/dports/sysutils/fselect/fselect-0.7.7/src/util/ |
H A D | top_n.rs | 72 top_n.insert("xyz", 2); in test_insert_to_limit() 78 top_n.insert("a", 1); in test_insert_past_limit_bigger_discarded() 79 top_n.insert("b", 2); in test_insert_past_limit_bigger_discarded() 80 top_n.insert("z", -1); in test_insert_past_limit_bigger_discarded() 87 top_n.insert("a", 1); in test_insert_past_limit_equal_discarded() 88 top_n.insert("b", 2); in test_insert_past_limit_equal_discarded() 89 top_n.insert("b", -1); in test_insert_past_limit_equal_discarded() 124 top_n.insert("z", 3); in test_limitless() 125 top_n.insert("y", 2); in test_limitless() 126 top_n.insert("a", 1); in test_limitless() [all …]
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/dports/math/R-cran-dplyr/dplyr/tests/testthat/ |
H A D | test-top-n.R | 3 top_four <- test_df %>% top_n(4, y) 11 expect_identical(top_n(df, 2)$x, c(10, 6)) 16 expect_identical(top_n(mtcars, 2, -disp), top_n(mtcars, -2, disp)) 20 expect_snapshot(res1 <- top_n(mtcars, n() * .5)) 21 expect_snapshot(res2 <- top_n(mtcars, 16)) 26 expect_identical(top_n(mtcars, n() * .5, disp), top_frac(mtcars, .5, disp)) 28 expect_snapshot(res1 <- top_n(mtcars, n() * .5))
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/dports/math/py-spopt/spopt-0.2.1/spopt/tests/ |
H A D | test_maxp.py | 39 top_n = 2 42 args = (self.mexico, w, attrs_name, threshold_name, threshold, top_n) 53 top_n = 2 56 args = (self.mexico, w, attrs_name, threshold_name, threshold, top_n) 67 top_n = 5 70 args = (self.mexico, w, attrs_name, threshold_name, threshold, top_n) 81 top_n = 5 84 args = (self.mexico, w, attrs_name, threshold_name, threshold, top_n)
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/dports/math/R-cran-dplyr/dplyr/man/ |
H A D | top_n.Rd | 3 \name{top_n} 4 \alias{top_n} 8 top_n(x, n, wt) 15 \item{n}{Number of rows to return for \code{top_n()}, fraction of rows to 26 \code{top_n()} has been superseded in favour of \code{\link[=slice_min]{slice_min()}}/\code{\link[=… 31 \code{top_n()} was superseded because the name was fundamentally confusing as 35 see an easy way to fix the existing \code{top_n()} function without breaking 41 df \%>\% top_n(2) # highest values 42 df \%>\% top_n(-2) # lowest values
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/dports/math/R-cran-dplyr/dplyr/tests/testthat/_snaps/ |
H A D | top-n.md | 1 # top_n() quotes n 4 res1 <- top_n(mtcars, n() * 0.5) 11 res2 <- top_n(mtcars, 16) 15 # top_frac() is a shorthand for top_n(n()*) 18 res1 <- top_n(mtcars, n() * 0.5)
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/dports/misc/py-xgboost/xgboost-1.5.1/R-package/R/ |
H A D | xgb.plot.shap.R | 107 xgb.plot.shap <- function(data, shap_contrib = NULL, features = NULL, top_n = 1, model = NULL, argument 118 top_n = top_n, 206 xgb.plot.shap.summary <- function(data, shap_contrib = NULL, features = NULL, top_n = 10, model = N… argument 209 …xgb.ggplot.shap.summary(data, shap_contrib, features, top_n, model, trees, target_class, approxcon… 221 xgb.shap.data <- function(data, shap_contrib = NULL, features = NULL, top_n = 1, model = NULL, argument 274 top_n <- top_n[1] 275 if (top_n < 1 | top_n > 100) stop("top_n: must be an integer within [1, 100]") 276 features <- imp$Feature[1:min(top_n, NROW(imp))]
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H A D | xgb.plot.importance.R | 61 xgb.plot.importance <- function(importance_matrix = NULL, top_n = NULL, measure = NULL, argument 90 if (!is.null(top_n)) { 91 top_n <- min(top_n, nrow(importance_matrix)) 92 importance_matrix <- head(importance_matrix, top_n)
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H A D | xgb.ggplot.R | 6 xgb.ggplot.importance <- function(importance_matrix = NULL, top_n = NULL, measure = NULL, argument 9 importance_matrix <- xgb.plot.importance(importance_matrix, top_n = top_n, measure = measure, 104 xgb.ggplot.shap.summary <- function(data, shap_contrib = NULL, features = NULL, top_n = 10, model =… argument 110 top_n = top_n,
