commit-live-students · TheSarang · Sep 24, 2018 · Sep 24, 2018 · Sep 24, 2018
diff --git a/__pycache__/__init__.cpython-36.pyc b/__pycache__/__init__.cpython-36.pyc
diff --git a/q01_k_means/__pycache__/__init__.cpython-36.pyc b/q01_k_means/__pycache__/__init__.cpython-36.pyc
diff --git a/q01_k_means/__pycache__/build.cpython-36.pyc b/q01_k_means/__pycache__/build.cpython-36.pyc
diff --git 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
@@ -9,8 +10,16 @@
 
 X_train = digits.images
 y_train = digits.target
+import numpy as np
+X = np.reshape(X_train, (len(X_train), -1))
+
+def k_means(X_train,y_train,cluster=10,random_state=9):
+    km = KMeans(init='random', n_clusters=cluster)
+    km.fit(X)
+
+    plt.imshow(X)
+    plt.show()
 
-# Write your solution here :
 
 
 

diff --git a/q01_k_means/tests/__pycache__/__init__.cpython-36.pyc b/q01_k_means/tests/__pycache__/__init__.cpython-36.pyc
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
diff --git a/q02_hierarchy_clustering/__pycache__/__init__.cpython-36.pyc b/q02_hierarchy_clustering/__pycache__/__init__.cpython-36.pyc
diff --git a/q02_hierarchy_clustering/__pycache__/build.cpython-36.pyc b/q02_hierarchy_clustering/__pycache__/build.cpython-36.pyc
diff --git a/q02_hierarchy_clustering/build.py b/q02_hierarchy_clustering/build.py
@@ -1,3 +1,4 @@
+# %load q02_hierarchy_clustering/build.py
 # Default imports
 
 import pandas as pd
@@ -9,7 +10,21 @@
 
 digits = datasets.load_digits()
 df = pd.DataFrame(scale(digits.data), index=digits.target)
+def hierarchy_clustering(df):
+    fig, (ax1, ax2, ax3, ax4) = plt.subplots(1, 4, figsize=(50, 18))
+
+    for linkage, cluster, ax in zip(
+            [hierarchy.complete(df), hierarchy.average(df), hierarchy.single(df), hierarchy.ward(df)],
+            ['c1', 'c2', 'c3', 'c4'],
+            [ax1, ax2, ax3, ax4]):
+        cluster = hierarchy.dendrogram(linkage, labels=df.index, p=12, truncate_mode='lastp', orientation='top',
+                                       color_threshold=0, leaf_font_size=10, distance_sort=True, ax=ax)
+    ax1.set_title('Complete Linkage')
+    ax2.set_title('Average Linkage')
+    ax3.set_title('Single Linkage')
+    ax4.set_title('Ward')
+    plt.show()
+
 
-# Write your solution here :
 
 
diff --git a/q02_hierarchy_clustering/tests/__pycache__/__init__.cpython-36.pyc b/q02_hierarchy_clustering/tests/__pycache__/__init__.cpython-36.pyc
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