public void EqualItemsMultipleMeasures()
{
// override the random number generator used by the decision logic so it can be manipulated
var mockRandom = new MockRandom();
DecisionLogic.SetRandomNumberGenerator(mockRandom);
// bias in favour of health 2x more than of pheromone
var biasedItem = new TestBiasedItem(1.0, 2.0);
var chosenItems = new List<TestMeasurableItem>();
// the numbers generated are distributed evenly based on the number of organisms
// therefore there should be each organism should be chosen once by the decision logic
var nextDouble = 0.0;
for (var i = 0; i < this.items.Count; i++)
{
mockRandom.SetNextDouble(nextDouble);
chosenItems.Add(DecisionLogic.MakeDecision(this.items, biasedItem));
nextDouble += 1.0 / this.items.Count;
}
// all items are equal, so the bias should not affect the frequency with which they are selected
// expecting each item to be chosen once, so expecting the same as the original list
var expectedItems = this.items;
var actualItems = chosenItems;
Assert.That(actualItems, Is.EqualTo(expectedItems));
}
public void EqualItemsSingleMeasure()
{
// override the random number generator used by the decision logic so it can be manipulated
var mockRandom = new MockRandom();
DecisionLogic.SetRandomNumberGenerator(mockRandom);
var biasedItem = new TestBiasedItem(1.0, 0);
var chosenItems = new List<TestMeasurableItem>();
// the numbers generated are distributed evenly based on the number of organisms
// therefore there should be each organism should be chosen once by the decision logic
var nextDouble = 0.0;
for (var i = 0; i < this.items.Count; i++)
{
mockRandom.SetNextDouble(nextDouble);
chosenItems.Add(DecisionLogic.MakeDecision(this.items, biasedItem));
nextDouble += 1.0 / this.items.Count;
}
// expecting each item to be chosen once, so expecting the same as the original list
var expectedItems = this.items;
var actualItems = chosenItems;
Assert.That(actualItems, Is.EqualTo(expectedItems));
}
public void InequalItemsMultipleBalancingMeasures()
{
// all items will have two measures, the second being the inverse of the first
// this will balance the overall weighting and give all items a perfectly even chance
var measurementIncrement = 1.0 / this.items.Count;
for (var i = 0; i < this.items.Count; i++)
{
var measurementLevel = (i + 1) * measurementIncrement;
this.items[i].SetPheromoneLevel(measurementLevel);
this.items[i].SetHealthLevel(1.0 - measurementLevel);
}
// override the random number generator used by the decision logic so it can be manipulated
// and set the base weighting to 0 (so that chance of being chosen is based directly on measurment level * bias)
var mockRandom = new MockRandom();
DecisionLogic.SetRandomNumberGenerator(mockRandom);
DecisionLogic.SetBaseWeighting(0.0);
// bias of both measurements are the same in order for them to balance
var biasedItem = new TestBiasedItem(1.0, 1.0);
var chosenItems = new List<TestMeasurableItem>();
var nextDouble = 0.0;
for (var i = 0; i < this.items.Count; i++)
{
mockRandom.SetNextDouble(nextDouble);
chosenItems.Add(DecisionLogic.MakeDecision(this.items, biasedItem));
nextDouble += 1.0 / this.items.Count;
}
// items are not equal, but the bias should evenly distribute the selection of items
var expectedItems = this.items;
var actualItems = chosenItems;
Assert.That(actualItems, Is.EqualTo(expectedItems));
}
public void InequalItemsSingleMeasure()
{
// all items will only have one measurement but the levels will all be different
// by an even spread based on how many items there are
var measurementIncrement = 1.0 / this.items.Count;
for (var i = 0; i < this.items.Count; i++)
{
var measurementLevel = (i + 1) * measurementIncrement;
this.items[i].SetPheromoneLevel(measurementLevel);
}
// override the random number generator used by the decision logic so it can be manipulated
// and set the base weighting to 0 (so that chance of being chosen is based directly on measurment level * bias)
var mockRandom = new MockRandom();
DecisionLogic.SetRandomNumberGenerator(mockRandom);
DecisionLogic.SetBaseWeighting(0.0);
var biasedItem = new TestBiasedItem(1.0, 0.0);
var chosenItems = new List<TestMeasurableItem>();
// the numbers generated need to reflect the range of measurement levels in the items
// if there are 8 items...
// - 8/8 pheromone -> 8 results
// - 7/8 pheromone -> 7 results
// - 6/8 pheromone -> 6 results
// - ...
// - 1/8 pheromone -> 1 result
// total number of results: 1 + 2 + 3 + 4 + ... = n(n + 1)/2 [sum of integers from 1 - n]
var numberOfResults = this.items.Count * (this.items.Count + 1) / 2.0;
var nextDouble = 0.0;
for (var i = 0; i < numberOfResults; i++)
{
mockRandom.SetNextDouble(nextDouble);
chosenItems.Add(DecisionLogic.MakeDecision(this.items, biasedItem));
nextDouble += 1.0 / numberOfResults;
}
// expecting each organism to be chosen a number of times proportional to the single measurement level used
var expectedItemCounts = new Dictionary<TestMeasurableItem, int>();
for (var i = 0; i < this.items.Count; i++)
{
expectedItemCounts.Add(this.items.ElementAt(i), i + 1);
}
var actualItemCounts = new Dictionary<TestMeasurableItem, int>();
foreach (var item in this.items)
{
var numberOfTimesChosen = chosenItems.Count(chosenItem => chosenItem.Equals(item));
actualItemCounts.Add(item, numberOfTimesChosen);
}
Assert.That(actualItemCounts, Is.EqualTo(expectedItemCounts));
}
public void InequalItemsMultipleUnbalancingMeasures()
{
// all items will have two measures, the second being double the inverse of the first
// this imbalance will be corrected by the bias, showing that the bias has the desired effect
var measurementIncrement = 1.0 / this.items.Count;
for (var i = 0; i < this.items.Count; i++)
{
var measurementLevel = (i + 1) * measurementIncrement;
this.items[i].SetPheromoneLevel(measurementLevel / 2.0);
this.items[i].SetHealthLevel(1 - measurementLevel);
}
// override the random number generator used by the decision logic so it can be manipulated
// and set the base weighting to 0 (so that chance of being chosen is based directly on measurment level * bias)
var mockRandom = new MockRandom();
DecisionLogic.SetRandomNumberGenerator(mockRandom);
DecisionLogic.SetBaseWeighting(0.0);
// pheromone bias is double that of health bias to compensate for the halving of the measurement level
var biasedItem = new TestBiasedItem(2.0, 1.0);
var chosenItems = new List<TestMeasurableItem>();
var nextDouble = 0.0;
for (var i = 0; i < this.items.Count; i++)
{
mockRandom.SetNextDouble(nextDouble);
chosenItems.Add(DecisionLogic.MakeDecision(this.items, biasedItem));
nextDouble += 1.0 / this.items.Count;
}
// items are not equal, but the bias should evenly compensate for the disproportionate measurements
// and evenly distribute the selection of items
var expectedItems = this.items;
var actualItems = chosenItems;
Assert.That(actualItems, Is.EqualTo(expectedItems));
}