print('{}/{} pro_a:{:.3f}, pro_b:{:.3f}, pro_c:{:.3f}'.format( d + 1, count, pro_A, pro_B, pro_C))

这是一行Python代码，使用了字符串格式化输出的方式，将d+1、count、pro_A、pro_B、pro_C的值按照一定的格式输出到控制台上。其中{}表示占位符，后面的数字表示占位符的位置，可以用多次{}来表示不同的位置。:.3f表示保留三位小数的浮点数。整个字符串使用了format()方法来进行格式化输出。

相关问题

给下面这段代码每行注释import os import json import torch from PIL import Image from torchvision import transforms from model import resnet34 def main(): device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") data_transform = transforms.Compose( [transforms.Resize(256), transforms.CenterCrop(224), transforms.ToTensor(), transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])]) # load image # 指向需要遍历预测的图像文件夹 imgs_root = "../dataset/val" assert os.path.exists(imgs_root), f"file: '{imgs_root}' dose not exist." # 读取指定文件夹下所有jpg图像路径 img_path_list = [os.path.join(imgs_root, i) for i in os.listdir(imgs_root) if i.endswith(".jpg")] # read class_indict json_path = './class_indices.json' assert os.path.exists(json_path), f"file: '{json_path}' dose not exist." json_file = open(json_path, "r") class_indict = json.load(json_file) # create model model = resnet34(num_classes=16).to(device) # load model weights weights_path = "./newresNet34.pth" assert os.path.exists(weights_path), f"file: '{weights_path}' dose not exist." model.load_state_dict(torch.load(weights_path, map_location=device)) # prediction model.eval() batch_size = 8 # 每次预测时将多少张图片打包成一个batch with torch.no_grad(): for ids in range(0, len(img_path_list) // batch_size): img_list = [] for img_path in img_path_list[ids * batch_size: (ids + 1) * batch_size]: assert os.path.exists(img_path), f"file: '{img_path}' dose not exist." img = Image.open(img_path) img = data_transform(img) img_list.append(img) # batch img # 将img_list列表中的所有图像打包成一个batch batch_img = torch.stack(img_list, dim=0) # predict class output = model(batch_img.to(device)).cpu() predict = torch.softmax(output, dim=1) probs, classes = torch.max(predict, dim=1) for idx, (pro, cla) in enumerate(zip(probs, classes)): print("image: {} class: {} prob: {:.3}".format(img_path_list[ids * batch_size + idx], class_indict[str(cla.numpy())], pro.numpy())) if __name__ == '__main__': main()

1. import os：导入操作系统模块
2. import json：导入处理json格式的模块
3. import torch：导入深度学习框架PyTorch模块
4. from PIL import Image：从PIL模块中导入Image模块，用于图像处理
5. from torchvision import transforms：从torchvision模块中导入transforms模块，用于数据预处理
6. from model import resnet34：从自定义模块model中导入resnet34模型
7. def main(): 定义一个名为main的函数
8. device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")：使用GPU加速计算，如果GPU可用，就使用GPU，否则使用CPU
9. data_transform = transforms.Compose([...])：定义一个数据预处理的组合操作，对图像进行缩放、中心裁剪、转换为张量并标准化
10. transforms.Resize(256)：将图像缩放至256*256大小
11. transforms.CenterCrop(224)：对缩放后的图像从中心裁剪出224*224大小的图像
12. transforms.ToTensor()：将图像转换为张量
13. transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])：将张量标准化，均值为0.485、0.456、0.406，标准差为0.229、0.224、0.225

