JFP | Javascript functional programming utilities | Functional Programming library

Here's a Haskell implementation of Melissa O'Neill's algorithm (from the linked article). Unlike the implementation that Gassa linked to, I've made minimal use of laziness, so that the performance analysis is clear -- O(n log n log log n), i.e., linearithmic in n log log n, the number of writes made by the imperative Sieve of Eratosthenes.

The heap implementation is just a tournament tree. The balancing logic is in push; by swapping the children every time, we ensure that, for every branch, the left subtree is the same size or one bigger compared to the right subtree, which ensures depth O(log n).

Ok I solved the problem on Windows 10 with Anaconda using python 3.6.5 and MSC v.1900 64 bit (informations given by running :

Why is not declared like this: instance {-# OVERLAPPING #-} => (f :<: (g :+: f)) where ... similar to the injection to the left.

The way the original code uses makes an instance head strictly more specific than the other. That is: f :<: (f :+: g) is more specific than f :<: (h :+: g), since the first only matches a (strict) subset of cases w.r.t. the latter.

This makes the compiler happy: the first instance can be tried. If it matches, great. If it does not match, and it does not unify, we can commit to the second more general instance. (If it does not match but unifies, then we are stuck and we can not commit to either instance.)

Doing it your way increases the possibility that constraint solving gets stuck, since when resolving F :<: (F :+: F) both instances apply. In original code, the first would be applied, since it is more specific.

Second question, is the inj = Inr . inj calling itself recursively?

No. The last inj is that define by the instance for f :<: g, so it is a different function. This often happens, e.g.

If you want to update the figure, you shouldn't create it in a function which is called several times. Instead you can create the figure in in the MainPage's init function and only update the content of its subplot. Therefore, the plot function could only clear the axes (not the figure!) and call the mandelbrot_image function to which the axes to plot to can be delivered as an argument. Finally the canvas has to be redrawn using canvas.draw() for the new plot to appear in the GUI.

I found an install process that seems to be working in jupyter notebook with Anaconda 4.3 for python 3.6.4 on Windows 10 win-64. Below I spell out the process that I followed. At the bottom I include a couple screenshots of the installed folders and files. If you have any suggestions on how to improve this process, please let me know.

This process is adapted from instructions at http://adataanalyst.com/machine-learning/installing-xgboost-for-windows-10/ which in turn are derived from http://stackoverflow.com/questions/33749735/how-to-install-xgboost-package-in-python-windows-platform.

PREP

1. If you don’t have git, install it and add it to your PATH.

2. As part of previous attempts to install xgboost I had recently updated numpy and scipy to latest versions

3. Download and install MinGW-64: http://sourceforge.net/projects/mingw-w64/

   a. In the Setting dialog, set the Architecture to “x86_64” (was i686) and the Threads to “win32” (was posix)

   b. I installed MinGW-64 to the default file path in C:\Program Files, so I added C:\Program Files\mingw-w64\x86_64-8.1.0-win32-seh-rt_v6-rev0\mingw64\bin to my PATH environment variable

   c. After installation finished, as suggested I went to the mingw64\bin folder and renamed mingw32-make to make

   • Actually, I made a copy of mingw32-make and named the copy make

   • Doing so may be the source of some of my troubles below, though I was able to get past them. I recommend you try renaming the file rather than leaving two copies of the same file with different names as I did

GET THE XGBOOST SOURCE CODE

1. Launch a Windows command prompt: Start | Windows System | Command Prompt

   • These steps may also work in MINGW64, which I switch to later, but here I try to faithfully record the steps I took as I followed the instructions I had)

   a. Enter cd c:\ where c:\ represents the location that you want to install xgboost. For me it was C:\...\Anaconda2\envs\py36\Lib\site-packages

   b. Enter git clone --recursive https://github.com/dmlc/xgboost

   • This will run and output a few dozen lines of output before displaying the prompt for the next step

   c. Enter cd xgboost

   d. Enter git submodule init

   • This did not produce any output and immediately displayed the prompt again

   e. Enter git submodule update

   • This did not produce any output and immediately displayed the prompt again

   f. Enter copy make\mingw64.mk config.mk

   • Output: "1 file(s) copied."

   • NOTE: Up to this point all commands were run in Windows command prompt. The next did not work there, so going forward I switched to the mingw64 terminal. I re-ran step "f" and continued at the next step.

2. Launch the mingw64 terminal: Start | MinGW--W64 project | Run terminal

   a. Enter cd C:\Users\karls\Anaconda2\envs\py36\Lib\site-packages\xgboost

   b. Enter copy make\mingw64.mk config.mk

   c. Enter make -j4

   • This command did not work. I tried dozens of variations based on suggested I googled: make.exe, makefile, cmake, pymake, make.py, mingw64-make, mingw64-make.exe, C:\Program Files\mingw-w64\x86_64-8.1.0-win32-seh-rt_v6-rev0\mingw64\bin\make, the list goes on. Nothing worked. I tried changing the directory to other folders inside of site-packages\xgboost. Finally, though the output was suspect, I hit upon the following and was able to proceed.

   d. Change directory to ...\site-packages\xgboost\make

   e. Enter mingw64.mk -j4

   • This popped up a "How do you want to open this file?" dialog, which was the most hopeful output I had seen yet. I did not open the file. Did the command do any good? I have no idea, but I went on to the next steps.

INSTALL THE PYTHON PACKAGE

f. Change directory to site-packages\xgboost\python-package

g. Enter python setup.py install

• This outputs several dozen lines: running this, creating that, writing and reading and copying, etc.

• I also had several "warning: no files found matching ..." lines.

• Presumably anything missing was dealt with in the followed lines of more writing, installing, running, creating, copying, byte-compiling, removing, processing, extracting, adding, and searching

• The final line read "Finished processing dependencies for xgboost==0.71"

At this point I was able to import and use xgboost in a jupyter notebook, so I did not take any further steps. However, the instructions I was following (linked above) included additional steps that you may find necessary. Please let me know if you see any errors in my process as I am still suspect that my install is unsusceptible to problems with updates later (for example, the directory seems to contain copies of the same files in multiple places).

Below are a couple screenshots of the installed directories:

The main problem here is that you create two different figures. The one that lives in the Tk frame is not the one you plot the mandelbrot image to.
So you need to work with the same figure throughout the code.
One option is to let the mandelbrot_image create the figure and return it to later be able to supply it to the FigureCanvas. See below for a complete solution.

An additional problem is that matplotlib does not have a figshow method. You probably want imshow()instead.

So apparently a new figure is created because of a call to pyplot.xticks(). While this behaviour is not reproducible in python 2.7 and I'm uncertain about the reasons for it, a solution is to use the API commands of the axes:

Do not use OpenMP with Rcpp's *Vector or *Matrix objects as they mask SEXP functions / memory allocations that are single-threaded. OpenMP is a multi-threaded approach.

This is why the code is crashing.

One way to get around this limitation is to use a non-R data structure to store the results. One of the following will be sufficient: arma::mat or Eigen::MatrixXd or std::vector... As I favor armadillo, I will change the res matrix to arma::mat from Rcpp::NumericMatrix. Thus, the following will execute your code in parallel:

You can install it through the conda-forge channel by running this command: