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

# # A Network Tour of Data Science
# ###       Xavier Bresson, Winter 2016/17
# ## Exercise 4 - Code 2 : Unsupervised Learning
# ## Unsupervised Clustering with Kernel K-Means  

# In[1]:


# Load libraries

# Math
import numpy as np

# Visualization 
get_ipython().run_line_magic('matplotlib', 'notebook')
import matplotlib.pyplot as plt
plt.rcParams.update({'figure.max_open_warning': 0})
from mpl_toolkits.axes_grid1 import make_axes_locatable
from scipy import ndimage

# Print output of LFR code
import subprocess

# Sparse matrix
import scipy.sparse
import scipy.sparse.linalg

# 3D visualization
import pylab
from mpl_toolkits.mplot3d import Axes3D
from matplotlib import pyplot

# Import data
import scipy.io

# Import functions in lib folder
import sys
sys.path.insert(1, 'lib')

# Import helper functions
get_ipython().run_line_magic('load_ext', 'autoreload')
get_ipython().run_line_magic('autoreload', '2')
from lib.utils import construct_kernel
from lib.utils import compute_kernel_kmeans_EM
from lib.utils import compute_kernel_kmeans_spectral
from lib.utils import compute_purity

# Import distance function
import sklearn.metrics.pairwise

# Remove warnings
import warnings
warnings.filterwarnings("ignore")


# In[2]:


# Load MNIST raw data images
mat = scipy.io.loadmat('datasets/mnist_raw_data.mat')
X = mat['Xraw']
n = X.shape[0]
d = X.shape[1]
Cgt = mat['Cgt'] - 1; Cgt = Cgt.squeeze()
nc = len(np.unique(Cgt))
print('Number of data =',n)
print('Data dimensionality =',d);
print('Number of classes =',nc);


# **Question 1a:** What is the clustering accuracy of standard/linear K-Means?<br>
# Hint: You may use functions *Ker=construct_kernel(X,'linear')* to compute the
# linear kernel and *[C_kmeans, En_kmeans]=compute_kernel_kmeans_EM(n_classes,Ker,Theta,10)* with *Theta= np.ones(n)* to run the standard K-Means algorithm, and *accuracy = compute_purity(C_computed,C_solution,n_clusters)* that returns the
# accuracy.

# In[3]:


# Your code here


# **Question 1b:** What is the clustering accuracy for the kernel K-Means algorithm with<br>
# (1) Gaussian Kernel for the EM approach and the Spectral approach?<br>
# (2) Polynomial Kernel for the EM approach and the Spectral approach?<br>
# Hint: You may use functions *Ker=construct_kernel(X,'gaussian')* and *Ker=construct_kernel(X,'polynomial',[1,0,2])* to compute the non-linear kernels<br>
# Hint: You may use functions *C_kmeans,__ = compute_kernel_kmeans_EM(K,Ker,Theta,10)* for the EM kernel KMeans algorithm and *C_kmeans,__ = compute_kernel_kmeans_spectral(K,Ker,Theta,10)* for the Spectral kernel K-Means algorithm.<br>

# In[4]:


# Your code here


# **Question 1c:** What is the clustering accuracy for the kernel K-Means algorithm with<br>
# (1) KNN_Gaussian Kernel for the EM approach and the Spectral approach?<br>
# (2) KNN_Cosine_Binary Kernel for the EM approach and the Spectral approach?<br>
# You can test for the value KNN_kernel=50.<br>
# Hint: You may use functions *Ker = construct_kernel(X,'kNN_gaussian',KNN_kernel)*
# and *Ker = construct_kernel(X,'kNN_cosine_binary',KNN_kernel)* to compute the
# non-linear kernels.

# In[5]:


# Your code here


# In[6]: