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mir.math.stat

This module contains base statistical algorithms.
Note that used specialized summing algorithms execute more primitive operations than vanilla summation. Therefore, if in certain cases maximum speed is required at expense of precision, one can use Summation.fast .
License:
Apache-2.0
Authors:
Shigeki Karita (original numir code), Ilya Yaroshenko, John Michael Hall
template statType(T, bool checkComplex = true)
template meanType(T)
struct MeanAccumulator(T, Summation summation);
Output range for mean.
Examples: 
import mir.ndslice.slice: sliced;

MeanAccumulator!(double, Summation.pairwise) x;
x.put([0.0, 1, 2, 3, 4].sliced);
assert(x.mean == 2);
x.put(5);
assert(x.mean == 2.5);
size_t count;
Summator!(T, summation) summator;
const pure nothrow @nogc @property @safe F mean(F = T)();
const pure nothrow @nogc @property @safe F sum(F = T)();
void put(Range)(Range r)
if (isIterable!Range);
void put()(T x);
void put(F = T)(MeanAccumulator!(F, summation) m);
template mean(F, Summation summation = Summation.appropriate)

template mean(Summation summation = Summation.appropriate)

template mean(F, string summation)

template mean(string summation)
Computes the mean of the input.
By default, if F is not floating point type or complex type, then the result will have a double type if F is implicitly convertible to a floating point type or a type for which isComplex!F is true.
Parameters:
F controls type of output
summation algorithm for calculating sums (default: Summation.appropriate)
Returns:
The mean of all the elements in the input, must be floating point or complex type
See Also:
Summation 
Examples: 
import mir.ndslice.slice: sliced;
import mir.complex;
alias C = Complex!double;

assert(mean([1.0, 2, 3]) == 2);
assert(mean([C(1, 3), C(2), C(3)]) == C(2, 1));

assert(mean!float([0, 1, 2, 3, 4, 5].sliced(3, 2)) == 2.5);

static assert(is(typeof(mean!float([1, 2, 3])) == float));
Examples:
Mean of vector 
import mir.ndslice.slice: sliced;

auto x = [0.0, 1.0, 1.5, 2.0, 3.5, 4.25,
          2.0, 7.5, 5.0, 1.0, 1.5, 0.0].sliced;
assert(x.mean == 29.25 / 12);
Examples:
Mean of matrix 
import mir.ndslice.fuse: fuse;

auto x = [
    [0.0, 1.0, 1.5, 2.0, 3.5, 4.25],
    [2.0, 7.5, 5.0, 1.0, 1.5, 0.0]
].fuse;

assert(x.mean == 29.25 / 12);
Examples:
Column mean of matrix 
import mir.ndslice.fuse: fuse;
import mir.ndslice.topology: alongDim, byDim, map;
import mir.algorithm.iteration: all;
import mir.math.common: approxEqual;

auto x = [
    [0.0, 1.0, 1.5, 2.0, 3.5, 4.25],
    [2.0, 7.5, 5.0, 1.0, 1.5, 0.0]
].fuse;
auto result = [1, 4.25, 3.25, 1.5, 2.5, 2.125];

// Use byDim or alongDim with map to compute mean of row/column.
assert(x.byDim!1.map!mean.all!approxEqual(result));
assert(x.alongDim!0.map!mean.all!approxEqual(result));

// FIXME
// Without using map, computes the mean of the whole slice
// assert(x.byDim!1.mean == x.sliced.mean);
// assert(x.alongDim!0.mean == x.sliced.mean);
Examples:
Can also set algorithm or output type 
import mir.ndslice.slice: sliced;
import mir.ndslice.topology: repeat;

//Set sum algorithm or output type

auto a = [1, 1e100, 1, -1e100].sliced;

auto x = a * 10_000;

assert(x.mean!"kbn" == 20_000 / 4);
assert(x.mean!"kb2" == 20_000 / 4);
assert(x.mean!"precise" == 20_000 / 4);
assert(x.mean!(double, "precise") == 20_000.0 / 4);

auto y = uint.max.repeat(3);
assert(y.mean!ulong == 12884901885 / 3);
Examples:
For integral slices, pass output type as template parameter to ensure output type is correct. 
import mir.math.common: approxEqual;
import mir.ndslice.slice: sliced;

auto x = [0, 1, 1, 2, 4, 4,
          2, 7, 5, 1, 2, 0].sliced;

auto y = x.mean;
assert(y.approxEqual(29.0 / 12, 1.0e-10));
static assert(is(typeof(y) == double));

assert(x.mean!float.approxEqual(29f / 12, 1.0e-10));
Examples:
Mean works for complex numbers and other user-defined types (provided they can be converted to a floating point or complex type) 
import mir.math.common: approxEqual;
import mir.ndslice.slice: sliced;
import mir.complex;
alias C = Complex!double;

auto x = [C(1.0, 2), C(2, 3), C(3, 4), C(4, 5)].sliced;
assert(x.mean.approxEqual(C(2.5, 3.5)));
Examples:
Compute mean tensors along specified dimention of tensors 
import mir.ndslice: alongDim, iota, as, map;
/++
  [[0,1,2],
   [3,4,5]]
 +/
auto x = iota(2, 3).as!double;
assert(x.mean == (5.0 / 2.0));

auto m0 = [(0.0+3.0)/2.0, (1.0+4.0)/2.0, (2.0+5.0)/2.0];
assert(x.alongDim!0.map!mean == m0);
assert(x.alongDim!(-2).map!mean == m0);

auto m1 = [(0.0+1.0+2.0)/3.0, (3.0+4.0+5.0)/3.0];
assert(x.alongDim!1.map!mean == m1);
assert(x.alongDim!(-1).map!mean == m1);

assert(iota(2, 3, 4, 5).as!double.alongDim!0.map!mean == iota([3, 4, 5], 3 * 4 * 5 / 2));
Examples:
Arbitrary mean 
assert(mean(1.0, 2, 3) == 2);
assert(mean!float(1, 2, 3) == 2);
meanType!F mean(Range)(Range r)
if (isIterable!Range);
Parameters:
Range r range, must be finite iterable
meanType!F mean(scope const F[] ar...);
Parameters:
F[] ar values
template hmeanType(T)
template hmean(F, Summation summation = Summation.appropriate)

template hmean(Summation summation = Summation.appropriate)

