调用Accord 算法的套路:
第一步:创建一个算法
var teacher = new xxx()
第二步: 训练这个算法
teacher.Learn(input, output)
第三步:让这个算法去预测
teacher.Decide(input)
Case00
// Create the learning algorithm with the chosen kernel var smo = new SequentialMinimalOptimization<Gaussian>() { Complexity = 100 // Create a hard-margin SVM }; // Use the algorithm to learn the svm var svm = smo.Learn(inputs, outputs); // Compute the machine‘s answers for the given inputs bool[] prediction = svm.Decide(inputs);
Case01:
// Create a Naive Bayes learning algorithm var teacher = new NaiveBayesLearning<NormalDistribution>(); // Use the learning algorithm to learn var nb = teacher.Learn(inputs, outputs); // Classify the samples using the model int[] answers = nb.Decide(inputs);
Case02
var teacher = new LinearCoordinateDescent(); // Teach the vector machine var svm = teacher.Learn(inputs, outputs); // Classify the samples using the model bool[] answers = svm.Decide(inputs);
Case03
// Create a new Sequential Minimal Optimization (SMO) learning // algorithm and estimate the complexity parameter C from data var teacher = new SequentialMinimalOptimization<Gaussian>() { UseComplexityHeuristic = true, UseKernelEstimation = true // estimate the kernel from the data }; // Teach the vector machine var svm = teacher.Learn(inputs, outputs); // Classify the samples using the model bool[] answers = svm.Decide(inputs);