mlf | Big Data Machine Learning Framework

This should do the trick

After a careful reading of the documentation of the existing APIs for ingressroutes, I found none that can be used, all of them are still in beta as Ingressroutes is not a native components of Kubernetes.

I also asked on Slack's Kubernetes, on the Kubernetes client channel.

Alan C on Kubernetes's slack, gave me that answer:

There's no class for ingressroutes. You can use the dynamic client to support arbitrary resource types (see link)

My solution is to use the python library subprocess to run the kubectl command line.

proc tabulate does not repeat values in the first column for each value in the second, because the output is meant for human reading. This is not the tool you need to write data to excel for further lookup.

MATCH is a great function in excel, but is not a good choice for your application, because

• it gives an error when it does not find what you look for, and that is why you need all labels in your output
• it only supports one criterion, so you need at least 3 of them
• it returns a position, so you still need an index function.

You can use lag to compare values from it's previous values.

Steps:

1. Create a blank plotting region by plot(..., type = "n").
2. Draw the rectangle.
3. Set the graphical parameter new by par(new = T).
4. Run your plotting code.
5. Reset graphical parameters.

For some reason your main activity layout is called R.layout.activity_splash I believe your intention was for it to be whatever main activity layout is called (the one that has R.id.fragments_container inside).

To require that all parameter values except $extraParameters be entered with their names, you can simply add the attribute [CmdletBinding(PositionalBinding=$false)] before the param keyword:

It is described in detail in the "Our track" section of the paper. I will try to provide a different and less formal explanation that will hopefully help you to build your own intuition.

Basically, the rigidly bound type variable is the MLF version of an ML weak variable. It could be illustrated using the examples from the paper. Given the function

If a=0, the series definition of MLf that you are using only applies when |z|<1. Indeed, when the base z is greater than 1 in absolute value, the powers z**k keep increasing, and the series diverges. Looking at its 100th, or another, partial sum is pointless, those sums have nothing to do with the function outside of the interval -1 < z < 1. Just use the formula 1/(1-z) for the case a=0.

The function is exp(z) and technically, it is represented by the power series z**k / k! for all z. But for large negative z this power series experiences catastrophic loss of significance: the individual terms are huge, for example, (-40)**40/factorial(40) is over 1e16, but their sum is tiny (exp(-40) is nearly zero). Since 1e16 approaches the limits of double precision, the output becomes dominated by the noise of truncating/rounding operations.

In general, evaluating polynomials by adding c(k) * z**k is not the best thing to do, both from the efficiency and precision standpoints. Horner's scheme is implemented in NumPy already, and using it simplifies the code: