diff --git a/__init__.pyc b/__init__.pyc index 6c0d88a..fb45092 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..032496e 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..f2a8c7c 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 @@ -10,7 +11,9 @@ y_train = digits.target # Write your solution here : +def k_means (X_train,y_train,cluster=10,random_state=9): + km=KMeans(init='random',n_clusters=10).fit(X_train) + plt.scatter(y_train,X_train[:,0,0],c=km,s=50) + plt.show() - - - +#k_means (X_train,y_train,cluster=10,random_state=9) diff --git a/q01_k_means/build.pyc b/q01_k_means/build.pyc index fa56657..181b506 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..62a7dba 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..1be215c 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..dcfc3fa 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..9229d51 100644 --- 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 @@ -10,3 +11,12 @@ df = pd.DataFrame(scale(digits.data), index=digits.target) # Write your solution here : +def hierarchy_clustering (df): + D=hierarchy.linkage(df,'average') + plt.figure(figsize=(25,10)) + plt.title("Hierarchial clustering dendogram") + plt.xlabel('sample index') + plt.ylabel('distance') + hierarchy.dendogram(D,leaf_rotation=90.,leaf_font_size=8.) + plt.show() +#hierarchy_clustering (df) diff --git a/q02_hierarchy_clustering/build.pyc b/q02_hierarchy_clustering/build.pyc index 59f6156..1536823 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..ffa80fd 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..47f05be 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