I have a dataset with a row that has unknowns which I thought I could use k-Nearest Neighbor on. When I read through the description of the function `knnimpute`

in Matlab it says that it replaces the NaN values with their closest neighbor column value. So I did a transpose on that column so all of the data is now in a single row. However, I get an error saying all the rows have NaN values in them. So I am a bit confused as to how to go about it.

Here's the code I ran:

```
knnimp = knnimpute(transpose(ds.stage),k);
```

I couldn't put a screen shot of the data but here's what it looks like (all of the data is in a row):

```
1 2 4 3 2 1 1 NaN 3 3 3 1 NaN 2 NaN
```

Here's the output I get after running the code on the transposed data:

```
All rows of the input data contains missing values. Unable to impute missing values.
```