From 62edd308c50cb8cb6e49d566d952aca1ed47cde1 Mon Sep 17 00:00:00 2001 From: Yu Gong Date: Wed, 20 Nov 2024 23:29:33 +0800 Subject: [PATCH 1/2] translate whole QML-zh_Hans.po --- po/QML-zh_Hans.po | 960 +++++++++++++++++++++++----------------------- 1 file changed, 487 insertions(+), 473 deletions(-) diff --git a/po/QML-zh_Hans.po b/po/QML-zh_Hans.po index a9536ceb..347ae068 100644 --- a/po/QML-zh_Hans.po +++ b/po/QML-zh_Hans.po @@ -89,11 +89,11 @@ msgstr "聚类分析" msgctxt "Description|" msgid "Density-Based" -msgstr "基于密度的" +msgstr "基于密度" msgctxt "Description|" msgid "Density-Based Clustering" -msgstr "密度聚类" +msgstr "基于密度聚类" msgctxt "Description|" msgid "Fuzzy C-Means" @@ -117,23 +117,23 @@ msgstr "随机森林聚类" msgctxt "DataSplit|" msgid "Data Split Preferences" -msgstr "数据分割选项" +msgstr "数据拆分设置" msgctxt "DataSplit|" msgid "Holdout Test Data" -msgstr "测试数据（留出法）" +msgstr "测试数据(留出法)" msgctxt "DataSplit|" msgid "Sample" -msgstr "样本" +msgstr "抽样" msgctxt "DataSplit|" msgid "% of all data" -msgstr "全部数据的%" +msgstr "%的数据" msgctxt "DataSplit|" msgid "Add generated indicator to data" -msgstr "将生成的指标添加到数据中" +msgstr "添加生成的指标到数据集" msgctxt "DataSplit|" msgid "None" @@ -141,7 +141,7 @@ msgstr "无" msgctxt "DataSplit|" msgid "Training and Validation Data" -msgstr "训练和验证数据" +msgstr "训练和验证集" msgctxt "DataSplit|" msgid "% for validation data" @@ -173,7 +173,7 @@ msgstr "训练参数" msgctxt "mlClassificationBoosting|" msgid "Algorithmic Settings" -msgstr "算法设置" +msgstr "算法参数" msgctxt "mlClassificationKnn|" msgid "Tables" @@ -189,7 +189,7 @@ msgstr "训练参数" msgctxt "mlClassificationKnn|" msgid "Algorithmic Settings" -msgstr "算法设置" +msgstr "算法参数" msgctxt "mlClassificationLda|" msgid "Tables" @@ -205,7 +205,7 @@ msgstr "先验和后验概率" msgctxt "mlClassificationLda|" msgid "Class means training data" -msgstr "" +msgstr "训练数据的类均值" msgctxt "mlClassificationLda|" msgid "Assumption Checks" @@ -213,7 +213,7 @@ msgstr "假设检验" msgctxt "mlClassificationLda|" msgid "Equality of class means" -msgstr "" +msgstr "类均值的相等性" msgctxt "mlClassificationLda|" msgid "Equality of covariance matrices" @@ -245,7 +245,7 @@ msgstr "训练参数" msgctxt "mlClassificationLda|" msgid "Algorithmic Settings" -msgstr "算法设置" +msgstr "算法参数" msgctxt "mlClassificationRandomForest|" msgid "Tables" @@ -261,7 +261,7 @@ msgstr "训练参数" msgctxt "mlClassificationRandomForest|" msgid "Algorithmic Settings" -msgstr "算法设置" +msgstr "算法参数" msgctxt "mlClusteringDensityBased|" msgid "Tables" @@ -277,11 +277,11 @@ msgstr "K-距离图" msgctxt "mlClusteringDensityBased|" msgid "Training Parameters" -msgstr "" +msgstr "训练参数" msgctxt "mlClusteringDensityBased|" msgid "Algorithmic Settings" -msgstr "训练参数" +msgstr "算法参数" msgctxt "mlClusteringDensityBased|" msgid "Normal" @@ -313,7 +313,7 @@ msgstr "训练参数" msgctxt "mlClusteringFuzzyCMeans|" msgid "Algorithmic Settings" -msgstr "算法设置" +msgstr "算法参数" msgctxt "mlClusteringHierarchical|" msgid "Tables" @@ -333,131 +333,131 @@ msgstr "训练参数" msgctxt "mlClusteringHierarchical|" msgid "Algorithmic Settings" -msgstr "算法设置" +msgstr "算法参数" msgctxt "mlClusteringHierarchical|" msgid "Euclidean" -msgstr "欧氏" +msgstr "欧式距离" msgctxt "mlClusteringHierarchical|" msgid "Average" -msgstr "Average" +msgstr "组平均" msgctxt "mlClusteringHierarchical|" msgid "Single" -msgstr "" +msgstr "单链" msgctxt "mlClusteringHierarchical|" msgid "Complete" -msgstr "" +msgstr "全链" msgctxt "mlClusteringHierarchical|" msgid "Centroid" -msgstr "" +msgstr "质心" msgctxt "mlClusteringHierarchical|" msgid "Median" -msgstr "" +msgstr "中位数" msgctxt "mlClusteringHierarchical|" msgid "Ward.D" -msgstr "" +msgstr "Ward.D" msgctxt "mlClusteringHierarchical|" msgid "Ward.D2" -msgstr "" +msgstr "Ward.D2" msgctxt "mlClusteringHierarchical|" msgid "McQuitty" -msgstr "" +msgstr "McQuitty" msgctxt "mlClusteringKMeans|" msgid "Tables" -msgstr "" +msgstr "表" msgctxt "mlClusteringKMeans|" msgid "Plots" -msgstr "" +msgstr "图" msgctxt "mlClusteringKMeans|" msgid "Training Parameters" -msgstr "" +msgstr "训练参数" msgctxt "mlClusteringKMeans|" msgid "Algorithmic Settings" -msgstr "" +msgstr "算法参数" msgctxt "mlClusteringRandomForest|" msgid "Tables" -msgstr "" +msgstr "表" msgctxt "mlClusteringRandomForest|" msgid "Plots" -msgstr "" +msgstr "图" msgctxt "mlClusteringRandomForest|" msgid "Training Parameters" -msgstr "" +msgstr "训练参数" msgctxt "mlClusteringRandomForest|" msgid "Algorithmic Settings" -msgstr "" +msgstr "算法参数" msgctxt "mlRegressionBoosting|" msgid "Tables" -msgstr "" +msgstr "表" msgctxt "mlRegressionBoosting|" msgid "Plots" -msgstr "" +msgstr "图" msgctxt "mlRegressionBoosting|" msgid "Training Parameters" -msgstr "" +msgstr "训练参数" msgctxt "mlRegressionBoosting|" msgid "Algorithmic Settings" -msgstr "" +msgstr "算法参数" msgctxt "mlRegressionKnn|" msgid "Tables" -msgstr "" +msgstr "表" msgctxt "mlRegressionKnn|" msgid "Plots" -msgstr "" +msgstr "图" msgctxt "mlRegressionKnn|" msgid "Training Parameters" -msgstr "" +msgstr "训练参数" msgctxt "mlRegressionKnn|" msgid "Algorithmic Settings" -msgstr "" +msgstr "算法参数" msgctxt "mlRegressionRandomForest|" msgid "Tables" -msgstr "" +msgstr "表" msgctxt "mlRegressionRandomForest|" msgid "Plots" -msgstr "" +msgstr "图" msgctxt "mlRegressionRandomForest|" msgid "Training Parameters" -msgstr "" +msgstr "训练参数" msgctxt "mlRegressionRandomForest|" msgid "Algorithmic Settings" -msgstr "" +msgstr "算法参数" msgctxt "mlRegressionRegularized|" msgid "Tables" -msgstr "" +msgstr "表" msgctxt "mlRegressionRegularized|" msgid "Plots" -msgstr "" +msgstr "图" msgctxt "mlRegressionRegularized|" msgid "Variable trace" @@ -465,466 +465,466 @@ msgstr "" msgctxt "mlRegressionRegularized|" msgid "Legend" -msgstr "" +msgstr "图例" msgctxt "mlRegressionRegularized|" msgid "λ evaluation" -msgstr "" +msgstr "λ估计" msgctxt "mlRegressionRegularized|" msgid "Training Parameters" -msgstr "" +msgstr "训练参数" msgctxt "mlRegressionRegularized|" msgid "Algorithmic Settings" -msgstr "" +msgstr "算法参数" msgctxt "mlRegressionRegularized|" msgid "Elastic net" -msgstr "" +msgstr "弹性网络" msgctxt "mlRegressionRegularized|" msgid "Lambda (λ)" -msgstr "" +msgstr "Lambda (λ)" msgctxt "mlRegressionRegularized|" msgid "Fixed" -msgstr "" +msgstr "固定" msgctxt "mlRegressionRegularized|" msgid "Optimized" -msgstr "" +msgstr "最优" msgctxt "Description|" msgid "Neural Network" -msgstr "" +msgstr "神经网络" msgctxt "Description|" msgid "Neural Network Regression" -msgstr "" +msgstr "神经网络回归" msgctxt "Description|" msgid "Neural Network Classification" -msgstr "" +msgstr "神经网络分类" msgctxt "Description|" msgid "Prediction" -msgstr "" +msgstr "预测" msgctxt "ExportResults|" msgid "Export Results" -msgstr "" +msgstr "导出结果" msgctxt "ExportResults|" msgid "Add predictions to data" -msgstr "" +msgstr "添加预测到数据集" msgctxt "ExportResults|" msgid "Column name" -msgstr "" +msgstr "列名" msgctxt "ExportResults|" msgid "e.g., predicted" -msgstr "" +msgstr "如预测" msgctxt "ExportResults|" msgid "Save as" -msgstr "" +msgstr "另存为" msgctxt "ExportResults|" msgid "e.g., location/model.jaspML" -msgstr "" +msgstr "如路径/model.jaspML" msgctxt "ExportResults|" msgid "Save trained model" -msgstr "" +msgstr "保存训练模型" msgctxt "mlClassificationNeuralNetwork|" msgid "Tables" -msgstr "" +msgstr "表" msgctxt "mlClassificationNeuralNetwork|" msgid "Plots" -msgstr "" +msgstr "图" msgctxt "mlClassificationNeuralNetwork|" msgid "Training Parameters" -msgstr "" +msgstr "训练参数" msgctxt "mlClassificationNeuralNetwork|" msgid "Algorithmic Settings" -msgstr "" +msgstr "算法参数" msgctxt "mlRegressionNeuralNetwork|" msgid "Tables" -msgstr "" +msgstr "表" msgctxt "mlRegressionNeuralNetwork|" msgid "Plots" -msgstr "" +msgstr "图" msgctxt "mlRegressionNeuralNetwork|" msgid "Training Parameters" -msgstr "" +msgstr "训练参数" msgctxt "mlRegressionNeuralNetwork|" msgid "Algorithmic Settings" -msgstr "" +msgstr "算法参数" msgctxt "DataSplit|" msgid "Column name" -msgstr "" +msgstr "列名" msgctxt "DataSplit|" msgid "Test set indicator" -msgstr "" +msgstr "测试集标签" msgctxt "mlClassificationLda|" msgid "Estimation method" -msgstr "" +msgstr "估计方法" msgctxt "mlClusteringDensityBased|" msgid "Epsilon neighborhood size" -msgstr "" +msgstr "Epsilon邻域大小" msgctxt "mlClusteringDensityBased|" msgid "Min. core points" -msgstr "" +msgstr "最小核心点" msgctxt "mlClusteringDensityBased|" msgid "Distance" -msgstr "" +msgstr "距离" msgctxt "mlClusteringFuzzyCMeans|" msgid "Max. iterations" -msgstr "" +msgstr "最大迭代次数" msgctxt "mlClusteringFuzzyCMeans|" msgid "Fuzziness parameter" -msgstr "" +msgstr "模糊度参数" msgctxt "mlClusteringHierarchical|" msgid "Distance" -msgstr "" +msgstr "距离" msgctxt "mlClusteringHierarchical|" msgid "Pearson" -msgstr "" +msgstr "皮尔森" msgctxt "mlClusteringHierarchical|" msgid "Linkage" -msgstr "" +msgstr "簇间距离" msgctxt "mlClusteringKMeans|" msgid "Max. iterations" -msgstr "" +msgstr "最大迭代次数" msgctxt "mlClusteringKMeans|" msgid "Random sets" -msgstr "" +msgstr "随机" msgctxt "mlClusteringKMeans|" msgid "Algorithm" -msgstr "" +msgstr "算法参数" msgctxt "mlClusteringRandomForest|" msgid "Trees" -msgstr "" +msgstr "树" msgctxt "mlRegressionBoosting|" msgid "Loss function" -msgstr "" +msgstr "损失函数" msgctxt "mlRegressionRegularized|" msgid "Convergence threshold" -msgstr "" +msgstr "收敛阈值" msgctxt "mlRegressionRegularized|" msgid "Penalty" -msgstr "" +msgstr "惩罚" msgctxt "mlRegressionRegularized|" msgid "Lasso" -msgstr "" +msgstr "Lasso回归" msgctxt "mlRegressionRegularized|" msgid "Ridge" -msgstr "" +msgstr "岭回归" msgctxt "mlRegressionRegularized|" msgid "Elastic net parameter (α)" -msgstr "" +msgstr "弹性网络参数(α)" msgctxt "mlRegressionRegularized|" msgid "Largest λ within 1 SE of min" -msgstr "" +msgstr "最小误+1se时最大λ" msgctxt "Description|" msgid "Decision Tree" -msgstr "" +msgstr "决策树" msgctxt "Description|" msgid "Decision Tree Regression" -msgstr "" +msgstr "决策树回归" msgctxt "Description|" msgid "Support Vector Machine" -msgstr "" +msgstr "支持向量机" msgctxt "Description|" msgid "Support Vector Machine Regression" -msgstr "" +msgstr "支持向量机回归" msgctxt "Description|" msgid "Decision Tree Classification" -msgstr "" +msgstr "决策树分类" msgctxt "Description|" msgid "Support Vector Machine Classification" -msgstr "" +msgstr "支持向量机分类" msgctxt "Description|" msgid "Neighborhood-Based" -msgstr "" +msgstr "基于邻域" msgctxt "Description|" msgid "Neighborhood-Based Clustering" -msgstr "" +msgstr "基于邻域聚类" msgctxt "mlClassificationDecisionTree|" msgid "Tables" -msgstr "" +msgstr "表" msgctxt "mlClassificationDecisionTree|" msgid "Plots" -msgstr "" +msgstr "图" msgctxt "mlClassificationDecisionTree|" msgid "Training Parameters" -msgstr "" +msgstr "训练参数" msgctxt "mlClassificationDecisionTree|" msgid "Algorithmic Settings" -msgstr "" +msgstr "算法参数" msgctxt "mlClassificationSvm|" msgid "Tables" -msgstr "" +msgstr "表" msgctxt "mlClassificationSvm|" msgid "Plots" -msgstr "" +msgstr "图" msgctxt "mlClassificationSvm|" msgid "Training Parameters" -msgstr "" +msgstr "训练参数" msgctxt "mlClassificationSvm|" msgid "Algorithmic Settings" -msgstr "" +msgstr "算法参数" msgctxt "mlClusteringKMeans|" msgid "Center type" -msgstr "" +msgstr "中心类型" msgctxt "mlClusteringKMeans|" msgid "Distance" -msgstr "" +msgstr "距离" msgctxt "mlClusteringKMeans|" msgid "Euclidean" -msgstr "" +msgstr "欧式距离" msgctxt "mlClusteringKMeans|" msgid "Manhattan" -msgstr "" +msgstr "曼哈顿距离" msgctxt "mlRegressionDecisionTree|" msgid "Tables" -msgstr "" +msgstr "表" msgctxt "mlRegressionDecisionTree|" msgid "Plots" -msgstr "" +msgstr "图" msgctxt "mlRegressionDecisionTree|" msgid "Training Parameters" -msgstr "" +msgstr "训练参数" msgctxt "mlRegressionDecisionTree|" msgid "Algorithmic Settings" -msgstr "" +msgstr "算法参数" msgctxt "mlRegressionSvm|" msgid "Tables" -msgstr "" +msgstr "表" msgctxt "mlRegressionSvm|" msgid "Plots" -msgstr "" +msgstr "图" msgctxt "mlRegressionSvm|" msgid "Training Parameters" -msgstr "" +msgstr "训练参数" msgctxt "mlRegressionSvm|" msgid "Algorithmic Settings" -msgstr "" +msgstr "算法参数" msgctxt "mlPrediction|" msgid "Trained model" -msgstr "" +msgstr "训练的模型" msgctxt "mlPrediction|" msgid "e.g., location/model.jaspML" -msgstr "" +msgstr "如路径/model.jaspML" msgctxt "mlPrediction|" msgid "Features" -msgstr "" +msgstr "特征" msgctxt "mlPrediction|" msgid "Algorithmic Settings" -msgstr "" +msgstr "算法参数" msgctxt "mlPrediction|" msgid "Tables" -msgstr "" +msgstr "表" msgctxt "mlPrediction|" msgid "to" -msgstr "" +msgstr "到" msgctxt "Description|" msgid "Linear" -msgstr "" +msgstr "线性" msgctxt "Description|" msgid "Linear Regression" -msgstr "" +msgstr "线性回归" msgctxt "AlgorithmicSettings|" msgid "Shrinkage" -msgstr "" +msgstr "压缩" msgctxt "AlgorithmicSettings|" msgid "" "A shrinkage parameter applied to each tree in the expansion. Also known as " "the learning rate or step-size reduction 0.001 to 0.1 usually work, but a " "smaller learning rate typically requires more trees." -msgstr "" +msgstr "应用于扩展中每棵树的压缩参数。也称为学习率或缩小步长，通常为0.001至0.1，但较小的学习率通常需要更多的树。" msgctxt "AlgorithmicSettings|" msgid "Interaction depth" -msgstr "" +msgstr "交互深度" msgctxt "AlgorithmicSettings|" msgid "" "Integer specifying the maximum depth of each tree (i.e., the highest level " "of variable interactions allowed. A value of 1 implies an additive model, a " "value of 2 implies a model with up to 2-way interactions, etc. Default is 1." -msgstr "" +msgstr "整数，指定每棵树的最大深度（即允许的最高变量交互水平）。值1意味着一个加法模型，值2意味着一个最多有2路交互作用的模型，以此类推。默认值为 1。" msgctxt "AlgorithmicSettings|" msgid "Min. observations in node" -msgstr "" +msgstr "节点中的最小观测" msgctxt "AlgorithmicSettings|" msgid "" "Integer specifying the minimum number of observations in the terminal nodes " "of the trees. Note that this is the actual number of observations, not the " "total weight." -msgstr "" +msgstr "整数，指定树的叶节点中观测值的最小数量。注意，这是实际观测值的数量，而不是总权重。" msgctxt "AlgorithmicSettings|" msgid "Training data used per tree" -msgstr "" +msgstr "每棵树使用的训练数据" msgctxt "AlgorithmicSettings|" msgid "" "Select the percentage of training data that is used to train each individual " "tree." -msgstr "" +msgstr "选择用于训练每棵树的训练数据的百分比" msgctxt "Deviance|" msgid "Deviance" -msgstr "" +msgstr "偏差" msgctxt "Deviance|" msgid "Shows the prediction error plotted against the number of trees." -msgstr "" +msgstr "显示预测误差与树的数量对比图。" msgctxt "ModelOptimization|" msgid "Number of Trees" -msgstr "" +msgstr "树的数量" msgctxt "ModelOptimization|" msgid "Choose how to optimize the model." -msgstr "" +msgstr "选择优化模型的方法。" msgctxt "ModelOptimization|" msgid "Fixed" -msgstr "" +msgstr "固定" msgctxt "ModelOptimization|" msgid "Enables you to use a user-specified number of decision trees." -msgstr "" +msgstr "允许用户指定数量的决策树。" msgctxt "ModelOptimization|" msgid "Trees" -msgstr "" +msgstr "树" msgctxt "ModelOptimization|" msgid "The number of trees." -msgstr "" +msgstr "树的数量" msgctxt "ModelOptimization|" msgid "Optimized" -msgstr "" +msgstr "最优" msgctxt "ModelOptimization|" msgid "" "Enables you to optimize the prediction error on a validation data set with " "respect to the number of trees." -msgstr "" +msgstr "使您能够根据树的数量优化验证数据集上的预测误差。" msgctxt "ModelOptimization|" msgid "Max. trees" -msgstr "" +msgstr "最大的树数目" msgctxt "ModelOptimization|" msgid "" "Sets the maximum number of possible decision trees to be considered. At " "default, this is set to 100." -msgstr "" +msgstr "设置决策树的最大数量。默认设置为 100。" msgctxt "Oob|" msgid "Out-of-bag improvement" -msgstr "" +msgstr "袋外改进" msgctxt "Oob|" msgid "" "Plots the number of trees against the out-of-bag classification accuracy " "improvement of the model. Accuracy is assessed for the training set." -msgstr "" +msgstr "树的数量与模型的袋外分类准确率的关系图。准确率是针对训练集进行评估的。" msgctxt "RelativeInfluence|" msgid "Relative influence" -msgstr "" +msgstr "相对影响" msgctxt "RelativeInfluence|" msgid "Shows the relative influence of the features." -msgstr "" +msgstr "显示特征的相对影响。" msgctxt "AlgorithmicSettings|" msgid "Min. observations for split" -msgstr "" +msgstr "分割的最小数据量" msgctxt "AlgorithmicSettings|" msgid "" "The minimum number of observations that must exist in a node in order for a " "split to be attempted." -msgstr "" +msgstr "节点中的最小观测数，以便尝试分割。" msgctxt "AlgorithmicSettings|" msgid "Min. observations in terminal" @@ -932,19 +932,19 @@ msgstr "" msgctxt "AlgorithmicSettings|" msgid "The minimum number of observations in any terminal node." -msgstr "" +msgstr "叶节点的最小观测数" msgctxt "AlgorithmicSettings|" msgid "Max. interaction depth" -msgstr "" +msgstr "最大交互深度" msgctxt "AlgorithmicSettings|" msgid "Set the maximum depth of any node of the final tree." -msgstr "" +msgstr "设置树的最大深度。" msgctxt "AttemptedSplits|" msgid "Attempted splits" -msgstr "" +msgstr "尝试过的拆分" msgctxt "AttemptedSplits|" msgid "" @@ -952,27 +952,27 @@ msgid "" "points, and the number of observations (which are not missing and are of " "positive weight) sent left or right by the split. It also shows the " "improvement in deviance given by the splits." -msgstr "" +msgstr "显示算法所做的拆分、相应的特征和拆分点，以及拆分向左或向右的观测值（不缺失且权重为正）的数量。它还显示了拆分后偏差的改善情况。" msgctxt "AttemptedSplits|" msgid "Only show splits in tree" -msgstr "" +msgstr "只显示树中的分割点" msgctxt "AttemptedSplits|" msgid "Remove splits that do not occur in the final tree from the table." -msgstr "" +msgstr "从表中删除最终树中没有出现的分割。" msgctxt "TreePlot|" msgid "Decision tree" -msgstr "" +msgstr "决策树" msgctxt "TreePlot|" msgid "Creates a plot that visualizes the decision tree and its leafs." -msgstr "" +msgstr "创建可视化决策树图。" msgctxt "AlgorithmicSettings|" msgid "Weights" -msgstr "" +msgstr "权重" msgctxt "AlgorithmicSettings|" msgid "Rectangular" @@ -1019,65 +1019,65 @@ msgid "" "Sets the weighting scheme for the nearest neighbors. The default rectangular " "option results in standard knn, while the other options expand the algorithm " "by weighing the nearest neighbors. See also the kknn package." -msgstr "" +msgstr "设置近邻的加权方案。默认矩形选项的结果是标准 knn，而其他选项则通过对近邻进行加权来扩展算法。另请参见 kknn 软件包。" msgctxt "AlgorithmicSettings|" msgid "Distance" -msgstr "" +msgstr "距离" msgctxt "AlgorithmicSettings|" msgid "Euclidian" -msgstr "" +msgstr "欧式距离" msgctxt "AlgorithmicSettings|" msgid "Manhattan" -msgstr "" +msgstr "曼哈顿距离" msgctxt "AlgorithmicSettings|" msgid "" "The distance metric to be used when determining the similarity between " "nearest neighbors. Can be either Euclidean or Manhattan distance." -msgstr "" +msgstr "确定近邻之间相似性时使用的距离度量。可以是欧氏距离或曼哈顿距离。" msgctxt "ModelOptimization|" msgid "Number of Nearest Neighbors" -msgstr "" +msgstr "最近邻的数量" msgctxt "ModelOptimization|" msgid "Enables you to use a user-specified number of nearest neighbors." -msgstr "" +msgstr "允许指定最近邻数量。" msgctxt "ModelOptimization|" msgid "Nearest neighbors" -msgstr "" +msgstr "最近邻" msgctxt "ModelOptimization|" msgid "The number of nearest neighbors to be used." -msgstr "" +msgstr "最近邻的数量。" msgctxt "ModelOptimization|" msgid "" "Enables you to optimize the prediction error on a validation data set with " "respect to the number of nearest neighbors." -msgstr "" +msgstr "根据最近邻数量优化验证数据集上的预测误差。" msgctxt "ModelOptimization|" msgid "Max. nearest neighbors" -msgstr "" +msgstr "最近邻的最大数量" msgctxt "ModelOptimization|" msgid "" "Sets the maximum number of possible nearest neighbors to be considered. At " "default, this is set to 10." -msgstr "" +msgstr "设置要考虑的可能近邻的最大数量。默认设置为 10。" msgctxt "OptimPlot|" msgid "Mean squared error" -msgstr "" +msgstr "均方误差" msgctxt "OptimPlot|" msgid "Classification accuracy" -msgstr "" +msgstr "分类准确率" msgctxt "OptimPlot|" msgid "" @@ -1086,27 +1086,27 @@ msgid "" "classification, plots the number of nearest neighbors against the " "classification accuracy of the model. Accuracy is assessed for the training " "(and validation) set." -msgstr "" +msgstr "对于回归，绘制近邻数与模型MSE的对比图。对训练集（和验证集）进行精度评估。对于分类，绘制近邻数与模型分类准确率的对比图。对训练集（和验证集）进行准确性评估。" msgctxt "WeightFunction|" msgid "Weight function" -msgstr "" +msgstr "权重函数" msgctxt "WeightFunction|" msgid "Shows how the weights are assigned as a function of the distance." -msgstr "" +msgstr "显示权重的分配与距离的函数关系。" msgctxt "ActivationFunctionPlot|" msgid "Activation function" -msgstr "" +msgstr "激活函数" msgctxt "ActivationFunctionPlot|" msgid "Creates a plot of the activation function." -msgstr "" +msgstr "绘制激活函数图。" msgctxt "AlgorithmicSettings|" msgid "Activation function" -msgstr "" +msgstr "激活函数" msgctxt "AlgorithmicSettings|" msgid "Linear" @@ -1118,7 +1118,7 @@ msgstr "" msgctxt "AlgorithmicSettings|" msgid "Logistic sigmoid" -msgstr "" +msgstr "sigmoid" msgctxt "AlgorithmicSettings|" msgid "Sine" @@ -1184,15 +1184,31 @@ msgid "" "- Mish: *f(x) = x * tanh(log(1 + e^x))*\n" "- Gaussian: *f(x) = e * (-x^2)*\n" "- GeLU: *f(x) = 0.5 * x * (1 + tanh(sqrt(2 / pi) * (x + 0.044715 * x^3)))*" -msgstr "" +msgstr "为每个隐藏层的信号设置激活函数。可用选项有：\n" +"- linear: *f(x) = x*\n" +"- Binary: *f(x) = 0 if x < 0, 1 if x > 0\n" +"- Logistic sigmoid: *f(x) = 1 / (1 + e^(-x))*\n" +"- Sine: *f(x) = sin(x)*\n" +"- Cosine: *f(x) = cos(x)*\n" +"- Inverse tangent: *f(x) = arctan(x)*\n" +"- Hyperbolic tangent: *f(x) = tanh(x)*\n" +"- ReLU: *f(x) = 0 if x < 0, x if x > 0*\n" +"- Softplus: *f(x) = log(1 + e^x)*\n" +"- Softsign: *f(x) = x / (abs(x) + 1)*\n" +"- ELU: *f(x) = e^x - 1 if x <= 0, x if x > 0*\n" +"- LReLU: *f(x) = 0.01 * x if x < 0, x if x > 0*\n" +"- SiLU: *f(x) = x / (1 + e^(-x))*\n" +"- Mish: *f(x) = x * tanh(log(1 + e^x))*\n" +"- Gaussian: *f(x) = e * (-x^2)*\n" +"- GeLU: *f(x) = 0.5 * x * (1 + tanh(sqrt(2 / pi) * (x + 0.044715 * x^3)))*" msgctxt "AlgorithmicSettings|" msgid "Algorithm" -msgstr "" +msgstr "算法" msgctxt "AlgorithmicSettings|" msgid "Backpropagation" -msgstr "" +msgstr "反向传播" msgctxt "AlgorithmicSettings|" msgid "rprop+" @@ -1220,110 +1236,110 @@ msgid "" "smallest absolute gradient, or `gprop-slr` for the globally convergent " "algorithm that modifies the learning rate associated with the smallest " "learning rate itself." -msgstr "" +msgstr "设置网络训练的算法。