for _ in range(n): a.append([c for c in input()])

This code snippet creates a 2D list called "a" which contains n sublists, where each sublist contains characters inputted by the user.

The "for _ in range(n)" loop is used to iterate n times and append a new sublist to "a" each time.

The "input()" function is used to prompt the user to enter a string of characters. The "for c in input()" loop is used to iterate through each character in the input string and append it to the current sublist being created.

For example, if n = 3 and the user enters the following three strings: "abc", "def", and "ghi", then the resulting "a" list would be:

[['a', 'b', 'c'], ['d', 'e', 'f'], ['g', 'h', 'i']]

相关问题

scaler = MinMaxScaler() train_data_scaled = scaler.fit_transform(train_data) X=[] y=[] for i in range(len(train_data_scaled)-max(n_predictions)): X.append(train_data_scaled[i:i+1,1:5]) y.append(train_data_scaled[i:i+1,0:1])基于以上代码帮我编写一个可以多步预测的LSTM模型

好的，以下是一个基于以上代码的多步预测的LSTM模型：

from sklearn.preprocessing import MinMaxScaler
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import LSTM, Dense

# 将数据进行归一化
scaler = MinMaxScaler()
train_data_scaled = scaler.fit_transform(train_data)

# 定义函数，用于将数据转换为LSTM所需的格式
def create_dataset(X, y, time_steps=1):
    Xs, ys = [], []
    for i in range(len(X) - time_steps):
        Xs.append(X[i:(i + time_steps)])
        ys.append(y[i + time_steps])
    return np.array(Xs), np.array(ys)

# 定义超参数
n_predictions = 3
n_features = 4
n_units = 32
n_epochs = 100
batch_size = 32

# 将数据转换为LSTM所需的格式
X, y = create_dataset(train_data_scaled[:, [0, 1, 2, 3]], train_data_scaled[:, [4]], n_predictions)

# 构建LSTM模型
model = Sequential()
model.add(LSTM(n_units, activation='relu', input_shape=(n_predictions, n_features)))
model.add(Dense(1))
model.compile(optimizer='adam', loss='mse')

# 训练模型
model.fit(X, y, epochs=n_epochs, batch_size=batch_size, verbose=1)

# 进行多步预测
predictions = []
current_batch = train_data_scaled[-n_predictions:, 1:5].reshape(1, n_predictions, n_features)
for i in range(n_predictions):
    current_pred = model.predict(current_batch)[0]
    predictions.append(current_pred)
    current_batch = np.append(current_batch[:, 1:, :], [[current_pred]], axis=1)

# 将预测结果进行逆归一化
predictions = scaler.inverse_transform(predictions)

该模型使用了一个LSTM层和一个全连接层，其中LSTM层的输出被连接到Dense层进行最终预测。训练过程中使用均方误差作为损失函数，Adam优化器进行参数更新。在进行多步预测时，模型首先使用最后n_predictions个数据点进行初始化，然后进行n_predictions次预测，每次预测使用上一次的预测结果作为输入。最后，将预测结果进行逆归一化，得到最终的预测结果。

用python实现找出一个自然数的全部因数，并存入列表，最后逐项输出。 Ls =[] Num = eval(input('请输入一个自然数')) for i in range(____, ______): if _________: Ls.append(_______) # 以下补充输出代码

Ls = []
Num = eval(input('请输入一个自然数: '))
for i in range(1, Num+1):
if Num % i == 0:
Ls.append(i)

# 输出
for factor in Ls:
print(factor)