Not sure when this happened but i love the new left-hand side navigation https://scikit-learn.org/dev/user_guide.html

(@adrinjalali did this maybe?)

However, when clicking the different entries, the result is inconsistent. For some, it shows a TOC that expands the existing toc with m

I just started going through the jupyter notebooks (great stuff, by the way), and when running the cells the first time I would always get some error like Polygon has no attribute normed. It turns out that the normed option in plt.hist has been deprecated (in matplotlib version 3.1.1 and later as far as I can tell), and should instead be replaced with density (they appear to operate simi

I don't know if this is worth documenting, but I was playing with the CLI referenced using pageres and came up with a fun little ("little" 😜 ) one-liner:

TMP=$(mktemp)".html";OUT=gitstats-"$(date '+%Y-%m-%d')".png;rm $OUT && git-stats --raw | git-stats-html -o $TMP && pageres $TMP 775x250 --filename="gitstats-<%=date%>" && imgcat $OUT

Aside from a little tempfile and date manipulation to

Need to do some better handling of low-observation models in plot_diagnostics. These are models that shouldn't really be estimated, and we can't really make the plots work, but we shouldn't raise exceptions.

• Any dataset with less than 10 observations will raise an error computing the error autocorrelations:

mod = sm.tsa.statespace.SARIMAX(np.random.normal(size=10), order=(10, 

From: https://boltons.readthedocs.io/en/latest/setutils.html#boltons.setutils.IndexedSet

As you can see, the IndexedSet is almost like a UniqueList, retaining only one copy of a given value, in the order it was first added.

This appears incorrect. Perhaps it is a remnant from an earlier version of the library?

In tests it could be ok to have copy/paste code but I believe that in common estimator checks we could remove the boilerplate of datasets generation for each test by creating a common fixture. Apart from that we could reduce the number of instances of the dataset(s) in order to speed up the e

I've noticed that there are a lot more tutorials/examples around VI
but less around sampling. I thought I might write up some of the sampling
examples as notebooks and add a sampling example for bayesian nets.

Is this something that would be helpful?

See:
gonum/gonum#845 (comment)

What would you like to submit? (put an 'x' inside the bracket that applies)

• [x ] question
• bug report
• feature request

Issue description

Is the matrix-matrix multiplication function (Dot(matrixA, matrixB)) documented anywhere? I can't find the documentation on the accord-framework.net website and the code appears to be generated from .tt files.

Tokei counts blank lines inside comment blocks as blank lines. Wouldn't they be considered as comment lines?

For example:

/* Copyright (C) 2007-2008 Foo
   Copyright (C) 2008      Foo

   File: test.c
  
*/

Tokei output:

-------------------------------------------------------------------------------
 Language            Files        Lines         Code     Comment

Hey there @JonnyWong16 , wanted to provide some content for the Wiki, and can't for the life of me figure out how to do a PR for Wiki content.

Below is an Nginx reverse proxy example that will allow users to access Tautulli via tautulli.url.com, and redirects HTTP requests to HTTPS. This configuration passes SSL Labs' test with an "A" grade.

DH Params creation vi

I see that the Gumbel distribution has been created based on this link: https://www.tensorflow.org/probability/api_docs/python/tfp/distributions/Gumbel
This is the xi =0 case per https://en.wikipedia.org/wiki/Generalized_extreme_value_distribution

I would like to use a more generalized version of the extreme value distrubutions allowing xi to be non-zero; Gumbel xi =0, Frechet xi > 0, and/or W

I don't know what went wrong.When I enter "./xlearn_train ./small_train.txt", it comes up "[ WARNING ] Validation file not found, xLearn has already disable early-stopping."

Hello.
I've come across what (to me) seems to be a problem with the FILENAME and FILENUM variables.

# mlr --version
Miller v5.6.2

# cat /tmp/csv1
A,B,C
_2GB,255,2
_4GB,120,4
_6GB,50,6
_10GB,10,10

# cat /tmp/csv2
FIRST,SECOND,THIRD,FOURTH
1,2,3,4
5,6,7,8
9,10,11,12
13,14,15,16

# mlr --icsv cat then put 'print FILENAME'   /tmp/csv1 /tmp/csv2
/tmp/csv1
A=_2GB,B=255,C=2
/

From the docs for https://simplestatistics.org/docs/#standardnormaltable:

The table used is the cumulative, and not cumulative from 0 to mean.

I'm having trouble understanding this sentence. What is meant by cumulative and not cumulative?

Checklist

Please ensure the following tasks are completed before filing a bug report.

• Read and understood the [Code of Conduct][code-of-conduct].
• Searched for existing issues and pull requests.

Description

Description of the issue.

The following code produces an array whose shape does not match what I'd expect:

Hi again,
a second issue I ran into is related to the userguide:
The example for Grouping on calculated columns regarding binning doesn't compile in v0.37.3 and apart from that doesn't lead to a reasonable result, as far as I see.

• Compilation isn't possible, as bin() returns a DoubleColumn (even if c

The help currently reads:
Vector S get; set;
Gets the singular values (Σ) of matrix in ascending value.

It should be:
descending order of magnitude.

I found several useful applications of pseudo-random number sampling in the past. In particular:

• Inverse transform sampling
• Gibbs sampling

(This issue serves a reminder to add the respective methods. Pull requests always welcome.)

Currently the main statistics table looks as follows:

The following historical commit information, by author, was found in the repository:

Author           Commits    Insertions      Deletions    % of changes
author 1              24           564             59            10.2
author 2              21           276             97            30.2

It would be useful to add an ext

The figures in index.rst have URLs that don't work. I looked for them in the source tree, but I don't find them there.