entropy | The main firmware

Try something like this:

The main issue is that you have "complete separation" in your dataset. With those predictors, you can identify Species_n without any error at all. In this kind of situation, the logistic model has no MLE, it improves more and more as the estimated coefficients get more extreme in the right direction.

The way to detect this is to look at the predicted probabilities or logits. When I ran your model once, I got estimates that were

It looks like, in calculate_community_modularity, you use greedy_modularity_communities to create a dict, modularity_dict, which maps a node in your graph to a community. If I understand correctly, you can take each subgraph community in modularity_dict and pass it into shannon_entropy to calculate the entropy for that community.

this is pseudo code, so there may be some errors. This should convey the principle, though.

after running calculate_community_modularity, you have a dict like this, where the key is each node, and the value is that which the community belongs to

The training is guided by the gradient of the loss with respect to the input.

The random value that you add to the loss in the second model is independent form the input, so it will not contribute to the gradient of the loss during training. When you are running the prediction you are taking the the model output (before the loss function), so that's not affected as well.

Have you looked into the recent additions that are included in JDK 17?

https://docs.oracle.com/en/java/javase/17/core/pseudorandom-number-generators.html#GUID-08E418B9-036F-4D11-8E1C-5EB19B23D8A1

There are plenty of algorithms available:

https://docs.oracle.com/en/java/javase/17/docs/api/java.base/java/util/random/package-summary.html#algorithms

For shuffling cards you likely don't need something that is cryptographically secure.

Using Collections.shuffle should do the trick if you provide a decent RNG.

https://docs.oracle.com/en/java/javase/17/docs/api/java.base/java/util/Collections.html#shuffle(java.util.List,java.util.Random)

In this example there is an SVG element for each image. This includes a with a unique id and a where the clip-path is defined. The also have a unique id. The result is that a unique image is making use of a unique clip-path, and the animation is started and ended with reference to that particular image.

If there are many images that need this clip-path we can agree that this is not an optimal solution. Reusing an already defined clip-path would be better, but as you discovered a "common" clip-path for all images will animate the clip-path on all the images at the same time. I have researched this and also tried a bit of JavaScript and reading the spec for begin-value without any clues on solving this.

Going for a CSS based animation could be a solution, but at the same time it it also limited what you can do in a setup like that.

From the docs, the input to CrossEntropyLoss "is expected to contain raw, unnormalized scores for each class". Those are typically called logits.

There are two questions:

• Scaling the logits should not yield the same cross-entropy. You might be thinking of a linear normalization, but the (implicit) softmax in the cross-entropy normalizes the exponential of the logits.
• This causes the learning to optimize toward larger values of the logits. This is exactly what you want because it means that the network is more "confident" of the classification prediction. (The posterior p(c|x) is closer to the ground truth.)

With available equations, we can program our own functions. Here, the two functions use equations present in the paper you suggested and one of the references the authors cite.

In my case, it works with a simple "show":