nn2 | * * Sferes2 module * * generic toolbox | Machine Learning library

some client library which is in Spark that is not available in somewhere in during build or edge node common place

This common place would be $SPARK_HOME/conf and/or $HADOOP_CONF_DIR. But if you are just running a regular Scala app with java jar or with IntelliJ, that has nothing to do with Spark.

... this values already present in hdfs-site.xml file and core-site.xml

Then, they should be read, accordingly, however overriding in the code shouldn't hurt either.

The values are necessary because they dicate where the actual namenodes are running; otherwise, it thinks mycluster is a real DNS name of only one server, when it isn't

I can't see your data, but I'm assuming this is a classification problem (you're predicting a binary outcome), and the predicted results you show above are probabilities produced by your neural network model.

The next step would be to apply a decision threshold to those probabilities. i.e. which of them should be 1 and which should be 0. e.g. you could keep the ratio of 0:1 the same as in your training data. Note that applying decisions to probabilities falls outside the modelling process.

Once you have a binary classification for each row in your test data, you can compare those predicted classifications to the actual classifications. This can be done in the form of a confusion matrix. In classification problems, accuracy is defined as the number of correct predictions divided by the total number of predictions.

Note that accuracy is not normally a very good indicator of model performance, especially with imbalanced datasets. It's better to assess your model based on the probabilities it produces rather than the classifications that follow your decision-making process. e.g. Look into using Brier scores as an alternative.

You get that error because the boxcox transformation does not take in zero. If you check the help page for BoxCoxTrans it writes:

If any(y <= 0) or if length(unique(y)) < numUnique, lambda is not estimated and no transformation is applied.

So if your preProcess() is run on a train set with no zeros in the columns, the boxcox transformation is applied, but it will not work on a test set with zeros.

In the book example above, most likely the seed was set with an older R version, so it works. If you are using a newer version of R, then it doesn't work. So if I check with your example:

To convert (bytes to numbers and back), you can use different methods:

There's a couple of problems with your code - firstly, you address the index as an integer even though you have a non-numerical index. Secondly, what you intend as your second assignment is really a boolean expression. The return statement makes sense only in a function body, but the rows that do not get an explicit assignment in the Category column, will be assigned nan anyway. Since you need more than one column, work with a DataFrame, not a Series. If you receive a Series S from somewhere else, you can create a DataFrame from it by e.g.

I think you just need to iterate over each within AllStations and return the nearest of the ContaminantStations that is within a radius.

This might work, although it's surely not the most elegant way:

The first thing you should do is create your own distance matrix. The rows should correspond to those in dat.1 and the columns to those in dat.2, and each entry in the matrix is the distance between the plot in the row and the plot in the column. You can do this manually by looping through your datasets and computing the Euclidean (or other) distance between the points. You can also use the match_on function in the optmatch package to do this with the following code:

There isn't a single init argument for keras Dense layers. You'll need to specify the initialization for kernel_initializer and bias_initializer separately.