data_norm = np.concatenate((data_norm, np.array([self.displaySamples]).reshape(-1, 1) / self.engine.P['MaxFeedbackVal']), -1)

这行代码是将一个新的列添加到`data_norm`数组中。具体来说，它使用`np.concatenate`函数将`data_norm`和一个新的列（通过将`self.displaySamples`转换为一维数组后进行重塑）进行连接。在连接时，使用`/ self.engine.P['MaxFeedbackVal']`将`self.displaySamples`的值除以`self.engine.P['MaxFeedbackVal']`，以进行归一化处理。

最后的参数`-1`表示在连接操作中将新的列添加到最后一列。这样，`data_norm`将成为一个包含原始数据以及新添加的归一化列的二维数组。

相关问题

# 提取特征并保存 def get_feat(image_list, name_list, label_list, savePath): i = 0 for image in image_list: try: # 如果是灰度图片 把3改为-1 image = np.reshape(image, (image_height, image_width, 3)) except: print('发送了异常，图片大小size不满足要求：',name_list[i]) continue gray = rgb2gray(image) / 255.0 # 这句话根据你的尺寸改改 fd = hog(gray, orientations=12,block_norm='L1', pixels_per_cell=[8, 8], cells_per_block=[4, 4], visualize=False, transform_sqrt=True) fd = np.concatenate((fd, [label_list[i]])) fd_name = name_list[i] + '.feat' fd_path = os.path.join(savePath, fd_name) joblib.dump(fd, fd_path) i += 1 print("Test features are extracted and saved.")加入颜色特征代码

以下是添加颜色特征的代码示例：

import cv2
import glob
import os
import numpy as np
from skimage.feature import hog
from skimage.color import rgb2gray
from sklearn.externals import joblib

def get_feat(image_list, name_list, label_list, savePath):
    i = 0
    for image in image_list:
        try:
            # 如果是灰度图片  把3改为-1
            image = np.reshape(image, (image_height, image_width, 3))
        except:
            print('发送了异常，图片大小size不满足要求：',name_list[i])
            continue
        gray = rgb2gray(image) / 255.0
        # 添加颜色特征
        hsv = cv2.cvtColor(image, cv2.COLOR_RGB2HSV)
        color_hist = cv2.calcHist([hsv], [0, 1, 2], None, [8, 8, 8], [0, 180, 0, 256, 0, 256])
        color_hist = cv2.normalize(color_hist, color_hist).flatten()
        # 使用 HOG 特征
        hog_feat = hog(gray, orientations=12, block_norm='L1', pixels_per_cell=[8, 8], cells_per_block=[4, 4], visualize=False, transform_sqrt=True)
        # 组合特征向量
        feat = np.concatenate((hog_feat, color_hist, [label_list[i]]))
        feat_name = name_list[i] + '.feat'
        feat_path = os.path.join(savePath, feat_name)
        joblib.dump(feat, feat_path)
        i += 1
    print("Features are extracted and saved.")

在这个示例中，首先使用 OpenCV 的 cvtColor 函数将图像从 RGB 转换为 HSV，然后使用 calcHist 函数计算 HSV 颜色直方图。接下来，使用 normalize 函数对颜色直方图进行归一化，并将其展平为一维数组。最后，将 HOG 特征和颜色特征连接起来，形成最终的特征向量。

Transformer and Self-Attention Pytorch代码

以下是使用PyTorch实现Transformer和Self-Attention的示例代码：

## Self-Attention

import torch
import torch.nn as nn

class SelfAttention(nn.Module):
    def __init__(self, embed_size, heads):
        super(SelfAttention, self).__init__()
        self.embed_size = embed_size
        self.heads = heads
        self.head_dim = embed_size // heads
        
        assert (self.head_dim * heads == embed_size), "Embed size needs to be divisible by heads"
        
        self.values = nn.Linear(self.head_dim, self.head_dim, bias=False)
        self.keys = nn.Linear(self.head_dim, self.head_dim, bias=False)
        self.queries = nn.Linear(self.head_dim, self.head_dim, bias=False)
        self.fc_out = nn.Linear(heads * self.head_dim, embed_size)
        
    def forward(self, values, keys, queries, mask):
        # Get number of training examples
        N = queries.shape[0]
        
        value_len, key_len, query_len = values.shape[1], keys.shape[1], queries.shape[1]
        
        # Split embedding into self.heads pieces
        values = values.reshape(N, value_len, self.heads, self.head_dim)
        keys = keys.reshape(N, key_len, self.heads, self.head_dim)
        queries = queries.reshape(N, query_len, self.heads, self.head_dim)
        
