pndas读取数据,降维处理后绘制数据散点图
时间: 2024-02-20 08:56:13 浏览: 86
with open(vocab_path, 'r', encoding='utf-8') as f:
vocab = f.read().splitlines()
return vocab
def preprocess(image_path, style_path, vocab_path):
# 加载词表
vocab = load_vocab(vocab好的,我可以回答这个问题。首先,您需要使用pandas库中的read_csv()方法读取您_path)
# 进行小波变换
cA, (cH, cV, cD) = wavelet_transform的数据文件。然后,您可以使用一些降维技术,如主成分分析(PCA)或线性(image_path)
# 将小波系数作为输入序列
src = [cA, cH, cV,判别分析(LDA),将数据降至二维或三维。最后,您可以使用matplotlib库中的scatter cD]
# 加载风格图像
style = Image.open(style_path)
# 将风格图像作为()方法来绘制数据的散点图。
以下是一个简单的示例代码,假设您的数据文件名为目标序列
tgt = style.convert('RGB').resize((len(src[0]), len(src[0][0]))).convert"data.csv":
```python
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.decomposition import PCA
# 读取('L')
tgt = np.array(tgt)
tgt = tgt.flatten().tolist()
# 将序列转换为索引数据
data = pd.read_csv('data.csv')
# 进行PCA降维处理
pca = PCA(n_components=2)
data
src = [[vocab.index(str(x)) for x in row] for row in src]
tgt = [vocab.index(str(x))_pca = pca.fit_transform(data)
# 绘制散点图
plt.scatter(data_pca[:, 0], data_pca[:, for x in tgt]
return src, tgt
def generate_image(image_path, style_path, vocab_path, model_path, output_path):
# 加载模型
checkpoint = torch.load(model_path, map_location='cpu')
transformer = Transformer(checkpoint['src1])
plt.show()
```
请注意,这只是一个简单的示例代码,您可能需要根据您的实际情况进行修改。
阅读全文
相关推荐














