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use crate::{sumn, re_error, RError, RE, MStats, MinMax, MutVecg, Stats, TriangMat, VecVec, Vecg};
use indxvec::{Mutops, Vecops};
use medians::{error::MedError, Medianf64};
use rayon::prelude::*;
impl<T> VecVec<T> for &[Vec<T>]
where
T: Clone + PartialOrd + Sync + Into<f64>,
Vec<Vec<T>>: IntoParallelIterator,
Vec<T>: IntoParallelIterator
{
/// Maps scalar valued closure onto all vectors in self and collects
fn scalar_fn(self,f: impl Fn(&[T]) -> Result<f64,RE>) -> Result<Vec<f64>,RE> {
self.iter().map(|s|-> Result<f64,RE> {
f(s) }).collect::<Result<Vec<f64>,RE>>()
}
/// Maps vector valued closure onto all vectors in self and collects
fn vector_fn(self,f: impl Fn(&[T]) -> Result<Vec<f64>,RE>) -> Result<Vec<Vec<f64>>,RE> {
self.iter().map(|s|-> Result<Vec<f64>,RE> {
f(s) }).collect::<Result<Vec<Vec<f64>>,RE>>()
}
/// Exact radii magnitudes to all member points from the Geometric Median.
/// More accurate and usually faster as well than the approximate `eccentricities` above,
/// especially when there are many points.
fn radii(self, gm: &[f64]) -> Result<Vec<f64>, RE> {
self.scalar_fn(|s| Ok(gm.vdist(s)))
}
/// Selects a column by number
fn column(self, cnum: usize) -> Vec<f64> {
self.iter().map(|row| row[cnum].clone().into()).collect()
}
/// Multithreaded transpose of vec of vecs matrix
fn transpose(self) -> Vec<Vec<f64>> {
(0..self[0].len())
.into_par_iter()
.map(|cnum| self.column(cnum))
.collect()
}
/// Normalize columns, so that they become unit row vectors
fn normalize(self) -> Result<Vec<Vec<f64>>, RE> {
(0..self[0].len())
.into_par_iter()
.map(|cnum| -> Result<Vec<f64>, RE> { self.column(cnum).vunit() })
.collect()
}
/// Householder's method returning triangular matrices (U',R), where
/// U are the reflector generators for use by house_uapply(m).
/// R is the upper triangular decomposition factor.
/// Here both U and R are returned for convenience in their transposed lower triangular forms.
/// Transposed input self for convenience, so that original columns get accessed easily as rows.
fn house_ur(self) -> Result<(TriangMat, TriangMat),RE> {
let n = self.len();
let d = self[0].len();
let min = if d <= n { d } else { n }; // minimal dimension
let mut r = self.transpose(); // self.iter().map(|s| s.tof64()).collect::<Vec<Vec<f64>>>(); // //
let mut ures = vec![0.; sumn(min)];
let mut rres = Vec::with_capacity(sumn(min));
for j in 0..min {
let Some(slc) = r[j].get(j..d)
else { return Err(RError::DataError("house_ur: failed to extract uvec slice".to_owned()));};
let uvec = slc.house_reflector();
for rlast in r.iter_mut().take(d).skip(j) {
let rvec = uvec.house_reflect::<f64>(&rlast.drain(j..d).collect::<Vec<f64>>());
rlast.extend(rvec);
// drained, reflected with this uvec, and rebuilt, all remaining rows of r
}
// these uvecs are columns, so they must saved column-wise
for (row, &usave) in uvec.iter().enumerate() {
ures[sumn(row + j) + j] = usave; // using triangular index
}
// save completed `r[j]` components only up to and including the diagonal
// we are not even storing the rest, so no need to set those to zero
for &rsave in r[j].iter().take(j + 1) {
rres.push(rsave)
}
}
Ok((
TriangMat {
kind: 3,
data: ures,
}, // transposed, non symmetric kind
TriangMat {
kind: 3,
data: rres,
}, // transposed, non symmetric kind
))
}
/// Joint probability density function of n matched slices of the same length
fn jointpdfn(self) -> Result<Vec<f64>, RE> {
let d = self[0].len(); // their common dimensionality (length)
for v in self.iter().skip(1) {
if v.len() != d {
return Err(RError::DataError(
"jointpdfn: all vectors must be of equal length!".to_owned(),
));
};
}
let mut res: Vec<f64> = Vec::with_capacity(d);
let mut tuples = self.transpose();
let df = tuples.len() as f64; // for turning counts to probabilities
// lexical sort to group together occurrences of identical tuples
tuples.sort_unstable_by(|a, b| {
let Some(x) = a.partial_cmp(b)
else { panic!("jointpdfn: comparison fail in f64 sort!"); }; x});
let mut count = 1_usize; // running count
let mut lastindex = 0; // initial index of the last unique tuple
tuples.iter().enumerate().skip(1).for_each(|(i, ti)| {
if ti > &tuples[lastindex] {
// new tuple ti (Vec<T>) encountered
res.push((count as f64) / df); // save frequency count as probability
lastindex = i; // current index becomes the new one
count = 1_usize; // reset counter
} else {
count += 1;
}
});
res.push((count as f64) / df); // flush the rest!
