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test.cpp
#include <sdtio.h> int main(void) { printf("Lorem ipsum. (ロレム・イプサム) 吾輩は猫である。"); }raw.githubusercontent.com/codelibs/fess-testdata/master/source_code/test.cpp -
test_nocharset.html
Lorem ipsum. (ロレム・イプサム) 吾輩は猫である。raw.githubusercontent.com/codelibs/fess-testdata/master/html/test_nocharset.html -
test.js
function foo() { var bar = "Lorem ipsum. (ロレム・イプサム) 吾輩は猫である。"; }raw.githubusercontent.com/codelibs/fess-testdata/master/source_code/test.js -
test.mm
<map version="0.7.1"> <node TEXT="Lorem ipsum. (ロレム・イプサム) 吾輩は猫である。"> </node> </map>raw.githubusercontent.com/codelibs/fess-testdata/master/xml/test.mm -
test.java
test.javapackage com.foo.bar; public class Test { public String...String test="Lorem ipsum. (ロレム・イプサム) 吾輩は猫である。"; } 5...raw.githubusercontent.com/codelibs/fess-testdata/master/source_code/test.java -
test.h
// Lorem ipsum. (ロレム・イプサム) 吾輩は猫である。 typedef int & Integer;raw.githubusercontent.com/codelibs/fess-testdata/master/source_code/test.h -
cross_validation.rst.txt
available data as a **test set** ``X_test, y_test``. Note that the...>>> X_train, X_test, y_train, y_test = train_test_split( ... X,...scikit-learn.org/stable/_sources/modules/cross_validation.rst.txt -
feature_selection.rst.txt
based on univariate statistical tests. It can be seen as a preprocessing...common univariate statistical tests for each feature: false positive...scikit-learn.org/stable/_sources/modules/feature_selection.rst.txt -
model_evaluation.rst.txt
ratio (pre-test and post-tests): .. math:: \text{post-test odds} =...>>> X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)...scikit-learn.org/stable/_sources/modules/model_evaluation.rst.txt -
plot_multi_metric_evaluation.rst.txt
``'_<scorer_name>'`` (``'mean_test_precision'``, ``'rank_test_precision'``, etc...)...style in (("train", "--"), ("test", "-")): sample_score_mean =...scikit-learn.org/stable/_sources/auto_examples/model_selection/plot_multi_metric_evaluation.rst.txt