#region License Information
/* HeuristicLab
* Copyright (C) 2002-2014 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
*
* This file is part of HeuristicLab.
*
* HeuristicLab is free software: you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation, either version 3 of the License, or
* (at your option) any later version.
*
* HeuristicLab is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU General Public License for more details.
*
* You should have received a copy of the GNU General Public License
* along with HeuristicLab. If not, see .
*/
#endregion
using System;
using System.Collections.Generic;
using System.Linq;
using HeuristicLab.Common;
using HeuristicLab.Random;
namespace HeuristicLab.Problems.Instances.DataAnalysis {
public static class ValueGenerator {
private static FastRandom rand = new FastRandom();
///
/// Generates a sequence of evenly spaced points by returning the start value and adding the stepwidth until the end is reached or surpassed.
///
///
/// The smallest and first value of the sequence.
/// The largest and last value of the sequence.
/// The step size between subsequent values.
/// Determines if the end should be included in the sequence regardless if the end is divisible by the stepwidth.
/// A sequence of values from start to end (inclusive)
[Obsolete("It is recommended to use the decimal overload to achieve a higher numerical accuracy.")]
public static IEnumerable GenerateSteps(double start, double end, double stepWidth, bool includeEnd = false) {
//mkommend: IEnumerable.Cast fails due to boxing and unboxing of the involved types
// http://referencesource.microsoft.com/#System.Core/System/Linq/Enumerable.cs#27bb217a6d5457ec
// http://blogs.msdn.com/b/ericlippert/archive/2009/03/19/representation-and-identity.aspx
return GenerateSteps((decimal)start, (decimal)end, (decimal)stepWidth, includeEnd).Select(x => (double)x);
}
///
/// Generates a sequence of evenly spaced points by returning the start value and adding the stepwidth until the end is reached or surpassed.
///
/// The smallest and first value of the sequence.
/// The largest and last value of the sequence.
/// The step size between subsequent values.
/// /// Determines if the end should be included in the sequence regardless if the end is divisible by the stepwidth.
/// A sequence of values from start to end
public static IEnumerable GenerateSteps(decimal start, decimal end, decimal stepWidth, bool includeEnd = false) {
if (stepWidth == 0)
throw new ArgumentException("The step width cannot be zero.");
if (start < end && stepWidth < 0)
throw new ArgumentException("The step width must be larger than zero for increasing sequences (start < end).");
if (start > end && stepWidth > 0)
throw new ArgumentException("The step width must be smaller than zero for decreasing sequences (start > end).");
decimal x = start;
while (x <= end) {
yield return x;
x += stepWidth;
}
if (x - stepWidth < end && includeEnd) yield return end;
}
///
/// Generates uniformly distributed values between start and end (inclusive!)
///
/// Number of values to generate.
/// The lower value (inclusive)
/// The upper value (inclusive)
/// An enumerable including n values in [start, end]
public static IEnumerable GenerateUniformDistributedValues(int n, double start, double end) {
for (int i = 0; i < n; i++) {
// we need to return a random value including end.
// so we cannot use rand.NextDouble() as it returns a value strictly smaller than 1.
double r = rand.NextUInt() / (double)uint.MaxValue; // r \in [0,1]
yield return r * (end - start) + start;
}
}
///
/// Generates normally distributed values sampling from N(mu, sigma)
///
/// Number of values to generate.
/// The mu parameter of the normal distribution
/// The sigma parameter of the normal distribution
/// An enumerable including n values ~ N(mu, sigma)
public static IEnumerable GenerateNormalDistributedValues(int n, double mu, double sigma) {
for (int i = 0; i < n; i++)
yield return NormalDistributedRandom.NextDouble(rand, mu, sigma);
}
// iterative approach
public static IEnumerable> GenerateAllCombinationsOfValuesInLists(List> lists) {
List> allCombinations = new List>();
if (lists.Count < 1) {
return allCombinations;
}
List> enumerators = new List>();
foreach (var list in lists) {
allCombinations.Add(new List());
enumerators.Add(list.GetEnumerator());
}
bool finished = !enumerators.All(x => x.MoveNext());
while (!finished) {
GetCurrentCombination(enumerators, allCombinations);
finished = MoveNext(enumerators, lists);
}
return allCombinations;
}
private static bool MoveNext(List> enumerators, List> lists) {
int cur = enumerators.Count - 1;
while (cur >= 0 && !enumerators[cur].MoveNext()) {
enumerators[cur] = lists[cur].GetEnumerator();
enumerators[cur].MoveNext();
cur--;
}
return cur < 0;
}
private static void GetCurrentCombination(List> enumerators, List> allCombinations) {
for (int i = 0; i < enumerators.Count(); i++) {
allCombinations[i].Add(enumerators[i].Current);
}
}
}
}