updated translation files

Author: github-actions

inst/qml/translations/jaspMachineLearning-zh_Hant.qm

@@ -1,15 +1,9 @@
 msgid ""
 msgstr ""
-"PO-Revision-Date: 2025-11-03 08:51+0000\n"
-"Last-Translator: Johannes Keyser <[email protected]>\n"
-"Language-Team: German <https://hosted.weblate.org/projects/jasp/"
-"jaspmachinelearning-qml/de/>\n"
-"Language: de\n"
 "MIME-Version: 1.0\n"
 "Content-Type: text/plain; charset=UTF-8\n"
 "Content-Transfer-Encoding: 8bit\n"
-"Plural-Forms: nplurals=2; plural=n != 1;\n"
-"X-Generator: Weblate 5.14.1-dev\n"
+"Plural-Forms: nplurals=2; plural=(n != 1);\n"
 "X-Language: de\n"
 "X-Source-Language: American English\n"
 "X-Qt-Contexts: true\n"

po/QML-de.po

@@ -1,15 +1,9 @@
 msgid ""
 msgstr ""
-"PO-Revision-Date: 2025-11-03 08:51+0000\n"
-"Last-Translator: 陳寬裕 <[email protected]>\n"
-"Language-Team: Chinese (Traditional Han script) <https://hosted.weblate.org/"
-"projects/jasp/jaspmachinelearning-qml/zh_Hant/>\n"
-"Language: zh_Hant\n"
 "MIME-Version: 1.0\n"
 "Content-Type: text/plain; charset=UTF-8\n"
 "Content-Transfer-Encoding: 8bit\n"
 "Plural-Forms: nplurals=1; plural=0;\n"
-"X-Generator: Weblate 5.14.1-dev\n"
 "X-Language: zh_Hant\n"
 "X-Qt-Contexts: true\n"
 
@@ -2041,7 +2035,8 @@ msgctxt "ClusterDetermination|"
 msgid ""
 "Enables you to generate a fixed amount of clusters. This allows you to "
 "generate your own specified number of clusters, and thus, optimize manually."
-msgstr "可讓你生成固定數量的集群。這表示你可以自行指定要分成的集群，並以此手動進行最"
+msgstr ""
+"可讓你生成固定數量的集群。這表示你可以自行指定要分成的集群，並以此手動進行最"
 "佳化。"
 
 msgctxt "ClusterDetermination|"
@@ -2073,9 +2068,9 @@ msgid ""
 "uses the similarity of observations within a cluster and their dissimilarity "
 "to other clusters for optimizing the clustering output."
 msgstr ""
-"最佳化方法的選項包括 AIC、BIC 和 Silhouette(輪廓係數)。AIC 是根據群內平方和("
-"群內變異量)、生成的群數，以及資料的維度數來最佳化群集結果。BIC 則是根據群內平"
-"方和(群內變異量)、生成的群數、維度數，以及樣本大小來最佳化群集結果。"
+"最佳化方法的選項包括 AIC、BIC 和 Silhouette(輪廓係數)。AIC 是根據群內平方和"
+"(群內變異量)、生成的群數，以及資料的維度數來最佳化群集結果。BIC 則是根據群內"
+"平方和(群內變異量)、生成的群數、維度數，以及樣本大小來最佳化群集結果。"
 "Silhouette(輪廓係數)則透過比較同一群內觀測值之間的相似程度，以及它們與其他群"
 "之間的不相似程度，來評估並最佳化群集結果。"