water.H2O.technote()方法的使用及代码示例

x33g5p2x  于2022-01-20 转载在 其他  
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本文整理了Java中water.H2O.technote()方法的一些代码示例，展示了H2O.technote()的具体用法。这些代码示例主要来源于Github/Stackoverflow/Maven等平台，是从一些精选项目中提取出来的代码，具有较强的参考意义，能在一定程度帮忙到你。H2O.technote()方法的具体详情如下：
包路径：water.H2O
类名称：H2O
方法名：technote

H2O.technote介绍

暂无

代码示例

代码示例来源：origin: h2oai/h2o-3

public DeepwaterMojoWriter(DeepWaterModel model) {
 super(model);
 _parms = model.get_params();
 _model_info = model.model_info();
 _output = model._output;
 if (_model_info._unstable) {
  throw new UnsupportedOperationException(technote(4, "Refusing to create a MOJO for an unstable model."));
 }
}

代码示例来源：origin: h2oai/h2o-3

public DeepLearningMojoWriter(DeepLearningModel model) {
 super(model);
 _parms = model.get_params();
 _model_info = model.model_info();
 _output = model._output;
 if (_model_info.isUnstable()) { // do not generate mojo for unstable model
  throw new UnsupportedOperationException(technote(4, "Refusing to create a MOJO for an unstable model."));
 }
}

代码示例来源：origin: h2oai/h2o-3

case bernoulli:
 if( _nclass != 2 /*&& !couldBeBool(_response)*/)
  error("_distribution", H2O.technote(2, "Binomial requires the response to be a 2-class categorical"));
 break;
case quasibinomial:
 if ( !_response.isNumeric() )
  error("_distribution", H2O.technote(2, "Quasibinomial requires the response to be numeric."));
 if ( _nclass != 2)
  error("_distribution", H2O.technote(2, "Quasibinomial requires the response to be binary."));
 break;
case modified_huber:
 if( _nclass != 2 /*&& !couldBeBool(_response)*/)
  error("_distribution", H2O.technote(2, "Modified Huber requires the response to be a 2-class categorical."));
 break;
case multinomial:
 if (!isClassifier()) error("_distribution", H2O.technote(2, "Multinomial requires an categorical response."));
 break;
case huber:
 if (isClassifier()) error("_distribution", H2O.technote(2, "Huber requires the response to be numeric."));
 break;
case poisson:
 if (isClassifier()) error("_distribution", H2O.technote(2, "Poisson requires the response to be numeric."));
 break;
case gamma:
 if (isClassifier()) error("_distribution", H2O.technote(2, "Gamma requires the response to be numeric."));
 break;
case tweedie:
 if (isClassifier()) error("_distribution", H2O.technote(2, "Tweedie requires the response to be numeric."));
 break;

代码示例来源：origin: h2oai/h2o-3

throw new IllegalArgumentException(technote(5, "Model is too large to fit into the DKV (larger than " + PrettyPrint.bytes(Value.MAX) + ")."));

代码示例来源：origin: h2oai/h2o-3

if(_response != null) {
 if(!isClassifier() && _response.isCategorical())
  error("_response", H2O.technote(2, "Regression requires numeric response, got categorical."));
 if ((_parms._solver.equals(Solver.GRADIENT_DESCENT_LH) || _parms._solver.equals(Solver.GRADIENT_DESCENT_SQERR)) && !_parms._family.equals(Family.ordinal))
  error("_solver", "Solvers GRADIENT_DESCENT_LH and GRADIENT_DESCENT_SQERR are only " +
  case binomial:
   if (!_response.isBinary() && _nclass != 2)
    error("_family", H2O.technote(2, "Binomial requires the response to be a 2-class categorical or a binary column (0/1)"));
   break;
  case multinomial:
   if (_nclass <= 2)
    error("_family", H2O.technote(2, "Multinomial requires a categorical response with at least 3 levels (for 2 class problem use family=binomial."));
   break;
  case poisson:
   break;
  case gamma:
   if (_nclass != 1) error("_distribution", H2O.technote(2, "Gamma requires the response to be numeric."));
   if (_response.min() <= 0) error("_family", "Response value for gamma distribution must be greater than 0.");
   break;
  case tweedie:
   if (_nclass != 1) error("_family", H2O.technote(2, "Tweedie requires the response to be numeric."));
   break;
  case quasibinomial:
   if (_nclass != 1) error("_family", H2O.technote(2, "Quasi_binomial requires the response to be numeric."));
   break;
  case ordinal:
   if (_nclass <= 2)

代码示例来源：origin: h2oai/h2o-3

throw new IllegalArgumentException(technote(5, "Model is too large"));

代码示例来源：origin: h2oai/h2o-3

dl.error("_loss", "Cannot use CrossEntropy loss for auto-encoder.");
if (!classification && _loss == CrossEntropy)
 dl.error("_loss", technote(2, "For CrossEntropy loss, the response must be categorical."));
 case gamma:
 case poisson:
  dl.error("_distribution", technote(2, _distribution  + " distribution is not allowed for classification."));
  break;
 case AUTO:
 case bernoulli:
 case modified_huber:
  dl.error("_distribution", technote(2, _distribution  + " distribution is not allowed for regression."));
  break;
 case tweedie:

代码示例来源：origin: ai.h2o/h2o-algos

public DeepwaterMojoWriter(DeepWaterModel model) {
 super(model);
 _parms = model.get_params();
 _model_info = model.model_info();
 _output = model._output;
 if (_model_info._unstable) {
  throw new UnsupportedOperationException(technote(4, "Refusing to create a MOJO for an unstable model."));
 }
}

代码示例来源：origin: ai.h2o/h2o-algos

public DeepLearningMojoWriter(DeepLearningModel model) {
 super(model);
 _parms = model.get_params();
 _model_info = model.model_info();
 _output = model._output;
 if (_model_info.isUnstable()) { // do not generate mojo for unstable model
  throw new UnsupportedOperationException(technote(4, "Refusing to create a MOJO for an unstable model."));
 }
}

代码示例来源：origin: ai.h2o/h2o-algos

case bernoulli:
 if( _nclass != 2 /*&& !couldBeBool(_response)*/)
  error("_distribution", H2O.technote(2, "Binomial requires the response to be a 2-class categorical"));
 break;
case quasibinomial:
 if ( !_response.isNumeric() )
  error("_distribution", H2O.technote(2, "Quasibinomial requires the response to be numeric."));
 if ( _nclass != 2)
  error("_distribution", H2O.technote(2, "Quasibinomial requires the response to be binary."));
 break;
case modified_huber:
 if( _nclass != 2 /*&& !couldBeBool(_response)*/)
  error("_distribution", H2O.technote(2, "Modified Huber requires the response to be a 2-class categorical."));
 break;
case multinomial:
 if (!isClassifier()) error("_distribution", H2O.technote(2, "Multinomial requires an categorical response."));
 break;
case huber:
 if (isClassifier()) error("_distribution", H2O.technote(2, "Huber requires the response to be numeric."));
 break;
case poisson:
 if (isClassifier()) error("_distribution", H2O.technote(2, "Poisson requires the response to be numeric."));
 break;
case gamma:
 if (isClassifier()) error("_distribution", H2O.technote(2, "Gamma requires the response to be numeric."));
 break;
case tweedie:
 if (isClassifier()) error("_distribution", H2O.technote(2, "Tweedie requires the response to be numeric."));
 break;

代码示例来源：origin: ai.h2o/h2o-ext-xgboost

throw new IllegalArgumentException(technote(11,
  "Data is too large to fit into the 32-bit Java float[] array that needs to be passed to the XGBoost C++ backend. Use H2O GBM instead."));

