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I am trying to get the grasp of rvalue references and move semantics with a simple self-made example but I can't understand a specific part. I have created the following class:

I am trying code from this page. I ran up to the part LR (tf-idf) and got the similar results

After that I decided to try GridSearchCV. My questions below:

1)

I'm trying to tune hyperparameters for KNN on a quite small datasets ( Kaggle Leaf which has around 990 lines ):

I'm trying to draw a partition border from a classification algorithm in a 3D plot in R (using plot3D). It's a relatively simple task if we only have two predictors, requiring only two axes to draw (e.g. using the partimat function). I haven't yet found a satisfactory way to draw a three predictor-based classification partition in 3D space.

To visualise the problem, let's start by building a partition for just two axes using a Linear Discriminant Analysis (LDA) classification algorithm on the iris dataset:

These are three different ways to run an individual fixed effect method which gives more or less the same results (see below). My main question is how to get predictive probabilities or average marginal effects using the second model (model_plm) or the third model(model_felm). I know how to do it using the first model (model_lm) and show an example below using ggeffects, but that only works when i have a small sample.

As i have over a million individual, my model only works using model_plm and model_felm. If i use model_lm, it takes a lot of time to run with one million individuals since they are controlled for in the model. I also get the following error: Error: vector memory exhausted (limit reached?). I checked many threads on StackOverflow to work around that error but nothing seems to solve it.

I was wondering whether there is an efficient way to work around this issue. My main interest is to extract the predicted probabilities of the interaction residence*union. I usually extract predictive probabilities or average marginal effects using one of these packages: ggeffects,emmeans or margins.

I'm currently trying to train a custom model with tensorflow to detect 17 landmarks/keypoints on each of 2 hands shown in an image (fingertips, first knuckles, bottom knuckles, wrist, and palm), for 34 points (and therefore 68 total values to predict for x & y). However, I cannot get the model to converge, with the output instead being an array of points that are pretty much the same for every prediction.

I started off with a dataset that has images like this:

each annotated to have the red dots correlate to each keypoint. To expand the dataset to try to get a more robust model, I took photos of the hands with various backgrounds, angles, positions, poses, lighting conditions, reflectivity, etc, as exemplified by these further images:

I have about 3000 images created now, with the landmarks stored inside a csv as such:

I have a train-test split of .67 train .33 test, with the images randomly selected to each. I load the images with all 3 color channels, and scale the both the color values & keypoint coordinates between 0 & 1.

I've tried a couple different approaches, each involving a CNN. The first keeps the images as they are, and uses a neural network model built as such:

I am trying to create an R Shiny app that calculates a score using ridge regression and then uses that in a random forest model. I saved both models as RDS and kept them in the same folder where the app.R is.

Then I read the models and data of predicted probabilities and define some functions:

I have the below conceptual problem which I can't get my head around.

Below is an example for survey data where I have a time column that indicates how long someone needs to respond to a certain question.

Now, I'm interested in how the amount of cleaning would change based on this threshold, i.e. what would happen if I increase the threshold, what would happen if I decrease it.

So my idea was to just create a ROC curve (or other model metrics) to have a visual cue about a potential threshold. The problem is that I don't have a machine-learning-like model that would give me class probabilities. So I was wondering if there's any way to create a ROC curve nonetheless with this type of data. I had the idea of just looping through my data at maybe 100 different thresholds, calculate false and true positive rates at each threshold and then do a simple line plot, but I was hoping for a more elegant solution that doesn't require me to loop.

Any ideas?

example data:

• time column indidates the time needed per case
• truth column indicates my current decision I want to compare against
• predicted column indicates the cleaning decision if I would cut at a time threshold of 2.5s. This is waht I need to change/loop through.

everyone and Professor John

We are using gekko to do MPC on tclab simulation model. We try to emulate the situation that on site the actuator deviates from MV calculated by gekko because of the problems of actuator.

If the deviation is in the fixed pattern, for example a quite big constant deviation happens for a long time and may come back then work well for a long time. We can deal with it by extra logic to detect deviation and add the deviation value to the mv calculated by gekko.

one day, I noticed that there could be meas for MV when fstatus = 1. So I gave it a try. I hope gekko could deal with the deviation by itself. for example, if mv from gekko is 10 and the measurement is 5 and the pattern continues, gekko may spit out a higher MV value than 10, for example 15 and measurement is 10.

In the simulation, when I set MV's fstatus=1, the MV's curve becomes to :

q1a is the q1 with manual deviation. In the above pic, q1a == q1. It looks like gekko takes one more step thinking about the MV's effect.

In the below pic, there are two times range, one with "q1a == q1+20" and the other with "q1a == q1 -20". q1a's value is fed to tclab and mv(q1)'s meas.

I do not understand why the q1 calculated by gekko is going up or going down when meas deviates despite the t1 is going far away from sp.

Edit: Example Code

See the screen shot below from "normal" HMI. The sluggish MV disappeared, so it maybe caused by bug in my code. But the up-going or down-going could still be seen. See my code below: