机器学习 demo分西瓜

2022-12-04 21:13:08

周老师的书，对神经网络写了一个小的Demo

是最简单的神经网络，只有一层的隐藏层。

这次练习依旧是对西瓜的好坏进行预测。

主要分了以下几个步骤

1、数据预处理

对西瓜的不同特性进行数学编码表示（0~1），我是直接编了对应数字。含糖量已经是一个0~1之间的数，所以就没有进行处理

青绿  1

乌黑 0.5

浅白  0

蜷缩  1

稍蜷 0.5

硬挺  0

浊响  1

沉闷 0.5

清脆  0

清晰  1

稍糊 0.5

模糊  0

凹陷  1

稍凹 0.5

平坦  0

硬滑  1

软黏  0

2、训练集和检测集

  1. package BP;
  2. public class TrainData {
  3. double[][] traindata;
  4. double[][] traindataoutput;
  5. double[][] testdata;
  6. double[][] testdataoutput;
  7. public TrainData(){
  8. traindata = new double[][]{
  9. new double[]{1,1,1,1,1,1,0.697,0.460},
  10. new double[]{0.5,1,0.5,1,1,1,0.774,0.376},
  11. new double[]{0.5,1,1,1,1,1,0.634,0.264},
  12. //new double[]{1,1,0.5,1,1,1,0.608,0.318,1},
  13. //new double[]{0,1,1,1,1,1,0.556,0.215,1},
  14. new double[]{1,0.5,1,1,0.5,0,0.403,0.237},
  15. new double[]{0.5,0.5,1,0.5,0.5,0,0.481,0.149},
  16. //new double[]{0.5,0.5,1,1,0.5,1,0.437,0.211,1},
  17. //new double[]{0.5,0.5,0.5,0.5,0.5,1,0.666,0.091,0},
  18. //new double[]{1,0,0,1,0,0,0.243,0.267,0},
  19. //new double[]{0,0,0,0,0,1,0.245,0.057,0},
  20. //new double[]{0,1,1,0,0,0,0.343,0.099,0},
  21. new double[]{1,0.5,1,0.5,1,1,0.639,0.161},
  22. new double[]{0,0.5,0,0.5,1,1,0.657,0.198},
  23. new double[]{0.5,0.5,1,1,0.5,0,0.360,0.370},
  24. new double[]{0,1,1,0,0,1,0.593,0.042},
  25. new double[]{1,1,0.5,0.5,0.5,1,0.719,0.103}
  26. };
  27. traindataoutput = new double[][]{
  28. new double[]{1},
  29. new double[]{1},
  30. new double[]{1},
  31. new double[]{1},
  32. new double[]{1},
  33. new double[]{0},
  34. new double[]{0},
  35. new double[]{0},
  36. new double[]{0},
  37. new double[]{0},
  38. };
  39. testdata = new double[][]{
  40. new double[]{1,1,0.5,1,1,1,0.608,0.318},
  41. new double[]{0,1,1,1,1,1,0.556,0.215},
  42. new double[]{0.5,0.5,1,1,0.5,1,0.437,0.211},
  43. new double[]{0.5,0.5,0.5,0.5,0.5,1,0.666,0.091},
  44. new double[]{1,0,0,1,0,0,0.243,0.267},
  45. new double[]{0,0,0,0,0,1,0.245,0.057},
  46. new double[]{0,1,1,0,0,0,0.343,0.099},
  47. };
  48. testdataoutput = new double[][]{
  49. new double[]{1},
  50. new double[]{1},
  51. new double[]{1},
  52. new double[]{0},
  53. new double[]{0},
  54. new double[]{0},
  55. new double[]{0},
  56. };
  57. }
  58. public static void main(String[] args){
  59. TrainData t = new TrainData();
  60. for(int i=0;i<t.traindata.length;i++){
  61. for(int j=0;j<9;j++)
  62. System.out.print(t.traindata[i][j]+ " ");
  63. System.out.println();
  64. }
  65. }
  66. }

3、BP主函数

  1. package BP;
  2. import java.util.Random;
  3. public class BP {
  4. int innum;
  5. int hiddennum;
  6. int outnum;
  7. //输入、隐藏、输出层
  8. public double[] input;
  9. public double[] hidden;
  10. //output为本神经网络计算出的输出值
  11. public double[] output;
  12. //realoutput为训练网络时，用户提供的真的输出值
  13. public double[] realoutput;
  14. //v[i,j]表示输入层i到隐层j  w[i,j]表示隐层i到输出层j
  15. public double[][] v;
  16. public double[][] w;
  17. //beta为隐层的阈值，afa为输出层阈值
  18. public double[] beta;
  19. public double[] afa;
  20. //学习率
  21. public double eta;
  22. //步长
  23. public double momentum;
  24. public final Random random;
  25. public BP(int inputnum,int hiddennum,int outputnum,double learningrate){
  26. innum = inputnum;
  27. this.hiddennum = hiddennum;
  28. outnum = outputnum;
  29. input = new double[inputnum + 1];
  30. hidden = new double[hiddennum + 1];
  31. output = new double[outputnum + 1];
  32. realoutput = new double[outputnum + 1];
  33. v = new double[inputnum + 1][hiddennum + 1];
  34. w = new double[hiddennum + 1][outputnum + 1];
  35. beta = new double[outputnum + 1];
  36. afa = new double[hiddennum + 1];
  37. for(int i=0;i<outputnum;i++)
  38. beta[i] = 0.0;
  39. for(int i=0;i<hiddennum;i++)
  40. afa[i] = 0.0;
  41. eta = learningrate;
  42. //随机数对结果影响较大
  43. random = new Random(19950326);
  44. randomizeWeights(w);
  45. randomizeWeights(v);
  46. }
  47. public void testData(double[] in){
  48. input = in;
  49. getNetOutput();
  50. }
  51. //只对本题目有用，output>0.5时为好西瓜，output<0.5时为坏西瓜
  52. public int predict(double[] in){
  53. testData(in);
  54. if(output[0]>0.5)
  55. return 1;
  56. else
  57. return 0;
  58. }
  59. //获得在test集上的正确率
  60. public double getAccuracy(double[][] in,double[][] out){
  61. int rightans = 0,wrongans = 0;
  62. for(int i=0;i<in.length;i++){
  63. if(predict(in[i])==(out[i][0])){
  64. //System.out.println("预测结果："+predict(in[i])+" 实际结果为："+out[i][0]);
  65. rightans++;
  66. }else{
  67. //System.out.println("预测结果："+predict(in[i])+" 实际结果为："+out[i][0]);
  68. wrongans++;
  69. }
  70. }
  71. System.out.println("对："+rightans+" 错："+wrongans);
  72. return (double)rightans/(double)(rightans+wrongans);
  73. }
  74. //times为进行几轮训练
  75. public void train(int times){
  76. TrainData t = new TrainData();
  77. double wu = 0.0,acc = 0.0;
  78. int n = t.traindata.length;
  79. for(int i=0;i<times;i++){
  80. wu = 0.0;
  81. for(int j=0;j<n;j++){
  82. traindata(t.traindata[j],t.traindataoutput[j]);
  83. wu += getDeviation();
  84. }
  85. wu = wu/((double)n);
  86. System.out.println("第"+i+"轮训练："+wu);
  87. acc = getAccuracy(t.testdata,t.testdataoutput);
  88. System.out.println("预测正确率为： "+acc);
  89. }
  90. }
  91. //对一个input输入进行训练
  92. public void traindata(double[] in,double[] out){
  93. input = in;
  94. realoutput = out;
  95. getNetOutput();
  96. adjustParameter();
  97. }
  98. //获得误差E
  99. public double getDeviation(){
  100. double e = 0.0;
  101. for(int i=0;i<outnum;i++)
  102. e += (output[i] - realoutput[i])*(output[i] - realoutput[i]);
  103. e *= 0.5;
  104. return e;
  105. }
  106. //调整权值
  107. public void adjustParameter(){
  108. double g[],e = 0.0;
  109. g = new double[outnum];
  110. int i,j;
  111. for(i=0;i<outnum;i++){
  112. g[i] = output[i]*(1-output[i])*(realoutput[i]-output[i]);
  113. beta[i] -= eta * g[i];
  114. for(j=0;j<hiddennum;j++){
  115. w[j][i] += eta * g[i] * hidden[j];
  116. }
  117. }
  118. for(i=0;i<hiddennum;i++){
  119. e = 0.0;
  120. for(j=0;j<outnum;j++)
  121. e += g[j]*w[i][j];
  122. e = hidden[i]*(1-hidden[i])*e;
  123. afa[i] -= eta * e;
  124. for(j=0;j<innum;j++)
  125. v[j][i] += eta * e * input[j];
  126. }
  127. }
  128. //获得output
  129. public void getNetOutput(){
  130. int i,j;
  131. double tmp=0.0;
  132. for(i=0;i<hiddennum;i++){
  133. tmp = 0.0;
  134. for(j=0;j<innum;j++)
  135. tmp += v[j][i]*input[j];
  136. hidden[i] = sigmoid(tmp-afa[i]);
  137. }
  138. for(i=0;i<outnum;i++){
  139. tmp = 0.0;
  140. for(j=0;j<hiddennum;j++)
  141. tmp += w[j][i]*hidden[j];
  142. output[i] = sigmoid(tmp-beta[i]);
  143. }
  144. }
  145. //对权值矩阵w、v进行初始随机化
  146. private void randomizeWeights(double[][] matrix) {
  147. for (int i = 0, len = matrix.length; i != len; i++)
  148. for (int j = 0, len2 = matrix[i].length; j != len2; j++) {
  149. double real = random.nextDouble();
  150. matrix[i][j] = random.nextDouble() > 0.5 ? real : -real;
  151. }
  152. }
  153. public void debug(){
  154. System.out.println("========begin=======");
  155. for(int i=0;i<innum;i++){
  156. for(int j=0;j<hiddennum;j++)
  157. System.out.print(v[i][j]+" ");
  158. System.out.println();
  159. }
  160. System.out.println();
  161. for(int i=0;i<hiddennum;i++){
  162. for(int j=0;j<outnum;j++)
  163. System.out.print(w[i][j]+" ");
  164. System.out.println();
  165. }
  166. System.out.println("========end=======");
  167. }
  168. public double sigmoid(double z){
  169. double s = 0.0;
  170. s = 1d/(1d + Math.exp(-z));
  171. return s;
  172. }
  173. public static void main(String[] args){
  174. BP bp = new BP(8,10,1,0.1);
  175. bp.train(50);
  176. }
  177. }

我要说的：

就结果来说，在验证集上的正确率可达到85%，当然很大程度上取决于BP初始化时random函数的种子。运气好的时候甚至能达到100%的正确率，运气不好的时候只有40%多，跟随便乱猜没什么区别。

想问大神。。。只能采用这种随机算法来找到一个最合适的ramdom种子值嘛？能不能用遗传这样的开放式算法进行搜索来找到最合适的随机值（我觉得随机的种子和随机结果并没有什么直接的关联，所以不知道能不能用遗传算法之列。。。）

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