some clean ups

shogun-toolbox · Jul 13, 2016 · a9cbb50 · a9cbb50
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diff --git a/doc/cookbook/source/examples/statistical_testing/linear_time_mmd.rst b/doc/cookbook/source/examples/statistical_testing/linear_time_mmd.rst
@@ -16,21 +16,31 @@ See :cite:`gretton2012kernel` for a detailed introduction.
 Example
 -------
 
-Imagine we have samples from :math:`p` and :math:`p`. We create CDenseFeatures (here 64 bit floats aka RealFeatures)as
+Imagine we have samples from :math:`p` and :math:`q`.
+As the linear time MMD is a streaming statistic, we need to pass it `CStreamingFeatures`.
+Here, we use synthetic data generators, but it is possible to construct `CStreamingFeatures` from (large) files.
 
 .. sgexample:: linear_time_mmd.sg:create_features
 
-We create an instance of :sgclass:`CLinearTimeMMD`, passing it data and the kernel to use, a :sgclass:`CGaussianKernel` here.
+We create an instance of :sgclass:`CLinearTimeMMD`, passing it data and the kernel to use,
 
 .. sgexample:: linear_time_mmd.sg:create_instance
 
+An important parameter for controlling the efficiency of the linear time MMD is block size of the number of samples that is processed at once. As a guideline, set as large as memory allows.
+
+.. sgexample::linear_time_mmd.sg:set_burst
+
 Computing the statistic is done as
 
-.. sgexample:: linear_time_mmd.sg:estimate_mmd
+.. sgexample::linear_time_mmd.sg:estimate_mmd
+
+We can perform the hypothesis test via computing the rejection threshold
+
+.. sgexample::linear_time_mmd.sg:perform_test_threshold
 
-We can perform the hypothesis test as
+Alternatively, we can compute the p-value for the above value of the statistic
 
-.. sgexample:: linear_time_mmd.sg:perform_test
+.. sgexample::linear_time_mmd.sg:perform_test_p_value
 
 ----------
 References

diff --git a/examples/meta/src/statistical_testing/linear_time_mmd.sg b/examples/meta/src/statistical_testing/linear_time_mmd.sg
@@ -13,12 +13,20 @@ mmd.set_num_samples_p(100)
 mmd.set_num_samples_q(100)
 #![create_instance]
 
+#![set_burst]
+mmd.set_num_blocks_per_burst(1000)
+#![set_burst]
+
 #![estimate_mmd]
 Real statistic = mmd.compute_statistic()
 #![estimate_mmd]
 
-#![perform_test]
+#![perform_test_threshold]
 Real alpha = 0.05
 Real threshold = mmd.compute_threshold(alpha)
+#![perform_test_threshold]
+
+#![perform_test_p_value]
 Real p_value = mmd.compute_p_value(statistic)
-#![perform_test]
+#![perform_test_p_value]
+