Question

我强烈怀疑它与本地错误有关，这似乎总是-1。

我的github page可以找到完整的代码。

我已根据this tutorial实施了感知器算法。

我的输入是一个单词特征向量，形式为：

示例：

Document 1 = ["I", "am", "awesome"]
Document 2 = ["I", "am", "great", "great"]

字典是：

["I", "am", "awesome", "great"]

因此，作为矢量的文档看起来像：

Document 1 = [1, 1, 1, 0]
Document 2 = [1, 1, 0, 2]

然而，当我运行算法时，我始终得到0.0，1.0或0.7071067811865476作为均方根误差的值，即Math.sqrt(globalError / inputSize)。< / p>

这不可能是正确的正弦我每次都会将权重初始化为不同的随机变量。另外我认为迭代次数相当低，因为我有数百个单词作为我的全局字典。

我一直在网上搜索，并通过调试器仔细挑选代码，我认为我已经正确地实现了算法，但是必须有一些我没有抓住的东西，也许你们其中一个人可以发现它。

这是三种不同执行的输出结果：

首先：

Iteration 1 : RMSE = 1.0
Iteration 2 : RMSE = 1.0
Iteration 3 : RMSE = 1.0
Iteration 4 : RMSE = 1.0
Iteration 5 : RMSE = 0.7071067811865476
Iteration 6 : RMSE = 0.0

=======
Decision boundary equation:
 bias: 4.9148000000000005
class = 1

第二

Iteration 1 : RMSE = 0.7071067811865476
Iteration 2 : RMSE = 1.0
Iteration 3 : RMSE = 0.7071067811865476
Iteration 4 : RMSE = 1.0
Iteration 5 : RMSE = 1.0
Iteration 6 : RMSE = 1.0
Iteration 7 : RMSE = 1.0
Iteration 8 : RMSE = 0.7071067811865476
Iteration 9 : RMSE = 0.0

=======
Decision boundary equation:
 bias: -4.8511
class = 1

第三

Iteration 1 : RMSE = 1.0
Iteration 2 : RMSE = 1.0
Iteration 3 : RMSE = 1.0
Iteration 4 : RMSE = 1.0
Iteration 5 : RMSE = 0.7071067811865476
Iteration 6 : RMSE = 0.0

=======
Decision boundary equation:
 bias: 9.7544
class = 1

代码如下所示：

public static void perceptron(Set<String> globoDict,
    Map<String, int[]> trainingPerceptronInput,
    Map<String, int[]> testPerceptronInput)
{
    final int globoDictSize = globoDict.size(); // number of features (x, y, z)

    // weights total 32 (31 for input variables and one for bias)
    double[] weights = new double[globoDictSize + 1];
    for (int i = 0; i < weights.length; i++) 
    {
        //weights[i] = Math.floor(Math.random() * 10000) / 10000;
        weights[i] = randomNumber(-10 , 10);
    }

    int inputSize = trainingPerceptronInput.size();
    int[] outputs = new int[inputSize];
    final double[][] a = initializeOutput(trainingPerceptronInput, globoDictSize, outputs);

    double globalError;
    int iteration = 0;
    do 
    {
        iteration++;
        globalError = 0;
        // loop through all instances (complete one epoch)
        for (int p = 0; p < inputSize; p++) 
        {
            // calculate predicted class
            int output = Prcptrn_CalcOutpt.calculateOutput(THETA, weights, a, p);
            // difference between predicted and actual class values
            double localError = outputs[p] - output;
            int i;
            for (i = 0; i < a.length; i++) 
            {
                weights[i] += LEARNING_RATE * localError * a[i][p];
            }
            weights[i] += LEARNING_RATE * localError;

            // summation of squared error (error value for all instances)
            globalError += localError * localError;
        }

        /* Root Mean Squared Error */
        System.out.println("Iteration "
            + iteration + " : RMSE = " + Math.sqrt(globalError / inputSize));
    } 
    while (globalError != 0 && iteration <= MAX_ITER);

    System.out.println("\n=======\nDecision boundary equation:");
    int i;
    for (i = 0; i < a.length; i++) 
    {
        System.out.print(" a");
        if (i < 10) System.out.print(0);
        System.out.println( i + " * " + weights[i]);


    }
    System.out.println(" bias: " + weights[i]);

    inputSize = testPerceptronInput.size();
    outputs = new int[inputSize];
    double[][] z = initializeOutput(testPerceptronInput, globoDictSize, outputs); 

    test_output = Prcptrn_CalcOutpt.calculateOutput(THETA, weights, z, 1);       

    System.out.println("class = " + test_output);
}

static double[][] initializeOutput( Map<String, int[]> perceptronInput, 
                                    int size, 
                                    int[] outputs)
{
    final int inputSize = perceptronInput.size();
    final double[][] a = new double[size][inputSize];

    // 2d array for features
    int[][] feature_matrix = new int[inputSize][size];
    String[] output_label = new String[inputSize];
    int x = 0;
    for (Entry<String, int[]> entry : perceptronInput.entrySet()) 
    {
        int[] container = entry.getValue();

        for (int j = 0; j < container.length; j++) 
        {
            feature_matrix[x][j] = container[j];
            output_label[x] = String.valueOf(entry.getKey());
        }
        x++;
    }

    for (x = 0; x < inputSize; x++) 
    {
        for (int i = 0; i < a.length; i++) 
        {
            a[i][x] = feature_matrix[x][i];
        }
        outputs[x] = output_label[x].equals(FILEPATH) ? 1 : 0;
    }

    return a;
}

public static double randomNumber(int min , int max) {
    DecimalFormat df = new DecimalFormat("#.####");
    double d = min + Math.random() * (max - min);
    String s = df.format(d);
    double x = Double.parseDouble(s);
    return x;
}

这是Prcptrn_CalcOutpt.calculateOutput：

static int calculateOutput(int theta, double[] weights, double[][] a, int index)
{
    double sum = 0;
    int i;
    for (i = 0; i < a.length; i++) 
    {
        sum += weights[i] * a[i][index];
    }
    sum += weights[i];
    return (sum >= theta) ? 1 : 0;
}

感知器算法计算局部误差/ RMSE

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