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C++ Metric::print方法代码示例

本文整理汇总了C++中Metric::print方法的典型用法代码示例。如果您正苦于以下问题：C++ Metric::print方法的具体用法？C++ Metric::print怎么用？C++ Metric::print使用的例子？那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类Metric的用法示例。


在下文中一共展示了Metric::print方法的4个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的C++代码示例。

示例1: train


//.........这里部分代码省略.........
      eval.overall_label_count += m_classifier._eval.overall_label_count;
      eval.correct_label_count += m_classifier._eval.correct_label_count;

      if ((curUpdateIter + 1) % m_options.verboseIter == 0) {
        //m_classifier.checkgrads(subExamples, curUpdateIter+1);
        std::cout << "current: " << updateIter + 1 << ", total block: " << batchBlock << std::endl;
        std::cout << "Cost = " << cost << ", Tag Correct(%) = " << eval.getAccuracy() << std::endl;
      }
      m_classifier.updateParams(m_options.regParameter, m_options.adaAlpha, m_options.adaEps);

    }

    if (devNum > 0) {
      bCurIterBetter = false;
      if (!m_options.outBest.empty())
        decodeInstResults.clear();
      metric_dev.reset();
      for (int idx = 0; idx < devExamples.size(); idx++) {
        vector<string> result_labels;
        predict(devExamples[idx].m_features, result_labels, devInsts[idx].words);

        if (m_options.seg)
          devInsts[idx].SegEvaluate(result_labels, metric_dev);
        else
          devInsts[idx].Evaluate(result_labels, metric_dev);

        if (!m_options.outBest.empty()) {
          curDecodeInst.copyValuesFrom(devInsts[idx]);
          curDecodeInst.assignLabel(result_labels);
          decodeInstResults.push_back(curDecodeInst);
        }
      }

      metric_dev.print();

      if (!m_options.outBest.empty() && metric_dev.getAccuracy() > bestDIS) {
        m_pipe.outputAllInstances(devFile + m_options.outBest, decodeInstResults);
        bCurIterBetter = true;
      }

      if (testNum > 0) {
        if (!m_options.outBest.empty())
          decodeInstResults.clear();
        metric_test.reset();
        for (int idx = 0; idx < testExamples.size(); idx++) {
          vector<string> result_labels;
          predict(testExamples[idx].m_features, result_labels, testInsts[idx].words);

          if (m_options.seg)
            testInsts[idx].SegEvaluate(result_labels, metric_test);
          else
            testInsts[idx].Evaluate(result_labels, metric_test);

          if (bCurIterBetter && !m_options.outBest.empty()) {
            curDecodeInst.copyValuesFrom(testInsts[idx]);
            curDecodeInst.assignLabel(result_labels);
            decodeInstResults.push_back(curDecodeInst);
          }
        }
        std::cout << "test:" << std::endl;
        metric_test.print();

        if (!m_options.outBest.empty() && bCurIterBetter) {
          m_pipe.outputAllInstances(testFile + m_options.outBest, decodeInstResults);
        }
      }
开发者ID:LeonCrashCode，项目名称:NNSegmentation，代码行数:67，代码来源:SparseLabeler.cpp

示例2: train


//.........这里部分代码省略.........
	      eval.overall_label_count += m_classifier._eval.overall_label_count;
	      eval.correct_label_count += m_classifier._eval.correct_label_count;
	
	      if ((idy + 1) % (m_options.verboseIter*10) == 0) {
	        std::cout << "current: " << idy + 1 << ", Cost = " << cost << ", Correct(%) = " << eval.getAccuracy() << std::endl;
	      }
	      m_classifier.updateParams(m_options.regParameter, m_options.adaAlpha, m_options.adaEps, m_options.clip);
	    }
	    std::cout << "current: " << iter + 1  << ", Correct(%) = " << eval.getAccuracy() << std::endl;
    }
    else{
    	if(iter == 0)eval.reset();
  		subInstances.clear();
      subInstGoldActions.clear();
    	for (int idy = 0; idy < m_options.batchSize; idy++) {
	      subInstances.push_back(trainInsts[indexes[idy]].chars);
	      subInstGoldActions.push_back(trainInstGoldactions[indexes[idy]]);    		
    	}
      double cost = m_classifier.train(subInstances, subInstGoldActions);

      eval.overall_label_count += m_classifier._eval.overall_label_count;
      eval.correct_label_count += m_classifier._eval.correct_label_count;
      
      if ((iter + 1) % (m_options.verboseIter) == 0) {
      	std::cout << "current: " << iter + 1 << ", Cost = " << cost << ", Correct(%) = " << eval.getAccuracy() << std::endl;
      	eval.reset();
      	bEvaluate = true;
      }
      
      m_classifier.updateParams(m_options.regParameter, m_options.adaAlpha, m_options.adaEps, m_options.clip);
    }
    
    if (bEvaluate && devNum > 0) {
      bCurIterBetter = false;
      if (!m_options.outBest.empty())
        decodeInstResults.clear();
      metric_dev.reset();
      for (int idx = 0; idx < devInsts.size(); idx++) {
        predict(devInsts[idx], curDecodeInst);
        devInsts[idx].evaluate(curDecodeInst, metric_dev);
        if (!m_options.outBest.empty()) {
          decodeInstResults.push_back(curDecodeInst);
        }
      }
      std::cout << "dev:" << std::endl;
      metric_dev.print();

      if (!m_options.outBest.empty() && metric_dev.getAccuracy() > bestFmeasure) {
        m_pipe.outputAllInstances(devFile + m_options.outBest, decodeInstResults);
        bCurIterBetter = true;
      }

      if (testNum > 0) {
        if (!m_options.outBest.empty())
          decodeInstResults.clear();
        metric_test.reset();
        for (int idx = 0; idx < testInsts.size(); idx++) {
          predict(testInsts[idx], curDecodeInst);
          testInsts[idx].evaluate(curDecodeInst, metric_test);
          if (bCurIterBetter && !m_options.outBest.empty()) {
            decodeInstResults.push_back(curDecodeInst);
          }
        }
        std::cout << "test:" << std::endl;
        metric_test.print();

        if (!m_options.outBest.empty() && bCurIterBetter) {
          m_pipe.outputAllInstances(testFile + m_options.outBest, decodeInstResults);
        }
      }

      for (int idx = 0; idx < otherInsts.size(); idx++) {
        std::cout << "processing " << m_options.testFiles[idx] << std::endl;
        if (!m_options.outBest.empty())
          decodeInstResults.clear();
        metric_test.reset();
        for (int idy = 0; idy < otherInsts[idx].size(); idy++) {
          predict(otherInsts[idx][idy], curDecodeInst);
          otherInsts[idx][idy].evaluate(curDecodeInst, metric_test);
          if (bCurIterBetter && !m_options.outBest.empty()) {
            decodeInstResults.push_back(curDecodeInst);
          }
        }
        std::cout << "test:" << std::endl;
        metric_test.print();

        if (!m_options.outBest.empty() && bCurIterBetter) {
          m_pipe.outputAllInstances(m_options.testFiles[idx] + m_options.outBest, decodeInstResults);
        }
      }


      if (m_options.saveIntermediate && metric_dev.getAccuracy() > bestFmeasure) {
        std::cout << "Exceeds best previous DIS of " << bestFmeasure << ". Saving model file.." << std::endl;
        bestFmeasure = metric_dev.getAccuracy();
        writeModelFile(modelFile);
      }
    }
  }
}
开发者ID:LinguList，项目名称:NNTransitionSegmentor，代码行数:101，代码来源:LSTMNCSegmentor.cpp

示例3: train


//.........这里部分代码省略.........
		}
		else {
			if (iter == 0)
				eval.reset();
			subInstances.clear();
			subInstGoldActions.clear();
			for (int idy = 0; idy < m_options.batchSize; idy++) {
				subInstances.push_back(trainInsts[indexes[idy]]);
				subInstGoldActions.push_back(trainInstGoldactions[indexes[idy]]);
			}
			double cost = m_classifier.train(subInstances, subInstGoldActions);

			eval.overall_label_count += m_classifier._eval.overall_label_count;
			eval.correct_label_count += m_classifier._eval.correct_label_count;

			if ((iter + 1) % (m_options.verboseIter) == 0) {
				std::cout << "current: " << iter + 1 << ", Cost = " << cost << ", Correct(%) = " << eval.getAccuracy() << std::endl;
				eval.reset();
				bEvaluate = true;
			}

			m_classifier.updateParams(m_options.regParameter, m_options.adaAlpha, m_options.adaEps);
		}

		if (bEvaluate && devNum > 0) {
			bCurIterBetter = false;
			if (!m_options.outBest.empty())
				decodeInstResults.clear();
			segMetric_dev.reset();
			postagMetric_dev.reset();
			for (int idx = 0; idx < devInsts.size(); idx++) {
				predict(devInsts[idx], curDecodeInst);
				devInsts[idx].evaluate(curDecodeInst, segMetric_dev, postagMetric_dev);
				if (!m_options.outBest.empty()) {
					decodeInstResults.push_back(curDecodeInst);
				}
			}
			std::cout << "dev:" << std::endl << "Seg: ";
			segMetric_dev.print();
			std::cout << "Postag: ";
			postagMetric_dev.print();

			if (!m_options.outBest.empty() && postagMetric_dev.getAccuracy() > bestPostagFmeasure) {
				m_pipe.outputAllInstances(devFile + m_options.outBest, decodeInstResults);
				bCurIterBetter = true;
			}

			if (testNum > 0) {
				if (!m_options.outBest.empty())
					decodeInstResults.clear();
				segMetric_test.reset();
				postagMetric_test.reset();
				for (int idx = 0; idx < testInsts.size(); idx++) {
					predict(testInsts[idx], curDecodeInst);
					testInsts[idx].evaluate(curDecodeInst, segMetric_test, postagMetric_test);
					if (bCurIterBetter && !m_options.outBest.empty()) {
						decodeInstResults.push_back(curDecodeInst);
					}
				}
				std::cout << "test:" << std::endl << "Seg: ";
				segMetric_test.print();
				std::cout << "Postag: ";
				postagMetric_test.print();

				if (!m_options.outBest.empty() && bCurIterBetter) {
					m_pipe.outputAllInstances(testFile + m_options.outBest, decodeInstResults);
				}
			}

			for (int idx = 0; idx < otherInsts.size(); idx++) {
				std::cout << "processing " << m_options.testFiles[idx] << std::endl;
				if (!m_options.outBest.empty())
					decodeInstResults.clear();
				segMetric_test.reset();
				postagMetric_test.reset();
				for (int idy = 0; idy < otherInsts[idx].size(); idy++) {
					predict(otherInsts[idx][idy], curDecodeInst);
					otherInsts[idx][idy].evaluate(curDecodeInst, segMetric_test, postagMetric_test);
					if (bCurIterBetter && !m_options.outBest.empty()) {
						decodeInstResults.push_back(curDecodeInst);
					}
				}
				std::cout << "test:" << std::endl << "Seg: ";
				segMetric_test.print();
				std::cout << "Postag: ";
				postagMetric_test.print();

				if (!m_options.outBest.empty() && bCurIterBetter) {
					m_pipe.outputAllInstances(m_options.testFiles[idx] + m_options.outBest, decodeInstResults);
				}
			}

			if (m_options.saveIntermediate && postagMetric_dev.getAccuracy() > bestPostagFmeasure) {
				std::cout << "Exceeds best previous DIS of " << bestPostagFmeasure << ". Saving model file.." << std::endl;
				bestPostagFmeasure = postagMetric_dev.getAccuracy();
				writeModelFile(modelFile);
			}
		}
	}
}
开发者ID:zhangmeishan，项目名称:NNTransitionPOSTagging，代码行数:101，代码来源:LinearSegmentor.cpp

示例4: train


//.........这里部分代码省略.........
        subExamples.clear();
        for (int idy = 0; idy < m_options.batchSize; idy++) {
          subExamples.push_back(trainExamples[indexes[idy]]);
        }
        int curUpdateIter = iter * m_options.verboseIter + updateIter;
        cost += m_classifier.process(subExamples, curUpdateIter);
        //m_classifier.checkgrads(subExamples, curUpdateIter);
        eval.overall_label_count += m_classifier._eval.overall_label_count;
        eval.correct_label_count += m_classifier._eval.correct_label_count;

        m_classifier.updateParams(m_options.regParameter, m_options.adaAlpha, m_options.adaEps);
      }
      std::cout << "current iter: " << iter + 1 << ", total iter: " << maxIter << std::endl;
      std::cout << "Cost = " << cost << ", SA Correct(%) = " << eval.getAccuracy() << std::endl;
    }

    if (devNum > 0) {
      bCurIterBetter = false;
      if (!m_options.outBest.empty())
        decodeInstResults.clear();
      metric_dev.reset();
      for (int idx = 0; idx < devExamples.size(); idx++) {
        string result_label;
        double confidence = predict(devExamples[idx].m_features, result_label);

        devInsts[idx].Evaluate(result_label, metric_dev);

        if (!m_options.outBest.empty()) {
          curDecodeInst.copyValuesFrom(devInsts[idx]);
          curDecodeInst.assignLabel(result_label, confidence);
          decodeInstResults.push_back(curDecodeInst);
        }
      }
      metric_dev.print();

      if ((!m_options.outBest.empty() && metric_dev.getAccuracy() > bestDIS)) {
        m_pipe.outputAllInstances(devFile + m_options.outBest, decodeInstResults);
        bCurIterBetter = true;
      }

      if (testNum > 0) {
        if (!m_options.outBest.empty())
          decodeInstResults.clear();
        metric_test.reset();
        for (int idx = 0; idx < testExamples.size(); idx++) {
          string result_label;
          double confidence = predict(testExamples[idx].m_features, result_label);
          testInsts[idx].Evaluate(result_label, metric_test);

          if (bCurIterBetter && !m_options.outBest.empty()) {
            curDecodeInst.copyValuesFrom(testInsts[idx]);
            curDecodeInst.assignLabel(result_label, confidence);
            decodeInstResults.push_back(curDecodeInst);
          }
        }
        std::cout << "test:" << std::endl;
        metric_test.print();

        if ((!m_options.outBest.empty() && bCurIterBetter)) {
          m_pipe.outputAllInstances(testFile + m_options.outBest, decodeInstResults);
        }
      }

      for (int idx = 0; idx < otherExamples.size(); idx++) {
        std::cout << "processing " << m_options.testFiles[idx] << std::endl;
        if (!m_options.outBest.empty())
开发者ID:SherlockYangBing，项目名称:TitleCaption，代码行数:67，代码来源:HCharDetector.cpp


注：本文中的Metric::print方法示例由纯净天空整理自Github/MSDocs等开源代码及文档管理平台，相关代码片段筛选自各路编程大神贡献的开源项目，源码版权归原作者所有，传播和使用请参考对应项目的License；未经允许，请勿转载。