CrossValidationValues Class |
Note: This API is now obsolete.
Namespace: Accord.MachineLearning
[SerializableAttribute] [ObsoleteAttribute("Please refer to CrossValidation<TModel, TInput> instead.")] public class CrossValidationValues : CrossValidationValues<Object>
The CrossValidationValues type exposes the following members.
Name | Description | |
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CrossValidationValues(Double, Double) |
Creates a new Cross-Validation Values class.
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CrossValidationValues(Object, Double, Double) |
Creates a new Cross-Validation Values class.
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CrossValidationValues(Double, Double, Double, Double) |
Creates a new Cross-Validation Values class.
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CrossValidationValues(Object, Double, Double, Double, Double) |
Creates a new Cross-Validation Values class.
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Name | Description | |
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Model |
Gets the model.
(Inherited from CrossValidationValuesTModel.) | |
Tag |
Gets or sets a tag for user-defined information.
(Inherited from CrossValidationValuesTModel.) | |
TrainingValue |
Gets the training value for the model.
(Inherited from CrossValidationValuesTModel.) | |
TrainingVariance |
Gets the variance of the training
value for the model, if available.
(Inherited from CrossValidationValuesTModel.) | |
ValidationValue |
Gets the validation value for the model.
(Inherited from CrossValidationValuesTModel.) | |
ValidationVariance |
Gets the variance of the validation
value for the model, if available.
(Inherited from CrossValidationValuesTModel.) |
Name | Description | |
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CreateTModel(TModel, Double, Double) |
Creates a new Cross-Validation Values class.
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CreateTModel(TModel, Double, Double, Double, Double) |
Creates a new Cross-Validation Values class.
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Equals | Determines whether the specified object is equal to the current object. (Inherited from Object.) | |
Finalize | Allows an object to try to free resources and perform other cleanup operations before it is reclaimed by garbage collection. (Inherited from Object.) | |
GetHashCode | Serves as the default hash function. (Inherited from Object.) | |
GetType | Gets the Type of the current instance. (Inherited from Object.) | |
MemberwiseClone | Creates a shallow copy of the current Object. (Inherited from Object.) | |
ToString | Returns a string that represents the current object. (Inherited from Object.) |
Name | Description | |
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HasMethod |
Checks whether an object implements a method with the given name.
(Defined by ExtensionMethods.) | |
IsEqual |
Compares two objects for equality, performing an elementwise
comparison if the elements are vectors or matrices.
(Defined by Matrix.) | |
To(Type) | Overloaded.
Converts an object into another type, irrespective of whether
the conversion can be done at compile time or not. This can be
used to convert generic types to numeric types during runtime.
(Defined by ExtensionMethods.) | |
ToT | Overloaded.
Converts an object into another type, irrespective of whether
the conversion can be done at compile time or not. This can be
used to convert generic types to numeric types during runtime.
(Defined by ExtensionMethods.) |
// Ensure results are reproducible Accord.Math.Random.Generator.Seed = 0; // This is a sample code on how to use Cross-Validation // to assess the performance of Support Vector Machines. // Consider the example binary data. We will be trying // to learn a XOR problem and see how well does SVMs // perform on this data. double[][] data = { new double[] { -1, -1 }, new double[] { 1, -1 }, new double[] { -1, 1 }, new double[] { 1, 1 }, new double[] { -1, -1 }, new double[] { 1, -1 }, new double[] { -1, 1 }, new double[] { 1, 1 }, new double[] { -1, -1 }, new double[] { 1, -1 }, new double[] { -1, 1 }, new double[] { 1, 1 }, new double[] { -1, -1 }, new double[] { 1, -1 }, new double[] { -1, 1 }, new double[] { 1, 1 }, }; int[] xor = // result of xor for the sample input data { -1, 1, 1, -1, -1, 1, 1, -1, -1, 1, 1, -1, -1, 1, 1, -1, }; // Create a new Cross-validation algorithm passing the data set size and the number of folds var crossvalidation = new CrossValidation<SupportVectorMachine<Linear, double[]>, double[]>() { K = 3, // Use 3 folds in cross-validation // Indicate how learning algorithms for the models should be created Learner = (s) => new SequentialMinimalOptimization<Linear, double[]>() { Complexity = 100 }, // Indicate how the performance of those models will be measured Loss = (expected, actual, p) => new ZeroOneLoss(expected).Loss(actual), Stratify = false, // do not force balancing of classes }; // If needed, control the parallelization degree crossvalidation.ParallelOptions.MaxDegreeOfParallelism = 1; // Compute the cross-validation var result = crossvalidation.Learn(data, xor); // Finally, access the measured performance. double trainingErrors = result.Training.Mean; // should be 0.30606060606060609 (+/- var. 0.083498622589531682) double validationErrors = result.Validation.Mean; // should be 0.3666666666666667 (+/- var. 0.023333333333333334) // If desired, compute an aggregate confusion matrix for the validation sets: GeneralConfusionMatrix gcm = result.ToConfusionMatrix(data, xor); double accuracy = gcm.Accuracy; // should be 0.625 double error = gcm.Error; // should be 0.375