template hmean(F, string summation)

template hmean(string summation)
Computes the harmonic mean of the input.
By default, if F is not floating point type or complex type, then the result will have a double type if F is implicitly convertible to a floating point type or a type for which isComplex!F is true.
Parameters:
F controls type of output
summation algorithm for calculating sums (default: Summation.appropriate)
Returns:
harmonic mean of all the elements of the input, must be floating point or complex type
See Also:
Summation 
Examples:
Harmonic mean of vector 
import mir.math.common: approxEqual;
import mir.ndslice.slice: sliced;

auto x = [20.0, 100.0, 2000.0, 10.0, 5.0, 2.0].sliced;

assert(x.hmean.approxEqual(6.97269));
Examples:
Harmonic mean of matrix 
import mir.math.common: approxEqual;
import mir.ndslice.fuse: fuse;

auto x = [
    [20.0, 100.0, 2000.0], 
    [10.0, 5.0, 2.0]
].fuse;

assert(x.hmean.approxEqual(6.97269));
Examples:
Column harmonic mean of matrix 
import mir.algorithm.iteration: all;
import mir.math.common: approxEqual;
import mir.ndslice: fuse;
import mir.ndslice.topology: alongDim, byDim, map;

auto x = [
    [20.0, 100.0, 2000.0],
    [ 10.0, 5.0, 2.0]
].fuse;

auto y = [13.33333, 9.52381, 3.996004];

// Use byDim or alongDim with map to compute mean of row/column.
assert(x.byDim!1.map!hmean.all!approxEqual(y));
assert(x.alongDim!0.map!hmean.all!approxEqual(y));
Examples:
Can also pass arguments to hmean 
import mir.math.common: approxEqual;
import mir.ndslice.topology: repeat;
import mir.ndslice.slice: sliced;

//Set sum algorithm or output type
auto x = [1, 1e-100, 1, -1e-100].sliced;

assert(x.hmean!"kb2".approxEqual(2));
assert(x.hmean!"precise".approxEqual(2));
assert(x.hmean!(double, "precise").approxEqual(2));

//Provide the summation type
assert(float.max.repeat(3).hmean!double.approxEqual(float.max));
Examples:
For integral slices, pass output type as template parameter to ensure output type is correct. 
import mir.math.common: approxEqual;
import mir.ndslice.slice: sliced;

auto x = [20, 100, 2000, 10, 5, 2].sliced;

auto y = x.hmean;

assert(y.approxEqual(6.97269));
static assert(is(typeof(y) == double));

assert(x.hmean!float.approxEqual(6.97269));
Examples:
hmean works for complex numbers and other user-defined types (provided they can be converted to a floating point or complex type) 
import mir.math.common: approxEqual;
import mir.ndslice.slice: sliced;
import mir.complex;
alias C = Complex!double;

auto x = [C(1, 2), C(2, 3), C(3, 4), C(4, 5)].sliced;
assert(x.hmean.approxEqual(C(1.97110904, 3.14849332)));
Examples:
Arbitrary harmonic mean 
import mir.math.common: approxEqual;
import mir.ndslice.slice: sliced;

auto x = hmean(20.0, 100, 2000, 10, 5, 2);
assert(x.approxEqual(6.97269));

auto y = hmean!float(20, 100, 2000, 10, 5, 2);
assert(y.approxEqual(6.97269));
hmeanType!F hmean(Range)(Range r)
if (isIterable!Range);
Parameters:
Range r range
hmeanType!F hmean(scope const F[] ar...);
Parameters:
F[] ar values
struct GMeanAccumulator(T) if (isMutable!T && isFloatingPoint!T);
Output range for gmean.
Examples: 
import mir.math.common: approxEqual;
import mir.ndslice.slice: sliced;

GMeanAccumulator!double x;
x.put([1.0, 2, 3, 4].sliced);
assert(x.gmean.approxEqual(2.21336384));
x.put(5);
assert(x.gmean.approxEqual(2.60517108));
size_t count;
ProdAccumulator!T prodAccumulator;
@property F gmean(F = T)()
if (isFloatingPoint!F);
void put(Range)(Range r)
if (isIterable!Range);
void put()(T x);
template gmeanType(T)
gmeanType!F gmean(F, Range)(Range r)
if (isFloatingPoint!F && isIterable!Range);

gmeanType!Range gmean(Range)(Range r)
if (isIterable!Range);
Computes the geometric average of the input.
By default, if F is not floating point type, then the result will have a double type if F is implicitly convertible to a floating point type.
Parameters:
Range r range, must be finite iterable
Returns:
The geometric average of all the elements in the input, must be floating point type
See Also:
prod 
gmeanType!F gmean(F)(scope const F[] ar...)
if (isFloatingPoint!F);
Parameters:
F[] ar values
Examples: 
import mir.math.common: approxEqual;
import mir.ndslice.slice: sliced;

assert(gmean([1.0, 2, 3]).approxEqual(1.81712059));

assert(gmean!float([1, 2, 3, 4, 5, 6].sliced(3, 2)).approxEqual(2.99379516));

static assert(is(typeof(gmean!float([1, 2, 3])) == float));
Examples:
Geometric mean of vector 
import mir.math.common: approxEqual;
import mir.ndslice.slice: sliced;

auto x = [3.0, 1.0, 1.5, 2.0, 3.5, 4.25,
          2.0, 7.5, 5.0, 1.0, 1.5, 2.0].sliced;

assert(x.gmean.approxEqual(2.36178395));
Examples:
Geometric mean of matrix 
import mir.math.common: approxEqual;
import mir.ndslice.fuse: fuse;

auto x = [
    [3.0, 1.0, 1.5, 2.0, 3.5, 4.25],
    [2.0, 7.5, 5.0, 1.0, 1.5, 2.0]
].fuse;

assert(x.gmean.approxEqual(2.36178395));
Examples:
Column gmean of matrix 
import mir.algorithm.iteration: all;
import mir.math.common: approxEqual;
import mir.ndslice.fuse: fuse;
import mir.ndslice.topology: alongDim, byDim, map;

auto x = [
    [3.0, 1.0, 1.5, 2.0, 3.5, 4.25],
    [2.0, 7.5, 5.0, 1.0, 1.5, 2.0]
].fuse;
auto result = [2.44948974, 2.73861278, 2.73861278, 1.41421356, 2.29128784, 2.91547594];

