water.fvec.Frame.clone()方法的使用及代码示例

x33g5p2x  于2022-01-19 转载在 其他  
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本文整理了Java中water.fvec.Frame.clone()方法的一些代码示例，展示了Frame.clone()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.clone()方法的具体详情如下：
包路径：water.fvec.Frame
类名称：Frame
方法名：clone

Frame.clone介绍

暂无

代码示例

代码示例来源：origin: h2oai/h2o-3

fr.clone(),  // train
fr.clone(),  // train

代码示例来源：origin: h2oai/h2o-3

try {
 dinfo = new DataInfo(
     fr.clone(),  // train

代码示例来源：origin: h2oai/h2o-3

@Test public void testAirlines1() { // just test that it works at all
 Frame fr = parse_test_file(Key.make("a.hex"), "smalldata/airlines/allyears2k_headers.zip");
 try {
  DataInfo dinfo = new DataInfo(
      fr.clone(),  // train
      null,        // valid
      1,           // num responses
      true,        // use all factor levels
      DataInfo.TransformType.STANDARDIZE,  // predictor transform
      DataInfo.TransformType.NONE,         // response  transform
      true,        // skip missing
      false,       // impute missing
      false,       // missing bucket
      false,       // weight
      false,       // offset
      false,       // fold
      Model.InteractionSpec.allPairwise(new String[]{fr.name(8),fr.name(16),fr.name(2)})  // interactions
  );
  dinfo.dropInteractions();
  dinfo.remove();
 } finally {
  fr.delete();
 }
}

代码示例来源：origin: h2oai/h2o-3

fr.clone(),  // train
fr.clone(),  // train

代码示例来源：origin: h2oai/h2o-3

model = new GLM(params).trainModel().get();
Scope.track_generic(model);
DataInfo tinfo = new DataInfo(train.clone(), null, 0, true, DataInfo.TransformType.STANDARDIZE,
    DataInfo.TransformType.NONE, false, false, false,
    /* weights */ false, /* offset */ false, /* fold */ false);

代码示例来源：origin: h2oai/h2o-2

new GLM2("GLM offset test on prostate.", Key.make(), modelKey, new GLM2.Source((Frame)fr.clone(), fr.vec("CAPSULE"), true, true), Family.binomial).setNonNegative(false).setRegularization(new double[]{1},new double[]{0.001607}).setBetaConstraints(betaConstraints).doInit().fork().get(); //.setHighAccuracy().doInit().fork().get();
model = DKV.get(modelKey).get();
Assert.assertTrue(model.get_params().state == Job.JobState.DONE);
new GLM2("GLM offset test on prostate.", Key.make(), modelKey, new GLM2.Source((Frame)fr.clone(), fr.vec("CAPSULE"), true, true), Family.binomial).setNonNegative(false).setRegularization(new double[]{1},new double[]{0.001607}).setBetaConstraints(betaConstraints).doInit().fork().get(); //.setHighAccuracy().doInit().fork().get();
model = DKV.get(modelKey).get();
System.out.println(model.coefficients());
betaConstraints = ParseDataset2.parse(parsed, new Key[]{betaConsKey});
try {
 new GLM2("GLM offset test on prostate.", Key.make(), modelKey, new GLM2.Source((Frame) fr.clone(), fr.vec("CAPSULE"), true, true), Family.binomial).setNonNegative(false).setRegularization(new double[]{1}, new double[]{0.001607}).setBetaConstraints(betaConstraints).doInit().fork().get(); //.setHighAccuracy().doInit().fork().get();
 assertTrue("should've thrown",false);
} catch(IllegalArgumentException t) {
betaConstraints = ParseDataset2.parse(parsed, new Key[]{betaConsKey});
try {
 new GLM2("GLM offset test on prostate.", Key.make(), modelKey, new GLM2.Source((Frame) fr.clone(), fr.vec("CAPSULE"), true, true), Family.binomial).setNonNegative(false).setRegularization(new double[]{1}, new double[]{0.001607}).setBetaConstraints(betaConstraints).doInit().fork().get(); //.setHighAccuracy().doInit().fork().get();
 assertTrue("should've thrown",false);
} catch(IllegalArgumentException t) {
betaConstraints = ParseDataset2.parse(parsed, new Key[]{betaConsKey});
try {
 new GLM2("GLM offset test on prostate.", Key.make(), modelKey, new GLM2.Source((Frame) fr.clone(), fr.vec("CAPSULE"), true, true), Family.binomial).setNonNegative(false).setRegularization(new double[]{1}, new double[]{0.001607}).setBetaConstraints(betaConstraints).doInit().fork().get(); //.setHighAccuracy().doInit().fork().get();
 assertTrue("should've thrown",false);
} catch(IllegalArgumentException t) {
betaConstraints = ParseDataset2.parse(parsed, new Key[]{betaConsKey});
try {
 new GLM2("GLM offset test on prostate.", Key.make(), modelKey, new GLM2.Source((Frame) fr.clone(), fr.vec("CAPSULE"), true, true), Family.binomial).setNonNegative(false).setRegularization(new double[]{1}, new double[]{0.001607}).setBetaConstraints(betaConstraints).doInit().fork().get(); //.setHighAccuracy().doInit().fork().get();

