Package quickml.supervised.regressionModel.LinearRegression

Examples of quickml.supervised.regressionModel.LinearRegression.RidgeLinearModel.predict()

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        double pythonRMSE = Math.sqrt(212.32/trainingData.size());
        double pythonEpsilon = pythonRMSE/25.0;
        double mse = 0;
        for (Instance<double[]> instance : trainingData) {
            double [] x = instance.getAttributes();
            logger.info("prediction " + ridgeLinearModel.predict(x) + ". label: " + instance.getLabel());
            mse+= Math.pow(ridgeLinearModel.predict(x) - (Double)instance.getLabel(), 2);
            logger.info("un-normalized mse " + mse);
        }
        mse/=trainingData.size();
        double RMSE = Math.sqrt(mse);
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        double pythonEpsilon = pythonRMSE/25.0;
        double mse = 0;
        for (Instance<double[]> instance : trainingData) {
            double [] x = instance.getAttributes();
            logger.info("prediction " + ridgeLinearModel.predict(x) + ". label: " + instance.getLabel());
            mse+= Math.pow(ridgeLinearModel.predict(x) - (Double)instance.getLabel(), 2);
            logger.info("un-normalized mse " + mse);
        }
        mse/=trainingData.size();
        double RMSE = Math.sqrt(mse);
        logger.info("mse_per_test_instance " + mse);
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