反向传播（backpropagation）选项是训练神经网络的标准算法，其他选项包括：“rprop+”（默认），用于带反向跟踪的弹性反向传播；“rprop-”，用于不带反向跟踪的弹性反向传播；“gprop-sag”，用于修改与最小绝对梯度相关的学习率的全局收敛算法；或 “gprop-slr”，用于修改与最小学习率本身相关的学习率的全局收敛算法。" msgctxt "AlgorithmicSettings|" msgid "Learning rate" -msgstr "" +msgstr "学习率" msgctxt "AlgorithmicSettings|" msgid "The learning rate used by the backpropagation algorithm." -msgstr "" +msgstr "反向传播算法使用的学习率。" msgctxt "AlgorithmicSettings|" msgid "Loss function" -msgstr "" +msgstr "损失函数" msgctxt "AlgorithmicSettings|" msgid "Sum of squares" -msgstr "" +msgstr "平方和" msgctxt "AlgorithmicSettings|" msgid "Cross-entropy" -msgstr "" +msgstr "交叉熵" msgctxt "AlgorithmicSettings|" msgid "The loss function used." -msgstr "" +msgstr "使用的损失函数。" msgctxt "AlgorithmicSettings|" msgid "Stopping criteria loss function" -msgstr "" +msgstr "损失函数停止条件" msgctxt "AlgorithmicSettings|" msgid "" "The threshold for the partial derivatives of the error function as stopping " "criteria." -msgstr "" +msgstr "停止标准：误差函数偏导数的阈值" msgctxt "AlgorithmicSettings|" msgid "Max. training repetitions" -msgstr "" +msgstr "最大训练重复次数" msgctxt "AlgorithmicSettings|" msgid "The maximum number of repetitions used in training the network." -msgstr "" +msgstr "训练网络时使用的最大重复次数" msgctxt "Coefficients|" msgid "Network weights" -msgstr "" +msgstr "网络权重" msgctxt "Coefficients|" msgid "" "Shows the connections in the neural network together with their weights." -msgstr "" +msgstr "显示神经网络中的连接及其权重。" msgctxt "ModelOptimization|" msgid "Network Topology" -msgstr "" +msgstr "网络拓扑" msgctxt "ModelOptimization|" msgid "Manual" -msgstr "" +msgstr "手动" msgctxt "ModelOptimization|" msgid "Specify the nodes in each hidden layer of the neural network." -msgstr "" +msgstr "指定神经网络每个隐藏层的节点。" msgctxt "ModelOptimization|" msgid "Nodes" -msgstr "" +msgstr "节点" msgctxt "ModelOptimization|" msgid "Hidden layer " -msgstr "" +msgstr "隐藏层" msgctxt "ModelOptimization|" msgid "Optimize the topology of the network using a genetic algorithm." -msgstr "" +msgstr "使用遗传算法优化网络拓扑结构" msgctxt "ModelOptimization|" msgid "Population size" -msgstr "" +msgstr "种群数量" msgctxt "ModelOptimization|" msgid "Size of population used in genetic optimization." -msgstr "" +msgstr "遗传优化中使用的种群数量" msgctxt "ModelOptimization|" msgid "Generations" -msgstr "" +msgstr "代数" msgctxt "ModelOptimization|" msgid "Number of generations used in genetic optimization." -msgstr "" +msgstr "遗传优化所用的代数" msgctxt "ModelOptimization|" msgid "Max. number of layers" -msgstr "" +msgstr "最大层数" msgctxt "ModelOptimization|" msgid "Max. nodes in each layer" -msgstr "" +msgstr "每层最大节点数" msgctxt "ModelOptimization|" msgid "Parent selection" -msgstr "" +msgstr "父代选择" msgctxt "ModelOptimization|" msgid "Roulette wheel" @@ -1347,19 +1363,19 @@ msgstr "" msgctxt "ModelOptimization|" msgid "How to select suviving networks." -msgstr "" +msgstr "如何选择suviving网络" msgctxt "ModelOptimization|" msgid "Candidates" -msgstr "" +msgstr "候选个体" msgctxt "ModelOptimization|" msgid "Number of candidates for tournament selection" -msgstr "" +msgstr "候选个体数" msgctxt "ModelOptimization|" msgid "Crossover method" -msgstr "" +msgstr "交叉方法" msgctxt "ModelOptimization|" msgid "Uniform" @@ -1375,11 +1391,11 @@ msgstr "" msgctxt "ModelOptimization|" msgid "How to crossover two candidate networks." -msgstr "" +msgstr "如何交叉两个候选网络" msgctxt "ModelOptimization|" msgid "Mutations" -msgstr "" +msgstr "突变" msgctxt "ModelOptimization|" msgid "Reset" @@ -1399,19 +1415,19 @@ msgstr "" msgctxt "ModelOptimization|" msgid "How to mutate a network." -msgstr "" +msgstr "如何改变网络" msgctxt "ModelOptimization|" msgid "Probability" -msgstr "" +msgstr "概率" msgctxt "ModelOptimization|" msgid "The mutation probability of a random network in each generation." -msgstr "" +msgstr "每一代随机网络的突变概率" msgctxt "ModelOptimization|" msgid "Survival method" -msgstr "" +msgstr "生存方法" msgctxt "ModelOptimization|" msgid "Fitness-based" @@ -1423,29 +1439,29 @@ msgstr "" msgctxt "ModelOptimization|" msgid "How to choose which networks survive and die in a generation." -msgstr "" +msgstr "在一代如何选择中网络生存和消亡" msgctxt "ModelOptimization|" msgid "Elitism" -msgstr "" +msgstr "精英" msgctxt "ModelOptimization|" msgid "Keep top networks from dying out." -msgstr "" +msgstr "防止顶层网络消亡" msgctxt "ModelOptimization|" msgid "Percentage of top networks to keep." -msgstr "" +msgstr "保留的顶层网络百分比" msgctxt "NetworkPlot|" msgid "Network structure" -msgstr "" +msgstr "网络结构" msgctxt "NetworkPlot|" msgid "" "Creates a plot that visualizes the structure (nodes and edges) of the " "network." -msgstr "" +msgstr "创建可视化网络结构（节点和边沿）的图表" msgctxt "OptimPlot|" msgid "" @@ -1455,132 +1471,132 @@ msgid "" "accuracy of the population of neural networks against the number of " "generations in the evoluationary optimization algorithm. Accuracy is " "assessed for the training (and validation) set." -msgstr "" +msgstr "对于回归，绘制神经网络群体的平均均方误差与进化优化算法的代数对比图。对于分类，绘制神经网络群的平均分类 “准确率 ”与进化优化算法的代数对比图。准确率针对训练（和验证）集进行评估。" msgctxt "AccuracyDecrease|" msgid "Mean decrease in accuracy" -msgstr "" +msgstr "准确率平均下降率" msgctxt "AccuracyDecrease|" msgid "" "Displays a figure with the mean decrease in accuracy per feature in the " "model." -msgstr "" +msgstr "显示模型中每个特征准确率的平均下降率" msgctxt "AlgorithmicSettings|" msgid "Features per split" -msgstr "" +msgstr "拆分使用的特征数" msgctxt "AlgorithmicSettings|" msgid "Auto" -msgstr "" +msgstr "自动" msgctxt "AlgorithmicSettings|" msgid "Manual" -msgstr "" +msgstr "手工" msgctxt "AlgorithmicSettings|" msgid "" "Set the number of feature variables that is used within each split in the " "decision trees. Defaults to auto." -msgstr "" +msgstr "设置决策树每次分割时使用特征的数量。默认为自动" msgctxt "AlgorithmicSettings|" msgid "The number of feature variables in each split." -msgstr "" +msgstr "每次分割的特征数量" msgctxt "ModelOptimization|" msgid "The number of trees to be used." -msgstr "" +msgstr "树的数量" msgctxt "NodePurity|" msgid "Total increase in node purity" -msgstr "" +msgstr "节点纯度的增长" msgctxt "NodePurity|" msgid "" "Displays a figure with total increase in node purity per feature in the " "model." -msgstr "" +msgstr "显示模型中每个特征节点纯度总增加量" msgctxt "Oob|" msgid "Out-of-bag error" -msgstr "" +msgstr "袋外误差" msgctxt "Oob|" msgid "Out-of-bag accuracy" -msgstr "" +msgstr "袋外准确率" msgctxt "Oob|" msgid "" "Plots the number of trees against the out-of-bag mean squared error " "(regression) or accuracy (classification) of the model." -msgstr "" +msgstr "将树的数量与模型的袋外均方误差（回归）或准确率（分类）进行对比分析" msgctxt "CoefficientTable|" msgid "Coefficients" -msgstr "" +msgstr "系数" msgctxt "CoefficientTable|" msgid "Shows a table containing the regression coefficients." -msgstr "" +msgstr "显示包含回归系数的表" msgctxt "CoefficientTable|" msgid "Confidence interval" -msgstr "" +msgstr "置信区间" msgctxt "CoefficientTable|" msgid "Display confidence intervals around estimated regression coefficients." -msgstr "" +msgstr "显示回归系数的置信区间" msgctxt "CoefficientTable|" msgid "The confidence level for the interval." -msgstr "" +msgstr "区间的置信水平" msgctxt "CoefficientTable|" msgid "Display equation" -msgstr "" +msgstr "显示公式" msgctxt "CoefficientTable|" msgid "" "Display the regression equation with the estimated values of the " "coefficients." -msgstr "" +msgstr "显示带有系数估计值的回归方程" msgctxt "Intercept|" msgid "Include intercept" -msgstr "" +msgstr "包括截距" msgctxt "Intercept|" msgid "Whether to include an intercept in the regression formula." -msgstr "" +msgstr "是否在回归公式中加入截距。" msgctxt "VariablesFormRegularizedRegression|" msgid "Target" -msgstr "" +msgstr "目标变量" msgctxt "VariablesFormRegularizedRegression|" msgid "In this box, the variable that needs to be predicted should be entered." -msgstr "" +msgstr "在此框中，输入需要预测的变量" msgctxt "VariablesFormRegularizedRegression|" msgid "Features" -msgstr "" +msgstr "特征" msgctxt "VariablesFormRegularizedRegression|" msgid "" "In this box, the variables that provide information about the target " "variable should be entered." -msgstr "" +msgstr "在此框中，输入为目标变量提供信息的变量" msgctxt "VariablesFormRegularizedRegression|" msgid "Weights" -msgstr "" +msgstr "权重" msgctxt "VariablesFormRegularizedRegression|" msgid "" "In this box, an optional variable containing case weights can be entered." -msgstr "" +msgstr "在此框中，输入包含样本权重的可选变量" msgctxt "AlgorithmicSettings|" msgid "Radial" @@ -1598,7 +1614,7 @@ msgctxt "AlgorithmicSettings|" msgid "" "The kernel used in training and predicting. Possible kernels are 'linear', " "'radial', 'polynomial', and 'sigmoid'." -msgstr "" +msgstr "用于训练和预测的内核。可能的核有'linear', 'radial', 'polynomial', and 'sigmoid'" msgctxt "AlgorithmicSettings|" msgid "Degree" @@ -1606,23 +1622,23 @@ msgstr "" msgctxt "AlgorithmicSettings|" msgid "The degree of polynomial used." -msgstr "" +msgstr "多项式的次数" msgctxt "AlgorithmicSettings|" msgid "Gamma parameter" -msgstr "" +msgstr "Gamma参数" msgctxt "AlgorithmicSettings|" msgid "The gamma parameter used." -msgstr "" +msgstr "Gamma参数" msgctxt "AlgorithmicSettings|" msgid "r parameter" -msgstr "" +msgstr "r参数" msgctxt "AlgorithmicSettings|" msgid "The complexity parameter used." -msgstr "" +msgstr "复杂度参数" msgctxt "AlgorithmicSettings|" msgid "Tolerance of termination criterion" @@ -1630,129 +1646,129 @@ msgstr "" msgctxt "AlgorithmicSettings|" msgid "The tolerance of termination criterion." -msgstr "" +msgstr "终止标准的容差" msgctxt "AlgorithmicSettings|" msgid "Epsilon" -msgstr "" +msgstr "epsilon参数" msgctxt "AlgorithmicSettings|" msgid "The epsilon parameter in the insensitive-loss function." -msgstr "" +msgstr "不敏感损失函数中的epsilon参数" msgctxt "SupportVectors|" msgid "Support vectors" -msgstr "" +msgstr "支持向量" msgctxt "SupportVectors|" msgid "" "Shows a table containing the data (points) indicated as support vectors by " "the algorithm." -msgstr "" +msgstr "显示作为支持向量的数据点的表格" msgctxt "AndrewsCurve|" msgid "Andrews curves" -msgstr "" +msgstr " 区分度曲线" msgctxt "AndrewsCurve|" msgid "" "Is a way to visualize structure in high-dimensional data. Lines that cluster " "are observations that are more alike." -msgstr "" +msgstr "是一种将多维数据可视化的方法，可以让我们看到不同类别之间的差异" msgctxt "ClusterDensity|" msgid "Cluster densities" -msgstr "" +msgstr "簇密度" msgctxt "ClusterDensity|" msgid "" "For each feature variable, generates a plot showing the overlapping " "densities for the clusters." -msgstr "" +msgstr "针对每个特征变量，生成显示簇重叠密度的曲线图" msgctxt "ClusterDensity|" msgid "Group into one figure" -msgstr "" +msgstr "组合成一个图形" msgctxt "ClusterDensity|" msgid "Group the density plots per feature into a single figure." -msgstr "" +msgstr "将每个特征的密度图组合成一张图" msgctxt "ClusterMatrix|" msgid "Cluster matrix plot" -msgstr "" +msgstr "聚类矩阵图" msgctxt "ClusterMatrix|" msgid "" "Creates a *n* x *n* plot that visualizes to which cluster every observation " "belongs according to the current model." -msgstr "" +msgstr "创建 *n* x *n* 图形，根据当前模型直观显示每个观测值所属的簇" msgctxt "ClusterMeans|" msgid "Cluster means" -msgstr "" +msgstr "簇平均值" msgctxt "ClusterMeans|" msgid "" "Creates a plot that visualizes and compares the mean of the feature " "variables in each cluster." -msgstr "" +msgstr "创建图表，直观显示并比较每个簇中特征变量的平均值" msgctxt "ClusterMeans|" msgid "Display barplot" -msgstr "" +msgstr "显示条形图" msgctxt "ClusterMeans|" msgid "Transform the cluster mean figure into a barplot." -msgstr "" +msgstr "将簇均值图转化为条形图" msgctxt "ClusterMeans|" msgid "Group into one figure" -msgstr "" +msgstr "组合成一个图形" msgctxt "ClusterMeans|" msgid "Group the plots per feature into a single figure." -msgstr "" +msgstr "将每个特征的图组合成一张图" msgctxt "DataSplit|" msgid "Data split" -msgstr "" +msgstr "数据拆分" msgctxt "DataSplit|" msgid "" "Shows how the data is split into training (and validation), and test set." -msgstr "" +msgstr "显示如何将数据分为训练集（和验证集）和测试集" msgctxt "DecisionBoundary|" msgid "Decision boundary matrix" -msgstr "" +msgstr "决策边界矩阵" msgctxt "DecisionBoundary|" msgid "" "Creates a *n* x *n* plot that visualizes how every observation would be " "classified if predicted through the current model. Boundaries between " "classes are visualized. Can only be made for numeric features." -msgstr "" +msgstr "创建 *n* x *n* 图形，直观显示每个观测值在当前模型预测下的分类情况,可视化类与类之间的界限。