aboutsummaryrefslogtreecommitdiff
path: root/Assignment 1/ex12.py
diff options
context:
space:
mode:
Diffstat (limited to 'Assignment 1/ex12.py')
-rw-r--r--Assignment 1/ex12.py11
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.