Golang Instances.Get方法代码示例

// Run applies a trained BinningFilter to a set of Instances,
// discretising any numeric attributes added.
//
// IMPORTANT: Run discretises in-place, so make sure to take
// a copy if the original instances are still needed
//
// IMPORTANT: This function panic()s if the filter has not been
// trained. Call Build() before running this function
//
// IMPORTANT: Call Build() after adding any additional attributes.
// Otherwise, the training structure will be out of date from
// the values expected and could cause a panic.
func (b *BinningFilter) Run(on *base.Instances) {
	if !b.trained {
		panic("Call Build() beforehand")
	}
	for attr := range b.Attributes {
		minVal := b.MinVals[attr]
		maxVal := b.MaxVals[attr]
		disc := 0
		// Casts to float32 to replicate a floating point precision error
		delta := float32(maxVal - minVal)
		delta /= float32(b.BinCount)
		for i := 0; i < on.Rows; i++ {
			val := on.Get(i, attr)
			if val <= minVal {
				disc = 0
			} else {
				disc = int(math.Floor(float64(float32(val-minVal) / delta)))
				if disc >= b.BinCount {
					disc = b.BinCount - 1
				}
			}
			on.Set(i, attr, float64(disc))
		}
		newAttribute := new(base.CategoricalAttribute)
		newAttribute.SetName(on.GetAttr(attr).GetName())
		for i := 0; i < b.BinCount; i++ {
			newAttribute.GetSysValFromString(fmt.Sprintf("%d", i))
		}
		on.ReplaceAttr(attr, newAttribute)
	}
}

// Predict outputs a base.Instances containing predictions from this tree
func (d *DecisionTreeNode) Predict(what *base.Instances) *base.Instances {
	outputAttrs := make([]base.Attribute, 1)
	outputAttrs[0] = what.GetClassAttr()
	predictions := base.NewInstances(outputAttrs, what.Rows)
	for i := 0; i < what.Rows; i++ {
		cur := d
		for {
			if cur.Children == nil {
				predictions.SetAttrStr(i, 0, cur.Class)
				break
			} else {
				at := cur.SplitAttr
				j := what.GetAttrIndex(at)
				if j == -1 {
					predictions.SetAttrStr(i, 0, cur.Class)
					break
				}
				classVar := at.GetStringFromSysVal(what.Get(i, j))
				if next, ok := cur.Children[classVar]; ok {
					cur = next
				} else {
					var bestChild string
					for c := range cur.Children {
						bestChild = c
						if c > classVar {
							break
						}
					}
					cur = cur.Children[bestChild]
				}
			}
		}
	}
	return predictions
}

func convertInstancesToLabelVec(X *base.Instances) []float64 {
	labelVec := make([]float64, X.Rows)
	for i := 0; i < X.Rows; i++ {
		labelVec[i] = X.Get(i, X.ClassIndex)
	}
	return labelVec
}

// Run discretises the set of Instances `on'
//
// IMPORTANT: ChiMergeFilter discretises in place.
func (c *ChiMergeFilter) Run(on *base.Instances) {
	if !c._Trained {
		panic("Call Build() beforehand")
	}
	for attr := range c.Tables {
		table := c.Tables[attr]
		for i := 0; i < on.Rows; i++ {
			val := on.Get(i, attr)
			dis := 0
			for j, k := range table {
				if k.Value < val {
					dis = j
					continue
				}
				break
			}
			on.Set(i, attr, float64(dis))
		}
		newAttribute := new(base.CategoricalAttribute)
		newAttribute.SetName(on.GetAttr(attr).GetName())
		for _, k := range table {
			newAttribute.GetSysValFromString(fmt.Sprintf("%f", k.Value))
		}
		on.ReplaceAttr(attr, newAttribute)
	}
}

func ChiMBuildFrequencyTable(attr int, inst *base.Instances) []*FrequencyTableEntry {
	ret := make([]*FrequencyTableEntry, 0)
	var attribute *base.FloatAttribute
	attribute, ok := inst.GetAttr(attr).(*base.FloatAttribute)
	if !ok {
		panic("only use Chi-M on numeric stuff")
	}
	for i := 0; i < inst.Rows; i++ {
		value := inst.Get(i, attr)
		valueConv := attribute.GetUsrVal(value)
		class := inst.GetClass(i)
		// Search the frequency table for the value
		found := false
		for _, entry := range ret {
			if entry.Value == valueConv {
				found = true
				entry.Frequency[class] += 1
			}
		}
		if !found {
			newEntry := &FrequencyTableEntry{
				valueConv,
				make(map[string]int),
			}
			newEntry.Frequency[class] = 1
			ret = append(ret, newEntry)
		}
	}

	return ret
}

func (lr *LinearRegression) Fit(inst *base.Instances) error {
	if inst.Rows < inst.GetAttributeCount() {
		return NotEnoughDataError
	}

	// 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(inst.Rows, 1, nil)
	explVariables := mat64.NewDense(inst.Rows, inst.GetAttributeCount(), nil)

	for i := 0; i < inst.Rows; i++ {
		observed.Set(i, 0, inst.Get(i, inst.ClassIndex)) // Set observed data

		for j := 0; j < inst.GetAttributeCount(); j++ {
			if j == 0 {
				// Set intercepts to 1.0
				// Could / should be done better: http://www.theanalysisfactor.com/interpret-the-intercept/
				explVariables.Set(i, 0, 1.0)
			} else {
				explVariables.Set(i, j, inst.Get(i, j-1))
			}
		}
	}

	n := inst.GetAttributeCount()
	qr := mat64.QR(explVariables)
	q := qr.Q()
	reg := qr.R()

	var transposed, qty mat64.Dense
	transposed.TCopy(q)
	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

	return nil
}

func (lr *LogisticRegression) Predict(X *base.Instances) *base.Instances {
	ret := X.GeneratePredictionVector()
	row := make([]float64, X.Cols-1)
	for i := 0; i < X.Rows; i++ {
		rowCounter := 0
		for j := 0; j < X.Cols; j++ {
			if j != X.ClassIndex {
				row[rowCounter] = X.Get(i, j)
				rowCounter++
			}
		}
		fmt.Println(Predict(lr.model, row), row)
		ret.Set(i, 0, Predict(lr.model, row))
	}
	return ret
}

func convertInstancesToProblemVec(X *base.Instances) [][]float64 {
	problemVec := make([][]float64, X.Rows)
	for i := 0; i < X.Rows; i++ {
		problemVecCounter := 0
		problemVec[i] = make([]float64, X.Cols-1)
		for j := 0; j < X.Cols; j++ {
			if j == X.ClassIndex {
				continue
			}
			problemVec[i][problemVecCounter] = X.Get(i, j)
			problemVecCounter++
		}
	}
	fmt.Println(problemVec, X)
	return problemVec
}

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

	ret := X.GeneratePredictionVector()
	for i := 0; i < X.Rows; i++ {
		var prediction float64 = lr.disturbance
		for j := 0; j < X.Cols; j++ {
			if j != X.ClassIndex {
				prediction += X.Get(i, j) * lr.regressionCoefficients[j]
			}
		}
		ret.Set(i, 0, prediction)
	}

	return ret, nil
}