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/dports/misc/xgboost/xgboost-1.5.1/R-package/R/ |
H A D | xgb.plot.shap.R | 107 xgb.plot.shap <- function(data, shap_contrib = NULL, features = NULL, top_n = 1, model = NULL, argument 118 top_n = top_n, 206 xgb.plot.shap.summary <- function(data, shap_contrib = NULL, features = NULL, top_n = 10, model = N… argument 209 …xgb.ggplot.shap.summary(data, shap_contrib, features, top_n, model, trees, target_class, approxcon… 221 xgb.shap.data <- function(data, shap_contrib = NULL, features = NULL, top_n = 1, model = NULL, argument 274 top_n <- top_n[1] 275 if (top_n < 1 | top_n > 100) stop("top_n: must be an integer within [1, 100]") 276 features <- imp$Feature[1:min(top_n, NROW(imp))]
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H A D | xgb.plot.importance.R | 61 xgb.plot.importance <- function(importance_matrix = NULL, top_n = NULL, measure = NULL, argument 90 if (!is.null(top_n)) { 91 top_n <- min(top_n, nrow(importance_matrix)) 92 importance_matrix <- head(importance_matrix, top_n)
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H A D | xgb.ggplot.R | 6 xgb.ggplot.importance <- function(importance_matrix = NULL, top_n = NULL, measure = NULL, argument 9 importance_matrix <- xgb.plot.importance(importance_matrix, top_n = top_n, measure = measure, 104 xgb.ggplot.shap.summary <- function(data, shap_contrib = NULL, features = NULL, top_n = 10, model =… argument 110 top_n = top_n,
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/dports/textproc/py-whoosh/Whoosh-2.7.4/src/whoosh/ |
H A D | searching.py | 957 self.top_n = top_n 992 return [Hit(self, self.top_n[i][1], i, self.top_n[i][0]) 998 return Hit(self, self.top_n[n][1], n, self.top_n[n][0]) 1005 yield Hit(self, self.top_n[i][1], i, self.top_n[i][0]) 1140 return len(self.top_n) 1159 r.top_n = copy.deepcopy(self.top_n) 1168 return self.top_n[n][0] 1174 return self.top_n[n][1] 1283 for item in results.top_n: 1299 self.top_n = items [all …]
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/dports/devel/ppl/ppl-1.2/src/ |
H A D | CO_Tree_templates.hh | 83 const dimension_type top_n = stack[stack_first_empty - 1].first; in CO_Tree() local 112 if (top_n == 0) { in CO_Tree() 116 if (top_n == 1) { in CO_Tree() 126 const dimension_type half = (top_n + 1) / 2; in CO_Tree() 128 stack[stack_first_empty ] = std::make_pair(top_n - half, 2); in CO_Tree()
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/dports/biology/py-goatools/goatools-1.1.6/goatools/grouper/ |
H A D | sorter.py | 95 top_n=None, use_sections=True): argument 114 assert top_n is not True and top_n is not False, \ 115 "top_n({T}) MUST BE None OR AN int".format(T=top_n) 120 if sec_sb is True or (sec_sb is not False and sec_sb is not None) or top_n is not None: 127 if top_n is not None: 128 nts_section = [(s, nts[:top_n]) for s, nts in nts_section]
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/dports/databases/jdb/jdb-1.14/ |
H A D | db2dcliff | 89 $top_n = undef; 97 $top_n = $dbopts->optarg; 106 die("$prog: -n N must be positive.\n") if (defined($top_n) && $top_n < 0); 210 if (defined($top_n)) { 218 my($last_ei) = $top_n - 1; $last_ei = $#ei if ($last_ei > $#ei);
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/dports/misc/py-xgboost/xgboost-1.5.1/R-package/man/ |
H A D | xgb.plot.shap.Rd | 11 top_n = 1, 42 feature importance is calculated, and \code{top_n} high ranked features are taken.} 44 \item{top_n}{when \code{features} is NULL, top_n [1, 100] most important features in a model are ta… 133 xgb.plot.shap(agaricus.test$data, contr, model = bst, top_n = 12, n_col = 3) 134 xgb.ggplot.shap.summary(agaricus.test$data, contr, model = bst, top_n = 12) # Summary plot 147 xgb.plot.shap(x, model = mbst, trees = trees0, target_class = 0, top_n = 4, 149 xgb.plot.shap(x, model = mbst, trees = trees0 + 1, target_class = 1, top_n = 4, 151 xgb.plot.shap(x, model = mbst, trees = trees0 + 2, target_class = 2, top_n = 4, 153 xgb.ggplot.shap.summary(x, model = mbst, target_class = 0, top_n = 4) # Summary plot
