Added README and LICENSE file

.travis.yml

@@ -0,0 +1,18 @@
+language: go
+go:
+  - 1.6
+  - 1.7
+
+install:
+  - go get github.com/gonum/matrix/mat64
+  - go get github.com/stretchr/testify/assert
+
+before_script:
+  - go get github.com/golang/lint
+
+script:
+  - make test
+  - make build
+
+after_success:
+  - bash <(curl -s https://codecov.io/bash)

LICENSE

@@ -0,0 +1,201 @@
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Makefile

@@ -1,12 +1,13 @@
+EXDIR=example
 BINARY=gopfield
 BUILD=go build
 CLEAN=go clean
 INSTALL=go install
 BUILDPATH=./_build
-PACKAGES=$(shell go list ./... | grep -v /vendor/)
+PACKAGES=$(shell go list ./... | grep -vE /vendor/)
 
 build: builddir
-	$(BUILD) -v -o $(BUILDPATH)/gopfield
+	$(BUILD) -v -o $(BUILDPATH)/gopfield example/main.go
 
 all: builddir build
 

README.md

@@ -1,2 +1,92 @@
 # gopfield
-Hopfield network in Go
+
+[![GoDoc](https://godoc.org/github.com/milosgajdos83/gopfield?status.svg)](https://godoc.org/github.com/milosgajdos83/gopfield)
+[![License](https://img.shields.io/:license-apache-blue.svg)](https://opensource.org/licenses/Apache-2.0)
+[![Travis CI](https://travis-ci.org/milosgajdos83/gopfield.svg?branch=master)](https://travis-ci.org/milosgajdos83/gopfield)
+[![Go Report Card](https://goreportcard.com/badge/github.com/milosgajdos83/gopfield)](https://goreportcard.com/report/github.com/milosgajdos83/gopfield)
+[![codecov](https://codecov.io/gh/milosgajdos83/gopfield/branch/master/graph/badge.svg)](https://codecov.io/gh/milosgajdos83/gopfield)
+
+This project provides an implementation of [Hopfield network](https://en.wikipedia.org/wiki/Hopfield_network) in Go. It implements both [Hebbian](https://en.wikipedia.org/wiki/Hopfield_network#Hebbian_learning_rule_for_Hopfield_networks) and [Storkey](https://en.wikipedia.org/wiki/Hopfield_network#The_Storkey_learning_rule) training algorithms. The goal is to provide a simple API to build Hopfield networks in `Go`.
+
+# Get started
+
+Get the source code:
+
+```
+$ go get -u github.com/milosgajdos83/gopfield
+```
+
+Run the tests:
+
+```
+$ make test
+```
+
+# Example
+
+You can see an example program below. It first creates a Hopfield network pattern based on arbitrary data. The data is encoded into binary values of +1/-1 (see the documentation) using `Encode` function. It is then stored in the network and then restored.
+
+```go
+func main() {
+        pattern := hopfield.Encode([]float64{0.2, -12.4, 0.0, 3.4})
+        // Create new Hopfield Network and set its size to the length of pattern
+	n, err := hopfield.NewNet(len(pattern))
+	if err != nil {
+		fmt.Fprintf(os.Stderr, "\nERROR: %s\n", err)
+		os.Exit(1)
+	}
+        fmt.Println("Storing:", pattern)
+	// store patterns in Hopfield network
+	if err := n.Store([]hopfield.Pattern{pattern}, "hebbian"); err != nil {
+		fmt.Fprintf(os.Stderr, "\nERROR: %s\n", err)
+		os.Exit(1)
+	}
+
+        // restore image from Hopfield network
+	res, err := n.Restore(pattern, 20, 10)
+	if err != nil {
+		fmt.Fprintf(os.Stderr, "\nERROR: %s\n", err)
+		os.Exit(1)
+	}
+        fmt.Println("Restored:", res)
+}
+```
+
+If you run this program, you will see the pattern being reconstructed correctly:
+
+```
+$ go run main.go
+Storing: [1 -1 -1 1]
+Restored: [1 -1 -1 1]
+```
+
+You can find a more elaborate example in the `example` directory of the project. The example directory also contains a `patterns` subdirectory which contains two [MNIST](http://yann.lecun.com/exdb/mnist/) images: 0 and 4. These are stored in the network. The example program then picks image `4` and adds some random noise to it. Finally it tries to reconstruct it from the network. See below how to use the example program:
+
+First you need to build it:
+
+```
+$ make build
+```
+
+If the build succeeds, you will find the built binary in `_build` directory of the project root. You can find out the cli options it provides:
+
+```
+$  _build/gopfield -h
+```
+
+Example run:
+
+```
+$ _build/gopfield -eqiters 40 -maxiters 100 -datadir ./example/patterns/ -output out.png -learning "storkey"
+```
+
+This will generate two files in directory: `noisy.png` and `out.png`.
+
+`noisy.png` image displays the file that was attempted to be reconstructed from the network:
+
+<img src="./example/noisy.png" alt="Corrupted image 4" width="200">
+
+
+`out.png` image shows the reconstucted image:
+
+<img src="./example/out.png" alt="Reconstructed image 4" width="200">