Ok(res)
}
/// Joint entropy of vectors of the same length
fn jointentropyn(self) -> Result<f64, RE> {
let jpdf = self.jointpdfn()?;
Ok(jpdf.iter().map(|&x| -x * (x.ln())).sum())
}
/// Dependence (component wise) of a set of vectors.
/// i.e. `dependencen` returns 0 iff they are statistically independent
/// bigger values when they are dependentent
fn dependencen(self) -> Result<f64, RE> {
Ok((0..self.len())
.into_par_iter()
.map(|i| self[i].entropy())
.sum::<f64>()
/ self.jointentropyn()?
- 1.0)
}
/// Flattened lower triangular part of a symmetric matrix for vectors in self.
/// The upper triangular part can be trivially generated for all j>i by: c(j,i) = c(i,j).
/// Applies closure f to compute a scalar binary relation between all pairs of vector
/// components of self.
/// The closure typically invokes one of the methods from Vecg trait (in vecg.rs),
/// such as dependencies.
/// Example call: `pts.transpose().crossfeatures(|v1,v2| v1.mediancorrf64(v2)?)?`
/// computes median correlations between all column vectors (features) in pts.
fn crossfeatures(self, f: fn(&[T], &[T]) -> f64) -> Result<TriangMat, RE> {
Ok(TriangMat {
kind: 2, // symmetric, non transposed
data: (0..self.len())
.into_par_iter()
.flat_map(|i| {
(0..i+1)
.map(|j| f(&self[i], &self[j]))
.collect::<Vec<f64>>()
})
.collect::<Vec<f64>>(),
})
}
/// Sum of nd points (or vectors)
fn sumv(self) -> Vec<f64> {
let mut resvec = vec![0_f64; self[0].len()];
for v in self {
resvec.mutvadd(v)
}
resvec
}
/// acentroid = multidimensional arithmetic mean
fn acentroid(self) -> Vec<f64> {
self.sumv().smult::<f64>(1. / (self.len() as f64))
}
/// multithreaded acentroid = multidimensional arithmetic mean
fn par_acentroid(self) -> Vec<f64> {
let sumvec = self
.par_iter()
.fold(
|| vec![0_f64; self[0].len()],
|mut vecsum: Vec<f64>, p| {
vecsum.mutvadd(p);
vecsum
},
)
.reduce(
|| vec![0_f64; self[0].len()],
|mut finalsum: Vec<f64>, partsum: Vec<f64>| {
finalsum.mutvadd::<f64>(&partsum);
finalsum
},
);
sumvec.smult::<f64>(1. / (self.len() as f64))
}
/// gcentroid = multidimensional geometric mean
fn gcentroid(self) -> Result<Vec<f64>,RE> {
let logvs = self.iter().map(|v|-> Result<Vec<f64>,RE> {
Ok(v.vunit()?.smult::<f64>(v.vmag().ln())) })
.collect::<Result<Vec<Vec<f64>>,RE>>()?;
let logcentroid = logvs.acentroid();
Ok(logcentroid.vunit()?.smult::<f64>(logcentroid.vmag().exp()))
}
/// hcentroid = multidimensional harmonic mean
fn hcentroid(self) -> Result<Vec<f64>,RE> {
let mut centre = vec![0_f64; self[0].len()];
for v in self {
centre.mutvadd::<f64>(&v.vinverse()?)
}
Ok(centre
.vinverse()?.smult::<f64>(self.len() as f64))
}
/// 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)
self.iter().enumerate().for_each(|(i, thisp)| {
self.iter().take(i).enumerate().for_each(|(j, thatp)| {
let d = thisp.vdist(thatp); // calculate each distance relation just once
dists[i] += d;
dists[j] += d; // but add it to both points' sums
})
});
dists
}
/// Points nearest and furthest from the geometric median.
