Question

我是机器学习的新手，也是accord.net的新手（我代码C＃）。

我想创建一个简单的项目，在那里我看一下振荡的简单时间序列数据，然后我希望accord.net学习它并预测下一个值是什么。

这就是数据（时间序列）应该是这样的：

X - Y

然后我希望它预测以下内容：

X - Y

你能帮我解决一些如何解决它的例子吗？

Answer 1

执行此操作的一种简单方法是使用Accord ID3决策树。

诀窍是找出要使用的输入 - 你不能只在X上训练 - 树不会从中学到关于X的未来值 - 但是你可以构建一些从X派生的特征（或者之前的） Y）的值将是有用的。

通常对于这样的问题 - 你会根据从Y的先前值（被预测的东西）而不是X得到的特征进行每次预测。但是假设你可以在每次预测之间顺序观察Y（你不能那么预测任何仲裁X）所以我会坚持提出的问题。

我开始构建一个Accord ID3决策树来解决下面这个问题。我使用了一些不同的x % n值作为特征 - 希望树可以解决这个问题。事实上，如果我添加(x-1) % 4作为一个功能，它可以在一个级别只使用该属性进行 - 但我想更重要的是让树找到模式。

以下是代码：

    // this is the sequence y follows
    int[] ysequence = new int[] { 1, 2, 3, 2 };

    // this generates the correct Y for a given X
    int CalcY(int x) => ysequence[(x - 1) % 4];

    // this generates some inputs - just a few differnt mod of x
    int[] CalcInputs(int x) => new int[] { x % 2, x % 3, x % 4, x % 5, x % 6 };


    // for http://stackoverflow.com/questions/40573388/simple-accord-net-machine-learning-example
    [TestMethod]
    public void AccordID3TestStackOverFlowQuestion2()
    {
        // build the training data set
        int numtrainingcases = 12;
        int[][] inputs = new int[numtrainingcases][];
        int[] outputs = new int[numtrainingcases];

        Console.WriteLine("\t\t\t\t x \t y");
        for (int x = 1; x <= numtrainingcases; x++)
        {
            int y = CalcY(x);
            inputs[x-1] = CalcInputs(x);
            outputs[x-1] = y;
            Console.WriteLine("TrainingData \t " +x+"\t "+y);
        }

        // define how many values each input can have
        DecisionVariable[] attributes =
        {
            new DecisionVariable("Mod2",2),
            new DecisionVariable("Mod3",3),
            new DecisionVariable("Mod4",4),
            new DecisionVariable("Mod5",5),
            new DecisionVariable("Mod6",6)
        };

        // define how many outputs (+1 only because y doesn't use zero)
        int classCount = outputs.Max()+1;

        // create the tree
        DecisionTree tree = new DecisionTree(attributes, classCount);

        // Create a new instance of the ID3 algorithm
        ID3Learning id3learning = new ID3Learning(tree);

        // Learn the training instances! Populates the tree
        id3learning.Learn(inputs, outputs);

        Console.WriteLine();
        // now try to predict some cases that werent in the training data
        for (int x = numtrainingcases+1; x <= 2* numtrainingcases; x++)
        {
            int[] query = CalcInputs(x);

            int answer = tree.Decide(query); // makes the prediction

            Assert.AreEqual(CalcY(x), answer); // check the answer is what we expected - ie the tree got it right
            Console.WriteLine("Prediction \t\t " + x+"\t "+answer);
        }
    }

这是它产生的输出：

                 x   y
TrainingData     1   1
TrainingData     2   2
TrainingData     3   3
TrainingData     4   2
TrainingData     5   1
TrainingData     6   2
TrainingData     7   3
TrainingData     8   2
TrainingData     9   1
TrainingData     10  2
TrainingData     11  3
TrainingData     12  2

Prediction       13  1
Prediction       14  2
Prediction       15  3
Prediction       16  2
Prediction       17  1
Prediction       18  2
Prediction       19  3
Prediction       20  2
Prediction       21  1
Prediction       22  2
Prediction       23  3
Prediction       24  2

希望有所帮助。

编辑：在评论之后，下面的示例被修改为训练目标（Y）的先前值 - 而不是从时间索引（X）导出的特征。这意味着你无法在系列开始时开始训练 - 因为你需要Y先前值的回溯历史。在这个例子中，我从x = 9开始，因为它保持相同的顺序。

        // this is the sequence y follows
    int[] ysequence = new int[] { 1, 2, 3, 2 };

    // this generates the correct Y for a given X
    int CalcY(int x) => ysequence[(x - 1) % 4];

    // this generates some inputs - just a few differnt mod of x
    int[] CalcInputs(int x) => new int[] { CalcY(x-1), CalcY(x-2), CalcY(x-3), CalcY(x-4), CalcY(x - 5) };
    //int[] CalcInputs(int x) => new int[] { x % 2, x % 3, x % 4, x % 5, x % 6 };


    // for http://stackoverflow.com/questions/40573388/simple-accord-net-machine-learning-example
    [TestMethod]
    public void AccordID3TestTestStackOverFlowQuestion2()
    {
        // build the training data set
        int numtrainingcases = 12;
        int starttrainingat = 9;
        int[][] inputs = new int[numtrainingcases][];
        int[] outputs = new int[numtrainingcases];

        Console.WriteLine("\t\t\t\t x \t y");
        for (int x = starttrainingat; x < numtrainingcases + starttrainingat; x++)
        {
            int y = CalcY(x);
            inputs[x- starttrainingat] = CalcInputs(x);
            outputs[x- starttrainingat] = y;
            Console.WriteLine("TrainingData \t " +x+"\t "+y);
        }

        // define how many values each input can have
        DecisionVariable[] attributes =
        {
            new DecisionVariable("y-1",4),
            new DecisionVariable("y-2",4),
            new DecisionVariable("y-3",4),
            new DecisionVariable("y-4",4),
            new DecisionVariable("y-5",4)
        };

        // define how many outputs (+1 only because y doesn't use zero)
        int classCount = outputs.Max()+1;

        // create the tree
        DecisionTree tree = new DecisionTree(attributes, classCount);

        // Create a new instance of the ID3 algorithm
        ID3Learning id3learning = new ID3Learning(tree);

        // Learn the training instances! Populates the tree
        id3learning.Learn(inputs, outputs);

        Console.WriteLine();
        // now try to predict some cases that werent in the training data
        for (int x = starttrainingat+numtrainingcases; x <= starttrainingat + 2 * numtrainingcases; x++)
        {
            int[] query = CalcInputs(x);

            int answer = tree.Decide(query); // makes the prediction

            Assert.AreEqual(CalcY(x), answer); // check the answer is what we expected - ie the tree got it right
            Console.WriteLine("Prediction \t\t " + x+"\t "+answer);
        }
    }

您还可以考虑对之前的Y值之间的差异进行培训 - 如果Y的绝对值不如相对变化那么重要，那么这种差异会更好。

简单的accord.net机器学习示例

1 个答案: