Example #1
0
// param base.IFixedDataGrid
// return base.IFixedDataGrid
func (p *AveragePerceptron) Predict(what base.FixedDataGrid) base.FixedDataGrid {

	if !p.trained {
		panic("Cannot call Predict on an untrained AveragePerceptron")
	}

	data := processData(what)

	allAttrs := base.CheckCompatible(what, p.TrainingData)
	if allAttrs == nil {
		// Don't have the same Attributes
		return nil
	}

	// 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)
		}
	}

	ret := base.GeneratePredictionVector(what)
	classAttr := ret.AllClassAttributes()[0]
	classSpec, err := ret.GetAttribute(classAttr)
	if err != nil {
		panic(err)
	}

	for i, datum := range data {
		result := p.score(datum)
		if result > 0.0 {
			ret.Set(classSpec, i, base.PackU64ToBytes(1))
		} else {
			ret.Set(classSpec, 1, []byte{0, 0, 0, 0, 0, 0, 0, 0})
		}
	}

	return ret
}
Example #2
0
// 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
	}

	// 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))

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

		// Reset maxMap
		for a := range maxmap {
			maxmap[a] = 0
		}

		// Refresh maxMap
		for _, elem := range values {
			label := base.GetClass(KNN.TrainingData, elem)
			if _, ok := maxmap[label]; ok {
				maxmap[label]++
			} else {
				maxmap[label] = 1
			}
		}

		// Sort the maxMap
		var maxClass string
		maxVal := -1
		for a := range maxmap {
			if maxmap[a] > maxVal {
				maxVal = maxmap[a]
				maxClass = a
			}
		}

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

	})

	return ret
}
Example #3
0
// 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
}