/// Returns struct MinMax{min,minindex,max,maxindex}
fn medout(self, gm: &[f64]) -> Result<MinMax<f64>,RE> {
Ok(self.scalar_fn(|s| Ok(gm.vdist(s)))?.minmax())
}
/// Radius of a point specified by its subscript.
fn radius(self, i: usize, gm: &[f64]) -> Result<f64, RE> {
if i > self.len() {
return Err(re_error("DataError","radius: invalid subscript"));
}
Ok(self[i].vdist::<f64>(gm))
}
/// Arith mean and std (in MStats struct), Median and MAD (in another MStats struct), Medoid and Outlier (in MinMax struct)
/// of scalar radii of points in self.
/// These are new robust measures of a cloud of multidimensional points (or multivariate sample).
fn eccinfo(self, gm: &[f64]) -> Result<(MStats, MStats, MinMax<f64>), RE>
where
Vec<f64>: FromIterator<f64>,
{
let rads: Vec<f64> = self.radii(gm)?;
Ok((rads.ameanstd()?, rads.medstats()?, rads.minmax()))
}
/// Quasi median, recommended only for comparison purposes
fn quasimedian(self) -> Result<Vec<f64>, RE> {
Ok((0..self[0].len())
.map(|colnum| self.column(colnum).median())
.collect::<Result<Vec<f64>, MedError<String>>>()?)
}
/// Geometric median's estimated error
fn gmerror(self, g: &[f64]) -> f64 {
let (gm, _, _) = self.nxnonmember(g);
gm.vdist::<f64>(g)
}
/// Proportions of points along each +/-axis (hemisphere)
/// Points that are perpendicular to axis get included in both +/-ve hemispheres.
/// Uses only the selected points specified in idx (e.g. the hull).
/// Self should normally be zero median vectors,
/// e.g. `self.translate(&median)`
fn sigvec(self, idx: &[usize]) -> Result<Vec<f64>, RE> {
let mut totpoints = idx.len();
let dims = self[0].len();
if self.is_empty() {
return Err(re_error("empty","sigvec given no data"));
};
let mut hemis = vec![0_f64; 2 * dims];
for &i in idx {
for (j, component) in self[i].iter().enumerate() {
let cf = component.clone().into();
if cf == 0. {
totpoints += 1;
hemis[j] += 1.;
hemis[dims + j] += 1.
} else if cf > 0. {
hemis[j] += 1.
} else {
hemis[dims + j] += 1.
};
}
}
let totf:f64 = totpoints as f64;
hemis
.iter_mut()
.for_each(|x:&mut f64| *x /= totf);
Ok(hemis)
}
/// madgm median of distances from gm: stable nd data spread measure
fn madgm(self, gm: &[f64]) -> Result<f64, RE> {
if self.is_empty() {
return Err(re_error("NoDataError","madgm given zero length vec!")); };
Ok(self.radii(gm)?.median()?)
}
/// stdgm mean of distances from gm: nd data spread measure, aka nd standard deviation
fn stdgm(self, gm: &[f64]) -> Result<f64,RE> {
if self.is_empty() {
return Err(re_error("NoDataError","stdgm given zero length vec!")); };
Ok( self.iter()
.map(|s| s.vdist(gm)).sum::<f64>()/self.len() as f64 )
}
/// Inner hull points from their square radii and
/// their ascending index `radindex`. Returns subset of `radindex`.
fn inner_hull(self, sqrads: &[f64], radindex: &[usize]) -> Vec<usize> {
radindex
.par_iter()
.filter_map(|&b| {
// test all points in ascending order
for &a in radindex {
// check against all points 'a' up to 'b'
if a == b {
return Some(b);
}; // b passed
// b lies inside of a => immediately reject b
if self[a].dotp(&self[b]) > sqrads[a] {
break;
};
}
None
})
.collect::<Vec<usize>>()
}
/// Outer hull points from their square radii and
/// their descending index `radindex`. Returns subset of `radindex`.
fn outer_hull(self, sqrads: &[f64], radindex: &[usize]) -> Vec<usize> {
radindex
.par_iter()
.filter_map(|&b| {
// test all points, in descending order
for &a in radindex {
if a == b {
return Some(b);
}; // b passed
// a lies outside of b => immediately reject b
if self[a].dotp(&self[b]) > sqrads[b] {
break;
};
}
None
})
.collect::<Vec<usize>>()
}
/// Measure of likelihood of zero median point **p** belonging to a zero median data cloud `self`.
