public void returns_the_same_as_mean_when_all_values_are_negative()
{
//arrange
var inputData = new[] { -1.0, -2.0 };
var mean = inputData.Mean();
//act
var lossMean = inputData.LossMean();
//assert
Assert.AreEqual(mean, lossMean);
}
public void returns_same_as_mean_when_all_values_are_positive()
{
//arrange
var inputData = new[] { 1.0, 2.0, 3.0 };
var mean = inputData.Mean();
//act
var gainMean = inputData.GainMean();
//assert
Assert.AreEqual(mean, gainMean);
}
public void only_uses_data_points_below_the_mean_of_all_data()
{
//arrange
var inputData = new[] { 1.0, 2.0, 3.0, 4.0 };
var mean = inputData.Mean();
var expectedSemiDeviation = inputData.Where(x => x < mean).StandardDeviation();
//act
var semiDeviation = inputData.SemiDeviation();
//assert
Assert.AreEqual(expectedSemiDeviation, semiDeviation);
}
public void handles_negative_values()
{
//arrange
var inputData = new[] { -1.0, 2.0, 3.0, 4.0 };
var mean = inputData.Mean();
var expectedSemiDeviation = inputData.Where(x => x < mean).StandardDeviation();
//act
var semiDeviation = inputData.SemiDeviation();
//assert
Assert.AreEqual(expectedSemiDeviation, semiDeviation);
}
public void FitsToMeanOnOrder0Polynomial()
{
// Mathematica: Fit[{{1,4.986},{2,2.347},{3,2.061},{4,-2.995},{5,-2.352},{6,-5.782}}, {1}, x]
// -> -0.289167
var x = Enumerable.Range(1, 6).Select(Convert.ToDouble).ToArray();
var y = new[] { 4.986, 2.347, 2.061, -2.995, -2.352, -5.782 };
var resp = Fit.Polynomial(x, y, 0);
Assert.AreEqual(1, resp.Length);
Assert.AreEqual(-0.289167, resp[0], 1e-4);
Assert.AreEqual(y.Mean(), resp[0], 1e-4);
}
