numpy-100 | 100 numpy exercises | Machine Learning library

by rougier Python Version: 1.1 License: MIT

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kandi X-RAY | numpy-100 Summary

numpy-100 is a Python library typically used in Artificial Intelligence, Machine Learning, Tensorflow, Numpy, Neural Network applications. numpy-100 has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has medium support. You can download it from GitHub.

100 numpy exercises (with solutions)

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numpy-100 has a medium active ecosystem.

It has 10394 star(s) with 5122 fork(s). There are 208 watchers for this library.

It had no major release in the last 12 months.

There are 27 open issues and 49 have been closed. On average issues are closed in 140 days. There are 18 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of numpy-100 is 1.1

Quality

numpy-100 has 0 bugs and 0 code smells.

Security

numpy-100 has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

numpy-100 code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

numpy-100 is licensed under the MIT License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

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numpy-100 releases are available to install and integrate.

Build file is available. You can build the component from source.

Top functions reviewed by kandi - BETA

kandi has reviewed numpy-100 and discovered the below as its top functions. This is intended to give you an instant insight into numpy-100 implemented functionality, and help decide if they suit your requirements.

Pick a random question
Print a question n
Creates a notebook
Create markdown file
Create a Jupyter notebook with random information
Read a Ktx file into a dictionary

Get all kandi verified functions for this library.

numpy-100 Key Features

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numpy-100 Examples and Code Snippets

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Community Discussions

Trending Discussions on numpy-100

How to tell if a given 2D array has null columns in python

Python fractal box count - fractal dimension

QUESTION

How to tell if a given 2D array has null columns in python

Asked 2019-Apr-30 at 13:58

1 Problem

I am practicing 100 Numpy exercises. Question 60 asks for how to tell if a given 2D array has null columns?

I am wondering whether it asks for checking a column fulled with 0 or fulled with nan?

2 Solutions I found

If null columns stands for column with its value all equal to 0, this answer satisfies.

...

ANSWER

Answered 2019-Apr-30 at 13:58

NaN or nan, either way, is the same as null. Meaning that there is no data there.

According to this post,

he main reason to use NaN (over None) is that it can be stored with numpy's float64 dtype, rather than the less efficient object dtype

As you can see, None, np.nan and 0 all behave slightly differently.

Source https://stackoverflow.com/questions/55922047

QUESTION

Python fractal box count - fractal dimension

Asked 2018-Dec-16 at 15:36

I have some images for which I want to calculate the Minkowski/box count dimension to determine the fractal characteristics in the image. Here are 2 example images:

10.jpg:

24.jpg:

I'm using the following code to calculate the fractal dimension:

...

ANSWER

Answered 2018-Mar-28 at 20:13

With fractal dimension of something physical the dimension might converge at different stages to different values. For example, a very thin line (but of finite width) would initially seem one dimensional, then eventual two dimensional as its width becomes of comparable size to the boxes used.

Lets see the dimensions that you have produced:

What do you see? Well the linear fits are not so good. And the dimensions is going towards a value of two. To diagnose, lets take a look at the grey-scale images produced, with the threshold that you have (that is, 0.9):

The nature picture has almost become an ink blob. The dimensions would go to a value of 2 very soon, as the graphs told us. That is because we pretty much lost the image. And now with a threshold of 50?

With new linear fits that are much better, the dimensions are 1.6 and 1.8 for urban and nature respectively. Keep in mind, that the urban picture actually has a lot of structure to it, in particular on the textured walls.

In future good threshold values would be ones closer to the mean of the grey scale images, that way your image does not turn into a blob of ink!

A good text book on this is "Fractals everywhere" by Michael F. Barnsley.

Source https://stackoverflow.com/questions/44793221

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install numpy-100

You can download it from GitHub.
You can use numpy-100 like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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