import numpy as np
import joblib
from Utils import get_color
import matplotlib.pyplot as plt
from DimensionReduction import get_normalize
# 获取一位受试者的训练数据
train_data = np.array(joblib.load('data/event_data_by_S_single.pkl')[0])
train_event = np.array(joblib.load('data/event_labels_by_S_single.pkl')[0])
non_p300_wave_avg = np.zeros(200)
p300_wave_avg = np.zeros(200)
for i in range(60):
wave = np.array(get_normalize(train_data[i])).T[0]
if train_event[i] == 0:
non_p300_wave_avg = non_p300_wave_avg + wave
else:
p300_wave_avg = p300_wave_avg + wave
non_p300_wave_avg = non_p300_wave_avg / 50
p300_wave_avg = p300_wave_avg / 10
print(train_data.shape)
print(train_data.shape, train_event.shape)
default_colors = ['r', 'b']
colors = get_color(range(8))
x = range(0, 800, 4)
return labels_predict, ARI(labels, labels_predict)
sim_data = Similarity(dim_data, alpha=0.6, beta=0.3)
pred, ari = calc_and_output(sim_data)
rel = RelevanceMatrix(pred)
print(len(rel[rel > 1e-10]))
sim_next = sim_data
l0 = len(sim_next[sim_next > 1e-10])
while l0 != 0:
print('l0:', l0)
# print(sim_next, rel)
sim_next = 0.01 * sim_next + 0.99 * rel
pred, ari = calc_and_output(sim_next)
rel = get_normalize(RelevanceMatrix(pred))
l0 = len(sim_next[sim_next > 1e-5])
# calc_and_output(sim_dis)
# calc_and_output(sim_cor)
# calc_and_output(sim_mu)
#
# x = []
# y = []
# z = []
# step = 0.1
# n_steps = int(1 / step) + 1
# m_z = np.zeros((n_steps, n_steps))
#
# range_index = np.arange(0, 1.1, 0.1)
#
# range_index = [round(i, 2) for i in range_index]
test_event_data_by_S = []
for i in range(5):
test_data = test_data_by_S[i]
test_event = test_event_by_S[i]
char_len = test_data.shape[0]
print(char_len)
event_data_s = []
for j in range(9):
char_name = char_names[j]
event = test_event[j]
for k in range(len(event)):
# 剔除实验开始结束时的标记 event
if k % 13 != 0:
# 开始和结束时间在数据表中的索引
event_start = event[k][1] - 1
event_end = event_start + 200
# 获取对应时间段的采样数据
event_data = get_normalize(
np.array(cb_filter(test_data[j])[event_start:event_end]))
event_data_s.append(event_data)
test_event_data_by_S.append(event_data_s)
joblib.dump(test_event_data_by_S, 'data/test_event_data_by_S.pkl')
event_data = np.zeros((200, 20))
for k in range(1, 13):
event_label = get_true_label(char_name, k)
event_labels.append(event_label)
event_data = np.zeros((200, 20))
for idx in range(1, len(event)): # 0 - 66
# 剔除实验开始结束时的标记 event 求五个轮次中同一行列的均值
if event[idx][0] == k:
# 开始和结束时间在数据表中的索引
event_start = event[idx][1] - 1
event_end = event_start + 200
# 获取对应时间段的采样数据
event_data = event_data + get_normalize(
np.array(
cb_filter(train_data[j])[event_start:event_end]))
event_data = event_data / 5
event_data_s.append(event_data)
print(len(event_data_s))
event_data_by_S.append(event_data_s)
event_labels_by_S.append(event_labels)
joblib.dump(event_data_by_S, 'data/event_data_by_S.pkl')
joblib.dump(event_labels_by_S, 'data/event_labels_by_S.pkl')
print(len(event_data_by_S[0]))
print(event_labels_by_S[0])