// Predict issues predictions. Each class-specific classifier is expected
// to output a value between 0 (indicating that a given instance is not
// a given class) and 1 (indicating that the given instance is definitely
// that class). For each instance, the class with the highest value is chosen.
// The result is undefined if several underlying models output the same value.
func (m *OneVsAllModel) Predict(what base.FixedDataGrid) (base.FixedDataGrid, error) {
	ret := base.GeneratePredictionVector(what)
	vecs := make([]base.FixedDataGrid, m.maxClassVal+1)
	specs := make([]base.AttributeSpec, m.maxClassVal+1)
	for i := uint64(0); i <= m.maxClassVal; i++ {
		f := m.filters[i]
		c := base.NewLazilyFilteredInstances(what, f)
		p, err := m.classifiers[i].Predict(c)
		if err != nil {
			return nil, err
		}
		vecs[i] = p
		specs[i] = base.ResolveAttributes(p, p.AllClassAttributes())[0]
	}
	_, rows := ret.Size()
	spec := base.ResolveAttributes(ret, ret.AllClassAttributes())[0]
	for i := 0; i < rows; i++ {
		class := uint64(0)
		best := 0.0
		for j := uint64(0); j <= m.maxClassVal; j++ {
			val := base.UnpackBytesToFloat(vecs[j].Get(specs[j], i))
			if val > best {
				class = j
				best = val
			}
		}
		ret.Set(spec, i, base.PackU64ToBytes(class))
	}
	return ret, nil
}

func (lr *LogisticRegression) Predict(X base.FixedDataGrid) base.FixedDataGrid {

	// Only support 1 class Attribute
	classAttrs := X.AllClassAttributes()
	if len(classAttrs) != 1 {
		panic(fmt.Sprintf("%d Wrong number of classes", len(classAttrs)))
	}
	// Generate return structure
	ret := base.GeneratePredictionVector(X)
	classAttrSpecs := base.ResolveAttributes(ret, classAttrs)
	// Retrieve numeric non-class Attributes
	numericAttrs := base.NonClassFloatAttributes(X)
	numericAttrSpecs := base.ResolveAttributes(X, numericAttrs)

	// Allocate row storage
	row := make([]float64, len(numericAttrSpecs))
	X.MapOverRows(numericAttrSpecs, func(rowBytes [][]byte, rowNo int) (bool, error) {
		for i, r := range rowBytes {
			row[i] = base.UnpackBytesToFloat(r)
		}
		val := Predict(lr.model, row)
		vals := base.PackFloatToBytes(val)
		ret.Set(classAttrSpecs[0], rowNo, vals)
		return true, nil
	})

	return ret
}

func findBestSplit(partition base.FixedDataGrid) {
	var delta float64
	delta = math.MinInt64

	attrs := partition.AllAttributes()
	classAttrs := partition.AllClassAttributes()
	candidates := base.AttributeDifferenceReferences(attrs, classAttrs)

	fmt.Println(delta)
	fmt.Println(classAttrs)
	fmt.Println(reflect.TypeOf(partition))
	fmt.Println(reflect.TypeOf(candidates))

	for i, n := range attrs {
		fmt.Println(i)
		//fmt.Println(partition)
		fmt.Println(reflect.TypeOf(n))
		attributeSpec, _ := partition.GetAttribute(n)

		fmt.Println(partition.GetAttribute(n))
		_, rows := partition.Size()
		for j := 0; j < rows; j++ {
			data := partition.Get(attributeSpec, j)
			fmt.Println(base.UnpackBytesToFloat(data))
		}

	}
}

// Predict outputs a base.Instances containing predictions from this tree
func (d *DecisionTreeNode) Predict(what base.FixedDataGrid) (base.FixedDataGrid, error) {
	predictions := base.GeneratePredictionVector(what)
	classAttr := getClassAttr(predictions)
	classAttrSpec, err := predictions.GetAttribute(classAttr)
	if err != nil {
		panic(err)
	}
	predAttrs := base.AttributeDifferenceReferences(what.AllAttributes(), predictions.AllClassAttributes())
	predAttrSpecs := base.ResolveAttributes(what, predAttrs)
	what.MapOverRows(predAttrSpecs, func(row [][]byte, rowNo int) (bool, error) {
		cur := d
		for {
			if cur.Children == nil {
				predictions.Set(classAttrSpec, rowNo, classAttr.GetSysValFromString(cur.Class))
				break
			} else {
				splitVal := cur.SplitRule.SplitVal
				at := cur.SplitRule.SplitAttr
				ats, err := what.GetAttribute(at)
				if err != nil {
					//predictions.Set(classAttrSpec, rowNo, classAttr.GetSysValFromString(cur.Class))
					//break
					panic(err)
				}

