public void Linear_Regression_Learner_Test()
{
var datum = new[]
{
new { X = 1.0, Y = 1.0 },
new { X = 2.0, Y = 2.0 },
new { X = 3.0, Y = 1.3 },
new { X = 4.0, Y = 3.75 },
new { X = 5.0, Y = 5.25 }
};
var d = Descriptor.New("DATUM")
.With("X").As(typeof(double))
.Learn("Y").As(typeof(double));
var generator = new LinearRegressionGenerator() { Descriptor = d };
var model = Learner.Learn(datum, 0.9, 5, generator);
Assert.True(0.8 >= model.Accuracy);
}
public void Linear_Regression_Test_House_Predictions_Regularized()
{
// test house prices based on ft-sq and no# bedrooms
Matrix x = new [,]
{
{2104, 3},
{1600, 3},
{2400, 3},
{1416, 2},
{3000, 4},
{1985, 4},
{1534, 3},
{1427, 3},
{1380, 3},
{1494, 3}
};
Vector y = new[]
{
399900,
329900,
369000,
232000,
539900,
299900,
314900,
198999,
212000,
242500
};
LinearRegressionGenerator generator = new LinearRegressionGenerator() { LearningRate = 0.01, MaxIterations = 400, Lambda = 1 };
var model = generator.Generate(x.Copy(), y.Copy());
var priceGrad = model.Predict(new Vector(new double[] { 1650, 3 }));
double actualGrad = 280942d;
Assert.AreEqual(actualGrad, System.Math.Round(priceGrad, 0));
}
public void Save_And_Load_LinearRegression()
{
Matrix x = new [,]
{
{2104, 3},
{1600, 3},
{2400, 3},
{1416, 2},
{3000, 4},
{1985, 4},
{1534, 3},
{1427, 3},
{1380, 3},
{1494, 3}
};
Vector y = new[]
{
399900,
329900,
369000,
232000,
539900,
299900,
314900,
198999,
212000,
242500
};
LinearRegressionGenerator generator = new LinearRegressionGenerator() { LearningRate = 0.01, MaxIterations = 400, Lambda = 1 };
var model = generator.Generate(x.Copy(), y.Copy()) as LinearRegressionModel;
Serialize(model);
var lmodel = Deserialize<LinearRegressionModel>();
Assert.AreEqual(model.Theta, lmodel.Theta);
}
public void Linear_Regression_Test_House_Predictions_Normal()
{
// test house prices based on ft-sq and no# bedrooms
Matrix x = new [,]
{
{2104, 3},
{1600, 3},
{2400, 3},
{1416, 2},
{3000, 4},
{1985, 4},
{1534, 3},
{1427, 3},
{1380, 3},
{1494, 3}
};
Vector y = new[]
{
399900,
329900,
369000,
232000,
539900,
299900,
314900,
198999,
212000,
242500
};
LinearRegressionGenerator generator = new LinearRegressionGenerator() { LearningRate = 0.01, MaxIterations = 400, Lambda = 0, NormalizeFeatures = true };
var model = generator.Generate(x.Copy(), y.Copy());
var priceEqns = model.Predict(new Vector(new double[] { 1650, 3 }));
// CK 150929: increased due to improvements in optimisation
double actualEqns = 295107.0d;
Almost.Equal(actualEqns, System.Math.Round(priceEqns, 0), 5000);
}
