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Machine Learning libraries for Go Lang - Linear regression, Logistic regression, etc.

package ml - Machine Learning Libraries ###import "github.com/alonsovidales/go_ml" Package ml provides some implementations of usefull machine learnin
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Category: Golang / Machine Learning
Watchers: 11
Star: 192
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Last update: Jan 20, 2022
Resource links
https://github.com/alonsovidales/go_ml

package ml - Machine Learning Libraries

###import "github.com/alonsovidales/go_ml"

GoDoc

Package ml provides some implementations of usefull machine learning algorithms for data mining and data analysis.

The implemented algorithms are:

- Linear Regression
- Logistic Regression
- Neural Networks
- Collaborative Filtering
- Gaussian Multivariate Distribution for anomaly detection systems

Is implemented too the fmincg function in order to calculate the optimal theta configuration to reduce the cost value for all the implemented solutions.

Author: Alonso Vidales [email protected]

Use of this source code is governed by a BSD-style. These programs and documents are distributed without any warranty, express or implied. All use of these programs is entirely at the user's own risk.

For further information about this libraries, please visit the online documentation on: http://godoc.org/github.com/alonsovidales/go_ml

3 Open More issues
Closed

What is the license applied to this repo?

I cannot find what license applies to this repository; if you haven't thought about it, could you think and add it? otherwise, can you make it more visible?

Thanks.

opened Feb 26, 2015 by ifraixedes 2
Open

Bias should be brought into result

func (rg *Regression) LinearHipotesis(x []float64) (r float64) { for i := 0; i < len(x); i++ { r += x[i] * rg.Theta[i] } return } There should be a bias variables added into result.

opened May 17, 2021 by dabney777 0
Open

Fix broken headings in Markdown files

GitHub changed the way Markdown headings are parsed, so this change fixes it.

See bryant1410/readmesfix for more information.

Tackles bryant1410/readmesfix#1

opened Apr 16, 2017 by bryant1410 0
Open

Hipotesis -> Hypothesis

Hypothesis is spelled out Hypothesis in english, not Hipotesis:

https://godoc.org/github.com/alonsovidales/go_ml#Regression.LinearHipotesis https://godoc.org/github.com/alonsovidales/go_ml#Regression.LogisticHipotesis

-s

opened Jun 9, 2016 by sbinet 0

Contents

    kanyun-inc Ytk-learn is a distributed machine learning library which implements most of popular machine learning algorithms(GBDT, GBRT, Mixture Logistic Regression, Gradient Boosting Soft Tree, Factorization Machines, Field-aware Factorization Machines, Logistic Regression, Softmax).
    nasciiboy regexp4 engine (Go-lang)
    lindahua Algorithms for regression analysis (e.g. linear regression and logistic regression)
    savan77 Machine Learning Tutorials in Python
    luccastera Machine Learning library for node.js
    dalys A list of awesome Linear resources, add-ons, integrations, links, etc.
    sajari Multivariable regression library in Go
    yandongliu javascript implementation of logistic regression/c4.5 decision tree
    Newmu Bare bones introduction to machine learning from linear regression to convolutional neural networks using Theano.
    denissimon A library for machine learning that builds predictions using a linear regression.
    yoshoku Rumale is a machine learning library in Ruby
    eriklindernoren Machine Learning From Scratch. Bare bones NumPy implementations of machine learning models and algorithms with a focus on accessibility. Aims to cover everything from linear regression to deep learning.
    pprett Bolt Online Learning Toolbox
    vinhkhuc Minimal tutorials for PyTorch
    KevinCoble A toolbox of AI modules written in Swift: Graphs/Trees, Support Vector Machines, Neural Networks, PCA, K-Means, Genetic Algorithms
    xlvector Golang machine learning lib
    h2oai H2O is an in-memory platform for distributed, scalable machine learning
    ne7ermore Deep-learning by using Pytorch. Basic nns like Logistic, CNN, RNN, LSTM and some examples are implemented by complex model.
    ICT-BDA Easy Machine Learning is a general-purpose dataflow-based system for easing the process of applying machine learning algorithms to real world tasks.
    dso-org Source code for deep symbolic regression.
    facebook Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.
    Share this repo

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