diff --git a/__init__.pyc b/__init__.pyc index 6c0d88a..f7dec2f 100644 Binary files a/__init__.pyc and b/__init__.pyc differ diff --git a/q01_k_means/__init__.pyc b/q01_k_means/__init__.pyc index bff55bc..eee3c94 100644 Binary files a/q01_k_means/__init__.pyc and b/q01_k_means/__init__.pyc differ diff --git a/q01_k_means/build.py b/q01_k_means/build.py index fca565c..622bf15 100644 --- a/q01_k_means/build.py +++ b/q01_k_means/build.py @@ -10,7 +10,18 @@ y_train = digits.target # Write your solution here : - - - - +import pandas as pd + +def k_means(X_train,y_train,cluster = 10,random_state=9): + cluster = [] + predictions = [] + train = X_train.reshape((X_train.shape[0],X_train.shape[1]*X_train.shape[2])) + + for i in range(10): + km = KMeans(n_clusters=i,random_state=random_state) + km.fit(train) + y_pred = km.predict(train) + clusters.append(i) + predictions.append(y_pred) + plt.plot(clusters,predictions) + plt.show() diff --git a/q01_k_means/build.pyc b/q01_k_means/build.pyc index fa56657..e51974a 100644 Binary files a/q01_k_means/build.pyc and b/q01_k_means/build.pyc differ diff --git a/q01_k_means/tests/__init__.pyc b/q01_k_means/tests/__init__.pyc index f6a37b9..aa55807 100644 Binary files a/q01_k_means/tests/__init__.pyc and b/q01_k_means/tests/__init__.pyc differ diff --git a/q01_k_means/tests/test_q01_k_means.pyc b/q01_k_means/tests/test_q01_k_means.pyc index ac55928..460bfc6 100644 Binary files a/q01_k_means/tests/test_q01_k_means.pyc and b/q01_k_means/tests/test_q01_k_means.pyc differ diff --git a/q02_hierarchy_clustering/__init__.pyc b/q02_hierarchy_clustering/__init__.pyc index 9e9464b..1793a8d 100644 Binary files a/q02_hierarchy_clustering/__init__.pyc and b/q02_hierarchy_clustering/__init__.pyc differ diff --git a/q02_hierarchy_clustering/build.py b/q02_hierarchy_clustering/build.py index 2ba8b26..a1cacf1 100644 --- a/q02_hierarchy_clustering/build.py +++ b/q02_hierarchy_clustering/build.py @@ -10,3 +10,11 @@ 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.,leaf_font_size=8.) + plt.show() diff --git a/q02_hierarchy_clustering/build.pyc b/q02_hierarchy_clustering/build.pyc index 59f6156..953d3c4 100644 Binary files a/q02_hierarchy_clustering/build.pyc and b/q02_hierarchy_clustering/build.pyc differ diff --git a/q02_hierarchy_clustering/tests/__init__.pyc b/q02_hierarchy_clustering/tests/__init__.pyc index bb41aea..76a0245 100644 Binary files a/q02_hierarchy_clustering/tests/__init__.pyc and b/q02_hierarchy_clustering/tests/__init__.pyc differ diff --git a/q02_hierarchy_clustering/tests/test_q02_hierarchy_clustering.pyc b/q02_hierarchy_clustering/tests/test_q02_hierarchy_clustering.pyc index d1b4567..3a7be05 100644 Binary files a/q02_hierarchy_clustering/tests/test_q02_hierarchy_clustering.pyc and b/q02_hierarchy_clustering/tests/test_q02_hierarchy_clustering.pyc differ