def test_varSelection(self):
linearSelection = model_order_reduction.variables_selection(self.X)
linearSelection.eigens = self.nPCtest
linearSelection.retained = self.nVarTest
linearSelection.method = self.selMethod1
labels1, ____ = linearSelection.fit()
linearSelection.method = self.selMethod2
labels2, ____ = linearSelection.fit()
linearSelection.method = self.selMethod3
labels3, ____ = linearSelection.fit()
self.assertEqual(len(labels1), self.nVarTest)
self.assertEqual(len(labels2), self.nVarTest)
self.assertEqual(len(labels3), self.nVarTest)
#set the initialization method
"initialization_method":
"uniform",
#enable additional options:
"write_stats":
False,
"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,
newDirName = "trainingMGPCAforPV2ndtry_time" + now.strftime(
"%Y_%m_%d-%H%M")
os.mkdir(newDirName)
os.chdir(newDirName)
# Write a txt to recap the prescribed settings
text_file = open("Recap_training.txt", "wt")
method_used = text_file.write("The PV method is: " + str(settings["method"]) +
". \n")
minVar = text_file.write(
"The number of considered variables for the model reduction is: {} \n".
format(settings["number_of_variables"]))
text_file.close()
# Retrieve the PVs name, and the corresponding numbers in the original matrix X
PV = model_order_reduction.variables_selection(X_preprocessed, settings)
PVs, numbers = PV.fit()
# Save the selected variables in a .txt, they can be useful later on
np.savetxt("Selected_variables.txt", numbers)
# Cut the dimensionality of the original matrix, considering only
# the selected 'm' variables
X_preprocessed_cut = X_preprocessed[:, numbers]
#perform the dimensionality reduction via Principal Component Analysis,
# and return the eigenvectors of the reduced manifold (PCs).
model = model_order_reduction.PCA(X_preprocessed, settings)
PCs, ____ = model.fit()
#compute the projection of the original points on the reduced