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

# ## Programming Exercise 7 - K-means Clustering and Principal Component Analysis
# 
# - [K-means on example dataset](#K-means-on-example-dataset)
# - [Image compression with K-means](#Image-compression-with-K-means)>
# - [PCA on example data set](#PCA-on-example-data-set)

# In[1]:


# %load ../../../standard_import.txt
import pandas as pd
import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt

from scipy.io import loadmat
from sklearn.cluster import KMeans
from sklearn.preprocessing import StandardScaler
from scipy import linalg

pd.set_option('display.notebook_repr_html', False)
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', 150)
pd.set_option('display.max_seq_items', None)
 
#%config InlineBackend.figure_formats = {'pdf',}
get_ipython().run_line_magic('matplotlib', 'inline')

import seaborn as sns
sns.set_context('notebook')
sns.set_style('white')


# ### K-means on example dataset

# In[2]:


data1 = loadmat('data/ex7data2.mat')
data1.keys()


# In[3]:


X1 = data1['X']
print('X1:', X1.shape)


# In[4]:


km1 = KMeans(3)
km1.fit(X1)


# In[5]:


plt.scatter(X1[:,0], X1[:,1], s=40, c=km1.labels_, cmap=plt.cm.prism) 
plt.title('K-Means Clustering Results with K=3')
plt.scatter(km1.cluster_centers_[:,0], km1.cluster_centers_[:,1], marker='+', s=100, c='k', linewidth=2);


# ### Image compression with K-means

# In[6]:


img = plt.imread('data/bird_small.png')
img_shape = img.shape
img_shape


# In[7]:


A = img/255


# In[8]:


AA = A.reshape(128*128,3)
AA.shape


# In[9]:


km2 = KMeans(16)
km2.fit(AA)


# In[10]:


B = km2.cluster_centers_[km2.labels_].reshape(img_shape[0], img_shape[1], 3)


# In[11]:


fig, (ax1, ax2) = plt.subplots(1,2, figsize=(13,9))
ax1.imshow(img)
ax1.set_title('Original')
ax2.imshow(B*255)
ax2.set_title('Compressed, with 16 colors')

for ax in fig.axes:
    ax.axis('off')


# ### PCA on example data set
# Using scipy instead of scikit-learn

# In[12]:


data2 = loadmat('data/ex7data1.mat')
data2.keys()


# In[13]:


X2 = data2['X']
print('X2:', X2.shape)


# In[14]:


# Standardizing the data.
scaler = StandardScaler()
scaler.fit(X2)


# In[15]:


U, S, V = linalg.svd(scaler.transform(X2).T)
print(U)
print(S)


# In[93]:


plt.scatter(X2[:,0], X2[:,1], s=30, edgecolors='b',facecolors='None', linewidth=1);
# setting aspect ratio to 'equal' in order to show orthogonality of principal components in the plot
plt.gca().set_aspect('equal')
plt.quiver(scaler.mean_[0], scaler.mean_[1], U[0,0], U[0,1], scale=S[1], color='r')
plt.quiver(scaler.mean_[0], scaler.mean_[1], U[1,0], U[1,1], scale=S[0], color='r');