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a7e95141d2
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b2a3bc393e
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@ -1,4 +1 @@
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Static
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Result
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.vscode
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__pycache__
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lazy-lock.json
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@ -1,5 +0,0 @@
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from .divSet import divSet
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from .loadData import load_data
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from .saveToXlsx import save_to_xlsx
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__all__ = ["divSet", "load_data", "save_to_xlsx"]
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from sklearn.model_selection import train_test_split
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from sklearn.preprocessing import StandardScaler, LabelEncoder
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def divSet(data, labels=None, test_size=0.2, random_state=None):
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"""Split data, scale features, and encode labels.
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This module is the canonical location for dataset splitting utilities.
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"""
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encoder = LabelEncoder()
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# 最后一列是标签
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X = data.iloc[:, :-1]
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y = data.iloc[:, -1]
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if labels:
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encoder.fit(labels)
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else:
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encoder.fit(y)
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# 优先使用分层抽样，尽量保证每个类别在训练集和测试集都出现。
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stratify_target = y if y.nunique() > 1 else None
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try:
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X_train, X_test, y_train, y_test = train_test_split(
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X, y, test_size=test_size, random_state=random_state, stratify=stratify_target
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)
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except ValueError:
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# 当样本过少等情况下分层失败，回退到普通随机划分。
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X_train, X_test, y_train, y_test = train_test_split(
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X, y, test_size=test_size, random_state=random_state
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)
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# 标准化特征
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scaler = StandardScaler()
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X_train = scaler.fit_transform(X_train)
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X_test = scaler.transform(X_test)
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# 编码标签
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y_train = encoder.transform(y_train.values)
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y_test = encoder.transform(y_test.values)
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return X_train, X_test, y_train, y_test, encoder
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__all__ = ["divSet"]
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@ -1,260 +0,0 @@
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import os
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import unicodedata
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import pandas as pd
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STATIC_PATH = './Static'
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DEFAULT_FILE_CLASSES = ('xlsx', 'xls', 'csv')
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# 从文件夹中读取所有数据文件，支持 xls/xlsx/csv
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# labelNames为label的名字，如果不提供则默认为文件名
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def load_data(folder, labelNames):
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# 检查folder参数
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if folder is None:
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raise ValueError("The 'folder' parameter is required.")
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# 检查labelNames参数
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if labelNames is None:
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raise ValueError("The 'labelNames' parameter is required if 'folder' does not contain labels.")
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folder = os.path.join(STATIC_PATH, folder)
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# 看看有没有元数据文件夹
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if not os.path.isdir(folder):
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raise ValueError(f"The folder '{folder}' does not exist.")
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file_classes = DEFAULT_FILE_CLASSES
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# 自动检测数据组织方式
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is_dir_mode = _detect_data_mode(folder=folder, labelNames=labelNames, fileClasses=file_classes)
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mode_name = 'multi-folder mode' if is_dir_mode else 'single-file mode'
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print(f"Auto detected data mode: {mode_name}")
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if not is_dir_mode:
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data = load_from_file(folder=folder, labelNames=labelNames, fileClasses=file_classes)
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else:
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data = load_from_folder(folder=folder, labelNames=labelNames, fileClasses=file_classes)
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print(data)
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return data
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def load_from_folder(folder, labelNames, fileClasses):
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all_features = []
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for labelName in labelNames:
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subfolder = os.path.join(folder, str(labelName))
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if os.path.exists(subfolder) and os.path.isdir(subfolder):
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fileNames = [f for f in os.listdir(subfolder) if _has_supported_extension(f, fileClasses)]
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max_row_length = get_max_row_len(subfolder, fileNames)
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features = []
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for fileName in fileNames:
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file_path = os.path.join(subfolder, fileName)
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features.append(load_xlsx(file_path, labelName, max_row_length, 'zero'))
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if features:
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all_features.append(pd.concat(features, ignore_index=True))
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# 将所有标签的数据合并
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return pd.concat(all_features, ignore_index=True)
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def load_from_file(folder, labelNames, fileClasses):
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# 构建期望的文件名（label + .扩展名），并在目录中进行健壮匹配
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# （去除零宽字符、Unicode 规范化、大小写不敏感）
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actual_file_names = []
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missing = []
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for labelName in labelNames:
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match = _find_matching_file_by_label(folder, labelName, fileClasses)
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if match is None:
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missing.append(f"{labelName}.<{'/'.join(fileClasses)}>")
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else:
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actual_file_names.append(match)
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if missing:
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available = sorted(os.listdir(folder))
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raise FileNotFoundError(
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"The following files were not found (after normalization): "
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+ ", ".join(missing)
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+ f". Available files: {available}"
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)
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# 获取数据的最大行数（使用实际匹配到的文件名）
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max_row_length = get_max_row_len(folder, actual_file_names)
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all_features = []
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for i, fileName in enumerate(actual_file_names):
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file_path = os.path.join(folder, fileName)
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features = load_xlsx(file_path, labelNames[i], max_row_length, 'zero')
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all_features.append(features)
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return pd.concat(all_features, ignore_index=True)
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def load_xlsx(fileName, labelName, max_row_length=1000, fill_rule=None):
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df = _read_data_file(fileName)
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# 提取偶数列
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features = df.iloc[0:, 1::2]
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features.dropna(inplace=True)
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features.reset_index(drop=True, inplace=True)
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features = features.T
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# 补全每一行到指定长度
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features = features.apply(lambda row: fill_to_len(row, max_row_length, fill_rule), axis=1)
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# 获取实际的列数
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actual_columns = features.shape[1]
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features['label'] = labelName
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features.columns = [f'feature{i+1}' for i in range(actual_columns)] + ['label']
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return features
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def fill_to_len(row, length=1000, rule=None):
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if len(row) >= length:
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return row.iloc[:length].reset_index(drop=True)
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fill_value = 0
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if rule == 'min':
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fill_value = row.min()
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elif rule == 'mean':
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fill_value = row.mean()
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elif rule == 'zero':
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fill_value = 0
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fill_values = pd.Series([fill_value] * (length - len(row)))
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return pd.concat([row, fill_values], ignore_index=True)
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def get_max_row_len(folder, filenames):
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max_len = 0
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for filename in filenames:
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df = _read_data_file(os.path.join(folder, filename))
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max_len = max(max_len, df.shape[0])
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return max_len
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def _read_data_file(file_path: str):
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ext = os.path.splitext(file_path)[1].lower()
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if ext == '.csv':
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return pd.read_csv(file_path)
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if ext in ('.xls', '.xlsx'):
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return pd.read_excel(file_path)
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raise ValueError(
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f"Unsupported file format: {ext}. Only .xls, .xlsx, and .csv are supported. "
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f"File: {file_path}"
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)
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def _has_supported_extension(filename: str, fileClasses) -> bool:
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ext = os.path.splitext(filename)[1].lower().lstrip('.')
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return ext in fileClasses
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# ---------- 内部工具函数：处理包含零宽字符或不同 Unicode 形式的文件名匹配 ----------
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def _strip_zero_width(s: str) -> str:
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# 移除常见零宽字符：U+200B, U+200C, U+200D, U+FEFF
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if not isinstance(s, str):
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return s
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return s.translate({
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0x200B: None,
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0x200C: None,
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0x200D: None,
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0xFEFF: None,
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})
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def _canonicalize_name(name: str) -> str:
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# 规范化到 NFKC，并移除零宽字符
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name = unicodedata.normalize('NFKC', name)
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name = _strip_zero_width(name)
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return name
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def _normalize_for_compare(name: str) -> str:
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# 进一步规范化用于宽松比较
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n = _canonicalize_name(name)
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n = n.replace('_', ' ')
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n = ' '.join(n.split())
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return n.lower()
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def _find_matching_file(folder: str, expected_name: str):
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# 首先进行严格匹配（规范化后相等）
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expected = _canonicalize_name(expected_name)
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try:
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entries = os.listdir(folder)
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except FileNotFoundError:
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return None
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for f in entries:
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if _canonicalize_name(f) == expected:
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return f
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# 次要策略：大小写不敏感比较
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expected_lower = expected.lower()
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for f in entries:
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if _canonicalize_name(f).lower() == expected_lower:
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return f
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# 宽松策略：将下划线当作空格处理，并折叠空白
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expected_relaxed = _normalize_for_compare(expected_name)
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for f in entries:
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if _normalize_for_compare(f) == expected_relaxed:
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return f
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return None
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def _find_matching_file_by_label(folder: str, label_name, fileClasses):
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for ext in fileClasses:
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expected_name = f"{label_name}.{ext}"
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match = _find_matching_file(folder, expected_name)
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if match is not None:
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return match
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return None
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def _detect_data_mode(folder: str, labelNames, fileClasses) -> bool:
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"""Auto detect data organization mode.
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Returns:
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True: multi-folder mode (folder/label/*.ext)
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False: single-file mode (folder/label.ext)
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"""
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# 判断是否满足多文件夹模式：每个 label 对应一个子目录，且至少有一个目标后缀文件
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has_all_label_subfolders = True
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for label in labelNames:
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subfolder = os.path.join(folder, str(label))
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if not (os.path.isdir(subfolder) and any(_has_supported_extension(f, fileClasses) for f in os.listdir(subfolder))):
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has_all_label_subfolders = False
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break
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# 判断是否满足单文件模式：每个 label 能匹配到对应文件
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has_all_label_files = True
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for label in labelNames:
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if _find_matching_file_by_label(folder, label, fileClasses) is None:
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has_all_label_files = False
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break
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if has_all_label_subfolders and not has_all_label_files:
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return True
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if has_all_label_files and not has_all_label_subfolders:
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return False
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if has_all_label_subfolders and has_all_label_files:
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raise ValueError(
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"Auto detect found both valid layouts under the same folder. "
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"Please keep only one layout type (either subfolders or root files) for each label."
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)
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raise ValueError(
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"Auto detect failed: neither single-file nor multi-folder layout matches all labels. "
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"Please verify folder structure and labelNames."
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)
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__all__ = ['load_data']
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@ -1,165 +0,0 @@
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import os
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import pandas as pd
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import matplotlib
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matplotlib.use('Agg')
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import matplotlib.pyplot as plt
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from mpl_toolkits.mplot3d import Axes3D
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import numpy as np
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def save_to_xlsx(project_name, file_name, data):
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folder_path = f'Result/{project_name}'
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os.makedirs(folder_path, exist_ok=True)
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data.to_excel(f'{folder_path}/{file_name}.xlsx', index=True)
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print('Save successed to ' + f'{folder_path}/{file_name}.xlsx')
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save_to_pic(project_name=project_name, file_name=file_name)
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return
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def save_to_pic(project_name, file_name):
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os.makedirs(f'Result/{project_name}', exist_ok=True)
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if file_name == 'pca_2d':
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draw_pca_2d(f'Result/{project_name}/{file_name}.xlsx')
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print('Save successed to ' + f'Result/{project_name}/{file_name}.png')
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elif file_name == 'pca_3d':
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draw_pca_3d(f'Result/{project_name}/{file_name}.xlsx')
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print('Save successed to ' + f'Result/{project_name}/{file_name}.png')
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elif file_name == 'acc_and_loss':
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draw_epoch_data(f'Result/{project_name}/{file_name}.xlsx')
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draw_last_epoch_bar_chart(f'Result/{project_name}/{file_name}.xlsx')
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print('Save successed to line graph and bar graph')
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elif file_name == 'cm':
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draw_and_save_cm(f'Result/{project_name}/{file_name}.xlsx')
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print('Save successed cm')
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elif file_name == 'cmn':
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draw_and_save_cm(f'Result/{project_name}/{file_name}.xlsx')
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print('Save successed cmn')
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else:
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print('unknow picture type')
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def draw_pca_2d(file_path):
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df = pd.read_excel(file_path)
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plt.figure(figsize=(8, 6))
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plt.scatter(df['PC1'], df['PC2'], c=df['labels'], cmap='viridis', edgecolor='k', alpha=0.6)
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plt.xlabel('PC1')
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plt.ylabel('PC2')
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plt.title('2D PCA')
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plt.colorbar(label='Labels')
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plt.savefig(file_path.replace('.xlsx', '.png'))
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plt.close()
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def draw_pca_3d(file_path):
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df = pd.read_excel(file_path)
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fig = plt.figure(figsize=(8, 6))
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ax = fig.add_subplot(111, projection='3d')
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scatter = ax.scatter(df['PC1'], df['PC2'], df['PC3'], c=df['labels'], cmap='viridis', edgecolor='k', alpha=0.6)
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ax.set_xlabel('PC1')
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ax.set_ylabel('PC2')
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ax.set_zlabel('PC3')
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ax.set_title('3D PCA')
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fig.colorbar(scatter, ax=ax, label='Labels')
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plt.savefig(file_path.replace('.xlsx', '.png'))
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def draw_epoch_data(file_path):
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df = pd.read_excel(file_path)
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epochs = df['epoch']
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train_loss = df['train_loss']
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train_accuracy = df['train_accuracy'] * 100
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test_accuracy = df['test_accuracy'] * 100
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f1_score = df['f1_score']
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precision = df['precision']
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recall = df['recall']
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fig, axs = plt.subplots(2, 3, figsize=(18, 12))
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axs[0, 0].plot(epochs, train_loss, 'b-', label='Train Loss')
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axs[0, 0].set_xlabel('Epoch')
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axs[0, 0].set_ylabel('Loss')
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axs[0, 0].set_title('Training Loss over Epochs')
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axs[0, 0].legend()
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axs[0, 1].plot(epochs, train_accuracy, 'g-', label='Train Accuracy')
|
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axs[0, 1].plot(epochs, test_accuracy, 'r-', label='Test Accuracy')
|
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axs[0, 1].set_xlabel('Epoch')
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axs[0, 1].set_ylabel('Accuracy (%)')
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axs[0, 1].set_title('Train and Test Accuracy over Epochs')
|
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axs[0, 1].legend()
|
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axs[0, 2].plot(epochs, f1_score, 'm-', label='F1 Score')
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axs[0, 2].set_xlabel('Epoch')
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axs[0, 2].set_ylabel('F1 Score')
|
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axs[0, 2].set_title('F1 Score over Epochs')
|
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axs[0, 2].legend()
|
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axs[1, 0].plot(epochs, precision, 'c-', label='Precision')
|
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axs[1, 0].set_xlabel('Epoch')
|
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axs[1, 0].set_ylabel('Precision')
|
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axs[1, 0].set_title('Precision over Epochs')
|
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axs[1, 0].legend()
|
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|
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axs[1, 1].plot(epochs, recall, 'y-', label='Recall')
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axs[1, 1].set_xlabel('Epoch')
|
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axs[1, 1].set_ylabel('Recall')
|
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axs[1, 1].set_title('Recall over Epochs')
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axs[1, 1].legend()
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axs[1, 2].axis('off')
|
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plt.tight_layout()
|
||||
plt.savefig(file_path.replace('.xlsx', '_epoch.png'))
|
||||
plt.close()
|
||||
|
||||
|
||||
def draw_last_epoch_bar_chart(file_path):
|
||||
df = pd.read_excel(file_path)
|
||||
last_epoch_data = df.iloc[-1]
|
||||
|
||||
metrics = ['train_loss', 'train_accuracy', 'test_accuracy', 'f1_score', 'precision', 'recall']
|
||||
values = [last_epoch_data[metric] for metric in metrics]
|
||||
labels = ['Train Loss', 'Train Accuracy', 'Test Accuracy', 'F1 Score', 'Precision', 'Recall']
|
||||
|
||||
values[1] *= 100
|
||||
values[2] *= 100
|
||||
|
||||
plt.figure(figsize=(10, 6))
|
||||
plt.bar(labels, values, color=['blue', 'green', 'red', 'magenta', 'cyan', 'yellow'])
|
||||
plt.xlabel('Metrics')
|
||||
plt.ylabel('Values')
|
||||
plt.title('Last Epoch Metrics')
|
||||
plt.ylim(bottom=0)
|
||||
|
||||
for i, value in enumerate(values):
|
||||
plt.text(i, value + 0.01, f'{value:.2f}', ha='center')
|
||||
|
||||
plt.tight_layout()
|
||||
plt.savefig(file_path.replace('.xlsx', '_last_epoch_bar.png'))
|
||||
plt.close()
|
||||
|
||||
|
||||
def draw_and_save_cm(file_path):
|
||||
df_cm = pd.read_excel(file_path)
|
||||
|
||||
labels = df_cm.columns[1:].tolist()
|
||||
cm = df_cm.iloc[:, 1:].to_numpy(dtype=float)
|
||||
|
||||
fig, axs = plt.subplots(1, 2, figsize=(12, 6))
|
||||
|
||||
axs[0].imshow(cm, interpolation='nearest', cmap='Blues')
|
||||
axs[0].set_title('Confusion Matrix')
|
||||
axs[0].set_xlabel('Predicted')
|
||||
axs[0].set_ylabel('True')
|
||||
axs[0].set_xticks(np.arange(len(labels)))
|
||||
axs[0].set_yticks(np.arange(len(labels)))
|
||||
axs[0].set_xticklabels(labels)
|
||||
axs[0].set_yticklabels(labels)
|
||||
|
||||
for i in range(len(labels)):
|
||||
for j in range(len(labels)):
|
||||
axs[0].text(j, i, f'{cm[i, j]}', ha='center', va='center')
|
||||
|
||||
plt.tight_layout()
|
||||
plt.savefig(file_path.replace('.xlsx', '.png'))
|
||||
plt.close()
|
||||
|
|
@ -1,96 +0,0 @@
|
|||
import torch
|
||||
import torch.nn as nn
|
||||
import numpy as np
|
||||
|
||||
from Qtorch.Models.Qnn import Qnn
|
||||
|
||||
|
||||
class QCNN(Qnn):
|
||||
def __init__(
|
||||
self,
|
||||
data,
|
||||
labels=None,
|
||||
conv_channels=(16, 32),
|
||||
kernel_size=3,
|
||||
hidden_size=128,
|
||||
dropout_rate=0.3,
|
||||
test_size=0.2,
|
||||
random_state=None,
|
||||
batch_size=64,
|
||||
learning_rate=0.00001,
|
||||
weight_decay=1e-5,
|
||||
lr_scheduler_patience=10,
|
||||
early_stop_patience=100,
|
||||
early_stop_threshold=0.99,
|
||||
):
|
||||
super(QCNN, self).__init__(
|
||||
data=data,
|
||||
labels=labels,
|
||||
test_size=test_size,
|
||||
random_state=random_state,
|
||||
batch_size=batch_size,
|
||||
learning_rate=learning_rate,
|
||||
weight_decay=weight_decay,
|
||||
lr_scheduler_patience=lr_scheduler_patience,
|
||||
early_stop_patience=early_stop_patience,
|
||||
early_stop_threshold=early_stop_threshold,
|
||||
)
|
||||
|
||||
self.conv_channels = tuple(conv_channels)
|
||||
self.kernel_size = kernel_size
|
||||
self.hidden_size = hidden_size
|
||||
self.dropout_rate = dropout_rate
|
||||
|
||||
self.feature_extractor = nn.Sequential()
|
||||
self.classifier = nn.Sequential()
|
||||
|
||||
# 构造 1D CNN 网络结构
|
||||
self.build_model(input_shape=self.X_train.shape[1:], num_classes=self.num_classes)
|
||||
self._model_built = True
|
||||
|
||||
def _transform_features(self, features):
|
||||
# 1D CNN 输入格式: [batch, channel=1, length]
|
||||
return torch.tensor(features, dtype=torch.float32).unsqueeze(1)
|
||||
|
||||
def build_model(self, input_shape, num_classes):
|
||||
if len(self.conv_channels) == 0:
|
||||
raise ValueError("'conv_channels' must contain at least one channel size.")
|
||||
|
||||
input_length = int(np.prod(input_shape))
|
||||
|
||||
conv_layers = []
|
||||
in_channels = 1
|
||||
for out_channels in self.conv_channels:
|
||||
conv_layers.append(nn.Conv1d(in_channels=in_channels, out_channels=out_channels, kernel_size=self.kernel_size))
|
||||
conv_layers.append(nn.ReLU())
|
||||
conv_layers.append(nn.MaxPool1d(kernel_size=2))
|
||||
in_channels = out_channels
|
||||
self.feature_extractor = nn.Sequential(*conv_layers)
|
||||
|
||||
conv_output_size = self._get_conv_output_size(input_length)
|
||||
self.classifier = nn.Sequential(
|
||||
nn.Linear(conv_output_size, self.hidden_size),
|
||||
nn.ReLU(),
|
||||
nn.Dropout(self.dropout_rate),
|
||||
nn.Linear(self.hidden_size, num_classes),
|
||||
)
|
||||
|
||||
self.__init_weights()
|
||||
|
||||
def _get_conv_output_size(self, input_length):
|
||||
x = torch.randn(1, 1, input_length)
|
||||
x = self.feature_extractor(x)
|
||||
return int(x.numel())
|
||||
|
||||
def forward(self, x):
|
||||
x = self.feature_extractor(x)
|
||||
x = x.view(x.size(0), -1)
|
||||
x = self.classifier(x)
|
||||
return x
|
||||
|
||||
def __init_weights(self):
|
||||
for m in self.modules():
|
||||
if isinstance(m, (nn.Conv1d, nn.Linear)):
|
||||
nn.init.xavier_uniform_(m.weight)
|
||||
if m.bias is not None:
|
||||
nn.init.zeros_(m.bias)
|
||||
|
|
@ -1,76 +0,0 @@
|
|||
import numpy as np
|
||||
import torch.nn as nn
|
||||
from Qtorch.Models.Qnn import Qnn
|
||||
|
||||
|
||||
class Qmlp(Qnn):
|
||||
|
||||
def __init__(self, data,
|
||||
hidden_layers,
|
||||
labels=None,
|
||||
dropout_rate=0.3,
|
||||
test_size = 0.2,
|
||||
random_state=None,
|
||||
batch_size=64,
|
||||
learning_rate=0.00001,
|
||||
weight_decay=1e-5,
|
||||
lr_scheduler_patience=10,
|
||||
early_stop_patience=100,
|
||||
early_stop_threshold=0.99,
|
||||
):
|
||||
super(Qmlp, self).__init__(
|
||||
data=data,
|
||||
labels=labels,
|
||||
test_size=test_size,
|
||||
random_state=random_state,
|
||||
batch_size=batch_size,
|
||||
learning_rate=learning_rate,
|
||||
weight_decay=weight_decay,
|
||||
lr_scheduler_patience=lr_scheduler_patience,
|
||||
early_stop_patience=early_stop_patience,
|
||||
early_stop_threshold=early_stop_threshold,
|
||||
)
|
||||
|
||||
self.hidden_layers = hidden_layers
|
||||
self.dropout_rate = dropout_rate
|
||||
self.layers = nn.ModuleList()
|
||||
|
||||
# 构造 MLP 网络结构
|
||||
self.build_model(input_shape=self.X_train.shape[1:], num_classes=self.num_classes)
|
||||
self._model_built = True
|
||||
|
||||
def build_model(self, input_shape, num_classes):
|
||||
if not self.hidden_layers:
|
||||
raise ValueError("'hidden_layers' must contain at least one layer size.")
|
||||
|
||||
input_size = int(np.prod(input_shape))
|
||||
self.layers = nn.ModuleList()
|
||||
|
||||
# 连接输入层和第一个隐藏层
|
||||
self.layers.append(nn.Linear(input_size, self.hidden_layers[0]))
|
||||
self.layers.append(nn.BatchNorm1d(self.hidden_layers[0]))
|
||||
self.layers.append(nn.ReLU())
|
||||
self.layers.append(nn.Dropout(self.dropout_rate))
|
||||
|
||||
# 创建隐藏层
|
||||
for i in range(1, len(self.hidden_layers)):
|
||||
self.layers.append(nn.Linear(self.hidden_layers[i-1], self.hidden_layers[i]))
|
||||
self.layers.append(nn.BatchNorm1d(self.hidden_layers[i]))
|
||||
self.layers.append(nn.ReLU())
|
||||
self.layers.append(nn.Dropout(self.dropout_rate))
|
||||
|
||||
# 创建输出层
|
||||
self.layers.append(nn.Linear(self.hidden_layers[-1], num_classes))
|
||||
self.__init_weights()
|
||||
|
||||
def forward(self, x):
|
||||
x = x.view(x.size(0), -1)
|
||||
for layer in self.layers:
|
||||
x = layer(x)
|
||||
return x
|
||||
|
||||
def __init_weights(self):
|
||||
for m in self.modules():
|
||||
if isinstance(m, nn.Linear):
|
||||
nn.init.xavier_uniform_(m.weight)
|
||||
nn.init.zeros_(m.bias)
|
||||
|
|
@ -1,233 +0,0 @@
|
|||
import torch
|
||||
import torch.nn as nn
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
from sklearn.decomposition import PCA
|
||||
from sklearn.metrics import confusion_matrix, f1_score, precision_score, recall_score
|
||||
from torch.utils.data import DataLoader, TensorDataset
|
||||
|
||||
from Qfunctions.divSet import divSet as DS
|
||||
|
||||
|
||||
class Qnn(nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
data,
|
||||
labels,
|
||||
test_size=0.2,
|
||||
random_state=None,
|
||||
batch_size=64,
|
||||
learning_rate=0.00001,
|
||||
weight_decay=1e-5,
|
||||
lr_scheduler_patience=10,
|
||||
early_stop_patience=100,
|
||||
early_stop_threshold=0.99,
|
||||
):
|
||||
|
||||
super(Qnn, self).__init__()
|
||||
|
||||
# 使用gpu进行加速, 没有gpu的话使用CPU
|
||||
self.DEVICE = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
|
||||
|
||||
# 训练配置，子类共享
|
||||
self.batch_size = batch_size
|
||||
self.learning_rate = learning_rate
|
||||
self.weight_decay = weight_decay
|
||||
self.lr_scheduler_patience = lr_scheduler_patience
|
||||
self.early_stop_patience = early_stop_patience
|
||||
self.early_stop_threshold = early_stop_threshold
|
||||
|
||||
# 划分测试集和训练集
|
||||
self.X_train, self.X_test, self.y_train, self.y_test, self.LABEL_ENCODER = DS(
|
||||
data=data, labels=labels, test_size=test_size, random_state=random_state
|
||||
)
|
||||
|
||||
self.labels = labels
|
||||
self.num_classes = len(labels) if labels is not None else int(np.max(self.y_train)) + 1
|
||||
|
||||
# 网络状态
|
||||
self._model_built = False
|
||||
|
||||
# 存储过程数据
|
||||
self.epoch_data = self._new_epoch_data()
|
||||
|
||||
# PCA 图片数据存储
|
||||
self.pca_2d, self.pca_3d = None, None
|
||||
|
||||
self.cm, self.cmn = None, None
|
||||
|
||||
def _new_epoch_data(self):
|
||||
return {
|
||||
'epoch': [],
|
||||
'train_loss': [],
|
||||
'train_accuracy': [],
|
||||
'test_accuracy': [],
|
||||
'precision': [],
|
||||
'recall': [],
|
||||
'f1_score': []
|
||||
}
|
||||
|
||||
def build_model(self, input_shape, num_classes):
|
||||
# 子类必须实现具体网络结构
|
||||
raise NotImplementedError("Subclasses must implement build_model(input_shape, num_classes)")
|
||||
|
||||
def _transform_features(self, features):
|
||||
# 默认输入格式: [batch, feature_dim]
|
||||
return torch.tensor(features, dtype=torch.float32)
|
||||
|
||||
def _prepare_data(self):
|
||||
|
||||
# 将data转换为tensor形式（子类可覆写 _transform_features）
|
||||
X_train_tensor = self._transform_features(self.X_train)
|
||||
y_train_tensor = torch.tensor(self.y_train, dtype=torch.long)
|
||||
|
||||
X_test_tensor = self._transform_features(self.X_test)
|
||||
y_test_tensor = torch.tensor(self.y_test, dtype=torch.long)
|
||||
|
||||
train_dataset = TensorDataset(X_train_tensor, y_train_tensor)
|
||||
test_dataset = TensorDataset(X_test_tensor, y_test_tensor)
|
||||
|
||||
train_loader = DataLoader(train_dataset, batch_size=self.batch_size, shuffle=True)
|
||||
test_loader = DataLoader(test_dataset, batch_size=self.batch_size, shuffle=False)
|
||||
|
||||
return train_loader, test_loader
|
||||
|
||||
def _train_model(self, train_loader, test_loader, epochs_times=100):
|
||||
|
||||
model = self.to(self.DEVICE)
|
||||
|
||||
criterion = nn.CrossEntropyLoss()
|
||||
optimizer = torch.optim.Adam(model.parameters(), lr=self.learning_rate, weight_decay=self.weight_decay)
|
||||
scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(
|
||||
optimizer,
|
||||
mode='min',
|
||||
factor=0.1,
|
||||
patience=self.lr_scheduler_patience,
|
||||
)
|
||||
best_test_accuracy = 0
|
||||
counter = 0
|
||||
|
||||
for epoch in range(epochs_times):
|
||||
|
||||
model.train()
|
||||
running_loss = 0.0
|
||||
correct_train = 0
|
||||
total_train = 0
|
||||
|
||||
for inputs, labels in train_loader:
|
||||
inputs, labels = inputs.to(self.DEVICE), labels.to(self.DEVICE)
|
||||
|
||||
optimizer.zero_grad()
|
||||
outputs = model(inputs)
|
||||
loss = criterion(outputs, labels)
|
||||
loss.backward()
|
||||
optimizer.step()
|
||||
|
||||
running_loss += loss.item()
|
||||
_, predicted = torch.max(outputs.data, 1)
|
||||
total_train += labels.size(0)
|
||||
correct_train += (predicted == labels).sum().item()
|
||||
train_accuracy = correct_train / total_train
|
||||
train_loss = running_loss / len(train_loader)
|
||||
|
||||
model.eval()
|
||||
correct_test = 0
|
||||
total_test = 0
|
||||
all_labels = []
|
||||
all_predicted = []
|
||||
all_prob = []
|
||||
with torch.no_grad():
|
||||
for inputs, labels in test_loader:
|
||||
inputs, labels = inputs.to(self.DEVICE), labels.to(self.DEVICE)
|
||||
outputs = model(inputs)
|
||||
prob = torch.nn.functional.softmax(outputs, dim=1)
|
||||
_, predicted = torch.max(outputs.data, 1)
|
||||
total_test += labels.size(0)
|
||||
correct_test += (predicted == labels).sum().item()
|
||||
all_labels.extend(labels.cpu().numpy())
|
||||
all_predicted.extend(predicted.cpu().numpy())
|
||||
all_prob.extend(prob.cpu().numpy())
|
||||
|
||||
test_accuracy = correct_test / total_test
|
||||
f1 = f1_score(all_labels, all_predicted, average='macro', zero_division=0)
|
||||
precision = precision_score(all_labels, all_predicted, average='macro', zero_division=0)
|
||||
recall = recall_score(all_labels, all_predicted, average='macro', zero_division=0)
|
||||
|
||||
if (epoch + 1) % 10 == 0:
|
||||
print('===============================================')
|
||||
print(f'Epoch [{epoch + 1} / {epochs_times}]:')
|
||||
print(f'Train Accuracy: {train_accuracy * 100:.2f}%, Test Accuracy: {test_accuracy*100:.2f}%, Loss: {train_loss:.4f}')
|
||||
print(f'Precision: {precision:.4f}, Recall: {recall:.4f}, F1 Score:{f1:.4f}, ')
|
||||
print('===============================================')
|
||||
|
||||
self.epoch_data['epoch'].append(epoch+1)
|
||||
self.epoch_data['train_loss'].append(train_loss)
|
||||
self.epoch_data['train_accuracy'].append(train_accuracy)
|
||||
self.epoch_data['test_accuracy'].append(test_accuracy)
|
||||
self.epoch_data['precision'].append(precision)
|
||||
self.epoch_data['recall'].append(recall)
|
||||
self.epoch_data['f1_score'].append(f1)
|
||||
|
||||
scheduler.step(train_loss)
|
||||
|
||||
if test_accuracy > best_test_accuracy:
|
||||
best_test_accuracy = test_accuracy
|
||||
counter = 0
|
||||
else:
|
||||
counter += 1
|
||||
|
||||
if counter >= self.early_stop_patience and best_test_accuracy >= self.early_stop_threshold:
|
||||
print(f"Early stopping at epoch {epoch+1}")
|
||||
break
|
||||
|
||||
# cmn为归一化矩阵
|
||||
# Keep matrix dimensions stable even when some classes do not appear in this split.
|
||||
cm_labels = np.arange(len(self.labels)) if self.labels is not None else None
|
||||
self.cm = confusion_matrix(all_labels, all_predicted, labels=cm_labels)
|
||||
self.cmn = confusion_matrix(all_labels, all_predicted, labels=cm_labels, normalize='true')
|
||||
|
||||
print(self.cm)
|
||||
return
|
||||
|
||||
def fit(self, epoch_times = 100):
|
||||
if not self._model_built:
|
||||
self.build_model(input_shape=self.X_train.shape[1:], num_classes=self.num_classes)
|
||||
self._model_built = True
|
||||
|
||||
# 每次训练前清空过程指标，避免重复累计
|
||||
self.epoch_data = self._new_epoch_data()
|
||||
|
||||
train_loader, test_loader = self._prepare_data()
|
||||
self._train_model(train_loader, test_loader, epochs_times=epoch_times)
|
||||
return
|
||||
|
||||
# 外部获取PCA图像数据的接口
|
||||
def get_PCA(self):
|
||||
|
||||
# PCA 2D 图像
|
||||
pca_2d = PCA(n_components=2) # 保留两个主成分
|
||||
principalComponents = pca_2d.fit_transform(self.X_train)
|
||||
df_pca2d =pd.DataFrame(data=principalComponents, columns=['PC1', 'PC2'])
|
||||
df_pca2d['labels'] = self.y_train
|
||||
|
||||
# PCA 3D 图像
|
||||
pca_3d = PCA(n_components=3) # 保留三个主成分
|
||||
principalComponents = pca_3d.fit_transform(self.X_train)
|
||||
df_pca3d = pd.DataFrame(data=principalComponents, columns=['PC1', 'PC2', 'PC3'])
|
||||
df_pca3d['labels'] = self.y_train
|
||||
|
||||
return df_pca2d, df_pca3d
|
||||
|
||||
# 外部获取混淆矩阵的接口
|
||||
def get_cm(self):
|
||||
label_names = self.labels if self.labels is not None else list(range(self.num_classes))
|
||||
return pd.DataFrame(self.cm, columns=label_names, index=label_names)
|
||||
|
||||
def get_cmn(self):
|
||||
label_names = self.labels if self.labels is not None else list(range(self.num_classes))
|
||||
return pd.DataFrame(self.cmn, columns=label_names, index=label_names)
|
||||
|
||||
# 外部获取迭代数据的接口
|
||||
def get_epoch_data(self):
|
||||
return pd.DataFrame(self.epoch_data)
|
||||
|
||||
|
|
@ -1,2 +0,0 @@
|
|||
# Qtorch/__init__.py
|
||||
from .Models import Qnn, Qmlp, Qcnn
|
||||
318
README.md
318
README.md
|
|
@ -1,318 +0,0 @@
|
|||
# Deeplearning 使用说明
|
||||
|
||||
## 1. Conda 环境迁移
|
||||
|
||||
环境文件在 `conda_env/`：
|
||||
|
||||
- `conda_env/environment.portable.yml`：通用迁移（推荐）
|
||||
- `conda_env/environment.lock.txt`：精确锁定（同系统/同架构优先）
|
||||
- `conda_env/env.yml`：历史文件
|
||||
|
||||
### 1.1 创建环境
|
||||
|
||||
```bash
|
||||
# 方式1（推荐）：通用创建
|
||||
conda env create -f conda_env/environment.portable.yml
|
||||
conda activate Deeplearning
|
||||
|
||||
# 方式2：精确复现
|
||||
conda create -n Deeplearning --file conda_env/environment.lock.txt
|
||||
conda activate Deeplearning
|
||||
|
||||
# 验证
|
||||
python -V
|
||||
python -c "import torch; print(torch.__version__)"
|
||||
```
|
||||
|
||||
### 1.2 同名环境已存在时
|
||||
|
||||
```bash
|
||||
# 方式A：保留旧环境，改名创建
|
||||
conda env create -f conda_env/environment.portable.yml -n Deeplearning_v2
|
||||
conda activate Deeplearning_v2
|
||||
|
||||
# 或者（lock 方式）
|
||||
conda create -n Deeplearning_v2 --file conda_env/environment.lock.txt
|
||||
conda activate Deeplearning_v2
|
||||
```
|
||||
|
||||
```bash
|
||||
# 方式B：删除旧环境后重建（谨慎）
|
||||
conda env remove -n Deeplearning
|
||||
conda env create -f conda_env/environment.portable.yml
|
||||
conda activate Deeplearning
|
||||
```
|
||||
|
||||
### 1.3 重新导出环境
|
||||
|
||||
```bash
|
||||
conda env export -n Deeplearning --no-builds > conda_env/environment.portable.yml
|
||||
conda list -n Deeplearning --explicit > conda_env/environment.lock.txt
|
||||
```
|
||||
|
||||
### 1.4 主要依赖包
|
||||
|
||||
训练与数据处理核心依赖：
|
||||
|
||||
- Python 3.12
|
||||
- pytorch / torchvision / torchaudio
|
||||
- pandas / numpy / scipy
|
||||
- scikit-learn
|
||||
- matplotlib / seaborn
|
||||
- openpyxl / xlrd（用于 xls/xlsx 读写）
|
||||
|
||||
说明：项目仓库已提供完整环境文件，优先用 `conda_env/environment.portable.yml` 或 `conda_env/environment.lock.txt` 创建环境。
|
||||
|
||||
### 1.5 依赖安装方式（Conda / pip）
|
||||
|
||||
方式 A（推荐，Conda 一步到位）：
|
||||
|
||||
```bash
|
||||
conda env create -f conda_env/environment.portable.yml
|
||||
conda activate Deeplearning
|
||||
```
|
||||
|
||||
方式 B（Conda 最小安装，适合自定义环境）：
|
||||
|
||||
```bash
|
||||
conda create -n Deeplearning python=3.12 -y
|
||||
conda activate Deeplearning
|
||||
|
||||
# GPU 机器（CUDA 12.4）
|
||||
conda install -y pytorch torchvision torchaudio pytorch-cuda=12.4 -c pytorch -c nvidia
|
||||
|
||||
# CPU 机器（无 CUDA）
|
||||
# conda install -y pytorch torchvision torchaudio cpuonly -c pytorch
|
||||
|
||||
conda install -y pandas numpy scipy scikit-learn matplotlib seaborn openpyxl xlrd
|
||||
```
|
||||
|
||||
方式 C（pip 安装，适合已有虚拟环境）：
|
||||
|
||||
```bash
|
||||
pip install torch torchvision torchaudio
|
||||
pip install pandas numpy scipy scikit-learn matplotlib seaborn openpyxl xlrd
|
||||
```
|
||||
|
||||
可选开发工具：
|
||||
|
||||
```bash
|
||||
pip install black autopep8 basedpyright
|
||||
```
|
||||
|
||||
## 2. 项目约定
|
||||
|
||||
### 2.1 输入数据格式
|
||||
每一类数据支持 `xls/xlsx/csv`。读取时默认取偶数列（索引 1,3,5...）作为特征，奇数列内容可忽略。
|
||||
|
||||
示意：
|
||||
|
||||
| 任意值 | 特征值 | 任意值 | 特征值 |
|
||||
|---|---|---|---|
|
||||
| arbitrary value | value | arbitrary value | value |
|
||||
|
||||
### 2.2 目录约定
|
||||
训练数据放在 `Static/`，输出结果放在 `Result/`。
|
||||
|
||||
推荐目录：
|
||||
|
||||
```text
|
||||
.
|
||||
├─ Static/
|
||||
│ └─ 20241009MaterialDiv/
|
||||
└─ Result/
|
||||
```
|
||||
|
||||
## 3. 快速开始
|
||||
|
||||
### 3.1 准备数据
|
||||
1. 将数据目录命名为 `日期+项目名`，例如 `20241009MaterialDiv`。
|
||||
2. 准备 `label_names`（建议英文或数字）。
|
||||
3. 将数据目录放入 `Static/`。
|
||||
|
||||
### 3.2 数据目录模板
|
||||
|
||||
单文件模式（每个标签一个文件）：
|
||||
|
||||
```text
|
||||
Static/
|
||||
20241009MaterialDiv/
|
||||
Acrlic.xlsx
|
||||
Ecoflex.xlsx
|
||||
PDMS.xlsx
|
||||
PLA.xlsx
|
||||
Wood.xlsx
|
||||
```
|
||||
|
||||
多子特征模式（每个标签一个子目录，目录下可有多个文件）：
|
||||
|
||||
```text
|
||||
Static/
|
||||
20241009MaterialDiv/
|
||||
Acrlic/
|
||||
sample_01.xlsx
|
||||
sample_02.xlsx
|
||||
Ecoflex/
|
||||
sample_01.xlsx
|
||||
sample_02.xlsx
|
||||
PDMS/
|
||||
sample_01.xlsx
|
||||
sample_02.xlsx
|
||||
PLA/
|
||||
sample_01.xlsx
|
||||
sample_02.xlsx
|
||||
Wood/
|
||||
sample_01.xlsx
|
||||
sample_02.xlsx
|
||||
```
|
||||
|
||||
命名规则（重要）：
|
||||
|
||||
- `label_names` 中每一项必须与文件名（单文件模式）或子文件夹名（多子特征模式）一致。
|
||||
- `label_names` 顺序就是标签编码顺序，训练结果和混淆矩阵按该顺序展示。
|
||||
|
||||
示例：
|
||||
|
||||
```python
|
||||
label_names = ['Acrlic', 'Ecoflex', 'PDMS', 'PLA', 'Wood']
|
||||
```
|
||||
|
||||
对应关系：
|
||||
|
||||
```text
|
||||
Acrlic <-> Acrlic.xlsx 或 Acrlic/
|
||||
Ecoflex <-> Ecoflex.xlsx 或 Ecoflex/
|
||||
PDMS <-> PDMS.xlsx 或 PDMS/
|
||||
PLA <-> PLA.xlsx 或 PLA/
|
||||
Wood <-> Wood.xlsx 或 Wood/
|
||||
```
|
||||
|
||||
### 3.3 通用：数据导入
|
||||
|
||||
```python
|
||||
from Qfunctions.loadData import load_data
|
||||
|
||||
projet_name = '20241009MaterialDiv'
|
||||
label_names = ['Acrlic', 'Ecoflex', 'PDMS', 'PLA', 'Wood']
|
||||
|
||||
# 自动识别数据模式（支持 xls/xlsx/csv）
|
||||
data = load_data(projet_name, label_names)
|
||||
```
|
||||
|
||||
### 3.4 模型调用
|
||||
|
||||
#### 3.4.1 MLP
|
||||
|
||||
```python
|
||||
from Qtorch.Models.Qmlp import Qmlp
|
||||
|
||||
model = Qmlp(
|
||||
data=data,
|
||||
labels=label_names,
|
||||
hidden_layers=[128, 256, 128],
|
||||
test_size=0.3,
|
||||
dropout_rate=0,
|
||||
)
|
||||
|
||||
model.fit(300)
|
||||
```
|
||||
|
||||
#### 3.4.2 1D CNN
|
||||
|
||||
```python
|
||||
from Qtorch.Models.Qcnn import QCNN
|
||||
|
||||
model = QCNN(
|
||||
data=data,
|
||||
labels=label_names,
|
||||
conv_channels=(16, 32),
|
||||
kernel_size=3,
|
||||
hidden_size=128,
|
||||
test_size=0.3,
|
||||
dropout_rate=0,
|
||||
)
|
||||
|
||||
model.fit(300)
|
||||
```
|
||||
|
||||
### 3.5 通用：结果获取与图表导出
|
||||
|
||||
```python
|
||||
from Qfunctions.saveToXlsx import save_to_xlsx
|
||||
|
||||
pca_2d, pca_3d = model.get_PCA()
|
||||
cm = model.get_cm()
|
||||
cmn = model.get_cmn()
|
||||
epoch_data = model.get_epoch_data()
|
||||
|
||||
save_to_xlsx(project_name=projet_name, file_name='pca_2d', data=pca_2d)
|
||||
save_to_xlsx(project_name=projet_name, file_name='pca_3d', data=pca_3d)
|
||||
save_to_xlsx(project_name=projet_name, file_name='cm', data=cm)
|
||||
save_to_xlsx(project_name=projet_name, file_name='cmn', data=cmn)
|
||||
save_to_xlsx(project_name=projet_name, file_name='acc_and_loss', data=epoch_data)
|
||||
```
|
||||
|
||||
### 3.6 每次运行参数与结果 YAML 存档
|
||||
|
||||
当前 `main.py` 会为每次运行创建时间戳目录：
|
||||
|
||||
- `Result/<project_name>/<YYYYMMDD_HHMMSS>/`
|
||||
|
||||
并在目录下自动保存：
|
||||
|
||||
- `run_params.yaml`：本次运行参数快照。
|
||||
- `run_result.yaml`：本次运行结果摘要（最佳轮次与最后轮次指标）。
|
||||
|
||||
这样可以直接对比不同运行的参数与结果变化。
|
||||
|
||||
## 4. load_data 参数说明
|
||||
|
||||
| 参数 | 类型 | 默认值 | 说明 |
|
||||
|---|---|---|---|
|
||||
| folder | str | 必填 | `Static/` 下的数据目录名 |
|
||||
| labelNames | list | 必填 | 类别名称列表，用于读取和排序标签 |
|
||||
|
||||
自动识别规则：
|
||||
|
||||
- 若每个 `label` 都对应 `folder/label/*.(xlsx|xls|csv)`，识别为多子特征模式。
|
||||
- 若每个 `label` 都对应 `folder/label.(xlsx|xls|csv)`，识别为单文件模式。
|
||||
- 超出支持范围的文件格式（仅支持 xls/xlsx/csv）会报错。
|
||||
- 若两种都成立（同名文件和同名子目录同时存在），会报错并提示只保留一种目录结构。
|
||||
- 若两种都不成立，会报错并提示检查目录结构或 `label_names`。
|
||||
|
||||
读取路径规则：
|
||||
|
||||
- 单文件模式：`./Static/folder/labelNames[i].(xlsx|xls|csv)`
|
||||
- 多子特征模式：`./Static/folder/labelNames[i]/*.(xlsx|xls|csv)`
|
||||
|
||||
## 5. 常见问题
|
||||
|
||||
### 5.1 找不到文件
|
||||
优先检查：
|
||||
|
||||
- `label_names` 与文件/文件夹是否同名
|
||||
- 文件后缀是否为 `.xls`、`.xlsx` 或 `.csv`（其他格式将报错）
|
||||
|
||||
## 6. TODO（后续计划）
|
||||
|
||||
### 阶段一：基础稳定与可维护性
|
||||
|
||||
- [ ] 固化模型基类契约：`build_model`、输入变换钩子、统一训练配置。
|
||||
- [ ] 封装 `QDL` 包结构（`qdl.data / qdl.models / qdl.export / qdl.api`）。
|
||||
- [ ] 增加兼容层：保留旧导入路径，逐步迁移到新包路径。
|
||||
- [ ] 增加最小测试集：`load_data`、`Qmlp.fit(1)`、`QCNN.fit(1)`、导出函数。
|
||||
|
||||
### 阶段二：高维与复合模型能力
|
||||
|
||||
- [ ] 将 batch 结构升级为字典（`x/y/lengths/mask/meta`），支持复合输入。
|
||||
- [ ] 在训练框架中加入钩子：`prepare_batch`、`forward_step`、`compute_loss`。
|
||||
- [ ] 引入编码器层（Encoder）抽象：按策略扩展（MLP/CNN/LSTM/GNN/Transformer），不按维度硬扩类。
|
||||
- [ ] 支持可变长时间序列（`collate_fn + lengths + mask`），作为后续复合模型基础。
|
||||
- [ ] 增加多分支融合模板（时序分支 + 静态分支），预留多任务损失组合。
|
||||
|
||||
### 阶段三：对外能力与发布
|
||||
|
||||
- [ ] 统一对外入口：提供高层 API（例如 `train_mlp`、`train_cnn1d`、`train_hybrid`）。
|
||||
- [ ] 在模型文档中预留扩展位：`3.4.3 LSTM`、`3.4.4 GNN`、`3.4.5 Hybrid`。
|
||||
- [ ] 完成打包配置（`pyproject.toml`）与本地可编辑安装说明。
|
||||
- [ ] 发布前回归：在 Conda 与 pip 环境分别跑通最小端到端流程。
|
||||
|
|
@ -1,49 +0,0 @@
|
|||
#!/bin/bash
|
||||
|
||||
# 输出的Markdown文件名
|
||||
output_file="python_files_output.md"
|
||||
|
||||
# 清空或创建输出文件
|
||||
> "$output_file"
|
||||
|
||||
# 递归函数来处理目录
|
||||
process_directory() {
|
||||
local dir="$1"
|
||||
local depth="$2"
|
||||
|
||||
# 添加目录作为标题
|
||||
echo "$(printf '%0.s#' $(seq 1 $depth)) ${dir##*/}" >> "$output_file"
|
||||
echo "" >> "$output_file"
|
||||
|
||||
# 遍历当前目录中的所有.py文件
|
||||
for file in "$dir"/*.py; do
|
||||
# 检查文件是否存在（以防止没有.py文件的情况）
|
||||
if [ -f "$file" ]; then
|
||||
# 将文件名作为子标题写入md文件
|
||||
echo "$(printf '%0.s#' $(seq 1 $((depth + 1)))) ${file##*/}" >> "$output_file"
|
||||
echo "" >> "$output_file"
|
||||
|
||||
# 添加代码块开始标记
|
||||
echo '```python' >> "$output_file"
|
||||
|
||||
# 将Python文件内容添加到md文件
|
||||
cat "$file" >> "$output_file"
|
||||
|
||||
# 添加代码块结束标记
|
||||
echo '```' >> "$output_file"
|
||||
echo "" >> "$output_file"
|
||||
fi
|
||||
done
|
||||
|
||||
# 递归处理子目录
|
||||
for subdir in "$dir"/*/; do
|
||||
if [ -d "$subdir" ]; then
|
||||
process_directory "$subdir" $((depth + 1))
|
||||
fi
|
||||
done
|
||||
}
|
||||
|
||||
# 从当前目录开始处理
|
||||
process_directory "." 1
|
||||
|
||||
echo "Markdown文件已生成: $output_file"
|
||||
|
|
@ -1,631 +0,0 @@
|
|||
#!/usr/bin/env python3
|
||||
"""
|
||||
数据质量检查脚本
|
||||
===================
|
||||
对 Static/ 下的数据目录执行完整性、统计、平衡性与离群值检查，
|
||||
生成详细报告输出到终端，并可保存为文本文件。
|
||||
|
||||
用法:
|
||||
python Scripts/check_data.py --folder 20260319Numbers --labels 0 1 2 3 4 5 6 7 8 9
|
||||
python Scripts/check_data.py --folder "20260408 grap" --labels 1 2 3 4 5 6 7 8 9 --output report.txt
|
||||
python Scripts/check_data.py -f 20260319Numbers -l 0 1 2 3 4 5 6 7 8 9
|
||||
|
||||
要求:
|
||||
在项目根目录 (Deeplearning/) 下运行，或通过 --root 指定项目根目录。
|
||||
"""
|
||||
|
||||
import os
|
||||
import sys
|
||||
import argparse
|
||||
import unicodedata
|
||||
from pathlib import Path
|
||||
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
|
||||
|
||||
# ============================================================
|
||||
# 工具函数（与 loadData.py 中逻辑保持一致）
|
||||
# ============================================================
|
||||
|
||||
DEFAULT_FILE_CLASSES = ("xlsx", "xls", "csv")
|
||||
|
||||
|
||||
def _has_supported_extension(filename: str, file_classes=DEFAULT_FILE_CLASSES) -> bool:
|
||||
ext = os.path.splitext(filename)[1].lower().lstrip(".")
|
||||
return ext in file_classes
|
||||
|
||||
|
||||
def _read_data_file(file_path: str) -> pd.DataFrame:
|
||||
ext = os.path.splitext(file_path)[1].lower()
|
||||
if ext == ".csv":
|
||||
return pd.read_csv(file_path)
|
||||
if ext in (".xls", ".xlsx"):
|
||||
return pd.read_excel(file_path)
|
||||
raise ValueError(
|
||||
f"Unsupported file format: {ext}. Only .xls, .xlsx, and .csv are supported. "
|
||||
f"File: {file_path}"
|
||||
)
|
||||
|
||||
|
||||
def _strip_zero_width(s: str) -> str:
|
||||
if not isinstance(s, str):
|
||||
return s
|
||||
return s.translate(
|
||||
{0x200B: None, 0x200C: None, 0x200D: None, 0xFEFF: None}
|
||||
)
|
||||
|
||||
|
||||
def _canonicalize_name(name: str) -> str:
|
||||
name = unicodedata.normalize("NFKC", name)
|
||||
name = _strip_zero_width(name)
|
||||
return name
|
||||
|
||||
|
||||
def _normalize_for_compare(name: str) -> str:
|
||||
n = _canonicalize_name(name)
|
||||
n = n.replace("_", " ")
|
||||
n = " ".join(n.split())
|
||||
return n.lower()
|
||||
|
||||
|
||||
def _find_matching_file(folder: str, expected_name: str):
|
||||
expected = _canonicalize_name(expected_name)
|
||||
try:
|
||||
entries = os.listdir(folder)
|
||||
except FileNotFoundError:
|
||||
return None
|
||||
for f in entries:
|
||||
if _canonicalize_name(f) == expected:
|
||||
return f
|
||||
expected_lower = expected.lower()
|
||||
for f in entries:
|
||||
if _canonicalize_name(f).lower() == expected_lower:
|
||||
return f
|
||||
expected_relaxed = _normalize_for_compare(expected_name)
|
||||
for f in entries:
|
||||
if _normalize_for_compare(f) == expected_relaxed:
|
||||
return f
|
||||
return None
|
||||
|
||||
|
||||
def _find_matching_file_by_label(folder: str, label_name, file_classes):
|
||||
for ext in file_classes:
|
||||
expected_name = f"{label_name}.{ext}"
|
||||
match = _find_matching_file(folder, expected_name)
|
||||
if match is not None:
|
||||
return match
|
||||
return None
|
||||
|
||||
|
||||
# ============================================================
|
||||
# 报告生成工具
|
||||
# ============================================================
|
||||
|
||||
class ReportBuffer:
|
||||
"""收集报告行，并同时输出到 stdout 和文件。"""
|
||||
|
||||
def __init__(self, output_path=None):
|
||||
self.lines: list[str] = []
|
||||
self.output_path = output_path
|
||||
|
||||
def add(self, text: str = ""):
|
||||
print(text)
|
||||
self.lines.append(text)
|
||||
|
||||
def save(self):
|
||||
if self.output_path:
|
||||
with open(self.output_path, "w", encoding="utf-8") as f:
|
||||
f.write("\n".join(self.lines) + "\n")
|
||||
print(f"\n报告已保存到: {self.output_path}")
|
||||
|
||||
|
||||
# ============================================================
|
||||
# 核心检查逻辑
|
||||
# ============================================================
|
||||
|
||||
def _extract_features(df: pd.DataFrame, source: str) -> pd.DataFrame:
|
||||
"""
|
||||
按项目约定提取偶数列作为特征（保持 int 列名以对齐）。
|
||||
返回特征 DataFrame（列名 0, 2, 4, ...）。
|
||||
"""
