fixed error, updated test

core/src/main/scala/org/apache/spark/api/java/JavaRDDLike.scala

@@ -484,7 +484,6 @@ trait JavaRDDLike[T, This <: JavaRDDLike[T, This]] extends Serializable {
    * @return the maximum of the RDD
    * */
   def max(comp: Comparator[T]): T = {
-    import scala.collection.JavaConversions._
     rdd.max()(Ordering.comparatorToOrdering(comp))
   }
 
@@ -495,7 +494,6 @@ trait JavaRDDLike[T, This <: JavaRDDLike[T, This]] extends Serializable {
    * @return the minimum of the RDD
    * */
   def min(comp: Comparator[T]): T = {
-    import scala.collection.JavaConversions._
     rdd.min()(Ordering.comparatorToOrdering(comp))
   }
 

core/src/main/scala/org/apache/spark/util/StatCounter.scala

@@ -29,8 +29,8 @@ class StatCounter(values: TraversableOnce[Double]) extends Serializable {
   private var n: Long = 0     // Running count of our values
   private var mu: Double = 0  // Running mean of our values
   private var m2: Double = 0  // Running variance numerator (sum of (x - mean)^2)
-  private var max_v: Double = Double.NegativeInfinity // Running max of our values
-  private var min_v: Double = Double.PositiveInfinity // Running min of our values
+  private var maxValue: Double = Double.NegativeInfinity // Running max of our values
+  private var minValue: Double = Double.PositiveInfinity // Running min of our values
 
   merge(values)
 
@@ -43,8 +43,8 @@ class StatCounter(values: TraversableOnce[Double]) extends Serializable {
     n += 1
     mu += delta / n
     m2 += delta * (value - mu)
-    max_v = math.max(max_v, value)
-    min_v = math.min(min_v, value)
+    maxValue = math.max(maxValue, value)
+    minValue = math.min(minValue, value)
     this
   }
 
@@ -63,8 +63,8 @@ class StatCounter(values: TraversableOnce[Double]) extends Serializable {
         mu = other.mu
         m2 = other.m2
         n = other.n  
-        max_v = other.max_v
-        min_v = other.min_v
+        maxValue = other.maxValue
+        minValue = other.minValue
       } else if (other.n != 0) {        
         val delta = other.mu - mu
         if (other.n * 10 < n) {
@@ -76,8 +76,8 @@ class StatCounter(values: TraversableOnce[Double]) extends Serializable {
         }
         m2 += other.m2 + (delta * delta * n * other.n) / (n + other.n)
         n += other.n
-        max_v = math.max(max_v, other.max_v)
-        min_v = math.min(min_v, other.min_v)
+        maxValue = math.max(maxValue, other.maxValue)
+        minValue = math.min(minValue, other.minValue)
       }
       this
     }
@@ -89,8 +89,8 @@ class StatCounter(values: TraversableOnce[Double]) extends Serializable {
     other.n = n
     other.mu = mu
     other.m2 = m2
-    other.max_v = max_v
-    other.min_v = min_v
+    other.maxValue = maxValue
+    other.minValue = minValue
     other
   }
 
@@ -100,9 +100,9 @@ class StatCounter(values: TraversableOnce[Double]) extends Serializable {
 
   def sum: Double = n * mu
 
-  def max: Double = max_v
+  def max: Double = maxValue
 
-  def min: Double = min_v
+  def min: Double = minValue
 
   /** Return the variance of the values. */
   def variance: Double = {
@@ -135,7 +135,7 @@ class StatCounter(values: TraversableOnce[Double]) extends Serializable {
   def sampleStdev: Double = math.sqrt(sampleVariance)
 
   override def toString: String = {
-    "(count: %d, mean: %f, stdev: %f, max: %f, min: $f)".format(count, mean, stdev, max, min)
+    "(count: %d, mean: %f, stdev: %f, max: %f, min: %f)".format(count, mean, stdev, max, min)
   }
 }
 

core/src/test/scala/org/apache/spark/PartitioningSuite.scala

@@ -172,7 +172,7 @@ class PartitioningSuite extends FunSuite with SharedSparkContext with PrivateMet
     assert(abs(1.0 - rdd.variance) < 0.01)
     assert(abs(1.0 - rdd.stdev) < 0.01)
     assert(stats.max === 4.0)
-    assert(stats.min === -1.0)
+    assert(stats.min === 2.0)
 
     // Add other tests here for classes that should be able to handle empty partitions correctly
   }

python/pyspark/statcounter.py

@@ -26,8 +26,8 @@ def __init__(self, values=[]):
         self.n = 0L    # Running count of our values
         self.mu = 0.0  # Running mean of our values
         self.m2 = 0.0  # Running variance numerator (sum of (x - mean)^2)
-        self.max_v = float("-inf")
-        self.min_v = float("inf")
+        self.maxValue = float("-inf")
+        self.minValue = float("inf")
 
         for v in values:
             self.merge(v)
@@ -38,10 +38,10 @@ def merge(self, value):
         self.n += 1
         self.mu += delta / self.n
         self.m2 += delta * (value - self.mu)
-        if self.max_v < value:
-            self.max_v = value
-        if self.min_v > value:
-            self.min_v = value
+        if self.maxValue < value:
+            self.maxValue = value
+        if self.minValue > value:
+            self.minValue = value
 
         return self
 
@@ -57,8 +57,8 @@ def mergeStats(self, other):
                 self.mu = other.mu
                 self.m2 = other.m2
                 self.n = other.n
-                self.max_v = other.max_v
-                self.min_v = other.min_v
+                self.maxValue = other.maxValue
+                self.minValue = other.minValue
 
             elif other.n != 0:        
                 delta = other.mu - self.mu
@@ -69,8 +69,8 @@ def mergeStats(self, other):
                 else:
                     self.mu = (self.mu * self.n + other.mu * other.n) / (self.n + other.n)
 
-                    self.max_v = max(self.max_v, other.max_v)
-                    self.min_v = min(self.min_v, other.min_v)
+                    self.maxValue = max(self.maxValue, other.maxValue)
+                    self.minValue = min(self.minValue, other.minValue)
 
                 self.m2 += other.m2 + (delta * delta * self.n * other.n) / (self.n + other.n)
                 self.n += other.n
@@ -90,10 +90,10 @@ def sum(self):
         return self.n * self.mu
 
     def min(self):
-        return self.min_v
+        return self.minValue
 
     def max(self):
-        return self.max_v
+        return self.maxValue
 
     # Return the variance of the values.
     def variance(self):