From dc77e1c88e7973ef2b7ec3afa2d22ec830a82dbe Mon Sep 17 00:00:00 2001
From: Camil Staps
Date: Mon, 21 Sep 2015 15:17:40 +0200
Subject: Assignment 1 continuing, only 1.2.2c-e to be done

---
 Assignment 1/ex12.py | 11 +++++++++++
 1 file changed, 11 insertions(+)

(limited to 'Assignment 1/ex12.py')

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.
-- 
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