The OpenD Programming Language

KurtosisAccumulator

mir stat descriptive univariate
  1. struct KurtosisAccumulator(T, KurtosisAlgo kurtosisAlgo, Summation summation)
  2. struct KurtosisAccumulator(T, KurtosisAlgo kurtosisAlgo, Summation summation)
  3. struct KurtosisAccumulator(T, KurtosisAlgo kurtosisAlgo, Summation summation)
  4. struct KurtosisAccumulator(T, KurtosisAlgo kurtosisAlgo, Summation summation)
  5. struct KurtosisAccumulator(T, KurtosisAlgo kurtosisAlgo, Summation summation)
    struct KurtosisAccumulator (
    T
    KurtosisAlgo kurtosisAlgo
    Summation summation
    ) if (
    isMutable!T &&
    kurtosisAlgo == KurtosisAlgo.assumeZeroMean
    ) {
    struct Summator(T, Summation summation) via import mir.math.sum : Summator;;
    enum bool isIterable(T); via import std.traits : isIterable;;
    alias S = Summator!(T, summation);
    this(Range r);
    this(T x);
    void put(Range r);
    void put(T x);
    void put(KurtosisAccumulator!(U, kurtosisAlgo, sumAlgo) v);
    size_t count();
    F mean();
    F variance(bool isPopulation);
    F centeredSumOfQuarts();
    F centeredSumOfCubes();
    F centeredSumOfSquares();
    F scaledSumOfCubes(bool isPopulation);
    F scaledSumOfQuarts(bool isPopulation);
    F skewness(bool isPopulation);
    F kurtosis(bool isPopulation, bool isRaw);
    }
  6. struct KurtosisAccumulator(T, KurtosisAlgo kurtosisAlgo, Summation summation)

Constructors

this
this(Range r)
this
this(T x)

Members

Aliases

S
alias S = Summator!(T, summation)

Functions

centeredSumOfCubes
F centeredSumOfCubes()
centeredSumOfQuarts
F centeredSumOfQuarts()
centeredSumOfSquares
F centeredSumOfSquares()
count
size_t count()
kurtosis
F kurtosis(bool isPopulation, bool isRaw)
mean
F mean()
put
void put(Range r)
put
void put(T x)
put
void put(KurtosisAccumulator!(U, kurtosisAlgo, sumAlgo) v)
scaledSumOfCubes
F scaledSumOfCubes(bool isPopulation)
scaledSumOfQuarts
F scaledSumOfQuarts(bool isPopulation)
skewness
F skewness(bool isPopulation)
variance
F variance(bool isPopulation)

Examples

assumeZeroMean

import mir.math.common: approxEqual, pow;
import mir.ndslice.slice: sliced;
import mir.stat.transform: center;

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;

KurtosisAccumulator!(double, KurtosisAlgo.assumeZeroMean, Summation.naive) v;
v.put(x);
assert(v.kurtosis(true, true).approxEqual((792.784119 / 12) / pow(54.765625 / 12, 2.0)));
assert(v.kurtosis(true, false).approxEqual((792.784119 / 12) / pow(54.765625 / 12, 2.0) - 3.0));
assert(v.kurtosis(false, false).approxEqual(792.784119 / pow(54.765625 / 11, 2.0) * (12.0 * 13.0) / (11.0 * 10.0 * 9.0) - 3.0 * (11.0 * 11.0) / (10.0 * 9.0)));
assert(v.kurtosis(false, true).approxEqual(792.784119 / pow(54.765625 / 11, 2.0) * (12.0 * 13.0) / (11.0 * 10.0 * 9.0) - 3.0 * (11.0 * 11.0) / (10.0 * 9.0) + 3.0));

v.put(4.0);
assert(v.kurtosis(true, true).approxEqual((1048.784119 / 13) / pow(70.765625 / 13, 2.0)));
assert(v.kurtosis(true, false).approxEqual((1048.784119 / 13) / pow(70.765625 / 13, 2.0) - 3.0));
assert(v.kurtosis(false, false).approxEqual(1048.784119 / pow(70.765625 / 12, 2.0) * (13.0 * 14.0) / (12.0 * 11.0 * 10.0) - 3.0 * (12.0 * 12.0) / (11.0 * 10.0)));
assert(v.kurtosis(false, true).approxEqual(1048.784119 / pow(70.765625 / 12, 2.0) * (13.0 * 14.0) / (12.0 * 11.0 * 10.0) - 3.0 * (12.0 * 12.0) / (11.0 * 10.0) + 3.0));

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