setk | Tools for Speech Enhancement integrated with Kaldi | Speech library

found the .columns() function

Class, or more accurately java.lang.Class is a system class, which means that Class.class.getResourceAsStream(...) only looks in the system ClassLoader for the resource, i.e. it doesn't use the ClassLoader that is responsible for the application code and resources.

Change code to MyFile.class.getResourceAsStream(...) or getClass().getResourceAsStream(...)

I see that you dealt with the most basic steps in your model creation, what you still need is to apply your k-means model on the vector that you want to make the clustering on (like what you did in line 10) then get your prediction, I mean what you have to do is to reDo the same work done in line 10 but on the new vector of features V. To understand this more I invite you to read this posted answer in StackOveflow: KMeans clustering in PySpark. I want to add also that the problem in the example that you are following is not due to the use of SparkSession or SparkContext as those are just an entry point to the Spark APIs, you can also get access to a sparContext through a sparkSession since it is unified by Databricks since Spark 2.0. The pyspark k-means is like the Scikit learn the only difference is the predefined functions in spark python API (PySpark).

you can use zipWith to combine both results and you will get returned a tuple2 or you can use zipWhen if you need request one to complete before request two.

I have managed to solve this myself. However, I will address the issue directly to the OpenCV developers so they can give me with the answer to it.

I'll try to answer your questions based on my reading of the material.

1. The reason for this loop is that the author sets a new seed for every loop using int(np.random.randint(100, size=1)). If the feature variables exhibit patterns that automatically group them into visible clusters, then the starting seed should not have an impact on the final cluster memberships. However, if the data is evenly distributed, then we might end up with different cluster members based on the initial random variable. I believe the author is changing these seeds for each run to test different initial distributions. Using setMaxIter would set maximum iterations for the same seed (initial distribution).
2. Similar to the above - the seed defines the initial distribution of k points around which you're going to cluster. Depending on your underlying data distribution, the clusters can converge in different final distributions.
3. The author has control over the seed, as discussed in points 1 and 2. You can see for what seed your code converges around clusters as desired, and for which you might not get convergence. Also, if you iterate for, say, 100 different seeds and your code still converges into the same final clusters, you can remove the default seed as it likely doesn't matter. Another use is from a more software engineering perspective, setting explicit seed is super important if you want to, for example, write tests for your code and don't want it to randomly fail.

From your log:

Maybe there is a problem with version libraries and imports, in my pc the code works fine.

I'll show you my .sbt and the output the code produces.

After a few more days of investigation, I was pointed to the (rather embarrassing) cause of the issue:

Pyspark has two machine learning libraries: pyspark.ml and pyspark.mllib and it turns out they don't go well together. Replacing from pyspark.mllib.linalg import DenseVector by from pyspark.ml.linalg import DenseVector resolves all the issues.