我对机器学习不熟悉，对accord.net（我的代码是c）。
我想创建一个简单的项目，在这里我可以看到一个简单的数据振荡时间序列，然后我想让accord.net学习它并预测下一个值是什么。
数据（时间序列）应该是这样的：
X-Y轴
1 - 1
2 - 2
3 - 3
4 - 2
5 - 1
6 - 2
7 - 3
8 - 2
9 - 1
我希望它能预测以下情况：
X-Y轴
10 - 2
11 - 3
12 - 2
13 - 1
14 - 2
15 - 3
你们能帮我举几个例子来解决这个问题吗？
最佳答案
一个简单的方法是使用accord id3决策树。
诀窍是找出要使用的输入-你不能只是在x上训练-树不会从中了解x的未来值-但是你可以构建一些从x（或y的以前值）派生的有用特性。
通常对于像这样的问题-你会根据从y（被预测的事物）而不是x的先前值中得到的特征来进行每个预测。但是这假设你可以在每个预测之间按顺序观察y（然后你不能预测任意的x），所以我将坚持提出的问题。
为了解决下面的问题，我尝试构建一个accord id3决策树。我使用了几个不同的值x % n作为特性-希望树能从中找出答案。事实上，如果我将(x-1) % 4作为一个特性添加进来，它就可以在一个级别上使用这个属性来完成这项工作——但我想重点是让树找到模式。
这里是代码：
// 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
希望能有所帮助。
编辑：下面的注释修改了示例，以训练目标（y）的先前值，而不是从时间索引（x）派生的特征。这意味着你不能在系列开始时就开始训练，因为你需要一个y以前值的历史记录。在这个例子中，我从x=9开始只是因为它保持了相同的序列。
// 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);
}
}
你也可以考虑训练一下y之前的值之间的差异，如果y的绝对值没有相对变化那么重要的话，这会更好。