ML04 Accord 调用实现机器算法的套路

// 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);

// 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);

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);

// 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);