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Golang PairwiseDistanceFunc.Distance方法代碼示例

本文整理匯總了Golang中github.com/sjwhitworth/golearn/metrics/pairwise.PairwiseDistanceFunc.Distance方法的典型用法代碼示例。如果您正苦於以下問題:Golang PairwiseDistanceFunc.Distance方法的具體用法?Golang PairwiseDistanceFunc.Distance怎麽用?Golang PairwiseDistanceFunc.Distance使用的例子?那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在github.com/sjwhitworth/golearn/metrics/pairwise.PairwiseDistanceFunc的用法示例。


在下文中一共展示了PairwiseDistanceFunc.Distance方法的3個代碼示例,這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚,您的評價將有助於係統推薦出更棒的Golang代碼示例。

示例1: computePairwiseDistances

func computePairwiseDistances(inst base.FixedDataGrid, attrs []base.Attribute, metric pairwise.PairwiseDistanceFunc) (*mat64.Dense, error) {
	// Compute pair-wise distances
	// First convert everything to floats
	mats, err := base.ConvertAllRowsToMat64(attrs, inst)
	if err != nil {
		return nil, err
	}

	// Next, do an n^2 computation of all pairwise distances
	_, rows := inst.Size()
	dist := mat64.NewDense(rows, rows, nil)
	for i := 0; i < rows; i++ {
		for j := i + 1; j < rows; j++ {
			d := metric.Distance(mats[i], mats[j])
			dist.Set(i, j, d)
			dist.Set(j, i, d)
		}
	}
	return dist, nil
}
開發者ID:CTLife,項目名稱:golearn,代碼行數:20,代碼來源:dbscan.go

示例2: Predict

func (KNN *KNNRegressor) Predict(vector *mat64.Dense, K int) float64 {
	// Get the number of rows
	rows, _ := KNN.Data.Dims()
	rownumbers := make(map[int]float64)
	labels := make([]float64, 0)

	// Check what distance function we are using
	var distanceFunc pairwise.PairwiseDistanceFunc
	switch KNN.DistanceFunc {
	case "euclidean":
		distanceFunc = pairwise.NewEuclidean()
	case "manhattan":
		distanceFunc = pairwise.NewManhattan()
	default:
		panic("unsupported distance function")
	}

	for i := 0; i < rows; i++ {
		row := KNN.Data.RowView(i)
		rowMat := utilities.FloatsToMatrix(row)
		distance := distanceFunc.Distance(rowMat, vector)
		rownumbers[i] = distance
	}

	sorted := utilities.SortIntMap(rownumbers)
	values := sorted[:K]

	var sum float64
	for _, elem := range values {
		value := KNN.Values[elem]
		labels = append(labels, value)
		sum += value
	}

	average := sum / float64(K)
	return average
}
開發者ID:hpxro7,項目名稱:golearn,代碼行數:37,代碼來源:knn.go

示例3: Predict

// Predict returns a classification for the vector, based on a vector input, using the KNN algorithm.
func (KNN *KNNClassifier) Predict(what base.FixedDataGrid) base.FixedDataGrid {
	// Check what distance function we are using
	var distanceFunc pairwise.PairwiseDistanceFunc
	switch KNN.DistanceFunc {
	case "euclidean":
		distanceFunc = pairwise.NewEuclidean()
	case "manhattan":
		distanceFunc = pairwise.NewManhattan()
	default:
		panic("unsupported distance function")
	}
	// Check Compatibility
	allAttrs := base.CheckCompatible(what, KNN.TrainingData)
	if allAttrs == nil {
		// Don't have the same Attributes
		return nil
	}

	// Use optimised version if permitted
	if KNN.AllowOptimisations {
		if KNN.DistanceFunc == "euclidean" {
			if KNN.canUseOptimisations(what) {
				return KNN.optimisedEuclideanPredict(what.(*base.DenseInstances))
			}
		}
	}
	fmt.Println("Optimisations are switched off")

	// Remove the Attributes which aren't numeric
	allNumericAttrs := make([]base.Attribute, 0)
	for _, a := range allAttrs {
		if fAttr, ok := a.(*base.FloatAttribute); ok {
			allNumericAttrs = append(allNumericAttrs, fAttr)
		}
	}

	// Generate return vector
	ret := base.GeneratePredictionVector(what)

	// Resolve Attribute specifications for both
	whatAttrSpecs := base.ResolveAttributes(what, allNumericAttrs)
	trainAttrSpecs := base.ResolveAttributes(KNN.TrainingData, allNumericAttrs)

	// Reserve storage for most the most similar items
	distances := make(map[int]float64)

	// Reserve storage for voting map
	maxmap := make(map[string]int)

	// Reserve storage for row computations
	trainRowBuf := make([]float64, len(allNumericAttrs))
	predRowBuf := make([]float64, len(allNumericAttrs))

	_, maxRow := what.Size()
	curRow := 0

	// Iterate over all outer rows
	what.MapOverRows(whatAttrSpecs, func(predRow [][]byte, predRowNo int) (bool, error) {

		if (curRow%1) == 0 && curRow > 0 {
			fmt.Printf("KNN: %.2f %% done\n", float64(curRow)*100.0/float64(maxRow))
		}
		curRow++

		// Read the float values out
		for i, _ := range allNumericAttrs {
			predRowBuf[i] = base.UnpackBytesToFloat(predRow[i])
		}

		predMat := utilities.FloatsToMatrix(predRowBuf)

		// Find the closest match in the training data
		KNN.TrainingData.MapOverRows(trainAttrSpecs, func(trainRow [][]byte, srcRowNo int) (bool, error) {
			// Read the float values out
			for i, _ := range allNumericAttrs {
				trainRowBuf[i] = base.UnpackBytesToFloat(trainRow[i])
			}

			// Compute the distance
			trainMat := utilities.FloatsToMatrix(trainRowBuf)
			distances[srcRowNo] = distanceFunc.Distance(predMat, trainMat)
			return true, nil
		})

		sorted := utilities.SortIntMap(distances)
		values := sorted[:KNN.NearestNeighbours]

		maxClass := KNN.vote(maxmap, values)

		base.SetClass(ret, predRowNo, maxClass)
		return true, nil

	})

	return ret
}
開發者ID:nickpoorman,項目名稱:golearn,代碼行數:97,代碼來源:knn.go


注:本文中的github.com/sjwhitworth/golearn/metrics/pairwise.PairwiseDistanceFunc.Distance方法示例由純淨天空整理自Github/MSDocs等開源代碼及文檔管理平台,相關代碼片段篩選自各路編程大神貢獻的開源項目,源碼版權歸原作者所有,傳播和使用請參考對應項目的License;未經允許,請勿轉載。