|
||||
# 偶数列索引: 1, 3, 5, ...
|
||||
even_cols = [c for i, c in enumerate(df.columns) if i % 2 == 1]
|
||||
if not even_cols:
|
||||
raise ValueError(f"没有找到偶数列（特征列）。请检查文件: {source}")
|
||||
features = df[even_cols].copy()
|
||||
|
||||
# 尝试转为数值
|
||||
for c in features.columns:
|
||||
features[c] = pd.to_numeric(features[c], errors="coerce")
|
||||
|
||||
return features
|
||||
|
||||
|
||||
def check_tabular_project(root: str, folder: str, labels: list[str], rp: ReportBuffer):
|
||||
"""完整检查流程"""
|
||||
data_dir = os.path.join(root, "Static", folder)
|
||||
if not os.path.isdir(data_dir):
|
||||
rp.add(f"[ERROR] 目录不存在: {data_dir}")
|
||||
rp.add("请确认 --folder 参数正确。")
|
||||
return
|
||||
|
||||
rp.add("=" * 64)
|
||||
rp.add(" Deeplearning 数据质量检查报告")
|
||||
rp.add("=" * 64)
|
||||
rp.add(f" 数据目录 : {data_dir}")
|
||||
rp.add(f" 标签数量 : {len(labels)}")
|
||||
rp.add(f" 标签列表 : {labels}")
|
||||
rp.add()
|
||||
|
||||
# ---- 第一步：检测数据模式 ----
|
||||
has_all_subfolders = True
|
||||
for lbl in labels:
|
||||
sub = os.path.join(data_dir, str(lbl))
|
||||
if not (os.path.isdir(sub) and any(_has_supported_extension(f) for f in os.listdir(sub))):
|
||||
has_all_subfolders = False
|
||||
break
|
||||
|
||||
has_all_files = True
|
||||
for lbl in labels:
|
||||
if _find_matching_file_by_label(data_dir, lbl, DEFAULT_FILE_CLASSES) is None:
|
||||
has_all_files = False
|
||||
break
|
||||
|
||||
if has_all_files and not has_all_subfolders:
|
||||
mode = "single_file"
|
||||
elif has_all_subfolders and not has_all_files:
|
||||
mode = "multi_folder"
|
||||
else:
|
||||
rp.add("[ERROR] 无法自动检测数据模式，或两种模式同时存在。")
|
||||
rp.add(f" has_all_files : {has_all_files}")
|
||||
rp.add(f" has_all_subfolders: {has_all_subfolders}")
|
||||
rp.add("请确保每个 label 对应唯一的文件或唯一的子目录。")
|
||||
return
|
||||
|
||||
if mode == "single_file":
|
||||
_check_single_file_mode(data_dir, labels, rp)
|
||||
else:
|
||||
_check_multi_folder_mode(data_dir, labels, rp)
|
||||
|
||||
rp.add()
|
||||
rp.add("=" * 64)
|
||||
rp.add(" 检查完成。")
|
||||
rp.add("=" * 64)
|
||||
|
||||
|
||||
def _check_single_file_mode(data_dir: str, labels: list[str], rp: ReportBuffer):
|
||||
rp.add()
|
||||
rp.add("── 数据模式: 单文件模式 ──")
|
||||
rp.add()
|
||||
|
||||
# 1. 定位实际文件名
|
||||
file_map: dict[str, str] = {}
|
||||
missing: list[str] = []
|
||||
for lbl in labels:
|
||||
match = _find_matching_file_by_label(data_dir, lbl, DEFAULT_FILE_CLASSES)
|
||||
if match:
|
||||
file_map[lbl] = match
|
||||
else:
|
||||
missing.append(lbl)
|
||||
|
||||
if missing:
|
||||
rp.add(f"[WARN] 以下标签找不到对应文件: {missing}")
|
||||
rp.add(f"当前目录内容: {sorted(os.listdir(data_dir))}")
|
||||
if not file_map:
|
||||
return
|
||||
labels = [l for l in labels if l in file_map]
|
||||
|
||||
# 2. 逐类读取
|
||||
all_features = [] # list of (label, pd.DataFrame)
|
||||
per_class_info: dict[str, dict] = {}
|
||||
col_counts: dict[str, int] = {}
|
||||
|
||||
for lbl in labels:
|
||||
fname = file_map[lbl]
|
||||
file_path = os.path.join(data_dir, fname)
|
||||
info: dict[str, object] = {"label": lbl, "file": fname, "warnings": []}
|
||||
|
||||
try:
|
||||
raw = _read_data_file(file_path)
|
||||
except Exception as e:
|
||||
info["error"] = str(e)
|
||||
per_class_info[lbl] = info
|
||||
rp.add(f"[ERROR] 读取文件失败: {file_path} — {e}")
|
||||
continue
|
||||
|
||||
info["raw_rows"] = raw.shape[0]
|
||||
info["raw_cols"] = raw.shape[1]
|
||||
|
||||
# NaN 在原始文件中
|
||||
total_nan = raw.isna().sum().sum()
|
||||
if total_nan > 0:
|
||||
info["warnings"].append(f"原始文件含 {total_nan} 个 NaN 单元格")
|
||||
|
||||
try:
|
||||
features = _extract_features(raw, fname)
|
||||
except ValueError as e:
|
||||
info["error"] = str(e)
|
||||
per_class_info[lbl] = info
|
||||
rp.add(f"[ERROR] 特征提取失败: {file_path} — {e}")
|
||||
continue
|
||||
|
||||
# 丢弃含 NaN 的行（同 loadData 的 dropna 逻辑）后统计
|
||||
clean = features.dropna()
|
||||
info["feature_cols"] = features.shape[1]
|
||||
info["samples_after_dropna"] = clean.shape[0]
|
||||
info["dropped_nan_rows"] = features.shape[0] - clean.shape[0]
|
||||
info["values"] = clean.values
|
||||
|
||||
col_counts[lbl] = features.shape[1]
|
||||
|
||||
if clean.shape[0] == 0:
|
||||
info["warnings"].append("去除 NaN 后无有效样本")
|
||||
|
||||
per_class_info[lbl] = info
|
||||
if clean.shape[0] > 0:
|
||||
all_features.append((lbl, clean))
|
||||
|
||||
# 列数一致性
|
||||
if len(set(col_counts.values())) > 1:
|
||||
rp.add()
|
||||
rp.add("[WARN] 各标签的特征列数不一致！")
|
||||
for lbl, cc in col_counts.items():
|
||||
rp.add(f" {lbl}: {cc} 列")
|
||||
rp.add("这会导致 load_data 时补零逻辑产生差异。")
|
||||
else:
|
||||
rp.add(f"[OK] 所有标签特征列数一致: {next(iter(col_counts.values()), 0)} 列")
|
||||
|
||||
# 样本数统计
|
||||
rp.add()
|
||||
rp.add("── 各类别样本数 ──")
|
||||
sample_counts: dict[str, int] = {}
|
||||
for lbl in labels:
|
||||
info = per_class_info.get(lbl, {})
|
||||
if "error" in info:
|
||||
rp.add(f" [{lbl}] 加载失败: {info['error']}")
|
||||
continue
|
||||
n = info.get("samples_after_dropna", 0)
|
||||
sample_counts[lbl] = n
|
||||
warnings = info.get("warnings", [])
|
||||
wflag = f" ⚠ {'; '.join(warnings)}" if warnings else ""
|
||||
rp.add(f" [{lbl}] {n} 行 (文件: {info.get('file','?')}, "
|
||||
f"原始 {info.get('raw_rows','?')} 行, "
|
||||
f"丢弃 NaN 行 {info.get('dropped_nan_rows',0)}){wflag}")
|
||||
|
||||
# 平衡性分析
|
||||
_analyze_balance(sample_counts, rp)
|
||||
|
||||
# 统计 + 离群值
|
||||
_analyze_statistics(all_features, rp)
|
||||
_analyze_outliers(all_features, rp)
|
||||
|
||||
|
||||
def _check_multi_folder_mode(data_dir: str, labels: list[str], rp: ReportBuffer):
|
||||
rp.add()
|
||||
rp.add("── 数据模式: 多子特征模式 ──")
|
||||
rp.add()
|
||||
|
||||
all_features = []
|
||||
per_class_info: dict[str, dict] = {}
|
||||
col_counts: dict[str, int] = {}
|
||||
|
||||
for lbl in labels:
|
||||
sub = os.path.join(data_dir, str(lbl))
|
||||
if not os.path.isdir(sub):
|
||||
per_class_info[lbl] = {"error": f"子目录不存在: {sub}"}
|
||||
rp.add(f"[ERROR] {lbl}: 子目录不存在")
|
||||
continue
|
||||
|
||||
files = sorted(
|
||||
[f for f in os.listdir(sub) if _has_supported_extension(f)]
|
||||
)
|
||||
if not files:
|
||||
per_class_info[lbl] = {"error": f"子目录下无支持的文件: {sub}"}
|
||||
rp.add(f"[ERROR] {lbl}: 子目录下无 .xlsx/.xls/.csv 文件")
|
||||
continue
|
||||
|
||||
class_frame_list = []
|
||||
single_file_cols = set()
|
||||
total_raw = 0
|
||||
total_dropped = 0
|
||||
failed_files = []
|
||||
|
||||
for fname in files:
|
||||
file_path = os.path.join(sub, fname)
|
||||
try:
|
||||
raw = _read_data_file(file_path)
|
||||
except Exception as e:
|
||||
failed_files.append(f" {fname}: {e}")
|
||||
continue
|
||||
|
||||
total_raw += raw.shape[0]
|
||||
try:
|
||||
features = _extract_features(raw, f"{lbl}/{fname}")
|
||||
except ValueError as e:
|
||||
failed_files.append(f" {fname}: {e}")
|
||||
continue
|
||||
|
||||
single_file_cols.add(features.shape[1])
|
||||
clean = features.dropna()
|
||||
total_dropped += features.shape[0] - clean.shape[0]
|
||||
if clean.shape[0] > 0:
|
||||
class_frame_list.append(clean)
|
||||
|
||||
info: dict[str, object] = {
|
||||
"label": lbl,
|
||||
"num_files": len(files),
|
||||
"raw_rows_total": total_raw,
|
||||
"dropped_nan_rows": total_dropped,
|
||||
"warnings": [],
|
||||
}
|
||||
|
||||
if failed_files:
|
||||
info["warnings"].append(f"{len(failed_files)} 个文件加载失败")
|
||||
for ff in failed_files:
|
||||
rp.add(f" [WARN] {ff}")
|
||||
|
||||
if len(single_file_cols) > 1:
|
||||
info["warnings"].append(
|
||||
f"子文件间列数不一致: {sorted(single_file_cols)}"
|
||||
)
|
||||
col_counts[lbl] = max(single_file_cols)
|
||||
elif single_file_cols:
|
||||
col_counts[lbl] = single_file_cols.pop()
|
||||
else:
|
||||
col_counts[lbl] = 0
|
||||
|
||||
if class_frame_list:
|
||||
combined = pd.concat(class_frame_list, ignore_index=True)
|
||||
info["samples_after_dropna"] = combined.shape[0]
|
||||
info["feature_cols"] = combined.shape[1]
|
||||
info["values"] = combined.values
|
||||
all_features.append((lbl, combined))
|
||||
else:
|
||||
info["samples_after_dropna"] = 0
|
||||
info["warnings"].append("无有效样本")
|
||||
|
||||
per_class_info[lbl] = info
|
||||
|
||||
# 列数一致性
|
||||
non_zero = {l: c for l, c in col_counts.items() if c > 0}
|
||||
if non_zero and len(set(non_zero.values())) > 1:
|
||||
rp.add()
|
||||
rp.add("[WARN] 各标签的特征列数不一致（将使用零填充对齐）:")
|
||||
for lbl, cc in col_counts.items():
|
||||
rp.add(f" {lbl}: {cc} 列")
|
||||
elif non_zero:
|
||||
rp.add(f"[OK] 所有标签特征列数一致: {next(iter(non_zero.values()))} 列")
|
||||
|
||||
# 样本数统计
|
||||
rp.add()
|
||||
rp.add("── 各类别样本数 ──")
|
||||
sample_counts: dict[str, int] = {}
|
||||
for lbl in labels:
|
||||
info = per_class_info.get(lbl, {})
|
||||
if "error" in info:
|
||||
rp.add(f" [{lbl}] 加载失败: {info['error']}")
|
||||
continue
|
||||
n = info.get("samples_after_dropna", 0)
|
||||
sample_counts[lbl] = n
|
||||
wflag = ""
|
||||
if info.get("warnings"):
|
||||
wflag = f" ⚠ {'; '.join(info['warnings'])}"
|
||||
rp.add(f" [{lbl}] {n} 行 "
|
||||
f"(来自 {info.get('num_files','?')} 个文件, "
|
||||
f"原始 {info.get('raw_rows_total','?')} 行, "
|
||||
f"丢弃 NaN 行 {info.get('dropped_nan_rows',0)}){wflag}")
|
||||
|
||||
_analyze_balance(sample_counts, rp)
|
||||
_analyze_statistics(all_features, rp)
|
||||
_analyze_outliers(all_features, rp)
|
||||
|
||||
|
||||
# ============================================================
|
||||
# 分析子模块
|
||||
# ============================================================
|
||||
|
||||
def _analyze_balance(counts: dict[str, int], rp: ReportBuffer):
|
||||
rp.add()
|
||||
rp.add("── 类别平衡性分析 ──")
|
||||
if not counts:
|
||||
rp.add("无有效样本，跳过。")
|
||||
return
|
||||
|
||||
values = list(counts.values())
|
||||
total = sum(values)
|
||||
n_classes = len(values)
|
||||
avg = total / n_classes if n_classes else 0
|
||||
min_count = min(values)
|
||||
max_count = max(values)
|
||||
|
||||
rp.add(f" 总样本数 : {total}")
|
||||
rp.add(f" 类别数 : {n_classes}")
|
||||
rp.add(f" 平均每类 : {avg:.1f}")
|
||||
rp.add(f" 最少样本类 : {min(counts, key=counts.get)} ({min_count})")
|
||||
rp.add(f" 最多样本类 : {max(counts, key=counts.get)} ({max_count})")
|
||||
|
||||
if min_count == 0:
|
||||
rp.add(" [ERROR] 存在样本数为 0 的类别，训练将无法进行！")
|
||||
return
|
||||
|
||||
ratio = max_count / min_count if min_count > 0 else float("inf")
|
||||
rp.add(f" 不平衡比例 : {ratio:.2f}:1 (max/min)")
|
||||
|
||||
std_val = float(np.std(values))
|
||||
cv = std_val / avg if avg > 0 else 0
|
||||
rp.add(f" 变异系数(CV): {cv:.4f}")
|
||||
|
||||
if ratio > 5:
|
||||
rp.add(" [WARN] 类别严重不平衡 (>5:1)，建议进行数据增强或使用类别权重。")
|
||||
elif ratio > 3:
|
||||
rp.add(" [WARN] 类别较不平衡 (>3:1)，可考虑采样策略。")
|
||||
else:
|
||||
rp.add(" [OK] 类别基本平衡。")
|
||||
|
||||
# 训练-测试划分预估
|
||||
rp.add()
|
||||
rp.add(" ── 训练/测试划分预估 (test_size=0.2, stratify) ──")
|
||||
for lbl, cnt in sorted(counts.items()):
|
||||
test_n = max(1, int(cnt * 0.2))
|
||||
train_n = cnt - test_n
|
||||
rp.add(f" [{lbl}] 训练 {train_n} / 测试 {test_n} (总计 {cnt})")
|
||||
|
||||
|
||||
def _analyze_statistics(
|
||||
all_features: list[tuple[str, pd.DataFrame]],
|
||||
rp: ReportBuffer,
|
||||
):
|
||||
rp.add()
|
||||
rp.add("── 特征统计信息 ──")
|
||||
if not all_features:
|
||||
rp.add("无有效数据，跳过。")
|
||||
return
|
||||
|
||||
# 全局特征统计（将各 array 零填充到相同列数）
|
||||
max_cols = max(f[1].shape[1] for f in all_features)
|
||||
padded_arrays = []
|
||||
for _, df in all_features:
|
||||
val = df.values
|
||||
if val.shape[1] < max_cols:
|
||||
pad = np.zeros((val.shape[0], max_cols - val.shape[1]), dtype=val.dtype)
|
||||
val = np.hstack([val, pad])
|
||||
padded_arrays.append(val)
|
||||
all_values = np.vstack(padded_arrays)
|
||||
rp.add(f" 全局特征维度: {all_values.shape} (样本数 × 特征数, 零填充对齐到 {max_cols} 列)")
|
||||
rp.add(f" 全局均值 : {np.mean(all_values):.6f}")
|
||||
rp.add(f" 全局标准差 : {np.std(all_values):.6f}")
|
||||
rp.add(f" 全局最小值 : {np.min(all_values):.6f}")
|
||||
rp.add(f" 全局最大值 : {np.max(all_values):.6f}")
|
||||
rp.add(f" 全局中位数 : {np.median(all_values):.6f}")
|
||||
|
||||
# 每个特征维度的统计
|
||||
rp.add()
|
||||
n_cols = min(all_values.shape[1], 12)
|
||||
rp.add(f" ── 前 {n_cols} 个特征维度的分布 (均值 ± 标准差) ──")
|
||||
for j in range(n_cols):
|
||||
col = all_values[:, j]
|
||||
rp.add(
|
||||
f" feature{j+1}: "
|
||||
f"μ={np.mean(col):.4f} σ={np.std(col):.4f} "
|
||||
f"[{np.min(col):.4f}, {np.max(col):.4f}] "
|
||||
f"med={np.median(col):.4f}"
|
||||
)
|
||||
|
||||
# 每个类别的简要统计
|
||||
rp.add()
|
||||
rp.add(" ── 各类别特征统计 ──")
|
||||
for lbl, df in all_features:
|
||||
val = df.values
|
||||
rp.add(
|
||||
f" [{lbl}] "
|
||||
f"μ={np.mean(val):.4f} σ={np.std(val):.4f} "
|
||||
f"范围 [{np.min(val):.4f}, {np.max(val):.4f}]"
|
||||
)
|
||||
|
||||
|
||||
def _analyze_outliers(
|
||||
all_features: list[tuple[str, pd.DataFrame]],
|
||||
rp: ReportBuffer,
|
||||
):
|
||||
rp.add()
|
||||
rp.add("── 离群值检测 (基于 IQR) ──")
|
||||
if not all_features:
|
||||
rp.add("无有效数据，跳过。")
|
||||
return
|
||||
|
||||
total_outlier_samples = 0
|
||||
total_samples = 0
|
||||
|
||||
for lbl, df in all_features:
|
||||
val = df.values
|
||||
total_samples += val.shape[0]
|
||||
q1 = np.percentile(val, 25, axis=0)
|
||||
q3 = np.percentile(val, 75, axis=0)
|
||||
iqr = q3 - q1
|
||||
lower = q1 - 1.5 * iqr
|
||||
upper = q3 + 1.5 * iqr
|
||||
|
||||
outlier_mask = np.any((val < lower) | (val > upper), axis=1)
|
||||
n_outliers = int(np.sum(outlier_mask))
|
||||
total_outlier_samples += n_outliers
|
||||
pct = n_outliers / val.shape[0] * 100 if val.shape[0] > 0 else 0
|
||||
status = "[OK]" if pct < 10 else "[WARN]" if pct < 25 else "[ERROR]"
|
||||
rp.add(f" [{lbl}] 离群样本: {n_outliers}/{val.shape[0]} ({pct:.1f}%) {status}")
|
||||
|
||||
overall_pct = total_outlier_samples / total_samples * 100 if total_samples > 0 else 0
|
||||
rp.add()
|
||||
rp.add(f" 整体离群比例: {total_outlier_samples}/{total_samples} ({overall_pct:.1f}%)")
|
||||
|
||||
if overall_pct > 20:
|
||||
rp.add(" [WARN] 超过 20% 数据为离群值，请确认数据清洗是否正确。")
|
||||
elif overall_pct > 10:
|
||||
rp.add(" [INFO] 离群值比例偏高，训练时可能影响收敛。")
|
||||
else:
|
||||
rp.add(" [OK] 离群值比例正常。")
|
||||
|
||||
|
||||
# ============================================================
|
||||
# 命令行入口
|
||||
# ============================================================
|
||||
|
||||
def main():
|
||||
parser = argparse.ArgumentParser(
|
||||
description="Deeplearning 数据质量检查脚本",
|
||||
formatter_class=argparse.RawDescriptionHelpFormatter,
|
||||
epilog="""
|
||||
示例:
|
||||
python Scripts/check_data.py -f 20260319Numbers -l 0 1 2 3 4 5 6 7 8 9
|
||||
python Scripts/check_data.py -f "20260408 grap" -l 1 2 3 4 5 6 7 8 9 -o report.txt
|
||||
python Scripts/check_data.py -f 20260319Numbers -l 0 1 2 3 4 5 6 7 8 9 -r /path/to/project
|
||||
""",
|
||||
)
|
||||
parser.add_argument(
|
||||
"-f", "--folder",
|
||||
required=True,
|
||||
help="Static/ 下的数据目录名（例如 20260319Numbers）",
|
||||
)
|
||||
parser.add_argument(
|
||||
"-l", "--labels",
|
||||
nargs="+",
|
||||
required=True,
|
||||
help="类别标签列表，空格分隔（例如 0 1 2 3 4 或 A B C）",
|
||||
)
|
||||
parser.add_argument(
|
||||
"-o", "--output",
|
||||
default=None,
|
||||
help="将报告保存到指定文件路径",
|
||||
)
|
||||
parser.add_argument(
|
||||
"-r", "--root",
|
||||
default=None,
|
||||
help="项目根目录（默认为脚本的上级目录，即 Deeplearning/）",
|
||||
)
|
||||
|
||||
args = parser.parse_args()
|
||||
|
||||
# 确定项目根目录
|
||||
if args.root:
|
||||
root = args.root
|
||||
else:
|
||||
script_dir = Path(__file__).resolve().parent
|
||||
root = str(script_dir.parent)
|
||||
|
||||
root = os.path.abspath(root)
|
||||
|
||||
if not os.path.isdir(root):
|
||||
print(f"[ERROR] 项目根目录不存在: {root}")
|
||||
sys.exit(1)
|
||||
|
||||
rp = ReportBuffer(output_path=args.output)
|
||||
check_tabular_project(
|
||||
root=root,
|
||||
folder=args.folder,
|
||||
labels=args.labels,
|
||||
rp=rp,
|
||||
)
|
||||
rp.save()
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
171
Scripts/test.py
171
Scripts/test.py
|
|
@ -1,171 +0,0 @@
|
|||
import matplotlib.pyplot as plt
|
||||
import matplotlib.patches as patches
|
||||
|
||||
def draw_diagram():
|
||||
# Setup the figure
|
||||
fig, ax = plt.subplots(figsize=(10, 8))
|
||||
ax.set_xlim(0, 10)
|
||||
ax.set_ylim(0, 8)
|
||||
ax.axis('off') # Turn off axis
|
||||
|
||||
# Font settings
|
||||
font_formula = {'family': 'sans-serif', 'weight': 'bold', 'size': 14}
|
||||
font_label = {'family': 'sans-serif', 'weight': 'bold', 'size': 16}
|
||||
|
||||
# --- PART 1: TOP LEFT (The "Acrylate" Base) ---
|
||||
# Replacing the SiO2 Block with an Acrylate Group structure
|
||||
# We will draw a stylized Acrylic Acid molecule acting as the "Anchor"
|
||||
|
||||
# Label "A"
|
||||
ax.text(1.0, 7.5, "A", **font_label)
|
||||
|
||||
# Label "Acrylate" (replaces SiO2)
|
||||
ax.text(1.0, 4.5, "Acrylate\n(丙烯酸酯)", ha='center', va='center', fontsize=14, weight='bold')
|
||||
|
||||
# Draw Acrylate structure (Vertical orientation to mimic the wall)
|
||||
# CH2=CH-C(=O)-OH
|
||||
# C=C
|
||||
ax.text(1.5, 6.5, "CH", **font_formula, ha='right')
|
||||
ax.text(1.6, 6.4, "2", fontsize=10, weight='bold', ha='left') # subscript
|
||||
|
||||
ax.plot([1.5, 1.5], [6.3, 5.8], 'k-', lw=2) # Double bond line 1
|
||||
ax.plot([1.4, 1.4], [6.3, 5.8], 'k-', lw=2) # Double bond line 2
|
||||
|
||||
ax.text(1.5, 5.6, "CH", **font_formula, ha='center')
|
||||
|
||||
ax.plot([1.5, 1.5], [5.4, 4.9], 'k-', lw=2) # Single bond
|
||||
|
||||
ax.text(1.5, 4.7, "C", **font_formula, ha='center')
|
||||
|
||||
# Carbonyl O
|
||||
ax.plot([1.4, 1.1], [4.7, 4.7], 'k-', lw=2) # Double bond to O (sideways)
|
||||
ax.plot([1.4, 1.1], [4.8, 4.8], 'k-', lw=2)
|
||||
ax.text(0.9, 4.7, "O", **font_formula, ha='center', va='center')
|
||||
|
||||
# Hydroxyl OH (The reactive site)
|
||||
ax.plot([1.6, 1.9], [4.7, 4.7], 'k-', lw=2)
|
||||
ax.text(2.1, 4.7, "OH", **font_formula, ha='left', va='center')
|
||||
|
||||
|
||||
# --- PART 2: TOP RIGHT (The Fluorinated Alcohol) ---
|
||||
# Replacing FDTES with Perfluoroethylethanol (C2F5-CH2-CH2-OH)
|
||||
# Structure: HO - CH2 - CH2 - CF2 - CF3
|
||||
|
||||
# The Plus Sign
|
||||
ax.text(3.5, 5.5, "+", fontsize=40, weight='bold', color='#0070C0', ha='center')
|
||||
|
||||
# Start coordinates for alcohol
|
||||
start_x = 4.5
|
||||
y_level = 5.5
|
||||
|
||||
# HO-
|
||||
ax.text(start_x, y_level, "HO", **font_formula, ha='right')
|
||||
ax.plot([start_x + 0.1, start_x + 0.5], [y_level, y_level], 'k-', lw=2)
|
||||
|
||||
# -CH2-
|
||||
ax.text(start_x + 0.8, y_level, "CH", **font_formula, ha='center')
|
||||
ax.text(start_x + 1.05, y_level-0.1, "2", fontsize=10, weight='bold')
|
||||
ax.plot([start_x + 1.2, start_x + 1.6], [y_level, y_level], 'k-', lw=2)
|
||||
|
||||
# -CH2-
|
||||
ax.text(start_x + 1.9, y_level, "CH", **font_formula, ha='center')
|
||||
ax.text(start_x + 2.15, y_level-0.1, "2", fontsize=10, weight='bold')
|
||||
ax.plot([start_x + 2.3, start_x + 2.7], [y_level, y_level], 'k-', lw=2)
|
||||
|
||||
# -CF2- (Perfluoro group starts)
|
||||
ax.text(start_x + 3.0, y_level, "C", **font_formula, ha='center')
|
||||
# F on top
|
||||
ax.plot([start_x + 3.0, start_x + 3.0], [y_level + 0.2, y_level + 0.5], 'k-', lw=2)
|
||||
ax.text(start_x + 3.0, y_level + 0.6, "F", **font_formula, ha='center')
|
||||
# F on bottom
|
||||
ax.plot([start_x + 3.0, start_x + 3.0], [y_level - 0.2, y_level - 0.5], 'k-', lw=2)
|
||||
ax.text(start_x + 3.0, y_level - 0.8, "F", **font_formula, ha='center')
|
||||
|
||||
ax.plot([start_x + 3.3, start_x + 3.7], [y_level, y_level], 'k-', lw=2)
|
||||
|
||||
# -CF3 (End of chain)
|
||||
ax.text(start_x + 4.0, y_level, "C", **font_formula, ha='center')
|
||||
# F on top
|
||||
ax.plot([start_x + 4.0, start_x + 4.0], [y_level + 0.2, y_level + 0.5], 'k-', lw=2)
|
||||
ax.text(start_x + 4.0, y_level + 0.6, "F", **font_formula, ha='center')
|
||||
# F on bottom
|
||||
ax.plot([start_x + 4.0, start_x + 4.0], [y_level - 0.2, y_level - 0.5], 'k-', lw=2)
|
||||
ax.text(start_x + 4.0, y_level - 0.8, "F", **font_formula, ha='center')
|
||||
# F on right
|
||||
ax.plot([start_x + 4.2, start_x + 4.5], [y_level, y_level], 'k-', lw=2)
|
||||
ax.text(start_x + 4.7, y_level, "F", **font_formula, ha='center')
|
||||
|
||||
|
||||
# --- PART 3: THE ARROW ---
|
||||
ax.arrow(5.0, 4.0, 0, -1.0, head_width=0.3, head_length=0.3, fc='#0070C0', ec='#0070C0', lw=3)
|
||||
|
||||
|
||||
# --- PART 4: THE PRODUCT (Bottom) ---
|
||||
# Fluorinated Acrylate Monomer
|
||||
|
||||
prod_y = 1.5
|
||||
|
||||
# Acrylate part (Left side of product)
|
||||
ax.text(1.5, prod_y + 1.0, "CH", **font_formula, ha='right')
|
||||
ax.text(1.6, prod_y + 0.9, "2", fontsize=10, weight='bold', ha='left')
|
||||
|
||||
ax.plot([1.5, 1.5], [prod_y + 0.8, prod_y + 0.3], 'k-', lw=2)
|
||||
ax.plot([1.4, 1.4], [prod_y + 0.8, prod_y + 0.3], 'k-', lw=2)
|
||||
|
||||
ax.text(1.5, prod_y + 0.1, "CH", **font_formula, ha='center')
|
||||
|
||||
ax.plot([1.5, 1.5], [prod_y - 0.1, prod_y - 0.6], 'k-', lw=2)
|
||||
|
||||
ax.text(1.5, prod_y - 0.8, "C", **font_formula, ha='center')
|
||||
|
||||
# Carbonyl O
|
||||
ax.plot([1.4, 1.1], [prod_y - 0.8, prod_y - 0.8], 'k-', lw=2)
|
||||
ax.plot([1.4, 1.1], [prod_y - 0.7, prod_y - 0.7], 'k-', lw=2)
|
||||
ax.text(0.9, prod_y - 0.8, "O", **font_formula, ha='center', va='center')
|
||||
|
||||
# Ester Oxygen (Replacing the OH group interaction)
|
||||
ax.plot([1.7, 2.0], [prod_y - 0.8, prod_y - 0.8], 'k-', lw=2)
|
||||
ax.text(2.2, prod_y - 0.8, "O", **font_formula, ha='center', va='center')
|
||||
|
||||
# Link to Spacer
|
||||
ax.plot([2.4, 3.5], [prod_y - 0.8, prod_y - 0.8], 'k-', lw=2) # Long bond to accommodate layout
|
||||
|
||||
# Fluorinated Chain (Right side of product)
|
||||
# -CH2-
|
||||
ax.text(3.8, prod_y - 0.8, "CH", **font_formula, ha='center', va='center')
|
||||
ax.text(4.05, prod_y - 0.9, "2", fontsize=10, weight='bold')
|
||||
ax.plot([4.2, 4.6], [prod_y - 0.8, prod_y - 0.8], 'k-', lw=2)
|
||||
|
||||
# -CH2-
|
||||
ax.text(4.9, prod_y - 0.8, "CH", **font_formula, ha='center', va='center')
|
||||
ax.text(5.15, prod_y - 0.9, "2", fontsize=10, weight='bold')
|
||||
ax.plot([5.3, 5.7], [prod_y - 0.8, prod_y - 0.8], 'k-', lw=2)
|
||||
|
||||
# -CF2-
|
||||
ax.text(6.0, prod_y - 0.8, "C", **font_formula, ha='center', va='center')
|
||||
# F top/bottom
|
||||
ax.plot([6.0, 6.0], [prod_y - 0.6, prod_y - 0.3], 'k-', lw=2)
|
||||
ax.text(6.0, prod_y - 0.1, "F", **font_formula, ha='center')
|
||||
ax.plot([6.0, 6.0], [prod_y - 1.0, prod_y - 1.3], 'k-', lw=2)
|
||||
ax.text(6.0, prod_y - 1.6, "F", **font_formula, ha='center')
|
||||
|
||||
ax.plot([6.3, 6.7], [prod_y - 0.8, prod_y - 0.8], 'k-', lw=2)
|
||||
|
||||
# -CF3
|
||||
ax.text(7.0, prod_y - 0.8, "C", **font_formula, ha='center', va='center')
|
||||
# F top/bottom
|
||||
ax.plot([7.0, 7.0], [prod_y - 0.6, prod_y - 0.3], 'k-', lw=2)
|
||||
ax.text(7.0, prod_y - 0.1, "F", **font_formula, ha='center')
|
||||
ax.plot([7.0, 7.0], [prod_y - 1.0, prod_y - 1.3], 'k-', lw=2)
|
||||
ax.text(7.0, prod_y - 1.6, "F", **font_formula, ha='center')
|
||||
# F right
|
||||
ax.plot([7.2, 7.5], [prod_y - 0.8, prod_y - 0.8], 'k-', lw=2)
|
||||
ax.text(7.7, prod_y - 0.8, "F", **font_formula, ha='center', va='center')
|
||||
|
||||
# Save the figure
|
||||
plt.savefig("reaction_diagram.png", bbox_inches='tight', dpi=300)
|
||||
plt.close()
|
||||
|
||||
if __name__ == "__main__":
|
||||
draw_diagram()
|
||||
print("Diagram generated as reaction_diagram.png")
|
||||
|
|
@ -1,657 +0,0 @@
|
|||
#!/usr/bin/env python3
|
||||
"""
|
||||
数据可视化脚本
|
||||
===============
|
||||
加载 Static/ 下的数据目录，生成多种可视化图表：
|
||||
- 类别分布柱状图
|
||||
- 特征分布直方图（各类叠加）
|
||||
- 特征箱线图（前 N 个特征）
|
||||
- PCA 降维散点图 + 置信椭圆
|
||||
- t-SNE 降维散点图
|
||||
- 各类别均值/标准差对比热力图
|
||||
- 全局特征相关性热力图
|
||||
- 全局特征分布概览
|
||||
|
||||
用法:
|
||||
python Scripts/visualize.py -f 20260319Numbers -l 0 1 2 3 4 5 6 7 8 9
|
||||
python Scripts/visualize.py -f "20260408 grap" -l 1 2 3 4 5 6 7 8 9 --max-features 20
|
||||
python Scripts/visualize.py -f 20260319Numbers -l 0 1 2 3 4 5 6 7 8 9 --no-tsne
|
||||
|
||||
输出目录: Visualizations/<folder>/
|
||||
"""
|
||||
|
||||
import os
|
||||
import sys
|
||||
import argparse
|
||||
import unicodedata
|
||||
from pathlib import Path
|
||||
import warnings
|
||||
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
import matplotlib
|
||||
matplotlib.use("Agg")
|
||||
import matplotlib.pyplot as plt
|
||||
import seaborn as sns
|
||||
|
||||
warnings.filterwarnings("ignore", category=FutureWarning)
|
||||
warnings.filterwarnings("ignore", category=UserWarning)
|
||||
|
||||
# ============================================================
|
||||
# 数据加载工具（与 check_data.py 保持一致）
|
||||
# ============================================================
|
||||
|
||||
DEFAULT_FILE_CLASSES = ("xlsx", "xls", "csv")
|
||||
|
||||
|
||||
def _has_supported_extension(filename: str, file_classes=DEFAULT_FILE_CLASSES) -> bool:
|
||||
ext = os.path.splitext(filename)[1].lower().lstrip(".")
|
||||
return ext in file_classes
|
||||
|
||||
|
||||
def _read_data_file(file_path: str) -> pd.DataFrame:
|
||||
ext = os.path.splitext(file_path)[1].lower()
|
||||
if ext == ".csv":
|
||||
return pd.read_csv(file_path)
|
||||
if ext in (".xls", ".xlsx"):
|
||||
return pd.read_excel(file_path)
|
||||
raise ValueError(f"Unsupported file format: {ext}: {file_path}")
|
||||
|
||||
|
||||
def _strip_zero_width(s: str) -> str:
|
||||
if not isinstance(s, str):
|
||||
return s
|
||||
return s.translate({0x200B: None, 0x200C: None, 0x200D: None, 0xFEFF: None})
|
||||
|
||||
|
||||
def _canonicalize_name(name: str) -> str:
|
||||
name = unicodedata.normalize("NFKC", name)
|
||||
return _strip_zero_width(name)
|
||||
|
||||
|
||||
def _normalize_for_compare(name: str) -> str:
|
||||
n = _canonicalize_name(name)
|
||||
n = n.replace("_", " ")
|
||||
n = " ".join(n.split())
|
||||
return n.lower()
|
||||
|
||||
|
||||
def _find_matching_file(folder: str, expected_name: str):
|
||||
expected = _canonicalize_name(expected_name)
|
||||
try:
|
||||
entries = os.listdir(folder)
|
||||
except FileNotFoundError:
|
||||
return None
|
||||
for f in entries:
|
||||
if _canonicalize_name(f) == expected:
|
||||
return f
|
||||
expected_lower = expected.lower()
|
||||
for f in entries:
|
||||
if _canonicalize_name(f).lower() == expected_lower:
|
||||
return f
|
||||
expected_relaxed = _normalize_for_compare(expected_name)
|
||||
for f in entries:
|
||||
if _normalize_for_compare(f) == expected_relaxed:
|
||||
return f
|
||||
return None
|
||||
|
||||
|
||||
def _find_matching_file_by_label(folder: str, label_name, file_classes):
|
||||
for ext in file_classes:
|
||||
expected_name = f"{label_name}.{ext}"
|
||||
match = _find_matching_file(folder, expected_name)
|
||||
if match is not None:
|
||||
return match
|
||||
return None
|
||||
|
||||
|
||||
def _extract_features(df: pd.DataFrame, source: str) -> pd.DataFrame:
|
||||
even_cols = [c for i, c in enumerate(df.columns) if i % 2 == 1]
|
||||
if not even_cols:
|
||||
raise ValueError(f"没有找到偶数列（特征列）。请检查文件: {source}")
|
||||
features = df[even_cols].copy()
|
||||
for c in features.columns:
|
||||
features[c] = pd.to_numeric(features[c], errors="coerce")
|
||||
return features
|
||||
|
||||
|
||||
# ============================================================
|
||||
# 数据加载
|
||||
# ============================================================
|
||||
|
||||
def load_all_data(root: str, folder: str, labels: list[str]):
|
||||
data_dir = os.path.join(root, "Static", folder)
|
||||
if not os.path.isdir(data_dir):
|
||||
print(f"[ERROR] 目录不存在: {data_dir}")
|
||||
sys.exit(1)
|
||||
|
||||
has_all_files = all(
|
||||
_find_matching_file_by_label(data_dir, lbl, DEFAULT_FILE_CLASSES) is not None
|
||||
for lbl in labels
|
||||
)
|
||||
has_all_subfolders = all(
|
||||
os.path.isdir(os.path.join(data_dir, str(lbl)))
|
||||
and any(_has_supported_extension(f) for f in os.listdir(os.path.join(data_dir, str(lbl))))
|
||||
for lbl in labels
|
||||
)
|
||||
|
||||
if has_all_files and not has_all_subfolders:
|
||||
return _load_single_file_mode(data_dir, labels)
|
||||
elif has_all_subfolders and not has_all_files:
|
||||
return _load_multi_folder_mode(data_dir, labels)
|
||||
else:
|
||||
print("[WARN] 数据模式不明确，尝试单文件模式...")
|
||||
return _load_single_file_mode(data_dir, labels)
|
||||
|
||||
|
||||
def _load_single_file_mode(data_dir: str, labels: list[str]):
|
||||
all_features = []
|
||||
col_counts = {}
|
||||
label_names = []
|
||||
|
||||
for lbl in labels:
|
||||
fname = _find_matching_file_by_label(data_dir, lbl, DEFAULT_FILE_CLASSES)
|
||||
if fname is None:
|
||||
print(f"[WARN] 标签 {lbl} 找不到文件，跳过")
|
||||
continue
|
||||
file_path = os.path.join(data_dir, fname)
|
||||
try:
|
||||
raw = _read_data_file(file_path)
|
||||
except Exception as e:
|
||||
print(f"[ERROR] 读取 {file_path} 失败: {e}")
|
||||
continue
|
||||
try:
|
||||
features = _extract_features(raw, fname)
|
||||
except ValueError as e:
|
||||
print(f"[ERROR] {e}")
|
||||
continue
|
||||
|
||||
clean = features.dropna()
|
||||
if clean.shape[0] == 0:
|
||||
print(f"[WARN] 标签 {lbl} 去除 NaN 后无样本，跳过")
|
||||
continue
|
||||
|
||||
col_counts[lbl] = clean.shape[1]
|
||||
all_features.append((lbl, clean))
|
||||
label_names.append(lbl)
|
||||
|
||||
return _build_arrays(all_features, label_names, col_counts)
|
||||
|
||||
|
||||
def _load_multi_folder_mode(data_dir: str, labels: list[str]):
|
||||
all_features = []
|
||||
col_counts = {}
|
||||
label_names = []
|
||||
|
||||
for lbl in labels:
|
||||
sub = os.path.join(data_dir, str(lbl))
|
||||
if not os.path.isdir(sub):
|
||||
print(f"[WARN] 标签 {lbl} 子目录不存在，跳过")
|
||||
continue
|
||||
|
||||
files = sorted([f for f in os.listdir(sub) if _has_supported_extension(f)])
|
||||
if not files:
|
||||
print(f"[WARN] 标签 {lbl} 子目录无文件，跳过")
|
||||
continue
|
||||
|
||||
frames = []
|
||||
max_cols_in_class = 0
|
||||
for fname in files:
|
||||
file_path = os.path.join(sub, fname)
|
||||
try:
|
||||
raw = _read_data_file(file_path)
|
||||
except Exception as e:
|
||||
print(f"[WARN] 读取 {file_path} 失败: {e}")
|
||||
continue
|
||||
try:
|
||||
feat = _extract_features(raw, f"{lbl}/{fname}")
|
||||
except ValueError as e:
|
||||
print(f"[WARN] {e}")
|
||||
continue
|
||||
clean = feat.dropna()
|
||||
if clean.shape[0] > 0:
|
||||
frames.append(clean)
|
||||
max_cols_in_class = max(max_cols_in_class, clean.shape[1])
|
||||
|
||||
if not frames:
|
||||
print(f"[WARN] 标签 {lbl} 无有效样本，跳过")
|
||||
continue
|
||||
|
||||
padded_frames = []
|
||||
for f in frames:
|
||||
if f.shape[1] < max_cols_in_class:
|
||||
pad = np.zeros((f.shape[0], max_cols_in_class - f.shape[1]))
|
||||
padded = pd.DataFrame(
|
||||
np.hstack([f.values, pad]),
|
||||
columns=list(f.columns) + [f"_pad_{i}" for i in range(max_cols_in_class - f.shape[1])],
|
||||
)
|
||||
padded_frames.append(padded)
|
||||
else:
|
||||
padded_frames.append(f)
|
||||
|
||||
combined = pd.concat(padded_frames, ignore_index=True)
|
||||
col_counts[lbl] = combined.shape[1]
|
||||
all_features.append((lbl, combined))
|
||||
label_names.append(lbl)
|
||||
|
||||
return _build_arrays(all_features, label_names, col_counts)
|
||||
|
||||
|
||||
def _build_arrays(all_features, label_names, col_counts):
|
||||
if not all_features:
|
||||
print("[ERROR] 没有加载到任何有效数据")
|
||||
sys.exit(1)
|
||||
|
||||
max_cols = max(c for c in col_counts.values())
|
||||
|
||||
X_list = []
|
||||
y_list = []
|
||||
for idx, (lbl, df) in enumerate(all_features):
|
||||
val = df.values
|
||||
if val.shape[1] < max_cols:
|
||||
pad = np.zeros((val.shape[0], max_cols - val.shape[1]), dtype=val.dtype)
|
||||
val = np.hstack([val, pad])
|
||||
X_list.append(val)
|
||||
y_list.append(np.full(val.shape[0], idx, dtype=int))
|
||||
|
||||
X = np.vstack(X_list)
|
||||
y = np.concatenate(y_list)
|
||||
|
||||
return X, y, label_names, all_features, col_counts
|
||||
|
||||
|
||||
# ============================================================
|
||||
# 可视化函数
|
||||
# ============================================================
|
||||
|
||||
TAB10 = plt.cm.tab10.colors
|
||||
|
||||
|
||||
def _ensure_dir(path: str):
|
||||
os.makedirs(path, exist_ok=True)
|
||||
|
||||
|
||||
def plot_class_distribution(y, label_names, out_dir: str):
|
||||
fig, ax = plt.subplots(figsize=(max(8, len(label_names) * 0.6), 5))
|
||||
counts = [int(np.sum(y == i)) for i in range(len(label_names))]
|
||||
colors = [TAB10[i % 10] for i in range(len(label_names))]
|
||||
bars = ax.bar(label_names, counts, color=colors, edgecolor="white", linewidth=0.8)
|
||||
for bar, cnt in zip(bars, counts):
|
||||
ax.text(bar.get_x() + bar.get_width() / 2, bar.get_height() + max(counts) * 0.01,
|
||||
str(cnt), ha="center", va="bottom", fontsize=9)
|
||||
ax.set_xlabel("类别")
|
||||
ax.set_ylabel("样本数")
|
||||
ax.set_title(f"类别分布 (总计 {sum(counts)} 样本, {len(label_names)} 类)")
|
||||
fig.tight_layout()
|
||||
path = os.path.join(out_dir, "01_class_distribution.png")
|
||||
fig.savefig(path, dpi=150)
|
||||
plt.close(fig)
|
||||
print(f"[OK] {path}")
|
||||
|
||||
|
||||
def plot_feature_histograms(all_features, out_dir: str, max_features: int = 12):
|
||||
n_features = min(max_features, all_features[0][1].shape[1])
|
||||
n_cols = 4
|
||||
n_rows = (n_features + n_cols - 1) // n_cols
|
||||
|
||||
fig, axes = plt.subplots(n_rows, n_cols, figsize=(4 * n_cols, 3 * n_rows))
|
||||
axes = axes.flatten() if n_rows * n_cols > 1 else [axes]
|
||||
|
||||
colors = [TAB10[i % 10] for i in range(len(all_features))]
|
||||
|
||||
for j in range(n_features):
|
||||
ax = axes[j]
|
||||
for idx, (lbl, df) in enumerate(all_features):
|
||||
if j < df.shape[1]:
|
||||
col = df.iloc[:, j].values
|
||||
ax.hist(col, bins=40, density=True, alpha=0.4, color=colors[idx],
|
||||
label=f"类 {lbl}")
|
||||
ax.set_title(f"Feature {j+1}")
|
||||
ax.set_xlabel("值")
|
||||
ax.set_ylabel("密度")
|
||||
if n_features <= 8:
|
||||
ax.legend(fontsize=7, loc="upper right")
|
||||
|
||||
for j in range(n_features, len(axes)):
|
||||
axes[j].set_visible(False)
|
||||
|
||||
if n_features > 8:
|
||||
handles = [plt.Rectangle((0, 0), 1, 1, color=colors[i], alpha=0.4)
|
||||
for i in range(len(all_features))]
|
||||
fig.legend(handles, [lbl for lbl, _ in all_features],
|
||||
loc="lower center", ncol=min(10, len(all_features)), fontsize=7)
|
||||
|
||||
fig.suptitle(f"特征分布直方图（各类叠加，前 {n_features} 维）", fontsize=13, y=1.01)
|
||||
fig.tight_layout()
|
||||
path = os.path.join(out_dir, "02_feature_histograms.png")
|
||||
fig.savefig(path, dpi=150, bbox_inches="tight")
|
||||
plt.close(fig)
|
||||
print(f"[OK] {path}")
|
||||
|
||||
|
||||
def plot_feature_boxplots(all_features, out_dir: str, max_features: int = 20):
|
||||
n_features = min(max_features, all_features[0][1].shape[1])
|
||||
n_cols = 4
|
||||
n_rows = (n_features + n_cols - 1) // n_cols
|
||||
|
||||
fig, axes = plt.subplots(n_rows, n_cols, figsize=(4.5 * n_cols, 3.5 * n_rows))
|
||||
axes = axes.flatten() if n_rows * n_cols > 1 else [axes]
|
||||
|
||||
for j in range(n_features):
|
||||
ax = axes[j]
|
||||
data_list = []
|
||||
positions = []
|
||||
labels_for_box = []
|
||||
for idx, (lbl, df) in enumerate(all_features):
|
||||
if j < df.shape[1]:
|
||||
data_list.append(df.iloc[:, j].values)
|
||||
positions.append(idx + 1)
|
||||
labels_for_box.append(str(lbl))
|
||||
|
||||
bp = ax.boxplot(data_list, positions=positions, labels=labels_for_box,
|
||||
patch_artist=True, widths=0.6, showfliers=True,
|
||||
flierprops={"marker": ".", "markersize": 2, "alpha": 0.3})
|
||||
for patch, idx in zip(bp["boxes"], range(len(data_list))):
|
||||
patch.set_facecolor(TAB10[idx % 10])
|
||||
patch.set_alpha(0.6)
|
||||
ax.set_title(f"Feature {j+1}")
|
||||
ax.set_xlabel("类别")
|
||||
ax.tick_params(axis="x", rotation=0, labelsize=8)
|
||||
|
||||
for j in range(n_features, len(axes)):
|
||||
axes[j].set_visible(False)
|
||||
|
||||
fig.suptitle(f"特征箱线图（各类别对比，前 {n_features} 维）", fontsize=13, y=1.01)
|
||||
fig.tight_layout()
|
||||
path = os.path.join(out_dir, "03_feature_boxplots.png")
|
||||
fig.savefig(path, dpi=150, bbox_inches="tight")
|
||||
plt.close(fig)
|
||||
print(f"[OK] {path}")
|
||||
|
||||
|
||||
def _plot_confidence_ellipse(ax, mean, cov, color, alpha=0.2, n_std=1.0):
|
||||
from matplotlib.patches import Ellipse
|
||||
vals, vecs = np.linalg.eigh(cov)
|
||||
order = vals.argsort()[::-1]
|
||||
vals = vals[order]
|
||||
vecs = vecs[:, order]
|
||||
angle = np.degrees(np.arctan2(vecs[1, 0], vecs[0, 0]))
|
||||
width, height = 2 * n_std * np.sqrt(vals)
|
||||
ellipse = Ellipse(xy=mean, width=width, height=height, angle=angle,
|
||||
facecolor=color, alpha=alpha, edgecolor=color, linewidth=0.8)
|
||||
ax.add_patch(ellipse)
|
||||
|
||||
|
||||
def plot_pca(X, y, label_names, out_dir: str):
|
||||
from sklearn.decomposition import PCA
|
||||
|
||||
pca = PCA(n_components=2, random_state=42)
|
||||
X_pca = pca.fit_transform(X)
|
||||
|
||||
fig, axes = plt.subplots(1, 2, figsize=(14, 6))
|
||||
colors = [TAB10[i % 10] for i in range(len(label_names))]
|
||||
|
||||
# 散点图
|
||||
ax = axes[0]
|
||||
for i, lbl in enumerate(label_names):
|
||||
mask = y == i
|
||||
ax.scatter(X_pca[mask, 0], X_pca[mask, 1], c=[colors[i]], label=f"类 {lbl}",
|
||||
alpha=0.5, s=3, edgecolors="none")
|
||||
ax.set_xlabel(f"PC1 ({pca.explained_variance_ratio_[0]*100:.1f}%)")
|
||||
ax.set_ylabel(f"PC2 ({pca.explained_variance_ratio_[1]*100:.1f}%)")
|
||||
ax.set_title("PCA 降维散点图")
|
||||
ax.legend(fontsize=7, markerscale=3, loc="best")
|
||||
|
||||
# 质心+椭圆
|
||||
ax2 = axes[1]
|
||||
for i, lbl in enumerate(label_names):
|
||||
mask = y == i
|
||||
class_points = X_pca[mask]
|
||||
mean = class_points.mean(axis=0)
|
||||
ax2.scatter(mean[0], mean[1], c=[colors[i]], s=80, marker="X",
|
||||
edgecolors="black", linewidths=0.8, zorder=5)
|
||||
ax2.annotate(str(lbl), (mean[0], mean[1]), fontsize=8, ha="center", va="bottom",
|
||||
fontweight="bold", xytext=(0, 4), textcoords="offset points")
|
||||
if class_points.shape[0] > 2:
|
||||
cov = np.cov(class_points.T)
|
||||
_plot_confidence_ellipse(ax2, mean, cov, color=colors[i], alpha=0.25)
|
||||
|
||||
ax2.set_xlabel(f"PC1 ({pca.explained_variance_ratio_[0]*100:.1f}%)")
|
||||
ax2.set_ylabel(f"PC2 ({pca.explained_variance_ratio_[1]*100:.1f}%)")
|
||||
ax2.set_title("PCA 质心 + 1σ 置信椭圆")
|
||||
|
||||
fig.suptitle("PCA 降维分析", fontsize=14)
|
||||
fig.tight_layout()
|
||||
path = os.path.join(out_dir, "04_pca.png")
|
||||
fig.savefig(path, dpi=150)
|
||||
plt.close(fig)
|
||||
print(f"[OK] {path}")
|
||||
|
||||
# 方差解释率
|
||||
fig2, ax = plt.subplots(figsize=(8, 4))
|
||||
n = min(30, len(pca.explained_variance_ratio_))
|
||||
cumsum = np.cumsum(pca.explained_variance_ratio_[:n])
|
||||
ax.bar(range(1, n + 1), pca.explained_variance_ratio_[:n],
|
||||
alpha=0.6, color="steelblue", label="个体")
|
||||
ax.plot(range(1, n + 1), cumsum, "ro-", markersize=4, label="累计")
|
||||
ax.set_xlabel("主成分")
|
||||
ax.set_ylabel("方差解释率")
|
||||
ax.set_title("PCA 方差解释率")
|
||||
ax.legend()
|
||||
fig2.tight_layout()
|
||||
path2 = os.path.join(out_dir, "04_pca_variance.png")
|
||||
fig2.savefig(path2, dpi=150)
|
||||
plt.close(fig2)
|
||||
print(f"[OK] {path2}")
|
||||
|
||||
|
||||
def plot_tsne(X, y, label_names, out_dir: str, max_samples: int = 5000):
|
||||
from sklearn.manifold import TSNE
|
||||
|
||||
if X.shape[0] > max_samples:
|
||||
print(f"[INFO] t-SNE: 样本过多 ({X.shape[0]}), 分层抽样至 {max_samples}")
|
||||
indices = []
|
||||
per_class = max_samples // len(label_names)
|
||||
for i in range(len(label_names)):
|
||||
idx_i = np.where(y == i)[0]
|
||||
if len(idx_i) <= per_class:
|
||||
indices.extend(idx_i.tolist())
|
||||
else:
|
||||
rng = np.random.RandomState(42)
|
||||
indices.extend(rng.choice(idx_i, per_class, replace=False).tolist())
|
||||
indices = np.array(indices)
|
||||
X_sub = X[indices]
|
||||
y_sub = y[indices]
|
||||
else:
|
||||
X_sub = X
|
||||
y_sub = y
|
||||
|
||||
print("[INFO] 正在计算 t-SNE（可能需要一些时间）...")
|
||||
perplexity = min(50, max(5, X_sub.shape[0] // 3))
|
||||
tsne = TSNE(n_components=2, random_state=42, perplexity=perplexity,
|
||||
n_iter=1000, verbose=0)
|
||||
X_tsne = tsne.fit_transform(X_sub)
|
||||
|
||||
colors = [TAB10[i % 10] for i in range(len(label_names))]
|
||||
|
||||
fig, ax = plt.subplots(figsize=(10, 8))
|
||||
for i, lbl in enumerate(label_names):
|
||||
mask = y_sub == i
|
||||
ax.scatter(X_tsne[mask, 0], X_tsne[mask, 1], c=[colors[i]], label=f"类 {lbl}",
|
||||
alpha=0.5, s=3, edgecolors="none")
|
||||
ax.set_xlabel("t-SNE 1")
|
||||
ax.set_ylabel("t-SNE 2")
|
||||
ax.set_title(f"t-SNE 降维散点图 (n={X_sub.shape[0]}, perplexity={perplexity})")
|
||||
ax.legend(fontsize=7, markerscale=3, loc="best")
|
||||
fig.tight_layout()
|
||||
path = os.path.join(out_dir, "05_tsne.png")
|
||||
fig.savefig(path, dpi=150)
|
||||
plt.close(fig)
|
||||
print(f"[OK] {path}")
|
||||
|
||||
|
||||
def plot_class_mean_std_heatmap(all_features, label_names, out_dir: str, max_features: int = 30):
|
||||
n_features = min(max_features, all_features[0][1].shape[1])
|
||||
n_classes = len(label_names)
|
||||
|
||||
mean_matrix = np.zeros((n_classes, n_features))
|
||||
std_matrix = np.zeros((n_classes, n_features))
|
||||
|
||||
for i, (lbl, df) in enumerate(all_features):
|
||||
for j in range(min(n_features, df.shape[1])):
|
||||
col = df.iloc[:, j].values
|
||||
mean_matrix[i, j] = np.mean(col)
|
||||
std_matrix[i, j] = np.std(col)
|
||||
for j in range(df.shape[1], n_features):
|
||||
mean_matrix[i, j] = 0.0
|
||||
std_matrix[i, j] = 0.0
|
||||
|
||||
fig, axes = plt.subplots(1, 2, figsize=(max(10, n_features * 0.35), max(5, n_classes * 0.5)))
|
||||
|
||||
sns.heatmap(mean_matrix, ax=axes[0], cmap="RdBu_r", center=0,
|
||||
xticklabels=[f"F{i+1}" for i in range(n_features)] if n_features <= 30 else False,
|
||||
yticklabels=label_names,
|
||||
annot=n_features <= 20, fmt=".3f" if n_features <= 20 else "",
|
||||
linewidths=0.5, cbar_kws={"label": "均值", "shrink": 0.8})
|
||||
axes[0].set_title("各类别特征均值")
|
||||
axes[0].set_xlabel("特征维度")
|
||||
|
||||
sns.heatmap(std_matrix, ax=axes[1], cmap="YlOrRd",
|
||||
xticklabels=[f"F{i+1}" for i in range(n_features)] if n_features <= 30 else False,
|
||||
yticklabels=label_names,
|
||||
annot=n_features <= 20, fmt=".3f" if n_features <= 20 else "",
|
||||
linewidths=0.5, cbar_kws={"label": "标准差", "shrink": 0.8})
|
||||
axes[1].set_title("各类别特征标准差")
|
||||
axes[1].set_xlabel("特征维度")
|
||||
|
||||
fig.suptitle("各类别特征统计对比", fontsize=13)
|
||||
fig.tight_layout()
|
||||
path = os.path.join(out_dir, "06_class_mean_std_heatmap.png")
|
||||
fig.savefig(path, dpi=150, bbox_inches="tight")
|
||||
plt.close(fig)
|
||||
print(f"[OK] {path}")
|
||||
|
||||
|
||||
def plot_correlation_heatmap(X, out_dir: str, max_features: int = 30):
|
||||
n_features = min(max_features, X.shape[1])
|
||||
X_sub = X[:, :n_features]
|
||||
|
||||
corr = np.corrcoef(X_sub.T)
|
||||
|
||||
fig, ax = plt.subplots(figsize=(max(10, n_features * 0.5), max(8, n_features * 0.45)))
|
||||
sns.heatmap(corr, ax=ax, cmap="RdBu_r", center=0, vmin=-1, vmax=1,
|
||||
xticklabels=[f"F{i+1}" for i in range(n_features)] if n_features <= 30 else False,
|
||||
yticklabels=[f"F{i+1}" for i in range(n_features)] if n_features <= 30 else False,
|
||||
linewidths=0.1, cbar_kws={"label": "Pearson r", "shrink": 0.8})
|
||||
ax.set_title(f"特征相关性矩阵 (前 {n_features} 维)")
|
||||
fig.tight_layout()
|
||||
path = os.path.join(out_dir, "07_correlation_heatmap.png")
|
||||
fig.savefig(path, dpi=150)
|
||||
plt.close(fig)
|
||||
print(f"[OK] {path}")
|
||||
|
||||
|
||||
def plot_global_distribution(X, out_dir: str):
|
||||
fig, axes = plt.subplots(1, 2, figsize=(12, 5))
|
||||
|
||||
all_vals = X.flatten()
|
||||
axes[0].hist(all_vals, bins=100, color="steelblue", alpha=0.7, edgecolor="white",
|
||||
linewidth=0.3)
|
||||
axes[0].axvline(np.mean(all_vals), color="red", linestyle="--",
|
||||
label=f"均值={np.mean(all_vals):.4f}")
|
||||
axes[0].axvline(np.median(all_vals), color="orange", linestyle="--",
|
||||
label=f"中位数={np.median(all_vals):.4f}")
|
||||
axes[0].set_xlabel("特征值")
|
||||
axes[0].set_ylabel("频数")
|
||||
axes[0].set_title("全局特征值分布")
|
||||
axes[0].legend()
|
||||
|
||||
means = np.mean(X, axis=0)
|
||||
stds = np.std(X, axis=0)
|
||||
n_features = min(X.shape[1], 50)
|
||||
axes[1].errorbar(range(1, n_features + 1), means[:n_features], yerr=stds[:n_features],
|
||||
fmt="o", markersize=3, capsize=2, color="steelblue", alpha=0.7)
|
||||
axes[1].set_xlabel("特征维度")
|
||||
axes[1].set_ylabel("均值 ± 标准差")
|
||||
axes[1].set_title(f"各维度均值与标准差 (前 {n_features} 维)")
|
||||
|
||||
fig.suptitle("全局特征概览", fontsize=13)
|
||||
fig.tight_layout()
|
||||
path = os.path.join(out_dir, "08_global_distribution.png")
|
||||
fig.savefig(path, dpi=150)
|
||||
plt.close(fig)
|
||||
print(f"[OK] {path}")
|
||||
|
||||
|
||||
# ============================================================
|
||||
# 命令行入口
|
||||
# ============================================================
|
||||
|
||||
def main():
|
||||
parser = argparse.ArgumentParser(
|
||||
description="Deeplearning 数据可视化脚本",
|
||||
formatter_class=argparse.RawDescriptionHelpFormatter,
|
||||
epilog="""
|
||||
示例:
|
||||
python Scripts/visualize.py -f 20260319Numbers -l 0 1 2 3 4 5 6 7 8 9
|
||||
python Scripts/visualize.py -f "20260408 grap" -l 1 2 3 4 5 6 7 8 9
|
||||
python Scripts/visualize.py -f 20260319Numbers -l 0 1 2 3 4 5 6 7 8 9 --no-tsne --max-features 15
|
||||
""",
|
||||
)
|
||||
parser.add_argument("-f", "--folder", required=True,
|
||||
help="Static/ 下的数据目录名")
|
||||
parser.add_argument("-l", "--labels", nargs="+", required=True,
|
||||
help="类别标签列表，空格分隔")
|
||||
parser.add_argument("-r", "--root", default=None,
|
||||
help="项目根目录（默认为 Deeplearning/）")
|
||||
parser.add_argument("--max-features", type=int, default=20,
|
||||
help="可视化中显示的最大特征维度数 (默认 20)")
|
||||
parser.add_argument("--no-tsne", action="store_true",
|
||||
help="跳过 t-SNE 计算")
|
||||
parser.add_argument("--no-pca", action="store_true",
|
||||
help="跳过 PCA 计算")
|
||||
parser.add_argument("--tsne-max-samples", type=int, default=5000,
|
||||
help="t-SNE 最大抽样数 (默认 5000)")
|
||||
|
||||
args = parser.parse_args()
|
||||
|
||||
if args.root:
|
||||
root = args.root
|
||||
else:
|
||||
root = str(Path(__file__).resolve().parent.parent)
|
||||
root = os.path.abspath(root)
|
||||
|
||||
print(f"加载数据: {root}/Static/{args.folder}")
|
||||
X, y, label_names, all_features, col_counts = load_all_data(root, args.folder, args.labels)
|
||||
print(f" 样本数: {X.shape[0]}, 特征维度: {X.shape[1]}, 类别数: {len(label_names)}")
|
||||
for lbl in label_names:
|
||||
cnt = int(np.sum(y == label_names.index(lbl)))
|
||||
print(f" 类 {lbl}: {cnt} 样本, {col_counts.get(lbl, X.shape[1])} 列")
|
||||
|
||||
out_dir = os.path.join(root, "Visualizations", args.folder)
|
||||
_ensure_dir(out_dir)
|
||||
print(f"\n输出目录: {out_dir}\n")
|
||||
|
||||
plt.rcParams["font.family"] = "sans-serif"
|
||||
plt.rcParams["font.sans-serif"] = ["DejaVu Sans"]
|
||||
|
||||
print("生成可视化图表...\n")
|
||||
plot_class_distribution(y, label_names, out_dir)
|
||||
plot_feature_histograms(all_features, out_dir,
|
||||
max_features=min(args.max_features, X.shape[1]))
|
||||
plot_feature_boxplots(all_features, out_dir,
|
||||
max_features=min(args.max_features, X.shape[1]))
|
||||
if not args.no_pca:
|
||||
plot_pca(X, y, label_names, out_dir)
|
||||
if not args.no_tsne:
|
||||
plot_tsne(X, y, label_names, out_dir, max_samples=args.tsne_max_samples)
|
||||
plot_class_mean_std_heatmap(all_features, label_names, out_dir,
|
||||
max_features=min(args.max_features, X.shape[1]))
|
||||
plot_correlation_heatmap(X, out_dir, max_features=min(30, X.shape[1]))
|
||||
plot_global_distribution(X, out_dir)
|
||||
|
||||
print(f"\n全部图表已保存到: {out_dir}")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
|
|
@ -1,180 +0,0 @@
|
|||
name: Deeplearning
|
||||
channels:
|
||||
- pytorch
|
||||
- nvidia
|
||||
- defaults
|
||||
dependencies:
|
||||
- _libgcc_mutex=0.1=main
|
||||
- _openmp_mutex=5.1=1_gnu
|
||||
- blas=1.0=mkl
|
||||
- bottleneck=1.3.7=py312ha883a20_0
|
||||
- brotli=1.0.9=h5eee18b_8
|
||||
- brotli-bin=1.0.9=h5eee18b_8
|
||||
- brotli-python=1.0.9=py312h6a678d5_8
|
||||
- bzip2=1.0.8=h5eee18b_6
|
||||
- ca-certificates=2024.11.26=h06a4308_0
|
||||
- certifi=2024.12.14=py312h06a4308_0
|
||||
- charset-normalizer=3.3.2=pyhd3eb1b0_0
|
||||
- contourpy=1.2.0=py312hdb19cb5_0
|
||||
- cuda-cudart=12.4.127=0
|
||||
- cuda-cupti=12.4.127=0
|
||||
- cuda-libraries=12.4.0=0
|
||||
- cuda-nvrtc=12.4.127=0
|
||||
- cuda-nvtx=12.4.127=0
|
||||
- cuda-opencl=12.4.127=0
|
||||
- cuda-runtime=12.4.0=0
|
||||
- cudatoolkit=11.5.1=hcf5317a_9
|
||||
- cycler=0.11.0=pyhd3eb1b0_0
|
||||
- cyrus-sasl=2.1.28=h52b45da_1
|
||||
- dbus=1.13.18=hb2f20db_0
|
||||
- debugpy=1.6.7=py312h6a678d5_0
|
||||
- et_xmlfile=1.1.0=py312h06a4308_1
|
||||
- expat=2.6.2=h6a678d5_0
|
||||
- ffmpeg=4.3=hf484d3e_0
|
||||
- filelock=3.13.1=py312h06a4308_0
|
||||
- fontconfig=2.14.1=h55d465d_3
|
||||
- fonttools=4.51.0=py312h5eee18b_0
|
||||
- freetype=2.12.1=h4a9f257_0
|
||||
- glib=2.78.4=h6a678d5_0
|
||||
- glib-tools=2.78.4=h6a678d5_0
|
||||
- gmp=6.2.1=h295c915_3
|
||||
- gnutls=3.6.15=he1e5248_0
|
||||
- gst-plugins-base=1.14.1=h6a678d5_1
|
||||
- gstreamer=1.14.1=h5eee18b_1
|
||||
- icu=73.1=h6a678d5_0
|
||||
- idna=3.7=py312h06a4308_0
|
||||
- intel-openmp=2023.1.0=hdb19cb5_46306
|
||||
- jinja2=3.1.4=py312h06a4308_0
|
||||
- joblib=1.4.2=py312h06a4308_0
|
||||
- jpeg=9e=h5eee18b_3
|
||||
- kiwisolver=1.4.4=py312h6a678d5_0
|
||||
- krb5=1.20.1=h143b758_1
|
||||
- lame=3.100=h7b6447c_0
|
||||
- lcms2=2.12=h3be6417_0
|
||||
- ld_impl_linux-64=2.38=h1181459_1
|
||||
- lerc=3.0=h295c915_0
|
||||
- libbrotlicommon=1.0.9=h5eee18b_8
|
||||
- libbrotlidec=1.0.9=h5eee18b_8
|
||||
- libbrotlienc=1.0.9=h5eee18b_8
|
||||
- libclang=14.0.6=default_hc6dbbc7_1
|
||||
- libclang13=14.0.6=default_he11475f_1
|
||||
- libcublas=12.4.2.65=0
|
||||
- libcufft=11.2.0.44=0
|
||||
- libcufile=1.9.1.3=0
|
||||
- libcups=2.4.2=h2d74bed_1
|
||||
- libcurand=10.3.5.147=0
|
||||
- libcusolver=11.6.0.99=0
|
||||
- libcusparse=12.3.0.142=0
|
||||
- libdeflate=1.17=h5eee18b_1
|
||||
- libedit=3.1.20230828=h5eee18b_0
|
||||
- libffi=3.4.4=h6a678d5_1
|
||||
- libgcc-ng=11.2.0=h1234567_1
|
||||
- libgfortran-ng=11.2.0=h00389a5_1
|
||||
- libgfortran5=11.2.0=h1234567_1
|
||||
- libglib=2.78.4=hdc74915_0
|
||||
- libgomp=11.2.0=h1234567_1
|
||||
- libiconv=1.16=h5eee18b_3
|
||||
- libidn2=2.3.4=h5eee18b_0
|
||||
- libjpeg-turbo=2.0.0=h9bf148f_0
|
||||
- libllvm14=14.0.6=hdb19cb5_3
|
||||
- libnpp=12.2.5.2=0
|
||||
- libnvfatbin=12.4.127=0
|
||||
- libnvjitlink=12.4.99=0
|
||||
- libnvjpeg=12.3.1.89=0
|
||||
- libpng=1.6.39=h5eee18b_0
|
||||
- libpq=12.17=hdbd6064_0
|
||||
- libstdcxx-ng=11.2.0=h1234567_1
|
||||
- libtasn1=4.19.0=h5eee18b_0
|
||||
- libtiff=4.5.1=h6a678d5_0
|
||||
- libunistring=0.9.10=h27cfd23_0
|
||||
- libuuid=1.41.5=h5eee18b_0
|
||||
- libwebp-base=1.3.2=h5eee18b_0
|
||||
- libxcb=1.15=h7f8727e_0
|
||||
- libxkbcommon=1.0.1=h097e994_2
|
||||
- libxml2=2.13.1=hfdd30dd_2
|
||||
- llvm-openmp=14.0.6=h9e868ea_0
|
||||
- lz4-c=1.9.4=h6a678d5_1
|
||||
- markupsafe=2.1.3=py312h5eee18b_0
|
||||
- matplotlib=3.8.4=py312h06a4308_0
|
||||
- matplotlib-base=3.8.4=py312h526ad5a_0
|
||||
- mkl=2023.1.0=h213fc3f_46344
|
||||
- mkl-service=2.4.0=py312h5eee18b_1
|
||||
- mkl_fft=1.3.8=py312h5eee18b_0
|
||||
- mkl_random=1.2.4=py312hdb19cb5_0
|
||||
- mpmath=1.3.0=py312h06a4308_0
|
||||
- mysql=5.7.24=h721c034_2
|
||||
- ncurses=6.4=h6a678d5_0
|
||||
- nettle=3.7.3=hbbd107a_1
|
||||
- networkx=3.3=py312h06a4308_0
|
||||
- numexpr=2.8.7=py312hf827012_0
|
||||
- numpy=1.26.4=py312hc5e2394_0
|
||||
- numpy-base=1.26.4=py312h0da6c21_0
|
||||
- openh264=2.1.1=h4ff587b_0
|
||||
- openjpeg=2.5.2=he7f1fd0_0
|
||||
- openpyxl=3.1.5=py312h5eee18b_0
|
||||
- openssl=3.0.15=h5eee18b_0
|
||||
- packaging=24.1=py312h06a4308_0
|
||||
- pandas=2.2.2=py312h526ad5a_0
|
||||
- pcre2=10.42=hebb0a14_1
|
||||
- pillow=10.4.0=py312h5eee18b_0
|
||||
- pip=24.2=py312h06a4308_0
|
||||
- ply=3.11=py312h06a4308_1
|
||||
- pybind11-abi=5=hd3eb1b0_0
|
||||
- pyopengl=3.1.1a1=py312h06a4308_0
|
||||
- pyparsing=3.0.9=py312h06a4308_0
|
||||
- pyqt=5.15.10=py312h6a678d5_0
|
||||
- pyqt5-sip=12.13.0=py312h5eee18b_0
|
||||
- pysocks=1.7.1=py312h06a4308_0
|
||||
- python=3.12.4=h5148396_1
|
||||
- python-dateutil=2.9.0post0=py312h06a4308_2
|
||||
- python-tzdata=2023.3=pyhd3eb1b0_0
|
||||
- pytorch=2.4.0=py3.12_cuda12.4_cudnn9.1.0_0
|
||||
- pytorch-cuda=12.4=hc786d27_6
|
||||
- pytorch-mutex=1.0=cuda
|
||||
- pytz=2024.1=py312h06a4308_0
|
||||
- pyyaml=6.0.1=py312h5eee18b_0
|
||||
- qt-main=5.15.2=h53bd1ea_10
|
||||
- readline=8.2=h5eee18b_0
|
||||
- requests=2.32.3=py312h06a4308_0
|
||||
- scikit-learn=1.5.1=py312h526ad5a_0
|
||||
- scipy=1.13.1=py312hc5e2394_0
|
||||
- seaborn=0.13.2=py312h06a4308_0
|
||||
- setuptools=72.1.0=py312h06a4308_0
|
||||
- sip=6.7.12=py312h6a678d5_0
|
||||
- six=1.16.0=pyhd3eb1b0_1
|
||||
- sqlite=3.45.3=h5eee18b_0
|
||||
- sympy=1.12=py312h06a4308_0
|
||||
- tbb=2021.8.0=hdb19cb5_0
|
||||
- threadpoolctl=3.5.0=py312he106c6f_0
|
||||
- tk=8.6.14=h39e8969_0
|
||||
- torchaudio=2.4.0=py312_cu124
|
||||
- torchtriton=3.0.0=py312
|
||||
- torchvision=0.19.0=py312_cu124
|
||||
- tornado=6.4.1=py312h5eee18b_0
|
||||
- tqdm=4.66.5=py312he106c6f_0
|
||||
- typing_extensions=4.11.0=py312h06a4308_0
|
||||
- tzdata=2024a=h04d1e81_0
|
||||
- unicodedata2=15.1.0=py312h5eee18b_0
|
||||
- urllib3=2.2.2=py312h06a4308_0
|
||||
- wheel=0.43.0=py312h06a4308_0
|
||||
- xlrd=2.0.1=pyhd3eb1b0_1
|
||||
- xz=5.4.6=h5eee18b_1
|
||||
- yaml=0.2.5=h7b6447c_0
|
||||
- zlib=1.2.13=h5eee18b_1
|
||||
- zstd=1.5.5=hc292b87_2
|
||||
- pip:
|
||||
- autopep8==2.3.1
|
||||
- basedpyright==1.16.0
|
||||
- black==24.8.0
|
||||
- click==8.1.7
|
||||
- fsspec==2024.6.1
|
||||
- graphviz==0.20.3
|
||||
- greenlet==3.0.3
|
||||
- msgpack==1.0.8
|
||||
- mypy-extensions==1.0.0
|
||||
- nodejs-wheel-binaries==20.16.0
|
||||
- pathspec==0.12.1
|
||||
- platformdirs==4.2.2
|
||||
- pycodestyle==2.12.1
|
||||
- pynvim==0.5.0
|
||||
prefix: /home/qyhhh/.miniconda3/envs/Deeplearning
|
||||
|
|
@ -1,163 +0,0 @@
|
|||
# This file may be used to create an environment using:
|
||||
# $ conda create --name <env> --file <this file>
|
||||
# platform: linux-64
|
||||
# created-by: conda 26.1.1
|
||||
@EXPLICIT
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/_libgcc_mutex-0.1-main.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/blas-1.0-mkl.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/ca-certificates-2024.11.26-h06a4308_0.conda
|
||||
https://conda.anaconda.org/nvidia/linux-64/cuda-cudart-12.4.127-0.tar.bz2
|
||||
https://conda.anaconda.org/nvidia/linux-64/cuda-cupti-12.4.127-0.tar.bz2
|
||||
https://conda.anaconda.org/nvidia/linux-64/cuda-nvrtc-12.4.127-0.tar.bz2
|
||||
https://conda.anaconda.org/nvidia/linux-64/cuda-nvtx-12.4.127-0.tar.bz2
|
||||
https://conda.anaconda.org/nvidia/linux-64/cuda-opencl-12.4.127-0.tar.bz2
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/ld_impl_linux-64-2.38-h1181459_1.conda
|
||||
https://conda.anaconda.org/nvidia/linux-64/libcublas-12.4.2.65-0.tar.bz2
|
||||
https://conda.anaconda.org/nvidia/linux-64/libcufft-11.2.0.44-0.tar.bz2
|
||||
https://conda.anaconda.org/nvidia/linux-64/libcufile-1.9.1.3-0.tar.bz2
|
||||
https://conda.anaconda.org/nvidia/linux-64/libcurand-10.3.5.147-0.tar.bz2
|
||||
https://conda.anaconda.org/nvidia/linux-64/libcusolver-11.6.0.99-0.tar.bz2
|
||||
https://conda.anaconda.org/nvidia/linux-64/libcusparse-12.3.0.142-0.tar.bz2
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/libgfortran5-11.2.0-h1234567_1.conda
|
||||
https://conda.anaconda.org/nvidia/linux-64/libnpp-12.2.5.2-0.tar.bz2
|
||||
https://conda.anaconda.org/nvidia/linux-64/libnvfatbin-12.4.127-0.tar.bz2
|
||||
https://conda.anaconda.org/nvidia/linux-64/libnvjitlink-12.4.99-0.tar.bz2
|
||||
https://conda.anaconda.org/nvidia/linux-64/libnvjpeg-12.3.1.89-0.tar.bz2
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/libstdcxx-ng-11.2.0-h1234567_1.conda
|
||||
https://repo.anaconda.com/pkgs/main/noarch/pybind11-abi-5-hd3eb1b0_0.conda
|
||||
https://conda.anaconda.org/pytorch/noarch/pytorch-mutex-1.0-cuda.tar.bz2
|
||||
https://repo.anaconda.com/pkgs/main/noarch/tzdata-2024a-h04d1e81_0.conda
|
||||
https://conda.anaconda.org/nvidia/linux-64/cuda-libraries-12.4.0-0.tar.bz2
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/libgfortran-ng-11.2.0-h00389a5_1.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/libgomp-11.2.0-h1234567_1.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/_openmp_mutex-5.1-1_gnu.conda
|
||||
https://conda.anaconda.org/nvidia/linux-64/cuda-runtime-12.4.0-0.tar.bz2
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/libgcc-ng-11.2.0-h1234567_1.conda
|
||||
https://conda.anaconda.org/pytorch/linux-64/pytorch-cuda-12.4-hc786d27_6.tar.bz2
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/bzip2-1.0.8-h5eee18b_6.conda
|
||||
https://conda.anaconda.org/nvidia/linux-64/cudatoolkit-11.5.1-hcf5317a_9.tar.bz2
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/expat-2.6.2-h6a678d5_0.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/gmp-6.2.1-h295c915_3.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/icu-73.1-h6a678d5_0.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/jpeg-9e-h5eee18b_3.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/lame-3.100-h7b6447c_0.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/lerc-3.0-h295c915_0.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/libbrotlicommon-1.0.9-h5eee18b_8.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/libdeflate-1.17-h5eee18b_1.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/libffi-3.4.4-h6a678d5_1.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/libiconv-1.16-h5eee18b_3.conda
|
||||
https://conda.anaconda.org/pytorch/linux-64/libjpeg-turbo-2.0.0-h9bf148f_0.tar.bz2
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/libtasn1-4.19.0-h5eee18b_0.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/libunistring-0.9.10-h27cfd23_0.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/libuuid-1.41.5-h5eee18b_0.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/libwebp-base-1.3.2-h5eee18b_0.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/libxcb-1.15-h7f8727e_0.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/lz4-c-1.9.4-h6a678d5_1.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/ncurses-6.4-h6a678d5_0.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/openh264-2.1.1-h4ff587b_0.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/openssl-3.0.15-h5eee18b_0.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/tbb-2021.8.0-hdb19cb5_0.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/xz-5.4.6-h5eee18b_1.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/yaml-0.2.5-h7b6447c_0.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/zlib-1.2.13-h5eee18b_1.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/intel-openmp-2023.1.0-hdb19cb5_46306.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/libbrotlidec-1.0.9-h5eee18b_8.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/libbrotlienc-1.0.9-h5eee18b_8.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/libcups-2.4.2-h2d74bed_1.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/libedit-3.1.20230828-h5eee18b_0.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/libidn2-2.3.4-h5eee18b_0.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/libllvm14-14.0.6-hdb19cb5_3.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/libpng-1.6.39-h5eee18b_0.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/libxml2-2.13.1-hfdd30dd_2.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/llvm-openmp-14.0.6-h9e868ea_0.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/nettle-3.7.3-hbbd107a_1.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/pcre2-10.42-hebb0a14_1.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/readline-8.2-h5eee18b_0.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/tk-8.6.14-h39e8969_0.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/zstd-1.5.5-hc292b87_2.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/brotli-bin-1.0.9-h5eee18b_8.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/freetype-2.12.1-h4a9f257_0.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/gnutls-3.6.15-he1e5248_0.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/krb5-1.20.1-h143b758_1.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/libclang13-14.0.6-default_he11475f_1.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/libglib-2.78.4-hdc74915_0.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/libtiff-4.5.1-h6a678d5_0.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/libxkbcommon-1.0.1-h097e994_2.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/mkl-2023.1.0-h213fc3f_46344.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/sqlite-3.45.3-h5eee18b_0.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/brotli-1.0.9-h5eee18b_8.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/cyrus-sasl-2.1.28-h52b45da_1.conda