				var classVar string
				if _, ok := ats.GetAttribute().(*base.FloatAttribute); ok {
					// If it's a numeric Attribute (e.g. FloatAttribute) check that
					// the value of the current node is greater than the old one
					classVal := base.UnpackBytesToFloat(what.Get(ats, rowNo))
					if classVal > splitVal {
						classVar = "1"
					} else {
						classVar = "0"
					}
				} else {
					classVar = ats.GetAttribute().GetStringFromSysVal(what.Get(ats, rowNo))
				}
				if next, ok := cur.Children[classVar]; ok {
					cur = next
				} else {
					// Suspicious of this
					var bestChild string
					for c := range cur.Children {
						bestChild = c
						if c > classVar {
							break
						}
					}
					cur = cur.Children[bestChild]
				}
			}
		}
		return true, nil
	})
	return predictions, nil
}

func getNumericAttributeEntropy(f base.FixedDataGrid, attr *base.FloatAttribute) (float64, float64) {

	// Resolve Attribute
	attrSpec, err := f.GetAttribute(attr)
	if err != nil {
		panic(err)
	}

	// Build sortable vector
	_, rows := f.Size()
	refs := make([]numericSplitRef, rows)
	f.MapOverRows([]base.AttributeSpec{attrSpec}, func(val [][]byte, row int) (bool, error) {
		cls := base.GetClass(f, row)
		v := base.UnpackBytesToFloat(val[0])
		refs[row] = numericSplitRef{v, cls}
		return true, nil
	})

	// Sort
	sort.Sort(splitVec(refs))

	generateCandidateSplitDistribution := func(val float64) map[string]map[string]int {
		presplit := make(map[string]int)
		postplit := make(map[string]int)
		for _, i := range refs {
			if i.val < val {
				presplit[i.class]++
			} else {
				postplit[i.class]++
			}
		}
		ret := make(map[string]map[string]int)
		ret["0"] = presplit
		ret["1"] = postplit
		return ret
	}

	minSplitEntropy := math.Inf(1)
	minSplitVal := math.Inf(1)
	// Consider each possible function
	for i := 0; i < len(refs)-1; i++ {
		val := refs[i].val + refs[i+1].val
		val /= 2
		splitDist := generateCandidateSplitDistribution(val)
		splitEntropy := getSplitEntropy(splitDist)
		if splitEntropy < minSplitEntropy {
			minSplitEntropy = splitEntropy
			minSplitVal = val
		}
	}

	return minSplitEntropy, minSplitVal
}

func processData(x base.FixedDataGrid) instances {
	_, rows := x.Size()

	result := make(instances, rows)

	// Retrieve numeric non-class Attributes
	numericAttrs := base.NonClassFloatAttributes(x)
	numericAttrSpecs := base.ResolveAttributes(x, numericAttrs)

	// Retrieve class Attributes
	classAttrs := x.AllClassAttributes()
	if len(classAttrs) != 1 {
		panic("Only one classAttribute supported!")
	}

	// Check that the class Attribute is categorical
	// (with two values) or binary
	classAttr := classAttrs[0]
	if attr, ok := classAttr.(*base.CategoricalAttribute); ok {
		if len(attr.GetValues()) != 2 {
			panic("To many values for Attribute!")
		}
	} else if _, ok := classAttr.(*base.BinaryAttribute); ok {
	} else {
		panic("Wrong class Attribute type!")
	}

	// Convert each row
	x.MapOverRows(numericAttrSpecs, func(row [][]byte, rowNo int) (bool, error) {
		// Allocate a new row
		probRow := make([]float64, len(numericAttrSpecs))

		// Read out the row
		for i, _ := range numericAttrSpecs {
			probRow[i] = base.UnpackBytesToFloat(row[i])
		}

		// Get the class for the values
		class := base.GetClass(x, rowNo)
		instance := instance{class, probRow}
		result[rowNo] = instance
		return true, nil
	})
	return result
}

