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use std::iter::FromIterator;
use crate::{Med, MStats, MinMax, MutVecg, MutVecf64, Stats, Vecg, Vecf64, VecVec};
pub use indxvec::{merge::*,Indices};
impl<T> VecVec<T> for &[Vec<T>] where T: Copy+PartialOrd+std::fmt::Display,
f64: From<T> {
/// acentroid = simple multidimensional arithmetic mean
/// # Example
/// ```
/// use rstats::{Vecg,VecVec,VecVecg,genvec};
/// let pts = genvec(15,15,255,30);
/// let dist = pts.distsum(&pts.acentroid());
/// assert_eq!(dist, 4.14556218326653_f64);
/// ```
fn acentroid(self) -> Vec<f64> {
let mut centre = vec![0_f64; self[0].len()];
for v in self { centre.mutvadd(&v) }
centre.mutsmultf64(1.0 / (self.len() as f64));
centre
}
/// gcentroid = multidimensional geometric mean
/// # Example
/// ```
/// use rstats::{Vecg,VecVec,VecVecg,genvec};
/// let pts = genvec(15,15,255,30);
/// let centre = pts.gcentroid();
/// let dist = pts.distsum(¢re);
/// assert_eq!(dist,4.897594485332543_f64);
/// ```
fn gcentroid(self) -> Vec<f64> {
let n = self.len() as f64; // number of points
let d = self[0].len(); // dimensions
let mut centre = vec![0_f64; d];
let mut lnvec = vec![0_f64; d];
for v in self {
for i in 0..d { lnvec[i] = f64::from(v[i]).ln() }
centre.mutvaddf64(&lnvec)
}
centre.iter().map(|comp| (comp/n).exp()).collect()
}
/// hcentroid = multidimensional harmonic mean
/// # Example
/// ```
/// use rstats::{Vecg,VecVec,VecVecg,genvec};
/// let pts = genvec(15,15,255,30);
/// let centre = pts.hcentroid();
/// let dist = pts.distsum(¢re);
/// assert_eq!(dist,5.1272881071877014_f64);
/// ```
fn hcentroid(self) -> Vec<f64> {
let mut centre = vec![0_f64; self[0].len()];
for v in self {
centre.mutvaddf64(&v.vinverse().unwrap())
}
centre.smultf64(1.0/(self.len() as f64)).vinverse().unwrap()
}
/// For each member point, gives its sum of distances to all other points and their MinMax
fn distsums(self) -> Vec<f64> {
let n = self.len();
let mut dists = vec![0_f64; n]; // distances accumulator for all points
// examine all unique pairings (lower triangular part of symmetric flat matrix)
for i in 1..n {
let thisp = &self[i];
for j in 0..i {
let thatp = &self[j];
let d = thisp.vdist(&thatp); // calculate each distance relation just once
dists[i] += d;
dists[j] += d; // but add it to both points
}
}
dists
}
/// The sum of distances from one member point, given by its `indx`, to all the other points in self.
/// For all the points, use more efficient `distsums`.
fn distsuminset(self, indx: usize) -> f64 {
let n = self.len();
let mut sum = 0_f64;
let thisp = &self[indx];
for i in 0..n {
if i == indx {
continue;
};
sum += self[i].vdist(&thisp)
}
sum
}
/// Medoid and Outlier (Medout)
/// Medoid is the member point (point belonging to the set of points `self`),
/// which has the least sum of distances to all other points.
/// Outlier is the point with the greatest sum of distances.
/// In other words, they are the members nearest and furthest from the geometric median.
/// Returns struct MinMax{min,minindex,max,maxindex}
/// # Example
/// ```
/// use rstats::{Vecg,VecVec,VecVecg,genvec};
/// let pts = genvec(15,15,255,30);
/// let mm = pts.medout();
/// assert_eq!(mm.min,4.812334638782327_f64);
/// ```
fn medout(self) -> MinMax<f64> {
minmax(&self.distsums())
}
/// Finds approximate vectors from each member point towards the geometric median.
/// Twice as fast as doing them individually, using symmetry.
fn eccentricities(self) -> Vec<Vec<f64>> {
let n = self.len();
// allocate vectors for the results
let mut eccs = vec![vec![0_f64; self[0].len()]; n];
let mut recips = vec![0_f64; n];
// ecentricities vectors accumulator for all points
// examine all unique pairings (lower triangular part of symmetric flat matrix)
for i in 1..n {
let thisp = &self[i];
for j in 0..i {
// calculate each unit vector between any pair of points just once
let dvmag = self[j].vdist(&thisp);
if !dvmag.is_normal() { continue }
let rec = 1.0_f64/dvmag;
eccs[i].mutvaddf64(&self[j].smultf64(rec));
recips[i] += rec;
// mind the vector's opposite orientations w.r.t. to the two points!
eccs[j].mutvsubf64(&self[j].smultf64(rec));
recips[j] += rec;
}
}
for i in 0..n {
eccs[i].mutsmultf64(1.0/recips[i]);
eccs[i].mutvsub(&self[i])
}
eccs
}
/// Exact eccentricity vectors from all member points by first finding the Geometric Median.
/// Usually faster than the approximate `eccentricities` above, especially when there are many points.
fn exacteccs(self, eps:f64) -> Vec<Vec<f64>> {
let mut eccs = Vec::with_capacity(self.len()); // Vectors for the results
let gm:Vec<f64> = self.gmedian(eps);
for v in self {
eccs.push(gm.vsub(&v))
}
eccs
}
/// Estimated (computed) eccentricity vector for a member point.
/// It points towards the geometric median.
/// The true geometric median is as yet unknown.
/// The true geometric median would return zero vector.
/// The member point in question is specified by its index `indx`.
/// This function is suitable for a single member point.
/// When eccentricities of all the points are wanted, use `exacteccs` above.
fn eccmember(self, indx: usize) -> Vec<f64> {
self.nxmember(indx).vsub(&self[indx])
}
/// Estimated (computed) eccentricity vector for a non member point.
/// The true geometric median is as yet unknown.
/// Returns the eccentricity vector.
/// The true geometric median would return zero vector.
/// This function is suitable for a single non-member point.
fn eccnonmember(self, p:&[f64]) -> Vec<f64> {
self.nxnonmember(p).vsubf64(p)
}
/// Mean and Std (in MStats struct), Median and quartiles (in Med struct), Median and Outlier (in MinMax struct)
/// of scalar eccentricities of points in self.
/// These are new robust measures of a cloud of multidimensional points (or multivariate sample).
fn eccinfo(self, eps: f64) -> (MStats, Med, MinMax<f64>) where Vec<f64>:FromIterator<f64> {
let gm:Vec<f64> = self.gmedian(eps);
let eccs:Vec<f64> = self.iter().map(|v| gm.vdist(&v)).collect();
(eccs.ameanstd().unwrap(),eccs.median().unwrap(),minmax(&eccs))
}
/// GM and sorted eccentricities magnitudes.
/// Describing a set of points `self` in n dimensions
fn sortedeccs(self, ascending:bool, eps:f64) -> ( Vec<f64>,Vec<f64> ) {
let mut eccs = Vec::with_capacity(self.len());
let gm = self.gmedian(eps);
for v in self { // collect raw ecentricities magnitudes
eccs.push(gm.vdist(&v))
}
( gm, sortm(&eccs,ascending) )
}
/// Eccentricities of Medoid and Outlier.
/// Same as just the third element of a tuple returned by eccinfo
/// # Example
/// ```
/// use rstats::{Vecg,VecVec,genvec};
/// pub const EPS:f64 = 1e-7;
/// let d = 6_usize;
/// let pt = genvec(d,24,7,13); // random test data 5x20
/// let mm = pt.emedoid(EPS);
/// assert_eq!(mm.minindex,10); // index of e-medoid
/// assert_eq!(mm.maxindex,20); // index of e-outlier
/// ```
fn emedoid(self, eps: f64) -> MinMax<f64> where Vec<f64>:FromIterator<f64> {
let gm:Vec<f64> = self.gmedian(eps);
let eccs:Vec<f64> = self.iter().map(|v| gm.vdist(&v)).collect();
minmax(&eccs)
}
/// Initial (first) point for geometric medians.