|
||||
https://conda.anaconda.org/pytorch/linux-64/ffmpeg-4.3-hf484d3e_0.tar.bz2
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/fontconfig-2.14.1-h55d465d_3.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/glib-tools-2.78.4-h6a678d5_0.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/lcms2-2.12-h3be6417_0.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/libclang-14.0.6-default_hc6dbbc7_1.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/libpq-12.17-hdbd6064_0.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/openjpeg-2.5.2-he7f1fd0_0.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/python-3.12.4-h5148396_1.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/brotli-python-1.0.9-py312h6a678d5_8.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/certifi-2024.12.14-py312h06a4308_0.conda
|
||||
https://repo.anaconda.com/pkgs/main/noarch/charset-normalizer-3.3.2-pyhd3eb1b0_0.conda
|
||||
https://repo.anaconda.com/pkgs/main/noarch/cycler-0.11.0-pyhd3eb1b0_0.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/debugpy-1.6.7-py312h6a678d5_0.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/et_xmlfile-1.1.0-py312h06a4308_1.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/filelock-3.13.1-py312h06a4308_0.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/glib-2.78.4-h6a678d5_0.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/idna-3.7-py312h06a4308_0.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/joblib-1.4.2-py312h06a4308_0.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/kiwisolver-1.4.4-py312h6a678d5_0.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/markupsafe-2.1.3-py312h5eee18b_0.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/mkl-service-2.4.0-py312h5eee18b_1.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/mpmath-1.3.0-py312h06a4308_0.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/mysql-5.7.24-h721c034_2.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/networkx-3.3-py312h06a4308_0.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/packaging-24.1-py312h06a4308_0.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/pillow-10.4.0-py312h5eee18b_0.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/ply-3.11-py312h06a4308_1.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/pyopengl-3.1.1a1-py312h06a4308_0.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/pyparsing-3.0.9-py312h06a4308_0.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/pyqt5-sip-12.13.0-py312h5eee18b_0.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/pysocks-1.7.1-py312h06a4308_0.conda
|
||||
https://repo.anaconda.com/pkgs/main/noarch/python-tzdata-2023.3-pyhd3eb1b0_0.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/pytz-2024.1-py312h06a4308_0.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/pyyaml-6.0.1-py312h5eee18b_0.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/setuptools-72.1.0-py312h06a4308_0.conda
|
||||
https://repo.anaconda.com/pkgs/main/noarch/six-1.16.0-pyhd3eb1b0_1.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/threadpoolctl-3.5.0-py312he106c6f_0.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/tornado-6.4.1-py312h5eee18b_0.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/tqdm-4.66.5-py312he106c6f_0.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/typing_extensions-4.11.0-py312h06a4308_0.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/unicodedata2-15.1.0-py312h5eee18b_0.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/wheel-0.43.0-py312h06a4308_0.conda
|
||||
https://repo.anaconda.com/pkgs/main/noarch/xlrd-2.0.1-pyhd3eb1b0_1.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/dbus-1.13.18-hb2f20db_0.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/fonttools-4.51.0-py312h5eee18b_0.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/gstreamer-1.14.1-h5eee18b_1.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/jinja2-3.1.4-py312h06a4308_0.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/numpy-base-1.26.4-py312h0da6c21_0.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/openpyxl-3.1.5-py312h5eee18b_0.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/pip-24.2-py312h06a4308_0.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/python-dateutil-2.9.0post0-py312h06a4308_2.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/sip-6.7.12-py312h6a678d5_0.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/sympy-1.12-py312h06a4308_0.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/urllib3-2.2.2-py312h06a4308_0.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/gst-plugins-base-1.14.1-h6a678d5_1.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/requests-2.32.3-py312h06a4308_0.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/qt-main-5.15.2-h53bd1ea_10.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/pyqt-5.15.10-py312h6a678d5_0.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/bottleneck-1.3.7-py312ha883a20_0.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/contourpy-1.2.0-py312hdb19cb5_0.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/matplotlib-3.8.4-py312h06a4308_0.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/matplotlib-base-3.8.4-py312h526ad5a_0.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/mkl_fft-1.3.8-py312h5eee18b_0.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/mkl_random-1.2.4-py312hdb19cb5_0.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/numpy-1.26.4-py312hc5e2394_0.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/numexpr-2.8.7-py312hf827012_0.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/scipy-1.13.1-py312hc5e2394_0.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/pandas-2.2.2-py312h526ad5a_0.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/scikit-learn-1.5.1-py312h526ad5a_0.conda
|
||||
https://repo.anaconda.com/pkgs/main/linux-64/seaborn-0.13.2-py312h06a4308_0.conda
|
||||
https://conda.anaconda.org/pytorch/linux-64/pytorch-2.4.0-py3.12_cuda12.4_cudnn9.1.0_0.tar.bz2
|
||||
https://conda.anaconda.org/pytorch/linux-64/torchaudio-2.4.0-py312_cu124.tar.bz2
|
||||
https://conda.anaconda.org/pytorch/linux-64/torchtriton-3.0.0-py312.tar.bz2
|
||||
https://conda.anaconda.org/pytorch/linux-64/torchvision-0.19.0-py312_cu124.tar.bz2
|
||||
|
|
@ -1,179 +0,0 @@
|
|||
name: Deeplearning
|
||||
channels:
|
||||
- defaults
|
||||
- nvidia
|
||||
- pytorch
|
||||
dependencies:
|
||||
- _libgcc_mutex=0.1
|
||||
- _openmp_mutex=5.1
|
||||
- blas=1.0
|
||||
- bottleneck=1.3.7
|
||||
- brotli=1.0.9
|
||||
- brotli-bin=1.0.9
|
||||
- brotli-python=1.0.9
|
||||
- bzip2=1.0.8
|
||||
- ca-certificates=2024.11.26
|
||||
- certifi=2024.12.14
|
||||
- charset-normalizer=3.3.2
|
||||
- contourpy=1.2.0
|
||||
- cuda-cudart=12.4.127
|
||||
- cuda-cupti=12.4.127
|
||||
- cuda-libraries=12.4.0
|
||||
- cuda-nvrtc=12.4.127
|
||||
- cuda-nvtx=12.4.127
|
||||
- cuda-opencl=12.4.127
|
||||
- cuda-runtime=12.4.0
|
||||
- cudatoolkit=11.5.1
|
||||
- cycler=0.11.0
|
||||
- cyrus-sasl=2.1.28
|
||||
- dbus=1.13.18
|
||||
- debugpy=1.6.7
|
||||
- et_xmlfile=1.1.0
|
||||
- expat=2.6.2
|
||||
- ffmpeg=4.3
|
||||
- filelock=3.13.1
|
||||
- fontconfig=2.14.1
|
||||
- fonttools=4.51.0
|
||||
- freetype=2.12.1
|
||||
- glib=2.78.4
|
||||
- glib-tools=2.78.4
|
||||
- gmp=6.2.1
|
||||
- gnutls=3.6.15
|
||||
- gst-plugins-base=1.14.1
|
||||
- gstreamer=1.14.1
|
||||
- icu=73.1
|
||||
- idna=3.7
|
||||
- intel-openmp=2023.1.0
|
||||
- jinja2=3.1.4
|
||||
- joblib=1.4.2
|
||||
- jpeg=9e
|
||||
- kiwisolver=1.4.4
|
||||
- krb5=1.20.1
|
||||
- lame=3.100
|
||||
- lcms2=2.12
|
||||
- ld_impl_linux-64=2.38
|
||||
- lerc=3.0
|
||||
- libbrotlicommon=1.0.9
|
||||
- libbrotlidec=1.0.9
|
||||
- libbrotlienc=1.0.9
|
||||
- libclang=14.0.6
|
||||
- libclang13=14.0.6
|
||||
- libcublas=12.4.2.65
|
||||
- libcufft=11.2.0.44
|
||||
- libcufile=1.9.1.3
|
||||
- libcups=2.4.2
|
||||
- libcurand=10.3.5.147
|
||||
- libcusolver=11.6.0.99
|
||||
- libcusparse=12.3.0.142
|
||||
- libdeflate=1.17
|
||||
- libedit=3.1.20230828
|
||||
- libffi=3.4.4
|
||||
- libgcc-ng=11.2.0
|
||||
- libgfortran-ng=11.2.0
|
||||
- libgfortran5=11.2.0
|
||||
- libglib=2.78.4
|
||||
- libgomp=11.2.0
|
||||
- libiconv=1.16
|
||||
- libidn2=2.3.4
|
||||
- libjpeg-turbo=2.0.0
|
||||
- libllvm14=14.0.6
|
||||
- libnpp=12.2.5.2
|
||||
- libnvfatbin=12.4.127
|
||||
- libnvjitlink=12.4.99
|
||||
- libnvjpeg=12.3.1.89
|
||||
- libpng=1.6.39
|
||||
- libpq=12.17
|
||||
- libstdcxx-ng=11.2.0
|
||||
- libtasn1=4.19.0
|
||||
- libtiff=4.5.1
|
||||
- libunistring=0.9.10
|
||||
- libuuid=1.41.5
|
||||
- libwebp-base=1.3.2
|
||||
- libxcb=1.15
|
||||
- libxkbcommon=1.0.1
|
||||
- libxml2=2.13.1
|
||||
- llvm-openmp=14.0.6
|
||||
- lz4-c=1.9.4
|
||||
- markupsafe=2.1.3
|
||||
- matplotlib=3.8.4
|
||||
- matplotlib-base=3.8.4
|
||||
- mkl=2023.1.0
|
||||
- mkl-service=2.4.0
|
||||
- mkl_fft=1.3.8
|
||||
- mkl_random=1.2.4
|
||||
- mpmath=1.3.0
|
||||
- mysql=5.7.24
|
||||
- ncurses=6.4
|
||||
- nettle=3.7.3
|
||||
- networkx=3.3
|
||||
- numexpr=2.8.7
|
||||
- numpy=1.26.4
|
||||
- numpy-base=1.26.4
|
||||
- openh264=2.1.1
|
||||
- openjpeg=2.5.2
|
||||
- openpyxl=3.1.5
|
||||
- openssl=3.0.15
|
||||
- packaging=24.1
|
||||
- pandas=2.2.2
|
||||
- pcre2=10.42
|
||||
- pillow=10.4.0
|
||||
- pip=24.2
|
||||
- ply=3.11
|
||||
- pybind11-abi=5
|
||||
- pyopengl=3.1.1a1
|
||||
- pyparsing=3.0.9
|
||||
- pyqt=5.15.10
|
||||
- pyqt5-sip=12.13.0
|
||||
- pysocks=1.7.1
|
||||
- python=3.12.4
|
||||
- python-dateutil=2.9.0post0
|
||||
- python-tzdata=2023.3
|
||||
- pytorch=2.4.0
|
||||
- pytorch-cuda=12.4
|
||||
- pytorch-mutex=1.0
|
||||
- pytz=2024.1
|
||||
- pyyaml=6.0.1
|
||||
- qt-main=5.15.2
|
||||
- readline=8.2
|
||||
- requests=2.32.3
|
||||
- scikit-learn=1.5.1
|
||||
- scipy=1.13.1
|
||||
- seaborn=0.13.2
|
||||
- setuptools=72.1.0
|
||||
- sip=6.7.12
|
||||
- six=1.16.0
|
||||
- sqlite=3.45.3
|
||||
- sympy=1.12
|
||||
- tbb=2021.8.0
|
||||
- threadpoolctl=3.5.0
|
||||
- tk=8.6.14
|
||||
- torchaudio=2.4.0
|
||||
- torchtriton=3.0.0
|
||||
- torchvision=0.19.0
|
||||
- tornado=6.4.1
|
||||
- tqdm=4.66.5
|
||||
- typing_extensions=4.11.0
|
||||
- tzdata=2024a
|
||||
- unicodedata2=15.1.0
|
||||
- urllib3=2.2.2
|
||||
- wheel=0.43.0
|
||||
- xlrd=2.0.1
|
||||
- xz=5.4.6
|
||||
- yaml=0.2.5
|
||||
- zlib=1.2.13
|
||||
- zstd=1.5.5
|
||||
- pip:
|
||||
- autopep8==2.3.1
|
||||
- basedpyright==1.16.0
|
||||
- black==24.8.0
|
||||
- click==8.1.7
|
||||
- fsspec==2024.6.1
|
||||
- graphviz==0.20.3
|
||||
- greenlet==3.0.3
|
||||
- msgpack==1.0.8
|
||||
- mypy-extensions==1.0.0
|
||||
- nodejs-wheel-binaries==20.16.0
|
||||
- pathspec==0.12.1
|
||||
- platformdirs==4.2.2
|
||||
- pycodestyle==2.12.1
|
||||
- pynvim==0.5.0
|
||||
|
|
@ -0,0 +1,46 @@
|
|||
G = require('G')
|
||||
|
||||
if not G.g.vscode then
|
||||
|
||||
local lazypath = G.fn.stdpath("data") .. "/lazy/lazy.nvim"
|
||||
|
||||
if not G.loop.fs_stat(lazypath) then
|
||||
G.fn.system({
|
||||
"git",
|
||||
"clone",
|
||||
"--filter=blob:none",
|
||||
"https://github.com/folke/lazy.nvim.git",
|
||||
"--branch=stable",
|
||||
lazypath,
|
||||
})
|
||||
|
||||
|
||||
G.fn.system({
|
||||
"git",
|
||||
"config",
|
||||
"--global",
|
||||
"credential.helper",
|
||||
"store",
|
||||
})
|
||||
end
|
||||
|
||||
G.opt.rtp:prepend(lazypath)
|
||||
|
||||
require("keymap")
|
||||
|
||||
|
||||
if G.g.vscode then
|
||||
require('vscode')
|
||||
else
|
||||
require("options")
|
||||
local clone_prefix = G.use_ssh and "git@github.com:%s.git" or "https://github.com/%s.git"
|
||||
require("lazy").setup(
|
||||
require('plugs'), {
|
||||
lockfile = G.fn.stdpath("data") .. "/lazy/lazy-lock.json",
|
||||
git = {
|
||||
url_format = clone_prefix,
|
||||
}
|
||||
}
|
||||
)
|
||||
end
|
||||
end
|
||||
|
|
@ -0,0 +1,59 @@
|
|||
local G = {}
|
||||
|
||||
G.use_ssh = false
|
||||
|
||||
G.g = vim.g
|
||||
G.b = vim.b
|
||||
G.o = vim.o
|
||||
G.fn = vim.fn
|
||||
G.api = vim.api
|
||||
G.opt = vim.opt
|
||||
G.loop = vim.loop
|
||||
G.lb = vim.lsp.buf
|
||||
G.dic = vim.diagnostic
|
||||
G.cgp = vim.nvim_create_augroup
|
||||
|
||||
function G.map(maps)
|
||||
for _, map in pairs(maps) do
|
||||
if #map == 3 then
|
||||
vim.keymap.set(map[1], map[2], map[3], { noremap = true })
|
||||
elseif #map == 4 then
|
||||
vim.keymap.set(map[1], map[2], map[3], map[4])
|
||||
else
|
||||
print("太多变量了")
|
||||
end
|
||||
--G.api.nvim_set_keymap(map[1], map[2], map[3], map[4])
|
||||
end
|
||||
end
|
||||
|
||||
function G.delmap(maps)
|
||||
for _, map in pairs(maps) do
|
||||
if #map == 2 then
|
||||
vim.keymap.del(map[1], map[2], {})
|
||||
elseif #map == 3 then
|
||||
vim.keymap.del(map[1], map[2], map[3])
|
||||
else
|
||||
print("太多变量了")
|
||||
end
|
||||
end
|
||||
end
|
||||
|
||||
function G.cmd(cmd)
|
||||
G.api.nvim_command(cmd)
|
||||
end
|
||||
|
||||
function G.exec(c)
|
||||
G.api.nvim_exec(c)
|
||||
end
|
||||
|
||||
function G.eval(c)
|
||||
return G.api.nvim_eval(c)
|
||||
end
|
||||
|
||||
function G.au(even, opts)
|
||||
return G.api.nvim_create_autocmd(even, opts)
|
||||
end
|
||||
|
||||
G.g.mapleader = ' '
|
||||
|
||||
return G
|
||||
|
|
@ -0,0 +1,46 @@
|
|||
local opt = { noremap = true }
|
||||
|
||||
-- base
|
||||
G.map({
|
||||
{ 'n', '<leader>nh', ':nohlsearch<CR>', opt },
|
||||
{ 'n', '<leader>rp', ':%s/', opt },
|
||||
|
||||
{ 'v', 'L', '$', opt },
|
||||
{ 'v', 'H', '^', opt },
|
||||
{ 'n', 'L', '$', opt },
|
||||
{ 'n', 'H', '^', opt },
|
||||
{ 'v', '>', '>gv', opt },
|
||||
{ 'v', '<', '<gv', opt },
|
||||
|
||||
{ 'n', '>', '>>', opt },
|
||||
{ 'n', '<', '<<', opt },
|
||||
{ 'n', '?', ':set hlsearch<CR>?', opt },
|
||||
{ 'n', '/', ':set hlsearch<CR>/', opt },
|
||||
|
||||
{ 'n', '<A-l>', ':tabn<CR>', opt },
|
||||
{ 'n', '<A-h>', ':tabp<CR>', opt },
|
||||
|
||||
|
||||
{ 'n', '<c-j>', '<c-w>j', opt },
|
||||
{ 'n', '<c-h>', '<c-w>h', opt },
|
||||
{ 'n', '<c-k>', '<c-w>k', opt },
|
||||
{ 'n', '<c-l>', '<c-w>l', opt },
|
||||
|
||||
|
||||
{ 'n', '<c-c>', ':q<CR>', opt },
|
||||
{ 'n', '<c-S>', ':w !sudo tee %<CR>', opt },
|
||||
{ 'n', '<c-q>', ':q!<CR>', opt },
|
||||
{ 'v', '<cs-y>', '"+y', opt },
|
||||
|
||||
|
||||
|
||||
{ 'n', '<leader>y', 'ggyG', opt },
|
||||
{ 'n', '<leader>p', 'ggpG', opt },
|
||||
{ 'n', '<leader>v', 'ggVG', opt },
|
||||
|
||||
{ 'n', '<up>', ':res -5<CR>', opt },
|
||||
{ 'n', '<down>', ':res +5<CR>', opt },
|
||||
{ 'n', '<left>', ':vertical resize -5<CR>', opt },
|
||||
{ 'n', '<right>', ':vertical resize +5<CR>', opt },
|
||||
|
||||
})
|
||||
|
|
@ -0,0 +1,22 @@
|
|||
return {
|
||||
-- capabilities = require("cmp_nvim_lsp").default_capabilities(),
|
||||
settings = {
|
||||
basedpyright = {
|
||||
analysis = {
|
||||
autoSearchPaths = true,
|
||||
diagnosticMode = "openFilesOnly",
|
||||
useLibraryCodeForTypes = true,
|
||||
typeCheckingMode = "standard"
|
||||
},
|
||||
},
|
||||
},
|
||||
on_attach = function()
|
||||
G.api.nvim_create_user_command('R', function()
|
||||
G.cmd [[set splitbelow]]
|
||||
G.cmd [[sp]]
|
||||
G.cmd [[term python3 %]]
|
||||
G.cmd [[resize 10]]
|
||||
G.cmd [[startinsert]]
|
||||
end, {})
|
||||
end
|
||||
}
|
||||
|
|
@ -0,0 +1,11 @@
|
|||
return{
|
||||
on_attach = function()
|
||||
G.api.nvim_create_user_command('R', function()
|
||||
G.cmd [[set splitbelow]]
|
||||
G.cmd [[sp]]
|
||||
G.cmd [[term sh %]]
|
||||
G.cmd [[resize 10]]
|
||||
G.cmd [[startinsert]]
|
||||
end, {})
|
||||
end
|
||||
}
|
||||
|
|
@ -0,0 +1,11 @@
|
|||
return {
|
||||
on_attach = function()
|
||||
G.api.nvim_create_user_command('R', function()
|
||||
G.cmd [[set splitbelow]]
|
||||
G.cmd [[sp]]
|
||||
G.cmd [[term g++ "%" -o "%<" && ./"%<" && rm "%<"]]
|
||||
G.cmd [[resize 10]]
|
||||
G.cmd [[startinsert]]
|
||||
end, {})
|
||||
end
|
||||
}
|
||||
|
|
@ -0,0 +1,18 @@
|
|||
return {
|
||||
on_attach = function()
|
||||
G.api.nvim_create_user_command('R', function()
|
||||
G.cmd [[set splitbelow]]
|
||||
G.cmd [[sp]]
|
||||
G.cmd [[term go run %]]
|
||||
G.cmd [[resize 10]]
|
||||
G.cmd [[startinsert]]
|
||||
end, {})
|
||||
G.api.nvim_create_user_command('Rgin', function()
|
||||
G.cmd [[set splitbelow]]
|
||||
G.cmd [[sp]]
|
||||
G.cmd [[term go run ./main.go]]
|
||||
G.cmd [[resize 10]]
|
||||
G.cmd [[startinsert]]
|
||||
end, {})
|
||||
end
|
||||
}
|
||||
|
|
@ -0,0 +1,4 @@
|
|||
return {
|
||||
on_attach = function()
|
||||
end
|
||||
}
|
||||
|
|
@ -0,0 +1,32 @@
|
|||
local runtime_path = vim.split(package.path, ';')
|
||||
|
||||
table.insert(runtime_path, "lua/?.lua")
|
||||
table.insert(runtime_path, "lua/?/init.lua")
|
||||
|
||||
return {
|
||||
|
||||
capabilities = require('cmp_nvim_lsp').default_capabilities(),
|
||||
|
||||
settings = {
|
||||
|
||||
Lua = {
|
||||
runtime = {
|
||||
-- Tell the language server which version of Lua you're using (most likely LuaJIT in the case of Neovim)
|
||||
version = 'LuaJIT',
|
||||
-- Setup your lua path
|
||||
path = runtime_path,
|
||||
},
|
||||
diagnostics = {
|
||||
globals = {'vim', 'G', 'yield', 'Candidate'},
|
||||
},
|
||||
workspace = {
|
||||
-- Make the server aware of Neovim runtime files
|
||||
library = vim.api.nvim_get_runtime_file("", true),
|
||||
},
|
||||
-- Do not send telemetry data containing a randomized but unique identifier
|
||||
telemetry = {
|
||||
enable = false
|
||||
},
|
||||
},
|
||||
},
|
||||
}
|
||||
|
|
@ -0,0 +1,2 @@
|
|||
return {
|
||||
}
|
||||
|
|
@ -0,0 +1,11 @@
|
|||
return {
|
||||
on_attach = function()
|
||||
G.api.nvim_create_user_command('R', function()
|
||||
G.cmd [[set splitbelow]]
|
||||
G.cmd [[sp]]
|
||||
G.cmd [[term python3 %]]
|
||||
G.cmd [[resize 10]]
|
||||
G.cmd [[startinsert]]
|
||||
end, {})
|
||||
end
|
||||
}
|
||||
|
|
@ -0,0 +1,12 @@
|
|||
return {
|
||||
capabilities = require('cmp_nvim_lsp').default_capabilities(),
|
||||
settings = {
|
||||
yaml = {
|
||||
schemas = {
|
||||
["https://json.schemastore.org/github-workflow.json"] = "/.github/workflows/*",
|
||||
["../path/relative/to/file.yml"] = "/.github/workflows/*",
|
||||
["/path/from/root/of/project"] = "/.github/workflows/*",
|
||||
},
|
||||
},
|
||||
}
|
||||
}
|
||||
|
|
@ -0,0 +1,91 @@
|
|||
--
|
||||
G.opt.ttimeout = true
|
||||
G.opt.ttimeoutlen = 100
|
||||
|
||||
-- 行号
|
||||
G.opt.nu = true
|
||||
G.opt.rnu = true
|
||||
G.opt.scrolloff = 999
|
||||
|
||||
-- 自动保存
|
||||
G.opt.autowrite = true
|
||||
G.opt.autowriteall = true
|
||||
|
||||
-- tab键
|
||||
G.opt.sw = 2
|
||||
G.opt.ts = 2
|
||||
G.opt.softtabstop = 2
|
||||
G.opt.smarttab = true
|
||||
G.opt.expandtab = true
|
||||
G.opt.autoindent = true
|
||||
|
||||
-- 光标
|
||||
G.opt.cursorline = true
|
||||
|
||||
-- 分屏
|
||||
G.opt.splitright = true
|
||||
G.opt.splitbelow = true
|
||||
|
||||
-- 搜索
|
||||
G.opt.ignorecase = true
|
||||
G.opt.incsearch = true
|
||||
|
||||
-- 不换行
|
||||
G.opt.textwidth = 999
|
||||
G.opt.wrap = false
|
||||
|
||||
|
||||
-- 文件判断
|
||||
G.cmd("filetype plugin indent on")
|
||||
|
||||
-- 取消换行注释
|
||||
G.au({ "BufEnter" }, {
|
||||
pattern = { "*" },
|
||||
callback = function()
|
||||
-- vim.opt.formatoptions = vim.opt.formatoptions - { "c", "r", "o" }
|
||||
G.opt.formatoptions = G.opt.formatoptions
|
||||
- "o" -- O and o, don't continue comments
|
||||
+ "r" -- But do continue when pressing enter.
