def calculate_12_month(start_month):
# for dense_model ssta and ha
predict_data = np.empty([1, 20, 27, 2])
data_y = np.empty([1, 20, 27, 2])
data_realistic = np.empty([1, 20, 27, 2])
if is_seasonal_circle:
data_sc = np.empty([1, 1], dtype='int32')
if model_type == 'conv':
data_x = np.empty([1, 20, 27, 2])
else:
data_x = np.empty([1, 1080])
predict_data[0] = file_helper_unformatted.read_data_sstaha(start_month)
if is_retrain:
predict_data = file_helper_unformatted.exchange_rows(predict_data)
# data preprocess z-zero
if data_preprocess_method == 'preprocess_Z':
predict_data = data_preprocess.preprocess_Z(predict_data, 0)
# data preprocess dimensionless
if data_preprocess_method == 'dimensionless':
redict_data = data_preprocess.dimensionless(predict_data, 0)
# data preprocess 0-1
if data_preprocess_method == 'preprocess_01':
predict_data = data_preprocess.preprocess_01(predict_data, 0)
# data preprocess no month mean
if data_preprocess_method == 'nomonthmean':
predict_data = data_preprocess.no_month_mean(predict_data, 0)
if model_type == 'conv':
data_x[0] = predict_data[0]
else:
data_x[0] = np.reshape(predict_data[0], (1, 1080))
ssta_error = np.empty([directly_month])
for i in range(directly_month):
if is_seasonal_circle:
data_sc[0] = [(start_month + i) % 12]
data_x = model.predict([data_x, data_sc])
else:
data_x = model.predict(data_x)
if model_type == 'conv':
data_y[0] = data_x[0]
else:
data_y[0] = np.reshape(data_x[0], (20, 27, 2))
# data preprocess z-zero
if data_preprocess_method == 'preprocess_Z':
data_y = data_preprocess.preprocess_Z(data_y, 1)
# data preprocess dimensionless
if data_preprocess_method == 'dimensionless':
data_y = data_preprocess.dimensionless(data_y, 1)
# data preprocess 0-1
if data_preprocess_method == 'preprocess_01':
data_y = data_preprocess.preprocess_01(data_y, 1)
# data preprocess no month mean
if data_preprocess_method == 'nomonthmean':
data_y = data_preprocess.no_month_mean(data_y, 1)
data_realistic[0] = file_helper_unformatted.read_data_sstaha(
start_month + i + 1)
if is_retrain:
data_realistic = file_helper_unformatted.exchange_rows(
data_realistic)
# ssta_error[i] = calculate_error(data_y[0], data_realistic[0])
ssta_error[i] = math_tool.calculate_rmse(data_y[0], data_realistic[0])
print(ssta_error)
ssta_error2 = ssta_error.copy()
for i in range(1, directly_month):
ssta_error2[i] = (ssta_error[i] - ssta_error[i - 1]) / 10000
ssta_error2[0] = ssta_error2[0] / 10000
return ssta_error2
# predict_data = np.empty([1, 540])
# predict_data[0] = file_helper.read_data(file_num)
# file_helper.write_data(file_num, model.predict(predict_data)[0])
# for dense_model ssta and ha
predict_data = np.empty([1, 20, 27, 2])
data_y = np.empty([1, 20, 27, 2])
if is_seasonal_circle:
data_sc = np.empty([1, 1], dtype='int32')
if model_type == 'conv':
data_x = np.empty([1, 20, 27, 2])
elif model_type == 'dense':
data_x = np.empty([1, 1080])
for start_month in range(file_num, file_num + month - interval, interval):
predict_data[0] = file_helper_unformatted.read_data_sstaha(start_month)
nino34 = [index_calculation.get_nino34(predict_data[0])]
# data preprocess z-zero
if data_preprocess_method == 'preprocess_Z':
predict_data = data_preprocess.preprocess_Z(predict_data, 0)
# data preprocess dimensionless
if data_preprocess_method == 'dimensionless':
redict_data = data_preprocess.dimensionless(predict_data, 0)
# data preprocess 0-1
if data_preprocess_method == 'preprocess_01':
predict_data = data_preprocess.preprocess_01(predict_data, 0)
# data preprocess no month mean
if data_preprocess_method == 'nomonthmean':
predict_data = data_preprocess.no_month_mean(predict_data, 0)
data_yy = np.empty([1, 20, 27, 2])
if is_seasonal_circle:
data_sc = np.empty([1, 1], dtype='int32')
data_y = np.empty([1, 20, 27, 2])
if is_nino_output:
nino34_output = []
rmse_sst = []
nino_temp = 0
nino34 = []