// Use byDim or alongDim with map to compute mean of row/column.
assert(x.byDim!1.map!gmean.all!approxEqual(result));
assert(x.alongDim!0.map!gmean.all!approxEqual(result));

// FIXME
// Without using map, computes the mean of the whole slice
// assert(x.byDim!1.gmean.all!approxEqual(result));
// assert(x.alongDim!0.gmean.all!approxEqual(result));
Examples:
Can also set output type 
import mir.math.common: approxEqual;
import mir.ndslice.slice: sliced;
import mir.ndslice.topology: repeat;

auto x = [5120.0, 7340032, 32, 3758096384].sliced;

assert(x.gmean!float.approxEqual(259281.45295212));

auto y = uint.max.repeat(2);
assert(y.gmean!float.approxEqual(cast(float) uint.max));
Examples:
For integral slices, pass output type as template parameter to ensure output type is correct. 
import mir.math.common: approxEqual;
import mir.ndslice.slice: sliced;

auto x = [5, 1, 1, 2, 4, 4,
          2, 7, 5, 1, 2, 10].sliced;

auto y = x.gmean;
static assert(is(typeof(y) == double));

assert(x.gmean!float.approxEqual(2.79160522));
Examples:
gean works for user-defined types, provided the nth root can be taken for them 
static struct Foo {
    float x;
    alias x this;
}

import mir.math.common: approxEqual;
import mir.ndslice.slice: sliced;

auto x = [Foo(1.0), Foo(2.0), Foo(3.0)].sliced;
assert(x.gmean.approxEqual(1.81712059));
Examples:
Compute gmean tensors along specified dimention of tensors 
import mir.algorithm.iteration: all;
import mir.math.common: approxEqual;
import mir.ndslice.fuse: fuse;
import mir.ndslice.topology: alongDim, iota, map;

auto x = [
    [1.0, 2, 3],
    [4.0, 5, 6]
].fuse;

assert(x.gmean.approxEqual(2.99379516));

auto result0 = [2.0, 3.16227766, 4.24264069];
assert(x.alongDim!0.map!gmean.all!approxEqual(result0));
assert(x.alongDim!(-2).map!gmean.all!approxEqual(result0));

auto result1 = [1.81712059, 4.93242414];
assert(x.alongDim!1.map!gmean.all!approxEqual(result1));
assert(x.alongDim!(-1).map!gmean.all!approxEqual(result1));

auto y = [
    [
        [1.0, 2, 3],
        [4.0, 5, 6]
    ], [
        [7.0, 8, 9],
        [10.0, 9, 10]
    ]
].fuse;

auto result3 = [
    [2.64575131, 4.0,        5.19615242],
    [6.32455532, 6.70820393, 7.74596669]
];
assert(y.alongDim!0.map!gmean.all!approxEqual(result3));
Examples:
Arbitrary gmean 
import mir.math.common: approxEqual;

assert(gmean(1.0, 2, 3).approxEqual(1.81712059));
assert(gmean!float(1, 2, 3).approxEqual(1.81712059));
template median(F, bool allowModify = false)

template median(bool allowModify = false)
Computes the median of slice.
By default, if F is not floating point type or complex type, then the result will have a double type if F is implicitly convertible to a floating point type or a type for which isComplex!F is true.
Can also pass a boolean variable, allowModify, that allows the input slice to be modified. By default, a reference-counted copy is made.
Parameters:
F output type
allowModify Allows the input slice to be modified, default is false
Returns:
the median of the slice
See Also:
mean 
@nogc meanType!F median(Iterator, size_t N, SliceKind kind)(Slice!(Iterator, N, kind) slice);
Parameters:
Slice!(Iterator, N, kind) slice slice
meanType!(T[]) median(T)(scope const T[] ar...);
Parameters:
T[] ar array
auto median(T)(T withAsSlice)
if (hasAsSlice!T);
Parameters:
T withAsSlice input that satisfies hasAsSlice
Examples:
Median of vector 
import mir.ndslice.slice: sliced;

auto x0 = [9.0, 1, 0, 2, 3, 4, 6, 8, 7, 10, 5].sliced;
assert(x0.median == 5);

auto x1 = [9.0, 1, 0, 2, 3, 4, 6, 8, 7, 10].sliced;
assert(x1.median == 5);
Examples:
Median of dynamic array 
auto x0 = [9.0, 1, 0, 2, 3, 4, 6, 8, 7, 10, 5];
assert(x0.median == 5);

auto x1 = [9.0, 1, 0, 2, 3, 4, 6, 8, 7, 10];
assert(x1.median == 5);
Examples:
Median of matrix 
import mir.ndslice.fuse: fuse;

auto x0 = [
    [9.0, 1, 0, 2,  3], 
    [4.0, 6, 8, 7, 10]
].fuse;

assert(x0.median == 5);
Examples:
Row median of matrix 
import mir.algorithm.iteration: all;
import mir.math.common: approxEqual;
import mir.ndslice.fuse: fuse;
import mir.ndslice.slice: sliced;
import mir.ndslice.topology: alongDim, byDim, map;

auto x = [
    [0.0, 1.0, 1.5, 2.0, 3.5, 4.25], 
    [2.0, 7.5, 5.0, 1.0, 1.5, 0.0]
].fuse;

auto result = [1.75, 1.75].sliced;

// Use byDim or alongDim with map to compute median of row/column.
assert(x.byDim!0.map!median.all!approxEqual(result));
assert(x.alongDim!1.map!median.all!approxEqual(result));
Examples:
Can allow original slice to be modified or set output type 
import mir.ndslice.slice: sliced;

auto x0 = [9.0, 1, 0, 2, 3, 4, 6, 8, 7, 10, 5].sliced;
assert(x0.median!true == 5);

auto x1 = [9, 1, 0, 2, 3, 4, 6, 8, 7, 10].sliced;
assert(x1.median!(float, true) == 5);
Examples:
Arbitrary median 
assert(median(0, 1, 2, 3, 4) == 2);
Examples:
For integral slices, can pass output type as template parameter to ensure output type is correct 
import mir.ndslice.slice: sliced;

auto x = [9, 1, 0, 2, 3, 4, 6, 8, 7, 10].sliced;
assert(x.median!float == 5f);

auto y = x.median;
assert(y == 5.0);
static assert(is(typeof(y) == double));
template center(alias centralTendency = mean!(Summation.appropriate))
Centers slice, which must be a finite iterable.
By default, slice is centered by the mean. A custom function may also be provided using centralTendency.
Returns:
The elements in the slice with the average subtracted from them.
Examples:
Center vector 
import mir.algorithm.iteration: all;
import mir.math.common: approxEqual;
import mir.ndslice.slice: sliced;

auto x = [1.0, 2, 3, 4, 5, 6].sliced;
assert(x.center.all!approxEqual([-2.5, -1.5, -0.5, 0.5, 1.5, 2.5]));

// Can center using different functions
assert(x.center!hmean.all!approxEqual([-1.44898, -0.44898, 0.55102, 1.55102, 2.55102, 3.55102]));
assert(x.center!gmean.all!approxEqual([-1.99379516, -0.99379516, 0.00620483, 1.00620483, 2.00620483, 3.00620483]));
assert(x.center!median.all!approxEqual([-2.5, -1.5, -0.5, 0.5, 1.5, 2.5]));
Examples:
Center dynamic array 
import mir.algorithm.iteration: all;
import mir.math.common: approxEqual;

auto x = [1.0, 2, 3, 4, 5, 6];
assert(x.center.all!approxEqual([-2.5, -1.5, -0.5, 0.5, 1.5, 2.5]));
Examples:
Center matrix 
import mir.algorithm.iteration: all;
import mir.math.common: approxEqual;
import mir.ndslice: fuse;

auto x = [
    [0.0, 1, 2], 
    [3.0, 4, 5]
].fuse;

auto y = [
    [-2.5, -1.5, -0.5], 
    [ 0.5,  1.5,  2.5]
].fuse;

assert(x.center.all!approxEqual(y));
Examples:
Column center matrix 
import mir.algorithm.iteration: all, equal;
import mir.math.common: approxEqual;
import mir.ndslice: fuse;
import mir.ndslice.topology: alongDim, byDim, map;

auto x = [
    [20.0, 100.0, 2000.0],
    [10.0,   5.0,    2.0]
].fuse;

auto result = [
    [ 5.0,  47.5,  999],
    [-5.0, -47.5, -999]
].fuse;

// Use byDim with map to compute average of row/column.
auto xCenterByDim = x.byDim!1.map!center;
auto resultByDim = result.byDim!1;
assert(xCenterByDim.equal!(equal!approxEqual)(resultByDim));

auto xCenterAlongDim = x.alongDim!0.map!center;
auto resultAlongDim = result.alongDim!0;
assert(xCenterByDim.equal!(equal!approxEqual)(resultAlongDim));
Examples:
Can also pass arguments to average function used by center 
import mir.ndslice.slice: sliced;

//Set sum algorithm or output type
auto a = [1, 1e100, 1, -1e100];

auto x = a.sliced * 10_000;

//Due to Floating Point precision, subtracting the mean from the second
//and fourth numbers in `x` does not change the value of the result
auto result = [5000, 1e104, 5000, -1e104].sliced;

assert(x.center!(mean!"kbn") == result);
assert(x.center!(mean!"kb2") == result);
assert(x.center!(mean!"precise") == result);
Examples:
Passing a centered input to variance or standardDeviation with the assumeZeroMean algorithm is equivalent to calculating variance or standardDeviation on the original input. 
import mir.algorithm.iteration: all;
import mir.math.common: approxEqual;
import mir.ndslice.slice: sliced;

auto x = [1.0, 2, 3, 4, 5, 6].sliced;
assert(x.center.variance!"assumeZeroMean".approxEqual(x.variance));
assert(x.center.standardDeviation!"assumeZeroMean".approxEqual(x.standardDeviation));
auto center(Iterator, size_t N, SliceKind kind)(Slice!(Iterator, N, kind) slice);

auto center(T)(T[] array);

auto center(T)(T withAsSlice)
if (hasAsSlice!T);
Parameters:
Slice!(Iterator, N, kind) slice slice
struct MapSummator(alias fun, T, Summation summation) if (isMutable!T);
Output range that applies function fun to each input before summing
Examples: 
import mir.math.common: powi;
import mir.ndslice.slice: sliced;

alias f = (double x) => (powi(x, 2));
MapSummator!(f, double, Summation.pairwise) x;
x.put([0.0, 1, 2, 3, 4].sliced);
assert(x.sum == 30.0);
x.put(5);
assert(x.sum == 55.0);
Summator!(T, summation) summator;
@property F sum(F = T)();
void put(Range)(Range r)
if (isIterable!Range);
void put()(T x);
enum VarianceAlgo: int;
Variance algorithms.
See Also: Algorithms for calculating variance.
online
Performs Welford's online algorithm for updating variance. Can also put another VarianceAccumulator of the same type, which uses the parallel algorithm from Chan et al., described above.
naive
Calculates variance using E(x^^2) - E(x)^2 (alowing for adjustments for population/sample variance). This algorithm can be numerically unstable.
twoPass
Calculates variance using a two-pass algorithm whereby the input is first centered and then the sum of squares is calculated from that.
assumeZeroMean
Calculates variance assuming the mean of the dataseries is zero.
struct VarianceAccumulator(T, VarianceAlgo varianceAlgo, Summation summation) if (isMutable!T && (varianceAlgo == VarianceAlgo.naive));
Examples: 
import mir.math.common: approxEqual;
import mir.ndslice.slice: sliced;

auto x = [0.0, 1.0, 1.5, 2.0, 3.5, 4.25,
          2.0, 7.5, 5.0, 1.0, 1.5, 0.0].sliced;

enum PopulationTrueCT = true;
enum PopulationFalseCT = false;
bool PopulationTrueRT = true;
bool PopulationFalseRT = false;

