LM | Python module to train simple multi | Machine Learning library

There is no need to do this using a loop.

You can simply suspend all execution for n seconds by using Sys.sleep(n). So to suspend for 2 hours you can use Sys.sleep(2*60*60)

My problem is that the intercept for my second regression is wrong, in fact I do not even get an intercept when looking at the summary, unlike with the first regression.

That is because your second model specifies no intercept, since you use ... ~ 0 + ...

Also, your first model doesn't make sense because it includes spending twice. The second entry for spending will be ignored by lm

It’s not a bug. Floating point arithmetic has rounding errors. For single arithmetic operations + - * / sqrt the results should be the same, but for floating-point functions you can’t really expect it.

In this case it seems the compiler itself produced the results at compile time. The processor you use is unlikely to make a difference. And we don’t know whether the new version is more or less precise than the old one.

Currently (2022-02-05), CRAN builds R binaries for Apple silicon using Apple clang (from Command Line Tools for Xcode 12.4) and an experimental build of gfortran.

If you obtain R from CRAN (i.e., here), then you need to replicate CRAN's compiler setup on your system before building R packages that contain C/C++/Fortran code from their sources (and before using Rcpp, etc.). This requirement ensures that your package builds are compatible with R itself.

A further complication is the fact that Apple clang doesn't support OpenMP, so you need to do even more work to compile programs that make use of multithreading. You could circumvent the issue by building R itself and all R packages from sources with LLVM clang, which does support OpenMP, but this approach is onerous and "for experts only". There is another approach that has been tested by a few people, including Simon Urbanek, the maintainer of R for macOS. It is experimental and also "for experts only", but seems to work on my machine and is simpler than trying to build R yourself.

Warning: These instructions come with no warranty and could break at any time. They assume some level of familiarity with C/C++/Fortran program compilation, Makefile syntax, and Unix shells. As usual, sudo at your own risk.

I will try to address compilers and OpenMP support at the same time. I am going to assume that you are starting from nothing. Feel free to skip steps you've already taken, though you might find a fresh start helpful.

I've tested these instructions on a machine running Big Sur, and at least one person has tested them on a machine running Monterey. I would be glad to hear from others.

1. Download an R binary from CRAN here and install. Be sure to select the binary built for Apple silicon.

2. Run

In addition to the provided comments:

%$% also called the Exposition pipe vs. %>%:

This is a short summary of this article https://towardsdatascience.com/3-lesser-known-pipe-operators-in-tidyverse-111d3411803a

"The key difference in using %$% or %>% lies in the type of arguments of used functions."

One advantage, and as far as I can understand it, for me the only one to use %$% over %>% is the fact that we can avoid repetitive input of the dataframe name in functions that have no data as an argument.

For example the lm() has a data argument. In this case we can use both %>% and %$% interchangeable.

But in functions like the cor() which has no data argument:

Fun question! I haven't encountered it yet, but there might be a package to help do this automatically. Here's a manual approach using two hacks:

1. the clip = "off" parameter of the coord_* functions, to allow us to add annotations outside the plot area.
2. building a density plot, extracting its coordinates, and then rotating and translating those.

First, we can make a density plot of the change from initial to final, seeing a left skewed distribution:

Since stdout is line buffered, putchar doesn't write to the terminal directly; it puts the character into a buffer, which is flushed when a newline is encountered. And the buffer for stdout happens to be located on the heap following your heap_book allocation.

So at some point in your copy, you putchar all the characters of your secretinfo method. They are now in the output buffer. A little later, heap_book[i] is within the stdout buffer itself, so you encounter the copy of secretinfo that is there. When you putchar it, you effectively create another copy a little further along in the buffer, and the process repeats.

You can verify this in your debugger. The address of the stdout buffer, on glibc, can be found with p stdout->_IO_buf_base. In my test it's exactly 160 bytes past heap_book.

In your second call it looks like poly() is computed first before subsetting. Compare the outputs of model.frame() below:

Set the model_complexity of mp.Pose to 0.

As the documentation states:

MODEL_COMPLEXITY Complexity of the pose landmark model: 0, 1 or 2. Landmark accuracy as well as inference latency generally go up with the model complexity. Default to 1.

This is the best solution I've found, also use this.