diff --git a/__pycache__/__init__.cpython-36.pyc b/__pycache__/__init__.cpython-36.pyc
index e366269..5205629 100644
Binary files a/__pycache__/__init__.cpython-36.pyc and b/__pycache__/__init__.cpython-36.pyc differ
diff --git a/q01_k_means/__pycache__/__init__.cpython-36.pyc b/q01_k_means/__pycache__/__init__.cpython-36.pyc
index 5030f09..507c94f 100644
Binary files a/q01_k_means/__pycache__/__init__.cpython-36.pyc and b/q01_k_means/__pycache__/__init__.cpython-36.pyc differ
diff --git a/q01_k_means/__pycache__/build.cpython-36.pyc b/q01_k_means/__pycache__/build.cpython-36.pyc
index d257235..660843e 100644
Binary files a/q01_k_means/__pycache__/build.cpython-36.pyc and b/q01_k_means/__pycache__/build.cpython-36.pyc differ
diff --git a/q01_k_means/build.py b/q01_k_means/build.py
index 925feb4..ecce760 100644
--- a/q01_k_means/build.py
+++ b/q01_k_means/build.py
@@ -1,3 +1,4 @@
+# %load q01_k_means/build.py
 # Default imports
 from sklearn.cluster import KMeans
 import matplotlib.pyplot as plt
@@ -7,12 +8,21 @@
 
 digits = datasets.load_digits()
 
-X_train = digits.images
+X_train = digits.data
 y_train = digits.target
 
 # Write your solution here :
+def k_means(X_train, y_train, cluster = 10, random_state = 9):
+    
+    k_model = KMeans(init='random',n_clusters=cluster, random_state=random_state)
+    k_model.fit(X_train)
+    
+    for i in range(10):
+        plt.figure(figsize=(1,1))
+        plt.imshow(k_model.cluster_centers_[i].reshape(8,8))
+        plt.show()
 
 
 
-
+k_means(X_train, y_train, cluster = 10, random_state = 9)
 
diff --git a/q01_k_means/tests/__pycache__/__init__.cpython-36.pyc b/q01_k_means/tests/__pycache__/__init__.cpython-36.pyc
index 4d0c118..82893e8 100644
Binary files a/q01_k_means/tests/__pycache__/__init__.cpython-36.pyc and b/q01_k_means/tests/__pycache__/__init__.cpython-36.pyc differ
diff --git a/q01_k_means/tests/__pycache__/test_q01_k_means.cpython-36.pyc b/q01_k_means/tests/__pycache__/test_q01_k_means.cpython-36.pyc
index c05e1f0..67ec8cd 100644
Binary files a/q01_k_means/tests/__pycache__/test_q01_k_means.cpython-36.pyc and b/q01_k_means/tests/__pycache__/test_q01_k_means.cpython-36.pyc differ
diff --git a/q02_hierarchy_clustering/__pycache__/__init__.cpython-36.pyc b/q02_hierarchy_clustering/__pycache__/__init__.cpython-36.pyc
index 8510bd5..e1bc51a 100644
Binary files a/q02_hierarchy_clustering/__pycache__/__init__.cpython-36.pyc and b/q02_hierarchy_clustering/__pycache__/__init__.cpython-36.pyc differ
diff --git a/q02_hierarchy_clustering/__pycache__/build.cpython-36.pyc b/q02_hierarchy_clustering/__pycache__/build.cpython-36.pyc
index 5171cbf..d2bf3c4 100644
Binary files a/q02_hierarchy_clustering/__pycache__/build.cpython-36.pyc and b/q02_hierarchy_clustering/__pycache__/build.cpython-36.pyc differ
diff --git a/q02_hierarchy_clustering/build.py b/q02_hierarchy_clustering/build.py
index 724237c..23a9eb3 100644
--- a/q02_hierarchy_clustering/build.py
+++ b/q02_hierarchy_clustering/build.py
@@ -1,15 +1,46 @@
+# %load q02_hierarchy_clustering/build.py
 # Default imports
 
 import pandas as pd
 import matplotlib.pyplot as plt
-plt.switch_backend('agg')
+#plt.switch_backend('agg')
 from sklearn.preprocessing import scale
 from scipy.cluster import hierarchy
 from sklearn import datasets
-
 digits = datasets.load_digits()
-df = pd.DataFrame(scale(digits.data), index=digits.target)
-
+#df = pd.DataFrame(scale(digits.data), index=digits.target)
 # Write your solution here :
+def hierarchy_clustering(X_train):
+    
+    df = pd.DataFrame(scale(X_train))
+    plt.subplot(1,4,1)
+    Z = hierarchy.linkage(df,'complete')
+    hierarchy.dendrogram(Z,leaf_rotation=90,leaf_font_size=8)
+    plt.title('Complete linkage')
+    plt.plot()
+
+    plt.subplot(1,4,2)
+    Z = hierarchy.linkage(df,'average')
+    hierarchy.dendrogram(Z,leaf_rotation=90,leaf_font_size=8)
+    plt.title('Average linkage')
+    plt.plot()
+
+    plt.subplot(1,4,3)
+    Z = hierarchy.linkage(df,'single')
+    hierarchy.dendrogram(Z,leaf_rotation=90,leaf_font_size=8)
+    plt.title('Single linkage')
+    plt.plot()
+
+    plt.subplot(1,4,4)
+    Z = hierarchy.linkage(df,'ward')
+    hierarchy.dendrogram(Z,leaf_rotation=90,leaf_font_size=8)
+    plt.title('Ward')
+    plt.plot()
+
+    plt.show()
+
+hierarchy_clustering(digits.data)
+
+
 
 
diff --git a/q02_hierarchy_clustering/tests/__pycache__/__init__.cpython-36.pyc b/q02_hierarchy_clustering/tests/__pycache__/__init__.cpython-36.pyc
index c783cf0..06062f3 100644
Binary files a/q02_hierarchy_clustering/tests/__pycache__/__init__.cpython-36.pyc and b/q02_hierarchy_clustering/tests/__pycache__/__init__.cpython-36.pyc differ
diff --git a/q02_hierarchy_clustering/tests/__pycache__/test_q02_hierarchy_clustering.cpython-36.pyc b/q02_hierarchy_clustering/tests/__pycache__/test_q02_hierarchy_clustering.cpython-36.pyc
index 180ff26..bb5ce5a 100644
Binary files a/q02_hierarchy_clustering/tests/__pycache__/test_q02_hierarchy_clustering.cpython-36.pyc and b/q02_hierarchy_clustering/tests/__pycache__/test_q02_hierarchy_clustering.cpython-36.pyc differ