VarianceAccumulator!(double, VarianceAlgo.naive, Summation.naive) v;
v.put(x);
assert(v.variance(PopulationTrueRT).approxEqual(54.76562 / 12));
assert(v.variance(PopulationTrueCT).approxEqual(54.76562 / 12));
assert(v.variance(PopulationFalseRT).approxEqual(54.76562 / 11));
assert(v.variance(PopulationFalseCT).approxEqual(54.76562 / 11));

v.put(4.0);
assert(v.variance(PopulationTrueRT).approxEqual(57.01923 / 13));
assert(v.variance(PopulationTrueCT).approxEqual(57.01923 / 13));
assert(v.variance(PopulationFalseRT).approxEqual(57.01923 / 12));
assert(v.variance(PopulationFalseCT).approxEqual(57.01923 / 12));
this(Range)(Range r)
if (isIterable!Range);
this()(T x);
MeanAccumulator!(T, summation) meanAccumulator;
@property size_t count();
@property F mean(F = T)();
Summator!(T, summation) sumOfSquares;
void put(Range)(Range r)
if (isIterable!Range);
void put()(T x);
@property F variance(F = T)(bool isPopulation);
struct VarianceAccumulator(T, VarianceAlgo varianceAlgo, Summation summation) if (isMutable!T && (varianceAlgo == VarianceAlgo.online));
Examples: 
import mir.math.common: approxEqual;
import mir.ndslice.slice: sliced;

auto x = [0.0, 1.0, 1.5, 2.0, 3.5, 4.25,
          2.0, 7.5, 5.0, 1.0, 1.5, 0.0].sliced;

enum PopulationTrueCT = true;
enum PopulationFalseCT = false;
bool PopulationTrueRT = true;
bool PopulationFalseRT = false;

VarianceAccumulator!(double, VarianceAlgo.online, Summation.naive) v;
v.put(x);

assert(v.variance(PopulationTrueRT).approxEqual(54.76562 / 12));
assert(v.variance(PopulationTrueCT).approxEqual(54.76562 / 12));
assert(v.variance(PopulationFalseRT).approxEqual(54.76562 / 11));
assert(v.variance(PopulationFalseCT).approxEqual(54.76562 / 11));

v.put(4.0);
assert(v.variance(PopulationTrueRT).approxEqual(57.01923 / 13));
assert(v.variance(PopulationTrueCT).approxEqual(57.01923 / 13));
assert(v.variance(PopulationFalseRT).approxEqual(57.01923 / 12));
assert(v.variance(PopulationFalseCT).approxEqual(57.01923 / 12));
Examples: 
import mir.math.common: approxEqual;
import mir.ndslice.slice: sliced;

auto x = [0.0, 1.0, 1.5, 2.0, 3.5, 4.25].sliced;
auto y = [2.0, 7.5, 5.0, 1.0, 1.5, 0.0].sliced;

enum PopulationTrueCT = true;
enum PopulationFalseCT = false;
bool PopulationTrueRT = true;
bool PopulationFalseRT = false;

VarianceAccumulator!(double, VarianceAlgo.online, Summation.naive) v;
v.put(x);
assert(v.variance(PopulationTrueRT).approxEqual(12.55208 / 6));
assert(v.variance(PopulationTrueCT).approxEqual(12.55208 / 6));
assert(v.variance(PopulationFalseRT).approxEqual(12.55208 / 5));
assert(v.variance(PopulationFalseCT).approxEqual(12.55208 / 5));

v.put(y);
assert(v.variance(PopulationTrueRT).approxEqual(54.76562 / 12));
assert(v.variance(PopulationTrueCT).approxEqual(54.76562 / 12));
assert(v.variance(PopulationFalseRT).approxEqual(54.76562 / 11));
assert(v.variance(PopulationFalseCT).approxEqual(54.76562 / 11));
this(Range)(Range r)
if (isIterable!Range);
this()(T x);
MeanAccumulator!(T, summation) meanAccumulator;
@property size_t count();
@property F mean(F = T)();
Summator!(T, summation) centeredSumOfSquares;
void put(Range)(Range r)
if (isIterable!Range);
void put()(T x);
void put()(VarianceAccumulator!(T, varianceAlgo, summation) v);
@property F variance(F = T)(bool isPopulation);
struct VarianceAccumulator(T, VarianceAlgo varianceAlgo, Summation summation) if (isMutable!T && (varianceAlgo == VarianceAlgo.twoPass));
Examples: 
import mir.math.common: approxEqual;
import mir.ndslice.slice: sliced;

auto x = [0.0, 1.0, 1.5, 2.0, 3.5, 4.25,
          2.0, 7.5, 5.0, 1.0, 1.5, 0.0].sliced;

enum PopulationTrueCT = true;
enum PopulationFalseCT = false;
bool PopulationTrueRT = true;
bool PopulationFalseRT = false;

auto v = VarianceAccumulator!(double, VarianceAlgo.twoPass, Summation.naive)(x);
assert(v.variance(PopulationTrueRT).approxEqual(54.76562 / 12));
assert(v.variance(PopulationTrueCT).approxEqual(54.76562 / 12));
assert(v.variance(PopulationFalseRT).approxEqual(54.76562 / 11));
assert(v.variance(PopulationFalseCT).approxEqual(54.76562 / 11));
MeanAccumulator!(T, summation) meanAccumulator;
@property size_t count();
@property F mean(F = T)();
Summator!(T, summation) centeredSumOfSquares;
this(Iterator, size_t N, SliceKind kind)(Slice!(Iterator, N, kind) slice);
this(U)(U[] array);
this(T)(T withAsSlice)
if (hasAsSlice!T);
this()(T x);
@property F variance(F = T)(bool isPopulation);
struct VarianceAccumulator(T, VarianceAlgo varianceAlgo, Summation summation) if (isMutable!T && (varianceAlgo == VarianceAlgo.assumeZeroMean));
Examples: 
import mir.math.common: approxEqual;
import mir.ndslice.slice: sliced;

auto a = [0.0, 1.0, 1.5, 2.0, 3.5, 4.25,
          2.0, 7.5, 5.0, 1.0, 1.5, 0.0].sliced;
auto x = a.center;

enum PopulationTrueCT = true;
enum PopulationFalseCT = false;
bool PopulationTrueRT = true;
bool PopulationFalseRT = false;