只能用于数字特征。" msgctxt "DecisionBoundary|" msgid "Legend" -msgstr "" +msgstr "图例" msgctxt "DecisionBoundary|" msgid "Show a legend next to the figure." -msgstr "" +msgstr "显示图例" msgctxt "DecisionBoundary|" msgid "Add data points" -msgstr "" +msgstr "添加数据点" msgctxt "DecisionBoundary|" msgid "Show the observations in the data set as points in the plot." -msgstr "" +msgstr "将数据集中的观测显示为图中的点" msgctxt "ElbowMethod|" msgid "Elbow method" -msgstr "" +msgstr "肘部法" msgctxt "ElbowMethod|" msgid "" @@ -1760,29 +1776,29 @@ msgid "" "number of clusters on the x-axis. This plot can be used for determining the " "optimal number of clusters. The plot shows three curves using AIC, BIC, and " "'elbow method' optimization." -msgstr "" +msgstr "生成一幅图，Y轴为总内平方和，X轴为聚类个数。该图可用于确定最佳聚类数。该图显示了使用 AIC、BIC 和 “肘部法”优化的三条曲线" msgctxt "PredictivePerformance|" msgid "Predictive performance" -msgstr "" +msgstr "预测性能" msgctxt "PredictivePerformance|" msgid "" "Plots the true values of the observations in the test set against their " "predicted values." -msgstr "" +msgstr "绘制测试集的真实值与预测值的对比图" msgctxt "RocCurve|" msgid "ROC curves" -msgstr "" +msgstr "ROC曲线" msgctxt "RocCurve|" msgid "Displays ROC curves for each class predicted against all other classes." -msgstr "" +msgstr "显示针对所有其他类别预测的每个类别的 ROC 曲线。" msgctxt "Tsne|" msgid "t-SNE cluster plot" -msgstr "" +msgstr "t-SNE聚类图" msgctxt "Tsne|" msgid "" @@ -1794,39 +1810,39 @@ msgid "" "distances between observations and clusters. To recreate the same t-SNE plot " "across several clustering analyses you can set their seed to the same value, " "as the t-SNE algorithm uses random starting values." -msgstr "" +msgstr "t-SNE图用于可视化高维数据，在二维空间中数据之间的相对距离。t-SNE的坐标轴无法解释。t-SNE图旨在显示观测值和簇之间的相对距离。要在多个聚类分析中重现相同的t-SNE图，需要将它们的种子值设置为相同，因为t-SNE算法使用随机初始值" msgctxt "Tsne|" msgid "Legend" -msgstr "" +msgstr "图例" msgctxt "Tsne|" msgid "Show a legend next to the figure." -msgstr "" +msgstr "显示图例" msgctxt "Tsne|" msgid "Add data labels" -msgstr "" +msgstr "添加数据标签" msgctxt "Tsne|" msgid "" "Add the row numbers of the observations in the data set as labels to the " "plot." -msgstr "" +msgstr "将数据集中观测值的行号作为标签添加到绘图中" msgctxt "ClassProportions|" msgid "Class proportions" -msgstr "" +msgstr "类的分布" msgctxt "ClassProportions|" msgid "" "Displays a table that shows the proportions of each class in the data set, " "training (and validaton), and test set." -msgstr "" +msgstr "显示一个表格，显示数据集、训练集（和验证集）以及测试集中每个类的比例。" msgctxt "ClusterInfo|" msgid "Cluster information" -msgstr "" +msgstr "簇信息" msgctxt "ClusterInfo|" msgid "" @@ -1834,108 +1850,108 @@ msgid "" "cluster heterogeneity. The latter is the cluster within sum of squares " "divided by its total over the various clusters. These outputs are shown by " "default." -msgstr "" +msgstr "显示每个簇的大小和簇内异质性的解释比例。后者是簇内的内平方和除以各簇的总和。默认显示这些输出结果" msgctxt "ClusterInfo|" msgid "Within sum of squares" -msgstr "" +msgstr "内平方和" msgctxt "ClusterInfo|" msgid "" "Adds a row with the within sum of squares of each cluster to the table. This " "option is selected by default." -msgstr "" +msgstr "在表中显示每个簇的内平方和。默认选择此选项" msgctxt "ClusterInfo|" msgid "Silhouette score" -msgstr "" +msgstr "轮廓系数" msgctxt "ClusterInfo|" msgid "Adds a row with the silhouette score of each cluster to the table." -msgstr "" +msgstr "在表中显示每个簇的轮廓系数。" msgctxt "ClusterInfo|" msgid "Centers" -msgstr "" +msgstr "中心" msgctxt "ClusterInfo|" msgid "" "Adds a row with the center per feature of each cluster to the table. The " "center can be the mean, median or mode depending on the clustering algorithm." -msgstr "" +msgstr "在表格中添加一行，显示每个特征的每个簇的中心，中心可以是平均值、中位数或中心点，具体取决于聚类算法" msgctxt "ClusterInfo|" msgid "Between sum of squares" -msgstr "" +msgstr "外平方和" msgctxt "ClusterInfo|" msgid "" "Adds a note with the between sum of squares of the cluster model to the " "table." -msgstr "" +msgstr "在表中添加聚类模型的外平方和。" msgctxt "ClusterInfo|" msgid "Total sum of squares" -msgstr "" +msgstr "整体平方和" msgctxt "ClusterInfo|" msgid "" "Adds a note with the total sum of squares of the cluster model to the table." -msgstr "" +msgstr "在表中添加聚类模型总平方和。" msgctxt "ClusterMeans|" msgid "Shows a table containing the cluster means for each feature variable." -msgstr "" +msgstr "表中显示包含各个特征的簇的均值。" msgctxt "ConfusionMatrix|" msgid "Confusion matrix" -msgstr "" +msgstr "混淆矩阵" msgctxt "ConfusionMatrix|" msgid "" "Displays a table that shows the observed classes against the predicted " "classes. Used to assess model accuracy." -msgstr "" +msgstr "显示实际的类与预测类的对照表，用于评估模型的准确性。" msgctxt "ConfusionMatrix|" msgid "Display proportions" -msgstr "" +msgstr "显示比例" msgctxt "ConfusionMatrix|" msgid "Displays proportions in the confusion matrix instead of counts." -msgstr "" +msgstr "显示比例而不是数值" msgctxt "ExplainPredictions|" msgid "Explain predictions" -msgstr "" +msgstr "解释预测结果" msgctxt "ExplainPredictions|" msgid "Cases" -msgstr "" +msgstr "案例" msgctxt "ExplainPredictions|" msgid "The test set index of the first row to be displayed in the table." -msgstr "" +msgstr "测试集的起始行号" msgctxt "ExplainPredictions|" msgid "to" -msgstr "" +msgstr "到" msgctxt "ExplainPredictions|" msgid "The test set index of the last row to be displayed in the table." -msgstr "" +msgstr "测试集的结束行号" msgctxt "FeatureImportance|" msgid "Feature importance" -msgstr "" +msgstr "特征重要性" msgctxt "FeatureImportance|" msgid "Shows the available feature importance metrics for the fitted model." -msgstr "" +msgstr "显示模型中特征的重要性" msgctxt "ModelPerformance|" msgid "Model performance" -msgstr "" +msgstr "模型性能" msgctxt "ModelPerformance|" msgid "" @@ -1943,43 +1959,43 @@ msgid "" "include mean squared error (MSE), root mean squared error (RMSE), R2 and more. For classification, these metrics include precision, recall, " "the F1-score, support, AUC (area under the ROC curve) and more." -msgstr "" +msgstr "显示可用的度量方法. 对于回归，这些指标包括均方误差 (MSE)、均方根误差 (RMSE)、R2等。对于分类，这些指标包括精度、召回率、F1分数、支持度、AUC（ROC曲线下方面积）等。" msgctxt "ClusterDetermination|" msgid "Cluster Determination" -msgstr "" +msgstr "确定簇数量" msgctxt "ClusterDetermination|" msgid "Choose how to determine the number of clusters in the model." -msgstr "" +msgstr "确定簇数目" msgctxt "ClusterDetermination|" msgid "Fixed" -msgstr "" +msgstr "固定" 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 "" +msgstr "生成固定数目的簇，从而进行手动优化。" msgctxt "ClusterDetermination|" msgid "Clusters" -msgstr "" +msgstr "簇" msgctxt "ClusterDetermination|" msgid "The number of clusters to be fitted." -msgstr "" +msgstr "拟合簇的数目" msgctxt "ClusterDetermination|" msgid "Optimized according to" -msgstr "" +msgstr "根据以下标准进行优化" msgctxt "ClusterDetermination|" msgid "" "Enables you to choose an optimization method. BIC optimization is set as " "default." -msgstr "" +msgstr "可以选择优化方法。默认设置为 BIC 优化。" msgctxt "ClusterDetermination|" msgid "" @@ -1991,41 +2007,41 @@ msgid "" "the sample size for optimizing the clustering output. The silhouette value " "uses the similarity of observations within a cluster and their dissimilarity " "to other clusters for optimizing the clustering output." -msgstr "" +msgstr "优化方法。选项有AIC、BIC和轮廓系数。AIC使用内部平方和、生成的簇数和维数来优化聚类输出。BIC使用内部平方和、生成的簇数、维数和样本量来优化聚类输出。轮廓系数使用聚类内观测值的相似性及与其他聚类的不相似性来优化聚类输出。" msgctxt "ClusterDetermination|" msgid "Max. clusters" -msgstr "" +msgstr "最大簇数量" msgctxt "ClusterDetermination|" msgid "" "Sets the maximum number of possible clusters to be generated. At default, " "this is set to 10." -msgstr "" +msgstr "设置可能生成的簇的最大数量。默认设置为 10" msgctxt "DataSplit|" msgid "Choose how to create the test set." -msgstr "" +msgstr "选择如何创建测试集" msgctxt "DataSplit|" msgid "" "Choose a percentage to randomly sample from your data to derive prediction " "error. Generates an internal indicator variable that indicates whether the " "observation is included (1) or excluded (0) from the test set." -msgstr "" +msgstr "从数据中按照百分比进行随机抽样；生成内部变量，表示观测是否包含（1）或排除（0）在测试集中" msgctxt "DataSplit|" msgid "The percentage of observations to use for the test set." -msgstr "" +msgstr "用于测试集的观测的百分比" msgctxt "DataSplit|" msgid "" "Add the generated test set indicator from the option above to your data set." -msgstr "" +msgstr "将上述选项生成的测试集指标添加到数据集中" msgctxt "DataSplit|" msgid "The column name for the generated test set indicator." -msgstr "" +msgstr "测试集指标的列名" msgctxt "DataSplit|" msgid "" @@ -2034,60 +2050,60 @@ msgid "" "test set) and 1 (included in the test set). The data will then be split into " "a training (and validation if requested) set (0), and a test set (1) " "according to your indicator." -msgstr "" +msgstr "使用指标变量为测试集选择数据。该指标应该是数据中的一列，只包含0（排除在测试集中）和1（包含在测试集中）。然后，数据将分成训练集（和验证集，如果需要的话）（0）和测试集（1）。" msgctxt "DataSplit|" msgid "The variable in the data set that is used as the test set indicator." -msgstr "" +msgstr "数据集中用作测试集指标的变量" msgctxt "DataSplit|" msgid "Choose how to create the validation set." -msgstr "" +msgstr "选择如何创建验证集。" msgctxt "DataSplit|" msgid "" "Randomly sample a percentage from the remaining training data (after " "selecting the test set)." -msgstr "" +msgstr "从剩余的训练数据中随机抽样一个百分比（在选择测试集之后）" msgctxt "DataSplit|" msgid "The percentage of observations to use for the validation set." -msgstr "" +msgstr "用于验证集的观测的百分比" msgctxt "DataSplit|" msgid "Partition the remaining data in *k* parts." -msgstr "" +msgstr "将剩余数据分成 *k* 部分" msgctxt "DataSplit|" msgid "The number of folds to be used." -msgstr "" +msgstr "使用的折数" msgctxt "DataSplit|" msgid "Partition the remaining data in *n* parts." -msgstr "" +msgstr "将剩余数据分成 *n* 部分" msgctxt "ExportResults|" msgid "" "Generates a new column in your dataset with the values of your regression " "result. This gives you the option to inspect, cluster, or predict the " "generated values." -msgstr "" +msgstr "在数据集中生成一列新的回归结果值。您可以选择对生成的值进行检查、聚类或预测。" msgctxt "ExportResults|" msgid "The column name for the predicted values." -msgstr "" +msgstr "预测值的列名" msgctxt "ExportResults|" msgid "The file path for the saved model." -msgstr "" +msgstr "保存模型的路径" msgctxt "ExportResults|" msgid "When clicked, the model is exported to the specified file path." -msgstr "" +msgstr "点击后，模型将导出到指定的路径中" msgctxt "ScaleVariables|" msgid "Scale features" -msgstr "" +msgstr "特征缩放" msgctxt "ScaleVariables|" msgid "" @@ -2096,68 +2112,68 @@ msgid "" "scale. As a result, standardizing provides numerical stability. JASP uses " "the Z-score standardization of a mean of 0 and a standard deviation of 1. " "This option is selected by default." -msgstr "" +msgstr "将数据集中的连续特征标准化。标准化可确保不同尺度的特征在相似尺度范围内，可以提供数值稳定性。JASP使用平均值为0、标准差为1的Z-score标准化。默认情况下选择该选项" msgctxt "SetSeed|" msgid "Set seed" -msgstr "" +msgstr "设置种子" msgctxt "SetSeed|" msgid "" "Gives the option to set a seed for your analysis. Setting a seed will " "exclude random processes influencing an analysis. For example, setting a " "seed makes it possible to re-run analyses with the same data splits." -msgstr "" +msgstr "为分析设置种子。设置种子将消除分析的随机性。