Diff: MCSVM.cpp

Revision:

0:3f38e74a4a77

Child:

1:1a0d5152d50b

--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/MCSVM.cpp	Thu Jan 15 08:22:02 2015 +0000
@@ -0,0 +1,201 @@
+#include "MCSVM.hpp"
+
+// コンストラクタ. 適宜追加予定
+MCSVM::MCSVM(int    class_n,
+             int    sample_dim,
+             int    sample_n, 
+             float* sample_data,
+             int*   sample_mclabel)
+  : SVM(sample_dim, sample_n, sample_data, sample_mclabel)
+{
+  this->n_class = class_n;
+  this->maxFailcount = 5;
+
+  int n_kC2 = (n_class) * (n_class - 1) / 2;
+  
+  mc_alpha   = new float[n_sample * n_kC2];
+  mc_label   = new int[n_sample * n_kC2];
+
+  // ラベルの生成
+  int tmp_lab;
+  for (int ci = 0; ci < n_class; ci++) {
+    for (int cj = ci + 1; cj < n_class; cj++) {
+      // i < jであることから, ラベルiは負例, ラベルjは正例に割り当てる.
+      // いずれのラベルにも該当しないデータは欠損とし,ラベル0とする.
+      for (int l=0; l < n_sample; l++) {
+        if (this->label[l] == ci) {
+          tmp_lab = -1;
+        } else if (this->label[l] == cj) {
+          tmp_lab = 1;
+        } else {
+          tmp_lab = 0;
+        }
+        MATRIX_AT(mc_label,n_sample,INX_KSVM_IJ(n_class,ci,cj),l) = tmp_lab;
+        //printf("l %d : %d -> %d \n", l, label[l], tmp_lab);
+      }
+
+    }
+  }
+
+}
+
+// 領域開放
+MCSVM::~MCSVM(void)
+{
+    delete [] mc_alpha;
+    delete [] mc_label;
+}
+
+// 全SVMの学習.
+int MCSVM::learning(void)
+{
+  int status, fail_count;
+  int* tmp_label = new int[n_sample];
+  // 元のラベルを退避
+  memcpy(tmp_label,label, sizeof(int) * n_sample);
+  for (int ci = 0; ci < n_class; ci++) {
+    for (int cj = ci + 1; cj < n_class; cj++) {
+      // 2値ラベルを取得する.
+      memcpy(label, &(MATRIX_AT(mc_label, n_sample, INX_KSVM_IJ(n_class,ci,cj), 0)), sizeof(int) * n_sample);
+      // 学習 - 学習失敗の場合はリトライする.
+      fail_count = 0;
+      do {
+        if (fail_count >= maxFailcount) {
+          fprintf(stderr, "Learning failed %d times at %d,%d classifier, give up \r\n", fail_count, ci, cj);
+          return MCSVM_NOT_LEARN;
+        }
+        status = SVM::learning();
+
+        if ( (status == SVM_NOT_CONVERGENCED)
+            || (status == SVM_DETECT_BAD_VAL) ) {
+          fail_count++;
+        }
+      } while (status != SVM_LEARN_SUCCESS);
+      // 学習結果の係数とSVラベルの取得
+      memcpy(&(MATRIX_AT(mc_alpha, n_sample, INX_KSVM_IJ(n_class,ci,cj), 0)), this->alpha, sizeof(float) * n_sample);
+    }
+  }
+
+  // 元のラベルを復帰
+  memcpy(this->label, tmp_label, sizeof(int) * n_sample);
+  delete [] tmp_label;
+  return MCSVM_LEARN_SUCCESS;
+
+}
+
+// 未知データのラベルを推定する.
+int MCSVM::predict_label(float* data) 
+{
+  // 単位ステップ関数による決定的識別
+  float net;
+  int* result_label_count = new int[n_class];
+
+  int tmp_ci, tmp_cj;
+  memset(result_label_count, 0, sizeof(int) * n_class);
+  for (int ci = 0; ci < n_class; ci++) {
+    for (int cj = ci + 1; cj < n_class; cj++) {
+
+      // インデックスをi < jに
+      tmp_ci = ci; tmp_cj = cj;
+      if ( ci > cj ) {
+        tmp_cj = ci; tmp_ci = cj;
+      }
+      // 係数とラベルを取得し,ci,cjを識別するSVMを構成
