def test_VQPCA(self):
VQPCA = clustering.lpca(self.X)
VQPCA.eigens = self.nPCtest
VQPCA.clusters = self.nKtest
VQPCA.initialization = 'uniform'
VQPCA.writeFolder = False
try:
idx = VQPCA.fit()
passed = True
except:
passed = False
self.assertEqual(passed, True)
"evaluate_clustering":
True,
}
X = readCSV(file_options["path_to_file"], file_options["input_file_name"])
print("matrix dimensions: {}".format(X.shape))
if settings["select_variables"]:
PVs = model_order_reduction.variables_selection(X, settings)
labels, numbers = PVs.fit()
redX = X[:, numbers]
print("The new data dimensions are: {}".format(X.shape))
model = clustering.lpca(redX, settings)
index = model.fit()
if settings["evaluate_clustering"]:
#evaluate the clustering solution
PHC_coeff, PHC_deviations = evaluate_clustering_PHC(X,
index,
method='PHC_standard')
print(PHC_coeff)
#evaluate the clustering solution by means of the Davies-Bouldin index
X_tilde = center_scale(X, center(X, method=settings["centering_method"]),
scale(X, method=settings["scaling_method"]))
DB = evaluate_clustering_DB(X_tilde, index)
False, # --> call the method to classify a new matrix Y on the basis of the lpca clustering
"write_on_txt":
True, # --> write the idx vector containing the label for each observation
"evaluate_clustering":
True, # --> enable the calculation of indeces to evaluate the goodness of the clustering
}
# Load the matrices and the mesh, to plot the classification results
X = readCSV(file_options["path_to_file"], file_options["input_file_name"])
Y = readCSV(file_options["path_to_file"], file_options["test_file_name"])
mesh = np.genfromtxt(mesh_options["path_to_file"] + "/" +
mesh_options["mesh_file_name"],
delimiter=',')
# Start the clustering step
model = clustering.lpca(X, settings_clustering)
index = model.fit()
# Start the classification step
classifier = classification.VQPCA(X, index, Y)
classification_vector = classifier.fit()
# Plot the results
matplotlib.rcParams.update({'font.size': 14, 'text.usetex': True})
fig = plt.figure()
axes = fig.add_axes([0.15, 0.15, 0.7, 0.7], frameon=True)
axes.scatter(mesh[:, 0],
mesh[:, 1],
c=classification_vector,
alpha=1,
cmap='gnuplot')