scaled_data = pickle.load( open("save_scaled_data.p", "rb")) print "plotting correlation 123" one_list_data = list(itertools.chain.from_iterable(scaled_data)) print len(scaled_data) print len(one_list_data) print print len(scaled_data[0]) print print len(one_list_data[0]), one_list_data[0] print len(zip(*one_list_data)) print len(zip(*one_list_data)[0]) # feat_list = zip(*one_list_data) feature_names = FEATURE_NAMES plot_correlation(one_list_data, feature_names, True) plot_correlation(one_list_data, feature_names, False) # plot_correlation(one_list_data)
data_new = pickle.load( open("save_scaled_data_new.p", "rb")) annotations_new = pickle.load( open("save_an_new.p", "rb")) print "..loaded" print len(data) print 0 d = list(itertools.chain.from_iterable(data)) d2 = list(itertools.chain.from_iterable(data_new)) print len(d) print len(d[0]) print "---" # assert 0 plot_correlation(d, FEATURE_NAMES, False) plot_correlation(d, FEATURE_NAMES, False) #=============================================================================== # print "removing candidates from data" # # data = data_new # annotations = annotations_new #=============================================================================== # # print map(sum, annotations) # # # print filter_dead(data[0], ) # # # # # print len([filter_dead(d,a) for d,a in zip(data,annotations) ])