/// This is not nearly as fast as is simple distance of **p** from **gm** but it is more sophisticated,
/// taking into account the local shape of s near **p**.
/// Returns the number of points belonging to s falling outside the normal through **p**.
/// All outer hull points have by definition `insideness = 0`.
/// Mahalanobis distance has the same goal but is less specific.
/// When self contains only precomputed outer hull points, the computation will be faster.
fn insideness(self, p: &[f64]) -> usize {
let sqrad = p.vmagsq();
let mut count = 0_usize;
for a in self {
if a.dotp(p) > sqrad { count += 1; };
};
count
}
/// Collects indices of inner (or core) hull and outer hull, from zero median points in self.
/// Defining plane of a point A goes through A and is normal to the zero median vector **a**.
/// B is an inner hull point, when it lies inside all other points' defining planes.
/// B is an outer hull point, when there is no other point beyond its own defining plane.
/// B can belong to both hulls, as when all the points lie on a hyper-sphere around gm.
/// The testing is done in increasing (decreasing) radius order.
/// B lies outside the defining plane of **a**, when its projection onto unit **a** exceeds `|a|`:
/// `|b|cos(θ) > |a| => a*b > |a|^2`,
/// such B immediately fails as a candidate for the inner hull.
/// Working with square magnitudes, `|a|^2` saves taking square roots and dividing the dot product by |a|.
/// Similarly for the outer hull, where A and B simply swap roles.
fn hulls(self) -> (Vec<usize>, Vec<usize>) {
let sqradii = self.par_iter().map(|s| s.vmagsq()).collect::<Vec<f64>>();
let mut radindex = sqradii.hashsort_indexed(|x| *x); // ascending square radii
let innerindex = self.inner_hull(&sqradii,&radindex);
radindex.mutrevs(); // make the order of points descending
let outerindex = self.outer_hull(&sqradii,&radindex);
(innerindex, outerindex)
}
/// Initial (first) point for geometric medians.
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.iter().map(|pi| pi.clone().into().powi(2)).sum::<f64>(); // vmag();
if mag.is_normal() {
// skip if p is at the origin
let rec = 1.0_f64 / (mag.sqrt());
// the sum of reciprocals of magnitudes for the final scaling
rsum += rec;
// add this unit vector to their sum
vsum.mutvadd::<f64>(&p.smult::<f64>(rec))
}
}
vsum.mutsmult::<f64>(1.0 / rsum); // scale by the sum of reciprocals
vsum // good initial gm
}
/// Like gmparts, except only does one iteration from any non-member point g
fn nxnonmember(self, g: &[f64]) -> (Vec<f64>, Vec<f64>, f64) {
// vsum is the sum vector of unit vectors towards the points
let mut vsum = vec![0_f64; self[0].len()];
let mut recip = 0_f64;
for x in self {
// |x-p| done in-place for speed. Could have simply called x.vdist(p)
let mag: f64 = x
.iter()
.zip(g)
.map(|(xi, &gi)| (xi.clone().into() - gi).powi(2))
.sum::<f64>();
if mag.is_normal() {
// ignore this point should distance be zero
let rec = 1.0_f64 / (mag.sqrt()); // reciprocal of distance (scalar)
// vsum increments by components
vsum.iter_mut()
.zip(x)
.for_each(|(vi, xi)| *vi += xi.clone().into() * rec);
recip += rec // add separately the reciprocals for final scaling
}
}
(
vsum.iter().map(|vi| vi / recip).collect::<Vec<f64>>(),
vsum,
recip,
)
}
/// 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 has 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.
