@Test public void testCPU() { System.out.println("CPU"); MathEx.setSeed(19650218); LinearModel model = RidgeRegression.fit(CPU.formula, CPU.data, 0.1); System.out.println(model); double[] prediction = CrossValidation.regression(10, CPU.formula, CPU.data, (f, x) -> RidgeRegression.fit(f, x, 0.1)); double rmse = RMSE.of(CPU.y, prediction); System.out.println("10-CV RMSE = " + rmse); assertEquals(55.268333864, rmse, 1E-7); }
public static LinearModel fit(Formula formula, DataFrame data) { return fit(formula, data, new Properties()); }
RidgeRegression { public static LinearModel fit(Formula formula, DataFrame data) { return fit(formula, data, new Properties()); } }
RidgeRegression { public static LinearModel fit(Formula formula, DataFrame data) { return fit(formula, data, new Properties()); }  }
RidgeRegression { public static LinearModel fit(Formula formula, DataFrame data) { return fit(formula, data, new Properties()); }  static LinearModel fit(Formula formula, DataFrame data); static LinearModel fit(Formula formula, DataFrame data, Properties prop); static LinearModel fit(Formula formula, DataFrame data, double lambda); static LinearModel fit(Formula formula, DataFrame data, double[] weights, double[] lambda, double[] beta0); }
RidgeRegression { public static LinearModel fit(Formula formula, DataFrame data) { return fit(formula, data, new Properties()); }  static LinearModel fit(Formula formula, DataFrame data); static LinearModel fit(Formula formula, DataFrame data, Properties prop); static LinearModel fit(Formula formula, DataFrame data, double lambda); static LinearModel fit(Formula formula, DataFrame data, double[] weights, double[] lambda, double[] beta0);  }