def logistic(InputFileName):
raw_data = load_data(InputFileName)
all_normalised_data = append_classifications(raw_data)
all_normalised_data = normalise(all_normalised_data)
# all_normalised_data = all_normalised_data
training_data = append_features(all_normalised_data)
expander = FeatureExpander(training_data)
inclusion_list = []
inclusion_list.append(0) # last sv
inclusion_list.append(0) # last change in sv
inclusion_list.append(0) # mean of prev 10 rows sv
inclusion_list.append(0) # std dev of prev 10 rows sv
inclusion_list.append(0) # last sp
inclusion_list.append(0) # last change in sp
inclusion_list.append(0) # mean of prev 10 rows sp
inclusion_list.append(0) # std dev of prev 10 rows sp
expanded = expander.expand_features(inclusion_list)
write_to_file(expanded, fp_out)
[expanded_CV1, expanded_CV2] = split_data(expanded)
THETA_CV1 = regression(expanded_CV1)
THETA_CV2 = regression(expanded_CV2)
print THETA_CV1
print THETA_CV2
return evaluate(expanded_CV1, expanded_CV2, THETA_CV1, THETA_CV2)
def linear(InputFileName):
raw_data = load_data(InputFileName)
training_data = append_features(raw_data)
expander = FeatureExpander(training_data)
inclusion_list = []
inclusion_list.append(2) # last sv
inclusion_list.append(0) # last change in sv
inclusion_list.append(0) # mean of prev 10 rows sv
inclusion_list.append(0) # std dev of prev 10 rows sv
inclusion_list.append(0) # last sp
inclusion_list.append(0) # last change in sp
inclusion_list.append(0) # mean of prev 10 rows sp
inclusion_list.append(0) # std dev of prev 10 rows sp
expanded = expander.expand_features(inclusion_list)
write_to_file(expanded, fp_out)
[expanded_CV1, expanded_CV2] = split_data(expanded)
THETA_CV1 = regression(expanded_CV1)
THETA_CV2 = regression(expanded_CV2)
result = evaluate(expanded_CV1,expanded_CV2,THETA_CV1,THETA_CV2)
print THETA_CV1
print THETA_CV2
return result
