From db2309142981324c5ff8917e9f0cc95b5529dfe5 Mon Sep 17 00:00:00 2001
From: Camil Staps
Date: Sun, 13 Sep 2015 18:14:38 +0200
Subject: Assignment 1, 1.1 & 1.2 start

---
 Assignment 1/ex11.py | 72 ++++++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 72 insertions(+)
 create mode 100644 Assignment 1/ex11.py

(limited to 'Assignment 1/ex11.py')

diff --git a/Assignment 1/ex11.py b/Assignment 1/ex11.py
new file mode 100644
index 0000000..b3c3683
--- /dev/null
+++ b/Assignment 1/ex11.py	
@@ -0,0 +1,72 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Wed Sep  9 16:03:05 2015
+
+@author: camilstaps
+"""
+
+from __future__ import print_function
+import numpy as np
+
+# 1.1.1
+
+vector_x = np.array([6,7,8,9,10,11,12])
+vector_y = np.array([3,7,11,15,19,23,27])
+vector_w = np.array([1,1,0,0.5,1,1.5,2,0,0])
+vector_s = np.array([100,98.8,97.6,96.4,95.2])
+vector_z = np.array([0.7 + 0.3 * i for i in range(0,7)])
+
+# 1.1.1 a
+vector_v = np.multiply(3, vector_x) + vector_y
+print("1.1.1 a:", vector_v)
+
+# 1.1.1 b
+dot_product = np.dot(vector_x, vector_y)
+print("1.1.1 b:", dot_product)
+
+# 1.1.1 c
+vector_t = np.multiply(np.pi, vector_s + 4)
+print("1.1.1 c:", vector_t)
+
+# 1.1.1 d
+vector_z = vector_z - 1
+print("1.1.1 d:", vector_z)
+
+# 1.1.1 e
+vector_x = np.concatenate((vector_x[:4], [4,4,4]))
+print("1.1.1 e:", vector_x)
+
+# 1.1.1 f
+vector_r = np.multiply(2, vector_w) - 5
+print("1.1.1 f:", vector_r)
+
+# 1.1.2
+
+matrix_m = np.asmatrix([[1,2,3],[6,8,4],[6,7,5]])
+matrix_n = np.asmatrix([[4,6],[7,2],[5,1]])
+matrix_p = np.asmatrix([[2,5],[5,5]])
+
+# 1.1.2 a
+matrix_a = matrix_m * matrix_n + matrix_n
+print("1.1.2 a:", matrix_a)
+
+# 1.1.2 b
+matrix_b = np.dot(np.transpose(matrix_n), matrix_m)
+print("1.1.2 b:", matrix_b)
+
+# 1.1.2 c
+matrix_c = np.invert(matrix_p) + matrix_p
+print("1.1.2 c:", matrix_c)
+
+# 1.1.2 d
+#matrix_d = matrix_a * matrix_c * (matrix_c + matrix_b)
+print("1.1.2 d: C and B cannot be added")
+
+# 1.1.2 e
+# Only square matrices have eigenvalues (see for example
+# http://math.stackexchange.com/q/583938/63495), so N is omitted
+val_m, vec_m = np.linalg.eig(matrix_m)
+val_p, vec_p = np.linalg.eig(matrix_p)
+print("1.1.2 e (M):", val_m, vec_m)
+print("1.1.2 e (N): non-square matrices don't have eigenv*s")
+print("1.1.2 e (P):", val_p, vec_p)
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