From 37fd0c3f585ec90d4abd4aa897cb0d30490ebe7e Mon Sep 17 00:00:00 2001 From: cmercier Date: Tue, 12 May 2020 14:32:35 +0200 Subject: [PATCH] Formatting. --- .../bayesian_linear_regression_main.cpp | 30 +++++++++---------- 1 file changed, 15 insertions(+), 15 deletions(-) diff --git a/src/mlpack/methods/bayesian_linear_regression/bayesian_linear_regression_main.cpp b/src/mlpack/methods/bayesian_linear_regression/bayesian_linear_regression_main.cpp index 31771f5caf..31781c4bec 100644 --- a/src/mlpack/methods/bayesian_linear_regression/bayesian_linear_regression_main.cpp +++ b/src/mlpack/methods/bayesian_linear_regression/bayesian_linear_regression_main.cpp @@ -42,8 +42,8 @@ PROGRAM_INFO("BayesianLinearRegression", "This procedure includes the Ockham's razor that penalizes over complex " "solutions. " "\n\n" - "This program is able to train a Bayesian linear regression model or load a " - "model from file, output regression predictions for a test set, and save " + "This program is able to train a Bayesian linear regression model or load " + "a model from file, output regression predictions for a test set, and save " "the trained model to a file. The Bayesian linear regression algorithm is " "described in more detail below:" "\n\n" @@ -51,8 +51,8 @@ PROGRAM_INFO("BayesianLinearRegression", "dimension, t is a vector of targets, alpha is the precision of the " "gaussian prior distribtion of w, and w is solution to determine. " "\n\n" - "The Bayesian linear regression comptutes the posterior distribution of the " - "parameters by the Bayes's rule : " + "The Bayesian linear regression comptutes the posterior distribution of " + "the parameters by the Bayes's rule : " "\n\n" " p(w|X) = p(X,t|w) * p(w|alpha) / p(X)" "\n\n" @@ -62,13 +62,13 @@ PROGRAM_INFO("BayesianLinearRegression", "and " + PRINT_PARAM_STRING("scale") + " parameters control the " "centering and the normalizing options. A trained model can be saved with " "the " + PRINT_PARAM_STRING("output_model") + ". If no training is desired " - "at all, a model can be passed via the "+ PRINT_PARAM_STRING("input_model") + - " parameter." + "at all, a model can be passed via the " + + PRINT_PARAM_STRING("input_model") + " parameter." "\n\n" "The program can also provide predictions for test data using either the " - "trained model or the given input model. Test points can be specified with" - " the " + PRINT_PARAM_STRING("test") + " parameter. Predicted responses " - "to the test points can be saved with the " + + "trained model or the given input model. Test points can be specified " + "with the " + PRINT_PARAM_STRING("test") + " parameter. Predicted " + "responses to the test points can be saved with the " + PRINT_PARAM_STRING("output_predictions") + " output parameter. The " "corresponding standard deviation can be save by precising the " + PRINT_PARAM_STRING("output_std") + " parameter." @@ -89,7 +89,7 @@ PROGRAM_INFO("BayesianLinearRegression", " responses to " + PRINT_DATASET("test_predictions") + ": " "\n\n" + PRINT_CALL("bayesian_linear_regression", "input_model", - "bayesian_linear_regression_model", "test", "test", + "bayesian_linear_regression_model", "test", "test", "output_predictions", "test_predictions")); PARAM_MATRIX_IN("input", "Matrix of covariates (X).", "i"); @@ -97,13 +97,13 @@ PARAM_MATRIX_IN("input", "Matrix of covariates (X).", "i"); PARAM_MATRIX_IN("responses", "Matrix of responses/observations (y).", "r"); PARAM_MODEL_IN(BayesianLinearRegression, "input_model", "Trained " - "BayesianLinearRegression model to use.", "m"); + "BayesianLinearRegression model to use.", "m"); PARAM_MODEL_OUT(BayesianLinearRegression, "output_model", "Output " - "BayesianLinearRegression model.", "M"); + "BayesianLinearRegression model.", "M"); PARAM_MATRIX_IN("test", "Matrix containing points to regress on (test " - "points).", "t"); + "points).", "t"); PARAM_MATRIX_OUT("output_predictions", "If --test_file is specified, this " "file is where the predicted responses will be saved.", "o"); @@ -114,8 +114,8 @@ PARAM_MATRIX_OUT("output_std", "If --std_file is specified, this file is where " PARAM_INT_IN("center", "Center the data and fit the intercept. Set to 0 to " "disable", - "c", - 1); + "c", + 1); PARAM_INT_IN("scale", "Scale each feature by their standard deviations. " "set to 1 to scale.",