From: johncreed Date: Wed, 31 Jul 2019 15:23:51 +0000 (+0800) Subject: Remove trailing white-space X-Git-Tag: v240~12^2 X-Git-Url: https://granicus.if.org/sourcecode?a=commitdiff_plain;h=27a1f46950124068ca3dd8b20b5302201ce19b9b;p=liblinear Remove trailing white-space --- diff --git a/README b/README index 74def8a..4dbca97 100644 --- a/README +++ b/README @@ -141,8 +141,8 @@ Option -v randomly splits the data into n parts and calculates cross validation accuracy on them. Option -C conducts cross validation under different parameters and finds -the best one. This option is supported only by -s 0, -s 2 (for finding -C) and -s 11 (for finding C, p). If the solver is not specified, -s 2 +the best one. This option is supported only by -s 0, -s 2 (for finding +C) and -s 11 (for finding C, p). If the solver is not specified, -s 2 is used. Formulations: @@ -274,12 +274,12 @@ For parameter selection by -C, users can specify other solvers (currently -s 0, -s 2 and -s 11 are supported) and different number of CV folds. Further, users can use the -c option to specify the smallest C value of the -search range. This option is useful when users want to +search range. This option is useful when users want to rerun the parameter selection procedure from a specified C under a different setting, such as a stricter stopping tolerance -e 0.0001 in the above example. Similarly, for --s 11, users can use the -p option to specify the -maximal p value of the search range. +-s 11, users can use the -p option to specify the +maximal p value of the search range. > train -c 10 -w1 2 -w2 5 -w3 2 four_class_data_file @@ -458,9 +458,9 @@ in linear.h, so you can check the version number. conducts cross validation many times under parameters C = start_C, 2*start_C, 4*start_C, 8*start_C, ..., and finds the best one with the highest cross validation accuracy. For -s 11, it conducts cross - validation many times with a two-fold loop. The outer loop considers a + validation many times with a two-fold loop. The outer loop considers a default sequence of p = 19/20*max_p, ..., 1/20*max_p, 0 and - under each p value the inner loop considers a sequence of parameters + under each p value the inner loop considers a sequence of parameters C = start_C, 2*start_C, 4*start_C, ..., and finds the best one with the lowest mean squared error. @@ -469,12 +469,12 @@ in linear.h, so you can check the version number. all folds become stable or C reaches max_C. If start_p <= 0, then this procedure calculates a maximal p for prob as - the start_p. Otherwise, the procedure starts with the first - i/20*max_p <= start_p so the outer sequence is i/20*max_p, + the start_p. Otherwise, the procedure starts with the first + i/20*max_p <= start_p so the outer sequence is i/20*max_p, (i-1)/20*max_p, ..., 0. - - The best C, the best p, and the corresponding accuracy (or MSE) are - assigned to *best_C, *best_p and *best_score, respectively. For + + The best C, the best p, and the corresponding accuracy (or MSE) are + assigned to *best_C, *best_p and *best_score, respectively. For classification, *best_p is not used, and the returned value is -1. - Function: double predict(const model *model_, const feature_node *x); diff --git a/matlab/README b/matlab/README index 58651f6..a084a6e 100644 --- a/matlab/README +++ b/matlab/README @@ -133,11 +133,11 @@ If the '-v' option is specified, cross validation is conducted and the returned model is just a scalar: cross-validation accuracy for classification and mean-squared error for regression. -If the '-C' option is specified, best parameters are found by cross +If the '-C' option is specified, best parameters are found by cross validation. The parameter selection utility is supported only by -s 0, --s 2 (for finding C) and -s 11 (for finding C, p). The returned -model is a three dimensional vector with the best C, the best p, and -the corresponding cross-validation accuracy or mean squared error. The +-s 2 (for finding C) and -s 11 (for finding C, p). The returned +model is a three dimensional vector with the best C, the best p, and +the corresponding cross-validation accuracy or mean squared error. The returned best p for -s 0 and -s 2 is set to -1 because the p parameter is not used by classification models. diff --git a/python/README b/python/README index 4644e4e..f4e52f6 100644 --- a/python/README +++ b/python/README @@ -335,13 +335,13 @@ The above command loads structure. If '-v' is specified, cross validation is conducted and the returned model is just a scalar: cross-validation accuracy for classification and mean-squared error for regression. - + If the '-C' option is specified, best parameters are found - by cross validation. The parameter selection utility is supported + by cross validation. The parameter selection utility is supported only by -s 0, -s 2 (for finding C) and -s 11 (for finding C, p). - The returned structure is a triple with the best C, the best p, - and the corresponding cross-validation accuracy or mean squared - error. The returned best p for -s 0 and -s 2 is set to -1 because + The returned structure is a triple with the best C, the best p, + and the corresponding cross-validation accuracy or mean squared + error. The returned best p for -s 0 and -s 2 is set to -1 because the p parameter is not used by classification models.