def calculate_probability_matrix(clustering):
"""
"""
num_clusters = len(clustering.clusters)
total_elements,cluster_sizes = clusTools.get_cluster_sizes(clustering.clusters) #@UnusedVariable
class_list = clustering.gen_class_list()
prob_matrix = []
for i in range(num_clusters):
row = [0.]*num_clusters
prob_matrix.append(row)
prob_increments = []
for i in range(num_clusters):
prob_increments.append(1./cluster_sizes[i])
for i in range(len(class_list)-1):
current_cluster = class_list[i]
next_cluster = class_list[i+1]
prob_matrix[current_cluster][next_cluster] += prob_increments[current_cluster]
return prob_matrix
def calculate_probability_matrix(clustering):
"""
"""
num_clusters = len(clustering.clusters)
total_elements,cluster_sizes = clusTools.get_cluster_sizes(clustering.clusters) #@UnusedVariable
class_list = clustering.gen_class_list()
prob_matrix = []
for i in range(num_clusters):
row = [0.]*num_clusters
prob_matrix.append(row)
prob_increments = []
for i in range(num_clusters):
prob_increments.append(1./cluster_sizes[i])
for i in range(len(class_list)-1):
current_cluster = class_list[i]
next_cluster = class_list[i+1]
prob_matrix[current_cluster][next_cluster] += prob_increments[current_cluster]
return prob_matrix
def analysis_function_mean_cluster_size(self, clustering):
"""
Returns the mean cluster size.
"""
sizes = get_cluster_sizes(clustering.clusters)[1]
return numpy.mean(sizes)
def test_get_sizes(self):
myclusters = []
for c in test_data.clusters:
myclusters.append(cluster_from_tuple(c))
sizes = [5,4,4,4,3]
numpy.testing.assert_array_equal(sizes, get_cluster_sizes(myclusters)[1], "Cluster sizes are different")
def analysis_function_mean_cluster_size(self,clustering):
"""
Returns the mean cluster size.
"""
sizes = get_cluster_sizes(clustering.clusters)[1]
return numpy.mean(sizes)