[FLINK-2950] [ml] [docs] Fix markdown rendering problem in SVM documentation
- Remove unnecessary indentation of table - Fix wrong `strong` end tag - Simplify lambda expression in map operation This closes #1312 Project: http://git-wip-us.apache.org/repos/asf/flink/repo Commit: http://git-wip-us.apache.org/repos/asf/flink/commit/d5a6b13a Tree: http://git-wip-us.apache.org/repos/asf/flink/tree/d5a6b13a Diff: http://git-wip-us.apache.org/repos/asf/flink/diff/d5a6b13a Branch: refs/heads/master Commit: d5a6b13ab1ff6f42369b6b1cd2aad73bd6910362 Parents: 8051222 Author: Chiwan Park <chiwanp...@apache.org> Authored: Sun Nov 1 11:15:26 2015 +0900 Committer: Stephan Ewen <se...@apache.org> Committed: Mon Nov 30 17:44:13 2015 +0100 ---------------------------------------------------------------------- docs/libs/ml/svm.md | 202 +++++++++++++++++++++++------------------------ 1 file changed, 101 insertions(+), 101 deletions(-) ---------------------------------------------------------------------- http://git-wip-us.apache.org/repos/asf/flink/blob/d5a6b13a/docs/libs/ml/svm.md ---------------------------------------------------------------------- diff --git a/docs/libs/ml/svm.md b/docs/libs/ml/svm.md index 89f7e70..c344979 100644 --- a/docs/libs/ml/svm.md +++ b/docs/libs/ml/svm.md @@ -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 @@ -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)
