predicator | Rust crate for generating and running dynamic code | Compiler library

I have wrote the knn cross validation method below using the iris dataset in R. How would I get the best value of k from this and create a confusion matrix based on this? Any help would be great.

Say I have a 5D tensor x and a 1D boolean mask m where m.shape[0] == x.shape[0], and I want to decide which sub-network should be applied on each 4D sample inside x based on the corresponding boolean entry of m.

To my knowledge tf.cond accepts scalar predicator only. Though tf.boolean_mask may be helpful to split the samples inside a batch into two subsets according to m as desired, I am not sure how to re-pack the outputs back into one 5D tensor without messing up the original sample order. Any hints?

Using pickers at the top of columns in a grid of UICollectionViewCells to control contents of each column independently:

Each picker should update only the values of the cells in that column (only column 1 updated in image).

While the data updates, the label of the pickers become very confused!

1) When the user picks a new value at the top of column 1 or 4 - for example going from Masculine to Neutral.

1a) the data updates for the column - to Neutral - but the label on the used picker reverts to the previous value - Masculine.

1b) meanwhile, the label of the not selected picker (col. 1 or 4 - NOT 3) updates to the value selected in the selected picker (without changing the values in the not selected column)

In image, column 1 was updated from Masculine to Neutral (Das Kind is Neutral; den Ball is Masculine):

2) Column 3 is different when selecting a value.

2a) The data and picker label update correctly (no reversion to previous picker label value).

2b) If columns 1 and 4 have varying labels - eg col 1 Masculine, and col 4 Neutral they swap picker label values (no change to their data)!

I'm instantiating my pickers in a custom collection view cell from the collection view cell at item method. I'm using the indexPath.item to differentiate the cell and pickers (caseOfPickers) - that at least seems to control the data.

I have been working with Linked-Data lately and it has been causing me a headache. Although I have read a lot of pages on the RDF format I dont understand entirely if it is used to hold data, e.g. for a database, or if it is something like a way to model how data interconnect with each other. I apologize if my question is obvious, but I havent been able to clarify this myself.

An example of a document I came across lately:

The Europian Commision has about 13485 Skills and Competences listed in their database as described here: https://ec.europa.eu/esco/portal/skill

However when I download a document with their Skills&Competences from this source https://ec.europa.eu/esco/portal/download I get an RDF document, which doesnt seem to hold 13485 entries. This is how the document looks like:

I followed the following documentation by Google to create ML engine and I deployed my online predicator there:

https://cloud.google.com/ml-engine/docs/scikit/quickstart

I know that it's possible to access to the engine by RESTful api as described in below: https://cloud.google.com/ml-engine/docs/v1/predict-request#request-body

But I want all clients can access the API related to my model without OAuth or any type of authentication. How can I do this?

I followed the following documentation by Google to create ML engine and I deployed my online predicator there:

https://cloud.google.com/ml-engine/docs/scikit/quickstart

Now my question is what is the simplest way I can connect my iOS application to the prepared ML engine?

got TypeError: Don't know how to wrap : . at 0x000001B879FD3D08>

when run

I need to pass a list of Enum Bucket types between Mapper and Reducer, I have implemented custom BucketArrayWritable according to implementation-of-an-arraywritable-for-a-custom-hadoop-type, and the Bucket Enum has a no-argument constructor, but I always get the error