[MINOR] Fix typos in comments and testcase name of code
## What changes were proposed in this pull request? This PR fixes typos in comments and testcase name of code. ## How was this patch tested? manual. Author: Dongjoon Hyun <dongjoon@apache.org> Closes #11481 from dongjoon-hyun/minor_fix_typos_in_code.
This commit is contained in:
Dongjoon Hyun
Sean Owen
parent
52035d1036
commit
941b270b70
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@ -28,7 +28,7 @@ import org.apache.spark._
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* of them will be combined together, showed in one line.
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*/
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private[spark] class ConsoleProgressBar(sc: SparkContext) extends Logging {
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// Carrige return
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// Carriage return
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val CR = '\r'
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// Update period of progress bar, in milliseconds
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val UPDATE_PERIOD = 200L
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@ -26,11 +26,11 @@ package org.apache.sparktest
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*/
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class ImplicitSuite {
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// We only want to test if `implict` works well with the compiler, so we don't need a real
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// We only want to test if `implicit` works well with the compiler, so we don't need a real
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// SparkContext.
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def mockSparkContext[T]: org.apache.spark.SparkContext = null
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// We only want to test if `implict` works well with the compiler, so we don't need a real RDD.
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// We only want to test if `implicit` works well with the compiler, so we don't need a real RDD.
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def mockRDD[T]: org.apache.spark.rdd.RDD[T] = null
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def testRddToPairRDDFunctions(): Unit = {
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@ -563,7 +563,7 @@ def main():
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# backwards compatibility checks
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if build_tool == "sbt":
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# Note: compatiblity tests only supported in sbt for now
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# Note: compatibility tests only supported in sbt for now
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detect_binary_inop_with_mima()
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# run the test suites
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@ -228,7 +228,7 @@ class MyJavaLogisticRegressionModel
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* Create a copy of the model.
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* The copy is shallow, except for the embedded paramMap, which gets a deep copy.
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* <p>
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* This is used for the defaul implementation of [[transform()]].
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* This is used for the default implementation of [[transform()]].
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*
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* In Java, we have to make this method public since Java does not understand Scala's protected
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* modifier.
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@ -47,7 +47,7 @@ if __name__ == "__main__":
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# $example on$
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data = sc.textFile('data/mllib/sample_naive_bayes_data.txt').map(parseLine)
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# Split data aproximately into training (60%) and test (40%)
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# Split data approximately into training (60%) and test (40%)
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training, test = data.randomSplit([0.6, 0.4], seed=0)
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# Train a naive Bayes model.
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@ -47,7 +47,7 @@ if __name__ == "__main__":
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# Instantiate regression metrics to compare predicted and actual ratings
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metrics = RegressionMetrics(scoreAndLabels)
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# Root mean sqaured error
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# Root mean squared error
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print("RMSE = %s" % metrics.rootMeanSquaredError)
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# R-squared
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@ -16,7 +16,7 @@
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#
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# This example uses text8 file from http://mattmahoney.net/dc/text8.zip
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# The file was downloadded, unziped and split into multiple lines using
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# The file was downloaded, unzipped and split into multiple lines using
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#
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# wget http://mattmahoney.net/dc/text8.zip
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# unzip text8.zip
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@ -30,7 +30,7 @@ import breeze.linalg.{DenseVector, Vector}
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* org.apache.spark.mllib.classification.LogisticRegressionWithLBFGS based on your needs.
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*/
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object LocalFileLR {
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val D = 10 // Numer of dimensions
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val D = 10 // Number of dimensions
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val rand = new Random(42)
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case class DataPoint(x: Vector[Double], y: Double)
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@ -35,7 +35,7 @@ import org.apache.spark._
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* org.apache.spark.mllib.classification.LogisticRegressionWithLBFGS based on your needs.
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*/
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object SparkHdfsLR {
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val D = 10 // Numer of dimensions
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val D = 10 // Number of dimensions
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val rand = new Random(42)
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case class DataPoint(x: Vector[Double], y: Double)
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@ -36,7 +36,7 @@ import org.apache.spark._
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*/
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object SparkLR {
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val N = 10000 // Number of data points
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val D = 10 // Numer of dimensions
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val D = 10 // Number of dimensions
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val R = 0.7 // Scaling factor
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val ITERATIONS = 5
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val rand = new Random(42)
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@ -60,10 +60,10 @@ object RDDRelation {
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// Write out an RDD as a parquet file with overwrite mode.
