def run_example():
""" Load a data table, compute a list of clusters and plot a list of clusters. Set DESKTOP = True/False to use either matplotlib or simplegui """
data_table = load_data_table(DATA_3108_URL)
#data_table = load_data_table(DATA_111_URL)
singleton_list = []
for line in data_table:
singleton_list.append(
alg_cluster.Cluster(set([line[0]]), line[1], line[2], line[3],
line[4]))
# ************** Here we have to choose the type of clustering we want to use for visualization ********************
#cluster_list = sequential_clustering(singleton_list, 15); print "Displaying", len(cluster_list), "sequential clusters"
#cluster_list = alg_project3_solution.hierarchical_clustering(singleton_list, 9); print "Displaying", len(cluster_list), "hierarchical clusters"
cluster_list = alg_project3_solution.kmeans_clustering(
singleton_list, 9, 5)
print "Displaying", len(cluster_list), "k-means clusters"
if DESKTOP: # draw the clusters using matplotlib or simplegui
alg_clusters_matplotlib.plot_clusters(data_table, cluster_list, False)
# alg_clusters_matplotlib.plot_clusters(data_table, cluster_list, True) # add cluster centers
else:
alg_clusters_simplegui.PlotClusters(
data_table,
cluster_list) # use toggle in GUI to add cluster centers
def run_example():
"""
Load a data table, compute a list of clusters and
plot a list of clusters
Set DESKTOP = True/False to use either matplotlib or simplegui
"""
data_table = load_data_table(DATA_3108_URL)
singleton_list = []
for line in data_table:
singleton_list.append(
alg_cluster.Cluster(set([line[0]]), line[1], line[2], line[3],
line[4]))
#cluster_list = sequential_clustering(singleton_list, 15)
cluster_list = hierarchical_clustering(singleton_list, 15)
print "Displaying", len(cluster_list), "sequential clusters"
#cluster_list = alg_project3_solution.hierarchical_clustering(singleton_list, 9)
#print "Displaying", len(cluster_list), "hierarchical clusters"
#cluster_list = alg_project3_solution.kmeans_clustering(singleton_list, 9, 5)
#print "Displaying", len(cluster_list), "k-means clusters"
# draw the clusters using matplotlib or simplegui
if DESKTOP:
alg_clusters_matplotlib.plot_clusters(data_table, cluster_list, True)
else:
alg_clusters_simplegui.PlotClusters(data_table, cluster_list)
def run_example():
"""
Load a data table, compute a list of clusters and
plot a list of clusters
Set DESKTOP = True/False to use either matplotlib or simplegui
"""
data_table = load_data_table(DATA_111_URL)
singleton_list = []
for line in data_table:
singleton_list.append(
alg_cluster.Cluster(set([line[0]]), line[1], line[2], line[3],
line[4]))
#cluster_list = sequential_clustering(singleton_list, 15)
#print "Displaying", len(cluster_list), "sequential clusters"
#cluster_list = closest_pairs_and_clustering_algorithms.hierarchical_clustering(singleton_list, 9)
#print "Displaying", len(cluster_list), "hierarchical clusters"
cluster_list = closest_pairs_and_clustering_algorithms.kmeans_clustering(
singleton_list, 9, 5)
print "Displaying", len(cluster_list), "k-means clusters"
# draw the clusters using matplotlib or simplegui
if DESKTOP:
#alg_clusters_matplotlib.plot_clusters(data_table, cluster_list, False)
alg_clusters_matplotlib.plot_clusters(data_table, cluster_list,
True) #add cluster centers
else:
alg_clusters_simplegui.PlotClusters(
data_table,
cluster_list) # use toggle in GUI to add cluster centers
def run_question(number, data_set):
"""
Load a data table, compute a list of clusters and
plot a list of clusters.
Set DESKTOP = True/False to use either matplotlib or simplegui
"""
global DESKTOP
print "Loading data table ..."
data_table = load_data_table(data_set)
print "Data table loaded. Creating clusters ..."
singleton_list = []
# set correct number of clusters
if number in [2, 3]:
num_clusters = 15
elif number in [5, 6]:
num_clusters = 9
print "\nQuestion number: ", number
print "Number of clusters to be calculated: ", num_clusters
# parse data_table into cluster objects
for line in data_table:
singleton_list.append(
alg_cluster.Cluster(set([line[0]]), line[1], line[2], line[3],
line[4]))
print "\nCluster list created. Passing list to hierarchical_clustering ..."