+      memcpy(this->alpha, &(MATRIX_AT(mc_alpha, n_sample, INX_KSVM_IJ(n_class,tmp_ci,tmp_cj), 0)), sizeof(float) * n_sample);
+      memcpy(this->label, &(MATRIX_AT(mc_label, n_sample, INX_KSVM_IJ(n_class,tmp_ci,tmp_cj), 0)), sizeof(float) * n_sample);
+
+      // 識別:識別されたクラスに投票.
+      net = SVM::predict_net(data);
+      //printf("ci:%d cj:%d >> net : %f \n", ci, cj, net);
+      if ( net < 0 ) {
+        result_label_count[ci]++;
+      } else if ( net >= 0 ) {
+        result_label_count[cj]++;
+      }
+
+    }
+    //printf("sum_net[%d] : %f \n", ci, sum_net[ci]);
+  }
+
+  // 判定:最大頻度のクラスに判定する.
+  int max,argmax;
+  max = 0;
+  for (int i = 0; i < n_class; i++) {
+    //printf("%d : %d \n", i, result_label_count[i]);
+    if ( result_label_count[i] > max ) {
+      max = result_label_count[i];
+      argmax = i;
+    }
+  }
+
+  delete [] result_label_count;
+  return argmax;
+
+}
+
+// 未知データの識別確率を推定する.
+float MCSVM::predict_probability(float* data) 
+{
+  // シグモイド関数による確率的識別
+  float prob;
+  float* result_label_prob = new float[n_class];
+  int tmp_ci, tmp_cj;
+  memset(result_label_count, 0, sizeof(int) * n_class);
+  for (int ci = 0; ci < n_class; ci++) {
+    for (int cj = ci + 1; cj < n_class; cj++) {
+
+      // インデックスをci < cjに : 負例はci, 正例はcj
+      tmp_ci = ci; tmp_cj = cj;
+      if ( ci > cj ) {
+        tmp_cj = ci; tmp_ci = cj;
+      }
+      // 係数とラベルを取得し,ci,cjを識別するSVMを構成
+      memcpy(this->alpha, &(MATRIX_AT(mc_alpha, n_sample, INX_KSVM_IJ(n_class,tmp_ci,tmp_cj), 0)), sizeof(float) * n_sample);
+      memcpy(this->label, &(MATRIX_AT(mc_label, n_sample, INX_KSVM_IJ(n_class,tmp_ci,tmp_cj), 0)), sizeof(float) * n_sample);
+
+      // 確率識別:確率の足し上げ
+      prob = SVM::predict_probability(data);
+      if ( prob > float(0.5) ) {
+          result_label_count[cj] += prob;
+      } else {
+          result_label_count[ci] += (1-prob);
+      }
+
+    }
+    //printf("sum_net[%d] : %f \n", ci, sum_net[ci]);
+  }
+
+  // 判定:最大確率和
+  // おそらくラベル識別との整合性は取れる...はず
+  float max = 0;
+  for (int i = 0; i < n_class; i++) {
+    //printf("%d : %d \n", i, result_label_count[i]);
+    if ( result_label_prob[i] > max ) {
+      max = result_label_prob[i];
+    }
+  }
+
+  delete [] result_label_prob;
+  // 平均確率を返す.
+  return (max / n_class);
+
+}
+
+// override
+float* MCSVM::get_alpha(void) {
+    return (float *)mc_alpha;
+}
+
+// override
+void MCSVM::set_alpha(float* mcalpha_data, int nsample, int nclass) {
+    if ( nsample != n_sample ) {
+        fprintf( stderr, " set_alpha : number of sample isn't match : n_samle= %d, arg= %d \r\n", n_sample, nsample);
+        return;
+    } else if ( nclass != n_class ) {
+        fprintf( stderr, " set_alpha : number of class isn't match : n_class= %d, nclass= %d \r\n", n_class, nclass);
+        return;
+    }
+    int nC2 = n_class * (n_class - 1)/2;
+    memcpy(mc_alpha, mcalpha_data, sizeof(float) * nsample * (n_class * (n_class - 1) / 2));
+    status = SVM_SET_ALPHA;
+}