Trait rstats::Vecf64 [−][src]
pub trait Vecf64 {}Show methods
fn smult(self, s: f64) -> Vec<f64>; fn sadd(self, s: f64) -> Vec<f64>; fn dotp(self, v: &[f64]) -> f64; fn vinverse(self) -> Vec<f64>; fn cosine(self, _v: &[f64]) -> f64; fn vsub(self, v: &[f64]) -> Vec<f64>; fn vadd(self, v: &[f64]) -> Vec<f64>; fn vmag(self) -> f64; fn vmagsq(self) -> f64; fn vdist(self, v: &[f64]) -> f64; fn vdistsq(self, v: &[f64]) -> f64; fn vunit(self) -> Vec<f64>; fn varea(self, v: &[f64]) -> f64; fn vsim(self, v: &[f64]) -> f64; fn vdisim(self, v: &[f64]) -> f64; fn correlation(self, _v: &[f64]) -> f64; fn kendalcorr(self, _v: &[f64]) -> f64; fn spearmancorr(self, _v: &[f64]) -> f64; fn kazutsugi(self) -> f64; fn autocorr(self) -> f64; fn minmax(self) -> (f64, usize, f64, usize); fn lintrans(self) -> Vec<f64>; fn binsearch(self, v: f64) -> usize; fn sortf(self) -> Vec<f64>; fn sortm(self) -> Vec<f64>; fn mergerank(self) -> Vec<usize>; fn mergesort(self, i: usize, n: usize) -> Vec<usize>;
Expand description
Vector algebra on one or two vectors.
Required methods
fn smult(self, s: f64) -> Vec<f64>
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fn smult(self, s: f64) -> Vec<f64>
[src]Scalar multiplication of a vector
fn sadd(self, s: f64) -> Vec<f64>
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fn sadd(self, s: f64) -> Vec<f64>
[src]Scalar addition to vector
fn dotp(self, v: &[f64]) -> f64
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fn dotp(self, v: &[f64]) -> f64
[src]Scalar product of two vectors
fn vinverse(self) -> Vec<f64>
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fn vinverse(self) -> Vec<f64>
[src]Inverse vecor of magnitude 1/|v|
fn cosine(self, _v: &[f64]) -> f64
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fn cosine(self, _v: &[f64]) -> f64
[src]Cosine = a.dotp(b)/(a.vmag*b.vmag)
fn vsub(self, v: &[f64]) -> Vec<f64>
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fn vsub(self, v: &[f64]) -> Vec<f64>
[src]Vector subtraction
fn vadd(self, v: &[f64]) -> Vec<f64>
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fn vadd(self, v: &[f64]) -> Vec<f64>
[src]Vector addition
fn vmag(self) -> f64
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fn vmag(self) -> f64
[src]Vector magnitude
fn vmagsq(self) -> f64
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fn vmagsq(self) -> f64
[src]Vector magnitude squared
fn vdist(self, v: &[f64]) -> f64
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fn vdist(self, v: &[f64]) -> f64
[src]Euclidian distance between two points
fn vdistsq(self, v: &[f64]) -> f64
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fn vdistsq(self, v: &[f64]) -> f64
[src]vdist between two points squared
fn vunit(self) -> Vec<f64>
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fn vunit(self) -> Vec<f64>
[src]Unit vector
fn varea(self, v: &[f64]) -> f64
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fn varea(self, v: &[f64]) -> f64
[src]Area of parallelogram between two vectors (magnitude of cross product)
fn vsim(self, v: &[f64]) -> f64
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fn vsim(self, v: &[f64]) -> f64
[src]Area proportional to the swept arc Vector similarity in the interval [0,1]: (1+cos(theta))/2
fn vdisim(self, v: &[f64]) -> f64
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fn vdisim(self, v: &[f64]) -> f64
[src]Vector dissimilarity in the interval [0,1]: (1-cos(theta))/2
fn correlation(self, _v: &[f64]) -> f64
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fn correlation(self, _v: &[f64]) -> f64
[src]Correlation
fn kendalcorr(self, _v: &[f64]) -> f64
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fn kendalcorr(self, _v: &[f64]) -> f64
[src]Kendall’s tau-b (rank order) correlation
fn spearmancorr(self, _v: &[f64]) -> f64
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fn spearmancorr(self, _v: &[f64]) -> f64
[src]Spearman’s rho (rank differences) correlation
fn kazutsugi(self) -> f64
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fn kazutsugi(self) -> f64
[src]Kazutsugi Spearman’s corelation against just five distances (to outcomes classes)
fn autocorr(self) -> f64
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fn autocorr(self) -> f64
[src]Autocorrelation
fn minmax(self) -> (f64, usize, f64, usize)
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fn minmax(self) -> (f64, usize, f64, usize)
[src]Minimum, minimum’s index, maximum, maximum’s index.
Implementations on Foreign Types
impl Vecf64 for &[f64]
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impl Vecf64 for &[f64]
[src]fn dotp(self, v: &[f64]) -> f64
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fn dotp(self, v: &[f64]) -> f64
[src]Scalar product of two f64 slices.
Must be of the same length - no error checking (for speed)
fn cosine(self, v: &[f64]) -> f64
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fn cosine(self, v: &[f64]) -> f64
[src]Cosine of an angle between two vectors.
Example
use rstats::Vecf64; let v1 = vec![1_f64,2.,3.,4.,5.,6.,7.,8.,9.,10.,11.,12.,13.,14.]; let v2 = vec![14_f64,1.,13.,2.,12.,3.,11.,4.,10.,5.,9.,6.,8.,7.]; assert_eq!(v1.cosine(&v2),0.7517241379310344);
fn vdist(self, v: &[f64]) -> f64
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fn vdist(self, v: &[f64]) -> f64
[src]Euclidian distance between two n dimensional points (vectors).
