commit-live-students · soham009 · Jan 2, 2019 · Jan 2, 2019
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
@@ -1,18 +1,37 @@
+# %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
-
+import pandas as pd
 
 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, y_train)
+    a = X_train[(y_train == 0) & (kmeans.labels_ == 0)][0:20]
+    b = X_train[(y_train == 1) & (kmeans.labels_ == 1)][0:20]
+    c = X_train[(y_train == 2) & (kmeans.labels_ == 2)][0:20]
+    d = X_train[(y_train == 3) & (kmeans.labels_ == 3)][0:20]
+    e = X_train[(y_train == 4) & (kmeans.labels_ == 4)][0:20]
+    f = X_train[(y_train == 5) & (kmeans.labels_ == 5)][0:20]
+    g = X_train[(y_train == 6) & (kmeans.labels_ == 6)][0:20]
+    h = X_train[(y_train == 7) & (kmeans.labels_ == 7)][0:20]
+    i = X_train[(y_train == 8) & (kmeans.labels_ == 8)][0:20]
+    j = X_train[(y_train == 9) & (kmeans.labels_ == 9)][0:20]
+
+    for char in (a, b, c, d, e, f, g, h, i, j):
+        for index in range(0, len(char)):
+            plt.subplot(10, 20, index + 1)
+            plt.axis('off')
+            plt.imshow(char[index])
+        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
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
@@ -1,5 +1,3 @@
-# Default imports
-
 import pandas as pd
 import matplotlib.pyplot as plt
 plt.switch_backend('agg')
@@ -11,5 +9,19 @@
 df = pd.DataFrame(scale(digits.data), index=digits.target)
 
 # Write your solution here :
+def hierarchy_clustering(df):
+    Z = hierarchy.linkage(df, 'average')
+    plt.figure(figsize=(25, 10))
+    plt.title('Hierarchical Clustering Dendrogram')
+    plt.xlabel('sample index')
+    plt.ylabel('distance')
+    hierarchy.dendrogram(
+    Z,
+    leaf_rotation=90.,  # rotates the x axis labels
+    leaf_font_size=8.,  # font size for the x axis labels
+    )
+    plt.show()
+
+hierarchy_clustering(df)
 
 
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