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)