Popularity
5.9
Stable
Activity
0.0
Stable
310
16
28
Programming language: Go
License: GNU Affero General Public License v3.0
Tags:
Machine Learning
regommend alternatives and similar packages
Based on the "Machine Learning" category.
Alternatively, view regommend alternatives based on common mentions on social networks and blogs.
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Gorgonia
Gorgonia is a library that helps facilitate machine learning in Go. -
gosseract
Go package for OCR (Optical Character Recognition), by using Tesseract C++ library -
m2cgen
Transform ML models into a native code (Java, C, Python, Go, JavaScript, Visual Basic, C#, R, PowerShell, PHP, Dart, Haskell, Ruby, F#, Rust) with zero dependencies -
gago
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ocrserver
A simple OCR API server, seriously easy to be deployed by Docker, on Heroku as well -
onnx-go
onnx-go gives the ability to import a pre-trained neural network within Go without being linked to a framework or library. -
shield
Bayesian text classifier with flexible tokenizers and storage backends for Go -
neat
Plug-and-play, parallel Go framework for NeuroEvolution of Augmenting Topologies (NEAT). -
go-featureprocessing
๐ฅ Fast, simple sklearn-like feature processing for Go -
neural-go
A multilayer perceptron network implemented in Go, with training via backpropagation. -
go-cluster
k-modes and k-prototypes clustering algorithms implementation in Go
WorkOS - The modern identity platform for B2B SaaS
The APIs are flexible and easy-to-use, supporting authentication, user identity, and complex enterprise features like SSO and SCIM provisioning.
Promo
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README
regommend
Recommendation engine for Go
Installation
Make sure you have a working Go environment (Go 1.2 or higher is required). See the install instructions.
To install regommend, simply run:
go get github.com/muesli/regommend
To compile it from source:
cd $GOPATH/src/github.com/muesli/regommend
go get -u -v
go build && go test -v
Example
package main
import (
"github.com/muesli/regommend"
"fmt"
)
func main() {
// Accessing a new regommend table for the first time will create it.
books := regommend.Table("books")
booksChrisRead := make(map[interface{}]float64)
booksChrisRead["1984"] = 5.0
booksChrisRead["Robinson Crusoe"] = 4.0
booksChrisRead["Moby-Dick"] = 3.0
books.Add("Chris", booksChrisRead)
booksJayRead := make(map[interface{}]float64)
booksJayRead["1984"] = 5.0
booksJayRead["Robinson Crusoe"] = 4.0
booksJayRead["Gulliver's Travels"] = 4.5
books.Add("Jay", booksJayRead)
recs, _ := books.Recommend("Chris")
for _, rec := range recs {
fmt.Println("Recommending", rec.Key, "with score", rec.Distance)
}
neighbors, _ := books.Neighbors("Chris")
...
}
To run this example, go to example/ and run:
go run example.go