influence | Find Social Influencers | Social Channel Utils library

You are using scalatest version 2.2.6:

According to MSDN:Sample, Maybe you need to set a pin's signal state to indicate the data has been sent/received in your microcontroller program. More details reside in your serial communication transmission of data standard. And you should write code according to the result of WaitCommEvent(hCom, &dwEvtMask, &o); like the linked sample.

The most naive way to do it would be to instantiate both models, sum the two predictions and compute the loss with it. This will backpropagate through both models:

When BP or SP is used in addressing, the default segment register is SS, otherwise it's DS. Rewrite the first column of memory dump table with linear address, i.e. instead of seg:offs calculate 16*seg+offs. This gives addresses

I am going to focus on this part (since we know very little about the rest of the code) :

event.target.files[0].initialCroppedAreaPixels just returns undefined, which I don't understand, because in the next line, the file that gets logged into the console always has the initialCroppedAreaPixels attribute.

In your js console the first console.log return the value of an attribute at a specific time. The second one return an object (the object itself and not its value at a specific time).

to convince you, open a js console and do the following experiment :

This could be achieved like so:

1. Extract the category labels including the sample sizes from you person variable using e.g. string::str_extract and assign it to cat.
2. Add scales = "free_y" to facet_grid.

EDIT The sample sizes could be remove from the question using e.g. gsub:

The difference between micro and macro averages becomes apparent in imbalanced datasets.

The micro average is a global strategy that basically ignores that there is a distinction between classes. It is calculated by counting the total true positives, false negatives and false positives over all classes.

In classification tasks where the underlying problem is not a multilabel classification, the micro average actually equals the accuracy score. See that your micro precision and recall are equal. Compute the accuracy score and compare, you will see no difference.

In case of macro average, the precision and recall are calculated for each label separately and reported as their unweighted mean. Depending on how your classifier performs on each class, this can heavily influence the result.

You can also refer to this answer of mine, where it has been addressed in a bit more detail.

You can add domain knowledge or constraints to structure learning in a couple of ways.

• If you want to specify the network structure and parameters using domain knowledge, you can build the network manually using custom.fit.

• If you want to estimate the structure of the BN from data then you can impose constraints on edge direction & edge presence using the whitelist and blacklist parameters in the structure learning algorithms.

• A prior can be placed on the edges in structure learning (e.g. prior="cs", where "If prior is cs, beta is a data frame with columns from, to and prob specifying the prior probability for a set of arcs. A uniform probability distribution is assumed for the remaining arcs."). There are other priors that can be used.

To convert the JSON string into Python objects you can use: