Golang evaluation.GetConfusionMatrix函数代码示例

原作者: [db:作者] 来自: [db:来源] 收藏邀请

本文整理汇总了Golang中github.com/sjwhitworth/golearn/evaluation.GetConfusionMatrix函数的典型用法代码示例。如果您正苦于以下问题：Golang GetConfusionMatrix函数的具体用法？Golang GetConfusionMatrix怎么用？Golang GetConfusionMatrix使用的例子？那么恭喜您, 这里精选的函数代码示例或许可以为您提供帮助。

在下文中一共展示了GetConfusionMatrix函数的20个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于我们的系统推荐出更棒的Golang代码示例。

示例1: main

func main() {

	var tree base.Classifier

	rand.Seed(time.Now().UTC().UnixNano())

	// Load in the iris dataset
	iris, err := base.ParseCSVToInstances("../datasets/iris_headers.csv", true)
	if err != nil {
		panic(err)
	}

	// Discretise the iris dataset with Chi-Merge
	filt := filters.NewChiMergeFilter(iris, 0.99)
	filt.AddAllNumericAttributes()
	filt.Build()
	filt.Run(iris)

	// Create a 60-40 training-test split
	insts := base.InstancesTrainTestSplit(iris, 0.60)

	//
	// First up, use ID3
	//
	tree = trees.NewID3DecisionTree(0.6)
	// (Parameter controls train-prune split.)

	// Train the ID3 tree
	tree.Fit(insts[0])

	// Generate predictions
	predictions := tree.Predict(insts[1])

	// Evaluate
	fmt.Println("ID3 Performance")
	cf := eval.GetConfusionMatrix(insts[1], predictions)
	fmt.Println(eval.GetSummary(cf))

	//
	// Next up, Random Trees
	//

	// Consider two randomly-chosen attributes
	tree = trees.NewRandomTree(2)
	tree.Fit(insts[0])
	predictions = tree.Predict(insts[1])
	fmt.Println("RandomTree Performance")
	cf = eval.GetConfusionMatrix(insts[1], predictions)
	fmt.Println(eval.GetSummary(cf))

	//
	// Finally, Random Forests
	//
	tree = ensemble.NewRandomForest(100, 3)
	tree.Fit(insts[0])
	predictions = tree.Predict(insts[1])
	fmt.Println("RandomForest Performance")
	cf = eval.GetConfusionMatrix(insts[1], predictions)
	fmt.Println(eval.GetSummary(cf))
}

开发者ID:24hours，项目名称:golearn，代码行数:60，代码来源:trees.go

示例2: TestRandomForest1

func TestRandomForest1(testEnv *testing.T) {
	inst, err := base.ParseCSVToInstances("../examples/datasets/iris_headers.csv", true)
	if err != nil {
		panic(err)
	}

	rand.Seed(time.Now().UnixNano())
	trainData, testData := base.InstancesTrainTestSplit(inst, 0.6)
	filt := filters.NewChiMergeFilter(inst, 0.90)
	for _, a := range base.NonClassFloatAttributes(inst) {
		filt.AddAttribute(a)
	}
	filt.Train()
	trainDataf := base.NewLazilyFilteredInstances(trainData, filt)
	testDataf := base.NewLazilyFilteredInstances(testData, filt)
	rf := new(BaggedModel)
	for i := 0; i < 10; i++ {
		rf.AddModel(trees.NewRandomTree(2))
	}
	rf.Fit(trainDataf)
	fmt.Println(rf)
	predictions := rf.Predict(testDataf)
	fmt.Println(predictions)
	confusionMat := eval.GetConfusionMatrix(testDataf, predictions)
	fmt.Println(confusionMat)
	fmt.Println(eval.GetMacroPrecision(confusionMat))
	fmt.Println(eval.GetMacroRecall(confusionMat))
	fmt.Println(eval.GetSummary(confusionMat))
}

开发者ID:Gudym，项目名称:golearn，代码行数:29，代码来源:bagging_test.go

示例3: main

func main() {
	// Load in a dataset, with headers. Header attributes will be stored.
	// Think of instances as a Data Frame structure in R or Pandas.
	// You can also create instances from scratch.
	rawData, err := base.ParseCSVToInstances("datasets/iris.csv", false)
	if err != nil {
		panic(err)
	}

	// Print a pleasant summary of your data.
	fmt.Println(rawData)

	//Initialises a new KNN classifier
	cls := knn.NewKnnClassifier("euclidean", 2)

	//Do a training-test split
	trainData, testData := base.InstancesTrainTestSplit(rawData, 0.50)
	cls.Fit(trainData)

	//Calculates the Euclidean distance and returns the most popular label
	predictions := cls.Predict(testData)
	fmt.Println(predictions)

	// Prints precision/recall metrics
	confusionMat, err := evaluation.GetConfusionMatrix(testData, predictions)
	if err != nil {
		panic(fmt.Sprintf("Unable to get confusion matrix: %s", err.Error()))
	}
	fmt.Println(evaluation.GetSummary(confusionMat))
}

开发者ID:raghavkgarg，项目名称:gotutorial，代码行数:30，代码来源:ml1.go

示例4: TestPruning

func TestPruning(testEnv *testing.T) {
	inst, err := base.ParseCSVToInstances("../examples/datasets/iris_headers.csv", true)
	if err != nil {
		panic(err)
	}
	trainData, testData := base.InstancesTrainTestSplit(inst, 0.6)
	filt := filters.NewChiMergeFilter(inst, 0.90)
	filt.AddAllNumericAttributes()
	filt.Build()
	fmt.Println(testData)
	filt.Run(testData)
	filt.Run(trainData)
	root := NewRandomTree(2)
	fittrainData, fittestData := base.InstancesTrainTestSplit(trainData, 0.6)
	root.Fit(fittrainData)
	root.Prune(fittestData)
	fmt.Println(root)
	predictions := root.Predict(testData)
	fmt.Println(predictions)
	confusionMat := eval.GetConfusionMatrix(testData, predictions)
	fmt.Println(confusionMat)
	fmt.Println(eval.GetMacroPrecision(confusionMat))
	fmt.Println(eval.GetMacroRecall(confusionMat))
	fmt.Println(eval.GetSummary(confusionMat))
}

开发者ID:hsinhoyeh，项目名称:golearn，代码行数:25，代码来源:tree_test.go

示例5: TestRandomForest1

func TestRandomForest1(testEnv *testing.T) {
	inst, err := base.ParseCSVToInstances("../examples/datasets/iris_headers.csv", true)
	if err != nil {
		panic(err)
	}

	rand.Seed(time.Now().UnixNano())
	insts := base.InstancesTrainTestSplit(inst, 0.6)
	filt := filters.NewChiMergeFilter(inst, 0.90)
	filt.AddAllNumericAttributes()
	filt.Build()
	filt.Run(insts[1])
	filt.Run(insts[0])
	rf := new(BaggedModel)
	for i := 0; i < 10; i++ {
		rf.AddModel(trees.NewRandomTree(2))
	}
	rf.Fit(insts[0])
	fmt.Println(rf)
	predictions := rf.Predict(insts[1])
	fmt.Println(predictions)
	confusionMat := eval.GetConfusionMatrix(insts[1], predictions)
	fmt.Println(confusionMat)
	fmt.Println(eval.GetMacroPrecision(confusionMat))
	fmt.Println(eval.GetMacroRecall(confusionMat))
	fmt.Println(eval.GetSummary(confusionMat))
}

开发者ID:24hours，项目名称:golearn，代码行数:27，代码来源:bagging_test.go

示例6: TestPredict

func TestPredict(t *testing.T) {

	a := NewAveragePerceptron(10, 1.2, 0.5, 0.3)

	if a == nil {

		t.Errorf("Unable to create average perceptron")
	}

	absPath, _ := filepath.Abs("../examples/datasets/house-votes-84.csv")
	rawData, err := base.ParseCSVToInstances(absPath, true)
	if err != nil {
		t.Fail()
	}

	trainData, testData := base.InstancesTrainTestSplit(rawData, 0.5)
	a.Fit(trainData)

	if a.trained == false {
		t.Errorf("Perceptron was not trained")
	}

	predictions := a.Predict(testData)
	cf, err := evaluation.GetConfusionMatrix(testData, predictions)
	if err != nil {
		t.Errorf("Couldn't get confusion matrix: %s", err)
		t.Fail()
	}
	fmt.Println(evaluation.GetSummary(cf))
	fmt.Println(trainData)
	fmt.Println(testData)
	if evaluation.GetAccuracy(cf) < 0.65 {
		t.Errorf("Perceptron not trained correctly")
	}
}

开发者ID:CTLife，项目名称:golearn，代码行数:35，代码来源:average_test.go

示例7: BenchmarkKNNWithNoOpts

func BenchmarkKNNWithNoOpts(b *testing.B) {
	// Load
	train, test := readMnist()
	cls := NewKnnClassifier("euclidean", 1)
	cls.AllowOptimisations = false
	cls.Fit(train)
	predictions := cls.Predict(test)
	c, err := evaluation.GetConfusionMatrix(test, predictions)
	if err != nil {
		panic(err)
	}
	fmt.Println(evaluation.GetSummary(c))
	fmt.Println(evaluation.GetAccuracy(c))
}

开发者ID:CTLife，项目名称:golearn，代码行数:14，代码来源:knn_bench_test.go

示例8: TestMultiSVMUnweighted

func TestMultiSVMUnweighted(t *testing.T) {
	Convey("Loading data...", t, func() {
		inst, err := base.ParseCSVToInstances("../examples/datasets/articles.csv", false)
		So(err, ShouldBeNil)
		X, Y := base.InstancesTrainTestSplit(inst, 0.4)

		m := NewMultiLinearSVC("l1", "l2", true, 1.0, 1e-4, nil)
		m.Fit(X)

		Convey("Predictions should work...", func() {
			predictions, err := m.Predict(Y)
			cf, err := evaluation.GetConfusionMatrix(Y, predictions)
			So(err, ShouldEqual, nil)
			So(evaluation.GetAccuracy(cf), ShouldBeGreaterThan, 0.70)
		})
	})
}

开发者ID:CTLife，项目名称:golearn，代码行数:17，代码来源:multisvc_test.go

示例9: main

func main() {
	data, err := base.ParseCSVToInstances("iris_headers.csv", true)
	if err != nil {
		panic(err)
	}

	cls := knn.NewKnnClassifier("euclidean", 2)

	trainData, testData := base.InstancesTrainTestSplit(data, 0.8)
	cls.Fit(trainData)

	predictions := cls.Predict(testData)
	fmt.Println(predictions)

	confusionMat := evaluation.GetConfusionMatrix(testData, predictions)
	fmt.Println(evaluation.GetSummary(confusionMat))
}

开发者ID:vkarthi46，项目名称:ml-algorithms-simple，代码行数:17，代码来源:golearn_sample.go

示例10: NewTestTrial

func NewTestTrial(filename string, split float64) bool {
	cls := knn.NewKnnClassifier("euclidean", 2)
	data := CSVtoKNNData(filename)
	train, test := base.InstancesTrainTestSplit(data, split)

	cls.Fit(train)
	//Calculates the Euclidean distance and returns the most popular label
	predictions := cls.Predict(test)
	fmt.Println(predictions)

	confusionMat, err := evaluation.GetConfusionMatrix(test, predictions)
	if err != nil {
		panic(fmt.Sprintf("Unable to get confusion matrix: %s", err.Error()))
	}
	fmt.Println(evaluation.GetSummary(confusionMat))

	return true
}

开发者ID:postfix，项目名称:education，代码行数:18，代码来源:knn.go

示例11: TestRandomForest

func TestRandomForest(t *testing.T) {
	Convey("Given a valid CSV file", t, func() {
		inst, err := base.ParseCSVToInstances("../examples/datasets/iris_headers.csv", true)
		So(err, ShouldBeNil)

		Convey("When Chi-Merge filtering the data", func() {
			filt := filters.NewChiMergeFilter(inst, 0.90)
			for _, a := range base.NonClassFloatAttributes(inst) {
				filt.AddAttribute(a)
			}
			filt.Train()
			instf := base.NewLazilyFilteredInstances(inst, filt)

			Convey("Splitting the data into test and training sets", func() {
				trainData, testData := base.InstancesTrainTestSplit(instf, 0.60)

				Convey("Fitting and predicting with a Random Forest", func() {
					rf := NewRandomForest(10, 3)
					err = rf.Fit(trainData)
					So(err, ShouldBeNil)

					predictions, err := rf.Predict(testData)
					So(err, ShouldBeNil)

					confusionMat, err := evaluation.GetConfusionMatrix(testData, predictions)
					So(err, ShouldBeNil)

					Convey("Predictions should be somewhat accurate", func() {
						So(evaluation.GetAccuracy(confusionMat), ShouldBeGreaterThan, 0.35)
					})
				})
			})
		})

		Convey("Fitting with a Random Forest with too many features compared to the data", func() {
			rf := NewRandomForest(10, len(base.NonClassAttributes(inst))+1)
			err = rf.Fit(inst)

			Convey("Should return an error", func() {
				So(err, ShouldNotBeNil)
			})
		})
	})
}

开发者ID:CTLife，项目名称:golearn，代码行数:44，代码来源:randomforest_test.go

示例12: TestRandomForest1

func TestRandomForest1(testEnv *testing.T) {
	inst, err := base.ParseCSVToInstances("../examples/datasets/iris_headers.csv", true)
	if err != nil {
		panic(err)
	}
	trainData, testData := base.InstancesTrainTestSplit(inst, 0.60)
	filt := filters.NewChiMergeFilter(trainData, 0.90)
	filt.AddAllNumericAttributes()
	filt.Build()
	filt.Run(testData)
	filt.Run(trainData)
	rf := NewRandomForest(10, 3)
	rf.Fit(trainData)
	predictions := rf.Predict(testData)
	fmt.Println(predictions)
	confusionMat := eval.GetConfusionMatrix(testData, predictions)
	fmt.Println(confusionMat)
	fmt.Println(eval.GetSummary(confusionMat))
}

开发者ID:hsinhoyeh，项目名称:golearn，代码行数:19，代码来源:randomforest_test.go

示例13: main

func main() {
	rawData, err := base.ParseCSVToInstances("../datasets/iris_headers.csv", true)
	if err != nil {
		panic(err)
	}

	//Initialises a new KNN classifier
	cls := knn.NewKnnClassifier("euclidean", 2)

	//Do a training-test split
	trainData, testData := base.InstancesTrainTestSplit(rawData, 0.50)
	cls.Fit(trainData)

	//Calculates the Euclidean distance and returns the most popular label
	predictions := cls.Predict(testData)
	fmt.Println(predictions)

	// Prints precision/recall metrics
	confusionMat := evaluation.GetConfusionMatrix(testData, predictions)
	fmt.Println(evaluation.GetSummary(confusionMat))
}

开发者ID:JacobXie，项目名称:golearn，代码行数:21，代码来源:knnclassifier_iris.go

示例14: TestMultiSVMWeighted

func TestMultiSVMWeighted(t *testing.T) {
	Convey("Loading data...", t, func() {
		weights := make(map[string]float64)
		weights["Finance"] = 0.1739
		weights["Tech"] = 0.0750
		weights["Politics"] = 0.4928

		inst, err := base.ParseCSVToInstances("../examples/datasets/articles.csv", false)
		So(err, ShouldBeNil)
		X, Y := base.InstancesTrainTestSplit(inst, 0.4)

		m := NewMultiLinearSVC("l1", "l2", true, 0.62, 1e-4, weights)
		m.Fit(X)

		Convey("Predictions should work...", func() {
			predictions, err := m.Predict(Y)
			cf, err := evaluation.GetConfusionMatrix(Y, predictions)
			So(err, ShouldEqual, nil)
			So(evaluation.GetAccuracy(cf), ShouldBeGreaterThan, 0.70)
		})
	})
}

