sample_seq = data[0,:] IndexError: too many indices for array: array is 1-dimensional, but 2 were indexed
时间: 2024-05-02 09:18:48 浏览: 25
This error message suggests that the variable "data" is only a one-dimensional array, but the code is trying to index it with two indices.
To fix this error, you should only use one index to access the elements in the array. For example, if you want to access the first element in the array, you can use:
```
sample_seq = data[0]
```
This will assign the first element of the array to the variable "sample_seq".
相关问题
``` x_seq=PALL(:,1)```如何获取三个向量的第i个值
假设有三个向量x_seq、y_seq、z_seq,你想要获取它们的第i个值,可以按照以下步骤操作:
1. 创建三个向量x_seq、y_seq、z_seq。假设这些向量已经被创建并赋值。
2. 获取第i个值。使用以下代码可以获取第i个值:
```python
i = 3 # i表示你想要获取的位置,这里设置为3
x = x_seq[i-1] # 获取x_seq的第i个值
y = y_seq[i-1] # 获取y_seq的第i个值
z = z_seq[i-1] # 获取z_seq的第i个值
```
这里需要注意的是,Python中的列表索引是从0开始的,因此在获取第i个值时需要将i减1。上述代码将获取x_seq的第3个值、y_seq的第3个值和z_seq的第3个值。
def PrepareDataset(speed_matrix, BATCH_SIZE = 40, seq_len = 10, pred_len = 1, train_propotion = 0.7, valid_propotion = 0.2):
"""
Function to prepare dataset for training and validation
Args:
speed_matrix: numpy array of shape (num_days, num_timesteps, num_nodes)
BATCH_SIZE: batch size for training (default = 40)
seq_len: sequence length (number of timesteps) for input (default = 10)
pred_len: number of timesteps to predict (default = 1)
train_propotion: proportion of dataset to use for training (default = 0.7)
valid_propotion: proportion of dataset to use for validation (default = 0.2)
Returns:
train_data: PyTorch DataLoader object for training data
valid_data: PyTorch DataLoader object for validation data
"""
# Calculate number of days
num_days = speed_matrix.shape[0]
# Calculate number of nodes
num_nodes = speed_matrix.shape[2]
# Calculate total number of timesteps
total_timesteps = num_days * speed_matrix.shape[1]
# Create input and target sequences
input_seq = []
target_seq = []
# Loop through each day
for day in range(num_days):
# Loop through each timestep
for timestep in range(speed_matrix.shape[1] - seq_len - pred_len):
# Extract input sequence
input_seq.append(speed_matrix[day, timestep:timestep+seq_len, :])
# Extract target sequence
target_seq.append(speed_matrix[day, timestep+seq_len:timestep+seq_len+pred_len, :])
# Convert input and target sequences to numpy arrays
input_seq = np.array(input_seq)
target_seq = np.array(target_seq)
# Split dataset into training, validation, and testing sets
train_size = int(total_timesteps * train_propotion)
valid_size = int(total_timesteps * valid_propotion)
test_size = total_timesteps - train_size - valid_size
train_input = input_seq[:train_size]
train_target = target_seq[:train_size]
valid_input = input_seq[train_size:train_size+valid_size]
valid_target = target_seq[train_size:train_size+valid_size]
test_input = input_seq[train_size+valid_size:]
test_target = target_seq[train_size+valid_size:]
# Convert training and validation data to PyTorch DataLoader objects
train_data = DataLoader(TensorDataset(torch.Tensor(train_input), torch.Tensor(train_target)), batch_size=BATCH_SIZE, shuffle=True)
valid_data = DataLoader(TensorDataset(torch.Tensor(valid_input), torch.Tensor(valid_target)), batch_size=BATCH_SIZE, shuffle=True)
return train_data, valid_data
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