Remove unhelpful docstring
@@ -32,13 +32,6 @@ public class RegressionModelBuilder<OUTPUT> {
this.outputTransform = outputTransform;
}
- /**
- * Sets the regularization to be used by the model.
- * This can be any of the types from {@link io.improbable.keanu.model.regression.RegressionRegularization RegressionRegularization}
- *
- * @param regularization the type of regularization to be used by the resulting model
- * @return An updated version of this builder
- */
public RegressionModelBuilder withRegularization(RegressionRegularization regularization) {
this.regularization = regularization;
return this;
@@ -50,7 +43,7 @@ public class RegressionModelBuilder<OUTPUT> {
* @param means An array of means of the distribution describing the prior belief about the regression weights
* @param scaleParameters An array of scale parameters of the distribution describing the prior belief about the regression weights.
* This will represent sigmas if no or ridge regularization is used and will represent betas if lasso regularization is used.
- * @return An updated version of this builder
+ * @return this
*/
public RegressionModelBuilder withPriorOnWeights(double[] means, double[] scaleParameters) {
RegressionWeights.checkArrayHasCorrectNumberOfFeatures(means, getFeatureCount());
@@ -68,7 +61,7 @@ public class RegressionModelBuilder<OUTPUT> {
* @param mean The mean of the distribution describing the prior belief about the regression intercept
* @param scaleParameter The scale parameter of the distribution describing the prior belief about the regression intercept.
* This will represent sigmas if no or ridge regularization is used and will represent betas if lasso regularization is used.
- * @return An updated version of this builder
+ * @return this
*/
public RegressionModelBuilder withPriorOnIntercept(double mean, double scaleParameter) {
this.priorOnInterceptMean = mean;
@@ -82,7 +75,7 @@ public class RegressionModelBuilder<OUTPUT> {
* @param mean The mean of the distribution describing the prior belief about both the regression intercept and weights
* @param scaleParameter The scale parameter of the distribution describing the prior belief about both regression intercept and weights.
* This will represent sigmas if no or ridge regularization is used and will represent betas if lasso regularization is used.
- * @return An updated version of this builder
+ * @return this
*/
public RegressionModelBuilder withPriorOnWeightsAndIntercept(double mean, double scaleParameter) {
withPriorOnWeights(RegressionWeights.fillPriorOnWeights(this.inputTrainingData.getShape(), mean), RegressionWeights.fillPriorOnWeights(this.inputTrainingData.getShape(), scaleParameter));