// Transform converts the given byte sequence using the old Attribute into the new
// byte sequence.
//
// If the old Attribute has a categorical value of at most two items, then a zero or
// non-zero byte sequence is returned.
//
// If the old Attribute has a categorical value of at most n-items, then a non-zero
// or zero byte sequence is returned based on the value of the new Attribute passed in.
//
// If the old Attribute is a float, it's value's unpacked and we check for non-zeroness
//
// If the old Attribute is a BinaryAttribute, just return the input
func (b *BinaryConvertFilter) Transform(a base.Attribute, n base.Attribute, attrBytes []byte) []byte {
	ret := make([]byte, 1)
	// Check for CategoricalAttribute
	if _, ok := a.(*base.CategoricalAttribute); ok {
		// Unpack byte value
		val := base.UnpackBytesToU64(attrBytes)
		// If it's a two-valued one, check for non-zero
		if b.twoValuedCategoricalAttributes[a] {
			if val > 0 {
				ret[0] = 1
			} else {
				ret[0] = 0
			}
		} else if an, ok := b.nValuedCategoricalAttributeMap[a]; ok {
			// If it's an n-valued one, check the new Attribute maps onto
			// the unpacked value
			if af, ok := an[val]; ok {
				if af.Equals(n) {
					ret[0] = 1
				} else {
					ret[0] = 0
				}
			} else {
				panic("Categorical value not defined!")
			}
		} else {
			panic(fmt.Sprintf("Not a recognised Attribute %v", a))
		}
	} else if _, ok := a.(*base.BinaryAttribute); ok {
		// Binary: just return the original value
		ret = attrBytes
	} else if _, ok := a.(*base.FloatAttribute); ok {
		// Float: check for non-zero
		val := base.UnpackBytesToFloat(attrBytes)
		if val > 0 {
			ret[0] = 1
		} else {
			ret[0] = 0
		}
	} else {
		panic(fmt.Sprintf("Unrecognised Attribute: %v", a))
	}
	return ret
}

// Transform returns the byte sequence after discretisation
func (c *ChiMergeFilter) Transform(a base.Attribute, n base.Attribute, field []byte) []byte {
	// Do we use this Attribute?
	if !c.attrs[a] {
		return field
	}
	// Find the Attribute value in the table
	table := c.tables[a]
	dis := 0
	val := base.UnpackBytesToFloat(field)
	for j, k := range table {
		if k.Value < val {
			dis = j
			continue
		}
		break
	}

	return base.PackU64ToBytes(uint64(dis))
}

func convertInstancesToLabelVec(X base.FixedDataGrid) []float64 {
	// Get the class Attributes
	classAttrs := X.AllClassAttributes()
	// Only support 1 class Attribute
	if len(classAttrs) != 1 {
		panic(fmt.Sprintf("%d ClassAttributes (1 expected)", len(classAttrs)))
	}
	// ClassAttribute must be numeric
	if _, ok := classAttrs[0].(*base.FloatAttribute); !ok {
		panic(fmt.Sprintf("%s: ClassAttribute must be a FloatAttribute", classAttrs[0]))
	}
	// Allocate return structure
	_, rows := X.Size()
	labelVec := make([]float64, rows)
	// Resolve class Attribute specification
	classAttrSpecs := base.ResolveAttributes(X, classAttrs)
	X.MapOverRows(classAttrSpecs, func(row [][]byte, rowNo int) (bool, error) {
		labelVec[rowNo] = base.UnpackBytesToFloat(row[0])
		return true, nil
	})
	return labelVec
}

func convertInstancesToProblemVec(X base.FixedDataGrid) [][]float64 {
	// Allocate problem array
	_, rows := X.Size()
	problemVec := make([][]float64, rows)

	// Retrieve numeric non-class Attributes
	numericAttrs := base.NonClassFloatAttributes(X)
	numericAttrSpecs := base.ResolveAttributes(X, numericAttrs)

	// Convert each row
	X.MapOverRows(numericAttrSpecs, func(row [][]byte, rowNo int) (bool, error) {
		// Allocate a new row
		probRow := make([]float64, len(numericAttrSpecs))
		// Read out the row
		for i, _ := range numericAttrSpecs {
			probRow[i] = base.UnpackBytesToFloat(row[i])
		}
		// Add the row
		problemVec[rowNo] = probRow
		return true, nil
	})
	return problemVec
}

func (lr *LinearRegression) Predict(X base.FixedDataGrid) (base.FixedDataGrid, error) {
	if !lr.fitted {
		return nil, NoTrainingDataError
	}

	ret := base.GeneratePredictionVector(X)
	attrSpecs := base.ResolveAttributes(X, lr.attrs)
	clsSpec, err := ret.GetAttribute(lr.cls)
	if err != nil {
		return nil, err
	}

	X.MapOverRows(attrSpecs, func(row [][]byte, i int) (bool, error) {
		var prediction float64 = lr.disturbance
		for j, r := range row {
			prediction += base.UnpackBytesToFloat(r) * lr.regressionCoefficients[j]
		}

		ret.Set(clsSpec, i, base.PackFloatToBytes(prediction))
		return true, nil
	})

	return ret, nil
}

// 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

//.........这里部分代码省略.........

// 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
}

// Predict uses the underlying network to produce predictions for the
// class variables of X.
//
// Can only predict one CategoricalAttribute at a time, or up to n
// FloatAttributes. Set or unset ClassAttributes to work around this
// limitation.
func (m *MultiLayerNet) Predict(X base.FixedDataGrid) base.FixedDataGrid {

	// Create the return vector
	ret := base.GeneratePredictionVector(X)

	// Make sure everything's a FloatAttribute
	insts := m.convertToFloatInsts(X)

	// Get the input/output Attributes
	inputAttrs := base.NonClassAttributes(insts)
	outputAttrs := ret.AllClassAttributes()

	// Compute layers
	layers := 2 + len(m.layers)

	// Check that we're operating in a singular mode
	floatMode := 0
	categoricalMode := 0
	for _, a := range outputAttrs {
		if _, ok := a.(*base.CategoricalAttribute); ok {
			categoricalMode++
		} else if _, ok := a.(*base.FloatAttribute); ok {
			floatMode++
		} else {
			panic("Unsupported output Attribute type!")
		}
	}

	if floatMode > 0 && categoricalMode > 0 {
		panic("Can't predict a mix of float and categorical Attributes")
	} else if categoricalMode > 1 {
		panic("Can't predict more than one categorical class Attribute")
	}

	// Create the activation vector
	a := mat64.NewDense(m.network.size, 1, make([]float64, m.network.size))

	// Resolve the input AttributeSpecs
	inputAs := base.ResolveAttributes(insts, inputAttrs)

	// Resolve the output Attributespecs
	outputAs := base.ResolveAttributes(ret, outputAttrs)

	// Map over each input row
	insts.MapOverRows(inputAs, func(row [][]byte, rc int) (bool, error) {
		// Clear the activation vector
		for i := 0; i < m.network.size; i++ {
			a.Set(i, 0, 0.0)
		}
		// Build the activation vector
		for i, vb := range row {
			if cIndex, ok := m.attrs[inputAs[i].GetAttribute()]; !ok {
				panic("Can't resolve the Attribute!")
			} else {
				a.Set(cIndex, 0, base.UnpackBytesToFloat(vb))
			}
		}
		// Robots, activate!
		m.network.Activate(a, layers)

		// Decide which class to set
		if floatMode > 0 {
			for _, as := range outputAs {
				cIndex := m.attrs[as.GetAttribute()]
				ret.Set(as, rc, base.PackFloatToBytes(a.At(cIndex, 0)))
			}
		} else {
			maxIndex := 0
			maxVal := 0.0
			for i := m.classAttrOffset; i < m.classAttrOffset+m.classAttrCount; i++ {
				val := a.At(i, 0)
				if val > maxVal {
					maxIndex = i
					maxVal = val
				}
			}
			maxIndex -= m.classAttrOffset
			ret.Set(outputAs[0], rc, base.PackU64ToBytes(uint64(maxIndex)))
		}
		return true, nil
	})

	return ret

}

//.........这里部分代码省略.........
		m.network.SetBias(nodeOffset, rand.NormFloat64()*0.1)
	}

	// Connect final hidden layer with the output layer
	layerOffset = len(inputAttrsVec)
	for i, l := range m.layers {
		if i == len(m.layers)-1 {
			for j := 1; j <= l; j++ {
				nodeOffset1 := layerOffset + j
				for k := 1; k <= len(classAttrsVec); k++ {
					nodeOffset2 := layerOffset + l + k
					m.network.SetWeight(nodeOffset1, nodeOffset2, rand.NormFloat64()*0.1)
				}
			}
		}
		layerOffset += l
	}

	// Connect input layer with first hidden layer (or output layer
	for i := 1; i <= len(inputAttrsVec); i++ {
		nextLayerLen := 0
		if len(m.layers) > 0 {
			nextLayerLen = m.layers[0]
		} else {
			nextLayerLen = len(classAttrsVec)
		}
		for j := 1; j <= nextLayerLen; j++ {
			nodeOffset := len(inputAttrsVec) + j
			v := rand.NormFloat64() * 0.1
			m.network.SetWeight(i, nodeOffset, v)
		}
	}

	// Create the training activation vector
	trainVec := mat64.NewDense(size, 1, make([]float64, size))
	// Create the error vector
	errVec := mat64.NewDense(size, 1, make([]float64, size))