代码示例来源：origin: h2oai/h2o-3

@Test public void testIris1() {  // test that getting sparseRows and denseRows produce the same results
 Frame fr = parse_test_file(Key.make("a.hex"), "smalldata/iris/iris_wheader.csv");
 fr.swap(1,4);
 Model.InteractionPair[] ips = Model.InteractionPair.generatePairwiseInteractionsFromList(0, 1);
 DataInfo di=null;
 try {
  di = new DataInfo(
      fr.clone(),  // train
      null,        // valid
      1,           // num responses
      true,        // use all factor levels
      DataInfo.TransformType.NONE,  // predictor transform
      DataInfo.TransformType.NONE,  // response  transform
      true,        // skip missing
      false,       // impute missing
      false,       // missing bucket
      false,       // weight
      false,       // offset
      false,       // fold
      Model.InteractionSpec.allPairwise(new String[]{fr.name(0),fr.name(1)})          // interactions
  );
  checker(di,false);
 } finally {
  fr.delete();
  if( di!=null ) {
   di.dropInteractions();
   di.remove();
  }
 }
}

代码示例来源：origin: h2oai/h2o-3

);
DataInfo dinfo = new DataInfo(
    fr.clone(),  // train

代码示例来源：origin: h2oai/h2o-3

@Test public void testIris2() {  // test that getting sparseRows and denseRows produce the same results
 Frame fr = parse_test_file(Key.make("a.hex"), "smalldata/iris/iris_wheader.csv");
 fr.swap(1,4);
 Model.InteractionPair[] ips = Model.InteractionPair.generatePairwiseInteractionsFromList(0, 1);
 DataInfo di=null;
 try {
  di = new DataInfo(
      fr.clone(),  // train
      null,        // valid
      1,           // num responses
      true,        // use all factor levels
      DataInfo.TransformType.STANDARDIZE,  // predictor transform
      DataInfo.TransformType.NONE,  // response  transform
      true,        // skip missing
      false,       // impute missing
      false,       // missing bucket
      false,       // weight
      false,       // offset
      false,       // fold
      Model.InteractionSpec.allPairwise(new String[]{fr.name(0),fr.name(1)})          // interactions
  );
  checker(di,true);
 } finally {
  fr.delete();
  if( di!=null ) {
   di.dropInteractions();
   di.remove();
  }
 }
}

代码示例来源：origin: h2oai/h2o-3

if (_parms._link == Link.family_default)
 _parms._link = _parms._family.defaultLink;
_dinfo = new DataInfo(_train.clone(), _valid, 1, _parms._use_all_factor_levels || _parms._lambda_search, _parms._standardize ? DataInfo.TransformType.STANDARDIZE : DataInfo.TransformType.NONE, DataInfo.TransformType.NONE, _parms._missing_values_handling == MissingValuesHandling.Skip, _parms._missing_values_handling == MissingValuesHandling.MeanImputation, false, hasWeightCol(), hasOffsetCol(), hasFoldCol(), _parms.interactionSpec());