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df.write.mode(SaveMode.Overwrite).parquet("pair.parquet")
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// Read in parquet file. Parquet files are self-describing so the schmema is preserved.
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// Read in parquet file. Parquet files are self-describing so the schema is preserved.
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val parquetFile = sqlContext.read.parquet("pair.parquet")
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// Queries can be run using the DSL on parequet files just like the original RDD.
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// Queries can be run using the DSL on parquet files just like the original RDD.
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parquetFile.where($"key" === 1).select($"value".as("a")).collect().foreach(println)
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// These files can also be registered as tables.
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@ -44,7 +44,7 @@ object TwitterPopularTags {
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val filters = args.takeRight(args.length - 4)
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// Set the system properties so that Twitter4j library used by twitter stream
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// can use them to generat OAuth credentials
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// can use them to generate OAuth credentials
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System.setProperty("twitter4j.oauth.consumerKey", consumerKey)
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System.setProperty("twitter4j.oauth.consumerSecret", consumerSecret)
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System.setProperty("twitter4j.oauth.accessToken", accessToken)
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@ -21,7 +21,7 @@ import org.apache.spark.SparkFunSuite
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class EdgeSuite extends SparkFunSuite {
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test ("compare") {
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// decending order
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// descending order
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val testEdges: Array[Edge[Int]] = Array(
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Edge(0x7FEDCBA987654321L, -0x7FEDCBA987654321L, 1),
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Edge(0x2345L, 0x1234L, 1),
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@ -541,7 +541,7 @@ object PrefixSpan extends Logging {
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}
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/**
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* Represents a frequence sequence.
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* Represents a frequent sequence.
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* @param sequence a sequence of itemsets stored as an Array of Arrays
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* @param freq frequency
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* @tparam Item item type
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@ -688,7 +688,7 @@ object Unidoc {
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"-noqualifier", "java.lang"
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),
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// Use GitHub repository for Scaladoc source linke
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// Use GitHub repository for Scaladoc source links
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unidocSourceBase := s"https://github.com/apache/spark/tree/v${version.value}",
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scalacOptions in (ScalaUnidoc, unidoc) ++= Seq(
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@ -127,7 +127,7 @@ class PrefixSpanModel(JavaModelWrapper):
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@since("1.6.0")
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def freqSequences(self):
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"""Gets frequence sequences"""
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"""Gets frequent sequences"""
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return self.call("getFreqSequences").map(lambda x: PrefixSpan.FreqSequence(x[0], x[1]))
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@ -72,7 +72,7 @@ import org.apache.spark.annotation.DeveloperApi
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* all variables defined by that code. To extract the result of an
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* interpreted line to show the user, a second "result object" is created
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* which imports the variables exported by the above object and then
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* exports members called "$eval" and "$print". To accomodate user expressions
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* exports members called "$eval" and "$print". To accommodate user expressions
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* that read from variables or methods defined in previous statements, "import"
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* statements are used.
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*
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@ -1515,7 +1515,7 @@ import org.apache.spark.annotation.DeveloperApi
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exprTyper.symbolOfLine(code)
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/**
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* Constucts type information based on the provided expression's final
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* Constructs type information based on the provided expression's final
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* result or the definition provided.
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*
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* @param expr The expression or definition
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@ -29,7 +29,7 @@ private case object OracleDialect extends JdbcDialect {
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override def getCatalystType(
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sqlType: Int, typeName: String, size: Int, md: MetadataBuilder): Option[DataType] = {
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// Handle NUMBER fields that have no precision/scale in special way
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// because JDBC ResultSetMetaData converts this to 0 procision and -127 scale
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// because JDBC ResultSetMetaData converts this to 0 precision and -127 scale
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// For more details, please see
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// https://github.com/apache/spark/pull/8780#issuecomment-145598968
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// and
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@ -23,7 +23,7 @@ import org.apache.spark.sql.execution.streaming.{CompositeOffset, LongOffset, Of
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trait OffsetSuite extends SparkFunSuite {
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/** Creates test to check all the comparisons of offsets given a `one` that is less than `two`. */
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def compare(one: Offset, two: Offset): Unit = {
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test(s"comparision $one <=> $two") {
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test(s"comparison $one <=> $two") {
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assert(one < two)
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assert(one <= two)
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assert(one <= one)
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