: RealNumber> BBDTree<T> {
) -> T {
let d = centroids[0].len();
// Determine which mean the node mean is closest to
let mut min_dist =
Euclidian::squared_distance(&self.nodes[node].center, &centroids[candidates[0]]);
let mut closest = candidates[0];
@@ -114,9 +113,7 @@ impl<T: RealNumber> BBDTree<T> {
}
}
// If this is a non-leaf node, recurse if necessary
if !self.nodes[node].lower.is_none() {
// Build the new list of candidates
let mut new_candidates = vec![0; k];
let mut newk = 0;
@@ -133,7 +130,6 @@ impl<T: RealNumber> BBDTree<T> {
}
}
// Recurse if there's at least two
if newk > 1 {
return self.filter(
self.nodes[node].lower.unwrap(),
@@ -155,7 +151,6 @@ impl<T: RealNumber> BBDTree<T> {
}
}
// Assigns all data within this node to a single mean
for i in 0..d {
sums[closest][i] = sums[closest][i] + self.nodes[node].sum[i];
}
@@ -203,14 +198,11 @@ impl<T: RealNumber> BBDTree<T> {
fn build_node<M: Matrix<T>>(&mut self, data: &M, begin: usize, end: usize) -> usize {
let (_, d) = data.shape();
// Allocate the node
let mut node = BBDTreeNode::new(d);
// Fill in basic info
node.count = end - begin;
node.index = begin;
// Calculate the bounding box
let mut lower_bound = vec![T::zero(); d];
let mut upper_bound = vec![T::zero(); d];
@@ -231,7 +223,6 @@ impl<T: RealNumber> BBDTree<T> {
}
}
// Calculate bounding box stats
let mut max_radius = T::from(-1.).unwrap();
let mut split_index = 0;
for i in 0..d {
@@ -243,7 +234,6 @@ impl<T: RealNumber> BBDTree<T> {
}
}
// If the max spread is 0, make this a leaf node
if max_radius < T::from(1E-10).unwrap() {
node.lower = Option::None;
node.upper = Option::None;
@@ -262,9 +252,6 @@ impl<T: RealNumber> BBDTree<T> {
return self.add_node(node);
}
// Partition the data around the midpoint in this dimension. The
// partitioning is done in-place by iterating from left-to-right and
// right-to-left in the same way that partioning is done in quicksort.
let split_cutoff = node.center[split_index];
let mut i1 = begin;
let mut i2 = end - 1;
@@ -291,11 +278,9 @@ impl<T: RealNumber> BBDTree<T> {
}
}
// Create the child nodes
node.lower = Option::Some(self.build_node(data, begin, begin + size));
node.upper = Option::Some(self.build_node(data, begin + size, end));
// Calculate the new sum and opt cost
for i in 0..d {
node.sum[i] =
self.nodes[node.lower.unwrap()].sum[i] + self.nodes[node.upper.unwrap()].sum[i];
src/cluster/kmeans.rs
-5
View File
@@ -222,12 +222,8 @@ impl<T: RealNumber + Sum> KMeans<T> {
let mut row = vec![T::zero(); m];
// pick the next center
for j in 1..k {
// Loop over the samples and compare them to the most recent center. Store
// the distance from each sample to its closest center in scores.
for i in 0..n {
// compute the distance between this sample and the current center
data.copy_row_as_vec(i, &mut row);
let dist = Euclidian::squared_distance(&row, &centroid);
@@ -257,7 +253,6 @@ impl<T: RealNumber + Sum> KMeans<T> {
for i in 0..n {
data.copy_row_as_vec(i, &mut row);
// compute the distance between this sample and the current center
let dist = Euclidian::squared_distance(&row, &centroid);
if dist < d[i] {
src/linalg/evd.rs
-13
View File
@@ -103,9 +103,7 @@ fn tred2<T: RealNumber, M: BaseMatrix<T>>(V: &mut M, d: &mut Vec<T>, e: &mut Vec
d[i] = V.get(n - 1, i);
}
// Householder reduction to tridiagonal form.
for i in (1..n).rev() {
// Scale to avoid under/overflow.
let mut scale = T::zero();
let mut h = T::zero();
for k in 0..i {
@@ -119,7 +117,6 @@ fn tred2<T: RealNumber, M: BaseMatrix<T>>(V: &mut M, d: &mut Vec<T>, e: &mut Vec
V.set(j, i, T::zero());
}
} else {
// Generate Householder vector.
for k in 0..i {
d[k] = d[k] / scale;
h = h + d[k] * d[k];
@@ -136,7 +133,6 @@ fn tred2<T: RealNumber, M: BaseMatrix<T>>(V: &mut M, d: &mut Vec<T>, e: &mut Vec
e[j] = T::zero();
}
// Apply similarity transformation to remaining columns.
for j in 0..i {
f = d[j];
V.set(j, i, f);
@@ -169,7 +165,6 @@ fn tred2<T: RealNumber, M: BaseMatrix<T>>(V: &mut M, d: &mut Vec<T>, e: &mut Vec
d[i] = h;
}
// Accumulate transformations.
for i in 0..n - 1 {
V.set(n - 1, i, V.get(i, i));
V.set(i, i, T::one());
@@ -210,7 +205,6 @@ fn tql2<T: RealNumber, M: BaseMatrix<T>>(V: &mut M, d: &mut Vec<T>, e: &mut Vec<
let mut f = T::zero();
let mut tst1 = T::zero();
for l in 0..n {
// Find small subdiagonal element
tst1 = T::max(tst1, d[l].abs() + e[l].abs());
let mut m = l;
@@ -226,8 +220,6 @@ fn tql2<T: RealNumber, M: BaseMatrix<T>>(V: &mut M, d: &mut Vec<T>, e: &mut Vec<
}
}
// If m == l, d[l] is an eigenvalue,
// otherwise, iterate.
if m > l {
let mut iter = 0;
loop {
@@ -236,7 +228,6 @@ fn tql2<T: RealNumber, M: BaseMatrix<T>>(V: &mut M, d: &mut Vec<T>, e: &mut Vec<
panic!("Too many iterations");
}
// Compute implicit shift
let mut g = d[l];
let mut p = (d[l + 1] - g) / (T::two() * e[l]);
let mut r = p.hypot(T::one());
@@ -252,7 +243,6 @@ fn tql2<T: RealNumber, M: BaseMatrix<T>>(V: &mut M, d: &mut Vec<T>, e: &mut Vec<
}
f = f + h;
// Implicit QL transformation.
p = d[m];
let mut c = T::one();
let mut c2 = c;
@@ -273,7 +263,6 @@ fn tql2<T: RealNumber, M: BaseMatrix<T>>(V: &mut M, d: &mut Vec<T>, e: &mut Vec<
p = c * d[i] - s * g;
d[i + 1] = h + s * (c * g + s * d[i]);
// Accumulate transformation.
for k in 0..n {
h = V.get(k, i + 1);
V.set(k, i + 1, s * V.get(k, i) + c * h);
@@ -284,7 +273,6 @@ fn tql2<T: RealNumber, M: BaseMatrix<T>>(V: &mut M, d: &mut Vec<T>, e: &mut Vec<
e[l] = s * p;
d[l] = c * p;
// Check for convergence.
if e[l].abs() <= tst1 * T::epsilon() {
break;
}
@@ -294,7 +282,6 @@ fn tql2<T: RealNumber, M: BaseMatrix<T>>(V: &mut M, d: &mut Vec<T>, e: &mut Vec<
e[l] = T::zero();
}
// Sort eigenvalues and corresponding vectors.
for i in 0..n - 1 {
let mut k = i;
let mut p = d[i];