	// Resolve training AttributeSpecs
	trainAs := base.ResolveAllAttributes(insts)

	// Feed-forward, compute error and update for each training example
	// until convergence (what's that)
	for iteration := 0; iteration < m.MaxIterations; iteration++ {
		totalError := 0.0
		maxRow := 0
		insts.MapOverRows(trainAs, func(row [][]byte, i int) (bool, error) {

			maxRow = i
			// Clear vectors
			for i := 0; i < size; i++ {
				trainVec.Set(i, 0, 0.0)
				errVec.Set(i, 0, 0.0)
			}

			// Build vectors
			for i, vb := range row {
				v := base.UnpackBytesToFloat(vb)
				if attrIndex, ok := trainingAttrs[trainAs[i].GetAttribute()]; ok {
					// Add to Activation vector
					trainVec.Set(attrIndex, 0, v)
				} else if attrIndex, ok := classAttrs[trainAs[i].GetAttribute()]; ok {
					// Set to error vector
					errVec.Set(attrIndex, 0, v)
				} else {
					panic("Should be able to find this Attribute!")
				}
			}

			// Activate the network
			m.network.Activate(trainVec, totalLayers-1)

			// Compute the error
			for a := range classAttrs {
				cIndex := classAttrs[a]
				errVec.Set(cIndex, 0, errVec.At(cIndex, 0)-trainVec.At(cIndex, 0))
			}

			// Update total error
			totalError += math.Abs(errVec.Sum())

			// Back-propagate the error
			b := m.network.Error(trainVec, errVec, totalLayers)

			// Update the weights
			m.network.UpdateWeights(trainVec, b, m.LearningRate)

			// Update the biases
			m.network.UpdateBias(b, m.LearningRate)

			return true, nil
		})

		totalError /= float64(maxRow)
		// If we've converged, no need to carry on
		if totalError < m.Convergence {
			break
		}
	}
}

func (lr *LinearRegression) Fit(inst base.FixedDataGrid) error {

	// Retrieve row size
	_, rows := inst.Size()

	// Validate class Attribute count
	classAttrs := inst.AllClassAttributes()
	if len(classAttrs) != 1 {
		return fmt.Errorf("Only 1 class variable is permitted")
	}
	classAttrSpecs := base.ResolveAttributes(inst, classAttrs)

	// Retrieve relevant Attributes
	allAttrs := base.NonClassAttributes(inst)
	attrs := make([]base.Attribute, 0)
	for _, a := range allAttrs {
		if _, ok := a.(*base.FloatAttribute); ok {
			attrs = append(attrs, a)
		}
	}

	cols := len(attrs) + 1

	if rows < cols {
		return NotEnoughDataError
	}

	// Retrieve relevant Attribute specifications
	attrSpecs := base.ResolveAttributes(inst, attrs)

	// Split into two matrices, observed results (dependent variable y)
	// and the explanatory variables (X) - see http://en.wikipedia.org/wiki/Linear_regression
	observed := mat64.NewDense(rows, 1, nil)
	explVariables := mat64.NewDense(rows, cols, nil)

	// Build the observed matrix
	inst.MapOverRows(classAttrSpecs, func(row [][]byte, i int) (bool, error) {
		val := base.UnpackBytesToFloat(row[0])
		observed.Set(i, 0, val)
		return true, nil
	})

	// Build the explainatory variables
	inst.MapOverRows(attrSpecs, func(row [][]byte, i int) (bool, error) {
		// Set intercepts to 1.0
		explVariables.Set(i, 0, 1.0)
		for j, r := range row {
			explVariables.Set(i, j+1, base.UnpackBytesToFloat(r))
		}
		return true, nil
	})

	n := cols
	qr := new(mat64.QR)
	qr.Factorize(explVariables)
	var q, reg mat64.Dense
	q.QFromQR(qr)
	reg.RFromQR(qr)

	var transposed, qty mat64.Dense
	transposed.Clone(q.T())
	qty.Mul(&transposed, observed)

	regressionCoefficients := make([]float64, n)
	for i := n - 1; i >= 0; i-- {
		regressionCoefficients[i] = qty.At(i, 0)
		for j := i + 1; j < n; j++ {
			regressionCoefficients[i] -= regressionCoefficients[j] * reg.At(i, j)
		}
		regressionCoefficients[i] /= reg.At(i, i)
	}

	lr.disturbance = regressionCoefficients[0]
	lr.regressionCoefficients = regressionCoefficients[1:]
	lr.fitted = true
	lr.attrs = attrs
	lr.cls = classAttrs[0]
	return nil
}