def process(data, batch_size, shuffle): load = data[data.columns[1]] load = load.tolist() data = data.values.tolist() load = (load - n) / (m - n)#归一化操作 seq = [] for i in range(len(data) - 24): train_seq = []#shape:24*1 train_label = [] for j in range(...
function [train_data,test_data]=LSTM_data_process() %% 数据加载并完成初始归一化 train_data_initial= [0.4413 0.4707 0.6953 0.8133 0.4379 0.4677 0.6981 0.8002 0.4517 0.4725 0.7006 0.8201; 0.4379 0.4677 0.6981 0.8002 0.4517 0.4725 0.7006 0.8201 0.4557 0.4790 0.7019 0.8211; 0.4517 0.4725 0.7006 0.8201 ...
%% 数据加载,并归一化处理 [train_data,test_data]=LSTM_data_process(); data_length=size(train_data,1); data_num=size(train_data,2); %% 网络参数初始化 % 结点数设置 input_num=12; cell_num=18; output_num=4; % 网络中门的偏置 bias_input_gate=rand(1,cell_num); bias_forget_gate=rand...
from sklearn.model_selection import train_test_split data_prepro = data_process(data, 30, 7) X_train, X_test, y_train, y_test = train_test_split(data_prepro['datain'], data_prepro['dataout'], test_size=0.2) X_train = torch.from_numpy(X_train.astype(np.float32)) X_test =...
from data_read import date_process train_x,train_y=date_process() # 序列长度 int_sequence_len = train_x.shape[1] # 每个序列的长度 int_a = train_x.shape[2] # 输出几个元素 几步: out_len = train_y.shape[1] # 划分验证集和测试集 ...
defprocess_data(self,n):ifself.data is None:self.getData()feature=[self.data.iloc[i:i+n].values.tolist()foriinrange(len(self.data)-n+2)ifi+n<len(self.data)]label=[self.data.open.values[i+n]foriinrange(len(self.data)-n+2)ifi+n<len(self.data)]train_x=feature[:500]test_x...
true_result=[]; %% 对每个分量建模 for i=1:c disp(['对第',num2str(i),'个分量建模']) [x,y]=data_process(imf(i,:),24); %归一化 [xs,mappingx]=mapminmax(x',0,1);x=xs'; [ys,mappingy]=mapminmax(y',0,1);y=ys';
data_process │ ├── cnews_data_process.py │ ├── __init__.py │ ├── medical_question_data_process.py │ └── weibo_data_process.py ├── inference.py ├── logger │ ├── __init__.py │ ├── logger_config.yml │ ├── logger.py ├── model │ ├── ...
batch_size) for Dtr, Val, path in zip(Dtrs, Vals, LSTM_PATHS): train(args, Dtr, Val, path) Dtrs, Vals, Dtes, m, n = load_data(args, flag, batch_size=1) m_test(args, Dtes, LSTM_PATHS, m, n) 模型测试:多步预测的每一步,都需要用不同的模型来进行预测。值得注意的是,在正式...
]m,n=np.max(train[train.columns[1]]),np.min(train[train.columns[1]])defprocess(data,...