# calculate clusters
if number == 0:
cluster_list = sequential_clustering(singleton_list, 15)
print "Displaying", len(cluster_list), "sequential clusters"
elif number in [2, 5]:
cluster_list = cpf.hierarchical_clustering(singleton_list,
num_clusters)
print "Displaying", len(cluster_list), "hierarchical clusters"
elif number in [3, 6]:
cluster_list = cpf.kmeans_clustering(singleton_list, num_clusters, 5)
print "Displaying", len(cluster_list), "k-means clusters"
else:
"Please pass a valid number to run_question. Valid options are 0, 2, 3, 5, or 6."
# draw the clusters using matplotlib or simplegui
if DESKTOP:
# alg_clusters_matplotlib.plot_clusters(data_table, cluster_list, False)
alg_clusters_matplotlib.plot_clusters(data_table, cluster_list,
True) #add cluster centers
else:
alg_clusters_simplegui.PlotClusters(
data_table,
cluster_list) # use toggle in GUI to add cluster centers
def run_kmeans_example():
"""
Load a data table, compute a list of clusters and
plot a list of clusters
Set DESKTOP = True/False to use either matplotlib or simplegui
"""
data_table = load_data_table(DATA_896_URL)
singleton_list = []
for line in data_table:
singleton_list.append(
alg_cluster.Cluster(set([line[0]]), line[1], line[2], line[3],
line[4]))
def compute_distortion(cluster_list):
error = 0
for cluster in cluster_list:
error += cluster.cluster_error(data_table)
return error
error = []
for cluster_num in range(6, 21):
cluster_list = kmeans_clustering(singleton_list, cluster_num, 5)
error.append(compute_distortion(cluster_list))
singleton_list = []
for line in data_table:
singleton_list.append(
alg_cluster.Cluster(set([line[0]]), line[1], line[2], line[3],
line[4]))
return error
print("Displaying", len(cluster_list), "kmeans clusters")
# cluster_list = alg_project3_solution.hierarchical_clustering(singleton_list, 9)
# print "Displaying", len(cluster_list), "hierarchical clusters"
# cluster_list = alg_project3_solution.kmeans_clustering(singleton_list, 9, 5)
# print "Displaying", len(cluster_list), "k-means clusters"
# draw the clusters using matplotlib or simplegui
if DESKTOP:
# alg_clusters_matplotlib.plot_clusters(data_table, cluster_list, False)
alg_clusters_matplotlib.plot_clusters(data_table, cluster_list,
True) # add cluster centers
else:
alg_clusters_simplegui.PlotClusters(
data_table,
cluster_list) # use toggle in GUI to add cluster centers
def Question5():
data_table = load_data_table(DATA_111_URL)
singleton_list = []
for line in data_table:
singleton_list.append(
alg_cluster.Cluster(set([line[0]]), line[1], line[2], line[3],
line[4]))
cluster_list = module3_project.hierarchical_clustering(singleton_list, 9)
print "Displaying", len(cluster_list), "hierarchical clusters"
# draw the clusters using matplotlib or simplegui
if DESKTOP:
alg_clusters_matplotlib.plot_clusters(data_table, cluster_list, True)
else:
alg_clusters_simplegui.PlotClusters(data_table, cluster_list)
def run_example():
"""
Load a data table, compute a list of clusters and
plot a list of clusters
Set DESKTOP = True/False to use either matplotlib or simplegui
"""
data_table = load_data_table(DATA_111_URL)
singleton_list = []
for line in data_table:
singleton_list.append(
alg_cluster.Cluster(set([line[0]]), line[1], line[2], line[3],
line[4]))
# cluster_list = sequential_clustering(singleton_list, 15)
# print "Displaying", len(cluster_list), "sequential clusters"
# Question 2 answer: uncomment bottom two lines in block comment with
# data_table being equal to load_data_table(DATA_3108_URL)
# cluster_list = alg_project3_solution.hierarchical_clustering(singleton_list, 9)
# print "Displaying", len(cluster_list), "hierarchical clusters"
# Question 3 answer: uncomment bottom two lines in block comment with
# data_table being equal to load_data_table(DATA_3108_URL)
# cluster_list = alg_project3_solution.kmeans_clustering(singleton_list, 15, 5)
# print "Displaying", len(cluster_list), "k-means clusters"
# Question 5 answer: uncomment bottom two lines in block comment with
# data_table being equal to load_data_table(DATA_111_URL)
# cluster_list = alg_project3_solution.hierarchical_clustering(singleton_list, 9)
# print "Displaying", len(cluster_list), "hierarchical clusters"
# Question 6 answer: uncomment bottom two lines in block comment with
# data_table being equal to load_data_table(DATA_111_URL)
# cluster_list = alg_project3_solution.kmeans_clustering(singleton_list, 9, 5)
# print "Displaying", len(cluster_list), "k-means clusters"
# draw the clusters using matplotlib or simplegui
if DESKTOP:
alg_clusters_matplotlib.plot_clusters(data_table, cluster_list, True)
else:
alg_clusters_simplegui.PlotClusters(
data_table,
cluster_list) # use toggle in GUI to add cluster centers
def run_example():
"""
Load a data table, compute a list of clusters and
plot a list of clusters
Set DESKTOP = True/False to use either matplotlib or simplegui
"""
#data_table = load_data_table(DATA_3108_URL)
#data_table = load_data_table(DATA_111_URL)
#data_table = load_data_table(DATA_290_URL)
data_table = load_data_table(DATA_896_URL)
singleton_list = []
for line in data_table:
singleton_list.append(alg_cluster.Cluster(set([line[0]]), line[1], line[2], line[3], line[4]))
#cluster_list = sequential_clustering(singleton_list, 9)
#print "Displaying", len(cluster_list), "sequential clusters"
#start_time = time.clock()
cluster_list = alg_project3_solution.hierarchical_clustering(singleton_list, 9)
#print "hierarchical:",compute_distortion(cluster_list)
#print "time taken for plot:"+ str(time.clock() - start_time)
print "Displaying", len(cluster_list), "hierarchical clusters"
#start_time = time.clock()
#cluster_list = alg_project3_solution.kmeans_clustering(singleton_list, 9, 5)
#print "kmeans: ",compute_distortion(cluster_list)
#print "time taken for plot:"+ str(time.clock() - start_time)
#print "Displaying", len(cluster_list), "k-means clusters"
# draw the clusters using matplotlib or simplegui
if DESKTOP:
alg_clusters_matplotlib.plot_clusters(data_table, cluster_list, False)
alg_clusters_matplotlib.plot_clusters(data_table, cluster_list, True) #add cluster centers
#print ""
else:
alg_clusters_simplegui.PlotClusters(data_table, cluster_list) # use toggle in GUI to add cluster centers
def run_example():
"""
Load a data table, compute a list of clusters and
plot a list of clusters
Set DESKTOP = True/False to use either matplotlib or simplegui
"""
data_table = load_data_table(DATA_896_URL)
singleton_list = []
for line in data_table:
singleton_list.append(
alg_cluster.Cluster(set([line[0]]), line[1], line[2], line[3],
line[4]))
# cluster_list = sequential_clustering(singleton_list, 15)
# print "Displaying", len(cluster_list), "sequential clusters"
cluster_list = alg_project3_solution.hierarchical_clustering(
singleton_list, 9)
print "Displaying", len(cluster_list), "hierarchical clusters"
# cluster_list = alg_project3_solution.kmeans_clustering(singleton_list, 9, 5)
# print "Displaying", len(cluster_list), "k-means clusters"
# calculate cluster_error of hierarchical_clustering and kmeans_clustering
hc_error = compute_distortion(
alg_project3_solution.hierarchical_clustering(singleton_list, 9),
data_table)
kmc_error = compute_distortion(
alg_project3_solution.kmeans_clustering(singleton_list, 9, 5),
data_table)
print "cluster_error:\nhierarchical_clustring: ", hc_error, "\nkmeans_clustering: ", kmc_error
# draw the clusters using matplotlib or simplegui
if DESKTOP:
# alg_clusters_matplotlib.plot_clusters(data_table, cluster_list, False)
alg_clusters_matplotlib.plot_clusters(data_table, cluster_list,
True) #add cluster centers
else:
alg_clusters_simplegui.PlotClusters(
data_table,
cluster_list) # use toggle in GUI to add cluster centers
def run_question(number, data_set):
"""
Load a data table, compute a list of clusters and
plot a list of clusters.
Set DESKTOP = True/False to use either matplotlib or simplegui
"""
global DESKTOP
print "Loading data table ..."
data_table = load_data_table(data_set)
print "Data table loaded. Creating clusters ..."
singleton_list = []
for line in data_table:
singleton_list.append(
alg_cluster.Cluster(set([line[0]]), line[1], line[2], line[3],
line[4]))
print "Cluster list created. Passing list to hierarchical_clustering ..."
if number == 0:
cluster_list = sequential_clustering(singleton_list, 15)
print "Displaying", len(cluster_list), "sequential clusters"
elif number in [2, 5]:
cluster_list = cpf.hierarchical_clustering(singleton_list, 9)
print "Displaying", len(cluster_list), "hierarchical clusters"
elif number in [3, 6]:
cluster_list = cpf.kmeans_clustering(singleton_list, 9, 5)
print "Displaying", len(cluster_list), "k-means clusters"
else:
"Please pass a valid number to run_question."
# draw the clusters using matplotlib or simplegui
if DESKTOP:
# alg_clusters_matplotlib.plot_clusters(data_table, cluster_list, False)
alg_clusters_matplotlib.plot_clusters(data_table, cluster_list,
True) #add cluster centers
else:
alg_clusters_simplegui.PlotClusters(
data_table,
cluster_list) # use toggle in GUI to add cluster centers
def run_example(table_type, clustering_algorithm, num_cluster, plot=True):
"""
Load a data table, compute a list of clusters and
plot a list of clusters
Set DESKTOP = True/False to use either matplotlib or simplegui
"""
data_table = load_data_table(table_type)
singleton_list = []
for line in data_table:
singleton_list.append(
alg_cluster.Cluster(set([line[0]]), line[1], line[2], line[3],
line[4]))
#cluster_list = sequential_clustering(singleton_list, 9)
#print "Displaying", len(cluster_list), "sequential clusters"
if clustering_algorithm == "h":
cluster_list = alg_project3_solution.hierarchical_clustering(
singleton_list, num_cluster)
print "get", len(cluster_list), "hierarchical clusters"
elif clustering_algorithm == "k":
cluster_list = alg_project3_solution.kmeans_clustering(
singleton_list, num_cluster, 5)
print "get", len(cluster_list), "k-means clusters"
# draw the clusters using matplotlib or simplegui
if plot:
if DESKTOP:
#alg_clusters_matplotlib.plot_clusters(data_table, cluster_list, False)
alg_clusters_matplotlib.plot_clusters(data_table, cluster_list,
True) #add cluster centers
else:
alg_clusters_simplegui.PlotClusters(
data_table,
cluster_list) # use toggle in GUI to add cluster centers
return (cluster_list, data_table)
def run_example():
"""
Load a data table, compute a list of clusters and
plot a list of clusters
Set DESKTOP = True/False to use either matplotlib or simplegui
"""
data_table = load_data_table(DATA_290_URL)
singleton_list = []
for line in data_table:
singleton_list.append(
alg_cluster.Cluster(set([line[0]]), line[1], line[2], line[3],
line[4]))
# cluster_list = sequential_clustering(singleton_list, 15)
# print "Displaying", len(cluster_list), "sequential clusters"
# cluster_list = alg_project3_solution.hierarchical_clustering(singleton_list, 16)
# distortion = application3.compute_distortion(cluster_list, data_table)
# print "Displaying", len(cluster_list), "hierarchical clusters, distortion:", distortion
cluster_list = alg_project3_solution.kmeans_clustering(
singleton_list, 16, 5)
distortion = application3.compute_distortion(cluster_list, data_table)
print "Displaying", len(
cluster_list), "k-means clusters, distortion:", distortion
# draw the clusters using matplotlib or simplegui
if DESKTOP:
alg_clusters_matplotlib.plot_clusters(data_table, cluster_list, True)
# alg_clusters_matplotlib.plot_clusters(data_table, cluster_list, True) #add cluster centers
else:
alg_clusters_simplegui.PlotClusters(
data_table,
cluster_list) # use toggle in GUI to add cluster centers
def run_example():
"""
Load a data table, compute a list of clusters and
plot a list of clusters
Set DESKTOP = True/False to use either matplotlib or simplegui
"""
data_table = load_data_table('unifiedCancerData_111.csv')
singleton_list = []
for line in data_table:
singleton_list.append(
alg_cluster.Cluster(set([line[0]]), line[1], line[2], line[3],
line[4]))
# cluster_list = sequential_clustering(singleton_list, 15)
# print "Displaying", len(cluster_list), "sequential clusters"
# cluster_list = alg_project3_solution.hierarchical_clustering(singleton_list, 9)
# print 'hierarchical error', app3_7.compute_distortion(cluster_list, data_table)
# print "Displaying", len(cluster_list), "hierarchical clusters"
cluster_list = alg_project3_solution.kmeans_clustering(
singleton_list, 9, 5)
print 'k means error', app3_7.compute_distortion(cluster_list, data_table)
print "Displaying", len(cluster_list), "k-means clusters"
# draw the clusters using matplotlib or simplegui
if DESKTOP:
# alg_clusters_matplotlib.plot_clusters(data_table, cluster_list, False)
alg_clusters_matplotlib.plot_clusters(data_table, cluster_list,
True) #add cluster centers
else:
alg_clusters_simplegui.PlotClusters(
data_table,
cluster_list) # use toggle in GUI to add cluster centers
def run_example(table, method):
"""
Load a data table, compute a list of clusters and
plot a list of clusters
Set DESKTOP = True/False to use either matplotlib or simplegui
"""
#data_table = load_data_table(DATA_3108_URL)
#data_table = load_data_table(DATA_290_URL)
data_table = load_data_table(table)
singleton_list = []
for line in data_table:
singleton_list.append(
alg_cluster.Cluster(set([line[0]]), line[1], line[2], line[3],
line[4]))
#cluster_list = sequential_clustering(singleton_list, 15)
#print "Displaying", len(cluster_list), "sequential clusters"
cluster_distortion_dict = {}
start = 20
end = 6
count = start
new_list = list(singleton_list)
while count >= end:
if method == 'h_cluster':
cluster_list = alg_project3_solution.hierarchical_clustering(
new_list, count)
cluster_distortion_dict[count] = compute_distortion(
cluster_list, data_table)
new_list = cluster_list
elif method == 'k_cluster':
cluster_list = alg_project3_solution.kmeans_clustering(
singleton_list, count, 5)
cluster_distortion_dict[count] = compute_distortion(
cluster_list, data_table)
#new_list = cluster_list
count -= 1
#print "Displaying", len(cluster_list), "hierarchical clusters"
#print "Displaying", len(cluster_list), "hierarchical clusters cluster error"
#cluster_list = alg_project3_solution.kmeans_clustering(singleton_list, 9, 5)
#print "Displaying", len(cluster_list), "k-means clusters"
#print "Displaying", len(cluster_list), "k-means clusters cluster error"
# draw the clusters using matplotlib or simplegui
if DESKTOP:
#alg_clusters_matplotlib.plot_clusters(data_table, cluster_list, False)
#alg_clusters_matplotlib.plot_clusters(data_table, cluster_list, True) #add cluster centers
#print compute_distortion(cluster_list, data_table)
return cluster_distortion_dict
else:
alg_clusters_simplegui.PlotClusters(
data_table,
cluster_list) # use toggle in GUI to add cluster centers
def run_example():
"""
Load a data table, compute a list of clusters and
plot a list of clusters
Set DESKTOP = True/False to use either matplotlib or simplegui
"""
algo_used = 1 # 1: sequential clusters, 2: hierarchical clusters, 3: k-means clusters
data_urls = [DATA_3108_URL, DATA_896_URL, DATA_290_URL, DATA_111_URL]
source = 3 # pick which data source url
data_table = load_data_table(data_urls[source - 1])
def clustering(algo_used, num_clusters, num_iter = 5):
"""
Uses specified algorithm to cluster data
input: int for specified algorithm, data_table
output: cluster_list
"""
singleton_list = []
for line in data_table:
singleton_list.append(alg_cluster.Cluster(set([line[0]]), line[1], line[2], line[3], line[4]))
if algo_used == 1:
cluster_list = sequential_clustering(singleton_list, num_clusters)
print "Displaying", len(cluster_list), "sequential clusters"
elif algo_used == 2:
cluster_list = prj3.hierarchical_clustering(singleton_list, num_clusters)
print "Displaying", len(cluster_list), "hierarchical clusters"
elif algo_used == 3:
cluster_list = prj3.kmeans_clustering(singleton_list, num_clusters, num_iter)
print "Displaying", len(cluster_list), "k-means clusters"
return cluster_list
def gen_random_clusters(num_clusters):
"""
Creates a list of clusters where each cluster in this list corresponds to one randomly generated point in the 2 x 2 square
Input: number of clusters (int)
Output: list of random clusters that is num_clusters long (list)
"""
cluster_list = []
for cluster in xrange(num_clusters):
x = random.choice([1, -1]) * random.random()
y = random.choice([1, -1]) * random.random()
cluster_list.append(alg_cluster.Cluster(set([]), x, y, 1, 0))
return cluster_list
def question_one():
"""
Function for answering first question
"""
xvals = range(2, 200)
slow_yvals = []
fast_yvals = []
for num in xvals:
cluster_list = gen_random_clusters(num)
initial = time.time()
answer = prj3.slow_closest_pairs(cluster_list)
final = time.time()
slow_yvals.append(final - initial)
for num in xvals:
cluster_list = gen_random_clusters(num)
initial = time.time()
answer = prj3.fast_closest_pair(cluster_list)
final = time.time()
fast_yvals.append(final - initial)
slow_line = plt.plot(xvals, slow_yvals, color='r', label="Slow Closest Pair")
fast_line = plt.plot(xvals, fast_yvals, color='b', label="Fast Closest Pair")
plt.legend(loc=2)
plt.title("Efficiency of Slow and Fast Closest Pairs Algorithms")
plt.xlabel("Number of Clusters")
plt.ylabel("Run Times in Milliseconds")
plt.show()
def compute_distortion(cluster_list):
"""
Takes a list of clusters and uses cluster_error to compute its distortion.