VarianceAccumulator!(double, VarianceAlgo.assumeZeroMean, Summation.naive) v;
v.put(x);

assert(v.variance(PopulationTrueRT).approxEqual(54.76562 / 12));
assert(v.variance(PopulationTrueCT).approxEqual(54.76562 / 12));
assert(v.variance(PopulationFalseRT).approxEqual(54.76562 / 11));
assert(v.variance(PopulationFalseCT).approxEqual(54.76562 / 11));

v.put(4.0);
assert(v.variance(PopulationTrueRT).approxEqual(70.76562 / 13));
assert(v.variance(PopulationTrueCT).approxEqual(70.76562 / 13));
assert(v.variance(PopulationFalseRT).approxEqual(70.76562 / 12));
assert(v.variance(PopulationFalseCT).approxEqual(70.76562 / 12));
Examples: 
import mir.math.common: approxEqual;
import mir.ndslice.slice: sliced;

auto a = [0.0, 1.0, 1.5, 2.0, 3.5, 4.25,
          2.0, 7.5, 5.0, 1.0, 1.5, 0.0].sliced;
auto b = a.center;
auto x = b[0 .. 6];
auto y = b[6 .. $];

enum PopulationTrueCT = true;
enum PopulationFalseCT = false;
bool PopulationTrueRT = true;
bool PopulationFalseRT = false;

VarianceAccumulator!(double, VarianceAlgo.assumeZeroMean, Summation.naive) v;
v.put(x);
assert(v.variance(PopulationTrueRT).approxEqual(13.492188 / 6));
assert(v.variance(PopulationTrueCT).approxEqual(13.492188 / 6));
assert(v.variance(PopulationFalseRT).approxEqual(13.492188 / 5));
assert(v.variance(PopulationFalseCT).approxEqual(13.492188 / 5));

v.put(y);
assert(v.variance(PopulationTrueRT).approxEqual(54.76562 / 12));
assert(v.variance(PopulationTrueCT).approxEqual(54.76562 / 12));
assert(v.variance(PopulationFalseRT).approxEqual(54.76562 / 11));
assert(v.variance(PopulationFalseCT).approxEqual(54.76562 / 11));
@property size_t count();
@property F mean(F = T)();
Summator!(T, summation) centeredSumOfSquares;
this(Range)(Range r)
if (isIterable!Range);
this()(T x);
void put(Range)(Range r)
if (isIterable!Range);
void put()(T x);
void put()(VarianceAccumulator!(T, varianceAlgo, summation) v);
@property F variance(F = T)(bool isPopulation);
template variance(F, VarianceAlgo varianceAlgo = VarianceAlgo.online, Summation summation = Summation.appropriate)

template variance(VarianceAlgo varianceAlgo = VarianceAlgo.online, Summation summation = Summation.appropriate)

template variance(F, string varianceAlgo, string summation = "appropriate")

template variance(string varianceAlgo, string summation = "appropriate")
Calculates the variance of the input
By default, if F is not floating point type or complex type, then the result will have a double type if F is implicitly convertible to a floating point type or a type for which isComplex!F is true.
Parameters:
F controls type of output
varianceAlgo algorithm for calculating variance (default: VarianceAlgo.online)
summation algorithm for calculating sums (default: Summation.appropriate)
Returns:
The variance of the input, must be floating point or complex type
Examples: 
import mir.math.common: approxEqual;
import mir.ndslice.slice: sliced;
import mir.complex;
alias C = Complex!double;

assert(variance([1.0, 2, 3]).approxEqual(2.0 / 2));
assert(variance([1.0, 2, 3], true).approxEqual(2.0 / 3));

assert(variance([C(1, 3), C(2), C(3)]).approxEqual(C(-4, -6) / 2));

assert(variance!float([0, 1, 2, 3, 4, 5].sliced(3, 2)).approxEqual(17.5 / 5));

static assert(is(typeof(variance!float([1, 2, 3])) == float));
Examples:
Variance of vector 
import mir.math.common: approxEqual;
import mir.ndslice.slice: sliced;

auto x = [0.0, 1.0, 1.5, 2.0, 3.5, 4.25,
          2.0, 7.5, 5.0, 1.0, 1.5, 0.0].sliced;

assert(x.variance.approxEqual(54.76562 / 11));
Examples:
Variance of matrix 
import mir.math.common: approxEqual;
import mir.ndslice.fuse: fuse;

auto x = [
    [0.0, 1.0, 1.5, 2.0, 3.5, 4.25],
    [2.0, 7.5, 5.0, 1.0, 1.5, 0.0]
].fuse;

assert(x.variance.approxEqual(54.76562 / 11));
Examples:
Column variance of matrix 
import mir.algorithm.iteration: all;
import mir.math.common: approxEqual;
import mir.ndslice.fuse: fuse;
import mir.ndslice.topology: alongDim, byDim, map;

auto x = [
    [0.0,  1.0, 1.5, 2.0], 
    [3.5, 4.25, 2.0, 7.5],
    [5.0,  1.0, 1.5, 0.0]
].fuse;
auto result = [13.16667 / 2, 7.041667 / 2, 0.1666667 / 2, 30.16667 / 2];

// Use byDim or alongDim with map to compute variance of row/column.
assert(x.byDim!1.map!variance.all!approxEqual(result));
assert(x.alongDim!0.map!variance.all!approxEqual(result));

// FIXME
// Without using map, computes the variance of the whole slice
// assert(x.byDim!1.variance == x.sliced.variance);
// assert(x.alongDim!0.variance == x.sliced.variance);
Examples:
Can also set algorithm type 
import mir.math.common: approxEqual;
import mir.ndslice.slice: sliced;

auto a = [0.0, 1.0, 1.5, 2.0, 3.5, 4.25,
          2.0, 7.5, 5.0, 1.0, 1.5, 0.0].sliced;

auto x = a + 1_000_000_000;

auto y = x.variance;
assert(y.approxEqual(54.76562 / 11));

// The naive algorithm is numerically unstable in this case
auto z0 = x.variance!"naive";
assert(!z0.approxEqual(y));

// But the two-pass algorithm provides a consistent answer
auto z1 = x.variance!"twoPass";
assert(z1.approxEqual(y));

// And the assumeZeroMean algorithm is way off
auto z2 = x.variance!"assumeZeroMean";
assert(z2.approxEqual(1.2e19 / 11));
Examples:
Can also set algorithm or output type 
import mir.math.common: approxEqual;
import mir.ndslice.slice: sliced;
import mir.ndslice.topology: repeat;

//Set population variance, variance algorithm, sum algorithm or output type

auto a = [1.0, 1e100, 1, -1e100].sliced;
auto x = a * 10_000;

bool populationTrueRT = true;
bool populationFalseRT = false;
enum PopulationTrueCT = true;

