本文整理了Java中water.fvec.Frame.hasNAs()方法的一些代码示例，展示了Frame.hasNAs()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。Frame.hasNAs()方法的具体详情如下：
包路径：water.fvec.Frame
类名称：Frame
方法名：hasNAs

Frame.hasNAs介绍

暂无

代码示例

代码示例来源：origin: h2oai/h2o-3

@Override
public void init(boolean expensive) {
 super.init(expensive);
 if (_parms._max_iterations < 1 || _parms._max_iterations > 1e6) {
  error("_max_iterations", "max_iterations must be between 1 and 1e6 inclusive");
 }
 if (_train == null) {
  return;
 }
 _ncolExp = hex.util.LinearAlgebraUtils.numColsExp(_train,_parms._use_all_factor_levels);
 // if (_ncolExp < 2) error("_train", "_train must have more than one column when categoricals are expanded");
 // TODO: Initialize _parms._k = min(ncolExp(_train), nrow(_train)) if not set
 int k_min = (int)Math.min(_ncolExp, _train.numRows());
 if (_parms._k < 1) {
  _parms._k = k_min;
  warn("_k", "_k is set to be "+k_min);
 } else if (_parms._k > k_min) {
  error("_k", "_k must be between 1 and " + k_min);
 }
 if (!_parms._use_all_factor_levels && _parms._pca_method == PCAParameters.Method.GLRM) {
  error("_use_all_factor_levels", "GLRM only implemented for _use_all_factor_levels = true");
 }
 if (_parms._pca_method != PCAParameters.Method.GLRM && expensive && error_count() == 0) {
  if (!(_train.hasNAs()) || _parms._impute_missing)  {
   checkMemoryFootPrint();  // perform memory check here if dataset contains no NAs or if impute_missing enabled
  }
 }
}

代码示例来源：origin: h2oai/h2o-3

@Override public void init(boolean expensive) {
 super.init(expensive);
 if (_parms._max_iterations < 1)
  error("_max_iterations", "max_iterations must be at least 1");
 if(_train == null) return;
  if (_callFromGLRM)  // when used to initialize GLRM, need to treat binary numeric columns with binary loss as numeric columns
   _ncolExp = _glrmModel._output._catOffsets[_glrmModel._output._catOffsets.length-1]+_glrmModel._output._nnums;
  else
   _ncolExp = LinearAlgebraUtils.numColsExp(_train,_parms._use_all_factor_levels);
 if (_ncolExp > MAX_COLS_EXPANDED) {
  warn("_train", "_train has " + _ncolExp + " columns when categoricals are expanded. " +
      "Algorithm may be slow.");
 }
 if(_parms._nv < 1 || _parms._nv > _ncolExp)
  error("_nv", "Number of right singular values must be between 1 and " + _ncolExp);
 if (expensive && error_count() == 0) {
  if (!(_train.hasNAs()) || _parms._impute_missing)  {
   checkMemoryFootPrint();  // perform memory check here if dataset contains no NAs or if impute_missing enabled
  }
 }
}

代码示例来源：origin: h2oai/h2o-3

boolean frameHasNas = tranRebalanced.hasNAs();

代码示例来源：origin: h2oai/h2o-3

if ((!_parms._impute_missing) && tranRebalanced.hasNAs()) { // remove NAs rows
 tinfo = new DataInfo(_train, _valid, 0, _parms._use_all_factor_levels, _parms._transform,
     DataInfo.TransformType.NONE, /* skipMissing */ !_parms._impute_missing, /* imputeMissing */
DKV.put(dinfo._key, dinfo);
if (!_parms._impute_missing && tranRebalanced.hasNAs()) {

代码示例来源：origin: h2oai/h2o-3

_state = new ComputationState(_job, _parms, _dinfo, null, nclasses());
boolean skippingRows = (_parms._missing_values_handling == MissingValuesHandling.Skip && _train.hasNAs());
if (hasWeightCol() || skippingRows) { // need to re-compute means and sd
 boolean setWeights = skippingRows;// && _parms._lambda_search && _parms._alpha[0] > 0;

代码示例来源：origin: ai.h2o/h2o-algos

@Override public void init(boolean expensive) {
 super.init(expensive);
 if (_parms._max_iterations < 1)
  error("_max_iterations", "max_iterations must be at least 1");
 if(_train == null) return;
  if (_callFromGLRM)  // when used to initialize GLRM, need to treat binary numeric columns with binary loss as numeric columns
   _ncolExp = _glrmModel._output._catOffsets[_glrmModel._output._catOffsets.length-1]+_glrmModel._output._nnums;
  else
   _ncolExp = LinearAlgebraUtils.numColsExp(_train,_parms._use_all_factor_levels);
 if (_ncolExp > MAX_COLS_EXPANDED) {
  warn("_train", "_train has " + _ncolExp + " columns when categoricals are expanded. " +
      "Algorithm may be slow.");
 }
 if(_parms._nv < 1 || _parms._nv > _ncolExp)
  error("_nv", "Number of right singular values must be between 1 and " + _ncolExp);
 if (expensive && error_count() == 0) {
  if (!(_train.hasNAs()) || _parms._impute_missing)  {
   checkMemoryFootPrint();  // perform memory check here if dataset contains no NAs or if impute_missing enabled
  }
 }
}

代码示例来源：origin: ai.h2o/h2o-algos

@Override
public void init(boolean expensive) {
 super.init(expensive);
 if (_parms._max_iterations < 1 || _parms._max_iterations > 1e6) {
  error("_max_iterations", "max_iterations must be between 1 and 1e6 inclusive");
 }
 if (_train == null) {
  return;
 }
 _ncolExp = hex.util.LinearAlgebraUtils.numColsExp(_train,_parms._use_all_factor_levels);
 // if (_ncolExp < 2) error("_train", "_train must have more than one column when categoricals are expanded");
 // TODO: Initialize _parms._k = min(ncolExp(_train), nrow(_train)) if not set
 int k_min = (int)Math.min(_ncolExp, _train.numRows());
 if (_parms._k < 1) {
  _parms._k = k_min;
  warn("_k", "_k is set to be "+k_min);
 } else if (_parms._k > k_min) {
  error("_k", "_k must be between 1 and " + k_min);
 }
 if (!_parms._use_all_factor_levels && _parms._pca_method == PCAParameters.Method.GLRM) {
  error("_use_all_factor_levels", "GLRM only implemented for _use_all_factor_levels = true");
 }
 if (_parms._pca_method != PCAParameters.Method.GLRM && expensive && error_count() == 0) {
  if (!(_train.hasNAs()) || _parms._impute_missing)  {
   checkMemoryFootPrint();  // perform memory check here if dataset contains no NAs or if impute_missing enabled
  }
 }
}

代码示例来源：origin: ai.h2o/sparkling-water-ml

if (user_points.hasNAs()) {
  error("_initial_weights", "Initial weights cannot contain missing values.");

代码示例来源：origin: ai.h2o/h2o-algos

boolean frameHasNas = tranRebalanced.hasNAs();

代码示例来源：origin: ai.h2o/h2o-algos

if ((!_parms._impute_missing) && tranRebalanced.hasNAs()) { // remove NAs rows
 tinfo = new DataInfo(_train, _valid, 0, _parms._use_all_factor_levels, _parms._transform,
     DataInfo.TransformType.NONE, /* skipMissing */ !_parms._impute_missing, /* imputeMissing */
DKV.put(dinfo._key, dinfo);
if (!_parms._impute_missing && tranRebalanced.hasNAs()) {

代码示例来源：origin: ai.h2o/h2o-algos

_state = new ComputationState(_job, _parms, _dinfo, null, nclasses());
boolean skippingRows = (_parms._missing_values_handling == MissingValuesHandling.Skip && _train.hasNAs());
if (hasWeightCol() || skippingRows) { // need to re-compute means and sd
 boolean setWeights = skippingRows;// && _parms._lambda_search && _parms._alpha[0] > 0;