function seq = GenChaos(init,rv_bit,len) % init: 设定初始值 % thd : 门限 % len : 生成序列长度 % rv_bit: 舍弃的位数 x = init; % 初始值 thd = 0; % 门限 S=rv_bit; % 舍弃的位数 T=len; % 截断的位数 H=zeros(1,T); % 码序列 for i=1:S %舍去前S位 x=1-2*x*x; end for i=1:T %从S+1为开始截断，截断T位 if x>thd H(i)=1; else H(i)=0; end x=1-2*x*x; end seq = H; end这个程序运行的流程

这个程序是用来生成伪随机码的 MATLAB 函数，其主要流程如下：

1. 初始化参数：包括初始值、门限、舍弃的位数和生成序列长度；
2. 进行迭代，得到伪随机序列；
3. 对伪随机序列进行截断，得到最终的伪随机码。

具体而言，下面是 GenChaos 函数的 MATLAB 代码：

function seq = GenChaos(init, rv_bit, len)

% 参数初始化
x = init;       % 初始值
thd = 0;        % 门限
S = rv_bit;     % 舍弃的位数
T = len;        % 截断的位数

% 生成伪随机序列
for i = 1:S
    x = 1 - 2 * x * x;
end
H = zeros(1, T);
for i = 1:T
    if x > thd
        H(i) = 1;
    else
        H(i) = 0;
    end
    x = 1 - 2 * x * x;
end

% 截断伪随机序列，得到伪随机码
seq = H;
end

用户可以通过调整函数输入参数 `init`、`rv_bit` 和 `len`，来改变初始值、舍弃的位数和生成序列长度。需要注意的是，在生成伪随机序列时，需要进行一定次数的迭代，以消除初始值对结果的影响。在截断伪随机序列时，可以通过调整门限 `thd` 的值，来控制伪随机码的分布。

相关问题

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

解释下面这段代码 def pad_sequence(seq_feature, batch_first=True, padding_value=0, max_len=966): """对长度不同于模型输入的音频进行padding或截断""" feature_shape = seq_feature.shape feat_len = feature_shape[0] if feat_len > max_len: # truncate to max length seq_feature = seq_feature[:max_len].unsqueeze(0) return seq_feature batch_size = 1 trailing_dims = feature_shape[1:] if batch_first: out_dims = (batch_size, max_len) + trailing_dims else: out_dims = (max_len, batch_size) + trailing_dims out_tensor = seq_feature.data.new(*out_dims).fill_(padding_value) if batch_first: out_tensor[0, :feat_len, ...] = seq_feature else: out_tensor[:feat_len, 0, ...] = seq_feature return out_tensor

这段代码是用于对长度不同于模型输入的音频进行padding或截断的。具体来说，它的输入是一个形状为(seq_len, feature_dim)的特征序列，其中seq_len表示该特征序列的长度，feature_dim表示每个特征向量的维度。该函数首先获取输入序列的长度feat_len，如果feat_len大于max_len，就将输入序列截断到max_len的长度。如果feat_len小于等于max_len，就对输入序列进行padding，使其长度等于max_len。padding使用的值为padding_value。

输出为形状为(batch_size, max_len, feature_dim)的张量，其中batch_size表示批大小，如果batch_first为True，则batch_size为1，特征序列在第二个维度上，即张量的形状为(batch_size, seq_len, feature_dim)，否则特征序列在第一个维度上，即张量的形状为(seq_len, batch_size, feature_dim)。输出张量中，长度小于等于原始特征序列长度feat_len的部分是原始特征序列，其余部分都是padding_value。