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import numpy | |
import csv | |
def write_to_csv(ratios, file_name): | |
rows = [] | |
for i in range(len(ratios)): | |
row = [] | |
for key, value in ratios[i].items(): | |
row.append(value) | |
rows.append(row) | |
with open(file_name, 'a', newline='') as csvfile: | |
csvwriter = csv.writer(csvfile) | |
csvwriter.writerows(rows) | |
csvfile.close() | |
def write_line_to_csv(ratios, file_name): | |
row = [] | |
for key, value in ratios.items(): | |
row.append(value) | |
with open(file_name, 'a', newline='') as csvfile: | |
csvwriter = csv.writer(csvfile) | |
csvwriter.writerow(row) | |
def standard_deviation(values): | |
_mean = numpy.mean(values) | |
differences = [(value - _mean) ** 2 for value in values] | |
sum_of_differences = sum(differences) | |
return (sum_of_differences / (len(values) - 1)) ** 0.5 | |
def normalization(ratios_list): | |
_mean = numpy.mean(list(ratios_list.values())) | |
_deviation = standard_deviation(list(ratios_list.values())) | |
z_score = {} | |
for z_key, z_i in ratios_list.items(): | |
z_score[z_key] = (z_i - _mean) / _deviation | |
lb = 0 | |
ub = 1.618 | |
_min = min(z_score.values()) | |
_max = max(z_score.values()) | |
linear = {} | |
for z_key, z_i in z_score.items(): | |
linear[z_key] = lb + (z_i - _min) * (ub - lb) / (_max - _min) | |
return linear | |