例如：设置相同种子值可以生成相同的数据拆分，复现分析。" msgctxt "SetSeed|" msgid "The value of the seed." -msgstr "" +msgstr "种子值" msgctxt "VariablesFormClassification|" msgid "Target" -msgstr "" +msgstr "目标变量" msgctxt "VariablesFormClassification|" msgid "In this box, the variable that needs to be predicted should be entered." -msgstr "" +msgstr "目标变量" msgctxt "VariablesFormClassification|" msgid "Features" -msgstr "" +msgstr "特征" msgctxt "VariablesFormClassification|" msgid "" "In this box, the variables that provide information about the target " "variable should be entered." -msgstr "" +msgstr "在此框中，输入为目标变量提供信息的变量" msgctxt "VariablesFormClustering|" msgid "Features" -msgstr "" +msgstr "特征" msgctxt "VariablesFormClustering|" msgid "" "In this box, the variables are need to be considered by the clustering " "algorithm should be entered." -msgstr "" +msgstr "在该框中，输入聚类算法需要的变量。" msgctxt "VariablesFormRegression|" msgid "Target" -msgstr "" +msgstr "目标变量" msgctxt "VariablesFormRegression|" msgid "In this box, the variable that needs to be predicted should be entered." -msgstr "" +msgstr "目标变量" msgctxt "VariablesFormRegression|" msgid "Features" -msgstr "" +msgstr "特征" msgctxt "VariablesFormRegression|" msgid "" "In this box, the variables that provide information about the target " "variable should be entered." -msgstr "" +msgstr "在此框中，输入为目标变量提供信息的变量" msgctxt "mlClassificationBoosting|" msgid "" @@ -2169,7 +2185,7 @@ msgid "" "### Assumptions\n" "- The target variable is a nominal or ordinal variable.\n" "- The feature variables consist of continuous, nominal, or ordinal variables." -msgstr "" +msgstr "提升法的工作原理是依次向决策树集合中添加树，每颗树尝试拟合模型的残差来修正模型。\n###假设\n目标变量是名义变量或有序变量。\n特征包括连续变量、名义变量或有序变量。" msgctxt "mlClassificationDecisionTree|" msgid "" @@ -2180,7 +2196,7 @@ msgid "" "### Assumptions\n" "- The target is a nominal or ordinal variable.\n" "- The feature variables consist of continuous, nominal, or ordinal variables." -msgstr "" +msgstr "决策树是一种有监督的学习算法，使用决策树作为预测模型。\n###假设\n目标是一个名义变量或有序变量。\n特征变量包括连续变量、名义变量或有序变量。" msgctxt "mlClassificationKnn|" msgid "" @@ -2192,7 +2208,7 @@ msgid "" "### Assumptions\n" "- The target is a nominal or ordinal variable.\n" "- The feature variables consist of continuous, nominal, or ordinal variables." -msgstr "" +msgstr "K最近邻是一种分类方法，它通过查看与新观测值最相似的*k*个特征观测值来预测其类别分配。近邻的数量与模型的复杂性有内在联系，因为数量越少，模型的灵活性就越大。\n###假设\n目标是一个名义变量或有序变量。\n特征变量包括连续变量、名义变量或有序变量。" msgctxt "mlClassificationLda|" msgid "" @@ -2210,11 +2226,11 @@ msgid "" "can be checked with the corresponding table.\n" "- Multicollinearity: The classes should not correlate within each other, can " "be checked with the corresponding table." -msgstr "" +msgstr "线性判别分析（LDA）是一种分类方法，其目的是找到*p-1*个分量，这些分量对目标变量中的类别区分效果最佳。LDA是一种线性分类器，这意味着类与类之间的决策边界是线性的。\n###假设\n-目标变量是一个名义变量或序数变量。\n-特征变量由连续、名义或有序变量组成。\n-类均值相等：类均值应该相等，可以用相应的表格检查。\n-协方差矩阵相等：协方差矩阵应该相等，可以用对应表进行检查。\n-多重共线性：类之间不应该有相关性，可以用对应表进行检查。" msgctxt "mlClassificationLda|" msgid "Multivariate normality" -msgstr "" +msgstr "多变量正态性" msgctxt "mlClassificationNeuralNetwork|" msgid "" @@ -2228,7 +2244,7 @@ msgid "" "### Assumptions\n" "- The target is a nominal or ordinal variable.\n" "- The feature variables consist of continuous variables." -msgstr "" +msgstr "前馈神经网络是一种分类算法，其灵感来源于大脑的生物神经网络。神经元（节点）接收信号后进行处理，并向与其相连的神经元发送信号。节点上的信号是实数，通过激活函数发送信号，计算出该节点的输出。网络中的层数和节点数与模型的复杂性有内在联系，因为层数和节点数越多，模型的灵活性就越大。\n###假设\n-目标变量是名义或有序变量\n-特征变量由连续变量组成。" msgctxt "mlClassificationRandomForest|" msgid "" @@ -2240,7 +2256,7 @@ msgid "" "### Assumptions\n" "- The target variable is a nominal or ordinal variable.\n" "- The feature variables consist of continuous, nominal, or ordinal variables." -msgstr "" +msgstr "随机森林是一种分类方法，它由大量的单个决策树组成，这些决策树作为一个整体运行。\n###假设\n-目标变量是名义或有序变量。\n-特征变量由连续、名义或有序变量组成。" msgctxt "mlClassificationSvm|" msgid "" @@ -2252,7 +2268,7 @@ msgid "" "### Assumptions\n" "- The target is a nominal or ordinal variable.\n" "- The feature variables consist of continuous, nominal, or ordinal variables." -msgstr "" +msgstr "支持向量机是一种有监督的学习算法，它将训练实例映射到空间中的点上，从而最大化两个类别之间的间隔。然后，新的实例会被映射到相同的空间中，并根据它们属于间隔的哪一边来预测属于哪个类别。\n###假设\n- 目标变量是连续变量\n-特征变量由连续、名义或有序变量组成" msgctxt "mlClusteringDensityBased|" msgid "" @@ -2269,7 +2285,7 @@ msgid "" "### Assumptions\n" "- The data consists of continuous variables.\n" "- (Normally distributed data aids the clustering process)." -msgstr "" +msgstr "基于密度的聚类是一种软聚类方法，簇是由密度超过某个阈值的点组成的最大点集。密度产生于这样一个概念：对于聚类中的每个点，给定半径内的邻域必须至少包含一定数量的点，从而使该邻域的密度超过某个阈值。基于密度的聚类由密度高于簇外的点来识别。所有高密度点的集合称为密集区域集。未超过密度水平的点被识别为离群点。密度水平会影响生成簇的数量。\n###假设\n-数据由连续变量组成。\n-正态分布的数据有助于聚类过程。" msgctxt "mlClusteringFuzzyCMeans|" msgid "" @@ -2282,7 +2298,7 @@ msgid "" "### Assumptions\n" "- The data consists of continuous variables.\n" "- (Normally distributed data aids the clustering process)." -msgstr "" +msgstr "模糊c-均值聚类是一种软分区方法，其输出结果包含每个观测值与每个簇的关联程度。这样，数据观测值就有可能被部分分配到多个聚类中，并给出聚类成员资格的置信度”。模糊c-均值的方法与k-均值方法非常相似，只是它采用的是软方法。\n###假设\n-数据由连续变量组成。\n-正态分布的数据有助于聚类过程。" msgctxt "mlClusteringHierarchical|" msgid "" @@ -2294,7 +2310,7 @@ msgid "" "### Assumptions\n" "- The data consists of continuous variables.\n" "- (Normally distributed data aids the clustering process)." -msgstr "" +msgstr "层次聚类是一种硬分区算法，其目的是将数据划分为若干个簇，其中每个观测值只属于一个簇。数据的划分方式是，如果两个数据观测值属于同一簇，则它们之间的相似度最大；如果不属于同一簇，则它们之间的相似度最小。\n###假设\n-数据由连续变量组成。\n-正态分布的数据有助于聚类过程。" msgctxt "mlClusteringKMeans|" msgid "" @@ -2306,7 +2322,7 @@ msgid "" "### Assumptions\n" "- The data consists of continuous variables.\n" "- (Normally distributed data aids the clustering process)." -msgstr "" +msgstr "基于近邻的聚类方法是一套硬分区算法，其目的是将数据划分为若干个簇，其中每个观测值只属于一个簇。数据的划分方式是，如果两个数据观测值属于同一簇，则它们之间的相似程度最大；如果不属于同一簇，则它们之间的相似程度最小。\n###假设\n-数据由连续变量组成。\n-正态分布的数据有助于聚类过程。" msgctxt "mlClusteringRandomForest|" msgid "" @@ -2320,19 +2336,25 @@ msgid "" "### Assumptions\n" "- The data consists of continuous variables.\n" "- (Normally distributed data aids the clustering process)." -msgstr "" +msgstr "随机森林聚类是一种硬分区算法，旨在将数据划分为若干个群组，其中每个观测值只属于一个簇。这种方法以无监督方式使用随机森林算法，目标变量y设置为空。随机森林算法会生成一个邻近度矩阵，该矩阵会根据观测出现在同一个叶节点的频率来估算观测之间的距离。\n###假设\n-数据由连续变量组成。\n-正态分布数据有助于聚类过程。" + +msgctxt "mlPrediction|" +msgid "" +"The prediction analysis enables you to load a trained machine learning model " +"and apply it to new data." +msgstr "通过预测分析，您可以加载训练过的机器学习模型，并将其应用于新数据。" msgctxt "mlPrediction|" msgid "Cases" -msgstr "" +msgstr "案例" msgctxt "mlPrediction|" msgid "Add features" -msgstr "" +msgstr "添加特征" msgctxt "mlPrediction|" msgid "Explain predictions" -msgstr "" +msgstr "解释预测" msgctxt "mlRegressionBoosting|" msgid "" @@ -2342,11 +2364,11 @@ msgid "" "### Assumptions\n" "- The target variable is a continuous variable.\n" "- The feature variables consist of continuous, nominal, or ordinal variables." -msgstr "" +msgstr "提升法的工作原理是将特征依次添加到决策树集合中，每一个模型都会对前一个模型进行修正。提升法试图使新模型与前一个模型的残余误差相匹配。\n### 假设\n-目标变量是连续变量。\n-特征变量由连续、名义或有序变量组成。" msgctxt "mlRegressionBoosting|" msgid "The loss function used." -msgstr "" +msgstr "使用的损失函数。" msgctxt "mlRegressionDecisionTree|" msgid "" @@ -2357,7 +2379,7 @@ msgid "" "### Assumptions\n" "- The target variable is a continuous variable.\n" "- The feature variables consist of continuous, nominal, or ordinal variables." -msgstr "" +msgstr "决策树是一种监督学习算法，它使用决策树作为预测模型，从对项的观察（以树根为代表）到对项的目标值的结论（以树的端点为代表）。\n###假设\n-目标变量是连续变量。\n-特征变量由连续、名义或有序变量组成。" msgctxt "mlRegressionKnn|" msgid "" @@ -2369,29 +2391,29 @@ msgid "" "### Assumptions\n" "- The target variable is a continuous variable.\n" "- The feature variables consist of continuous, nominal, or ordinal variables." -msgstr "" +msgstr "K 最近邻是一种回归方法，它考察与新观测最相似的*k*个观测值，从而预测它们的值。最近邻的数量与模型的复杂性有内在联系，小的最近邻数量会增加模型的灵活性。\n###假设\n-目标变量是连续变量。\n-特征变量由连续、名义或序数变量组成。" msgctxt "mlRegressionLinear|" msgid "" "Linear regression allows the user to model a linear relationship between one " "or more features (predictors) and a continuous dependent (target) variable." -msgstr "" +msgstr "线性回归允许用户建立一个或多个特征（预测因子）与连续因变量（目标变量）之间的线性关系模型。" msgctxt "mlRegressionLinear|" msgid "Tables" -msgstr "" +msgstr "表" msgctxt "mlRegressionLinear|" msgid "Plots" -msgstr "" +msgstr "图" msgctxt "mlRegressionLinear|" msgid "Training Parameters" -msgstr "" +msgstr "训练参数" msgctxt "mlRegressionLinear|" msgid "Algorithmic Settings" -msgstr "" +msgstr "算法参数" msgctxt "mlRegressionNeuralNetwork|" msgid "" @@ -2405,7 +2427,7 @@ msgid "" "### Assumptions\n" "- The target variable is a continuous variable.\n" "- The feature variables consist of continuous." -msgstr "" +msgstr "前馈神经网络是一种预测算法，其灵感来源于大脑的生物神经网络。神经元（节点）接收信号后进行处理，并向与其相连的神经元发送信号。节点上的信号是实数，通过激活函数发送信号，计算出该节点的输出。网络中的层数和节点数与模型的复杂性有内在联系，因为层数和节点数越多，模型的灵活性就越大。\n###假设\n-目标变量是连续变量\n-特征变量由连续变量组成。" msgctxt "mlRegressionRandomForest|" msgid "" @@ -2415,7 +2437,7 @@ msgid "" "### Assumptions\n" "- The target variable is a continuous variable.\n" "- The feature variables consist of continuous, nominal, or ordinal variables." -msgstr "" +msgstr "随机森林是一种回归方法，它由大量的单个决策树组成，这些决策树作为一个整体运行。\n###假设\n-目标变量是连续变量。\n-特征变量由连续、名义或有序变量组成。" msgctxt "mlRegressionRegularized|" msgid "" @@ -2426,7 +2448,7 @@ msgid "" "### Assumptions\n" "- The target variable is a continuous variable.\n" "- The feature variables consist of continuous, nominal, or ordinal variables." -msgstr "" +msgstr "正则化线性回归是对线性回归的一种调整，将系数缩减为0。参数λ控制参数缩小的程度。\n###假设\n-目标变量是连续变量。\n-特征变量由连续、名义或序数变量组成。" msgctxt "mlRegressionSvm|" msgid "" @@ -2438,49 +2460,49 @@ msgid "" "### Assumptions\n" "- The target variable is a continuous variable.\n" "- The feature variables consist of continuous, nominal, or ordinal variable" -msgstr "" +msgstr "支持向量机是一种有监督的学习算法，它将训练实例映射到空间中的点上，从而最大化两个类别之间的间隔。然后，新的实例会被映射到相同的空间中，并根据它们属于间隔的哪一边来预测属于哪个类别。\n###假设\n- 目标变量是连续变量\n-特征变量由连续、名义或有序变量组成" msgctxt "Description|" msgid "Naive Bayes" -msgstr "" +msgstr "朴素贝叶斯" msgctxt "Description|" msgid "Naive Bayes Classification" -msgstr "" +msgstr "朴素贝叶斯分类" msgctxt "ModelOptimization|" msgid "Tree Complexity" -msgstr "" +msgstr "树的复杂度" msgctxt "ModelOptimization|" msgid "Enables you to use a user-specified complexity penalty." -msgstr "" +msgstr "可以指定复杂度惩罚" msgctxt "ModelOptimization|" msgid "Complexity penalty" -msgstr "" +msgstr "复杂度惩罚" msgctxt "ModelOptimization|" msgid "" "The complexity penalty to be used. Any split that does not decrease the " "overall lack of fit by a factor of this parameter is not attempted." -msgstr "" +msgstr "使用的复杂度惩罚。如果拆分后的整体欠拟合程度没有降低到一定程度，则不尝试拆分。" msgctxt "ModelOptimization|" msgid "" "Enables you to optimize the prediction error on a validation data set with " "respect to the complexity penalty." -msgstr "" +msgstr "通过该功能，您可以根据复杂性惩罚优化验证集上的预测误差。" msgctxt "ModelOptimization|" msgid "Max. complexity penalty" -msgstr "" +msgstr "最大复杂度惩罚" msgctxt "ModelOptimization|" msgid "" "Sets the maximum value of the complexity penalty to be considered. At " "default, this is set to 1." -msgstr "" +msgstr "设置复杂度惩罚的最大值。默认设置为 1" msgctxt "OptimPlot|" msgid "" @@ -2489,35 +2511,35 @@ msgid "" "classification, plots the complexity penalty against the classification " "accuracy of the model. Accuracy is assessed for the training (and " "validation) set." -msgstr "" +msgstr "对于回归，绘制复杂性惩罚与模型MSE的对比图。对训练集（和验证集）进行精度评估。对于分类，将复杂度惩罚与模型的分类准确率相对照。