コード例 #1
0
ファイル: one_v_all.go プロジェクト: CTLife/golearn
func (f *oneVsAllFilter) Transform(old, to base.Attribute, seq []byte) []byte {
	if !old.Equals(f.classAttr) {
		return seq
	}
	val := base.UnpackBytesToU64(seq)
	if val == f.classAttrVal {
		return base.PackFloatToBytes(1.0)
	}
	return base.PackFloatToBytes(0.0)
}
コード例 #2
0
ファイル: logistic.go プロジェクト: Gudym/golearn
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
}
コード例 #3
0
ファイル: linear_regression.go プロジェクト: CTLife/golearn
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
}
コード例 #4
0
ファイル: float.go プロジェクト: CTLife/golearn
func (f *FloatConvertFilter) Transform(a base.Attribute, n base.Attribute, attrBytes []byte) []byte {
	ret := make([]byte, 8)
	// 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 f.twoValuedCategoricalAttributes[a] {
			if val > 0 {
				ret = base.PackFloatToBytes(1.0)
			} else {
				ret = base.PackFloatToBytes(0.0)
			}
		} else if an, ok := f.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 = base.PackFloatToBytes(1.0)
				} else {
					ret = base.PackFloatToBytes(0.0)
				}
			} else {
				panic("Categorical value not defined!")
			}
		} else {
			panic(fmt.Sprintf("Not a recognised Attribute %v", a))
		}
	} else if _, ok := a.(*base.FloatAttribute); ok {
		// Binary: just return the original value
		ret = attrBytes
	} else if _, ok := a.(*base.BinaryAttribute); ok {
		// Float: check for non-zero
		if attrBytes[0] > 0 {
			ret = base.PackFloatToBytes(1.0)
		} else {
			ret = base.PackFloatToBytes(0.0)
		}
	} else {
		panic(fmt.Sprintf("Unrecognised Attribute: %v", a))
	}
	return ret
}
コード例 #5
0
ファイル: layered.go プロジェクト: nickpoorman/golearn
// 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

}
コード例 #6
0
ファイル: float_test.go プロジェクト: CTLife/golearn
func TestFloatFilter(t *testing.T) {

	Convey("Given a contrived dataset...", t, func() {

		// Read the contrived dataset
		inst, err := base.ParseCSVToInstances("./binary_test.csv", true)
		So(err, ShouldEqual, nil)

		// Add Attributes to the filter
		bFilt := NewFloatConvertFilter()
		bAttrs := base.NonClassAttributes(inst)
		for _, a := range bAttrs {
			bFilt.AddAttribute(a)
		}
		bFilt.Train()

		// Construct a LazilyFilteredInstances to handle it
		instF := base.NewLazilyFilteredInstances(inst, bFilt)

		Convey("All the non-class Attributes should be floats...", func() {
			// Check that all the Attributes are the right type
			for _, a := range base.NonClassAttributes(instF) {
				_, ok := a.(*base.FloatAttribute)
				So(ok, ShouldEqual, true)
			}
		})

		// Check that all the class Attributes made it
		Convey("All the class Attributes should have survived...", func() {
			origClassAttrs := inst.AllClassAttributes()
			newClassAttrs := instF.AllClassAttributes()
			intersectClassAttrs := base.AttributeIntersect(origClassAttrs, newClassAttrs)
			So(len(intersectClassAttrs), ShouldEqual, len(origClassAttrs))
		})
		// Check that the Attributes have the right names
		Convey("Attribute names should be correct...", func() {
			origNames := []string{"floatAttr", "shouldBe1Binary",
				"shouldBe3Binary_stoicism", "shouldBe3Binary_heroism",
				"shouldBe3Binary_romanticism", "arbitraryClass"}
			origMap := make(map[string]bool)
			for _, a := range origNames {
				origMap[a] = false
			}
			for _, a := range instF.AllAttributes() {
				name := a.GetName()
				_, ok := origMap[name]
				So(ok, ShouldBeTrue)

				origMap[name] = true
			}
			for a := range origMap {
				So(origMap[a], ShouldEqual, true)
			}
		})

		Convey("All Attributes should be the correct type...", func() {
			for _, a := range instF.AllAttributes() {
				if a.GetName() == "arbitraryClass" {
					_, ok := a.(*base.CategoricalAttribute)
					So(ok, ShouldEqual, true)
				} else {
					_, ok := a.(*base.FloatAttribute)
					So(ok, ShouldEqual, true)
				}
			}
		})

		// Check that the Attributes have been discretised correctly
		Convey("FloatConversion should have worked", func() {
			// Build Attribute map
			attrMap := make(map[string]base.Attribute)
			for _, a := range instF.AllAttributes() {
				attrMap[a.GetName()] = a
			}
			// For each attribute
			for name := range attrMap {
				So(name, ShouldBeIn, []string{
					"floatAttr",
					"shouldBe1Binary",
					"shouldBe3Binary_stoicism",
					"shouldBe3Binary_heroism",
					"shouldBe3Binary_romanticism",
					"arbitraryClass",
				})

				attr := attrMap[name]
				as, err := instF.GetAttribute(attr)
				So(err, ShouldEqual, nil)

				if name == "floatAttr" {
					So(instF.Get(as, 0), ShouldResemble, base.PackFloatToBytes(1.0))
					So(instF.Get(as, 1), ShouldResemble, base.PackFloatToBytes(1.0))
					So(instF.Get(as, 2), ShouldResemble, base.PackFloatToBytes(0.0))
				} else if name == "shouldBe1Binary" {
					So(instF.Get(as, 0), ShouldResemble, base.PackFloatToBytes(0.0))
					So(instF.Get(as, 1), ShouldResemble, base.PackFloatToBytes(1.0))
					So(instF.Get(as, 2), ShouldResemble, base.PackFloatToBytes(1.0))
				} else if name == "shouldBe3Binary_stoicism" {
					So(instF.Get(as, 0), ShouldResemble, base.PackFloatToBytes(1.0))
					So(instF.Get(as, 1), ShouldResemble, base.PackFloatToBytes(0.0))
					So(instF.Get(as, 2), ShouldResemble, base.PackFloatToBytes(0.0))
				} else if name == "shouldBe3Binary_heroism" {
					So(instF.Get(as, 0), ShouldResemble, base.PackFloatToBytes(0.0))
					So(instF.Get(as, 1), ShouldResemble, base.PackFloatToBytes(1.0))
					So(instF.Get(as, 2), ShouldResemble, base.PackFloatToBytes(0.0))
				} else if name == "shouldBe3Binary_romanticism" {
					So(instF.Get(as, 0), ShouldResemble, base.PackFloatToBytes(0.0))
					So(instF.Get(as, 1), ShouldResemble, base.PackFloatToBytes(0.0))
					So(instF.Get(as, 2), ShouldResemble, base.PackFloatToBytes(1.0))
				} else if name == "arbitraryClass" {
				}
			}
		})
	})
}