java – Mahout – 简单的分类问题
作者：互联网
我正在尝试构建一个简单的模型,可以将点分类为2D空间的2个分区：
>我通过指定几个点和它们所属的分区来训练模型.
>我使用模型来预测测试点可能落入的组(分类).
不幸的是,我没有得到预期的答案.我在代码中遗漏了什么,或者我做错了什么？
public class SimpleClassifier {
public static class Point{
public int x;
public int y;
public Point(int x,int y){
this.x = x;
this.y = y;
}
@Override
public boolean equals(Object arg0) {
Point p = (Point) arg0;
return( (this.x == p.x) &&(this.y== p.y));
}
@Override
public String toString() {
// TODO Auto-generated method stub
return this.x + " , " + this.y ;
}
}
public static void main(String[] args) {
Map<Point,Integer> points = new HashMap<SimpleClassifier.Point, Integer>();
points.put(new Point(0,0), 0);
points.put(new Point(1,1), 0);
points.put(new Point(1,0), 0);
points.put(new Point(0,1), 0);
points.put(new Point(2,2), 0);
points.put(new Point(8,8), 1);
points.put(new Point(8,9), 1);
points.put(new Point(9,8), 1);
points.put(new Point(9,9), 1);
OnlineLogisticRegression learningAlgo = new OnlineLogisticRegression();
learningAlgo = new OnlineLogisticRegression(2, 2, new L1());
learningAlgo.learningRate(50);
//learningAlgo.alpha(1).stepOffset(1000);
System.out.println("training model \n" );
for(Point point : points.keySet()){
Vector v = getVector(point);
System.out.println(point + " belongs to " + points.get(point));
learningAlgo.train(points.get(point), v);
}
learningAlgo.close();
//now classify real data
Vector v = new RandomAccessSparseVector(2);
v.set(0, 0.5);
v.set(1, 0.5);
Vector r = learningAlgo.classifyFull(v);
System.out.println(r);
System.out.println("ans = " );
System.out.println("no of categories = " + learningAlgo.numCategories());
System.out.println("no of features = " + learningAlgo.numFeatures());
System.out.println("Probability of cluster 0 = " + r.get(0));
System.out.println("Probability of cluster 1 = " + r.get(1));
}
public static Vector getVector(Point point){
Vector v = new DenseVector(2);
v.set(0, point.x);
v.set(1, point.y);
return v;
}
}
输出：
ans =
no of categories = 2
no of features = 2
Probability of cluster 0 = 3.9580985042775296E-4
Probability of cluster 1 = 0.9996041901495722
99％的输出显示集群1的概率更高.为什么？
解决方法:
问题是你没有包含偏见(拦截)术语,它总是1.
您需要在您的点类中添加偏差项(1).
这是许多有经验的人在机器学习中犯下的一个非常基本的错误.在学习理论上投入一些时间可能是个好主意. Andrew Ng’s lectures是一个值得学习的好地方.
要使代码得到预期的输出,需要更改以下内容.
>偏见术语补充说.
>学习参数太高.将其更改为10
现在你将得到0级的P(0)= 0.9999.
这是一个完整的工作示例,可以给出正确的结果：
import java.util.HashMap;
import java.util.Map;
import org.apache.mahout.classifier.sgd.L1;
import org.apache.mahout.classifier.sgd.OnlineLogisticRegression;
import org.apache.mahout.math.DenseVector;
import org.apache.mahout.math.RandomAccessSparseVector;
import org.apache.mahout.math.Vector;
class Point{
public int x;
public int y;
public Point(int x,int y){
this.x = x;
this.y = y;
}
@Override
public boolean equals(Object arg0) {
Point p = (Point) arg0;
return( (this.x == p.x) &&(this.y== p.y));
}
@Override
public String toString() {
return this.x + " , " + this.y ;
}
}
public class SimpleClassifier {
public static void main(String[] args) {
Map<Point,Integer> points = new HashMap<Point, Integer>();
points.put(new Point(0,0), 0);
points.put(new Point(1,1), 0);
points.put(new Point(1,0), 0);
points.put(new Point(0,1), 0);
points.put(new Point(2,2), 0);
points.put(new Point(8,8), 1);
points.put(new Point(8,9), 1);
points.put(new Point(9,8), 1);
points.put(new Point(9,9), 1);
OnlineLogisticRegression learningAlgo = new OnlineLogisticRegression();
learningAlgo = new OnlineLogisticRegression(2, 3, new L1());
learningAlgo.lambda(0.1);
learningAlgo.learningRate(10);
System.out.println("training model \n" );
for(Point point : points.keySet()){
Vector v = getVector(point);
System.out.println(point + " belongs to " + points.get(point));
learningAlgo.train(points.get(point), v);
}
learningAlgo.close();
Vector v = new RandomAccessSparseVector(3);
v.set(0, 0.5);
v.set(1, 0.5);
v.set(2, 1);
Vector r = learningAlgo.classifyFull(v);
System.out.println(r);
System.out.println("ans = " );
System.out.println("no of categories = " + learningAlgo.numCategories());
System.out.println("no of features = " + learningAlgo.numFeatures());
System.out.println("Probability of cluster 0 = " + r.get(0));
System.out.println("Probability of cluster 1 = " + r.get(1));
}
public static Vector getVector(Point point){
Vector v = new DenseVector(3);
v.set(0, point.x);
v.set(1, point.y);
v.set(2, 1);
return v;
}
}
输出：
2 , 2 belongs to 0
1 , 0 belongs to 0
9 , 8 belongs to 1
8 , 8 belongs to 1
0 , 1 belongs to 0
0 , 0 belongs to 0
1 , 1 belongs to 0
9 , 9 belongs to 1
8 , 9 belongs to 1
{0:2.470723149516907E-6,1:0.9999975292768505}
ans =
no of categories = 2
no of features = 3
Probability of cluster 0 = 2.470723149516907E-6
Probability of cluster 1 = 0.9999975292768505
请注意,我在SimpleClassifier类之外定义了类Point,但这只是为了使代码更具可读性并且不是必需的.
看看改变学习率时会发生什么.阅读有关交叉验证的说明,了解如何选择学习率.
Learning Rate => Probability of cluster 0
0.001 => 0.4991116089
0.01 => 0.492481585
0.1 => 0.469961472
1 => 0.5327745322
10 => 0.9745740393
100 => 0
1000 => 0
选择学习率：
>通过以固定学习率α开始,通过缓慢地让学习率α减小到零来进行随机梯度下降是常见的.
算法运行时,也可以确保参数收敛到
全局最小值而不仅仅是在最小值附近振荡.
>在这种情况下,当我们使用常数α时,您可以进行初始选择,运行梯度下降和观察成本函数,并相应地调整学习率.它被解释为here
标签：java,classification,mahout 来源： https://codeday.me/bug/20190530/1182708.html