开发者ID:CTLife，项目名称:golearn，代码行数:22，代码来源:multisvc_test.go

示例15: main

func main() {
	// Load and parse the data from csv files
	fmt.Println("Loading data...")
	trainData, err := base.ParseCSVToInstances("data/mnist_train.csv", true)
	if err != nil {
		panic(err)
	}
	testData, err := base.ParseCSVToInstances("data/mnist_test.csv", true)
	if err != nil {
		panic(err)
	}

	// Create a new linear SVC with some good default values
	classifier, err := linear_models.NewLinearSVC("l1", "l2", true, 1.0, 1e-4)
	if err != nil {
		panic(err)
	}

	// Don't output information on each iteration
	base.Silent()

	// Train the linear SVC
	fmt.Println("Training...")
	classifier.Fit(trainData)

	// Make predictions for the test data
	fmt.Println("Predicting...")
	predictions, err := classifier.Predict(testData)
	if err != nil {
		panic(err)
	}

	// Get a confusion matrix and print out some accuracy stats for our predictions
	confusionMat, err := evaluation.GetConfusionMatrix(testData, predictions)
	if err != nil {
		panic(fmt.Sprintf("Unable to get confusion matrix: %s", err.Error()))
	}
	fmt.Println(evaluation.GetSummary(confusionMat))
}

开发者ID:postfix，项目名称:golearn-digit-recognition，代码行数:39，代码来源:main.go

示例16: TestOneVsAllModel

func TestOneVsAllModel(t *testing.T) {

	classifierFunc := func(c string) base.Classifier {
		m, err := linear_models.NewLinearSVC("l1", "l2", true, 1.0, 1e-4)
		if err != nil {
			panic(err)
		}
		return m
	}

	Convey("Given data", t, func() {
		inst, err := base.ParseCSVToInstances("../examples/datasets/iris_headers.csv", true)
		So(err, ShouldBeNil)

		X, Y := base.InstancesTrainTestSplit(inst, 0.4)

		m := NewOneVsAllModel(classifierFunc)
		m.Fit(X)

		Convey("The maximum class index should be 2", func() {
			So(m.maxClassVal, ShouldEqual, 2)
		})

		Convey("There should be three of everything...", func() {
			So(len(m.filters), ShouldEqual, 3)
			So(len(m.classifiers), ShouldEqual, 3)
		})

		Convey("Predictions should work...", func() {
			predictions, err := m.Predict(Y)
			So(err, ShouldEqual, nil)
			cf, err := evaluation.GetConfusionMatrix(Y, predictions)
			So(err, ShouldEqual, nil)
			fmt.Println(evaluation.GetAccuracy(cf))
			fmt.Println(evaluation.GetSummary(cf))
		})
	})
}

开发者ID:CTLife，项目名称:golearn，代码行数:38，代码来源:one_v_all_test.go

示例17: TestBaggedModelRandomForest

func TestBaggedModelRandomForest(t *testing.T) {
	Convey("Given data", t, func() {
		inst, err := base.ParseCSVToInstances("../examples/datasets/iris_headers.csv", true)
		So(err, ShouldBeNil)

		Convey("Splitting the data into training and test data", func() {
			trainData, testData := base.InstancesTrainTestSplit(inst, 0.6)

			Convey("Filtering the split datasets", func() {
				rand.Seed(time.Now().UnixNano())
				filt := filters.NewChiMergeFilter(inst, 0.90)
				for _, a := range base.NonClassFloatAttributes(inst) {
					filt.AddAttribute(a)
				}
				filt.Train()
				trainDataf := base.NewLazilyFilteredInstances(trainData, filt)
				testDataf := base.NewLazilyFilteredInstances(testData, filt)

				Convey("Fitting and Predicting with a Bagged Model of 10 Random Trees", func() {
					rf := new(BaggedModel)
					for i := 0; i < 10; i++ {
						rf.AddModel(trees.NewRandomTree(2))
					}

					rf.Fit(trainDataf)
					predictions := rf.Predict(testDataf)

					confusionMat, err := evaluation.GetConfusionMatrix(testDataf, predictions)
					So(err, ShouldBeNil)

					Convey("Predictions are somewhat accurate", func() {
						So(evaluation.GetAccuracy(confusionMat), ShouldBeGreaterThan, 0.5)
					})
				})
			})
		})
	})
}

开发者ID:GeekFreaker，项目名称:golearn，代码行数:38，代码来源:bagging_test.go

示例18: TestRandomForest1

func TestRandomForest1(testEnv *testing.T) {
	inst, err := base.ParseCSVToInstances("../examples/datasets/iris_headers.csv", true)
	if err != nil {
		panic(err)
	}

	filt := filters.NewChiMergeFilter(inst, 0.90)
	for _, a := range base.NonClassFloatAttributes(inst) {
		filt.AddAttribute(a)
	}
	filt.Train()
	instf := base.NewLazilyFilteredInstances(inst, filt)

	trainData, testData := base.InstancesTrainTestSplit(instf, 0.60)

	rf := NewRandomForest(10, 3)
	rf.Fit(trainData)
	predictions := rf.Predict(testData)
	fmt.Println(predictions)
	confusionMat := eval.GetConfusionMatrix(testData, predictions)
	fmt.Println(confusionMat)
	fmt.Println(eval.GetSummary(confusionMat))
}

开发者ID:JacobXie，项目名称:golearn，代码行数:23，代码来源:randomforest_test.go

示例19: TestID3Classification

func TestID3Classification(testEnv *testing.T) {
	inst, err := base.ParseCSVToInstances("../examples/datasets/iris_headers.csv", true)
	if err != nil {
		panic(err)
	}
	filt := filters.NewBinningFilter(inst, 10)
	filt.AddAllNumericAttributes()
	filt.Build()
	filt.Run(inst)
	fmt.Println(inst)
	trainData, testData := base.InstancesTrainTestSplit(inst, 0.70)
	// Build the decision tree
	rule := new(InformationGainRuleGenerator)
	root := InferID3Tree(trainData, rule)
	fmt.Println(root)
	predictions := root.Predict(testData)
	fmt.Println(predictions)
	confusionMat := eval.GetConfusionMatrix(testData, predictions)
	fmt.Println(confusionMat)
	fmt.Println(eval.GetMacroPrecision(confusionMat))
	fmt.Println(eval.GetMacroRecall(confusionMat))
	fmt.Println(eval.GetSummary(confusionMat))
}

开发者ID:hsinhoyeh，项目名称:golearn，代码行数:23，代码来源:tree_test.go

示例20: TestRandomTreeClassification2

func TestRandomTreeClassification2(testEnv *testing.T) {
	inst, err := base.ParseCSVToInstances("../examples/datasets/iris_headers.csv", true)
	if err != nil {
		panic(err)
	}
	insts := base.InstancesTrainTestSplit(inst, 0.4)
	filt := filters.NewChiMergeFilter(inst, 0.90)
	filt.AddAllNumericAttributes()
	filt.Build()
	fmt.Println(insts[1])
	filt.Run(insts[1])
	filt.Run(insts[0])
	root := NewRandomTree(2)
	root.Fit(insts[0])
	fmt.Println(root)
	predictions := root.Predict(insts[1])
	fmt.Println(predictions)
	confusionMat := eval.GetConfusionMatrix(insts[1], predictions)
	fmt.Println(confusionMat)
	fmt.Println(eval.GetMacroPrecision(confusionMat))
	fmt.Println(eval.GetMacroRecall(confusionMat))
	fmt.Println(eval.GetSummary(confusionMat))
}

开发者ID:24hours，项目名称:golearn，代码行数:23，代码来源:tree_test.go

注：本文中的github.com/sjwhitworth/golearn/evaluation.GetConfusionMatrix函数示例由纯净天空整理自Github/MSDocs等源码及文档管理平台，相关代码片段筛选自各路编程大神贡献的开源项目，源码版权归原作者所有，传播和使用请参考对应项目的License；未经允许，请勿转载。