/++
Due to Floating Point precision, when centering `x`, subtracting the mean 
from the second and fourth numbers has no effect. Further, after centering 
and squaring `x`, the first and third numbers in the slice have precision 
too low to be included in the centered sum of squares. 
+/
assert(x.variance(populationFalseRT).approxEqual(2.0e208 / 3));
assert(x.variance(populationTrueRT).approxEqual(2.0e208 / 4));
assert(x.variance(PopulationTrueCT).approxEqual(2.0e208 / 4));

assert(x.variance!("online").approxEqual(2.0e208 / 3));
assert(x.variance!("online", "kbn").approxEqual(2.0e208 / 3));
assert(x.variance!("online", "kb2").approxEqual(2.0e208 / 3));
assert(x.variance!("online", "precise").approxEqual(2.0e208 / 3));
assert(x.variance!(double, "online", "precise").approxEqual(2.0e208 / 3));
assert(x.variance!(double, "online", "precise")(populationTrueRT).approxEqual(2.0e208 / 4));

auto y = uint.max.repeat(3);
auto z = y.variance!ulong;
assert(z == 0.0);
static assert(is(typeof(z) == double));
Examples:
For integral slices, pass output type as template parameter to ensure output type is correct. 
import mir.math.common: approxEqual;
import mir.ndslice.slice: sliced;

auto x = [0, 1, 1, 2, 4, 4,
          2, 7, 5, 1, 2, 0].sliced;

auto y = x.variance;
assert(y.approxEqual(50.91667 / 11));
static assert(is(typeof(y) == double));

assert(x.variance!float.approxEqual(50.91667 / 11));
Examples:
Variance works for complex numbers and other user-defined types (provided they can be converted to a floating point or complex type) 
import mir.math.common: approxEqual;
import mir.ndslice.slice: sliced;
import mir.complex;
alias C = Complex!double;

auto x = [C(1, 2), C(2, 3), C(3, 4), C(4, 5)].sliced;
assert(x.variance.approxEqual((C(0, 10)) / 3));
Examples:
Compute variance along specified dimention of tensors 
import mir.algorithm.iteration: all;
import mir.math.common: approxEqual;
import mir.ndslice.fuse: fuse;
import mir.ndslice.topology: as, iota, alongDim, map, repeat;

auto x = [
    [0.0, 1, 2],
    [3.0, 4, 5]
].fuse;

assert(x.variance.approxEqual(17.5 / 5));

auto m0 = [4.5, 4.5, 4.5];
assert(x.alongDim!0.map!variance.all!approxEqual(m0));
assert(x.alongDim!(-2).map!variance.all!approxEqual(m0));

auto m1 = [1.0, 1.0];
assert(x.alongDim!1.map!variance.all!approxEqual(m1));
assert(x.alongDim!(-1).map!variance.all!approxEqual(m1));

assert(iota(2, 3, 4, 5).as!double.alongDim!0.map!variance.all!approxEqual(repeat(3600.0 / 2, 3, 4, 5)));
Examples:
Arbitrary variance 
assert(variance(1.0, 2, 3) == 1.0);
assert(variance!float(1, 2, 3) == 1f);
meanType!F variance(Range)(Range r, bool isPopulation = false)
if (isIterable!Range);
Parameters:
Range r range, must be finite iterable
bool isPopulation true if population variance, false if sample variance (default)
meanType!F variance(scope const F[] ar...);
Parameters:
F[] ar values
template stdevType(T)
template standardDeviation(F, VarianceAlgo varianceAlgo = VarianceAlgo.online, Summation summation = Summation.appropriate)

template standardDeviation(VarianceAlgo varianceAlgo = VarianceAlgo.online, Summation summation = Summation.appropriate)

template standardDeviation(F, string varianceAlgo, string summation = "appropriate")

template standardDeviation(string varianceAlgo, string summation = "appropriate")
Calculates the standard deviation of the input
By default, if F is not floating point type, then the result will have a double type if F is implicitly convertible to a floating point type.
Parameters:
F controls type of output
varianceAlgo algorithm for calculating variance (default: VarianceAlgo.online)
summation algorithm for calculating sums (default: Summation.appropriate)
Returns:
The standard deviation of the input, must be floating point type type
Examples: 
import mir.math.common: approxEqual, sqrt;
import mir.ndslice.slice: sliced;

assert(standardDeviation([1.0, 2, 3]).approxEqual(sqrt(2.0 / 2)));
assert(standardDeviation([1.0, 2, 3], true).approxEqual(sqrt(2.0 / 3)));

assert(standardDeviation!float([0, 1, 2, 3, 4, 5].sliced(3, 2)).approxEqual(sqrt(17.5 / 5)));

static assert(is(typeof(standardDeviation!float([1, 2, 3])) == float));
Examples:
Standard deviation of vector 
import mir.math.common: approxEqual, sqrt;
import mir.ndslice.slice: sliced;

auto x = [0.0, 1.0, 1.5, 2.0, 3.5, 4.25,
          2.0, 7.5, 5.0, 1.0, 1.5, 0.0].sliced;

assert(x.standardDeviation.approxEqual(sqrt(54.76562 / 11)));
Examples:
Standard deviation of matrix 
import mir.math.common: approxEqual, sqrt;
import mir.ndslice.fuse: fuse;

auto x = [
    [0.0, 1.0, 1.5, 2.0, 3.5, 4.25],
    [2.0, 7.5, 5.0, 1.0, 1.5, 0.0]
].fuse;

assert(x.standardDeviation.approxEqual(sqrt(54.76562 / 11)));
Examples:
Column standard deviation of matrix 
import mir.algorithm.iteration: all;
import mir.math.common: approxEqual, sqrt;
import mir.ndslice.fuse: fuse;
import mir.ndslice.topology: alongDim, byDim, map;

auto x = [
    [0.0,  1.0, 1.5, 2.0], 
    [3.5, 4.25, 2.0, 7.5],
    [5.0,  1.0, 1.5, 0.0]
].fuse;
auto result = [13.16667 / 2, 7.041667 / 2, 0.1666667 / 2, 30.16667 / 2].map!sqrt;

// Use byDim or alongDim with map to compute standardDeviation of row/column.
assert(x.byDim!1.map!standardDeviation.all!approxEqual(result));
assert(x.alongDim!0.map!standardDeviation.all!approxEqual(result));

