#!/usr/bin/env python
# coding: utf-8

# # Vector manipulation in Python
# 
# In this lab, you will have the opportunity to practice once again with the NumPy library. This time, we will explore some advanced operations with arrays and matrices.
# 
# At the end of the previous module, we used PCA to transform a set of many variables into a set of only two uncorrelated variables. This process was made through a transformation of the data called rotation. 
# 
# In this week's assignment, you will need to find a transformation matrix from English to French vector space embeddings. Such a transformation matrix is nothing else but a matrix that rotates and scales vector spaces.
# 
# In this notebook, we will explain in detail the rotation transformation. 

# ## Transforming vectors
# 
# There are three main vector transformations:
# * Scaling
# * Translation
# * Rotation
# 
# In previous notebooks, we have applied the first two kinds of transformations. Now, let us learn how to use a fundamental transformation on vectors called _rotation_.
# 
# The rotation operation changes the direction of a vector, letting unaffected its dimensionality and its norm. Let us explain with some examples. 
# 
# In the following cells, we will define a NumPy matrix and a NumPy array. Soon we will explain how this is related to matrix rotation.

# In[1]:


import numpy as np                     # Import numpy for array manipulation
import matplotlib.pyplot as plt        # Import matplotlib for charts
from utils_nb import plot_vectors      # Function to plot vectors (arrows)


# ### Example 1

# In[2]:


# Create a 2 x 2 matrix
R = np.array([[2, 0],
              [0, -2]])


# In[3]:


x = np.array([[1, 1]]) # Create a 1 x 2 matrix


# The dot product between a vector and a square matrix produces a rotation and a scaling of the original vector. 
# 
# Remember that our recommended way to get the dot product in Python is np.dot(a, b):

# In[4]:


y = np.dot(x, R) # Apply the dot product between x and R
y


# We are going to use Pyplot to inspect the effect of the rotation on 2D vectors visually. For that, we have created a function `plot_vectors()` that takes care of all the intricate parts of the visual formatting. The code for this function is inside the `utils_nb.py` file. 
# 
# Now we can plot the vector $\vec x = [1, 1]$ in a cartesian plane. The cartesian plane will be centered at `[0,0]` and its x and y limits will be between `[-4, +4]`

# In[5]:


plot_vectors([x], axes=[4, 4], fname='transform_x.svg')


# Now, let's plot in the same system our vector $\vec x = [1, 1]$ and its dot product with the matrix
# 
# $$Ro = \begin{bmatrix} 2 & 0 \\ 0 & -2 \end{bmatrix}$$
# 
# $$y = x \cdot Ro = [[-2, 2]]$$

# In[6]:


plot_vectors([x, y], axes=[4, 4], fname='transformx_and_y.svg')


# Note that the output vector `y` (blue) is transformed in another vector. 

# ### Example 2
# 
# We are going to use Pyplot to inspect the effect of the rotation on 2D vectors visually. For that, we have created a function that takes care of all the intricate parts of the visual formatting. The following procedure plots an arrow within a Pyplot canvas.
# 
# Data that is composed of 2 real attributes is telling to belong to a $ RxR $ or $ R^2 $ space. Rotation matrices in $R^2$ rotate a given vector $\vec x$ by a counterclockwise angle $\theta$ in a fixed coordinate system. Rotation matrices are of the form:
# 
# $$Ro = \begin{bmatrix} cos \theta & -sin \theta \\ sin \theta & cos \theta \end{bmatrix}$$
# 
# The trigonometric functions in Numpy require the angle in radians, not in degrees. In the next cell, we define a rotation matrix that rotates vectors by $45^o$.

# In[7]:


angle = 100 * (np.pi / 180) #convert degrees to radians

Ro = np.array([[np.cos(angle), -np.sin(angle)],
              [np.sin(angle), np.cos(angle)]])

x2 = np.array([2, 2]).reshape(1, -1) # make it a row vector
y2 = np.dot(x2, Ro)

print('Rotation matrix')
print(Ro)
print('\nRotated vector')
print(y2)

print('\n x2 norm', np.linalg.norm(x2))
print('\n y2 norm', np.linalg.norm(y2))
print('\n Rotation matrix norm', np.linalg.norm(Ro))


# In[8]:


plot_vectors([x2, y2], fname='transform_02.svg')


# Some points to note:
# 
# * The norm of the input vector is the same as the norm of the output vector. Rotations matrices do not modify the norm of the vector, only its direction.
# * The norm of any $R^2$ rotation matrix is always $\sqrt 2 = 1.414221$

# ## Frobenius Norm
# 
# The Frobenius norm is the generalization to $R^2$ of the already known norm function for vectors 
# 
# $$\| \vec a \| = \sqrt {{\vec a} \cdot {\vec a}} $$
# 
# For a given $R^2$ matrix A, the frobenius norm is defined as:
# 
# $$\|\mathrm{A}\|_{F} \equiv \sqrt{\sum_{i=1}^{m} \sum_{j=1}^{n}\left|a_{i j}\right|^{2}}$$
# 

# In[9]:


A = np.array([[2, 2],
              [2, 2]])


# `np.square()` is a way to square each element of a matrix. It must be equivalent to use the * operator in Numpy arrays.

# In[10]:


A_squared = np.square(A)
A_squared


# Now you can sum over the elements of the resulting array, and then get the square root of the sum.

# In[11]:


A_Frobenius = np.sqrt(np.sum(A_squared))
A_Frobenius


# That was the extended version of the `np.linalg.norm()` function. You can check that it yields the same result.

# In[12]:


print('Frobenius norm of the Rotation matrix')
print(np.sqrt(np.sum(Ro * Ro)), '== ', np.linalg.norm(Ro))


# **Congratulations!! We've covered a few more matrix operations in this lab. This will come in handy in this week's programming assignment!**