diff options
Diffstat (limited to 'docs/mllib-linear-methods.md')
| -rw-r--r-- | docs/mllib-linear-methods.md | 13 |
1 files changed, 7 insertions, 6 deletions
diff --git a/docs/mllib-linear-methods.md b/docs/mllib-linear-methods.md index ebb555f974..40b7a7f807 100644 --- a/docs/mllib-linear-methods.md +++ b/docs/mllib-linear-methods.md @@ -63,7 +63,7 @@ methods MLlib supports: <tbody> <tr> <td>hinge loss</td><td>$\max \{0, 1-y \wv^T \x \}, \quad y \in \{-1, +1\}$</td> - <td>$\begin{cases}-y \cdot \x & \text{if $y \wv^T \x <1$}, \\ 0 & + <td>$\begin{cases}-y \cdot \x & \text{if $y \wv^T \x <1$}, \\ 0 & \text{otherwise}.\end{cases}$</td> </tr> <tr> @@ -225,10 +225,11 @@ algorithm for 200 iterations. import org.apache.spark.mllib.optimization.L1Updater val svmAlg = new SVMWithSGD() -svmAlg.optimizer.setNumIterations(200) - .setRegParam(0.1) - .setUpdater(new L1Updater) -val modelL1 = svmAlg.run(parsedData) +svmAlg.optimizer. + setNumIterations(200). + setRegParam(0.1). + setUpdater(new L1Updater) +val modelL1 = svmAlg.run(training) {% endhighlight %} Similarly, you can use replace `SVMWithSGD` by @@ -322,7 +323,7 @@ val valuesAndPreds = parsedData.map { point => val prediction = model.predict(point.features) (point.label, prediction) } -val MSE = valuesAndPreds.map{case(v, p) => math.pow((v - p), 2)}.reduce(_ + _) / valuesAndPreds.count +val MSE = valuesAndPreds.map{case(v, p) => math.pow((v - p), 2)}.mean() println("training Mean Squared Error = " + MSE) {% endhighlight %} |