jiangchengfeiyi-xiaochengxu/node_modules/mathjs/lib/cjs/function/statistics/median.js
2025-01-02 11:13:50 +08:00

114 lines
3.4 KiB
JavaScript

"use strict";
Object.defineProperty(exports, "__esModule", {
value: true
});
exports.createMedian = void 0;
var _collection = require("../../utils/collection.js");
var _array = require("../../utils/array.js");
var _factory = require("../../utils/factory.js");
var _improveErrorMessage = require("./utils/improveErrorMessage.js");
const name = 'median';
const dependencies = ['typed', 'add', 'divide', 'compare', 'partitionSelect'];
const createMedian = exports.createMedian = /* #__PURE__ */(0, _factory.factory)(name, dependencies, _ref => {
let {
typed,
add,
divide,
compare,
partitionSelect
} = _ref;
/**
* Recursively calculate the median of an n-dimensional array
* @param {Array} array
* @return {Number} median
* @private
*/
function _median(array) {
try {
array = (0, _array.flatten)(array.valueOf());
const num = array.length;
if (num === 0) {
throw new Error('Cannot calculate median of an empty array');
}
if (num % 2 === 0) {
// even: return the average of the two middle values
const mid = num / 2 - 1;
const right = partitionSelect(array, mid + 1);
// array now partitioned at mid + 1, take max of left part
let left = array[mid];
for (let i = 0; i < mid; ++i) {
if (compare(array[i], left) > 0) {
left = array[i];
}
}
return middle2(left, right);
} else {
// odd: return the middle value
const m = partitionSelect(array, (num - 1) / 2);
return middle(m);
}
} catch (err) {
throw (0, _improveErrorMessage.improveErrorMessage)(err, 'median');
}
}
// helper function to type check the middle value of the array
const middle = typed({
'number | BigNumber | Complex | Unit': function (value) {
return value;
}
});
// helper function to type check the two middle value of the array
const middle2 = typed({
'number | BigNumber | Complex | Unit, number | BigNumber | Complex | Unit': function (left, right) {
return divide(add(left, right), 2);
}
});
/**
* Compute the median of a matrix or a list with values. The values are
* sorted and the middle value is returned. In case of an even number of
* values, the average of the two middle values is returned.
* Supported types of values are: Number, BigNumber, Unit
*
* In case of a (multi dimensional) array or matrix, the median of all
* elements will be calculated.
*
* Syntax:
*
* math.median(a, b, c, ...)
* math.median(A)
*
* Examples:
*
* math.median(5, 2, 7) // returns 5
* math.median([3, -1, 5, 7]) // returns 4
*
* See also:
*
* mean, min, max, sum, prod, std, variance, quantileSeq
*
* @param {... *} args A single matrix or or multiple scalar values
* @return {*} The median
*/
return typed(name, {
// median([a, b, c, d, ...])
'Array | Matrix': _median,
// median([a, b, c, d, ...], dim)
'Array | Matrix, number | BigNumber': function (array, dim) {
// TODO: implement median(A, dim)
throw new Error('median(A, dim) is not yet supported');
// return reduce(arguments[0], arguments[1], ...)
},
// median(a, b, c, d, ...)
'...': function (args) {
if ((0, _collection.containsCollections)(args)) {
throw new TypeError('Scalar values expected in function median');
}
return _median(args);
}
});
});