scal | System Configuration Abstraction Layer

I was trying to use Logistic regression in OCaml. I need to use it as a blackbox for another problem I'm solving. I found the following site:

http://math.umons.ac.be/anum/en/software/OCaml/Logistic_Regression/

I pasted the following code (with a few modifications - I defined my own iris_features and iris_label) from this site into a file named logistic_regression.ml:

I am trying to implement some code from this old tutorial from 2015 (which is using a VERY old version of PyVISA, so everything is different now):

I would like to replicate this plot generated in the package BGVAR with ggplot2.

Here is some information on the plot command of BGVAR: https://github.com/mboeck11/BGVAR/blob/master/R/plot.R (more specifically, check plot.bgvar.irf).

From the vignette, consider this example:

I want to convert my dataframe to Seq in Zeppelin.

My Dataframe is as below

I am trying to run a PCA analysis on ocean temperature data using sklearn. First I use StandardScaler to standardize the data, then I run the PCA and create the reconstructions. I can get code to work fine up until that point. However, I cannot figure out how to apply the inverse of the StandardScaler back to the PCA reconstructions so that they are back in the original space and I can compare the reconstructions to the original unstandardized data. I've copied a short excerpt of the code I'm using below, along with the error I receive below it. None of the potential fixes I've found online have actually worked.

Stackoverflow community,

I am looking to plot the results of R's lm() as a plane in a 3d graph made with the scatterplot3d() command from the R package scatterplot3d. I keep getting multiple errors, depending on my method of trying to graph via the $plane3d() function.

First, some reproducible data - Step 1: making the data-frame

I am fitting a logistic growth model for each subject.

When I run my loop to fit the model for every patient. There is an error as some patients the model does not converge as they do not have a logistic growth. I want my loop to continue if there is an error in fitting the model and just have NA's in the dataset where I am capturing the coefficients

I can't figure it out what's wrong with my code, it's rly frustrating. I have to make inverse matrix function, what I thought I've done. I don't know why it doesn't work. The problem is probably in line with stars, after this step my matrix named mat is changed to identity matrix, but why? Before stars it prints my mat matrix normaly which I gave to function, but after stars it's a identity matrix, I don't understand why it happend. Here's what I have:

Similar issues have been discussed on this forum (e.g. here and here), but I have not found the one that solves my problem, so I apologize for a seemingly similar question.

I have a set of .txt files with UTF-8 encoding (see the screenshot). I am trying to run a topic model in R using tm package. However, despite using encoding = "UTF-8" when creating the corpus, I get obvious problems with encoding. For instance, I get < U+FB01 >scal instead of fiscal, in< U+FB02>uenc instead of influence, not all punctuation is removed and some letters are unrecognizable (e.g. quotations marks are still there in some cases like view” or plan’ or ændring or orphaned quotations marks like “ and ” or zit or years—thus with a dash which should have been removed). These terms also show up in topic distribution over terms. I had some problems with encoding before, but using "encoding = "UTF-8" to create the corpus used to solve the problem. It seem like it does not help this time.

I am on Windows 10 x64, R version 3.6.0 (2019-04-26) , 0.7-7 version of tm package (all up to date). I would greatly appreciate any advice on how to address the problem.