代码示例来源：origin: ai.h2o/h2o-ext-xgboost

case bernoulli:
 if( _nclass != 2 /*&& !couldBeBool(_response)*/)
  error("_distribution", technote(2, "Binomial requires the response to be a 2-class categorical"));
 break;
case modified_huber:
 if( _nclass != 2 /*&& !couldBeBool(_response)*/)
  error("_distribution", technote(2, "Modified Huber requires the response to be a 2-class categorical."));
 break;
case multinomial:
 if (!isClassifier()) error("_distribution", technote(2, "Multinomial requires an categorical response."));
 break;
case huber:
 if (isClassifier()) error("_distribution", technote(2, "Huber requires the response to be numeric."));
 break;
case poisson:
 if (isClassifier()) error("_distribution", technote(2, "Poisson requires the response to be numeric."));
 break;
case gamma:
 if (isClassifier()) error("_distribution", technote(2, "Gamma requires the response to be numeric."));
 break;
case tweedie:
 if (isClassifier()) error("_distribution", technote(2, "Tweedie requires the response to be numeric."));
 break;
case gaussian:
 if (isClassifier()) error("_distribution", technote(2, "Gaussian requires the response to be numeric."));
 break;
case laplace:
 if (isClassifier()) error("_distribution", technote(2, "Laplace requires the response to be numeric."));
 break;

代码示例来源：origin: ai.h2o/h2o-algos

throw new IllegalArgumentException(technote(5, "Model is too large to fit into the DKV (larger than " + PrettyPrint.bytes(Value.MAX) + ")."));

代码示例来源：origin: ai.h2o/h2o-algos

throw new IllegalArgumentException(technote(5, "Model is too large"));

代码示例来源：origin: ai.h2o/h2o-algos

if(_response != null) {
 if(!isClassifier() && _response.isCategorical())
  error("_response", H2O.technote(2, "Regression requires numeric response, got categorical."));
 if ((_parms._solver.equals(Solver.GRADIENT_DESCENT_LH) || _parms._solver.equals(Solver.GRADIENT_DESCENT_SQERR)) && !_parms._family.equals(Family.ordinal))
  error("_solver", "Solvers GRADIENT_DESCENT_LH and GRADIENT_DESCENT_SQERR are only " +
  case binomial:
   if (!_response.isBinary() && _nclass != 2)
    error("_family", H2O.technote(2, "Binomial requires the response to be a 2-class categorical or a binary column (0/1)"));
   break;
  case multinomial:
   if (_nclass <= 2)
    error("_family", H2O.technote(2, "Multinomial requires a categorical response with at least 3 levels (for 2 class problem use family=binomial."));
   break;
  case poisson:
   break;
  case gamma:
   if (_nclass != 1) error("_distribution", H2O.technote(2, "Gamma requires the response to be numeric."));
   if (_response.min() <= 0) error("_family", "Response value for gamma distribution must be greater than 0.");
   break;
  case tweedie:
   if (_nclass != 1) error("_family", H2O.technote(2, "Tweedie requires the response to be numeric."));
   break;
  case quasibinomial:
   if (_nclass != 1) error("_family", H2O.technote(2, "Quasi_binomial requires the response to be numeric."));
   break;
  case ordinal:
   if (_nclass <= 2)

代码示例来源：origin: ai.h2o/h2o-algos

dl.error("_loss", "Cannot use CrossEntropy loss for auto-encoder.");
if (!classification && _loss == CrossEntropy)
 dl.error("_loss", technote(2, "For CrossEntropy loss, the response must be categorical."));
 case gamma:
 case poisson:
  dl.error("_distribution", technote(2, _distribution  + " distribution is not allowed for classification."));
  break;
 case AUTO:
 case bernoulli:
 case modified_huber:
  dl.error("_distribution", technote(2, _distribution  + " distribution is not allowed for regression."));
  break;
 case tweedie:

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