## What changes were proposed in this pull request

When Kmeans using initMode = "random" and some random seed, it is possible the actual cluster size doesn't equal to the configured `k`.

In this case, summary(model) returns error due to the number of cols of coefficient matrix doesn't equal to k.

Example:
>  col1 <- c(1, 2, 3, 4, 0, 1, 2, 3, 4, 0)
>   col2 <- c(1, 2, 3, 4, 0, 1, 2, 3, 4, 0)
>   col3 <- c(1, 2, 3, 4, 0, 1, 2, 3, 4, 0)
>   cols <- as.data.frame(cbind(col1, col2, col3))
>   df <- createDataFrame(cols)
>
>   model2 <- spark.kmeans(data = df, ~ ., k = 5, maxIter = 10,  initMode = "random", seed = 22222, tol = 1E-5)
>
> summary(model2)
Error in `colnames<-`(`*tmp*`, value = c("col1", "col2", "col3")) :
  length of 'dimnames' [2] not equal to array extent
In addition: Warning message:
In matrix(coefficients, ncol = k) :
  data length [9] is not a sub-multiple or multiple of the number of rows [2]

Fix: Get the actual cluster size in the summary and use it to build the coefficient matrix.
## How was this patch tested?

Add unit tests.

Author: wm624@hotmail.com <wm624@hotmail.com>

Closes #16666 from wangmiao1981/kmeans.