Sometimes you have a set of actions that you use in different parts of the model. The next example shows how to fit a multiple linear regression model with theĪdditional constraint that none of its coefficients should be negative. Chapters 2 discusses the art of modeling, formal and conceptual. Weights = weights // (uncomment if you have a weighted problem)ĭouble ar2 = r2loss.Loss(predicted) // should be 0.51586887058782993 // Alternatively, we can also use the less generic, but maybe more user-friendly method directly: double ur2 = regression.CoefficientOfDetermination(inputs, outputs, adjust: true) // should be 0.51586887058782993 All SRAAM user inputs are organized in 3 standard Netlogo tabs and 5 tabs specific for SRAAM user inputs. ![]() (If you dont specify, the code is run by the observer. We can also compute other measures, such as the coefficient of determination r² using: double r2 = new RSquaredLoss(numberOfOutputs, outputs).Loss(predicted) // should be 0.55086630162967354 // Or the adjusted or weighted versions of r² using: var r2loss = new RSquaredLoss(numberOfOutputs, outputs) In NetLogo, you must specify which agents - turtles, patches, or the observer - are to run each command. And the squared error using the SquareLoss class: double error = new SquareLoss(outputs).Loss(predicted) We can compute the predicted points using: double predicted = regression.Transform(inputs) ![]() NetLogo colors an entire patch at once, lets set up the NetLogo world with a. MultipleLinearRegression regression = ols.Learn(inputs, outputs) Because we want to give our ants lots of room to maneuver, and because. Use Ordinary Least Squares to estimate a regression model: We will use Ordinary Least Squares to create a // linear regression model with an intercept term var ols = new OrdinaryLeastSquares() We can gather some info about the problem: int numberOfInputs = codebook.NumberOfInputs // should be 4 (since there are 4 variables) int numberOfOutputs = codebook.NumberOfOutputs // should be 12 (due their one-hot encodings) // Now we can use it to obtain double vectors: double inputs = codebook.ToDouble().Transform(instances) located in the same Z (z = 1) double outputs = , ![]() Now suppose you have some points double inputs = We have two input variables (x and y) // and we will be trying to find two parameters a and b and // an intercept term c. We will try to model a plane as an equation in the form // "ax + by + c = z".
0 Comments
Leave a Reply. |