代码示例来源：origin: h2oai/h2o-3

@Test public void testIris3() {  // test that getting sparseRows and denseRows produce the same results
 Frame fr = parse_test_file(Key.make("a.hex"), "smalldata/iris/iris_wheader.csv");
 fr.swap(2,4);
 Model.InteractionPair[] ips = Model.InteractionPair.generatePairwiseInteractionsFromList(0, 1, 2, 3);
 DataInfo di=null;
 try {
  di = new DataInfo(
      fr.clone(),  // train
      null,        // valid
      1,           // num responses
      true,        // use all factor levels
      DataInfo.TransformType.STANDARDIZE,  // predictor transform
      DataInfo.TransformType.NONE,  // response  transform
      true,        // skip missing
      false,       // impute missing
      false,       // missing bucket
      false,       // weight
      false,       // offset
      false,       // fold
      Model.InteractionSpec.allPairwise(new String[]{fr.name(0),fr.name(1),fr.name(2),fr.name(3)})          // interactions
  );
  checker(di,true);
 } finally {
  fr.delete();
  if( di!=null ) {
   di.dropInteractions();
   di.remove();
  }
 }
}

代码示例来源：origin: h2oai/h2o-2

p.source = (Frame)_train.clone();
p.response = _train.lastVec();
p.ignored_cols = null;

代码示例来源：origin: h2oai/h2o-3

try {
 di = new DataInfo(
     fr.clone(),  // train

代码示例来源：origin: h2oai/h2o-3

try {
 di = new DataInfo(
     fr.clone(),  // train

代码示例来源：origin: h2oai/h2o-2

new GLM2("GLM offset test on prostate.", Key.make(), modelKey, new GLM2.Source((Frame)fr.clone(), fr.vec("CAPSULE"), true, true), Family.binomial).setNonNegative(true).setRegularization(new double[]{1},new double[]{2.22E-5}).doInit().fork().get(); //.setHighAccuracy().doInit().fork().get();
model = DKV.get(modelKey).get();
Assert.assertTrue(model.get_params().state == Job.JobState.DONE);

代码示例来源：origin: h2oai/h2o-2

new GLM2("GLM offset test on prostate.",Key.make(),modelKey,new GLM2.Source((Frame)fr.clone(),fr.vec("CAPSULE"),false,false),Family.binomial).setRegularization(new double[]{0},new double[]{0}).doInit().fork().get(); //.setHighAccuracy().doInit().fork().get();
model = DKV.get(modelKey).get();
Assert.assertTrue(model.get_params().state == Job.JobState.DONE);
assertEquals(413, val.aic(),1e-1);
score = model.score((Frame)fr.clone());
Vec mu = score.vec("1");
final double [] exp_preds =

代码示例来源：origin: h2oai/h2o-2

try {
 GLM2.Source src = new GLM2.Source((Frame)fr.clone(), fr.vec("CAPSULE"), false, true);
 new GLM2("GLM offset test on prostate.", Key.make(), modelKey, src, Family.binomial).setNonNegative(false).setRegularization(new double[]{0},new double[]{0.000}).setBetaConstraints(betaConstraints).setHighAccuracy().doInit().fork().get(); //.setHighAccuracy().doInit().fork().get();
 model = DKV.get(modelKey).get();
  Assert.assertEquals(0,grad[i] + betaConstraints.vec("rho").at(i) * (beta[i] - betaConstraints.vec("beta_given").at(i)),1e-8);
 src = new GLM2.Source((Frame)fr.clone(), fr.vec("CAPSULE"), true, true);
 new GLM2("GLM offset test on prostate.", Key.make(), modelKey, src, Family.binomial).setNonNegative(false).setRegularization(new double[]{0},new double[]{0.000}).setBetaConstraints(betaConstraints).setHighAccuracy().doInit().fork().get(); //.setHighAccuracy().doInit().fork().get();
 model = DKV.get(modelKey).get();

代码示例来源：origin: ai.h2o/h2o-algos

if (_parms._link == Link.family_default)
 _parms._link = _parms._family.defaultLink;
_dinfo = new DataInfo(_train.clone(), _valid, 1, _parms._use_all_factor_levels || _parms._lambda_search, _parms._standardize ? DataInfo.TransformType.STANDARDIZE : DataInfo.TransformType.NONE, DataInfo.TransformType.NONE, _parms._missing_values_handling == MissingValuesHandling.Skip, _parms._missing_values_handling == MissingValuesHandling.MeanImputation, false, hasWeightCol(), hasOffsetCol(), hasFoldCol(), _parms.interactionSpec());

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