// Ensure results are reproducible Accord.Math.Random.Generator.Seed = 0; // This is a sample code on how to use Cross-Validation // to assess the performance of Hidden Markov Models. // Declare some testing data int[][] inputs = new int[][] { new int[] { 0,1,1,0 }, // Class 0 new int[] { 0,0,1,0 }, // Class 0 new int[] { 0,1,1,1,0 }, // Class 0 new int[] { 0,1,1,1,0 }, // Class 0 new int[] { 0,1,1,0 }, // Class 0 new int[] { 0,0,0,0,0 }, // Class 1 new int[] { 0,0,0,1,0 }, // Class 1 new int[] { 0,0,0,0,0 }, // Class 1 new int[] { 0,0,0 }, // Class 1 new int[] { 0,0,0,0 }, // Class 1 new int[] { 1,0,0,1 }, // Class 2 new int[] { 1,1,0,1 }, // Class 2 new int[] { 1,0,0,0,1 }, // Class 2 new int[] { 1,0,1 }, // Class 2 new int[] { 1,1,0,1 }, // Class 2 }; int[] outputs = new int[] { 0,0,0,0,0, // First 5 sequences are of class 0 1,1,1,1,1, // Middle 5 sequences are of class 1 2,2,2,2,2, // Last 5 sequences are of class 2 }; // Create a new Cross-validation algorithm passing the data set size and the number of folds var crossvalidation = new CrossValidation<HiddenMarkovClassifier, int[]>() { K = 3, // Use 3 folds in cross-validation Learner = (s) => new HiddenMarkovClassifierLearning() { Learner = (p) => new BaumWelchLearning() { NumberOfStates = 3 } }, Loss = (expected, actual, p) => { var cm = new GeneralConfusionMatrix(classes: p.Model.NumberOfClasses, expected: expected, predicted: actual); p.Variance = cm.Variance; return p.Value = cm.Kappa; }, Stratify = false, }; // If needed, control the parallelization degree crossvalidation.ParallelOptions.MaxDegreeOfParallelism = 1; // Compute the cross-validation var result = crossvalidation.Learn(inputs, outputs); // If desired, compute an aggregate confusion matrix for the validation sets: GeneralConfusionMatrix gcm = result.ToConfusionMatrix(inputs, outputs); // Finally, access the measured performance. double trainingErrors = result.Training.Mean; double validationErrors = result.Validation.Mean; double trainingErrorVar = result.Training.Variance; double validationErrorVar = result.Validation.Variance; double trainingErrorPooledVar = result.Training.PooledVariance; double validationErrorPooledVar = result.Validation.PooledVariance;
// Ensure we have reproducible results Accord.Math.Random.Generator.Seed = 0; // Get some data to be learned. We will be using the Wiconsin's // (Diagnostic) Breast Cancer dataset, where the goal is to determine // whether the characteristics extracted from a breast cancer exam // correspond to a malignant or benign type of cancer: var data = new WisconsinDiagnosticBreastCancer(); double[][] input = data.Features; // 569 samples, 30-dimensional features int[] output = data.ClassLabels; // 569 samples, 2 different class labels // Let's say we want to measure the cross-validation performance of // a decision tree with a maximum tree height of 5 and where variables // are able to join the decision path at most 2 times during evaluation: var cv = CrossValidation.Create( k: 10, // We will be using 10-fold cross validation learner: (p) => new C45Learning() // here we create the learning algorithm { Join = 2, MaxHeight = 5 }, // Now we have to specify how the tree performance should be measured: loss: (actual, expected, p) => new ZeroOneLoss(expected).Loss(actual), // This function can be used to perform any special // operations before the actual learning is done, but // here we will just leave it as simple as it can be: fit: (teacher, x, y, w) => teacher.Learn(x, y, w), // Finally, we have to pass the input and output data // that will be used in cross-validation. x: input, y: output ); // After the cross-validation object has been created, // we can call its .Learn method with the input and // output data that will be partitioned into the folds: var result = cv.Learn(input, output); // We can grab some information about the problem: int numberOfSamples = result.NumberOfSamples; // should be 569 int numberOfInputs = result.NumberOfInputs; // should be 30 int numberOfOutputs = result.NumberOfOutputs; // should be 2 double trainingError = result.Training.Mean; // should be 0.017771153143274855 double validationError = result.Validation.Mean; // should be 0.0755952380952381 // If desired, compute an aggregate confusion matrix for the validation sets: GeneralConfusionMatrix gcm = result.ToConfusionMatrix(input, output); double accuracy = gcm.Accuracy; // result should be 0.92442882249560632