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q01_k_means/build.py

@@ -1,18 +1,35 @@
+# %load q01_k_means/build.py
 # Default imports
 from sklearn.cluster import KMeans
 import matplotlib.pyplot as plt
 plt.switch_backend('agg')
 from sklearn import datasets
 
-
 digits = datasets.load_digits()
 
 X_train = digits.images
 y_train = digits.target
-
 # Write your solution here :
-
-
-
-
+def k_means(X_train,y_train, cluster = 10,random_state = 9):
+    X = X_train.reshape((len(X_train), -1))
+    kmeans = KMeans(n_clusters= cluster, random_state= random_state).fit(X)
+    len_list = []
+    limit = 21
+    for c in range(0, cluster):
+        print('C: ',c)
+        image = X_train[(kmeans.labels_ == c) & (y_train == c)]
+        temp = len(image)
+        for i in range(0, temp):
+            if(temp <= limit):
+                ax='ax'
+                fig = plt.figure()
+                value = i+1
+                ax += str(value)
+                ax = fig.add_subplot(1,temp,i+1)
+                plt.axis('off')
+                ax.imshow(image[i])
+                plt.show()
+
+            else:
+                break
 

q02_hierarchy_clustering/build.py

@@ -1,3 +1,4 @@
+# %load q02_hierarchy_clustering/build.py
 # Default imports
 
 import pandas as pd
@@ -11,5 +12,47 @@
 df = pd.DataFrame(scale(digits.data), index=digits.target)
 
 # Write your solution here :
+def hierarchy_clustering(df):
+    Z_avg = linkage(df, 'average') 
+    Z_single = linkage(df, 'single') 
+    Z_complete = linkage(df, 'complete') 
+    Z_ward = linkage(df, 'ward') 
 
+    plt.figure(figsize=(15, 10))
+    plt.subplot(411)
+    dendrogram(
+        Z_avg,
+        leaf_rotation=90.,  
+        leaf_font_size=3., 
+    )
+    plt.title('Average')
+
+    plt.figure(figsize=(15, 10))
+    plt.subplot(412)
+    dendrogram(
+        Z_single,
+        leaf_rotation=90.,  
+        leaf_font_size=3.,  
+    )
+    plt.title('Single')
+
+    plt.subplot(413)
+    dendrogram(
+        Z_complete,
+        leaf_rotation=90.,  
+        leaf_font_size=3.,  
+    )
+    plt.title('Complete')
+
+    plt.subplot(414)
+    dendrogram(
+        Z_ward,
+        leaf_rotation=90.,  
+        leaf_font_size=3.,  
+    )
+    plt.title('Ward')
+    plt.xlabel('sample index')
+    plt.ylabel('distance')
+
+    plt.show()