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# Load libraries
# Math
import numpy as np
# Visualization
%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
%load_ext autoreload
%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")
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# 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?
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.
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# Your code here
Ker = construct_kernel(X,'linear') # Compute linear Kernel for standard K-Means
Theta = np.ones(n) # Equal weight for each data
[C_kmeans,En_kmeans] = compute_kernel_kmeans_EM(nc,Ker,Theta,10)
acc= compute_purity(C_kmeans,Cgt,nc)
print('accuracy standard kmeans=',acc)
Question 1b: What is the clustering accuracy for the kernel K-Means algorithm with
(1) Gaussian Kernel for the EM approach and the Spectral approach?
(2) Polynomial Kernel for the EM approach and the Spectral approach?
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
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.
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# Your code here
Ker = construct_kernel(X,'gaussian') # Compute Gaussian Kernel
Theta = np.ones(n) # Equal weight for each data
C_kmeans,_ = compute_kernel_kmeans_EM(nc,Ker,Theta,10)
acc = compute_purity(C_kmeans,Cgt,nc)
print('accuracy non-linear kmeans with EM=',acc)
C_kmeans,_ = compute_kernel_kmeans_spectral(nc,Ker,Theta,10)
acc = compute_purity(C_kmeans,Cgt,nc)
print('accuracy non-linear kmeans with SPECTRAL=',acc)
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# Your code here
Ker = construct_kernel(X,'polynomial',[1,0,2])
Theta = np.ones(n) # Equal weight for each data
C_kmeans, En_kmeans = compute_kernel_kmeans_EM(nc,Ker,Theta,10)
acc = compute_purity(C_kmeans,Cgt,nc)
print('accuracy non-linear kmeans with EM=',acc)
[C_kmeans,En_kmeans] = compute_kernel_kmeans_spectral(nc,Ker,Theta,10)
acc = compute_purity(C_kmeans,Cgt,nc)
print('accuracy non-linear kmeans with SPECTRAL=',acc)
Question 1c: What is the clustering accuracy for the kernel K-Means algorithm with
(1) KNN_Gaussian Kernel for the EM approach and the Spectral approach?
(2) KNN_Cosine_Binary Kernel for the EM approach and the Spectral approach?
You can test for the value KNN_kernel=50.
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.
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# Your code here
KNN_kernel = 50
Ker = construct_kernel(X,'kNN_gaussian',KNN_kernel)
Theta = np.ones(n) # Equal weight for each data
C_kmeans,_ = compute_kernel_kmeans_EM(nc,Ker,Theta,10)
acc = compute_purity(C_kmeans,Cgt,nc)
print('accuracy non-linear kmeans with EM=',acc)
C_kmeans,_ = compute_kernel_kmeans_spectral(nc,Ker,Theta,10)
acc = compute_purity(C_kmeans,Cgt,nc)
print('accuracy non-linear kmeans with SPECTRAL=',acc)
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# Your code here
KNN_kernel = 50
Ker = construct_kernel(X,'kNN_cosine_binary',KNN_kernel)
Theta = np.ones(n) # Equal weight for each data
C_kmeans,_ = compute_kernel_kmeans_EM(nc,Ker,Theta,10)
acc = compute_purity(C_kmeans,Cgt,nc)
print('accuracy non-linear kmeans with EM=',acc)
C_kmeans,_ = compute_kernel_kmeans_spectral(nc,Ker,Theta,10)
acc = compute_purity(C_kmeans,Cgt,nc)
print('accuracy non-linear kmeans with SPECTRAL=',acc)
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