Refactoring some stuff

LICENSE.md

@@ -0,0 +1,21 @@
+The MIT License (MIT)
+
+Copyright (c) {{{year}}} {{{fullname}}}
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

README.md

@@ -1,10 +1,28 @@
 GoLearn
 =======
 
-A basic, but useful, machine learning library for Go.
+<img src="http://talks.golang.org/2013/advconc/gopherhat.jpg" width=125>
 
-To do:
+A small start on a machine learning library in Go.
 
-* Add more classifiers/regressors - random forests, logistic and linear regression
-* Add useful tools - K fold parameter search
-* Improve error handling
+Install
+=======
+
+```
+go get github.com/sjwhitworth/golearn
+cd src/github.com/sjwhitworth/golearn
+go get ./...
+```
+
+Examples
+=======
+
+```
+cd examples/
+go run knnclassifier_iris.go
+```
+
+Join the team
+=============
+
+If you'd like to contribute, please send me a mail at stephen dot whitworth at hailocab dot com.

base/domain.go

@@ -3,6 +3,10 @@
 
 package base
 
+import (
+	mat "github.com/skelterjohn/go.matrix"
+)
+
 // An object that can ingest some data and train on it.
 type Estimator interface {
 	Fit()
@@ -19,10 +23,7 @@ type Model interface {
 	Score()
 }
 
-// @todo;
-type BaseClassifier struct {
-}
-
-// @todo;
-type BaseRegressor struct {
+// @todo: Implement BaseEstimator setters and getters.
+type BaseEstimator struct {
+	Data *mat.DenseMatrix
 }

examples/knnclassifier_iris.go

@@ -2,19 +2,20 @@ package main
 
 import (
 	"fmt"
+
+	data "github.com/sjwhitworth/golearn/data"
+	knnclass "github.com/sjwhitworth/golearn/knn"
+	util "github.com/sjwhitworth/golearn/utilities"
 	mat "github.com/skelterjohn/go.matrix"
-	"golearn/data"
-	util "golearn/utilities"
 )
 
 func main() {
 	//Parses the infamous Iris data.
-	cols, rows, _, labels, data := base.ParseCsv("datasets/iris.csv", 4, []int{0, 1, 2})
+	cols, rows, _, labels, data := data.ParseCsv("datasets/iris.csv", 4, []int{0, 1, 2})
 
 	//Initialises a new KNN classifier
 	knn := knnclass.KNNClassifier{}
-	knn.C
-	knn.New("Testing", labels, data, rows, cols)
+	knn.New(labels, data, rows, cols, "euclidean")
 
 	for {
 		//Creates a random array of N float64s between 0 and 7

examples/knnregressor_random.go

@@ -1,31 +1,32 @@
 package main
 
 import (
-		mat "github.com/skelterjohn/go.matrix"
-		base "golearn/base"
-		util "golearn/utilities"
-		knnclass "golearn/knn"
-		"fmt"
-		)
+	"fmt"
 
-func main(){
+	data "github.com/sjwhitworth/golearn/data"
+	knnclass "github.com/sjwhitworth/golearn/knn"
+	util "github.com/sjwhitworth/golearn/utilities"
+	mat "github.com/skelterjohn/go.matrix"
+)
+
+func main() {
 	//Parses the infamous Iris data.
-	cols, rows, _, labels, data := base.ParseCsv("datasets/randomdata.csv", 2, []int{0,1})
+	cols, rows, _, labels, data := data.ParseCsv("datasets/randomdata.csv", 2, []int{0, 1})
 	newlabels := util.ConvertLabelsToFloat(labels)
 
 	//Initialises a new KNN classifier
 	knn := knnclass.KNNRegressor{}
-	knn.New("Testing", newlabels, data, rows, cols)
-	
+	knn.New(newlabels, data, rows, cols, "euclidean")
+
 	for {
 		//Creates a random array of N float64s between 0 and Y
 		randArray := util.RandomArray(2, 100)
 
 		//Initialises a vector with this array
-		random := mat.MakeDenseMatrix(randArray,1,2)
+		random := mat.MakeDenseMatrix(randArray, 1, 2)
 
 		//Calculates the Euclidean distance and returns the most popular label
 		outcome, _ := knn.Predict(random, 3)
 		fmt.Println(outcome)
 	}
-}
+}

golearn.go

@@ -0,0 +1 @@
+package golearn

knn/knn.go

@@ -1,47 +1,28 @@
 package knn
 
 import (
-	"fmt"
+	base "github.com/sjwhitworth/golearn/base"
+	util "github.com/sjwhitworth/golearn/utilities"
 	mat "github.com/skelterjohn/go.matrix"
-	base "golearn/base"
-	util "golearn/utilities"
-	"math"
 )
 
