def sort_by_standard_deviation():
global transposed_raw_data_matrix
global ALL_count
global MLL_count
global AML_count
global snr_tuples
global progressbar_total
sample_length = len(transposed_raw_data_matrix[0])
sample_count = len(transposed_raw_data_matrix)
MLL_end_index = ALL_count + MLL_count
for attribute_index in range(sample_length - 1):
attribute_list = list()
for sample_index in range(sample_count):
attribute_list.append(
transposed_raw_data_matrix[sample_index][attribute_index])
sd_value = statistics.standard_deviation(
attribute_list[:ALL_count]) + statistics.standard_deviation(
attribute_list[ALL_count:MLL_end_index]
) + statistics.standard_deviation(attribute_list[MLL_end_index:])
rounded_sd = math.ceil(sd_value * 10000) / 10000
snr_tuples.append((attribute_index, rounded_sd))
sort.randomized_quick_sort_for_tuples(snr_tuples, 0, len(snr_tuples) - 1)
progressbar.show(9,
progressbar_total,
prefix="Progress:",
suffix="Complete",
length=50)
def sort_by_SNR():
global data_matrix
global after_th_count
global snr_tuples
global progressbar_total
sample_length = len(data_matrix[0])
sample_count = len(data_matrix)
for attribute_index in range(sample_length - 1):
attribute_list = list()
for sample_index in range(sample_count):
attribute_list.append(data_matrix[sample_index][attribute_index])
snr_value = snr.mod_SNR(attribute_list[:after_th_count],
attribute_list[after_th_count:])
rounded_snr = math.ceil(snr_value * 10000) / 10000
snr_tuples.append((attribute_index, rounded_snr))
sort.randomized_quick_sort_for_tuples(snr_tuples, 0, len(snr_tuples) - 1)
progressbar.show(8,
progressbar_total,
prefix='Progress:',
suffix='Complete',
length=50)
def sort_by_standard_deviation():
global data_matrix
global ALL_count
global snr_tuples
global progressbar_total
sample_length = len(data_matrix[0])
sample_count = len(data_matrix)
for attribute_index in range(1, sample_length):
attribute_list = list()
for sample_index in range(sample_count):
attribute_list.append(data_matrix[sample_index][attribute_index])
sd_value = statistics.standard_deviation(
attribute_list[:ALL_count]) + statistics.standard_deviation(attribute_list[(ALL_count + 1):])
rounded_sd = math.ceil(sd_value * 10000) / 10000
snr_tuples.append((attribute_index, rounded_sd))
sort.randomized_quick_sort_for_tuples(snr_tuples, 0, len(snr_tuples) - 1)
progressbar.show(7, progressbar_total, prefix='Progress:',
suffix='Complete', length=50)