hac_model = HAC(test[1])
# Glass dataset
if "glass" in test[0]:
kmeans_sfs_glass = np.array([1,3])
kmeans_model.cluster(data_instances[:,kmeans_sfs_glass])
print "Kmeans SFS glass performance = %f" % kmeans_model.calculate_performance()
kmeans_ga_glass = np.array([0,1,2,3,4,5,6])
kmeans_model = KMeans(test[1])
kmeans_model.cluster(data_instances[:,kmeans_ga_glass])
print "Kmeans GA glass performance = %f" % kmeans_model.calculate_performance()
hac_sfs_glass = np.array([0])
hac_model.cluster(data_instances[:,hac_sfs_glass])
print "HAC SFS glass performance = %f" % hac_model.calculate_performance()
# Iris dataset
elif "iris" in test[0]:
kmeans_sfs_iris = np.array([1])
kmeans_model = KMeans(test[1])
kmeans_model.cluster(data_instances[:,kmeans_sfs_iris])
print "Kmeans SFS iris performance = %f" % kmeans_model.calculate_performance()
kmeans_ga_iris = np.array([0,1])
kmeans_model = KMeans(test[1])
kmeans_model.cluster(data_instances[:,kmeans_ga_iris])
print "Kmeans GA iris performance = %f" % kmeans_model.calculate_performance()
hac_sfs_iris = np.array([0])
hac_model.cluster(data_instances[:,hac_sfs_iris])
print "HAC SFS glass performance = %f" % hac_model.calculate_performance()
hac_ga_iris = np.array([1,2])
if "glass" in test[0]:
kmeans_sfs_glass = np.array([1, 3])
kmeans_model.cluster(data_instances[:, kmeans_sfs_glass])
print("K-means SFS glass performance = %f" %
kmeans_model.calculate_performance())
kmeans_ga_glass = np.array([0, 1, 2, 3, 4, 5, 6])
kmeans_model = KMeans(test[1])
kmeans_model.cluster(data_instances[:, kmeans_ga_glass])
print("K-means GA glass performance = %f" %
kmeans_model.calculate_performance())
hac_sfs_glass = np.array([0])
hac_model.cluster(data_instances[:, hac_sfs_glass])
print("HAC SFS glass performance = %f" %
hac_model.calculate_performance())
# Iris dataset
elif "iris" in test[0]:
kmeans_sfs_iris = np.array([1])
kmeans_model = KMeans(test[1])
kmeans_model.cluster(data_instances[:, kmeans_sfs_iris])
print("K-means SFS iris performance = %f" %
kmeans_model.calculate_performance())
kmeans_ga_iris = np.array([0, 1])
kmeans_model = KMeans(test[1])
kmeans_model.cluster(data_instances[:, kmeans_ga_iris])
print("K-means GA iris performance = %f" %
kmeans_model.calculate_performance())