对训练集（和验证集）进行准确率评估。" msgctxt "ModelOptimization|" msgid "Costs of Contraints Violation" -msgstr "" +msgstr "约束违反成本" msgctxt "ModelOptimization|" msgid "Enables you to use a user-specified cost of constraints violation." -msgstr "" +msgstr "使您可以使用指定的约束违反成本。" msgctxt "ModelOptimization|" msgid "Violation cost" -msgstr "" +msgstr "约束违反成本" msgctxt "ModelOptimization|" msgid "" "Enables you to optimize the prediction error on a validation data set with " "respect to the cost of constraints violation." -msgstr "" +msgstr "使您能够根据约束违反成本，优化验证数据集上的预测误差。" msgctxt "ModelOptimization|" msgid "Max. violation cost" -msgstr "" +msgstr "最大违反约束成本" msgctxt "ModelOptimization|" msgid "" "Sets the maximum value of the cost of constraints violation to be " "considered. At default, this is set to 5." -msgstr "" +msgstr "设置违反约束成本的最大值。默认设置为 5。" msgctxt "OptimPlot|" msgid "" @@ -2526,7 +2548,7 @@ msgid "" "classification, plots the cost of contraints violation against the " "classification accuracy of the model. Accuracy is assessed for the training " "(and validation) set." -msgstr "" +msgstr "对于回归，将违反约束条件的成本与模型的 MSE相比较。对训练集（和验证集）进行精度评估。对于分类，将违反约束条件的成本与模型的分类准确率进行对比。对训练集（和验证集）进行准确性评估。" msgctxt "mlClassificationNaiveBayes|" msgid "" @@ -2537,15 +2559,15 @@ msgid "" "- The target variable is a nominal or ordinal variable.\n" "- The features are independent.\n" "- The features are normally distributed given the target class." -msgstr "" +msgstr "朴素贝叶斯使用贝叶斯规则计算目标变量给定独立自变量情况下的条件后验概率。###假设\n-目标变量是名义变量或有序变量\n-特征是独立的\n-特征在目标类下是正态分布的\n" msgctxt "mlClassificationNaiveBayes|" msgid "Tables" -msgstr "" +msgstr "表" msgctxt "mlClassificationNaiveBayes|" msgid "Posterior statistics" -msgstr "" +msgstr "后验概率统计信息" msgctxt "mlClassificationNaiveBayes|" msgid "" @@ -2553,87 +2575,87 @@ msgid "" "contains the mean and standard deviation of the feature given the target " "class. For categorical features, the table displays the conditional " "probabilities given the target class." -msgstr "" +msgstr "显示后验统计量的表。对于数字特征，包含目标类别下特征的平均值和标准偏差。对于分类特征，显示目标类别的条件概率。" msgctxt "mlClassificationNaiveBayes|" msgid "Plots" -msgstr "" +msgstr "图" msgctxt "mlClassificationNaiveBayes|" msgid "Training Parameters" -msgstr "" +msgstr "训练参数" msgctxt "mlClassificationNaiveBayes|" msgid "Algorithmic Settings" -msgstr "" +msgstr "算法参数" msgctxt "mlClassificationNaiveBayes|" msgid "Smoothing parameter" -msgstr "" +msgstr "平滑参数" msgctxt "mlClassificationNaiveBayes|" msgid "" "A positive double controlling the amount of Laplace smoothing applied. The " "default (0) disables Laplace smoothing alltogether." -msgstr "" +msgstr "控制拉普拉斯平滑量的值。默认值（0）完全禁用拉普拉斯平滑。" msgctxt "FeatureImportance|" msgid "Permutations" -msgstr "" +msgstr "置换" msgctxt "FeatureImportance|" msgid "" "Sets the number of permutations on which the mean dropout loss is based." -msgstr "" +msgstr "设置平均dropout所依据的置换次数。" msgctxt "Description|" msgid "Model-Based" -msgstr "" +msgstr "基于模型" msgctxt "Description|" msgid "Model-Based Clustering" -msgstr "" +msgstr "基于模型聚类" msgctxt "ClusterMatrix|" msgid "Display components" -msgstr "" +msgstr "显示组成" msgctxt "mlClusteringModelBased|" msgid "" "Model-based clustering is based on parameterized finite Gaussian mixture " "models. The models are estimated by EM algorithm initialized by hierarchical " "model-based agglomerative clustering." -msgstr "" +msgstr "基于模型的聚类依赖参数化的有限高斯混合模型。模型通过EM算法进行估计，使用基于模型的层级聚类初始化。" msgctxt "mlClusteringModelBased|" msgid "Tables" -msgstr "" +msgstr "表" msgctxt "mlClusteringModelBased|" msgid "Parameter estimates" -msgstr "" +msgstr "参数估计" msgctxt "mlClusteringModelBased|" msgid "" "Shows tables containing the model parameters for each cluster and feature " "variable." -msgstr "" +msgstr "表包含每个簇和特征的模型参数。" msgctxt "mlClusteringModelBased|" msgid "Plots" -msgstr "" +msgstr "图" msgctxt "mlClusteringModelBased|" msgid "Training Parameters" -msgstr "" +msgstr "训练参数" msgctxt "mlClusteringModelBased|" msgid "Algorithmic Settings" -msgstr "" +msgstr "算法参数" msgctxt "mlClusteringModelBased|" msgid "Model" -msgstr "" +msgstr "模型" msgctxt "mlClusteringModelBased|" msgid "Auto" @@ -2693,27 +2715,27 @@ msgstr "" msgctxt "mlClusteringModelBased|" msgid "Choose the model to be fitted in the EM step of the clustering." -msgstr "" +msgstr "选择聚类EM步骤中要拟合模型。" msgctxt "mlClusteringModelBased|" msgid "Max. iterations" -msgstr "" +msgstr "最大迭代次数" msgctxt "mlClusteringModelBased|" msgid "The maximum number of iterations for the M-step in the algorithm." -msgstr "" +msgstr "算法中M步的最大迭代次数。" msgctxt "mlPrediction|" msgid "Predictions for new data" -msgstr "" +msgstr "新数据的预测" msgctxt "ConfusionMatrix|" msgid "Transpose matrix" -msgstr "" +msgstr "矩阵转置" msgctxt "ConfusionMatrix|" msgid "Transposes the confusion matrix." -msgstr "" +msgstr "转置混淆矩阵" msgctxt "ExplainPredictions|" msgid "" @@ -2721,19 +2743,19 @@ msgid "" "that can be attributed to individual model features. This feature uses the " "breakdown algoritm from the `ibreakdown` R package. For more details about " "this method, see Gosiewska and Biecek (2019)." -msgstr "" +msgstr "将模型预测结果分解为可归因于各特征的贡献。该功能使用R包ibreakdown中的分解算法。有关此方法的详情，请参阅Gosiewska and Biecek(2019)。" msgctxt "DataSplit|" msgid "e.g., testSet" -msgstr "" +msgstr "如测试集" msgctxt "Description|" msgid "Logistic / Multinomial" -msgstr "" +msgstr "逻辑斯蒂回归" msgctxt "Description|" msgid "Logistic / Multinomial Regression Classification" -msgstr "" +msgstr "逻辑斯蒂回归" msgctxt "mlClassificationLogisticMultinomial|" msgid "" @@ -2745,27 +2767,27 @@ msgid "" "handle target variables with more than two categories. Instead of predicting " "binary outcomes, multinomial regression is used for scenarios where the " "target variable has three or more unordered categories." -msgstr "" +msgstr "逻辑回归是一种统计方法，用于模拟二元目标变量与特征变量之间的关系。它使用逻辑斯蒂函数来预测概率，从而确保预测概率区间为[0,1]。多分类逻辑回归对逻辑回归进行了扩展，以处理目标变量具有两个以上类别的目标变量。" msgctxt "mlClassificationLogisticMultinomial|" msgid "Tables" -msgstr "" +msgstr "表" msgctxt "mlClassificationLogisticMultinomial|" msgid "Plots" -msgstr "" +msgstr "图" msgctxt "mlClassificationLogisticMultinomial|" msgid "Training Parameters" -msgstr "" +msgstr "训练参数" msgctxt "mlClassificationLogisticMultinomial|" msgid "Algorithmic Settings" -msgstr "" +msgstr "算法参数" msgctxt "mlClassificationLogisticMultinomial|" msgid "Link function (for binary classification)" -msgstr "" +msgstr "连接函数（用于二分类）" msgctxt "mlClassificationLogisticMultinomial|" msgid "Logit" @@ -2786,11 +2808,3 @@ msgstr "" msgctxt "mlClassificationLogisticMultinomial|" msgid "Log" msgstr "" - -msgctxt "mlPrediction|" -msgid "" -"The prediction analysis enables you to load a trained machine learning model " -"and apply it to new data. It is important that the features in the new " -"dataset have the same names as in the original dataset used for training." -msgstr "预测分析使您能够加载经过训练的机器学习模型并将其应用于新数据。新数据集中的特" -"征必须与训练数据集中的特征具有相同的名称。" From 58ff328bae504c89a9fb2c357bd3520ce765c63e Mon Sep 17 00:00:00 2001 From: Yu Gong Date: Wed, 20 Nov 2024 23:32:20 +0800 Subject: [PATCH 2/2] finish R-zh_Hans.po translate --- po/R-zh_Hans.po | 678 ++++++++++++++++++++++++------------------------ 1 file changed, 339 insertions(+), 339 deletions(-) diff --git a/po/R-zh_Hans.po b/po/R-zh_Hans.po index fecd977e..e5422a76 100644 --- a/po/R-zh_Hans.po +++ b/po/R-zh_Hans.po @@ -2,8 +2,8 @@ msgid "" msgstr "" "Project-Id-Version: jaspMachineLearning 0.17.2\n" "POT-Creation-Date: 2024-09-13 20:51\n" -"PO-Revision-Date: 2024-04-30 07:07+0000\n" -"Last-Translator: Cochrane \n" +"PO-Revision-Date: 2024-11-19 12:00+0000\n" +"Last-Translator: Yu Gong \n" "Language-Team: Chinese (Simplified) \n" "Language: zh_Hans\n" @@ -26,7 +26,7 @@ msgid "Random Forest Classification" msgstr "随机森林分类" msgid "Boosting Classification" -msgstr "增强(Boosting)分类" +msgstr "提升(Boosting)分类" msgid "Neural Network Classification" msgstr "神经网络分类" @@ -42,13 +42,13 @@ msgid "Naive Bayes Classification" msgstr "随机森林分类" msgid "Logistic / Multinomial Regression Classification" -msgstr "" +msgstr "逻辑斯蒂回归" msgid "Model Summary: %1$s" -msgstr "" +msgstr "模型概要：%1$s" msgid "Nearest neighbors" -msgstr "近邻" +msgstr "最近邻" msgid "Weights" msgstr "权重" @@ -60,16 +60,16 @@ msgid "Linear Discriminants" msgstr "线性判别" msgid "Method" -msgstr "" +msgstr "方法" msgid "Trees" msgstr "树" msgid "Features per split" -msgstr "" +msgstr "拆分使用的特征数" msgid "Shrinkage" -msgstr "" +msgstr "压缩" msgid "Hidden Layers" msgstr "隐藏层" @@ -78,74 +78,74 @@ msgid "Nodes" msgstr "节点" msgid "Complexity penalty" -msgstr "" +msgstr "复杂度惩罚" msgid "Splits" -msgstr "" +msgstr "拆分" msgid "Violation cost" -msgstr "" +msgstr "约束违反成本" msgid "Support Vectors" -msgstr "" +msgstr "支持向量" msgid "Smoothing" -msgstr "" +msgstr "平滑" msgid "Family" -msgstr "" +msgstr "函数族" msgid "Link" -msgstr "" +msgstr "链接" msgid "n(Train)" -msgstr "" +msgstr "n(训练)" msgid "n(Validation)" -msgstr "" +msgstr "n(验证)" msgid "n(Test)" -msgstr "" +msgstr "n(测试)" msgid "Validation Accuracy" msgstr "验证准确率" msgid "Test Accuracy" -msgstr "测试正确率" +msgstr "测试准确率" msgid "OOB Accuracy" -msgstr "" +msgstr "袋外准确率" msgid "Please provide a target variable and at least %i feature variable(s)." -msgstr "" +msgstr "请提供一个目标变量和至少 %i 个特征变量" msgid "The trained model is saved as %1$s." -msgstr "" +msgstr "训练的模型已保存为%1$s。" msgid "" "The trained model is not saved because the some of the variable names " "in the model contain spaces (i.e., ' ') or underscores (i.e., '_'). Please " "remove all such characters from the variable names and try saving the model " "again." -msgstr "" +msgstr "由于模型中的某些变量名包含空格或下划线，因此未保存训练好的模型。请删除变量名中的所有此类字符，并尝试重新保存模型。" msgid "The trained model is not saved until 'Save trained model' is checked." -msgstr "" +msgstr "只有选中“保存训练好的模型”，才能保存模型。" msgid "" "The model is optimized with respect to the validation set accuracy." -msgstr "" +msgstr "模型根据验证集精度进行了优化" msgid "" "The optimum number of nearest neighbors is the maximum number. You might " "want to adjust the range of optimization." -msgstr "" +msgstr "近邻的最佳数量是最大数量。您可能需要调整优化范围。" msgid "Manhattan" -msgstr "" +msgstr "曼哈顿距离" msgid "Euclidean" -msgstr "" +msgstr "欧式距离" msgid "Moment" msgstr "" @@ -160,162 +160,162 @@ msgid "t" msgstr "t" msgid "The model is optimized with respect to the out-of-bag accuracy." -msgstr "" +msgstr "该模型根据袋外准确率精度进行了优化。" msgid "The model is optimized with respect to the sum of squares." -msgstr "" +msgstr "该模型根据 平方和进行了优化" #, fuzzy msgid "Model Summary: Logistic Regression Classification" -msgstr "K近邻分类" +msgstr "模型概要：逻辑回归分类" msgid "Model Summary: Multinomial Regression Classification" -msgstr "" +msgstr "模型概要：多类逻辑回归分类" msgid "Confusion Matrix" -msgstr "" +msgstr "混淆矩阵" msgid "Observed" -msgstr "" +msgstr "观测" msgid "Predicted" -msgstr "" +msgstr "预测" msgid "Decision Boundary Matrix" -msgstr "" +msgstr "决策边界矩阵" msgid "" "Cannot create matrix: not enough numeric variables remain after removing " "factor variables. You need at least 2 numeric variables." -msgstr "" +msgstr "无法创建矩阵：删除因子变量后剩余的数值变量不够。