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cmercier
2020-05-12 14:32:35 +02:00
parent 072a6ede30
commit 37fd0c3f58
1 changed files with 15 additions and 15 deletions
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src/mlpack/methods/bayesian_linear_regression/bayesian_linear_regression_main.cpp
+15 -15
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@@ -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.",