input: list of clusters, original data table
output: cluster distortion int
"""
distortion = 0
for cluster in cluster_list:
distortion += cluster.cluster_error(data_table)
return distortion
def question_ten():
"""
Function for answering question 10
"""
xvals = xrange(6, 21)
kmeans_y = []
high_y = []
for clusters in xvals:
kmeans_y.append(compute_distortion(clustering(3, clusters)))
for clusters in xvals:
high_y.append(compute_distortion(clustering(2, clusters)))
kmeans_line = plt.plot(xvals, kmeans_y, color='r', label="K-Means Clustering")
high_line = plt.plot(xvals, high_y, color='b', label="Hierarchical Clustering")
plt.legend()
plt.title("Distortion Comparison Between Clustering Methods on 290 County Data Set")
plt.xlabel("Number of Output Clusters")
plt.ylabel("Distortion")
plt.show()
#question_one()
#question_ten()
# draw the clusters using matplotlib or simplegui
cluster_list = clustering(1, 5)
if DESKTOP:
alg_clusters_matplotlib.plot_clusters(data_table, cluster_list, True)
else:
alg_clusters_simplegui.PlotClusters(data_table, cluster_list)
def run_example():
"""
Load a data table, compute a list of clusters and
plot a list of clusters
Set DESKTOP = True/False to use either matplotlib or simplegui
"""
data_table = load_data_table(DATA_896_URL)
singleton_list = []
for line in data_table:
singleton_list.append(
alg_cluster.Cluster(set([line[0]]), line[1], line[2], line[3],
line[4]))
# cluster_list = sequential_clustering(singleton_list, 15)
# print "Displaying", len(cluster_list), "sequential clusters"
# question 5
# cluster_list = alg_project3_solution.hierarchical_clustering(singleton_list, 9)
# print "Displaying", len(cluster_list), "hierarchical clusters"
# question 6
# cluster_list = alg_project3_solution.kmeans_clustering(singleton_list, 9, 5)
# print "Displaying", len(cluster_list), "k-means clusters"
# question 7
# cluster_list = alg_project3_solution.kmeans_clustering(singleton_list, 9, 5)
# kmeans_result = alg_project3_solution.compute_distortion(cluster_list, data_table)
# print("Displaying", kmeans_result, "kmeans_result")
# cluster_list = alg_project3_solution.hierarchical_clustering(singleton_list, 9)
# hierarchical_result = alg_project3_solution.compute_distortion(cluster_list, data_table)
# print("Displaying", hierarchical_result, "hierarchical_result")
# question 10
kmeans_res = []
for clusters_number in range(6, 21):
cluster_list = alg_project3_solution.kmeans_clustering(
singleton_list, clusters_number, 5)
kmeans_res.append([
clusters_number,
alg_project3_solution.compute_distortion(cluster_list, data_table)
])
hier_res = []
for clusters_number in range(20, 5, -1):
cluster_list = alg_project3_solution.hierarchical_clustering(
singleton_list, clusters_number)
hier_res.append([
clusters_number,
alg_project3_solution.compute_distortion(cluster_list, data_table)
])
hier_res.reverse()
# draw the clusters using matplotlib or simplegui
if DESKTOP:
# alg_clusters_matplotlib.plot_clusters(data_table, cluster_list, False)
# alg_clusters_matplotlib.plot_clusters(data_table, cluster_list, True) # add cluster centers
create_separate_plots(kmeans_res, hier_res)
else:
alg_clusters_simplegui.PlotClusters(
data_table,
cluster_list) # use toggle in GUI to add cluster centers