至少需要2个数值变量。" msgid "ROC Curves Plot" -msgstr "" +msgstr "ROC曲线" msgid "False Positive Rate" -msgstr "" +msgstr "假正率" msgid "True Positive Rate" -msgstr "" +msgstr "真正率" msgid "Perfect separation" -msgstr "" +msgstr "完美分割" msgid "Andrews Curves Plot" -msgstr "" +msgstr "区分度曲线" msgid "Andrews curves require a minimum of 2 feature variables." -msgstr "" +msgstr "区分度曲线至少需要 2 个特征变量。" msgid "Box's M-test for Homogeneity of Covariance Matrices" -msgstr "" +msgstr "协方差矩阵同质性的Box M-检验" msgid "Model Performance Metrics" -msgstr "" +msgstr "模型性能指标" msgid "Support" -msgstr "" +msgstr "支持度" msgid "Accuracy" -msgstr "" +msgstr "准确率" msgid "Precision (Positive Predictive Value)" -msgstr "" +msgstr "精确率" msgid "Recall (True Positive Rate)" -msgstr "" +msgstr "召回率" msgid "False Discovery Rate" -msgstr "" +msgstr "错误发现率" msgid "F1 Score" -msgstr "" +msgstr "F1分数" msgid "Matthews Correlation Coefficient" -msgstr "" +msgstr "Matthews相关系数" msgid "Area Under Curve (AUC)" -msgstr "" +msgstr "曲线下方面积(AUC)" msgid "Negative Predictive Value" -msgstr "" +msgstr "负预测值" msgid "True Negative Rate" -msgstr "" +msgstr "真负率" msgid "False Negative Rate" -msgstr "" +msgstr "假负率" msgid "False Omission Rate" -msgstr "" +msgstr "错误遗漏率" msgid "Threat Score" -msgstr "" +msgstr "威胁分值" msgid "Statistical Parity" -msgstr "" +msgstr "统计均等" msgid "All metrics are calculated for every class against all other classes." -msgstr "" +msgstr "所有指标都是针对每个类和所有其他类计算的" msgid "Average / Total" -msgstr "" +msgstr "平均/总计" msgid "Class Proportions" -msgstr "" +msgstr "类比例" msgid "Data Set" -msgstr "" +msgstr "数据集" msgid "Training Set" -msgstr "" +msgstr "训练集" msgid "Validation Set" -msgstr "" +msgstr "验证集" msgid "Training and Validation Set" -msgstr "" +msgstr "训练与验证集" msgid "Test Set" -msgstr "" +msgstr "测试集" msgid "" "You have specified more clusters than distinct data points. Please choose a " "number lower than %s." -msgstr "" +msgstr "您指定的簇数多于不同的数据点。请选择一个小于%s 的数字。" msgid "" "R package error: The hclust clustering algorithm from the stats R package " "cannot handle data that has 65536 or more rows. We are working on a solution " "for this problem. Please try another algorithm in the meantime." -msgstr "" +msgstr "R软件包错误：stats软件包中的hclust聚类算法无法处理65536行或更多行的数据。我们正在努力解决这个问题。在此期间，请尝试其他算法。" msgid "K-Means Clustering" -msgstr "" +msgstr "K均值聚类" msgid "K-Medians Clustering" -msgstr "" +msgstr "K中位数聚类" msgid "K-Medoids Clustering" -msgstr "" +msgstr "K中心点聚类" msgid "Fuzzy C-Means Clustering" -msgstr "" +msgstr "模糊C-均值聚类" msgid "Hierarchical Clustering" -msgstr "" +msgstr "层次聚类" msgid "Density-Based Clustering" -msgstr "" +msgstr "密度聚类" msgid "Random Forest Clustering" -msgstr "" +msgstr "随机森林聚类" msgid "Model-Based Clustering" -msgstr "" +msgstr "基于模型聚类" msgid "Clusters" -msgstr "" +msgstr "簇" msgid "N" msgstr "" @@ -330,280 +330,280 @@ msgid "BIC" msgstr "BIC" msgid "Silhouette" -msgstr "" +msgstr "轮廓系数" msgid "Please provide at least 2 features." -msgstr "" +msgstr "请提供至少2个特征。" msgid "silhouette" -msgstr "" +msgstr "轮廓系数" msgid "The model is optimized with respect to the %s value." -msgstr "" +msgstr "根据 %s 值优化模型。" msgid "" "The optimum number of clusters is the maximum number of clusters. You might " "want to adjust the range of optimization." -msgstr "" +msgstr "簇的最佳数目就是簇的最大数目。您可能需要调整优化范围。" msgid "" "The model contains 0 clusters and only Noisepoints, we advise to change " "'Epsilon neighborhood size' and 'Min. core points' parameters under " "'Training parameters'." -msgstr "" +msgstr "该模型不包含簇，只有噪声点，建议更改“训练参数”下的“Epsilon邻域大小”和“最小核心点”参数。" msgid "" "The model contains 1 cluster and no Noisepoints. You may want to change " "'Epsilon neighborhood size' and 'Min. core points' parameters under " "'Training parameters'." -msgstr "" +msgstr "模型包含1个簇，没有噪音点。您可能需要更改“训练参数”下的“Epsilon邻域大小”和“最小核心点”参数。" msgid "spherical with equal volume" -msgstr "" +msgstr "等体积球形" msgid "spherical with unequal volume" -msgstr "" +msgstr "不等体积球形" msgid "diagonal with equal volume and shape" -msgstr "" +msgstr "等体积等形状的对角阵" msgid "diagonal with varying volume and equal shape" -msgstr "" +msgstr "体积不等，形状相同的对角阵" msgid "diagonal with equal volume and varying shape" -msgstr "" +msgstr "体积相等、形状不同的对角阵" msgid "diagonal with varying volume and shape" -msgstr "" +msgstr "体积和形状各异的对角阵" msgid "ellipsoidal with equal volume, shape, and orientation" -msgstr "" +msgstr "体积、形状和方向相同的椭圆体" msgid "ellipsoidal with equal shape and orientation" -msgstr "" +msgstr "形状和方向相同的椭圆体" msgid "ellipsoidal with equal volume and orientation" -msgstr "" +msgstr "等体积同方向的椭圆体" msgid "ellipsoidal with equal orientation" -msgstr "" +msgstr "等向椭圆体" msgid "ellipsoidal with equal volume and equal shape" -msgstr "" +msgstr "等体积等形状的椭圆体" msgid "ellipsoidal with equal shape" -msgstr "" +msgstr "等形状的椭圆体" msgid "ellipsoidal with equal volume" -msgstr "" +msgstr "等体积的椭圆体" msgid "ellipsoidal with varying volume, shape, and orientation" -msgstr "" +msgstr "具有不同体积、形状和方向的椭圆体" msgid "The model is %1$s." -msgstr "" +msgstr "模型为%1$s。" msgid "The features in the model are unstandardized." -msgstr "" +msgstr "模型中的特征未标准化。" msgid "Cluster Information" -msgstr "" +msgstr "簇信息" msgid "Cluster" -msgstr "" +msgstr "簇" msgid "Size" -msgstr "" +msgstr "大小" msgid "Explained proportion within-cluster heterogeneity" -msgstr "" +msgstr "簇内异质性的解释比例" msgid "Within sum of squares" -msgstr "" +msgstr "内平方和" msgid "Silhouette score" -msgstr "" +msgstr "轮廓系数" msgid "Center %s" -msgstr "" +msgstr "中心%s" msgid "Noisepoints" -msgstr "" +msgstr "噪声点" msgid "The Between Sum of Squares of the %1$s cluster model is %2$s" -msgstr "" +msgstr "聚类模型%1$s的外平方和为 %2$s" msgid "The Total Sum of Squares of the %1$s cluster model is %2$s" -msgstr "" +msgstr "聚类模型%1$s的总平方和为%2$s" msgid "Value" -msgstr "" +msgstr "值" msgid "Maximum diameter" -msgstr "" +msgstr "最大直径" msgid "Minimum separation" -msgstr "" +msgstr "最小间隔" msgid "Pearson's %s" -msgstr "" +msgstr "皮尔逊%s" msgid "Dunn index" -msgstr "" +msgstr "Dunn指数" msgid "Entropy" -msgstr "" +msgstr "熵" msgid "Calinski-Harabasz index" -msgstr "" +msgstr "Calinski-Harabasz指数" msgid "All metrics are based on the euclidean distance." -msgstr "" +msgstr "所有指标都基于欧几里得距离。" msgid "Evaluation metrics cannot be computed when there is only 1 cluster." -msgstr "" +msgstr "当只有一个簇时，无法计算指标。" msgid "t-SNE Cluster Plot" -msgstr "" +msgstr "t-SNE聚类图" msgid "Noisepoint" -msgstr "" +msgstr "噪声点" msgid "Elbow Method Plot" -msgstr "" +msgstr "肘部法图" msgid "WSS" -msgstr "" +msgstr "内平方和" msgid "Number of Clusters" -msgstr "" +msgstr "簇的数量" msgid "Lowest %s" -msgstr "" +msgstr "最低%s" msgid "Cluster Means" -msgstr "" +msgstr "簇均值" msgid "Cluster %s" -msgstr "" +msgstr "簇%s" msgid "Cluster Density Plots" -msgstr "" +msgstr "簇密度图" msgid "Density" -msgstr "" +msgstr "密度" msgid "Feature" -msgstr "" +msgstr "特征" msgid "All Features" -msgstr "" +msgstr "所有特征" msgid "Cluster Mean Plots" -msgstr "" +msgstr "簇均值图" msgid "Cluster Mean" -msgstr "" +msgstr "簇均值" msgid "" "Insufficient RAM available to compute the distance matrix. The analysis " "tried to allocate %s Gb" -msgstr "" +msgstr "可用内存不足，无法计算距离矩阵。分析尝试分配%sGb" msgid "An error occurred in the analysis: %1$s" -msgstr "" +msgstr "分析过程中出现错误：%1$s" msgid "Cluster Matrix Plot" -msgstr "" +msgstr "聚类矩阵图" msgid "The variable '%s' can't be both a feature and a test set indicator." -msgstr "" +msgstr "变量'%s'不能同时作为特征和测试集指标。" msgid "" "Your test set indicator should be binary, containing only 1 (included in " "test set) and 0 (excluded from test set)." -msgstr "" +msgstr "测试集指标应该是二进制的，只包含1（包含在 测试集中）和0（排除在测试集之外）。" msgid "" "There is only one observation in each level of the factor %1$s, please " "remove this factor as a feature." -msgstr "" +msgstr "因子%1$s的每个级别中只有一个观测值，请删除该因子。" msgid "" "There is only one observation in each level of the factors %1$s, please " "remove these factors as a feature." -msgstr "" +msgstr "因子%1$s的每个级别中只有一个观测值，请将这些因子作为特征删除。" msgid "" "You have specified more nearest neighbors than there are observations in the " "training set. Please choose a number lower than %d." -msgstr "" +msgstr "您指定的近邻数多于训练集中的观测值。请选择一个小于%d的数字。" msgid "" "You have specified more folds than there are observations in the training " "and validation set. Please choose a number lower than %d." -msgstr "" +msgstr "您指定的折叠数多于训练集和验证集中的观测数。请选择一个小于%d的数字。" msgid "K-Nearest Neighbors Regression" -msgstr "" +msgstr "K近邻回归" msgid "Regularized Linear Regression" -msgstr "" +msgstr "正则化线性回归" msgid "Random Forest Regression" -msgstr "" +msgstr "随机森林回归" msgid "Boosting Regression" -msgstr "" +msgstr "提升回归" msgid "Neural Network Regression" -msgstr "" +msgstr "神经网络回归" msgid "Decision Tree Regression" -msgstr "" +msgstr "决策树回归" msgid "Support Vector Machine Regression" -msgstr "" +msgstr "支持向量机回归" msgid "Linear Regression" -msgstr "" +msgstr "线性回归" msgid "Penalty" -msgstr "" +msgstr "惩罚" msgid "Loss function" -msgstr "" +msgstr "损失函数" msgid "Validation MSE" -msgstr "" +msgstr "验证MSE" msgid "Test MSE" -msgstr "" +msgstr "测试MSE" msgid "OOB Error" -msgstr "" +msgstr "袋外错误率" msgid "R%1$s" msgstr "" msgid "Adjusted R%1$s" -msgstr "" +msgstr "调整R%1$s" msgid "Please provide a target variable and at least %d feature variable(s)." -msgstr "" +msgstr "请提供一个目标变量和至少%d个特征变量" msgid "" "The model is optimized with respect to the validation set mean squared " "error." -msgstr "" +msgstr "根据验证集均方误差对模型进行了优化。" msgid "When %s is set to 0 linear regression is performed." -msgstr "" +msgstr "当%s设置为0时，将进行线性回归。" msgid "" "The model is optimized with respect to the out-of-bag mean squared error." -msgstr "" +msgstr "该模型根据袋外均方误差进行了优化。" msgid "Gaussian" msgstr "" @@ -615,46 +615,46 @@ msgid "MSE(scaled)" msgstr "" msgid "R%s cannot be computed due to lack of variance in the predictions." -msgstr "" +msgstr "由于预测中缺乏方差，因此无法计算R%s。" msgid "Predictive Performance Plot" -msgstr "" +msgstr "预测性能图" msgid "Observed Test Values" -msgstr "" +msgstr "观测测试值" msgid "Predicted Test Values" -msgstr "" +msgstr "预测测试值" msgid "Data Split" -msgstr "" +msgstr "数据拆分" msgid "Train: %d" -msgstr "" +msgstr "训练:%d" msgid "Test: %d" -msgstr "" +msgstr "测试:%d" msgid "Total: %d" -msgstr "" +msgstr "合计:%d" msgid "Validation: %d" -msgstr "" +msgstr "验证:%d" msgid "Train and validation: %d" -msgstr "" +msgstr "训练和验证:%d" msgid "Additive Explanations for Predictions of New Cases" -msgstr "" +msgstr "新案例预测的可加解释" msgid "Additive Explanations for Predictions of Test Set Cases" -msgstr "" +msgstr "测试集预测的可加解释" msgid "Case" -msgstr "" +msgstr "案例" msgid "Predicted (Prob.)" -msgstr "" +msgstr "预测（概率）" msgid "Base" msgstr "" @@ -666,63 +666,63 @@ msgstr "分析过程中发生了错误：%s" msgid "" "Displayed values represent feature contributions to the predicted value " "without features (column 'Base') for the test set." -msgstr "" +msgstr "显示值代表了特征对测试集（无Base列）预测值的贡献。" msgid "" "Displayed values represent feature contributions to the predicted class " "probability without features (column 'Base') for the test set." -msgstr "" +msgstr "显示值代表了特征对测试集（无Base列）预测概率值的贡献。" msgid "Feature Importance Metrics" -msgstr "" +msgstr "特征重要性度量" msgid "Mean dropout loss" -msgstr "" +msgstr "平均dropout损失" msgid "root mean squared error (RMSE)" -msgstr "" +msgstr "均方根误差 (RMSE)" msgid "1 - area under curve (AUC)" -msgstr "" +msgstr "1 - 曲线下方面积 (AUC)" msgid "cross entropy" -msgstr "" +msgstr "交叉熵" msgid "Mean dropout loss (defined as %1$s) is based on %2$s permutations." -msgstr "" +msgstr "平均dropout损失（定义为 %1$s）基于 %2$s 的置换。" msgid "The target variable should have at least 2 classes." -msgstr "" +msgstr "目标变量应至少有2个类" msgid "Linear Discriminant Coefficients" -msgstr "" +msgstr "线性判别系数" msgid "(Constant)" -msgstr "" +msgstr "（常量）" msgid "Prior and Posterior Class Probabilities" -msgstr "" +msgstr "类先验概率和后验概率" msgid "Prior" -msgstr "" +msgstr "先验概率" msgid "Posterior" -msgstr "" +msgstr "后验概率" msgid "Class Means in Training Data" -msgstr "" +msgstr "训练数据中的类平均" msgid "Linear Discriminant Matrix" -msgstr "" +msgstr "线性判别矩阵" msgid "Tests of Equality of Class Means" -msgstr "" +msgstr "类均值相等检验" msgid "The null hypothesis specifies equal class means." -msgstr "" +msgstr "零假设为类均值相等。" msgid "Tests of Equality of Covariance Matrices" -msgstr "" +msgstr "协方差矩阵相等性检验" msgid "df" msgstr "df" @@ -731,252 +731,252 @@ msgid "p" msgstr "p" msgid "The null hypothesis specifies equal covariance matrices." -msgstr "" +msgstr "零假设为协方差矩阵相等。" msgid "" "There are one or more levels in the target variable with less than two " "observations." -msgstr "" +msgstr "目标变量中有一个或多个水平的观测值少于两个" msgid "Box's M" -msgstr "" +msgstr "Box M检验" msgid "Pooled Within-Class Matrices Correlations" -msgstr "" +msgstr "池化类内矩阵相关性" msgid "Tests for Multivariate Normality" -msgstr "" +msgstr "多元正态性检验" msgid "Statistic" -msgstr "" +msgstr "统计量" msgid "Skewness" -msgstr "" +msgstr "偏度" msgid "Kurtosis" -msgstr "" +msgstr "峰度" msgid "" "Both p-values of the skewness and kurtosis statistics should be > 0.05 to " "conclude multivariate normality." -msgstr "" +msgstr "偏度和峰度统计量的p值都应>0.05以得出多变量正态性结论。" msgid "Regression Coefficients" -msgstr "" +msgstr "回归系数" msgid "Coefficient (%s)" -msgstr "" +msgstr "系数（%s）" msgid "Standard Error" -msgstr "" +msgstr "标准误" msgid "z" msgstr "" msgid "%1$s%% Confidence interval" -msgstr "" +msgstr "%1$s%%置信区间" msgid "Lower" -msgstr "" +msgstr "下限" msgid "Upper" -msgstr "" +msgstr "上限" msgid "The regression coefficients for numeric features are standardized." -msgstr "" +msgstr "数值特征的回归系数已标准化。" msgid "The regression coefficients are unstandardized." -msgstr "" +msgstr "回归系数未标准化。" msgid "" "Regression equation:\n" "%1$s" -msgstr "" +msgstr "回归方程：\n%1$s" msgid "Posterior Statistics" -msgstr "" +msgstr "后验统计" msgid "Feature: %1$s" -msgstr "" +msgstr "特征：%1$s" msgid "Mean" -msgstr "" +msgstr "均值" msgid "Std. deviation" -msgstr "" +msgstr "标准差" msgid "" "The table displays the mean and standard deviation of the feature given the " "target class." -msgstr "" +msgstr "表显示了目标类别下特征的均值和标准差。" msgid "" "The table displays the conditional probabilities given the target class." -msgstr "" +msgstr "表显示了目标类别的条件概率。" msgid "" "Analysis not possible: The algorithm did not converge within the maximum " "number of training repetitions (%1$s)." -msgstr "" +msgstr "无法进行分析：算法未在最大训练次数(%1$s)内收敛。" msgid "Optimizing network topology" -msgstr "" +msgstr "优化网络拓扑" msgid "K-Distance Plot" -msgstr "" +msgstr "K-距离图" msgid "Points Sorted by Distance" -msgstr "" +msgstr "按距离排序的点" msgid "" "%s-Nearest Neighbors \n" "Distance" -msgstr "" +msgstr "%s-近邻\n距离" msgid "Maximum curvature = %s" -msgstr "" +msgstr "最大曲率=%s" msgid "End of recursion reached without converging" -msgstr "" +msgstr "递归结束但未收敛" msgid "x and y must be numeric and finite. Missing values not allowed." -msgstr "" +msgstr "x和y必须是有限的数值。不允许有缺失值。" msgid "x and y must be of equal length." -msgstr "" +msgstr "x和y的长度必须相等。" msgid "Need more points to find cutoff." -msgstr "" +msgstr "需要更多的点才能找到截止点。" msgid "Need to specify fraction of maximum slope." -msgstr "" +msgstr "需要指定最大斜率的分数。" msgid "" "Fraction of maximum slope must be positive and be less than or equal to 1." -msgstr "" +msgstr "最大坡度的分数必须为正数，且小于或等于 1。" msgid "Method must be either 'first' or 'curvature'." -msgstr "" +msgstr "方法必须是“first”或“曲率”。" msgid "Cutoff point is beyond range. Returning NA." -msgstr "" +msgstr "截止点超出范围。返回 NA。" msgid "Dendrogram" -msgstr "" +msgstr "树图" msgid "" "The %1$s-component %2$s model could not be fitted, try a different model or " "a different number of clusters." -msgstr "" +msgstr "无法拟合%1$s-成分%2$s模型，请尝试不同的模型或不同的簇数。" msgid "Estimated Model Parameters" -msgstr "" +msgstr "估计模型参数" msgid "Mixing Probabilities" -msgstr "" +msgstr "混合概率" msgid "Mixing probability" -msgstr "" +msgstr "混合概率" msgid "Component %1$s" -msgstr "" +msgstr "成分%1$s" msgid "Means" -msgstr "" +msgstr "均值" msgid "Covariance Matrix for Component %1$s" -msgstr "" +msgstr "成分 %1$s 的协方差矩阵" msgid "Scale of the Covariance" -msgstr "" +msgstr "协方差标度" msgid "Scale" -msgstr "" +msgstr "标度" msgid "Shape of the Covariance Matrix" -msgstr "" +msgstr "协方差矩阵的形状" msgid "Eigenvalues of the Covariance Matrix for Component %1$s" -msgstr "" +msgstr "成分%1$s的协方差矩阵的特征值" msgid "Feature Importance" -msgstr "" +msgstr "特征重要性" msgid "Mean decrease in Gini Index" -msgstr "" +msgstr "基尼系数的平均降幅" msgid "K-nearest neighbors" -msgstr "" +msgstr "K最近邻" msgid "Linear discriminant" -msgstr "" +msgstr "线性判别" msgid "Linear" -msgstr "" +msgstr "线性" msgid "Boosting" -msgstr "" +msgstr "提升" msgid "Random forest" -msgstr "" +msgstr "随机森林" msgid "Regularized linear regression" -msgstr "" +msgstr "正则化线性回归" msgid "Neural network" -msgstr "" +msgstr "神经网络" msgid "Decision tree" -msgstr "" +msgstr "决策树" msgid "Support vector machine" -msgstr "" +msgstr "支持向量机" msgid "Naive Bayes" -msgstr "" +msgstr "朴素贝叶斯" msgid "Logistic regression" -msgstr "" +msgstr "逻辑回归" msgid "Multinomial regression" -msgstr "" +msgstr "多类逻辑回归" msgid "Error: The trained model is not created in JASP." -msgstr "" +msgstr "错误：未在JASP中创建训练有素的模型。" msgid "The trained model (type: %1$s) is currently not supported in JASP." -msgstr "" +msgstr "JASP目前不支持训练的模型（类型：%1$s）。" msgid "Error: The trained model is created using a different version of JASP." -msgstr "" +msgstr "错误：训练的模型是使用不同版本的JASP创建的。" msgid "Loaded Model" -msgstr "" +msgstr "已加载模型" msgid "Classification" -msgstr "" +msgstr "分类" msgid "Regression" -msgstr "" +msgstr "回归" msgid "Loaded Model: %1$s" -msgstr "" +msgstr "已加载模型:%1$s" msgid "" "The trained model is not applied because the the following features are " "missing: %1$s." -msgstr "" +msgstr "未应用模型，因为缺少以下功能：%1$s。" msgid "" "The following features are unused because they are not a feature variable in " "the trained model: %1$s." -msgstr "" +msgstr "以下特征未使用，因为它们不是训练模型中的特征变量：%1$s。" msgid "Nearest Neighbors" -msgstr "" +msgstr "最近邻" msgid "n(New)" -msgstr "" +msgstr "n(新)" msgid "Binomial" msgstr "" @@ -988,122 +988,122 @@ msgid "Logit" msgstr "" msgid "Predictions for New Data" -msgstr "" +msgstr "新数据的预测" msgid "Relative Influence" -msgstr "" +msgstr "相对影响" msgid "Warning." -msgstr "" +msgstr "警告。" #, fuzzy msgid "An error occurred when computing the mean dropout loss: %1$s" msgstr "分析过程中发生了错误：%s" msgid "Out-of-bag Improvement Plot" -msgstr "" +msgstr "袋外提升图" msgid "Training set" -msgstr "" +msgstr "训练集" msgid "" "OOB Change in \n" " Multinomial Deviance" -msgstr "" +msgstr "在多项式偏差中\n袋外误差的变化" msgid "" "OOB Change in \n" " Binomial Deviance" -msgstr "" +msgstr "在二项式偏差中\n袋外误差的变化" msgid "" "OOB Change in \n" "%s Deviance" -msgstr "" +msgstr "在%s偏差中\n袋外误差的变化" msgid "Plotting not possible: The model is based on only a single tree." -msgstr "" +msgstr "无法绘图： 模型只基于一棵树。" msgid "Number of Trees" -msgstr "" +msgstr "树的数量" msgid "Deviance Plot" -msgstr "" +msgstr "偏差图" msgid "Multinomial Deviance" -msgstr "" +msgstr "多项式偏差" msgid "Binomial Deviance" -msgstr "" +msgstr "二项式偏差" msgid "%s Deviance" -msgstr "" +msgstr "%s偏差" msgid "Relative Influence Plot" -msgstr "" +msgstr "相对影响图" msgid "" "The minimum number of observations per node is too large. Ensure that `2 * " "Min. observations in node (%1$i) + 1` < `Training data used per tree (%2$s) " "* available training data (%3$i)` (in this case the minimum can be %4$i at " "most)." -msgstr "" +msgstr "每个节点的最小观测数过大。确保`2*节点(%1$i)的最小观测数+1<每棵树使用的训练数据(%2$s)*可用训练数据(%3$i)`（这种情况下，最小值最多为%4$i）。" msgid "Relative Importance" -msgstr "" +msgstr "相对重要性" msgid "No splits were made in the tree." -msgstr "" +msgstr "树中没有拆分" msgid "Splits in Tree" -msgstr "" +msgstr "树中的拆分" msgid "Obs. in Split" -msgstr "" +msgstr "拆分中的观测" msgid "Split Point" -msgstr "" +msgstr "拆分点" msgid "Improvement" -msgstr "" +msgstr "改进" msgid "" "For each level of the tree, only the split with the highest improvement in " "deviance is shown." -msgstr "" +msgstr "在树的每一层中，只显示偏差改进最大的拆分。" msgid "Decision Tree Plot" -msgstr "" +msgstr "决策树图" msgid "Plotting not possible: No splits were made in the tree." -msgstr "" +msgstr "无法绘制图：树中没有拆分。" msgid "Plotting not possible: An error occurred while creating this plot: %s" -msgstr "" +msgstr "无法绘制图：创建图时发生错误：%s" msgid "Classification Accuracy Plot" -msgstr "" +msgstr "分类准确率图" msgid "Mean Squared Error Plot" -msgstr "" +msgstr "均方误差图" msgid "Classification Accuracy" -msgstr "" +msgstr "分类准确率" msgid "Mean Squared Error" -msgstr "" +msgstr "均方误差" msgid "Validation set" -msgstr "" +msgstr "验证集" msgid "Complexity Penalty" -msgstr "" +msgstr "复杂度惩罚" msgid "Number of Nearest Neighbors" -msgstr "" +msgstr "最近邻数量" msgid "Training and validation set" -msgstr "" +msgstr "训练集和验证集" msgid "Rectangular" msgstr "" @@ -1133,30 +1133,30 @@ msgid "Optimal" msgstr "" msgid "%1$s Weight Function" -msgstr "" +msgstr "%1$s权重函数" msgid "" "Plotting not possible: The selected weighting scheme cannot be visualized " "separately from the data." -msgstr "" +msgstr "无法绘图：所选加权方案无法与数据分开显示。" msgid "Proportion of Max. Distance" -msgstr "" +msgstr "最大距离的比例" msgid "Relative Weight" -msgstr "" +msgstr "相对权重" msgid "Network Weights" -msgstr "" +msgstr "网络权重" msgid "Node" -msgstr "" +msgstr "节点" msgid "Layer" -msgstr "" +msgstr "层" msgid "Weight" -msgstr "" +msgstr "权重" msgid "linear" msgstr "" @@ -1207,22 +1207,22 @@ msgid "gaussian error linear unit (GeLU)" msgstr "" msgid "The weights are input for the %1$s activation function." -msgstr "" +msgstr "权重是%1$s激活函数的输入。" msgid "Network Structure Plot" -msgstr "" +msgstr "网络结构图" msgid "Intercept" msgstr "截距" msgid "Input layer" -msgstr "" +msgstr "输入层" msgid "Output layer" -msgstr "" +msgstr "输出层" msgid "Hidden layer %1$s" -msgstr "" +msgstr "隐含层%1$s" msgid "Binary" msgstr "" @@ -1264,70 +1264,70 @@ msgid "GeLU" msgstr "" msgid "%1$s Activation Function" -msgstr "" +msgstr "%1$s激活函数" msgid "Input" -msgstr "" +msgstr "输入" msgid "Output" -msgstr "" +msgstr "输出" msgid "Generation" -msgstr "" +msgstr "代数" msgid "Mean decrease in accuracy" -msgstr "" +msgstr "准确率平均下降率" msgid "Total increase in node purity" -msgstr "" +msgstr "节点纯度的总增长" msgid "Out-of-bag Classification Accuracy Plot" -msgstr "" +msgstr "袋外分类准确率图" msgid "Out-of-bag Mean Squared Error Plot" -msgstr "" +msgstr "袋外均方误差图" msgid "Out-of-bag %sClassification Accuracy" -msgstr "" +msgstr "袋外%s分类准确率" msgid "Out-of-bag %sMean Squared Error" -msgstr "" +msgstr "袋外%s均方误差" msgid "Mean Decrease in Accuracy" -msgstr "" +msgstr "准确率平均下降率" msgid "Total Increase in Node Purity" -msgstr "" +msgstr "节点纯度总提高" msgid "L2 (Ridge)" -msgstr "" +msgstr "L2(岭回归)" msgid "L1 (Lasso)" -msgstr "" +msgstr "L1(Lasso)" msgid "Elastic Net" -msgstr "" +msgstr "弹性网络" msgid "Variable Trace Plot" msgstr "" msgid "Coefficients" -msgstr "" +msgstr "系数" msgid "Lambda Evaluation Plot" -msgstr "" +msgstr "Lambda评估图" msgid "Cross-Validated %sMean Squared Error" -msgstr "" +msgstr "交叉验证%s均方差" msgid "Min. CV MSE" -msgstr "" +msgstr "最小交叉验证均方差" msgid "%s 1 SE" -msgstr "" +msgstr "%s 1标准差" msgid "Row" -msgstr "" +msgstr "行" msgid "Cost of Constraints Violation" -msgstr "" +msgstr "约束违反成本"