def create_cluster_from_neuralgasnetwork(model: model,
a=0.5,
passes=80,
distance_toremove_edge=8):
data = model.mesures().values
model.setname("NEURALGAS avec distance_toremove=" +
str(distance_toremove_edge) + " passes=" + str(passes))
if not model.load_cluster():
model.start_treatment()
gng = GrowingNeuralGas(data)
gng.fit_network(e_b=0.05,
e_n=0.006,
distance_toremove_edge=distance_toremove_edge,
l=100,
a=0.5,
d=0.995,
passes=passes,
plot_evolution=False)
model.end_treatment()
print('Found %d clusters.' % gng.number_of_clusters())
model.clusters_from_real(gng.cluster_data(), "NEURALGAS_")
#gng.plot_clusters(gng.cluster_data())
return model
values = normalize(values).values
# returning values
return values
if __name__ == '__main__':
if os.path.exists('visualization/sequence'):
shutil.rmtree('visualization/sequence')
os.makedirs('visualization/sequence')
n_samples = 1500
data = None
#data = dataset() # in case the user wants to use a specific dataset
#data = datasets.load_breast_cancer()
#data = datasets.load_iris(n_samples=n_samples, random_state=8)
#data = datasets.make_blobs(n_samples=n_samples, random_state=8)
data = datasets.make_moons(n_samples=n_samples, noise=.05)
#data = datasets.make_circles(n_samples=n_samples, factor=.5, noise=.05)
#data = StandardScaler().fit_transform(data.data) # depends on the data variable
data = StandardScaler().fit_transform(data[0]) # depends on the data variable
print('Done.')
print('Fitting neural network...')
gng = GrowingNeuralGas(data)
gng.fit_network(e_b=0.1, e_n=0.006, a_max=10, l=200, a=0.5, d=0.995, passes=5, plot_evolution=True)
print('Found %d clusters.' % gng.number_of_clusters())
gng.plot_clusters(gng.cluster_data())
from gng import GrowingNeuralGas
from sklearn import datasets
from sklearn.preprocessing import StandardScaler
import os
import shutil
__authors__ = 'Adrien Guille'
__email__ = '*****@*****.**'
if __name__ == '__main__':
if os.path.exists('visualization/sequence'):
shutil.rmtree('visualization/sequence')
os.makedirs('visualization/sequence')
n_samples = 2000
dataset_type = 'moons'
data = None
print('Preparing data...')
if dataset_type == 'blobs':
data = datasets.make_blobs(n_samples=n_samples, random_state=8)
elif dataset_type == 'moons':
data = datasets.make_moons(n_samples=n_samples, noise=.05)
elif dataset_type == 'circles':
data = datasets.make_circles(n_samples=n_samples, factor=.5, noise=.05)
data = StandardScaler().fit_transform(data[0])
print('Done.')
print('Fitting neural network...')
gng = GrowingNeuralGas(data)
gng.fit_network(e_b=0.1, e_n=0.006, a_max=10, l=200, a=0.5, d=0.995, passes=8, plot_evolution=True)
print('Found %d clusters.' % gng.number_of_clusters())
gng.plot_clusters(gng.cluster_data())