// FIXME
// Without using map, computes the standardDeviation of the whole slice
// assert(x.byDim!1.standardDeviation == x.sliced.standardDeviation);
// assert(x.alongDim!0.standardDeviation == x.sliced.standardDeviation);
Examples:
Can also set algorithm type 
import mir.math.common: approxEqual, sqrt;
import mir.ndslice.slice: sliced;

auto a = [0.0, 1.0, 1.5, 2.0, 3.5, 4.25,
          2.0, 7.5, 5.0, 1.0, 1.5, 0.0].sliced;

auto x = a + 1_000_000_000;

auto y = x.standardDeviation;
assert(y.approxEqual(sqrt(54.76562 / 11)));

// The naive algorithm is numerically unstable in this case
auto z0 = x.standardDeviation!"naive";
assert(!z0.approxEqual(y));

// But the two-pass algorithm provides a consistent answer
auto z1 = x.standardDeviation!"twoPass";
assert(z1.approxEqual(y));
Examples:
Can also set algorithm or output type 
import mir.math.common: approxEqual, sqrt;
import mir.ndslice.slice: sliced;
import mir.ndslice.topology: repeat;

//Set population standard deviation, standardDeviation algorithm, sum algorithm or output type

auto a = [1.0, 1e100, 1, -1e100].sliced;
auto x = a * 10_000;

bool populationTrueRT = true;
bool populationFalseRT = false;
enum PopulationTrueCT = true;

/++
Due to Floating Point precision, when centering `x`, subtracting the mean 
from the second and fourth numbers has no effect. Further, after centering 
and squaring `x`, the first and third numbers in the slice have precision 
too low to be included in the centered sum of squares. 
+/
assert(x.standardDeviation(populationFalseRT).approxEqual(sqrt(2.0e208 / 3)));
assert(x.standardDeviation(populationTrueRT).approxEqual(sqrt(2.0e208 / 4)));
assert(x.standardDeviation(PopulationTrueCT).approxEqual(sqrt(2.0e208 / 4)));

assert(x.standardDeviation!("online").approxEqual(sqrt(2.0e208 / 3)));
assert(x.standardDeviation!("online", "kbn").approxEqual(sqrt(2.0e208 / 3)));
assert(x.standardDeviation!("online", "kb2").approxEqual(sqrt(2.0e208 / 3)));
assert(x.standardDeviation!("online", "precise").approxEqual(sqrt(2.0e208 / 3)));
assert(x.standardDeviation!(double, "online", "precise").approxEqual(sqrt(2.0e208 / 3)));
assert(x.standardDeviation!(double, "online", "precise")(populationTrueRT).approxEqual(sqrt(2.0e208 / 4)));

auto y = uint.max.repeat(3);
auto z = y.standardDeviation!ulong;
assert(z == 0.0);
static assert(is(typeof(z) == double));
Examples:
For integral slices, pass output type as template parameter to ensure output type is correct. 
import mir.math.common: approxEqual, sqrt;
import mir.ndslice.slice: sliced;

auto x = [0, 1, 1, 2, 4, 4,
          2, 7, 5, 1, 2, 0].sliced;

auto y = x.standardDeviation;
assert(y.approxEqual(sqrt(50.91667 / 11)));
static assert(is(typeof(y) == double));

assert(x.standardDeviation!float.approxEqual(sqrt(50.91667 / 11)));
Examples:
Variance works for other user-defined types (provided they can be converted to a floating point) 
import mir.ndslice.slice: sliced;

static struct Foo {
    float x;
    alias x this;
}

Foo[] foo = [Foo(1f), Foo(2f), Foo(3f)];
assert(foo.standardDeviation == 1f);
Examples:
Compute standard deviation along specified dimention of tensors 
import mir.algorithm.iteration: all;
import mir.math.common: approxEqual, sqrt;
import mir.ndslice.fuse: fuse;
import mir.ndslice.topology: as, iota, alongDim, map, repeat;

auto x = [
    [0.0, 1, 2],
    [3.0, 4, 5]
].fuse;

assert(x.standardDeviation.approxEqual(sqrt(17.5 / 5)));

auto m0 = repeat(sqrt(4.5), 3);
assert(x.alongDim!0.map!standardDeviation.all!approxEqual(m0));
assert(x.alongDim!(-2).map!standardDeviation.all!approxEqual(m0));

auto m1 = [1.0, 1.0];
assert(x.alongDim!1.map!standardDeviation.all!approxEqual(m1));
assert(x.alongDim!(-1).map!standardDeviation.all!approxEqual(m1));

assert(iota(2, 3, 4, 5).as!double.alongDim!0.map!standardDeviation.all!approxEqual(repeat(sqrt(3600.0 / 2), 3, 4, 5)));
Examples:
Arbitrary standard deviation 
import mir.math.common: sqrt;

assert(standardDeviation(1.0, 2, 3) == 1.0);
assert(standardDeviation!float(1, 2, 3) == 1f);
stdevType!F standardDeviation(Range)(Range r, bool isPopulation = false)
if (isIterable!Range);
Parameters:
Range r range, must be finite iterable
bool isPopulation true if population standard deviation, false if sample standard deviation (default)
stdevType!F standardDeviation(scope const F[] ar...);
Parameters:
F[] ar values
template simpleLinearRegression(Summation summation = Summation.kbn)

template simpleLinearRegression(string summation)
A linear regression model with a single explanatory variable.
Examples: 
import mir.math.common: approxEqual;
static immutable x = [0, 1, 2, 3];
static immutable y = [-1, 0.2, 0.9, 2.1];
auto params = x.simpleLinearRegression(y);
assert(params[0].approxEqual(-0.95)); // shift
assert(params[1].approxEqual(1)); // slope
@safe sumType!YRange[2] simpleLinearRegression(XRange, YRange)(XRange x, YRange y)
if (isInputRange!XRange && isInputRange!YRange && !(isArray!XRange && isArray!YRange) && isFloatingPoint!(sumType!YRange));

@safe sumType!(Y[])[2] simpleLinearRegression(X, Y)(scope const X[] x, scope const Y[] y);
Parameters:
XRange x x[i] points
YRange y f(x[i]) values
Returns:
The pair of shift and slope of the linear curve.