def DBN_Train(seq_len,win, lr):
data_src = LD.loadCsvData_Np("../data/co2-ppm-mauna-loa-19651980.csv", skiprows=1, ColumnList=[1])
# Australia/US British/US Canadian/US Dutch/US French/US German/US Japanese/US Swiss/US
data_t = data_src[:, 0:1].copy()
# 数据还源时使用
t_mean = np.mean(data_t)
t_min = np.min(data_t)
t_max = np.max(data_t)
# 数据预处理
result, x_result, y_result = LD.dataRecombine_Single(data_t,seq_len)
# print(x_result, y_result)
result_len = len(result)
row = round(0.8 * result.shape[0])
row = result_len - 87
windowSize = row
windowSize = win
# 数据归一化
# data_normalization = EvaluationIndex.归一化.normalization_max_min_负1_1(data_src)
x_result_GY = ((x_result - t_min) / (t_max - t_min)).copy()
y_result_GY = ((y_result - t_min) / (t_max - t_min)).copy()
# x_result = (x_result - t_mean) / np.std(x_result)
# y_result = (y_result - t_mean) / np.std(x_result)
y_rbf_all = []
y_test_all = []
rng = np.random.RandomState(1233)
for y_i in range(row, row+1):
if y_i < windowSize:
continue
x_train = x_result_GY[y_i - windowSize:y_i]
y_train = y_result_GY[y_i - windowSize:y_i]
x_test = x_result[y_i:y_i + 1]
y_test = y_result[y_i:y_i + 1]
net = DBN(layer_sizes=[seq_len,20,40], bp_layer=[1])
net.pretrain(x_train,lr=lr, epochs=200)
net.fineTune(x_train, y_train,lr=lr, epochs=10000)
y_rbf = net.predict(x_train)
import MDLearn.utils.EvalueationIndex as EI
# ei = EI.evalueationIndex(y_rbf, y_train)
# print("归一化训练ＲＭＳＥ")
# ei.show()
#
import MDLearn.utils.Draw as draw
# draw.plot_results_point(y_rbf, y_train)
y_rbf_haunYuan = y_rbf*(t_max - t_min)+t_min
y_train_haunYuan = y_result[y_i - windowSize:y_i]
print("还原训练ＲＭＳＥ")
ei = EI.evalueationIndex(y_rbf_haunYuan, y_train_haunYuan)
ei.show()
draw.plot_results_point(y_rbf_haunYuan, y_train_haunYuan)
'''DBN_T 效果不好 ＲＭＳＥ 在0.08左右'''
def DBN_BP_Test(seq_len, win, lr):
data_src = LD.loadCsvData_Np("../data/co2-ppm-mauna-loa-19651980.csv", skiprows=1, ColumnList=[1])
# Australia/US British/US Canadian/US Dutch/US French/US German/US Japanese/US Swiss/US
data_t =data_src[:,0:1].copy()
# 数据还源时使用
t_mean = np.mean(data_t)
t_min = np.min(data_t)
t_max = np.max(data_t)
# 数据预处理
result,x_result,y_result = LD.dataRecombine_Single(data_t, seq_len)
# print(x_result, y_result)
result_len = len(result)
row = round(0.8 * result.shape[0])
row = result_len - 87
windowSize = row
windowSize = win
# 数据归一化
# data_normalization = EvaluationIndex.归一化.normalization_max_min_负1_1(data_src)
x_result_GY = ((x_result - t_min) / (t_max - t_min)).copy()
y_result_GY = ((y_result - t_min) / (t_max - t_min)).copy()
# x_result = (x_result - t_mean) / np.std(x_result)
# y_result = (y_result - t_mean) / np.std(x_result)
y_rbf_all = []
y_test_all = []
rng = np.random.RandomState(1233)
for y_i in range(row, result_len):
if y_i < windowSize:
continue
x_train = x_result_GY[y_i - windowSize:y_i]
y_train = y_result_GY[y_i - windowSize:y_i]
x_test = x_result_GY[y_i:y_i + 1]
y_test = y_result_GY[y_i:y_i + 1]
# print(x_train, y_train)
# assert False
net = DBN(layer_sizes=[seq_len,20,40], bp_layer=[1])
net.pretrain(x_train,lr=lr, epochs=200)
# net.fineTune(x_train, y_train,lr=lr, epochs=10000)
bp = BP([seq_len,20,40, 1])
w_list, b_list = net.getHyperParameter()
bp.setHyperParameter(w_list, b_list)
bp.train(x_test, y_test, lr=lr, epochs=10000)
y_rbf = bp.predict(x_train)
y_rbf_all.append(y_rbf)
y_test_all.append(y_test)
# print(np.array(y_rbf_all).ravel())
# print(np.array(y_test_all).ravel())#, np.array(y_test_all))
# print("全部预测ＲＭＳＥ")
# y_rbf_all = np.array(y_rbf_all).ravel()
# y_test_all = np.array(y_test_all).ravel()
# ei = EI.evalueationIndex(y_rbf_all, y_test_all)
# ei.show()
import MDLearn.utils.Draw as draw
# draw.plot_results_point(y_rbf_all, y_test_all)
'''还原数据'''
print("DBN_BP_Test还原预测ＲＭＳＥ")
y_rbf_all = np.array(y_rbf_all)
y_test_all = np.array(y_test_all)
y_rbf_haunYuan = y_rbf_all * (t_max - t_min) + t_min
y_test_haunYuan = y_test_all * (t_max - t_min) + t_min
ei = EI.evalueationIndex(y_rbf_haunYuan, y_test_haunYuan)
ei.show()