import numpy as np n = int(input('输入每个维度的元素个数：')) a = np.arange(1,n**2+1).reshape(n,n) res = np.trace(a) print('矩阵的迹为', res, sep='')怎么用for循环来达到同样的效果

可以使用for循环来遍历矩阵的对角线元素，然后将它们累加起来，最终得到矩阵的迹。修改后的代码如下：

import numpy as np 

n = int(input('输入每个维度的元素个数：'))
a = np.arange(1,n**2+1).reshape(n,n)

res = 0
for i in range(n):
    res += a[i][i]

print('矩阵的迹为', res, sep='')

这样输出的结果与之前使用np.trace()函数得到的结果应该是一致的。

相关问题

import numpy as np n = int(input('输入每个维度的元素个数：')) a = np.arange(1,n**2+1).reshape(n,n) res = np.trace(a) print('矩阵的迹为',res)怎么把输出中的空格去掉

可以在print函数中使用参数sep来设置分隔符，将其设置为空字符串即可去掉输出中的空格。修改后的代码如下：

import numpy as np 

n = int(input('输入每个维度的元素个数：'))
a = np.arange(1,n**2+1).reshape(n,n)
res = np.trace(a)

print('矩阵的迹为', res, sep='')

这样输出的结果中就不会再有空格了。

将冒号后面的代码改写成一个nn.module类：import pandas as pd import numpy as np from sklearn.preprocessing import MinMaxScaler import matplotlib.pyplot as plt from keras.models import Sequential from keras.layers import Dense, LSTM data1 = pd.read_csv("终极1.csv", usecols=[17], encoding='gb18030') df = data1.fillna(method='ffill') data = df.values.reshape(-1, 1) scaler = MinMaxScaler(feature_range=(0, 1)) data = scaler.fit_transform(data) train_size = int(len(data) * 0.8) test_size = len(data) - train_size train, test = data[0:train_size, :], data[train_size:len(data), :] def create_dataset(dataset, look_back=1): dataX, dataY = [], [] for i in range(len(dataset)-look_back-1): a = dataset[i:(i+look_back), 0] dataX.append(a) dataY.append(dataset[i + look_back, 0]) return np.array(dataX), np.array(dataY) look_back = 30 trainX, trainY = create_dataset(train, look_back) testX, testY = create_dataset(test, look_back) trainX = np.reshape(trainX, (trainX.shape[0], 1, trainX.shape[1])) testX = np.reshape(testX, (testX.shape[0], 1, testX.shape[1])) model = Sequential() model.add(LSTM(50, input_shape=(1, look_back), return_sequences=True)) model.add(LSTM(50)) model.add(Dense(1)) model.compile(loss='mean_squared_error', optimizer='adam') model.fit(trainX, trainY, epochs=6, batch_size=1, verbose=2) trainPredict = model.predict(trainX) testPredict = model.predict(testX) trainPredict = scaler.inverse_transform(trainPredict) trainY = scaler.inverse_transform([trainY]) testPredict = scaler.inverse_transform(testPredict) testY = scaler.inverse_transform([testY])

以下是将代码改写成一个nn.module类：

import torch.nn as nn
import pandas as pd 
import numpy as np 
from sklearn.preprocessing import MinMaxScaler 
import matplotlib.pyplot as plt 
from keras.models import Sequential 
from keras.layers import Dense, LSTM 

class LSTMModel(nn.Module):
    def __init__(self, input_size, hidden_size, output_size, num_layers, look_back):
        super().__init__()
        self.hidden_size = hidden_size
        self.num_layers = num_layers
        self.look_back = look_back

        self.lstm1 = nn.LSTM(input_size, hidden_size, num_layers, batch_first=True)
        self.lstm2 = nn.LSTM(hidden_size, hidden_size, num_layers, batch_first=True)
        self.fc = nn.Linear(hidden_size, output_size)

    def forward(self, x):
        h0 = torch.zeros(self.num_layers, x.size(0), self.hidden_size).to(device)
        c0 = torch.zeros(self.num_layers, x.size(0), self.hidden_size).to(device)

        x = x.view(-1, self.look_back, 1)

        out, _ = self.lstm1(x, (h0, c0))
        out, _ = self.lstm2(out, (h0, c0))

        out = self.fc(out[:, -1, :])
        return out

data1 = pd.read_csv("终极1.csv", usecols=[17], encoding='gb18030') 
df = data1.fillna(method='ffill') 
data = df.values.reshape(-1, 1) 
scaler = MinMaxScaler(feature_range=(0, 1)) 
data = scaler.fit_transform(data) 
train_size = int(len(data) * 0.8) 
test_size = len(data) - train_size 
train, test = data[0:train_size, :], data[train_size:len(data), :] 

def create_dataset(dataset, look_back=1): 
    dataX, dataY = [], [] 
    for i in range(len(dataset)-look_back-1): 
        a = dataset[i:(i+look_back), 0] 
        dataX.append(a) 
        dataY.append(dataset[i + look_back, 0]) 
    return np.array(dataX), np.array(dataY) 

look_back = 30 
trainX, trainY = create_dataset(train, look_back) 
testX, testY = create_dataset(test, look_back) 
trainX = np.reshape(trainX, (trainX.shape[0], trainX.shape[1], 1)) 
testX = np.reshape(testX, (testX.shape[0], testX.shape[1], 1)) 

device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model = LSTMModel(input_size=1, hidden_size=50, output_size=1, num_layers=2, look_back=look_back).to(device)

criterion = nn.MSELoss()
optimizer = torch.optim.Adam(model.parameters(), lr=0.001)

num_epochs = 6
for epoch in range(num_epochs):
    for i, (inputs, labels) in enumerate(zip(trainX, trainY)):
        inputs = torch.from_numpy(inputs).float().to(device)
        labels = torch.from_numpy(labels).float().to(device)

        outputs = model(inputs)
        loss = criterion(outputs, labels)

        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

        if (i+1) % 100 == 0:
            print(f'Epoch [{epoch+1}/{num_epochs}], Step [{i+1}/{len(trainX)}], Loss: {loss.item():.4f}')

model.eval()
trainPredict = scaler.inverse_transform(model(torch.from_numpy(trainX).float().to(device)).detach().cpu().numpy())
testPredict = scaler.inverse_transform(model(torch.from_numpy(testX).float().to(device)).detach().cpu().numpy())
trainY = scaler.inverse_transform([trainY])
testY = scaler.inverse_transform([testY])

这个类接受5个参数，分别是：

- input_size: LSTM的输入维度，对于这个例子来说，input_size=1
- hidden_size: LSTM的隐藏层维度
- output_size: LSTM的输出维度，对于这个例子来说，output_size=1
- num_layers: LSTM的层数
- look_back: 每个样本的序列长度，对于这个例子来说，look_back=30