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Added documentation

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This commit is contained in:
Stephen Whitworth 2013-12-28 23:48:12 +00:00
parent 6366d3fa65
commit cc69c1ab2f
1 changed files with 14 additions and 19 deletions
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33
knn/knn.go
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@ -6,8 +6,8 @@ import (
"math"
"fmt"
"sort"
"base/base"
// "errors"
"../base"
// "errors""
)
//Sorts a map by value size in .s property
@ -41,6 +41,7 @@ func sortMap(m map[int]float64) []int {
return sm.s
}
//A KNN Classifier. Consists of a data matrix, associated labels in the same order as the matrix, and a name.
type KNNClassifier struct {
Data mat.DenseMatrix
Name string
@ -55,7 +56,7 @@ func RandomArray(n int) []float64 {
return ReturnedArray
}
//Mints a new classifier
//Mints a new classifier.
func (KNN *KNNClassifier) New(name string, labels []string, numbers []float64, x int, y int) {
// if x != len(KNN.Labels) {
@ -67,8 +68,7 @@ func (KNN *KNNClassifier) New(name string, labels []string, numbers []float64, x
KNN.Labels = labels
}
//Computes a variety of distance metrics between two vectors
//Only returns Euclidean distance at the moment
//Computes the Euclidean distance between two vectors.
func (KNN *KNNClassifier) ComputeDistance(vector *mat.DenseMatrix, testrow *mat.DenseMatrix) float64 {
var sum float64
@ -88,13 +88,12 @@ func (KNN *KNNClassifier) ComputeDistance(vector *mat.DenseMatrix, testrow *mat.
return eucdistance
}
//Returns a classification based on a vector input
//Just need to build the max voting function
//Returns a classification for the vector, based on a vector input, using the KNN algorithm.
func (KNN *KNNClassifier) Predict(vector *mat.DenseMatrix, K int) ([]string, []int) {
rows := KNN.Data.Rows()
rownumbers := make(map[int]float64)
labels := make([]string, K)
labels := make([]string, 1)
for i := 0; i < rows; i++{
row := KNN.Data.GetRowVector(i)
@ -112,18 +111,14 @@ func (KNN *KNNClassifier) Predict(vector *mat.DenseMatrix, K int) ([]string, []i
return labels, values
}
//Returns a label, given an index
func (KNN *KNNClassifier) GetLabel(index int) string {
return KNN.Labels[index]
}
func main(){
cols, rows, _, labels, data := base.ParseCsv("/Users/stephenwhitworth/Desktop/model.csv", 1, []int{2,3})
knn := KNNClassifier{}
random := mat.MakeDenseMatrix([]float64{4,4},1,2)
knn.New("Testing", labels, data, rows, cols)
for {
cols, rows, headers, labels, data := base.ParseCsv("/Users/stephenwhitworth/Desktop/Crmmodel.csv", 1, []int{2,3})
// values := []float64{1,1,1,1,4,4,4,4}
// knn := KNNClassifier{}
// random := mat.MakeDenseMatrix([]float64{4,4,4,4},1,4)
// knn.New("Testing", []string{"One", "Two"}, values,2,4)
// knn.Predict(random, 1)
labels, _ := knn.Predict(random, 20)
fmt.Println(labels)
}
}