CTotal = [0, 0, 0] MTotal = [0, 0, 0] for i in range(1, DIM + 1): select_arr.append(i) bubble_arr.append(i) insert_arr.append(i) myfile = open("sort_methods.txt", "w") # Все результаты запишем в файл print("\nУПОРЯДОЧЕННАЯ ПОСЛЕДОВАТЕЛЬНОСТЬ: Исходный массив") print(select_arr) count = [0, 0] count = algorithms.select(select_arr, DIM) print("\nУПОРЯДОЧЕННАЯ ПОСЛЕДОВАТЕЛЬНОСТЬ: Результирующий массив") print(select_arr) CTotal[0] = count[0] MTotal[0] = count[1] count = [0, 0] count = algorithms.insert(insert_arr, DIM) CTotal[1] = count[0] MTotal[1] = count[1] count = [0, 0] count = algorithms.bubble(bubble_arr, DIM) CTotal[2] = count[0] MTotal[2] = count[1] print("УПОРЯДОЧЕННАЯ ПОСЛЕДОВАТЕЛЬНОСТЬ:\n")
'num_constraints': 2000, 'gamma': 20.0 }, 'SDML': { 'balance_param': 0.5, 'sparsity_param': 0.1 }, 'LSML': {}, 'NCA': {}, 'LFDA': {}, 'RCA': {} } # ,'LFDA':{'k':2, 'dim': 50} 'NCA':{'learning_rate':0.01}'RCA':{ 'num_chunks':150, 'chunk_size':3} selected = ['GSEA', 'BASE', 'LMNN', 'SDML', 'LSML', 'LFDA', 'NCA'] options = algo.select(methods, selected) Result = algo.ALGO(GeneExp, Label, **options) Dist = Result.Dist Dist['SiamDen'] = np.load('Dist.npy') # Train = Result.inds_train # Test = Result.inds_test Train = np.load('inds_train.npy') Test = np.load('inds_test.npy') amin, amax = Dist['SiamDen'].min(), Dist['SiamDen'].max() Dist['SiamDen'] = 1.0 - (Dist['SiamDen'] - amin) / (amax - amin) perf.roc(Dist, Label, save_figure=True)
import random import collections import matplotlib.pyplot as plt from algorithms import select from branch_predictor import BranchPredictor import plotter history_length = 128 bp = BranchPredictor(history_length) data = list(range(200)) for _ in range(200): random.shuffle(data) result, result_length = select(data, 0, 100, 16, bp) bp.print_accuracies() plotter.generate_plot(bp, "expand")