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='')解释这段代码
时间: 2024-04-01 17:34:13 浏览: 19
这段代码计算一个n行n列的矩阵的迹,即矩阵对角线上元素的和。
首先通过input函数获取用户输入的矩阵维度n,然后使用numpy库中的arange函数生成一个从1到n^2的连续整数数组,并通过reshape函数将其转换为一个n行n列的矩阵。
接下来定义一个变量res,用来保存矩阵的迹,初值为0。然后使用for循环遍历矩阵的对角线元素,将其累加到res变量中。
最后使用print函数输出矩阵的迹,其中通过sep参数设置分隔符为空字符串,使输出结果中没有空格。
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:
```python
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:
```python
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
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