/// Same as eccnonmember('origin') but saving the subtractions of zeroes.
/// # Example
/// ```
/// use rstats::{Vecg,VecVec,VecVecg,genvec};
/// let pts = genvec(15,15,255,30);
/// let dist = pts.distsum(&pts.firstpoint());
/// assert_eq!(dist,4.132376831171272_f64);
/// ```
fn firstpoint(self) -> Vec<f64> {
let mut rsum = 0_f64;
let mut vsum = vec![0_f64; self[0].len()];
for p in self {
let mag = p.vmag();
if mag.is_normal() { // skip if p is at the origin
let rec = 1.0_f64/mag;
// the sum of reciprocals of magnitudes for the final scaling
rsum += rec;
// so not using simply .unitv
vsum.mutvaddf64(&p.smultf64(rec)) // add all unit vectors
}
}
vsum.mutsmultf64(1.0/rsum); // scale by the sum of reciprocals
vsum
}
/// Next approximate gm computed from a member point
/// specified by its index `indx` to self.
fn nxmember(self, indx: usize) -> Vec<f64> {
let n = self.len();
let mut vsum = vec![0_f64; self[0].len()];
let p = &self[indx];
let mut recip = 0_f64;
for i in 0..n {
if i == indx { continue }; // exclude this point
let pi = &self[i];
let mag = p.vdist(pi);
if !mag.is_normal() { continue } // too close to this one
let rec = 1.0_f64/mag;
vsum.mutvaddf64(&pi.smultf64(rec)); // add vector
recip += rec // add separately the reciprocals
}
vsum.mutsmultf64(1.0/recip);
vsum
}
/// Next approximate gm computed from a non-member point p
fn nxnonmember(self, p:&[f64]) -> Vec<f64> {
let mut vsum = vec![0_f64; self[0].len()];
let mut recip = 0_f64;
for x in self {
let magsq = x.vdistsqf64(p);
if !magsq.is_normal() { continue } // zero distance, safe to ignore
let rec = 1.0_f64/(magsq).sqrt();
vsum.mutvaddf64(&x.smultf64(rec)); // add vector
recip += rec // add separately the reciprocals
}
vsum.mutsmultf64(1.0/recip);
vsum
}
/// Geometric Median (gm) is the point that minimises the sum of distances to a given set of points.
/// It has (provably) only vector iterative solutions.
/// Search methods are slow and difficult in highly dimensional space.
/// Weiszfeld's fixed point iteration formula had known problems with sometimes failing to converge.
/// Especially, when the points are dense in the close proximity of the gm, or gm coincides with one of them.
/// However, these problems are fixed in my new algorithm here.
/// There will eventually be a multithreaded version.
fn gmedian(self, eps: f64) -> Vec<f64> {
let eps2 = eps.powi(2);
let mut point = self.acentroid(); // start iterating from the Centre
loop { // vector iteration till accuracy eps is reached
let nextp = self.nxnonmember(&point);
if nextp.vdistsqf64(&point) < eps2 { return nextp }; // termination
point = nextp;
}
}
/// Secant recovery from divergence
/// for finding the geometric median.
/// Initialised with first two points: the origin and the acentroid.
fn smedian(self, eps: f64) -> Vec<f64> {
let eps2 = eps.powi(2);
let mut p1 = self.acentroid();
let mut mag1:f64 = p1.iter().map(|c| c.powi(2)).sum();
loop {
let p2 = self.nxnonmember(&p1);
let e = p2.vsubf64(&p1); // new vector error, or eccentricity
let mag2:f64 = e.iter().map(|c| c.powi(2)).sum();
if mag2 < eps2 { return p2 }; // termination
p1.mutvaddf64(&e.smultf64(mag1/(mag1+mag2)));
mag1 = mag2;
};
}
}