Assignment 1 continuing, only 1.2.2c-e to be done

1 files changed, 11 insertions, 0 deletions

diff --git a/Assignment 1/ex12.py b/Assignment 1/ex12.py
index 3db49e1..5c467dc 100644
--- a/Assignment 1/ex12.py
+++ b/Assignment 1/ex12.py
@@ -47,3 +47,14 @@ handles = [pltpatches.Patch(label=k, color=v) for k, v in colors.iteritems()] +
 ax.legend(handles=handles, numpoints=1, loc=2)
 
 plt.show()
+
+# 1.2.2 a
+# PCA is a method that can be used to reduce dimensionality of a dataset. It 
+# can be used when some variables are correlated; we then basically rewrite one
+# of them as a function of the other. Of course, in general that implies data
+# loss.
+
+# 1.2.2 b
+# EVD is a way to rewrite a diagonalizable matrix into a canonical form (a
+# summation of products of eigenvalues and corresponding eigenvectors). SVD is 
+# a generalisation which can be applied to any matrix.