Reformat readme

Author: soloice

readme.md

@@ -4,11 +4,19 @@ Author: Soloice.
 
 Here are some instructions for the project:
 
-All source codes are in the folder 'src2/'. Two classes BinarySVM and MultiSVM are defined in the file svm.py.
-demo_test.py, multi_test.py and svm_test.py all used to debug the SMO algorithm: demo_test.py includes a data generator which generates 2-dimensional linear separable/almost-separable/circular data of 2 classes, then visualize the data points and train a BinarySVM.  Similarly, multi_test.py serves for testing MultiSVM.  In svm_test.py, some real data are extracted from the MNIST dataset and are visualized using the PCA technique.
-Finally, svm_test_full.py trains a SVM classifier on the whole MNIST data.
+Source code structure
+---------------------
+- All source codes are in the folder `src2/`.
+- Two classes BinarySVM and MultiSVM are defined in the file `svm.py`.
+- `demo_test.py`, `multi_test.py` and `svm_test.py` all used to debug the SMO algorithm: 
+  * `demo_test.py` includes a data generator which generates 2-dimensional linear separable/almost-separable/circular data of 2 classes, then visualize the data points and train a BinarySVM.  
+  * Similarly, `multi_test.py` serves for testing MultiSVM.  
+  * In `svm_test.py`, some real data are extracted from the MNIST dataset and are visualized using the PCA technique.
+- Finally, `svm_test_full.py` trains a SVM classifier on the whole MNIST data.
 
 
+Performance and Observations
+---------------------
 In my experiment, I found training an SVM with 'RBF' kernel is much faster than that with linear kernel.  I don't why.  Perhaps it is because in RKHS the data points are more separable thus facilitates the training procedure.
 For your reference, Training a MultiSVM classifier with 'RBF' kernel on 6/7 MNIST data (i.e., using 60k examples as the training set) takes 11462s on my workstation (32GB RAM, 1 CPU with 8 Intel(R) Xeon(R) CPU E5-1620 v2 @ 3.70GHz cores.)