main.go → example/main.go

@@ -67,6 +67,7 @@ func parseCliFlags() error {
 	return nil
 }
 
+// ReadImage reads an image file in path and returns it as image.Image or fails with error
 func ReadImage(path string) (image.Image, error) {
 	f, err := os.Open(path)
 	if err != nil {
@@ -82,6 +83,7 @@ func ReadImage(path string) (image.Image, error) {
 	return img, nil
 }
 
+// SaveImage saves img image in path or fails with error
 func SaveImage(path string, img image.Image) error {
 	f, err := os.Create(path)
 	if err != nil {
@@ -91,14 +93,12 @@ func SaveImage(path string, img image.Image) error {
 
 	switch filepath.Ext(path) {
 	case "jpeg":
-		return jpeg.Encode(f, img, &jpeg.Options{100})
+		return jpeg.Encode(f, img, &jpeg.Options{Quality: 100})
 	case "png":
 		return png.Encode(f, img)
-	default:
-		return png.Encode(f, img)
 	}
 
-	return nil
+	return fmt.Errorf("Unsupported image format: %s\n", filepath.Ext(path))
 }
 
 func main() {

hopfield/network.go

@@ -43,7 +43,7 @@ func NewNet(size int) (*Net, error) {
 
 	// allocate neurons slice and iitialize it
 	neurons := make([]*Neuron, size)
-	for i, _ := range neurons {
+	for i := range neurons {
 		neurons[i] = new(Neuron)
 	}
 	// allocate weights and bias matrices

hopfield/pattern.go

@@ -24,7 +24,7 @@ func Encode(data []float64) Pattern {
 }
 
 // AddNoise adds random noise to pattern and returns it. Noise is added by flipping the sign of existing pattern value.
-// It allows to specify the percentage of noise via pcnt paramter. AddNoise does not modify the pattern in place.
+// It allows to specify the percentage of noise via pcnt parameter. AddNoise does not modify the pattern in place.
 func AddNoise(p Pattern, pcnt int) Pattern {
 	np := make(Pattern, len(p))
 	copy(np, p)
@@ -54,7 +54,7 @@ func Image2Pattern(img image.Image) Pattern {
 	return Encode(pattern)
 }
 
-// Pattern2GrayImage turns passed in Hopfield network pattern to a lossy Gray scaled image from pattern.
+// Pattern2Image turns passed in Hopfield network pattern to a lossy Gray scaled image from pattern.
 // Data to pixel transformation is lossy: non-positive elements are transformed to 0, otherwise 255
 func Pattern2Image(p Pattern, r image.Rectangle) image.Image {
 	// pix is a slice that contains pixels