帮我为下面的代码加上注释：class SimpleDeepForest: def __init__(self, n_layers): self.n_layers = n_layers self.forest_layers = [] def fit(self, X, y): X_train = X for _ in range(self.n_layers): clf = RandomForestClassifier() clf.fit(X_train, y) self.forest_layers.append(clf) X_train = np.concatenate((X_train, clf.predict_proba(X_train)), axis=1) return self def predict(self, X): X_test = X for i in range(self.n_layers): X_test = np.concatenate((X_test, self.forest_layers[i].predict_proba(X_test)), axis=1) return self.forest_layers[-1].predict(X_test[:, :-2]) # 1. 提取序列特征（如：GC-content、序列长度等） def extract_features(fasta_file): features = [] for record in SeqIO.parse(fasta_file, "fasta"): seq = record.seq gc_content = (seq.count("G") + seq.count("C")) / len(seq) seq_len = len(seq) features.append([gc_content, seq_len]) return np.array(features) # 2. 读取相互作用数据并创建数据集 def create_dataset(rna_features, protein_features, label_file): labels = pd.read_csv(label_file, index_col=0) X = [] y = [] for i in range(labels.shape[0]): for j in range(labels.shape[1]): X.append(np.concatenate([rna_features[i], protein_features[j]])) y.append(labels.iloc[i, j]) return np.array(X), np.array(y) # 3. 调用SimpleDeepForest分类器 def optimize_deepforest(X, y): X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2) model = SimpleDeepForest(n_layers=3) model.fit(X_train, y_train) y_pred = model.predict(X_test) print(classification_report(y_test, y_pred)) # 4. 主函数 def main(): rna_fasta = "RNA.fasta" protein_fasta = "pro.fasta" label_file = "label.csv" rna_features = extract_features(rna_fasta) protein_features = extract_features(protein_fasta) X, y = create_dataset(rna_features, protein_features, label_file) optimize_deepforest(X, y) if __name__ == "__main__": main()

# Define a class named 'SimpleDeepForest'
class SimpleDeepForest:

# Initialize the class with 'n_layers' parameter
def __init__(self, n_layers):
self.n_layers = n_layers
self.forest_layers = []

# Define a method named 'fit' to fit the dataset into the classifier
def fit(self, X, y):
X_train = X

# Use the forest classifier to fit the dataset for 'n_layers' times
for _ in range(self.n_layers):
clf = RandomForestClassifier()
clf.fit(X_train, y)

# Append the classifier to the list of forest layers
self.forest_layers.append(clf)

# Concatenate the training data with the predicted probability of the last layer
X_train = np.concatenate((X_train, clf.predict_proba(X_train)), axis=1)

# Return the classifier
return self

# Define a method named 'predict' to make predictions on the test set
def predict(self, X):
X_test = X

# Concatenate the test data with the predicted probability of each layer
for i in range(self.n_layers):
X_test = np.concatenate((X_test, self.forest_layers[i].predict_proba(X_test)), axis=1)

# Return the predictions of the last layer
return self.forest_layers[-1].predict(X_test[:, :-2])

# Define a function named 'extract_features' to extract sequence features
def extract_features(fasta_file):
features = []

# Parse the fasta file to extract sequence features
for record in SeqIO.parse(fasta_file, "fasta"):
seq = record.seq
gc_content = (seq.count("G") + seq.count("C")) / len(seq)
seq_len = len(seq)
features.append([gc_content, seq_len])

# Return the array of features
return np.array(features)

# Define a function named 'create_dataset' to create the dataset
def create_dataset(rna_features, protein_features, label_file):
labels = pd.read_csv(label_file, index_col=0)
X = []
y = []

# Create the dataset by concatenating the RNA and protein features
for i in range(labels.shape[0]):
for j in range(labels.shape[1]):
X.append(np.concatenate([rna_features[i], protein_features[j]]))
y.append(labels.iloc[i, j])

# Return the array of features and the array of labels
return np.array(X), np.array(y)

# Define a function named 'optimize_deepforest' to optimize the deep forest classifier
def optimize_deepforest(X, y):
# Split the dataset into training set and testing set
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)

# Create an instance of the SimpleDeepForest classifier with 3 layers
model = SimpleDeepForest(n_layers=3)

# Fit the training set into the classifier
model.fit(X_train, y_train)

# Make predictions on the testing set
y_pred = model.predict(X_test)

# Print the classification report
print(classification_report(y_test, y_pred))

# Define the main function to run the program
def main():
rna_fasta = "RNA.fasta"
protein_fasta = "pro.fasta"
label_file = "label.csv"

# Extract the RNA and protein features
rna_features = extract_features(rna_fasta)
protein_features = extract_features(protein_fasta)

# Create the dataset
X, y = create_dataset(rna_features, protein_features, label_file)

# Optimize the DeepForest classifier
optimize_deepforest(X, y)

# Check if the program is being run as the main program
if __name__ == "__main__":
main()