def Validate(self, subset):
# Ensure that given the subset is valid.
for feat in subset:
if (0 > feat) or (feat > self.classifier.num_features):
print("ERROR: Subset does not match features.")
return 0
# Make copy of the dataset for feature selection.
# temp_normalized = self.classifier.normalized.copy()
to_delete = []
# Trim down to given subset.
if subset != [0]:
for col in range(self.classifier.num_features + 1)[1:]:
if col not in subset:
to_delete.append(col)
# K-fold vars.
fold_list = []
folded = []
num_correct = 0
print("\n--------------------------------------")
print("\nPerforming k-fold cross validation.\n")
# Perform k-fold cross validation.
for leave_out in range(self.classifier.num_points):
# Prepare temporary datasets for k-folding.
fold_list = self.classifier.normalized.copy()
folded = self.classifier.normalized[leave_out].copy()
del fold_list[leave_out]
# Remove the columns not specified by the subset.
fold_list = np.delete(fold_list, to_delete, axis=1)
folded = np.delete(folded, to_delete)
# Create classifier for folded data.
temp_c = Classifier()
temp_c.Train(fold_list)
result = temp_c.Test(folded)
# Increment if classification accurate.
if result == folded[0]:
num_correct += 1
return num_correct / self.classifier.num_points
