connectedness | easy way to check network connection

I'm making a randomly generated network where the node will send an update massage after asking if the other node already know the update. Currently the update status is still predefined. I'm getting this error message when running the simulation:

I'm making a randomly generated network where the node will send an update massage after asking if the other node already know the update. Currently the update status is still predefined. I'm getting this error message when running the simulation:

The Problem:

I've implemented Knuth's DLX "dancing links" algorithm for Pentominoes in two completely different ways and am still getting incorrect solutions. The trivial Wikipedia example works OK (https://en.wikipedia.org/wiki/Knuth%27s_Algorithm_X#Example), but more complex examples fail.

Debugging the full Pentominoes game requires a table with almost 2,000 entries, so I came up with a greatly reduced puzzle (pictured below) that is still complex enough to show the errant behavior.

Below is my trivial 3x5 Pentominoes example, using only 3 pieces to place. I can work through the algorithm with pen and paper, and sure enough my code is doing exactly what I told it to, but on the very first step, it nukes all of my rows! When I look at the connectedness, the columns certainly do seem to be OK. So clearly I'm misunderstanding something.

The Data Model:

This is the trivial solution I'm trying to get DLX to solve:

Below is the "moves" table, which encodes all the valid moves that the 3 pieces can make. (I filter out moves where a piece would create a hole size not divisible by 5)

• The left column is the encoded move, for example the first row is piece "L", placed at 0,0, then rotated ONE 90-degree turn counter-clockwise.
• vertical bar (|) delimiter
• First 3 columns are the selector bit for which piece I'm referring to. Since "l" is the first piece (of only 3), it has a 1 in the leftmost column.
• The next 15 columns are 1 bit for every spot on a 3x5 pentominoes board.

Dear fellow programmers,

I'm trying to analyze a high dimensional data set (31 variables, 1100 observations) through a penalized vector autoregression.

Since I'm using the techniques introduced by Diebold et. al (2019) to build a network of connectedness through variance decomposition matrices. I would like to use their package in r: https://www.rdocumentation.org/packages/vars/versions/1.5-3/topics/fevd

However, this package can only be used with a regular VAR estimate. I would like to use a penalized regression, such a LASSO. So How can I use their package in R, with a penalized VAR?

What did I try? Their is Lassovars package on github, however, I can't use it in the fevd() function. It says: only uses estimate from the Vars class.

Hope to hear from you!

Kind regards,

Bart

I'm working on a handwritten digit recognition problem, using OpenCV for preprocessing and Keras/Tensorflow for inference. I trained a model on the MNIST handwritten digit dataset, where each image is 28x28 pixels. Now I'm working with a new set of digits and I plan to do further training with the original model architecture and transfer learning via weight initialisation.

So here's my problem: I'm having an issue with losing certain features when I downsize to 28x28 pixels. Here's an example

That's meant to be a two, and the tiny gap in the top loop is important in helping differentiate it from a 9 or an 8. But my preprocessed version loses the gap, so the loop looks closed.

I have posted another question about how to do the downsizing without losing the features. On the other hand, maybe I'd like to downsize to a larger size like 56x56 pixels where I'm less likely to lose such features. How can I set things up such that this new size blends in with the model without rendering the pre-trained weights useless?

Here is the definition of the pre-trained model:

I am trying to create a graph with randomly connected nodes. Nodes should be connected randomly and if a node is already connected to another node, it shouldn't be connected to the same node again using different inout port.

In the docs there is this example to create a random graph:

I'm currently displaying serial output from my embedded device in a Mac OS terminal using the command

I have a set of data where the nodes have an attribute showing the name of the team to which they belong. I would like to work out the stats for a) the whole network and then b) the stats for each team comparing their connectivity etc. (Lets say I am comparing who sends emails to who and therefore the degree of connectedness of the team. Here is my sample code:

I am analysing network graph connectedness, measuring in_degrees and out_degrees of all nodes.

I am seeking to show the results as shown in this video at 02:00

As you can see the lecturer has plotted in_degrees on the x-axis and out_degrees on the y-axis. The colour scale represents the number of nodes which have those x,y characteristics. His example is simple with a minimum of 0 and a maximum of 3 on each axis.

I have produced data in the format of