python_ML | python code for some ML algorithm | Machine Learning library

by burness Python Version: Current License: No License

X-Ray Key Features Code Snippets Community Discussions(2)Vulnerabilities Install Support

kandi X-RAY | python_ML Summary

python_ML is a Python library typically used in Artificial Intelligence, Machine Learning, Example Codes applications. python_ML has no bugs, it has no vulnerabilities and it has low support. However python_ML build file is not available. You can download it from GitHub.

python code for some ML algorithm. 2014.01.30 Happy Chinese New Year everyday!!! No coding for a week now, enjoy yourself in these days.

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python_ML has a low active ecosystem.

It has 25 star(s) with 22 fork(s). There are 4 watchers for this library.

It had no major release in the last 6 months.

python_ML has no issues reported. There are 1 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of python_ML is current.

Quality

python_ML has 0 bugs and 0 code smells.

Security

python_ML has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

python_ML code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

python_ML does not have a standard license declared.

Check the repository for any license declaration and review the terms closely.

Without a license, all rights are reserved, and you cannot use the library in your applications.

Reuse

python_ML releases are not available. You will need to build from source code and install.

python_ML has no build file. You will be need to create the build yourself to build the component from source.

python_ML saves you 442 person hours of effort in developing the same functionality from scratch.

It has 1046 lines of code, 51 functions and 43 files.

It has high code complexity. Code complexity directly impacts maintainability of the code.

Top functions reviewed by kandi - BETA

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

Nn cost function
Gradient of sigmoid
Sigmoid function
Batch gradient update
Compute the cost function
Fit a multivariate Gaussian distribution
Fits the RVC cost function
Predict the model for the giventa1 and theta2
R Regularize the cost function
Sigmoid function

Get all kandi verified functions for this library.

python_ML Key Features

No Key Features are available at this moment for python_ML.

python_ML Examples and Code Snippets

No Code Snippets are available at this moment for python_ML.

Community Discussions

Trending Discussions on python_ML

Reading and adding a generic list with a C# application from a CSV file

GridSearchCV.fit() returns TypeError: Expected sequence or array-like, got estimator

QUESTION

Reading and adding a generic list with a C# application from a CSV file

Asked 2018-Dec-05 at 05:28

I need to pull data from the CSV file for my analysis to be done all the time. The file size has values close to 1 GB and total number of rows to read 20-30 million units

Expectations : As time to finish the job in less time and to look for a more optimum solution in memory usage.

Can you review the code and results I wrote and advise?

...

ANSWER

Answered 2018-Dec-04 at 23:18

This should dramatically reduce the total time and memory use, at the cost of it no longer making sense to measure the read, count, and list operations separately, as they now all happen as the file is streamed from disk:

Source https://stackoverflow.com/questions/53622623

QUESTION

GridSearchCV.fit() returns TypeError: Expected sequence or array-like, got estimator

Asked 2017-Feb-15 at 09:26

I am trying to do sentiment analysis on twitter data by following chapter 6 of the book Building Machine Learning Systems in Python.

I am using the dataset: https://raw.githubusercontent.com/zfz/twitter_corpus/master/full-corpus.csv

It uses a pipeline of tfidf vectorizer and naive bayes classifier as estimator.

Then I am using GridSearchCV() to find the best parameters for the estimator.

The code is as follows:

...

ANSWER

Answered 2017-Feb-15 at 09:26

This error is because You are passing wrong parameter by using scoring=f1_score into the GridSearchCV constructor. Have a look at documentation of GridSearchCV.

In scoring param, it asks for:

A string (see model evaluation documentation) or a scorer callable object / function with signature scorer(estimator, X, y). If None, the score method of the estimator is used.

You are passing a callable function with signature (y_true, y_pred[, ...]) which is wrong. Thats why you are getting the error. You should use a string as defined here to pass in scoring, or pass a callable with signature (estimator, X, y). This can be done by using make_scorer.

Change this line in your code:

Source https://stackoverflow.com/questions/42244860

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

Vulnerabilities

No vulnerabilities reported

Install python_ML

You can download it from GitHub.
You can use python_ML 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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