        # Transpose to get dimensions batch_size * self.heads * seq_len * self.head_dim
        values = values.permute(0, 2, 1, 3)
        keys = keys.permute(0, 2, 1, 3)
        queries = queries.permute(0, 2, 1, 3)
        
        # Calculate energy
        energy = torch.matmul(queries, keys.permute(0, 1, 3, 2))
        
        if mask is not None:
            energy = energy.masked_fill(mask == 0, float("-1e20"))
        
        # Apply softmax to get attention scores
        attention = torch.softmax(energy / (self.embed_size ** (1/2)), dim=-1)
        
        # Multiply attention scores with values
        out = torch.matmul(attention, values)
        
        # Concatenate and linearly transform output
        out = out.permute(0, 2, 1, 3).reshape(N, query_len, self.heads * self.head_dim)
        out = self.fc_out(out)
        
        return out

## Transformer

import torch
import torch.nn as nn
from torch.nn.modules.activation import MultiheadAttention

class TransformerBlock(nn.Module):
    def __init__(self, embed_size, heads, dropout, forward_expansion):
        super(TransformerBlock, self).__init__()
        self.attention = MultiheadAttention(embed_dim=embed_size, num_heads=heads)
        self.norm1 = nn.LayerNorm(embed_size)
        self.norm2 = nn.LayerNorm(embed_size)
        
        self.feed_forward = nn.Sequential(
            nn.Linear(embed_size, forward_expansion * embed_size),
            nn.ReLU(),
            nn.Linear(forward_expansion * embed_size, embed_size)
        )
        
        self.dropout = nn.Dropout(dropout)
        
    def forward(self, value, key, query, mask):
        attention_output, _ = self.attention(query, key, value, attn_mask=mask)
        
        x = self.dropout(self.norm1(attention_output + query))
        
        forward_output = self.feed_forward(x)
        out = self.dropout(self.norm2(forward_output + x))
        
        return out
        

class Encoder(nn.Module):
    def __init__(self, src_vocab_size, embed_size, num_layers, heads, device, forward_expansion, dropout, max_length):
        super(Encoder, self).__init__()
        self.embed_size = embed_size
        self.device = device
        self.word_embedding = nn.Embedding(src_vocab_size, embed_size)
        self.position_embedding = nn.Embedding(max_length, embed_size)
        
        self.layers = nn.ModuleList([
            TransformerBlock(embed_size, heads, dropout, forward_expansion) for _ in range(num_layers)
        ])
        
        self.dropout = nn.Dropout(dropout)
        
    def forward(self, x, mask):
        N, seq_length = x.shape
        positions = torch.arange(0, seq_length).expand(N, seq_length).to(self.device)
        
        out = self.dropout(self.word_embedding(x) + self.position_embedding(positions))
        
        for layer in self.layers:
            out = layer(out, out, out, mask)
            
        return out
    
    
class DecoderBlock(nn.Module):
    def __init__(self, embed_size, heads, forward_expansion, dropout, device):
        super(DecoderBlock, self).__init__()
        self.norm = nn.LayerNorm(embed_size)
        self.attention = MultiheadAttention(embed_size, heads)
        self.transformer_block = TransformerBlock(embed_size, heads, dropout, forward_expansion)
        self.dropout = nn.Dropout(dropout)
        
    def forward(self, x, value, key, src_mask, trg_mask):
        attention_output, _ = self.attention(x, x, x, attn_mask=trg_mask)
        query = self.dropout(self.norm(attention_output + x))
        out = self.transformer_block(value, key, query, src_mask)
        
        return out
    
    
class Decoder(nn.Module):
    def __init__(self, trg_vocab_size, embed_size, num_layers, heads, forward_expansion, dropout, device, max_length):
        super(Decoder, self).__init__()
        self.embed_size = embed_size
        self.device = device
        self.word_embedding = nn.Embedding(trg_vocab_size, embed_size)
        self.position_embedding = nn.Embedding(max_length, embed_size)
        
        self.layers = nn.ModuleList([
            DecoderBlock(embed_size, heads, forward_expansion, dropout, device) for _ in range(num_layers)
        ])
        
        self.fc_out = nn.Linear(embed_size, trg_vocab_size)
        self.dropout = nn.Dropout(dropout)
        
    def forward(self, x, enc_out, src_mask, trg_mask):
        N, seq_length = x.shape
        positions = torch.arange(0, seq_length).expand(N, seq_length).to(self.device)
        
        x = self.dropout(self.word_embedding(x) + self.position_embedding(positions))
        
        for layer in self.layers:
            x = layer(x, enc_out, enc_out, src_mask, trg_mask)
            
        out = self.fc_out(x)
        
        return out

这些代码可以用于实现Transformer和Self-Attention模型。但这只是示例，你需要根据你的数据和任务来调整这些代码中的各种超参数和结构。