diff options
Diffstat (limited to 'docs/mllib-statistics.md')
-rw-r--r-- | docs/mllib-statistics.md | 20 |
1 files changed, 10 insertions, 10 deletions
diff --git a/docs/mllib-statistics.md b/docs/mllib-statistics.md index de5d6485f9..be04d0b4b5 100644 --- a/docs/mllib-statistics.md +++ b/docs/mllib-statistics.md @@ -95,9 +95,9 @@ mat = ... # an RDD of Vectors # Compute column summary statistics. summary = Statistics.colStats(mat) -print summary.mean() -print summary.variance() -print summary.numNonzeros() +print(summary.mean()) +print(summary.variance()) +print(summary.numNonzeros()) {% endhighlight %} </div> @@ -183,12 +183,12 @@ seriesY = ... # must have the same number of partitions and cardinality as serie # Compute the correlation using Pearson's method. Enter "spearman" for Spearman's method. If a # method is not specified, Pearson's method will be used by default. -print Statistics.corr(seriesX, seriesY, method="pearson") +print(Statistics.corr(seriesX, seriesY, method="pearson")) data = ... # an RDD of Vectors # calculate the correlation matrix using Pearson's method. Use "spearman" for Spearman's method. # If a method is not specified, Pearson's method will be used by default. -print Statistics.corr(data, method="pearson") +print(Statistics.corr(data, method="pearson")) {% endhighlight %} </div> @@ -398,14 +398,14 @@ vec = Vectors.dense(...) # a vector composed of the frequencies of events # compute the goodness of fit. If a second vector to test against is not supplied as a parameter, # the test runs against a uniform distribution. goodnessOfFitTestResult = Statistics.chiSqTest(vec) -print goodnessOfFitTestResult # summary of the test including the p-value, degrees of freedom, - # test statistic, the method used, and the null hypothesis. +print(goodnessOfFitTestResult) # summary of the test including the p-value, degrees of freedom, + # test statistic, the method used, and the null hypothesis. mat = Matrices.dense(...) # a contingency matrix # conduct Pearson's independence test on the input contingency matrix independenceTestResult = Statistics.chiSqTest(mat) -print independenceTestResult # summary of the test including the p-value, degrees of freedom... +print(independenceTestResult) # summary of the test including the p-value, degrees of freedom... obs = sc.parallelize(...) # LabeledPoint(feature, label) . @@ -415,8 +415,8 @@ obs = sc.parallelize(...) # LabeledPoint(feature, label) . featureTestResults = Statistics.chiSqTest(obs) for i, result in enumerate(featureTestResults): - print "Column $d:" % (i + 1) - print result + print("Column $d:" % (i + 1)) + print(result) {% endhighlight %} </div> |