Slightly faster than vsub followed by vmag, as both are done in one loop
fn vdistsq(self, v: &[f64]) -> f64
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fn vdistsq(self, v: &[f64]) -> f64
[src]Euclidian distance squared between two n dimensional points (vectors).
Slightly faster than vsub followed by vmasq, as both are done in one loop
Same as vdist without taking the square root
fn varea(self, v: &[f64]) -> f64
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fn varea(self, v: &[f64]) -> f64
[src]Area of a parallelogram between two vectors.
Same as the magnitude of their cross product |a ^ b| = |a||b|sin(theta).
Attains maximum |a|.|b|
when the vectors are othogonal.
fn vsim(self, v: &[f64]) -> f64
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fn vsim(self, v: &[f64]) -> f64
[src]Area proportional to the swept arc up to angle theta.
Attains maximum of 2|a||b|
when the vectors have opposite orientations.
This is really |a||b|(1-cos(theta))
We define vector similarity S in the interval [0,1] as
S = (1+cos(theta))/2
fn vdisim(self, v: &[f64]) -> f64
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fn vdisim(self, v: &[f64]) -> f64
[src]We define vector dissimilarity D in the interval [0,1] as D = 1-S = (1-cos(theta))/2
fn correlation(self, v: &[f64]) -> f64
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fn correlation(self, v: &[f64]) -> f64
[src]Pearson’s correlation coefficient of a sample of two f64 variables.
Example
use rstats::Vecf64; let v1 = vec![1_f64,2.,3.,4.,5.,6.,7.,8.,9.,10.,11.,12.,13.,14.]; let v2 = vec![14_f64,1.,13.,2.,12.,3.,11.,4.,10.,5.,9.,6.,8.,7.]; assert_eq!(v1.correlation(&v2),-0.1076923076923077);
fn kendalcorr(self, v: &[f64]) -> f64
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fn kendalcorr(self, v: &[f64]) -> f64
[src]Kendall Tau-B correlation coefficient of a sample of two f64 variables. Defined by: tau = (conc - disc) / sqrt((conc + disc + tiesx) * (conc + disc + tiesy)) This is the simplest implementation with no sorting.
Example
use rstats::Vecf64; let v1 = vec![1_f64,2.,3.,4.,5.,6.,7.,8.,9.,10.,11.,12.,13.,14.]; let v2 = vec![14_f64,1.,13.,2.,12.,3.,11.,4.,10.,5.,9.,6.,8.,7.]; assert_eq!(v1.kendalcorr(&v2),-0.07692307692307693);
fn spearmancorr(self, v: &[f64]) -> f64
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fn spearmancorr(self, v: &[f64]) -> f64
[src]Spearman rho correlation coefficient of two f64 variables. This is the simplest implementation with no sorting.
Example
use rstats::Vecf64; let v1 = vec![1_f64,2.,3.,4.,5.,6.,7.,8.,9.,10.,11.,12.,13.,14.]; let v2 = vec![14_f64,1.,13.,2.,12.,3.,11.,4.,10.,5.,9.,6.,8.,7.]; assert_eq!(v1.spearmancorr(&v2),-0.1076923076923077);
fn kazutsugi(self) -> f64
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fn kazutsugi(self) -> f64
[src]Spearman correlation of five distances against Kazutsugi discrete outcomes [0.00,0.25,0.50,0.75,1.00], ranked as [4,3,2,1,0] (the order is swapped to penalise distances). The result is in the range [-1,1].
Example
use rstats::Vecf64; let v1:Vec<f64> = vec![4.,1.,2.,0.,3.]; assert_eq!(v1.kazutsugi(),0.3);
fn autocorr(self) -> f64
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fn autocorr(self) -> f64
[src](Auto)correlation coefficient of pairs of successive values of (time series) f64 variable.
Example
use rstats::Vecf64; let v1 = vec![1_f64,2.,3.,4.,5.,6.,7.,8.,9.,10.,11.,12.,13.,14.]; assert_eq!(v1.autocorr(),0.9984603532054123_f64);
fn minmax(self) -> (f64, usize, f64, usize)
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fn minmax(self) -> (f64, usize, f64, usize)
[src]Finds minimum, minimum’s index, maximum, maximum’s index of &f64 Here self is usually some data, rather than a vector
fn binsearch(self, v: f64) -> usize
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fn binsearch(self, v: f64) -> usize
[src]Counts how many items in sorted self are less than or equal to the value v, using binary search.
fn sortf(self) -> Vec<f64>
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fn sortf(self) -> Vec<f64>
[src]New sorted vector. Immutable sort. Copies self and then sorts it in place, leaving self unchanged. Calls mutsortf and that calls the standard self.sort_unstable_by
fn sortm(self) -> Vec<f64>
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fn sortm(self) -> Vec<f64>
[src]Immutable sort. Returns new sorted vector, just like ‘sortf’ above but using our indexing ‘mergesort’ below.
fn mergerank(self) -> Vec<usize>
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fn mergerank(self) -> Vec<usize>
[src]Ranking of self by inverting the (merge) sort index.
Sort index is in the order of sorted items, giving their indices to the original data.
Ranking is in the order of original data, giving their positions in the sort index.
Very fast ranking of many f64 items, ranking self
with only n*(log(n)+1) complexity.
fn mergesort(self, i: usize, n: usize) -> Vec<usize>
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fn mergesort(self, i: usize, n: usize) -> Vec<usize>
[src]Recursive non-destructive merge sort. The data is read-only, it is not moved or mutated.
Returns vector of indices to self from i to i+n, such that the indexed values are in sort order.
Thus we are moving the index values instead of the actual values.