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H A D | xgb.plot.shap.summary.Rd | 12 top_n = 10, 24 top_n = 10, 39 feature importance is calculated, and \code{top_n} high ranked features are taken.} 41 \item{top_n}{when \code{features} is NULL, top_n [1, 100] most important features in a model are ta…
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/dports/misc/xgboost/xgboost-1.5.1/R-package/man/ |
H A D | xgb.plot.shap.Rd | 11 top_n = 1, 42 feature importance is calculated, and \code{top_n} high ranked features are taken.} 44 \item{top_n}{when \code{features} is NULL, top_n [1, 100] most important features in a model are ta… 133 xgb.plot.shap(agaricus.test$data, contr, model = bst, top_n = 12, n_col = 3) 134 xgb.ggplot.shap.summary(agaricus.test$data, contr, model = bst, top_n = 12) # Summary plot 147 xgb.plot.shap(x, model = mbst, trees = trees0, target_class = 0, top_n = 4, 149 xgb.plot.shap(x, model = mbst, trees = trees0 + 1, target_class = 1, top_n = 4, 151 xgb.plot.shap(x, model = mbst, trees = trees0 + 2, target_class = 2, top_n = 4, 153 xgb.ggplot.shap.summary(x, model = mbst, target_class = 0, top_n = 4) # Summary plot
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H A D | xgb.plot.shap.summary.Rd | 12 top_n = 10, 24 top_n = 10, 39 feature importance is calculated, and \code{top_n} high ranked features are taken.} 41 \item{top_n}{when \code{features} is NULL, top_n [1, 100] most important features in a model are ta…
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/dports/www/chromium-legacy/chromium-88.0.4324.182/native_client/tools/ |
H A D | test_timing.py | 47 top_n = 10 59 top_n = int(val) 82 for time, name, mode in analyzer.Top(top_n):
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/dports/net/mpich/mpich-3.4.3/modules/ucx/test/gtest/common/ |
H A D | test_helpers.cc | 236 int top_n; in analyze_test_results() local 239 top_n = std::numeric_limits<int>::max(); in analyze_test_results() 241 top_n = atoi(env_p); in analyze_test_results() 242 if (!top_n) { in analyze_test_results() 294 top_n = std::min((int)test_results.size(), top_n); in analyze_test_results() 295 if (!top_n) { in analyze_test_results() 300 int max_index_size = ucs::to_string(top_n).size(); in analyze_test_results() 301 std::cout << std::endl << "TOP-" << top_n << " longest tests:" << std::endl; in analyze_test_results() 303 for (int i = 0; i < top_n; i++) { in analyze_test_results()
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/dports/www/chromium-legacy/chromium-88.0.4324.182/third_party/catapult/common/py_utils/py_utils/ |
H A D | memory_debug.py | 44 def LogHostMemoryUsage(top_n=10, level=logging.INFO): argument 61 logging.log(level, 'Memory usage of top %i processes groups', top_n) 76 top_n,
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/dports/www/qt5-webengine/qtwebengine-everywhere-src-5.15.2/src/3rdparty/chromium/third_party/catapult/common/py_utils/py_utils/ |
H A D | memory_debug.py | 44 def LogHostMemoryUsage(top_n=10, level=logging.INFO): argument 61 logging.log(level, 'Memory usage of top %i processes groups', top_n) 76 top_n,
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/dports/textproc/py-nltk/nltk-3.4.1/nltk/sentiment/ |
H A D | sentiment_analyzer.py | 79 def unigram_word_feats(self, words, top_n=None, min_freq=0): argument 93 for w, f in unigram_feats_freqs.most_common(top_n) 98 self, documents, top_n=None, min_freq=3, assoc_measure=BigramAssocMeasures.pmi argument 116 return finder.nbest(assoc_measure, top_n)
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/dports/mail/mimedefang/mimedefang-2.84/contrib/graphdefang-0.91/web/ |
H A D | graphdefang.cgi | 71 my $top_n = param('top_n'); 79 $settings{top_n} = $top_n;
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