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{ containsCollections, deepForEach, reduce } from '../../utils/collection';\nimport { arraySize } from '../../utils/array';\nimport { factory } from '../../utils/factory';\nimport { improveErrorMessage } from './utils/improveErrorMessage';\nvar name = 'mean';\nvar dependencies = ['typed', 'add', 'divide'];\nexport var createMean = /* #__PURE__ */factory(name, dependencies, function (_ref) {\n  var typed = _ref.typed,\n      add = _ref.add,\n      divide = _ref.divide;\n\n  /**\n   * Compute the mean value of matrix or a list with values.\n   * In case of a multi dimensional array, the mean of the flattened array\n   * will be calculated. When `dim` is provided, the maximum over the selected\n   * dimension will be calculated. Parameter `dim` is zero-based.\n   *\n   * Syntax:\n   *\n   *     math.mean(a, b, c, ...)\n   *     math.mean(A)\n   *     math.mean(A, dim)\n   *\n   * Examples:\n   *\n   *     math.mean(2, 1, 4, 3)                     // returns 2.5\n   *     math.mean([1, 2.7, 3.2, 4])               // returns 2.725\n   *\n   *     math.mean([[2, 5], [6, 3], [1, 7]], 0)    // returns [3, 5]\n   *     math.mean([[2, 5], [6, 3], [1, 7]], 1)    // returns [3.5, 4.5, 4]\n   *\n   * See also:\n   *\n   *     median, min, max, sum, prod, std, variance\n   *\n   * @param {... *} args  A single matrix or or multiple scalar values\n   * @return {*} The mean of all values\n   */\n  return typed(name, {\n    // mean([a, b, c, d, ...])\n    'Array | Matrix': _mean,\n    // mean([a, b, c, d, ...], dim)\n    'Array | Matrix, number | BigNumber': _nmeanDim,\n    // mean(a, b, c, d, ...)\n    '...': function _(args) {\n      if (containsCollections(args)) {\n        throw new TypeError('Scalar values expected in function mean');\n      }\n\n      return _mean(args);\n    }\n  });\n  /**\n   * Calculate the mean value in an n-dimensional array, returning a\n   * n-1 dimensional array\n   * @param {Array} array\n   * @param {number} dim\n   * @return {number} mean\n   * @private\n   */\n\n  function _nmeanDim(array, dim) {\n    try {\n      var sum = reduce(array, dim, add);\n      var s = Array.isArray(array) ? arraySize(array) : array.size();\n      return divide(sum, s[dim]);\n    } catch (err) {\n      throw improveErrorMessage(err, 'mean');\n    }\n  }\n  /**\n   * Recursively calculate the mean value in an n-dimensional array\n   * @param {Array} array\n   * @return {number} mean\n   * @private\n   */\n\n\n  function _mean(array) {\n    var sum;\n    var num = 0;\n    deepForEach(array, function (value) {\n      try {\n        sum = sum === undefined ? value : add(sum, value);\n        num++;\n      } catch (err) {\n        throw improveErrorMessage(err, 'mean', value);\n      }\n    });\n\n    if (num === 0) {\n      throw new Error('Cannot calculate the mean of an empty array');\n    }\n\n    return divide(sum, num);\n  }\n});"},"lineCount":null}},"error":null,"hash":"ad28dee680c32362cd2f4f895f57e82a","cacheData":{"env":{}}}