GeneticAlgorithmPython/example_clustering_3.py at master · winpython/GeneticAlgorithmPython · GitHub

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importnumpy
importmatplotlib.pyplot
importpygad

cluster1_num_samples=20
cluster1_x1_start=0
cluster1_x1_end=5
cluster1_x2_start=2
cluster1_x2_end=6
cluster1_x1=numpy.random.random(size=(cluster1_num_samples))
cluster1_x1=cluster1_x1* (cluster1_x1_end-cluster1_x1_start) +cluster1_x1_start
cluster1_x2=numpy.random.random(size=(cluster1_num_samples))
cluster1_x2=cluster1_x2* (cluster1_x2_end-cluster1_x2_start) +cluster1_x2_start

cluster2_num_samples=20
cluster2_x1_start=4
cluster2_x1_end=12
cluster2_x2_start=14
cluster2_x2_end=18
cluster2_x1=numpy.random.random(size=(cluster2_num_samples))
cluster2_x1=cluster2_x1* (cluster2_x1_end-cluster2_x1_start) +cluster2_x1_start
cluster2_x2=numpy.random.random(size=(cluster2_num_samples))
cluster2_x2=cluster2_x2* (cluster2_x2_end-cluster2_x2_start) +cluster2_x2_start

cluster3_num_samples=20
cluster3_x1_start=12
cluster3_x1_end=18
cluster3_x2_start=8
cluster3_x2_end=11
cluster3_x1=numpy.random.random(size=(cluster3_num_samples))
cluster3_x1=cluster3_x1* (cluster3_x1_end-cluster3_x1_start) +cluster3_x1_start
cluster3_x2=numpy.random.random(size=(cluster3_num_samples))
cluster3_x2=cluster3_x2* (cluster3_x2_end-cluster3_x2_start) +cluster3_x2_start

c1=numpy.array([cluster1_x1, cluster1_x2]).T
c2=numpy.array([cluster2_x1, cluster2_x2]).T
c3=numpy.array([cluster3_x1, cluster3_x2]).T

data=numpy.concatenate((c1, c2, c3), axis=0)

matplotlib.pyplot.scatter(cluster1_x1, cluster1_x2)
matplotlib.pyplot.scatter(cluster2_x1, cluster2_x2)
matplotlib.pyplot.scatter(cluster3_x1, cluster3_x2)
matplotlib.pyplot.title("Optimal Clustering")
matplotlib.pyplot.show()

defeuclidean_distance(X, Y):
"""
 Calculate the euclidean distance between X and Y. It accepts:
 :X should be a matrix of size (N, f) where N is the number of samples and f is the number of features for each sample.
 :Y should be of size f. In other words, it is a single sample.

 Returns a vector of N elements with the distances between the N samples and the Y.
 """

returnnumpy.sqrt(numpy.sum(numpy.power(X-Y, 2), axis=1))

defcluster_data(solution, solution_idx):
"""
 Clusters the data based on the current solution.
 """

globalnum_clusters, feature_vector_length, data
cluster_centers= [] # A list of size (C, f) where C is the number of clusters and f is the number of features representing each sample.
all_clusters_dists= [] # A list of size (C, N) where C is the number of clusters and N is the number of data samples. It holds the distances between each cluster center and all the data samples.
clusters= [] # A list with C elements where each element holds the indices of the samples within a cluster.
clusters_sum_dist= [] # A list with C elements where each element represents the sum of distances of the samples with a cluster.

forclust_idxinrange(num_clusters):
# Return the current cluster center.
cluster_centers.append(solution[feature_vector_length*clust_idx:feature_vector_length*(clust_idx+1)])
# Calculate the distance (e.g. euclidean) between the current cluster center and all samples.
cluster_center_dists=euclidean_distance(data, cluster_centers[clust_idx])
all_clusters_dists.append(numpy.array(cluster_center_dists))

cluster_centers=numpy.array(cluster_centers)
all_clusters_dists=numpy.array(all_clusters_dists)

# A 1D array that, for each sample, holds the index of the cluster with the smallest distance.
# In other words, the array holds the sample's cluster index.
cluster_indices=numpy.argmin(all_clusters_dists, axis=0)
forclust_idxinrange(num_clusters):
clusters.append(numpy.where(cluster_indices==clust_idx)[0])
# Calculate the sum of distances for the cluster.
iflen(clusters[clust_idx]) ==0:
# In case the cluster is empty (i.e. has zero samples).
clusters_sum_dist.append(0)
else:
# When the cluster is not empty (i.e. has at least 1 sample).
clusters_sum_dist.append(numpy.sum(all_clusters_dists[clust_idx, clusters[clust_idx]]))
# clusters_sum_dist.append(numpy.sum(euclidean_distance(data[clusters[clust_idx], :], cluster_centers[clust_idx])))

clusters_sum_dist=numpy.array(clusters_sum_dist)

returncluster_centers, all_clusters_dists, cluster_indices, clusters, clusters_sum_dist

deffitness_func(solution, solution_idx):
_, _, _, _, clusters_sum_dist=cluster_data(solution, solution_idx)

# The tiny value 0.00000001 is added to the denominator in case the average distance is 0.
fitness=1.0/ (numpy.sum(clusters_sum_dist) +0.00000001)

returnfitness

num_clusters=3
feature_vector_length=data.shape[1]
num_genes=num_clusters*feature_vector_length

ga_instance=pygad.GA(num_generations=100,
sol_per_pop=10,
init_range_low=0,
init_range_high=20,
num_parents_mating=5,
keep_parents=2,
num_genes=num_genes,
fitness_func=fitness_func,
suppress_warnings=True)

ga_instance.run()

best_solution, best_solution_fitness, best_solution_idx=ga_instance.best_solution()
print("Best solution is {bs}".format(bs=best_solution))
print("Fitness of the best solution is {bsf}".format(bsf=best_solution_fitness))
print("Best solution found after {gen} generations".format(gen=ga_instance.best_solution_generation))

cluster_centers, all_clusters_dists, cluster_indices, clusters, clusters_sum_dist=cluster_data(best_solution, best_solution_idx)

forcluster_idxinrange(num_clusters):
cluster_x=data[clusters[cluster_idx], 0]
cluster_y=data[clusters[cluster_idx], 1]
matplotlib.pyplot.scatter(cluster_x, cluster_y)
matplotlib.pyplot.scatter(cluster_centers[cluster_idx, 0], cluster_centers[cluster_idx, 1], linewidths=5)
matplotlib.pyplot.title("Clustering using PyGAD")
matplotlib.pyplot.show()