[FIX] Documentation links

Orange/widgets/evaluate/owtestlearners.py

@@ -125,7 +125,7 @@ class State(enum.Enum):
 
 
 class OWTestLearners(OWWidget):
-    name = "Test & Score"
+    name = "Test and Score"
     description = "Cross-validation accuracy estimation."
     icon = "icons/TestLearners1.svg"
     priority = 100

doc/visual-programming/source/index.rst

@@ -48,7 +48,7 @@ Data
    widgets/data/mergedata
    widgets/data/outliers
    widgets/data/preprocess
-   widgets/data/transform
+   widgets/data/applydomain
    widgets/data/purgedomain
    widgets/data/rank
    widgets/data/correlations
@@ -120,7 +120,7 @@ Unsupervised
    widgets/unsupervised/distancefile
    widgets/unsupervised/savedistancematrix
    widgets/unsupervised/hierarchicalclustering
-   widgets/unsupervised/kmeansclustering
+   widgets/unsupervised/kmeans
    widgets/unsupervised/louvainclustering
    widgets/unsupervised/mds
    widgets/unsupervised/tsne

doc/visual-programming/source/widgets/data/transform.md → doc/visual-programming/source/widgets/data/applydomain.md

@@ -1,5 +1,5 @@
-Transform
-=========
+Apply Domain
+============
 
 Given dataset and preprocessor transforms the dataset.
 
@@ -12,9 +12,9 @@ Given dataset and preprocessor transforms the dataset.
 
 - Transformed Data: transformed dataset
 
-**Transform** maps new data into a transformed space. For example, if we transform some data with PCA and wish to observe new data in the same space, we can use transform to map the new data into the PCA space created from the original data.
+**Apply Domain** maps new data into a transformed space. For example, if we transform some data with PCA and wish to observe new data in the same space, we can use transform to map the new data into the PCA space created from the original data.
 
-![](images/Transform.png)
+![](images/ApplyDomain.png)
 
 Widget accepts new data on the input and a preprocessor that was used to transform the old data.
 
@@ -23,10 +23,10 @@ Example
 
 We will use iris data from the [File](../data/file.md) widget for this example. To create two separate data sets, we will use [Select Rows](../data/selectrows.md) and set the condition to *iris is one of iris-setosa, iris-versicolor*. This will output a data set with a 100 rows, half of them belonging to iris-setosa class and the other half to iris-versicolor.
 
-We will transform the data with [PCA](../unsupervised/PCA.md) and select the first two components, which explain 96% of variance. Now, we would like to apply the same preprocessing on the 'new' data, that is the remaining 50 iris virginicas. Send the unused data from **Select Rows** to **Transform**. Make sure to use the *Unmatched Data* output from **Select Rows** widget. Then add the *Preprocessor* output from **PCA**.
+We will transform the data with [PCA](../unsupervised/PCA.md) and select the first two components, which explain 96% of variance. Now, we would like to apply the same preprocessing on the 'new' data, that is the remaining 50 iris virginicas. Send the unused data from **Select Rows** to **Apply Domain**. Make sure to use the *Unmatched Data* output from **Select Rows** widget. Then add the *Preprocessor* output from **PCA**.
 
-**Transform** will apply the preprocessor to the new data and output it. To add the new data to the old data, use [Concatenate](../data/concatenate.md). Use *Transformed Data* output from **PCA** as *Primary Data* and *Transformed Data* from **Transform** as *Additional Data*.
+**Apply Domain** will apply the preprocessor to the new data and output it. To add the new data to the old data, use [Concatenate](../data/concatenate.md). Use *Transformed Data* output from **PCA** as *Primary Data* and *Transformed Data* from **Apply Domain** as *Additional Data*.
 
 Observe the results in a [Data Table](../data/datatable.md) or in a [Scatter Plot](../visualize/scatterplot.md) to see the new data in relation to the old one.
 
-![](images/Transform-Example.png)
+![](images/ApplyDomain-Example.png)

doc/visual-programming/source/widgets/data/paintdata.md

@@ -21,6 +21,6 @@ The widget supports the creation of a new dataset by visually placing data point
 Example
 -------
 
-In the example below, we have painted a dataset with 4 classes. Such dataset is great for demonstrating k-means and hierarchical clustering methods. In the screenshot, we see that [k-Means](../unsupervised/kmeansclustering.md), overall, recognizes clusters better than [Hierarchical Clustering](../unsupervised/hierarchicalclustering.md). It returns a score rank, where the best score (the one with the highest value) means the most likely number of clusters. Hierarchical clustering, however, doesn’t group the right classes together. This is a great tool for learning and exploring statistical concepts.
+In the example below, we have painted a dataset with 4 classes. Such dataset is great for demonstrating k-means and hierarchical clustering methods. In the screenshot, we see that [k-Means](../unsupervised/kmeans.md), overall, recognizes clusters better than [Hierarchical Clustering](../unsupervised/hierarchicalclustering.md). It returns a score rank, where the best score (the one with the highest value) means the most likely number of clusters. Hierarchical clustering, however, doesn’t group the right classes together. This is a great tool for learning and exploring statistical concepts.
 
 ![](images/PaintData-Example.png)

doc/visual-programming/source/widgets/evaluation/confusionmatrix.md

@@ -1,5 +1,5 @@
-Predictions
-===========
+Confusion Matrix
+================
 
 Shows proportions between the predicted and actual class.