Finish assignment 4

2 files changed, 19 insertions, 14 deletions

diff --git a/Assignment 4/ex41.py b/Assignment 4/ex41.py
index 5ae66db..c3a9fc5 100644
--- a/Assignment 4/ex41.py
+++ b/Assignment 4/ex41.py
@@ -11,19 +11,27 @@ import sys
 sys.path.insert(0, './packages')
 
 import numpy as np
-from scipy import io as sciio
+import scipy.io as sciio
 from sklearn import cluster
 from clusterPlot import clusterPlot
 from clusterVal import clusterVal
 import matplotlib.pyplot as plt
 
 # 4.1.1
-n = 1
+plt.figure(figsize=(16,10))
+for n in range(1,5):
+    synth = sciio.loadmat('./data/synth' + str(n) + '.mat')
+    X = synth['X']
+    y = synth['y']
+    centroid, label, inertia = cluster.k_means(X, 4)
+    plt.subplot(2, 2, n)
+    clusterPlot(X, label, centroid, y)
+plt.show()
+
+n = 1    
 synth = sciio.loadmat('./data/synth' + str(n) + '.mat')
 X = synth['X']
 y = synth['y']
-centroid, label, inertia = cluster.k_means(X, 4)
-clusterPlot(X, label, centroid, y)
 
 # 4.1.2
 entropies, purities, rands, jaccards = [], [], [], []
@@ -35,8 +43,6 @@ for i in range(1, 11):
     rands.append(rand)
     jaccards.append(jaccard)
     
-print(entropies, purities, rands, jaccards)
-    
 x = np.arange(1,11)
 plt.figure(figsize=(8,8))
 plt.subplot(2,2,1)
@@ -56,8 +62,8 @@ plt.show()
 # 4.1.3
 faces = sciio.loadmat('./data/wildfaces.mat')
 X = faces['X']
-k = 0
-centroid, label, inertia = cluster.k_means(X, 10)
+k = 10
+centroid, label, inertia = cluster.k_means(X, k)
 
 n = 10
 plt.figure(figsize=(n*2,4))
@@ -73,7 +79,7 @@ plt.show()
 # 4.1.4
 digits = sciio.loadmat('./data/digits.mat')
 X = digits['X']
-k = 20
+k = 10
 
 plt.figure(figsize=(6,4))
 for k in range(0,24):
diff --git a/Assignment 4/packages/clusterPlot.py b/Assignment 4/packages/clusterPlot.py
index 2f37a3d..4abc2c0 100644
--- a/Assignment 4/packages/clusterPlot.py
+++ b/Assignment 4/packages/clusterPlot.py
@@ -4,7 +4,7 @@ Created on Mon Apr 14 09:01:18 2014
 

 """

 

-def clusterPlot(X, clusterid, centroids='None', y='None', covars='None', figsize=(16,10)):

+def clusterPlot(X, clusterid, centroids='None', y='None', covars='None', figsize=None):

     '''

     CLUSTERPLOT Plots a clustering of a data set as well as the true class

     labels. If data is more than 2-dimensional it should be first projected

@@ -27,7 +27,7 @@ def clusterPlot(X, clusterid, centroids='None', y='None', covars='None', figsize
     covars      M-by-M-by-K tensor of covariance matrices (optional)

     '''

     import numpy as np

-    from matplotlib.pyplot import figure, cm, plot, hold, legend, xlim, show

+    from matplotlib.pyplot import figure, cm, plot, hold, legend, xlim

 

 

     X = np.asarray(X)

@@ -43,7 +43,8 @@ def clusterPlot(X, clusterid, centroids='None', y='None', covars='None', figsize
     ncolors = np.max([C,K])

     

     # plot data points color-coded by class, cluster markers and centroids

-    figure(figsize=figsize)        

+    if figsize != None:

+        figure(figsize=figsize)        

     hold(True)

     colors = [0]*ncolors

     for color in range(ncolors):

@@ -71,5 +72,3 @@ def clusterPlot(X, clusterid, centroids='None', y='None', covars='None', figsize
     legend(legend_items, numpoints=1, markerscale=.75, prop={'size': 9})

     

     xlim(X[:,0].min()*1.1, X[:,0].max()*1.2)

-    

-    show()