# The time step for non-sequence prediction should be 1, but it was originally written as 0.
# So it caused the inconsistency.
for start_month in range(
file_num - prediction_month * directly_month, file_num + month -
prediction_month * directly_month + 1 - time_step, interval):
if not is_sequence:
predict_data[0] = file_helper_unformatted.read_data_sstaha(
start_month)
predict_data = data_preprocess.data_preprocess(predict_data, 0)
else:
for i in range(time_step):
predict_data[0][i] = file_helper_unformatted.read_data_sstaha(
start_month + i + 1)
predict_data[0] = data_preprocess.data_preprocess(
predict_data[0], 0)
if is_retrain:
predict_data = file_helper_unformatted.exchange_rows(predict_data)
if model_type == 'conv':
data_x[0] = predict_data[0]
elif model_type == 'dense':
data_x[0] = np.reshape(predict_data[0], (1, 1080))
def get_nino34_from_data(file_num, month):
nino3 = []
for i in range(file_num, file_num + month + 1):
data = file_helper_unformatted.read_data_sstaha(i)
nino3.append(get_nino34(data))
return nino3
last_month = 15
data_preprocess_method = name_list.data_preprocess_method
# for dense_model
# predict_data = np.empty([1, 540])
# predict_data[0] = file_helper.read_data(file_num)
# file_helper.write_data(file_num, model.predict(predict_data)[0])
# for dense_model ssta and ha
for last in range(last_month):
predict_data = np.empty([1, 20, 27, 2])
data_y = np.empty([1, 20, 27, 2])
if model_type == 'conv':
data_x = np.empty([1, 20, 27, 2])
else:
data_x = np.empty([1, 1080])
predict_data[0] = file_helper_unformatted.read_data_sstaha(file_num + last)
if is_retrain:
predict_data = file_helper_unformatted.exchange_rows(predict_data)
nino34 = [index_calculation.get_nino34(predict_data[0])]
# data preprocess z-zero
if data_preprocess_method == 'preprocess_Z':
predict_data = data_preprocess.preprocess_Z(predict_data, 0)
# data preprocess dimensionless
if data_preprocess_method == 'dimensionless':
redict_data = data_preprocess.dimensionless(predict_data, 0)
# data preprocess 0-1
if data_preprocess_method == 'preprocess_01':
predict_data = data_preprocess.preprocess_01(predict_data, 0)
# data preprocess no month mean
is_retrain = name_list.is_retrain
is_seasonal_circle = name_list.is_seasonal_circle
"""
file_num: the first prediction of data num
month: month num to predict
interval: Prediction interval
prediction_month: For the model to predict month num
directly_month: rolling run model times
"""
file_num = predict_369_data.file_num
month = predict_369_data.month
interval = 1
prediction_month = predict_369_data.prediction_month
directly_month = predict_369_data.directly_month
data_preprocess_method = name_list.data_preprocess_method
# make directory
file_path = "D:\msy\projects\zc\zcdata\data_networks\\" + model_name + '\\' + str(
directly_month) + '\\'
rmse_all = []
for start_month in range(file_num + prediction_month * directly_month,
file_num + month + 1, interval):
data_x = file_helper_unformatted.read_data_best(file_path, start_month)
data_from_data = file_helper_unformatted.read_data_sstaha(start_month)
rmse_all.append(
math_tool.calculate_rmse(data_x[:, :, 0], data_from_data[:, :, 0]))
print(rmse_all)
print(sum(rmse_all) / rmse_all.__len__())
directly_month = 12
data_preprocess_method = name_list.data_preprocess_method
# for dense_model
# predict_data = np.empty([1, 540])
# predict_data[0] = file_helper.read_data(file_num)
# file_helper.write_data(file_num, model.predict(predict_data)[0])
# for dense_model ssta and ha
predict_data = np.empty([1, 20, 27, 2])
data_y = np.empty([1, 20, 27, 2])
data_realistic = np.empty([1, 20, 27, 2])
if model_type == 'conv':
data_x = np.empty([1, 20, 27, 2])
else:
data_x = np.empty([1, 1080])
predict_data[0] = file_helper_unformatted.read_data_sstaha(file_num)
if is_retrain:
predict_data = file_helper_unformatted.exchange_rows(predict_data)
# data preprocess z-zero
if data_preprocess_method == 'preprocess_Z':
predict_data = data_preprocess.preprocess_Z(predict_data, 0)
# data preprocess dimensionless
if data_preprocess_method == 'dimensionless':
redict_data = data_preprocess.dimensionless(predict_data, 0)
# data preprocess 0-1
if data_preprocess_method == 'preprocess_01':
predict_data = data_preprocess.preprocess_01(predict_data, 0)
# data preprocess no month mean
if data_preprocess_method == 'nomonthmean':
predict_data = data_preprocess.no_month_mean(predict_data, 0)