 //A KNN Classifier. Consists of a data matrix, associated labels in the same order as the matrix, and a name.
 type KNNClassifier struct {
-	base.BaseClassifier
+	base.BaseEstimator
+	Labels       []string
+	DistanceFunc string
 }
 
 //Mints a new classifier.
-func (KNN *KNNClassifier) New(name string, labels []string, numbers []float64, x int, y int) {
+func (KNN *KNNClassifier) New(labels []string, numbers []float64, x int, y int, distfunc string) {
 
-	KNN.Data = *mat.MakeDenseMatrix(numbers, x, y)
-	KNN.Name = name
+	KNN.Data = mat.MakeDenseMatrix(numbers, x, y)
 	KNN.Labels = labels
+	KNN.DistanceFunc = distfunc
 }
 
-//Computes the Euclidean distance between two vectors.
-func (KNN *KNNClassifier) ComputeDistance(vector *mat.DenseMatrix, testrow *mat.DenseMatrix) float64 {
-	var sum float64
-
-	difference, err := testrow.MinusDense(vector)
-	flat := difference.Array()
-
-	if err != nil {
-		fmt.Println(err)
-	}
-
-	for _, i := range flat {
-		squared := math.Pow(i, 2)
-		sum += squared
-	}
-
-	eucdistance := math.Sqrt(sum)
-	return eucdistance
-}
-
-//Returns a classification for the vector, based on a vector input, using the KNN algorithm.
+// Returns a classification for the vector, based on a vector input, using the KNN algorithm.
+// @todo: Lots of room to improve this. V messy.
 func (KNN *KNNClassifier) Predict(vector *mat.DenseMatrix, K int) (string, []int) {
 
 	rows := KNN.Data.Rows()
@@ -51,7 +32,9 @@ func (KNN *KNNClassifier) Predict(vector *mat.DenseMatrix, K int) (string, []int
 
 	for i := 0; i < rows; i++ {
 		row := KNN.Data.GetRowVector(i)
-		eucdistance := KNN.ComputeDistance(row, vector)
+
+		//Will put code in to check errs later
+		eucdistance, _ := util.ComputeDistance(KNN.DistanceFunc, row, vector)
 		rownumbers[i] = eucdistance
 	}
 

knn/knnregressor.go

@@ -2,21 +2,23 @@ package knn
 
 import (
 	"fmt"
-	mat "github.com/skelterjohn/go.matrix"
-	base "golearn/base"
-	util "golearn/utilities"
 	"math"
+
+	util "github.com/sjwhitworth/golearn/utilities"
+	mat "github.com/skelterjohn/go.matrix"
 )
 
 //A KNN Regressor. Consists of a data matrix, associated result variables in the same order as the matrix, and a name.
 type KNNRegressor struct {
-	base.BaseRegressor
+	Data   *mat.DenseMatrix
+	Name   string
+	Labels []float64
 }
 
 //Mints a new classifier.
 func (KNN *KNNRegressor) New(name string, labels []float64, numbers []float64, x int, y int) {
 
-	KNN.Data = *mat.MakeDenseMatrix(numbers, x, y)
+	KNN.Data = mat.MakeDenseMatrix(numbers, x, y)
 	KNN.Name = name
 	KNN.Labels = labels
 }

utilities/distance.go

@@ -1,31 +1,36 @@
 package utilities
 
 import (
-	util "../utilities"
 	"fmt"
-	mat "github.com/skelterjohn/go.matrix"
 	"math"
+
+	mat "github.com/skelterjohn/go.matrix"
 )
 
-//Computes the Euclidean distance between two vectors.
-func ComputeDistance(metric string, vector *mat.DenseMatrix, testrow *mat.DenseMatrix) float64 {
+// Computes the 'distance' between two vectors, where the distance is one of the following methods -
+// euclidean (more to come)
+func ComputeDistance(metric string, vector *mat.DenseMatrix, testrow *mat.DenseMatrix) (float64, error) {
 	var sum float64
 
-	// Compute a variety of distance metrics
-	switch metric:
-	case "euclidean": {
-		difference, err := testrow.MinusDense(vector)
-		flat := difference.Array()
+	switch metric {
+	case "euclidean":
+		{
+			difference, err := testrow.MinusDense(vector)
+			flat := difference.Array()
 
-		if err != nil {
-			fmt.Println(err)
+			if err != nil {
+				fmt.Println(err)
+			}
+
+			for _, i := range flat {
+				squared := math.Pow(i, 2)
+				sum += squared
+			}
+
+			eucdistance := math.Sqrt(sum)
+			return eucdistance, nil
 		}
-
-		for _, i := range flat {
-			squared := math.Pow(i, 2)
-			sum += squared
-		}
-
-		eucdistance := math.Sqrt(sum)
-		return eucdistance
-	}
+	default:
+		return 0.0, fmt.Errorf("ValueError: %s is not an implemented distance method", metric)
+	}
+}