/// The sum of reciprocals is strictly increasing and so is used here as
/// easy to evaluate termination condition.
fn gmedian(self, eps: f64) -> Vec<f64> {
let mut g = self.acentroid(); // start iterating from the mean or vec![0_f64; self[0].len()];
let mut recsum = 0f64;
loop {
// vector iteration till accuracy eps is exceeded
let mut nextg = vec![0_f64; self[0].len()];
let mut nextrecsum = 0_f64;
for p in self {
// |p-g| done in-place for speed. Could have simply called p.vdist(g)
let mag: f64 = p
.iter()
.zip(&g)
.map(|(vi, gi)| (vi.clone().into() - gi).powi(2))
.sum();
if mag > eps {
let rec = 1.0_f64 / (mag.sqrt()); // reciprocal of distance (scalar)
// vsum increment by components
for (vi, gi) in p.iter().zip(&mut nextg) {
*gi += vi.clone().into() * rec
}
nextrecsum += rec // add separately the reciprocals for final scaling
} // else simply ignore this point v, should its distance from g be <= eps
}
nextg.iter_mut().for_each(|gi| *gi /= nextrecsum);
// eprintln!("recsum {}, nextrecsum {} diff {}",recsum,nextrecsum,nextrecsum-recsum);
if nextrecsum - recsum < eps {
return nextg;
}; // termination test
g = nextg;
recsum = nextrecsum;
}
}
/// Parallel (multithreaded) implementation of Geometric Median. Possibly the fastest you will find.
/// 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 hyper space.
/// Weiszfeld's fixed point iteration formula has known problems and sometimes fails to converge.
/// Specifically, when the points are dense in the close proximity of the gm, or gm coincides with one of them.
/// However, these problems are solved in my new algorithm here.
/// The sum of reciprocals is strictly increasing and so is used to easily evaluate the termination condition.
fn par_gmedian(self, eps: f64) -> Vec<f64> {
let mut g = self.par_acentroid(); // start iterating from the mean or vec![0_f64; self[0].len()];
let mut recsum = 0_f64;
loop {
// vector iteration till accuracy eps is exceeded
let (mut nextg, nextrecsum) = self
.par_iter()
.fold(
|| (vec![0_f64; self[0].len()], 0_f64),
|mut pair: (Vec<f64>, f64), p: &Vec<T>| {
// |p-g| done in-place for speed. Could have simply called p.vdist(g)
let mag: f64 = p
.iter()
.zip(&g)
.map(|(vi, gi)| (vi.clone().into() - gi).powi(2))
.sum();
// let (mut vecsum, mut recsum) = pair;
if mag > eps {
let rec = 1.0_f64 / (mag.sqrt()); // reciprocal of distance (scalar)
for (vi, gi) in p.iter().zip(&mut pair.0) {
*gi += vi.clone().into() * rec
}
pair.1 += rec; // add separately the reciprocals for the final scaling
} // else simply ignore this point should its distance from g be zero
pair
},
)
// must run reduce on the partial sums produced by fold
.reduce(
|| (vec![0_f64; self[0].len()], 0_f64),
|mut pairsum: (Vec<f64>, f64), pairin: (Vec<f64>, f64)| {
pairsum.0.mutvadd::<f64>(&pairin.0);
pairsum.1 += pairin.1;
pairsum
},
);
nextg.iter_mut().for_each(|gi| *gi /= nextrecsum);
if nextrecsum - recsum < eps {
return nextg;
}; // termination test
g = nextg;
recsum = nextrecsum;
}
}
/// Like `gmedian` but returns also the sum of unit vecs and the sum of reciprocals.
fn gmparts(self, eps: f64) -> (Vec<f64>, Vec<f64>, f64) {
let mut g = self.acentroid(); // start iterating from the Centre
let mut recsum = 0f64;
loop {
// vector iteration till accuracy eps is exceeded
let mut nextg = vec![0_f64; self[0].len()];
let mut nextrecsum = 0f64;
for x in self {
// for all points
// |x-g| done in-place for speed. Could have simply called x.vdist(g)
//let mag:f64 = g.vdist::<f64>(&x);
let mag = g
.iter()
.zip(x)
.map(|(&gi, xi)| (xi.clone().into() - gi).powi(2))
.sum::<f64>();
if mag.is_normal() {
let rec = 1.0_f64 / (mag.sqrt()); // reciprocal of distance (scalar)
// vsum increments by components
nextg
.iter_mut()
.zip(x)
.for_each(|(vi, xi)| *vi += xi.clone().into() * rec);
nextrecsum += rec // add separately the reciprocals for final scaling
} // else simply ignore this point should its distance from g be zero
}
if nextrecsum - recsum < eps {
return (
nextg
.iter()
.map(|&gi| gi / nextrecsum)
.collect::<Vec<f64>>(),
nextg,
nextrecsum,
);
}; // termination
nextg.iter_mut().for_each(|gi| *gi /= nextrecsum);
g = nextg;
recsum = nextrecsum;
}
}
}