'use strict';

Object.defineProperty(exports, "__esModule", {
  value: true
});
exports.linear = linear;
exports.log = log;

var _lodash = require('lodash');

var _lodash2 = _interopRequireDefault(_lodash);

function _interopRequireDefault(obj) { return obj && obj.__esModule ? obj : { default: obj }; }

function sum(data, fn) {
  return _lodash2.default.reduce(data, function (sum, d) {
    return sum + (d[1] == null ? 0 : fn(d));
  }, 0);
} /*
   * Licensed to Elasticsearch B.V. under one or more contributor
   * license agreements. See the NOTICE file distributed with
   * this work for additional information regarding copyright
   * ownership. Elasticsearch B.V. licenses this file to you under
   * the Apache License, Version 2.0 (the "License"); you may
   * not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   *    http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing,
   * software distributed under the License is distributed on an
   * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
   * KIND, either express or implied.  See the License for the
   * specific language governing permissions and limitations
   * under the License.
   */

/*
* Algorithms from
* copyright(c) 2013 Tom Alexander
* Licensed under the MIT license.
*/

function count(data) {
  return _lodash2.default.filter(data, function (d) {
    return d[1] == null ? false : true;
  }).length;
}

function mapTuples(data, fn) {
  return _lodash2.default.map(data, function (d) {
    return [d[0], fn(d)];
  });
}

function linear(data) {
  const xSum = sum(data, d => {
    return d[0];
  });
  const ySum = sum(data, d => {
    return d[1];
  });
  const xSqSum = sum(data, d => {
    return d[0] * d[0];
  });
  const xySum = sum(data, d => {
    return d[0] * d[1];
  });
  const observations = count(data);

  const gradient = (observations * xySum - xSum * ySum) / (observations * xSqSum - xSum * xSum);

  const intercept = ySum / observations - gradient * xSum / observations;

  return mapTuples(data, d => {
    return d[0] * gradient + intercept;
  });
}

function log(data) {
  const logXSum = sum(data, d => {
    return Math.log(d[0]);
  });
  const yLogXSum = sum(data, d => {
    return d[1] * Math.log(d[0]);
  });
  const ySum = sum(data, d => {
    return d[1];
  });
  const logXsqSum = sum(data, d => {
    return Math.pow(Math.log(d[0]), 2);
  });
  const observations = count(data);

  const b = (observations * yLogXSum - ySum * logXSum) / (observations * logXsqSum - logXSum * logXSum);

  const a = (ySum - b * logXSum) / observations;

  return mapTuples(data, d => {
    return a + b * Math.log(d[0]);
  });
}