diff options
Diffstat (limited to 'mllib')
-rw-r--r-- | mllib/src/main/scala/org/apache/spark/ml/r/GeneralizedLinearRegressionWrapper.scala | 8 | ||||
-rw-r--r-- | mllib/src/main/scala/org/apache/spark/ml/r/NaiveBayesWrapper.scala | 4 |
2 files changed, 6 insertions, 6 deletions
diff --git a/mllib/src/main/scala/org/apache/spark/ml/r/GeneralizedLinearRegressionWrapper.scala b/mllib/src/main/scala/org/apache/spark/ml/r/GeneralizedLinearRegressionWrapper.scala index 9618a3423e..5642abc645 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/r/GeneralizedLinearRegressionWrapper.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/r/GeneralizedLinearRegressionWrapper.scala @@ -67,8 +67,8 @@ private[r] object GeneralizedLinearRegressionWrapper data: DataFrame, family: String, link: String, - epsilon: Double, - maxit: Int): GeneralizedLinearRegressionWrapper = { + tol: Double, + maxIter: Int): GeneralizedLinearRegressionWrapper = { val rFormula = new RFormula() .setFormula(formula) val rFormulaModel = rFormula.fit(data) @@ -82,8 +82,8 @@ private[r] object GeneralizedLinearRegressionWrapper .setFamily(family) .setLink(link) .setFitIntercept(rFormula.hasIntercept) - .setTol(epsilon) - .setMaxIter(maxit) + .setTol(tol) + .setMaxIter(maxIter) val pipeline = new Pipeline() .setStages(Array(rFormulaModel, glr)) .fit(data) diff --git a/mllib/src/main/scala/org/apache/spark/ml/r/NaiveBayesWrapper.scala b/mllib/src/main/scala/org/apache/spark/ml/r/NaiveBayesWrapper.scala index 28925c79da..1dac246b03 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/r/NaiveBayesWrapper.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/r/NaiveBayesWrapper.scala @@ -56,7 +56,7 @@ private[r] object NaiveBayesWrapper extends MLReadable[NaiveBayesWrapper] { val PREDICTED_LABEL_INDEX_COL = "pred_label_idx" val PREDICTED_LABEL_COL = "prediction" - def fit(formula: String, data: DataFrame, laplace: Double): NaiveBayesWrapper = { + def fit(formula: String, data: DataFrame, smoothing: Double): NaiveBayesWrapper = { val rFormula = new RFormula() .setFormula(formula) .fit(data) @@ -70,7 +70,7 @@ private[r] object NaiveBayesWrapper extends MLReadable[NaiveBayesWrapper] { val features = featureAttrs.map(_.name.get) // assemble and fit the pipeline val naiveBayes = new NaiveBayes() - .setSmoothing(laplace) + .setSmoothing(smoothing) .setModelType("bernoulli") .setPredictionCol(PREDICTED_LABEL_INDEX_COL) val idxToStr = new IndexToString() |