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[FLINK-2950] [ml] [docs] Fix markdown rendering problem in SVM docume…
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…ntation

  - Remove unnecessary indentation of table
  - Fix wrong `strong` end tag
  - Simplify lambda expression in map operation

This closes apache#1312
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@chiwanpark @StephanEwen
chiwanpark authored and StephanEwen committed Nov 30, 2015
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202 changes: 101 additions & 101 deletions docs/libs/ml/svm.md
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Expand Up @@ -87,106 +87,106 @@ the algorithm's performance.

The SVM implementation can be controlled by the following parameters:

<table class="table table-bordered">
<thead>
<tr>
<th class="text-left" style="width: 20%">Parameters</th>
<th class="text-center">Description</th>
</tr>
</thead>

<tbody>
<tr>
<td><strong>Blocks</strong></td>
<td>
<p>
Sets the number of blocks into which the input data will be split.
On each block the local stochastic dual coordinate ascent method is executed.
This number should be set at least to the degree of parallelism.
If no value is specified, then the parallelism of the input DataSet is used as the number of blocks.
(Default value: <strong>None</strong>)
</p>
</td>
</tr>
<tr>
<td><strong>Iterations</strong></td>
<td>
<p>
Defines the maximum number of iterations of the outer loop method.
In other words, it defines how often the SDCA method is applied to the blocked data.
After each iteration, the locally computed weight vector updates have to be reduced to update the global weight vector value.
The new weight vector is broadcast to all SDCA tasks at the beginning of each iteration.
(Default value: <strong>10</strong>)
</p>
</td>
</tr>
<tr>
<td><strong>LocalIterations</strong></td>
<td>
<p>
Defines the maximum number of SDCA iterations.
In other words, it defines how many data points are drawn from each local data block to calculate the stochastic dual coordinate ascent.
(Default value: <strong>10</strong>)
</p>
</td>
</tr>
<tr>
<td><strong>Regularization</strong></td>
<td>
<p>
Defines the regularization constant of the SVM algorithm.
The higher the value, the smaller will the 2-norm of the weight vector be.
In case of a SVM with hinge loss this means that the SVM margin will be wider even though it might contain some false classifications.
(Default value: <strong>1.0</strong>)
</p>
</td>
</tr>
<tr>
<td><strong>Stepsize</strong></td>
<td>
<p>
Defines the initial step size for the updates of the weight vector.
The larger the step size is, the larger will be the contribution of the weight vector updates to the next weight vector value.
The effective scaling of the updates is $\frac{stepsize}{blocks}$.
This value has to be tuned in case that the algorithm becomes unstable.
(Default value: <strong>1.0</strong>)
</p>
</td>
</tr>
<tr>
<td><strong>ThresholdValue</strong></td>
<td>
<p>
Defines the limiting value for the decision function above which examples are labeled as
positive (+1.0). Examples with a decision function value below this value are classified
as negative (-1.0). In order to get the raw decision function values you need to indicate it by
using the OutputDecisionFunction parameter. (Default value: <strong>0.0</strong>)
</p>
</td>
</tr>
<tr>
<td><strong>OutputDecisionFunction</strong></td>
<td>
<p>
Determines whether the predict and evaluate functions of the SVM should return the distance
to the separating hyperplane, or binary class labels. Setting this to true will
return the raw distance to the hyperplane for each example. Setting it to false will
return the binary class label (+1.0, -1.0) (Default value: <strong>false<\strong>)
</p>
</td>
</tr>
<tr>
<td><strong>Seed</strong></td>
<td>
<p>
Defines the seed to initialize the random number generator.
The seed directly controls which data points are chosen for the SDCA method.
(Default value: <strong>Random Long Integer</strong>)
</p>
</td>
</tr>
</tbody>
</table>
<table class="table table-bordered">
<thead>
<tr>
<th class="text-left" style="width: 20%">Parameters</th>
<th class="text-center">Description</th>
</tr>
</thead>

<tbody>
<tr>
<td><strong>Blocks</strong></td>
<td>
<p>
Sets the number of blocks into which the input data will be split.
On each block the local stochastic dual coordinate ascent method is executed.
This number should be set at least to the degree of parallelism.
If no value is specified, then the parallelism of the input DataSet is used as the number of blocks.
(Default value: <strong>None</strong>)
</p>
</td>
</tr>
<tr>
<td><strong>Iterations</strong></td>
<td>
<p>
Defines the maximum number of iterations of the outer loop method.
In other words, it defines how often the SDCA method is applied to the blocked data.
After each iteration, the locally computed weight vector updates have to be reduced to update the global weight vector value.
The new weight vector is broadcast to all SDCA tasks at the beginning of each iteration.
(Default value: <strong>10</strong>)
</p>
</td>
</tr>
<tr>
<td><strong>LocalIterations</strong></td>
<td>
<p>
Defines the maximum number of SDCA iterations.
In other words, it defines how many data points are drawn from each local data block to calculate the stochastic dual coordinate ascent.
(Default value: <strong>10</strong>)
</p>
</td>
</tr>
<tr>
<td><strong>Regularization</strong></td>
<td>
<p>
Defines the regularization constant of the SVM algorithm.
The higher the value, the smaller will the 2-norm of the weight vector be.
In case of a SVM with hinge loss this means that the SVM margin will be wider even though it might contain some false classifications.
(Default value: <strong>1.0</strong>)
</p>
</td>
</tr>
<tr>
<td><strong>Stepsize</strong></td>
<td>
<p>
Defines the initial step size for the updates of the weight vector.
The larger the step size is, the larger will be the contribution of the weight vector updates to the next weight vector value.
The effective scaling of the updates is $\frac{stepsize}{blocks}$.
This value has to be tuned in case that the algorithm becomes unstable.
(Default value: <strong>1.0</strong>)
</p>
</td>
</tr>
<tr>
<td><strong>ThresholdValue</strong></td>
<td>
<p>
Defines the limiting value for the decision function above which examples are labeled as
positive (+1.0). Examples with a decision function value below this value are classified
as negative (-1.0). In order to get the raw decision function values you need to indicate it by
using the OutputDecisionFunction parameter. (Default value: <strong>0.0</strong>)
</p>
</td>
</tr>
<tr>
<td><strong>OutputDecisionFunction</strong></td>
<td>
<p>
Determines whether the predict and evaluate functions of the SVM should return the distance
to the separating hyperplane, or binary class labels. Setting this to true will
return the raw distance to the hyperplane for each example. Setting it to false will
return the binary class label (+1.0, -1.0) (Default value: <strong>false</strong>)
</p>
</td>
</tr>
<tr>
<td><strong>Seed</strong></td>
<td>
<p>
Defines the seed to initialize the random number generator.
The seed directly controls which data points are chosen for the SDCA method.
(Default value: <strong>Random Long Integer</strong>)
</p>
</td>
</tr>
</tbody>
</table>

## Examples

Expand All @@ -212,7 +212,7 @@ val svm = SVM()
svm.fit(trainingDS)

// Read the testing data set
val testingDS: DataSet[Vector] = env.readLibSVM(pathToTestingFile).map(lv => lv.vector)
val testingDS: DataSet[Vector] = env.readLibSVM(pathToTestingFile).map(_.vector)

// Calculate the predictions for the testing data set
val predictionDS: DataSet[(Vector, Double)] = svm.predict(testingDS)
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