feat:node-modules

node_modules/mathjs/lib/cjs/function/statistics/cumsum.js

@@ -0,0 +1,129 @@
+"use strict";
+
+Object.defineProperty(exports, "__esModule", {
+  value: true
+});
+exports.createCumSum = void 0;
+var _collection = require("../../utils/collection.js");
+var _factory = require("../../utils/factory.js");
+var _switch2 = require("../../utils/switch.js");
+var _improveErrorMessage = require("./utils/improveErrorMessage.js");
+var _array = require("../../utils/array.js");
+var _IndexError = require("../../error/IndexError.js");
+const name = 'cumsum';
+const dependencies = ['typed', 'add', 'unaryPlus'];
+const createCumSum = exports.createCumSum = /* #__PURE__ */(0, _factory.factory)(name, dependencies, _ref => {
+  let {
+    typed,
+    add,
+    unaryPlus
+  } = _ref;
+  /**
+   * Compute the cumulative sum of a matrix or a list with values.
+   * In case of a (multi dimensional) array or matrix, the cumulative sums
+   * along a specified dimension (defaulting to the first) will be calculated.
+   *
+   * Syntax:
+   *
+   *     math.cumsum(a, b, c, ...)
+   *     math.cumsum(A)
+   *
+   * Examples:
+   *
+   *     math.cumsum(2, 1, 4, 3)               // returns [2, 3, 7, 10]
+   *     math.cumsum([2, 1, 4, 3])             // returns [2, 3, 7, 10]
+   *     math.cumsum([[1, 2], [3, 4]])         // returns [[1, 2], [4, 6]]
+   *     math.cumsum([[1, 2], [3, 4]], 0)      // returns [[1, 2], [4, 6]]
+   *     math.cumsum([[1, 2], [3, 4]], 1)      // returns [[1, 3], [3, 7]]
+   *     math.cumsum([[2, 5], [4, 3], [1, 7]]) // returns [[2, 5], [6, 8], [7, 15]]
+   *
+   * See also:
+   *
+   *    mean, median, min, max, prod, std, variance, sum
+   *
+   * @param {... *} args  A single matrix or or multiple scalar values
+   * @return {*} The cumulative sum of all values
+   */
+  return typed(name, {
+    // sum([a, b, c, d, ...])
+    Array: _cumsum,
+    Matrix: function (matrix) {
+      return matrix.create(_cumsum(matrix.valueOf(), matrix.datatype()));
+    },
+    // sum([a, b, c, d, ...], dim)
+    'Array, number | BigNumber': _ncumSumDim,
+    'Matrix, number | BigNumber': function (matrix, dim) {
+      return matrix.create(_ncumSumDim(matrix.valueOf(), dim), matrix.datatype());
+    },
+    // cumsum(a, b, c, d, ...)
+    '...': function (args) {
+      if ((0, _collection.containsCollections)(args)) {
+        throw new TypeError('All values expected to be scalar in function cumsum');
+      }
+      return _cumsum(args);
+    }
+  });
+
+  /**
+     * Recursively calculate the cumulative sum of an n-dimensional array
+     * @param {Array} array
+     * @return {number} cumsum
+     * @private
+     */
+  function _cumsum(array) {
+    try {
+      return _cumsummap(array);
+    } catch (err) {
+      throw (0, _improveErrorMessage.improveErrorMessage)(err, name);
+    }
+  }
+  function _cumsummap(array) {
+    if (array.length === 0) {
+      return [];
+    }
+    const sums = [unaryPlus(array[0])]; // unaryPlus converts to number if need be
+    for (let i = 1; i < array.length; ++i) {
+      // Must use add below and not addScalar for the case of summing a
+      // 2+-dimensional array along the 0th dimension (the row vectors,
+      // or higher-d analogues, are literally added to each other).
+      sums.push(add(sums[i - 1], array[i]));
+    }
+    return sums;
+  }
+  function _ncumSumDim(array, dim) {
+    const size = (0, _array.arraySize)(array);
+    if (dim < 0 || dim >= size.length) {
+      // TODO: would be more clear when throwing a DimensionError here
+      throw new _IndexError.IndexError(dim, size.length);
+    }
+    try {
+      return _cumsumDimensional(array, dim);
+    } catch (err) {
+      throw (0, _improveErrorMessage.improveErrorMessage)(err, name);
+    }
+  }
+
+  /* Possible TODO: Refactor _reduce in collection.js to be able to work here as well */
+  function _cumsumDimensional(mat, dim) {
+    let i, ret, tran;
+    if (dim <= 0) {
+      const initialValue = mat[0][0];
+      if (!Array.isArray(initialValue)) {
+        return _cumsummap(mat);
+      } else {
+        tran = (0, _switch2._switch)(mat);
+        ret = [];
+        for (i = 0; i < tran.length; i++) {
+          ret[i] = _cumsumDimensional(tran[i], dim - 1);
+        }
+        return ret;
+      }
+    } else {
+      ret = [];
+      for (i = 0; i < mat.length; i++) {
+        ret[i] = _cumsumDimensional(mat[i], dim - 1);
+      }
+      return ret;
+    }
+  }
+});