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def forest_run(dimensions, patterns, pattern_labels, metric='qe', k_up=20, k_down=2, simulations=55, iterations=50):
"""
A method for watching Forest Gump run
:param dimensions: the dimensionality of the data
:param patterns: the data itself
:param metric: the quality metric
:param k_up: the maximum number of clusters
:param k_down: the minimum number of clusters
:param simulations: the number of simulations for each k
:param iterations: the number of iterations for each k-means pass
"""
# variable to store the best result
best_clustering = None
# the quality of that result
best_quality = 1000.00
# write results out to file while simulating
file_out = 'E:\Monte Carlo Final Results' + '_' + metric + '.csv'
with open(file_out, 'w', newline='') as f:
# different k values to test on
for i in range(k_down, k_up):
num_clusters = i
# number of retries / simulations
for j in range(simulations):
# create a clustering solution and apply k-means
clustering = Clustering(dimensions, num_clusters, patterns, 0.0001)
clustering.k_means_clustering(iterations)
# used to compute quality of the solution
quality = ClusteringQuality(clustering, 0.0001)
this_quality = 0.0
if metric == 'qe':
this_quality = quality.quantization_error()
if metric == 'si':
this_quality = quality.average_silhouette_index()
if metric == 'db':
this_quality = quality.davies_bouldin()
# update the best clustering
if this_quality < best_quality:
best_quality = this_quality
best_clustering = clustering
print("Updated best clustering")
# write result to the file
result = [num_clusters, this_quality]
for x in result:
f.write(str(x))
f.write(",")
f.write("\n")
f.flush()
print(j, result)
# print the actual clustering out to console
best_clustering.print_solution(pattern_labels)
if __name__ == "__main__":
# cProfile.run('forest_run()')
# set the number of dimensions in the data
dimensionality = 19
# load the data into an object
data = Data("E:\Website Documents\Clustering\Final Analysis\Final Data Set.csv")
# get the patterns from the object (list of lists)
pattern_labels = []
patterns_data, pattern_labels = data.load_data()
# specify the metric
# qe = quantization error
# si = silhouette index
# db = davies-bouldin
# forest_run(dimensionality, patterns_data)
forest_run(dimensionality, patterns_data, pattern_labels, simulations=1, k_down=8, k_up=9)
# forest_run(dimensionality, patterns_data, metric='si')
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