|
||||
end,
|
||||
})
|
||||
|
||||
G.au({ "InsertEnter" }, {
|
||||
pattern = { "*" },
|
||||
callback = function()
|
||||
G.opt.hlsearch = false
|
||||
end,
|
||||
})
|
||||
|
||||
G.au({ "VimEnter", "BufEnter" }, {
|
||||
pattern = { "*.code-snippets" },
|
||||
callback = function()
|
||||
G.cmd("setfiletype json")
|
||||
end,
|
||||
})
|
||||
|
||||
-- G.au({
|
||||
-- {"CmdlineEnter"},
|
||||
-- {
|
||||
-- if index
|
||||
-- }
|
||||
-- })
|
||||
|
||||
|
||||
local function isempty(s)
|
||||
return s == nil or s == ""
|
||||
end
|
||||
|
||||
local function use_if_defined(val, fallback)
|
||||
return val ~= nil and val or fallback
|
||||
end
|
||||
|
||||
|
||||
local conda_prefix = os.getenv("CONDA_PREFIX")
|
||||
if not isempty(conda_prefix) then
|
||||
vim.g.python_host_prog = use_if_defined(vim.g.python_host_prog, conda_prefix .. "/bin/python")
|
||||
vim.g.python3_host_prog = use_if_defined(vim.g.python3_host_prog, conda_prefix .. "/bin/python")
|
||||
else
|
||||
vim.g.python_host_prog = use_if_defined(vim.g.python_host_prog, "python")
|
||||
vim.g.python3_host_prog = use_if_defined(vim.g.python3_host_prog, "python3")
|
||||
end
|
||||
|
||||
|
|
@ -0,0 +1,32 @@
|
|||
return {
|
||||
|
||||
require('plugs.nvim-lspconfig'),
|
||||
require('plugs.nvimtree'), -- nvimtree
|
||||
require('plugs.theme'), -- theme
|
||||
require("plugs.edit-plugs"),
|
||||
require("plugs.dev"),
|
||||
|
||||
|
||||
-- leetcode刷题
|
||||
-- {
|
||||
-- "kawre/leetcode.nvim",
|
||||
-- build = ":TSUpdate html",
|
||||
-- dependencies = {
|
||||
-- "nvim-telescope/telescope.nvim",
|
||||
-- "nvim-lua/plenary.nvim", -- telescope 所需
|
||||
-- "MunifTanjim/nui.nvim",
|
||||
|
||||
-- -- 可选
|
||||
-- "nvim-treesitter/nvim-treesitter",
|
||||
-- "rcarriga/nvim-notify",
|
||||
-- "nvim-tree/nvim-web-devicons",
|
||||
-- },
|
||||
-- opts = {
|
||||
-- -- 配置放在这里
|
||||
-- cn = {
|
||||
-- enabled = true,
|
||||
-- },
|
||||
-- },
|
||||
-- },
|
||||
|
||||
}
|
||||
|
|
@ -0,0 +1,51 @@
|
|||
return {
|
||||
'lilydjwg/colorizer', -- 颜色识别
|
||||
-- "rest-nvim/rest.nvim",
|
||||
-- dependencies = { { "nvim-lua/plenary.nvim" } },
|
||||
-- config = function()
|
||||
-- require("rest-nvim").setup({
|
||||
-- --- Get the same options from Packer setup
|
||||
-- })
|
||||
-- end
|
||||
-- },
|
||||
-- {
|
||||
-- -- go开发
|
||||
-- "ray-x/go.nvim",
|
||||
-- dependencies = { -- optional packages
|
||||
-- "ray-x/guihua.lua",
|
||||
-- "neovim/nvim-lspconfig",
|
||||
-- "nvim-treesitter/nvim-treesitter",
|
||||
-- },
|
||||
-- config = function()
|
||||
-- require("go").setup()
|
||||
-- end,
|
||||
-- -- event = { "CmdlineEnter" },
|
||||
-- ft = { "go", 'gomod' },
|
||||
-- build = ':lua require("go.install").update_all_sync()' -- if you need to install/update all binaries
|
||||
-- },
|
||||
{
|
||||
-- conda 环境
|
||||
"kmontocam/nvim-conda",
|
||||
dependencies = { "nvim-lua/plenary.nvim" },
|
||||
},
|
||||
{
|
||||
-- 终端
|
||||
'akinsho/toggleterm.nvim',
|
||||
version = "*",
|
||||
config = function()
|
||||
require("toggleterm").setup {
|
||||
-- size can be a number or function which is passed the current terminal
|
||||
size = 10,
|
||||
open_mapping = [[<c-t>]],
|
||||
hide_numbers = true, -- hide the number column in toggleterm buffers
|
||||
shade_filetypes = {},
|
||||
shade_terminals = true,
|
||||
shading_factor = 1, -- the degree by which to darken to terminal colour, default: 1 for dark backgrounds, 3 for light
|
||||
start_in_insert = true,
|
||||
insert_mappings = true, -- whether or not the open mapping applies in insert mode
|
||||
persist_size = true,
|
||||
direction = 'horizontal',
|
||||
}
|
||||
end
|
||||
}
|
||||
}
|
||||
|
|
@ -0,0 +1,109 @@
|
|||
return {
|
||||
'vijaymarupudi/nvim-fzf', -- fzf
|
||||
{
|
||||
-- surround 和 wildfire 配合有神奇的效果
|
||||
'tpope/vim-surround',
|
||||
'gcmt/wildfire.vim',
|
||||
|
||||
-- 括号箭头
|
||||
'yaocccc/nvim-hlchunk',
|
||||
|
||||
},
|
||||
-- 多光标
|
||||
{
|
||||
'terryma/vim-multiple-cursors',
|
||||
},
|
||||
{
|
||||
--格式整理
|
||||
{
|
||||
'junegunn/vim-easy-align',
|
||||
config = function()
|
||||
G.map({
|
||||
{ "v", "ga", ":EasyAlign<CR>", { noremap = true } },
|
||||
{ "v", "=", ":EasyAlign<CR>", { noremap = true } },
|
||||
})
|
||||
end
|
||||
},
|
||||
},
|
||||
{
|
||||
-- 注释插件
|
||||
{
|
||||
'tpope/vim-commentary',
|
||||
}
|
||||
},
|
||||
{
|
||||
'github/copilot.vim', -- github copilot
|
||||
},
|
||||
{
|
||||
'windwp/nvim-autopairs',
|
||||
event = "InsertEnter",
|
||||
opts = {}, -- this is equalent to setup({}) function
|
||||
config = function()
|
||||
require('nvim-autopairs').setup({
|
||||
disable_filetype = { "vim" },
|
||||
})
|
||||
end
|
||||
},
|
||||
-- {
|
||||
-- 'kevinhwang91/nvim-ufo',
|
||||
-- dependencies = {
|
||||
-- 'kevinhwang91/promise-async'
|
||||
-- },
|
||||
-- config = function ()
|
||||
-- require("ufo").setup()
|
||||
-- end
|
||||
|
||||
-- },
|
||||
{
|
||||
-- hop
|
||||
"phaazon/hop.nvim",
|
||||
branch = "v2",
|
||||
keys = {
|
||||
"f", "F", "t", "T",
|
||||
"<c-f>"
|
||||
},
|
||||
lazy = true,
|
||||
config = function()
|
||||
require("hop").setup { keys = 'asdfghjkl;' }
|
||||
local hop = require('hop')
|
||||
local directions = require('hop.hint').HintDirection
|
||||
G.map({
|
||||
{ "n", "<c-f>", ":HopChar2MW<CR>", { noremap = true } },
|
||||
{ "n", "f",
|
||||
function()
|
||||
hop.hint_char1({
|
||||
direction = directions.AFTER_CURSOR,
|
||||
current_line_only = true
|
||||
})
|
||||
end, { noremap = true }
|
||||
},
|
||||
{ "n", "F",
|
||||
function()
|
||||
hop.hint_char1({
|
||||
direction = directions.BEFORE_CURSOR,
|
||||
current_line_only = true
|
||||
})
|
||||
end, { noremap = true }
|
||||
},
|
||||
{ "n", "t",
|
||||
function()
|
||||
hop.hint_char1({
|
||||
direction = directions.AFTER_CURSOR,
|
||||
current_line_only = true,
|
||||
hint_offset = -1
|
||||
})
|
||||
end, { noremap = true }
|
||||
},
|
||||
{ "n", "T",
|
||||
function()
|
||||
hop.hint_char1({
|
||||
direction = directions.BEFORE_CURSOR,
|
||||
current_line_only = true,
|
||||
hint_offset = -1
|
||||
})
|
||||
end, { noremap = true }
|
||||
},
|
||||
})
|
||||
end
|
||||
}
|
||||
}
|
||||
|
|
@ -0,0 +1,352 @@
|
|||
return {
|
||||
{
|
||||
-- lsp的config
|
||||
"neovim/nvim-lspconfig",
|
||||
dependencies = {
|
||||
"folke/neodev.nvim",
|
||||
},
|
||||
config = function()
|
||||
require 'neodev'.setup {}
|
||||
|
||||
-- 定义需要启用的服务器列表及其对应的配置
|
||||
local servers = {
|
||||
lua_ls = require('lsp.lua'),
|
||||
clangd = require('lsp.c'),
|
||||
bashls = require('lsp.bash'),
|
||||
basedpyright = require('lsp.basedpyright'),
|
||||
yamlls = require('lsp.yaml'),
|
||||
gopls = require('lsp.go'),
|
||||
jsonls = require('lsp.json'),
|
||||
}
|
||||
|
||||
-- 使用 Neovim 0.11+ 的新 API 进行配置和启用
|
||||
for server, config in pairs(servers) do
|
||||
vim.lsp.config(server, config)
|
||||
vim.lsp.enable(server)
|
||||
end
|
||||
-- require 'lspconfig'.lua_ls.setup(require('lsp.lua'))
|
||||
-- require 'lspconfig'.clangd.setup(require('lsp.c'))
|
||||
-- require 'lspconfig'.bashls.setup(require('lsp.bash'))
|
||||
-- require 'lspconfig'.basedpyright.setup(require('lsp.basedpyright'))
|
||||
-- require 'lspconfig'.yamlls.setup(require('lsp.yaml'))
|
||||
-- require 'lspconfig'.gopls.setup(require('lsp.go'))
|
||||
-- require 'lspconfig'.jsonls.setup(require('lsp.json'))
|
||||
|
||||
|
||||
G.map({
|
||||
|
||||
{ 'n', '<leader>rn', '<cmd>lua vim.lsp.buf.rename()<CR>' },
|
||||
|
||||
{ 'n', 'gd', '<cmd>lua vim.lsp.buf.definition()<CR>' },
|
||||
{ 'n', 'gh', '<cmd>lua vim.lsp.buf.hover()<CR>' },
|
||||
{ 'n', 'gD', '<cmd>lua vim.lsp.buf.declaration()<CR>' },
|
||||
{ 'n', 'gi', '<cmd>lua vim.lsp.buf.implementation()<CR>' },
|
||||
{ 'n', 'gr', '<cmd>lua vim.lsp.buf.references()<CR>' },
|
||||
{ 'n', '<cs-i>', '<cmd>lua vim.lsp.buf.format()<CR>' },
|
||||
|
||||
})
|
||||
end
|
||||
|
||||
},
|
||||
{
|
||||
-- lsp
|
||||
"williamboman/mason-lspconfig.nvim",
|
||||
dependencies = {
|
||||
"williamboman/mason.nvim", -- lsp 下载器
|
||||
},
|
||||
config = function()
|
||||
require "mason".setup {
|
||||
ui = {
|
||||
icons = {
|
||||
package_installed = "✓",
|
||||
package_pending = "➜",
|
||||
package_uninstalled = "✗"
|
||||
}
|
||||
}
|
||||
}
|
||||
require("mason-lspconfig").setup({
|
||||
ensure_installed = {
|
||||
"bashls",
|
||||
-- "basedpyright",
|
||||
"lua_ls",
|
||||
"jsonls",
|
||||
"yamlls",
|
||||
}
|
||||
})
|
||||
end
|
||||
},
|
||||
{ --
|
||||
'simrat39/symbols-outline.nvim',
|
||||
config = function()
|
||||
local opts = {
|
||||
highlight_hovered_item = true,
|
||||
show_guides = true,
|
||||
auto_preview = false,
|
||||
position = 'right',
|
||||
relative_width = true,
|
||||
width = 25,
|
||||
auto_close = false,
|
||||
show_numbers = false,
|
||||
show_relative_numbers = false,
|
||||
show_symbol_details = true,
|
||||
preview_bg_highlight = 'Pmenu',
|
||||
autofold_depth = nil,
|
||||
auto_unfold_hover = true,
|
||||
fold_markers = { '', '' },
|
||||
wrap = false,
|
||||
keymaps = { -- These keymaps can be a string or a table for multiple keys
|
||||
close = { "<Esc>", "q" },
|
||||
goto_location = "<Cr>",
|
||||
focus_location = "h",
|
||||
hover_symbol = "<C-space>",
|
||||
toggle_preview = "K",
|
||||
rename_symbol = "r",
|
||||
code_actions = "a",
|
||||
fold = "o",
|
||||
unfold = "l",
|
||||
fold_all = "W",
|
||||
unfold_all = "E",
|
||||
fold_reset = "R",
|
||||
},
|
||||
lsp_blacklist = {},
|
||||
symbol_blacklist = {},
|
||||
symbols = {
|
||||
File = { icon = "", hl = "@text.uri" },
|
||||
Module = { icon = "", hl = "@namespace" },
|
||||
Namespace = { icon = "", hl = "@namespace" },
|
||||
Package = { icon = "", hl = "@namespace" },
|
||||
Class = { icon = "𝓒", hl = "@type" },
|
||||
Method = { icon = "ƒ", hl = "@method" },
|
||||
Property = { icon = "", hl = "@method" },
|
||||
Field = { icon = "", hl = "@field" },
|
||||
Constructor = { icon = "", hl = "@constructor" },
|
||||
Enum = { icon = "ℰ", hl = "@type" },
|
||||
Interface = { icon = "ﰮ", hl = "@type" },
|
||||
Function = { icon = "", hl = "@function" },
|
||||
Variable = { icon = "", hl = "@constant" },
|
||||
Constant = { icon = "", hl = "@constant" },
|
||||
String = { icon = "𝓐", hl = "@string" },
|
||||
Number = { icon = "#", hl = "@number" },
|
||||
Boolean = { icon = "⊨", hl = "@boolean" },
|
||||
Array = { icon = "", hl = "@constant" },
|
||||
Object = { icon = "⦿", hl = "@type" },
|
||||
Key = { icon = "🔐", hl = "@type" },
|
||||
Null = { icon = "NULL", hl = "@type" },
|
||||
EnumMember = { icon = "", hl = "@field" },
|
||||
Struct = { icon = "𝓢", hl = "@type" },
|
||||
Event = { icon = "🗲", hl = "@type" },
|
||||
Operator = { icon = "+", hl = "@operator" },
|
||||
TypeParameter = { icon = "𝙏", hl = "@parameter" },
|
||||
Component = { icon = "", hl = "@function" },
|
||||
Fragment = { icon = "", hl = "@constant" },
|
||||
},
|
||||
}
|
||||
require("symbols-outline").setup(opts)
|
||||
G.map({
|
||||
{ "n", "<cs-o>", "<cmd>SymbolsOutline<cr>", { noremap = true } },
|
||||
})
|
||||
end
|
||||
},
|
||||
{
|
||||
-- lsp补全
|
||||
{
|
||||
"hrsh7th/nvim-cmp",
|
||||
dependencies = {
|
||||
'hrsh7th/cmp-nvim-lsp', -- { name = 'nvim_lua' }
|
||||
'hrsh7th/cmp-buffer', -- { name = 'buffer' },
|
||||
'hrsh7th/cmp-path', -- { name = 'path' }
|
||||
'hrsh7th/cmp-cmdline', -- { name = 'cmdline' }
|
||||
{
|
||||
'hrsh7th/vim-vsnip',
|
||||
config = function()
|
||||
G.g.vsnip_snippet_dir = G.fn.stdpath("config") .. "/snippets"
|
||||
end
|
||||
},
|
||||
'hrsh7th/cmp-vsnip',
|
||||
'onsails/lspkind-nvim',
|
||||
},
|
||||
config = function()
|
||||
local has_words_before = function()
|
||||
unpack = unpack or table.unpack
|
||||
local line, col = unpack(G.api.nvim_win_get_cursor(0))
|
||||
return col ~= 0 and G.api.nvim_buf_get_lines(0, line - 1, line, true)[1]:sub(col, col):match('%s') == nil
|
||||
end
|
||||
|
||||
local feedkey = function(key, mode)
|
||||
vim.api.nvim_feedkeys(vim.api.nvim_replace_termcodes(key, true, true, true), mode, true)
|
||||
end
|
||||
local cmp = require('cmp')
|
||||
local cmp_opt = {
|
||||
snippet = {
|
||||
expand = function(args)
|
||||
vim.fn["vsnip#anonymous"](args.body) -- For `vsnip` users.
|
||||
end,
|
||||
},
|
||||
sources = cmp.config.sources({
|
||||
{ name = 'nvim_lsp' },
|
||||
{ name = 'vsnip' },
|
||||
},
|
||||
{ name = 'buffer' },
|
||||
{ name = 'path' }
|
||||
),
|
||||
mapping = cmp.mapping.preset.insert({
|
||||
|
||||
["<CR>"] = cmp.mapping({
|
||||
i = cmp.mapping.confirm({ behavior = cmp.ConfirmBehavior.Replace, select = true }),
|
||||
c = function(fallback)
|
||||
if cmp.visible() then
|
||||
cmp.confirm({ behavior = cmp.ConfirmBehavior.Replace, select = true })
|
||||
else
|
||||
fallback()
|
||||
end
|
||||
end,
|
||||
}),
|
||||
|
||||
["<Tab>"] = cmp.mapping(function(fallback)
|
||||
if cmp.visible() then
|
||||
cmp.select_next_item()
|
||||
elseif vim.fn["vsnip#available"](1) == 1 then
|
||||
feedkey("<Plug>(vsnip-expand-or-jump)", "")
|
||||
elseif has_words_before() then
|
||||
cmp.complete()
|
||||
else
|
||||
fallback()
|
||||
end
|
||||
end, { "i", "s" }),
|
||||
|
||||
["<S-Tab>"] = cmp.mapping(function()
|
||||
if cmp.visible() then
|
||||
cmp.select_prev_item()
|
||||
elseif vim.fn["vsnip#jumpable"](-1) == 1 then
|
||||
feedkey("<Plug>(vsnip-jump-prev)", "")
|
||||
end
|
||||
end, { "i", "s" }),
|
||||
|
||||
}),
|
||||
|
||||
window = {
|
||||
completion = {
|
||||
winhighlight = "Normal:Pmenu,FloatBorder:Pmenu,Search:None",
|
||||
col_offset = -3,
|
||||
side_padding = 0,
|
||||
border = "rounded",
|
||||
scrollbar = false,
|
||||
},
|
||||
documentation = {
|
||||
winhighlight = "Normal:Pmenu,FloatBorder:Pmenu,Search:None",
|
||||
border = "rounded",
|
||||
scrollbar = false,
|
||||
},
|
||||
},
|
||||
|
||||
formatting = {
|
||||
fields = { "kind", "abbr", "menu" },
|
||||
format = function(entry, vim_item)
|
||||
local kind = require("lspkind").cmp_format({ mode = "symbol_text", maxwidth = 50, })(entry, vim_item)
|
||||
local strings = vim.split(kind.kind, "%s", { trimempty = true })
|
||||
kind.kind = " " .. (strings[1] or "") .. " "
|
||||
kind.menu = " (" .. (strings[2] or "") .. ")"
|
||||
return kind
|
||||
end,
|
||||
},
|
||||
}
|
||||
require('cmp').setup(cmp_opt)
|
||||
end,
|
||||
},
|
||||
|
||||
{
|
||||
-- 语法高亮 --
|
||||
'nvim-treesitter/nvim-treesitter',
|
||||
config = function()
|
||||
local treesitter_opt = {
|
||||
ensure_installed = {
|
||||
-- "c",
|
||||
-- "cpp",
|
||||
-- "python",
|
||||
-- "java",
|
||||
-- "lua",
|
||||
-- "bash",
|
||||
-- "vimdoc",
|
||||
},
|
||||
indent = { enable = true },
|
||||
ignore_install = {
|
||||
"txt",
|
||||
"go"
|
||||
},
|
||||
sync_install = false,
|
||||
auto_install = true,
|
||||
highlight = {
|
||||
enable = true,
|
||||
disable = function(_, buf)
|
||||
local max_filesize = 100 * 1024 -- 100 KB
|
||||
local ok, stats = pcall(G.loop.fs_stat, G.api.nvim_buf_get_name(buf))
|
||||
if ok and stats and stats.size > max_filesize then
|
||||
return true
|
||||
end
|
||||
end,
|
||||
additional_vim_regex_highlighting = false,
|
||||
},
|
||||
parsers = {
|
||||
html = {
|
||||
install_info = {
|
||||
url = "https://github.com/ikatyang/tree-sitter-vue",
|
||||
files = { "src/parser.c" },
|
||||
branch = "main"
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
require 'nvim-treesitter'.setup(treesitter_opt)
|
||||
require 'nvim-treesitter.install'.prefer_git = true
|
||||
if G.use_ssh then
|
||||
local parsers = require 'nvim-treesitter.parsers'.get_parser_configs()
|
||||
for _, p in pairs(parsers) do
|
||||
p.install_info.url = p.install_info.url:gsub("https://github/com/", "git@github.com:")
|
||||
end
|
||||
end
|
||||
end
|
||||
},
|
||||
},
|
||||
{
|
||||
'fgheng/winbar.nvim',
|
||||
config = function()
|
||||
require('winbar').setup({
|
||||
enabled = true, -- 是否启动winbar
|
||||
|
||||
-- show_file_path = true, -- 是否显示文件路径
|
||||
show_symbols = true, -- 是否显示函数标签
|
||||
|
||||
-- 颜色配置，为空，将使用默认配色
|
||||
colors = {
|
||||
path = '#aaffff', -- 路径的颜色，比如#ababab
|
||||
file_name = '#bbbbff', -- 文件名称的颜色，比如#acacac
|
||||
symbols = '#aaffaa', -- 函数颜色
|
||||
},
|
||||
|
||||
-- 图标配置
|
||||
icons = {
|
||||
seperator = '>', -- 路径分割符号
|
||||
editor_state = '●',
|
||||
lock_icon = '',
|
||||
},
|
||||
|
||||
-- 关闭winbar的窗口
|
||||
exclude_filetype = {
|
||||
'help',
|
||||
'startify',
|
||||
'dashboard',
|
||||
'packer',
|
||||
'neogitstatus',
|
||||
'NvimTree',
|
||||
'Trouble',
|
||||
'alpha',
|
||||
'lir',
|
||||
'Outline',
|
||||
'spectre_panel',
|
||||
'toggleterm',
|
||||
'qf',
|
||||
}
|
||||
})
|
||||
end
|
||||
},
|
||||
}
|
||||
|
|
@ -0,0 +1,40 @@
|
|||
return {
|
||||
"nvim-tree/nvim-tree.lua",
|
||||
dependencies = {
|
||||
'kyazdani42/nvim-web-devicons'
|
||||
},
|
||||
keys = {
|
||||
"<c-e>"
|
||||
},
|
||||
config = function()
|
||||
require 'nvim-web-devicons'.setup {}
|
||||
|
||||
require 'nvim-tree'.setup {
|
||||
sort_by = "case_sensitive",
|
||||
view = {
|
||||
width = 30,
|
||||
},
|
||||
filters = { dotfiles = true, },
|
||||
git = { enable = true },
|
||||
on_attach = function(bufnr)
|
||||
local api = require 'nvim-tree.api'
|
||||
api.config.mappings.default_on_attach(bufnr)
|
||||
|
||||
-- override a default
|
||||
G.map({
|
||||
{ 'n', 'v', api.node.open.vertical, { buffer = bufnr } },
|
||||
{ 'n', 's', api.node.open.horizontal, { buffer = bufnr } },
|
||||
})
|
||||
|
||||
G.delmap({
|
||||
{ 'n', '<C-e>', { buffer = bufnr } },
|
||||
{ 'n', '<C-v>', { buffer = bufnr } },
|
||||
{ 'n', '<C-x>', { buffer = bufnr } },
|
||||
})
|
||||
end
|
||||
}
|
||||
G.map({
|
||||
{ "n", "<C-e>", ":NvimTreeToggle<CR>", { noremap = true } },
|
||||
})
|
||||
end
|
||||
}
|
||||
|
|
@ -0,0 +1,82 @@
|
|||
return {
|
||||
{
|
||||
'folke/tokyonight.nvim',
|
||||
config = function()
|
||||
G.cmd("colorscheme tokyonight") -- 主题
|
||||
G.opt.background = 'dark' -- 背景
|
||||
end
|
||||
},
|
||||
{
|
||||
-- line插件
|
||||
'kdheepak/tabline.nvim',
|
||||
'nvim-lualine/lualine.nvim',
|
||||
config = function()
|
||||
require('lualine').setup {
|
||||
options = {
|
||||
icons_enabled = true,
|
||||
theme = 'auto',
|
||||
component_separators = { left = '', right = '' },
|
||||
section_separators = { left = '', right = '' },
|
||||
disabled_filetypes = {
|
||||
statusline = {},
|
||||
winbar = {},
|
||||
},
|
||||
ignore_focus = {},
|
||||
always_divide_middle = true,
|
||||
globalstatus = false,
|
||||
refresh = {
|
||||
statusline = 1000,
|
||||
tabline = 1000,
|
||||
winbar = 1000,
|
||||
}
|
||||
},
|
||||
sections = {
|
||||
lualine_a = { 'mode' },
|
||||
lualine_b = { 'branch', 'diff', 'diagnostics' },
|
||||
lualine_c = { {
|
||||
'filename',
|
||||
file_status = false,
|
||||
path = 1
|
||||
} },
|
||||
lualine_x = { 'encoding', 'fileformat', 'filetype' },
|
||||
lualine_y = { 'progress' },
|
||||
lualine_z = {}
|
||||
},
|
||||
inactive_sections = {
|
||||
lualine_a = {},
|
||||
lualine_b = {},
|
||||
lualine_c = { 'filename' },
|
||||
lualine_x = { 'location' },
|
||||
lualine_y = {},
|
||||
lualine_z = {}
|
||||
},
|
||||
tabline = {},
|
||||
winbar = {},
|
||||
inactive_winbar = {},
|
||||
extensions = {}
|
||||
}
|
||||
require('tabline').setup {
|
||||
-- Defaults configuration options
|
||||
enable = true,
|
||||
options = {
|
||||
-- If lualine is installed tabline will use separators configured in lualine by default.
|
||||
-- These options can be used to override those settings.
|
||||
section_separators = { ' ', ' ' },
|
||||
component_separators = { '', '' },
|
||||
max_bufferline_percent = 66, -- set to nil by default, and it uses vim.o.columns * 2/3
|
||||
show_tabs_always = false, -- this shows tabs only when there are more than one tab or if the first tab is named
|
||||
show_devicons = true, -- this shows devicons in buffer section
|
||||
show_bufnr = false, -- this appends [bufnr] to buffer section,
|
||||
show_filename_only = true, -- shows base filename only instead of relative path in filename
|
||||
modified_icon = "+ ", -- change the default modified icon
|
||||
modified_italic = false, -- set to true by default; this determines whether the filename turns italic if modified
|
||||
show_tabs_only = false, -- this shows only tabs instead of tabs + buffers
|
||||
},
|
||||
G.cmd [[
|
||||
set guioptions-=e " Use showtabline in gui vim
|
||||
set sessionoptions+=tabpages,globals " store tabpages and globals in session
|
||||
]]
|
||||
}
|
||||
end,
|
||||
},
|
||||
}
|
||||
|
|
@ -0,0 +1,4 @@
|
|||
require 'lazy'.setup({
|
||||
require('plugs.edit-plugs'),
|
||||
-- 'vijaymarupudi/nvim-fzf', -- fzf
|
||||
})
|
||||
175
main.py
175
main.py
|
|
@ -1,175 +0,0 @@
|
|||
import datetime
|
||||
from pathlib import Path
|
||||
|
||||
from Qtorch.Models.Qmlp import Qmlp
|
||||
from Qfunctions.loadData import load_data
|
||||
from Qfunctions.saveToXlsx import save_to_xlsx as save_to_xlsx
|
||||
|
||||
|
||||
def _to_builtin(value):
|
||||
if isinstance(value, dict):
|
||||
return {str(k): _to_builtin(v) for k, v in value.items()}
|
||||
if isinstance(value, (list, tuple)):
|
||||
return [_to_builtin(v) for v in value]
|
||||
if hasattr(value, 'item'):
|
||||
try:
|
||||
return value.item()
|
||||
except Exception:
|
||||
return str(value)
|
||||
return value
|
||||
|
||||
|
||||
def _yaml_scalar(value):
|
||||
if value is None:
|
||||
return 'null'
|
||||
if isinstance(value, bool):
|
||||
return 'true' if value else 'false'
|
||||
if isinstance(value, (int, float)):
|
||||
return str(value)
|
||||
text = str(value).replace('"', '\\"')
|
||||
return f'"{text}"'
|
||||
|
||||
|
||||
def _yaml_lines(value, indent=0):
|
||||
space = ' ' * indent
|
||||
|
||||
if isinstance(value, dict):
|
||||
if not value:
|
||||
return [space + '{}']
|
||||
lines = []
|
||||
for k, v in value.items():
|
||||
key = str(k)
|
||||
if isinstance(v, (dict, list, tuple)):
|
||||
lines.append(f'{space}{key}:')
|
||||
lines.extend(_yaml_lines(v, indent + 2))
|
||||
else:
|
||||
lines.append(f'{space}{key}: {_yaml_scalar(v)}')
|
||||
return lines
|
||||
|
||||
if isinstance(value, (list, tuple)):
|
||||
if not value:
|
||||
return [space + '[]']
|
||||
lines = []
|
||||
for item in value:
|
||||
if isinstance(item, (dict, list, tuple)):
|
||||
lines.append(f'{space}-')
|
||||
lines.extend(_yaml_lines(item, indent + 2))
|
||||
else:
|
||||
lines.append(f'{space}- {_yaml_scalar(item)}')
|
||||
return lines
|
||||
|
||||
return [space + _yaml_scalar(value)]
|
||||
|
||||
|
||||
def _save_yaml(file_path, data):
|
||||
built_data = _to_builtin(data)
|
||||
text = '\n'.join(_yaml_lines(built_data)) + '\n'
|
||||
with open(file_path, 'w', encoding='utf-8') as f:
|
||||
f.write(text)
|
||||
|
||||
def main():
|
||||
# 输入元数据文件夹名称
|
||||
projet_name = '20260512 Graps'
|
||||
# 请在[]内输入每一个分类的名称
|
||||
|
||||
label_names = ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I'] # label_names是大写的A-I
|
||||
hidden_layers = [256, 256]
|
||||
test_size = 0.5
|
||||
dropout_rate = 0
|
||||
epochs = 300
|
||||
|
||||
run_id = datetime.datetime.now().strftime('%Y%m%d_%H%M%S')
|
||||
run_project_name = f'{projet_name}/{run_id}'
|
||||
run_dir = Path('Result') / projet_name / run_id
|
||||
run_dir.mkdir(parents=True, exist_ok=True)
|
||||
|
||||
print(label_names)
|
||||
data = load_data(projet_name, label_names)
|
||||
|
||||
model = Qmlp(
|
||||
data=data,
|
||||
labels=label_names,
|
||||
hidden_layers=hidden_layers,
|
||||
test_size=test_size,
|
||||
dropout_rate=dropout_rate
|
||||
)
|
||||
# model = QCNN(
|
||||
# data=data,
|
||||
# labels=label_names,
|
||||
# conv_channels=(16, 32),
|
||||
# kernel_size=3,
|
||||
# hidden_size=128,
|
||||
# test_size=0.3,
|
||||
# dropout_rate=0
|
||||
# )
|
||||
|
||||
pca_2d, pca_3d = model.get_PCA()
|
||||
|
||||
model.fit(epochs)
|
||||
|
||||
cm = model.get_cm()
|
||||
cmn = model.get_cmn()
|
||||
epoch_data = model.get_epoch_data()
|
||||
|
||||
save_to_xlsx(project_name=run_project_name, file_name='pca_2d', data=pca_2d)
|
||||
save_to_xlsx(project_name=run_project_name, file_name='pca_3d', data=pca_3d)
|
||||
save_to_xlsx(project_name=run_project_name, file_name='cm', data=cm)
|
||||
save_to_xlsx(project_name=run_project_name, file_name='cmn', data=cmn)
|
||||
save_to_xlsx(project_name=run_project_name, file_name='acc_and_loss', data=epoch_data)
|
||||
|
||||
run_params = {
|
||||
'run_id': run_id,
|
||||
'project_name': projet_name,
|
||||
'label_names': label_names,
|
||||
'model': {
|
||||
'type': 'Qmlp',
|
||||
'hidden_layers': hidden_layers,
|
||||
'dropout_rate': dropout_rate,
|
||||
},
|
||||
'train': {
|
||||
'epochs': epochs,
|
||||
'test_size': test_size,
|
||||
},
|
||||
'created_at': datetime.datetime.now().isoformat(timespec='seconds'),
|
||||
}
|
||||
|
||||
if not epoch_data.empty:
|
||||
best_idx = int(epoch_data['test_accuracy'].idxmax())
|
||||
best_row = epoch_data.loc[best_idx].to_dict()
|
||||
last_row = epoch_data.iloc[-1].to_dict()
|
||||
run_result = {
|
||||
'run_id': run_id,
|
||||
'result_dir': str(run_dir),
|
||||
'best_epoch': int(best_row.get('epoch', 0)),
|
||||
'best_metrics': best_row,
|
||||
'last_epoch_metrics': last_row,
|
||||
'generated_files': [
|
||||
'pca_2d.xlsx',
|
||||
'pca_2d.png',
|
||||
'pca_3d.xlsx',
|
||||
'pca_3d.png',
|
||||
'cm.xlsx',
|
||||
'cm.png',
|
||||
'cmn.xlsx',
|
||||
'cmn.png',
|
||||
'acc_and_loss.xlsx',
|
||||
'acc_and_loss_epoch.png',
|
||||
'acc_and_loss_last_epoch_bar.png',
|
||||
],
|
||||
'created_at': datetime.datetime.now().isoformat(timespec='seconds'),
|
||||
}
|
||||
else:
|
||||
run_result = {
|
||||
'run_id': run_id,
|
||||
'result_dir': str(run_dir),
|
||||
'message': 'No epoch data generated.',
|
||||
'created_at': datetime.datetime.now().isoformat(timespec='seconds'),
|
||||
}
|
||||
|
||||
_save_yaml(run_dir / 'run_params.yaml', run_params)
|
||||
_save_yaml(run_dir / 'run_result.yaml', run_result)
|
||||
|
||||
print(f'Done. Result saved in: {run_dir}')
|
||||
|
||||
if __name__ == '__main__':
|
||||
main()
|
||||
|
|
@ -0,0 +1,26 @@
|
|||
{
|
||||
"for": {
|
||||
"prefix": "for",
|
||||
"body": [
|
||||
" for(int $1 = $2; $1 < $3; $1+=$4) {",
|
||||
" $5",
|
||||
" }"
|
||||
]
|
||||
},
|
||||
"for1": {
|
||||
"prefix": "for1",
|
||||
"body": [
|
||||
" for(int $1 = $2; $1 < $3; $1++) {",
|
||||
" $4",
|
||||
" }"
|
||||
]
|
||||
},
|
||||
"fori": {
|
||||
"prefix": "fori",
|
||||
"body": [
|
||||
" for(int i = $1; i < $2; i++) {",
|
||||
" $3",
|
||||
" }"
|
||||
]
|
||||
}
|
||||
}
|
||||
|
|
@ -0,0 +1,303 @@
|
|||
{
|
||||
"Class_Array_tree": {
|
||||
"prefix": "Class_Array_tree",
|
||||
"body": [
|
||||
"",
|
||||
"template <class T>",
|
||||
"class Array_tree {",
|
||||
" public:",
|
||||
" Array_tree() {}",
|
||||
" Array_tree(int n) { this->n = n, tree = vector<T>(n + 1); }",
|
||||
" void add(int id, T key) {",
|
||||
" for (int i = id; i <= n; i += lowbit(i)) tree[i] += key;",
|
||||
" }",
|
||||
"",
|
||||
" T get_sum(int id) {",
|
||||
" T sum = 0;",
|
||||
" for (int i = id; i; i -= lowbit(i)) sum += tree[i];",
|
||||
" return sum;",
|
||||
" }",
|
||||
"",
|
||||
" T get_sum(int l, int r) { return get_sum(r) - get_sum(l - 1); }",
|
||||
"",
|
||||
" private:",
|
||||
" int n;",
|
||||
" vector<T> tree;",
|
||||
" int lowbit(int x) { return x & -x; }",
|
||||
"};",
|
||||
""
|
||||
]
|
||||
},
|
||||
"Class_SGM_Tree": {
|
||||
"prefix": "Class_SGM_Tree",
|
||||
"body": [
|
||||
"",
|
||||
"class SGM_Tree {",
|
||||
" public:",
|
||||
" class point {",
|
||||
" public:",
|
||||
" ll sum, maxi, mini;",
|
||||
" };",
|
||||
"",
|
||||
" vll a, lazy;",
|
||||
" int n;",
|
||||
" ll sum, maxi, mini;",
|
||||
" vector<point> tree;",
|
||||
"",
|
||||
" SGM_Tree() {}",
|
||||
" SGM_Tree(int n, vi v) {",
|
||||
" // a下标默认从1开始,只需开n个点，不需要n + 1",
|
||||
" this->n = n;",
|
||||
" lazy = vll(n * 4 + 1);",
|
||||
" a.push_back(0);",
|
||||
" for (int i = 1; i <= n; i++) a.push_back(v[i]);",
|
||||
" tree = vector<point>(4 * n + 1), build(1, n, 1);",
|
||||
" }",
|
||||
" SGM_Tree(int n, vll v) {",
|
||||
" // a下标默认从1开始,只需开n个点，不需要n + 1",
|
||||
" this->n = n;",
|
||||
" lazy = vll(n * 4 + 1);",
|
||||
" a.push_back(0);",
|
||||
" for (int i = 1; i <= n; i++) a.push_back(v[i]);",
|
||||
" tree = vector<point>(4 * n + 1), build(1, n, 1);",
|
||||
" }",
|
||||
" SGM_Tree(int n, int* v) {",
|
||||
" // a下标默认从1开始,只需开n个点，不需要n + 1",
|
||||
" this->n = n;",
|
||||
" lazy = vll(n * 4 + 1);",
|
||||
" a.push_back(0);",
|
||||
" for (int i = 1; i <= n; i++) a.push_back(v[i]);",
|
||||
" tree = vector<point>(4 * n + 1), build(1, n, 1);",
|
||||
" }",
|
||||
"",
|
||||
" void push_up(int k) {",
|
||||
" int l = k * 2, r = k * 2 + 1;",
|
||||
" tree[k].sum = tree[l].sum + tree[r].sum;",
|
||||
" tree[k].maxi = max(tree[l].maxi, tree[r].maxi);",
|
||||
" tree[k].mini = min(tree[l].mini, tree[r].mini);",
|
||||
" }",
|
||||
"",
|
||||
" void push_down(int l, int r, int k) {",
|
||||
" if (lazy[k]) {",
|
||||
" int mid = l + r >> 1;",
|
||||
" lazy[k * 2] += lazy[k];",
|
||||
" lazy[k * 2 + 1] += lazy[k];",
|
||||
" tree[k * 2].sum += lazy[k] * (mid - l + 1);",
|
||||
" tree[k * 2 + 1].sum += lazy[k] * (r - mid);",
|
||||
" tree[k * 2].maxi += lazy[k];",
|
||||
" tree[k * 2 + 1].maxi += lazy[k];",
|
||||
" tree[k * 2].mini += lazy[k];",
|
||||
" tree[k * 2 + 1].mini += lazy[k];",
|
||||
" lazy[k] = 0;",
|
||||
" }",
|
||||
" }",
|
||||
"",
|
||||
" void get_updata(int l, int r, int k, ll value) {",
|
||||
" tree[k].sum += value * (r - l + 1);",
|
||||
" tree[k].maxi += value;",
|
||||
" tree[k].mini += value;",
|
||||
" lazy[k] += value;",
|
||||
" }",
|
||||
"",
|
||||
" void get(int k) {",
|
||||
" sum += tree[k].sum;",
|
||||
" maxi = max(maxi, tree[k].maxi);",
|
||||
" mini = min(mini, tree[k].mini);",
|
||||
" }",
|
||||
"",
|
||||
" void build(int l, int r, int k) {",
|
||||
" if (l == r) {",
|
||||
" tree[k].maxi = tree[k].mini = tree[k].sum = a[l];",
|
||||
" return;",
|
||||
" }",
|
||||
" int mid = l + r >> 1;",
|
||||
" build(l, mid, k * 2);",
|
||||
" build(mid + 1, r, k * 2 + 1);",
|
||||
" push_up(k);",
|
||||
" }",
|
||||
"",
|
||||
" void updata(int l, int r, int L, int R, int k, ll value) {",
|
||||
" if (L <= l && r <= R) {",
|
||||
" get_updata(l, r, k, value);",
|
||||
" return;",
|
||||
" }",
|
||||
" push_down(l, r, k);",
|
||||
" int mid = l + r >> 1;",
|
||||
" if (L <= mid) updata(l, mid, L, R, k * 2, value);",
|
||||
" if (R > mid) updata(mid + 1, r, L, R, k * 2 + 1, value);",
|
||||
" push_up(k);",
|
||||
" }",
|
||||
"",
|
||||
" void query(int l, int r, int L, int R, int k) {",
|
||||
" if (L <= l && r <= R) {",
|
||||
" get(k);",
|
||||
" return;",
|
||||
" }",
|
||||
" push_down(l, r, k);",
|
||||
" int mid = l + r >> 1;",
|
||||
" if (mid >= L) query(l, mid, L, R, 2 * k);",
|
||||
" if (mid < R) query(mid + 1, r, L, R, 2 * k + 1);",
|
||||
" }",
|
||||
"",
|
||||
" ll get_sum(int L, int R) {",
|
||||
" sum = 0;",
|
||||
" query(1, n, L, R, 1);",
|
||||
" return sum;",
|
||||
" }",
|
||||
"",
|
||||
" ll get_max(int L, int R) {",
|
||||
" maxi = -inf;",
|
||||
" query(1, n, L, R, 1);",
|
||||
" return maxi;",
|
||||
" }",
|
||||
"",
|
||||
" ll get_min(int L, int R) {",
|
||||
" mini = inf;",
|
||||
" query(1, n, L, R, 1);",
|
||||
" return mini;",
|
||||
" }",
|
||||
"};",
|
||||
""
|
||||
]
|
||||
},
|
||||
"Class_Dsu": {
|
||||
"prefix": "Class_Dsu",
|
||||
"body": [
|
||||
"",
|
||||
"class Dsu {",
|
||||
" public:",
|
||||
"",
|
||||
" vll fa, num;",
|
||||
"",
|
||||
" Dsu(int n) { fa = vll(n + 1), num = vll(n + 1); }",
|
||||
" int find(int x) {",
|
||||
" if (!fa[x]) return x;",
|
||||
" return fa[x] = find(fa[x]);",
|
||||
" }",
|
||||
"",
|
||||
" bool Dunion(int p, int q) {",
|
||||
" int v = find(p), u = find(q);",
|
||||
" if (v == u) return 0;",
|
||||
" fa[u] = v;",
|
||||
" num[v] += num[u];",
|
||||
" num[u] = num[v];",
|
||||
" return 1;",
|
||||
" }",
|
||||
" ",
|
||||
"};",
|
||||
"",
|
||||
"ll num(int x) { return num[find(x)]; }",
|
||||
""
|
||||
]
|
||||
},
|
||||
"class_Stmap": {
|
||||
"prefix": "Class_StMap",
|
||||
"body": [
|
||||
"",
|
||||
"class st_map {",
|
||||
" public:",
|
||||
" st_map() {}",
|
||||
" st_map(vll v) {",
|
||||
" this->n = v.size(), this->a = v;",
|
||||
" this->st = vector<array<ll, 31>>(n + 1);",
|
||||
" st_init();",
|
||||
" }",
|
||||
" int query(int l, int r) {",
|
||||
" int len = r - l + 1;",
|
||||
" int k = log(len) / log(2);",
|
||||
" return max(st[l][k], st[r - (1 << k) + 1][k]);",
|
||||
" }",
|
||||
"",
|
||||
" private:",
|
||||
" int n;",
|
||||
" vll a;",
|
||||
" vector<array<ll, 31>> st;",
|
||||
" void st_init() {",
|
||||
" for (int j = 0; j <= 17; j++) {",
|
||||
" for (int i = 1; i + (1 << j) - 1 <= n; i++) {",
|
||||
" if (j == 0)",
|
||||
" st[i][j] = a[i];",
|
||||
" else",
|
||||
" st[i][j] = max(st[i][j - 1], st[i + (1 << j - 1)][j - 1]);",
|
||||
" }",
|
||||
" }",
|
||||
" }",
|
||||
"};",
|
||||
""
|
||||
]
|
||||
},
|
||||
"Class_HJT_tree": {
|
||||
"prefix": "Class_HJT_tree",
|
||||
"body": [
|
||||
"",
|
||||
"template <class T>",
|
||||
"class HJT_tree {",
|
||||
" //处理数据默认下标从1开始",
|
||||
" public:",
|
||||
" //构造函数",
|
||||
" HJT_tree() {}",
|
||||
" HJT_tree(vector<T> v) {",
|
||||
" base = v, this->n = base.size() - 1;",
|
||||
" tree = vector<node>(n * 32), root.push_back(build(1, n));",
|
||||
" }",
|
||||
"",
|
||||
" void updata(int v, int x, T value) {",
|
||||
" //插入函数(版本，修改位置，修改值)",
|
||||
" root.push_back(insert(root[v], 1, n, x, value));",
|
||||
" }",
|
||||
"",
|
||||
" T query(int v, int x) {",
|
||||
" //查询函数（版本，查询位置）",
|
||||
" return get_se(root[v], 1, n, x, x);",
|
||||
" }",
|
||||
"",
|
||||
" T query(int v, int l, int r) {",
|
||||
" //查询函数（版本，查询区间）",
|
||||
" return get_se(root[v], 1, n, l, r);",
|
||||
" }",
|
||||
"",
|
||||
" private:",
|
||||
" vi root;",
|
||||
" vector<T> base;",
|
||||
" int n, idx = 0;",
|
||||
" struct node {",
|
||||
" int l, r;",
|
||||
" T data;",
|
||||
" };",
|
||||
" vector<node> tree;",
|
||||
" void pushup(int q) { tree[q].data = op(tree[q].l, tree[q].r); }",
|
||||
" T op(int l, int r) { return max(tree[l].data, tree[r].data); }",
|
||||
" T e() { return -inf; }",
|
||||
" int build(int l, int r) {",
|
||||
" int now = ++idx, mid = l + r >> 1;",
|
||||
" if (l != r)",
|
||||
" tree[now].l = build(l, mid), tree[now].r = build(mid + 1, r), pushup(now);",
|
||||
" return now;",
|
||||
" }",
|
||||
" int insert(int old, int l, int r, int x, int value) {",
|
||||
" int now = ++idx, mid = l + r >> 1;",
|
||||
" tree[now] = tree[old];",
|
||||
" if (l == r)",
|
||||
" tree[now].data = value;",
|
||||
" else {",
|
||||
" if (x <= mid)",
|
||||
" tree[now].l = insert(tree[old].l, l, mid, x, value);",
|
||||
" else",
|
||||
" tree[now].r = insert(tree[old].r, mid + 1, r, x, value);",
|
||||
" pushup(now);",
|
||||
" }",
|
||||
" return now;",
|
||||
" }",
|
||||
" T get_se(int v, int l, int r, int L, int R) {",
|
||||
" if (L <= l && r <= R) return tree[v].data;",
|
||||
" ll mid = l + r >> 1;",
|
||||
" T res = e();",
|
||||
" if (L <= mid) res = max(res, get_se(tree[v].l, l, mid, L, R));",
|
||||
" if (R > mid) res = max(res, get_se(tree[v].r, mid + 1, r, L, R));",
|
||||
" return res;",
|
||||
" }",
|
||||
"};",
|
||||
""
|
||||
]
|
||||
}
|
||||
}
|
||||
|
|
@ -0,0 +1,359 @@
|
|||
{
|
||||
"Graph": {
|
||||
"prefix": "Class_Graph",
|
||||
"body": [
|
||||
"",
|
||||
"template <class T>",
|
||||
"class Graph {",
|
||||
" public:",
|
||||
" struct edge {",
|
||||
" int next, to;",
|
||||
" T w;",
|
||||
" };",
|
||||
" int n;",
|
||||
" vector<vector<T>> maps;",
|
||||
" vector<T> dis;",
|
||||
" vector<edge> e;",
|
||||
" vi head, bj;",
|
||||
" Graph() {}",
|
||||
"",
|
||||
" Graph(int n) {",
|
||||
" this->n = n, this->m = n * (n - 1);",
|
||||
" head = vi(n + 1, -1), e = vector<edge>(m * 2 + 1);",
|
||||
" }",
|
||||
"",
|
||||
" Graph(int n, int m) {",
|
||||
" this->n = n, this->m = m, head = vi(n + 1, -1), e = vector<edge>(m * 2 + 1);",
|
||||
" }",
|
||||
"",
|
||||
" void add(int u, int v, T w) {",
|
||||
" e[cnt].to = v, e[cnt].next = head[u], e[cnt].w = w, head[u] = cnt++;",
|
||||
" }",
|
||||
"",
|
||||
" void add(int u, int v) {",
|
||||
" e[cnt].to = v, e[cnt].next = head[u], head[u] = cnt++;",
|
||||
" }",
|
||||
"",
|
||||
" private:",
|
||||
" int m, cnt = 0;",
|
||||
"};",
|
||||
"#define e g.e",
|
||||
"#define head g.head",
|
||||
"#define maps g.maps",
|
||||
"#define add g.add",
|
||||
""
|
||||
]
|
||||
},
|
||||
"Class_Graph_Dijkstra": {
|
||||
"prefix": "Class_Graph_Dijkstra",
|
||||
"body": [
|
||||
"",
|
||||
"#define dis g.dis",
|
||||
"#define bj g.bj",
|
||||
"ll dijkstra(Graph<ll> g, int st, int en) {",
|
||||
" dis = vll(g.n + 1, inf),",
|
||||
" bj = vi(g.n + 1);",
|
||||
" priority_queue<pll, vector<pll>, greater<pll>> q;",
|
||||
" dis[st] = 0;",
|
||||
" q.push({0, st});",
|
||||
" while (!q.empty()) {",
|
||||
" int u = q.top().y;",
|
||||
" q.pop();",
|
||||
" if (bj[u]) continue;",
|
||||
" bj[u] = 1;",
|
||||
" for (int i = head[u]; ~i; i = e[i].next) {",
|
||||
" int v = e[i].to;",
|
||||
" if (dis[v] > dis[u] + e[i].w) {",
|
||||
" dis[v] = dis[u] + e[i].w;",
|
||||
" q.push({dis[v], v});",
|
||||
" }",
|
||||
" }",
|
||||
" }",
|
||||
" if (dis[en] != inf)",
|
||||
" return dis[en];",
|
||||
" else",
|
||||
" return -1;",
|
||||
"}",
|
||||
""
|
||||
]
|
||||
},
|
||||
"Class_Graph_SPFA": {
|
||||
"prefix": "Class_Graph_SPFA",
|
||||
"body": [
|
||||
"",
|
||||
"ll spfa(Graph<ll> g, int st, int en) {",
|
||||
" vll bj(g.n + 1), dis(g.n + 1, inf), num(g.n + 1);",
|
||||
" queue<int> q;",
|
||||
" dis[st] = 0;",
|
||||
" bj[st] = 1;",
|
||||
" q.push(st);",
|
||||
" while (q.size()) {",
|
||||
" int u = q.front();",
|
||||
" if (!bj[u]) continue;",
|
||||
" if (num[u] > g.n + 1) return inf;//判断是否产生负环",
|
||||
" bj[u] = 0, num[u]++;",
|
||||
" for (int i = head[u]; ~i; i = e[i].next) {",
|
||||
" int w = e[i].w, v = e[i].to;",
|
||||
" if (dis[v] > dis[u] + w) {",
|
||||
" dis[v] = dis[u] + w;",
|
||||
" if (!bj[v]) q.push(v);",
|
||||
" }",
|
||||
" }",
|
||||
" }",
|
||||
" if(dis[en] == inf) return -1;",
|
||||
" return dis[en];",
|
||||
"}",
|
||||
""
|
||||
]
|
||||
},
|
||||
"Class_Graph_Kurskal": {
|
||||
"prefix": "Class_Graph_Kurskal",
|
||||
"body": [
|
||||
"",
|
||||
"ll kruskal(Graph<ll> g, Dsu d) {",
|
||||
" ll res = 0, cnt = 0;",
|
||||
" sort(e.begin(), e.end());",
|
||||
" for (auto i : e) {",
|
||||
" int u = i.u, v = i.v, w = i.w;",
|
||||
" int q = d.find(i.u), p = d.find(i.v);",
|
||||
" if (d.Dunion(q, p)) cnt++, res += w;",
|
||||
" if (cnt == n - 1) break;",
|
||||
" }",
|
||||
" return res;",
|
||||
"}",
|
||||
""
|
||||
]
|
||||
},
|
||||
"Class_Graph_Prim": {
|
||||
"prefix": "Class_Graph_Prim",
|
||||
"body": []
|
||||
},
|
||||
"Class_Graph_TreeDiam": {
|
||||
"prefix": "Class_Graph_TreeDiam",
|
||||
"description": "树的直径",
|
||||
"body": [
|
||||
"ll TreeDiam(Graph<ll> g) {",
|
||||
" vll dis1(n + 1), dis2(n + 1), p(n + 1), up(n + 1);",
|
||||
" function<ll(ll, ll)> dfs = [&](ll u, ll fa) {",
|
||||
" for (int i = head[u]; ~i; i = e[i].next) {",
|
||||
" int v = e[i].to, w = 1;",
|
||||
" if (v == fa) continue;",
|
||||
" ll x = dfs(v, u) + 1;",
|
||||
" if (x >= dis1[u])",
|
||||
" dis2[u] = dis1[u], dis1[u] = x, p[u] = v;",
|
||||
" else if (x >= dis2[u])",
|
||||
" dis2[u] = x;",
|
||||
" }",
|
||||
" return dis1[u];",
|
||||
" };",
|
||||
" function<void(ll, ll)> dfs0 = [&](ll u, ll fa) {",
|
||||
" for (int i = head[u]; ~i; i = e[i].next) {",
|
||||
" int v = e[i].to, w = 1;",
|
||||
" if (v == fa) continue;",
|
||||
" if (p[u] == v)",
|
||||
" up[v] = max(dis2[u], up[u]) + w;",
|
||||
" else",
|
||||
" up[v] = max(dis1[u], up[u]) + w;",
|
||||
" dfs0(v, u);",
|
||||
" }",
|
||||
" };",
|
||||
" dfs(1, -1), dfs0(1, -1);",
|
||||
" ll ans = -1;",
|
||||
" for (int i = 1; i <= n + m; i++) {",
|
||||
" if (dis1[i] == dis2[i] && dis1[i] == 0)",
|
||||
" ans = max(ans, up[i]);",
|
||||
" else",
|
||||
" ans = max(ans, max(up[i], dis1[i]));",
|
||||
" }",
|
||||
" return ans;",
|
||||
"}"
|
||||
]
|
||||
},
|
||||
"Class_Graph_SCC": {
|
||||
"prefix": "Class_Graph_SCC",
|
||||
"body": [
|
||||
"",
|
||||
"class SCC {",
|
||||
" public:",
|
||||
" stack<ll> stk;",
|
||||
" int timestamp = 0, scc_cnt = 0, n, m;",
|
||||
" vll dfn, low, in_stk, Size, id;",
|
||||
" Graph<ll> g;",
|
||||
"",
|
||||
" SCC() {}",
|
||||
"",
|
||||
" SCC(Graph<ll> g) {",
|
||||
" this->n = g.n, this->m = g.m, this->g = g;",
|
||||
" dfn = vll(n + 1, 0);",
|
||||
" low = vll(n + 1, 0);",
|
||||
" in_stk = vll(n + 1, 0);",
|
||||
" Size = vll(n + 1, 0);",
|
||||
" id = vll(n + 1, 0);",
|
||||
" for (int i = 1; i <= n; i++)",
|
||||
" if (!dfn[i]) tarjan(i);",
|
||||
" }",
|
||||
"",
|
||||
" void tarjan(int u) {",
|
||||
" dfn[u] = low[u] = ++timestamp;",
|
||||
" stk.push(u), in_stk[u] = 1;",
|
||||
" for (int i = head[u]; ~i; i = e[i].next) {",
|
||||
" int v = e[i].to;",
|
||||
" if (!dfn[v]) {",
|
||||
" tarjan(v);",
|
||||
" low[u] = min(low[u], low[v]);",
|
||||
" } else if (in_stk[v])",
|
||||
" low[u] = min(low[u], dfn[v]);",
|
||||
" }",
|
||||
" if (low[u] == dfn[u]) {",
|
||||
" ++scc_cnt;",
|
||||
" int v;",
|
||||
" do {",
|
||||
" v = stk.top();",
|
||||
" stk.pop();",
|
||||
" in_stk[v] = 0;",
|
||||
" id[v] = scc_cnt;",
|
||||
" Size[scc_cnt]++;",
|
||||
" } while (v != u);",
|
||||
" }",
|
||||
" return;",
|
||||
" }",
|
||||
"};",
|
||||
""
|
||||
]
|
||||
},
|
||||
"Class_Graph_Euler": {
|
||||
"prefix": "Class_Graph_Euler",
|
||||
"body": [
|
||||
"",
|
||||
"vll din, dout;",
|
||||
"",
|
||||
"class Euler {",
|
||||
" public:",
|
||||
" int n, m;",
|
||||
" Graph<ll> g;",
|
||||
" vector<bool> used;",
|
||||
" vll path;",
|
||||
" Euler() {}",
|
||||
" Euler(Graph<ll> g) {",
|
||||
" this->n = g.n, this->m = g.m, this->g = g;",
|
||||
" used = vector<bool>(n + 1);",
|
||||
" }",
|
||||
"",
|
||||
" void dfs_u(int u) {",
|
||||
" //无向图",
|
||||
" for (long long &i = head[u]; i; i = e[i].next) {",
|
||||
" long long j = i & 1 ? i + 1 : i - 1;",
|
||||
" if(used[i]) {",
|
||||
" i = e[i].next;",
|
||||
" continue;",
|
||||
" }",
|
||||
" used[j] = used[i] = true;",
|
||||
" int t = i / 2 + 1;",
|
||||
" dfs_u(e[i].to);",
|
||||
" path.push_back(t);",
|
||||
" }",
|
||||
" }",
|
||||
"",
|
||||
" void dfs_o(int u) {",
|
||||
" // 有向图",
|
||||
" for (long long &i = head[u]; i; i = e[i].next) {",
|
||||
" if(used[i]) {",
|
||||
" i = e[i].next;",
|
||||
" continue;",
|
||||
" }",
|
||||
" used[i] = true;",
|
||||
" int t = i + 1;",
|
||||
" dfs_o(e[i].to);",
|
||||
" path.push_back(t);",
|
||||
" }",
|
||||
" }",
|
||||
"",
|
||||
"};",
|
||||
"#define path eu.path",
|
||||
""
|
||||
]
|
||||
},
|
||||
"Class_Graph_LCA": {
|
||||
"prefix": "Class_Graph_LCA",
|
||||
"body": [
|
||||
"",
|
||||
"class LCA {",
|
||||
" public:",
|
||||
" int n;",
|
||||
" vll depth, fa[33];",
|
||||
"",
|
||||
" LCA() {}",
|
||||
" LCA(Graph<ll> g, int root) {",
|
||||
" n = g.n, depth = vll(n + 1, inf);",
|
||||
" for (int i = 0; i <= 32; i++) fa[i] = vll(n + 1);",
|
||||
" bfs(g, root);",
|
||||
" }",
|
||||
"",
|
||||
" void bfs(Graph<ll> g, int root) {",
|
||||
" depth[0] = 0, depth[root] = 1;",
|
||||
" queue<int> q;",
|
||||
" q.push(root);",
|
||||
" while (q.size()) {",
|
||||
" auto u = q.front();",
|
||||
" q.pop();",
|
||||
" for (int i = head[u]; ~i; i = e[i].next) {",
|
||||
" int v = e[i].to;",
|
||||
" if (depth[v] > depth[u] + 1) {",
|
||||
" depth[v] = depth[u] + 1;",
|
||||
" q.push(v);",
|
||||
" fa[0][v] = u;",
|
||||
" for (int k = 1; k <= 32; k++) fa[k][v] = fa[k - 1][fa[k - 1][v]];",
|
||||
" }",
|
||||
" }",
|
||||
" }",
|
||||
" }",
|
||||
"",
|
||||
" int query(int a, int b) {",
|
||||
" if (depth[a] < depth[b]) swap(a, b);",
|
||||
" for (int i = 32; i >= 0; i--)",
|
||||
" if (depth[fa[i][a]] >= depth[b]) a = fa[i][a];",
|
||||
" if (a == b) return a;",
|
||||
" for (int i = 32; i >= 0; i--)",
|
||||
" if (fa[i][a] != fa[i][b]) a = fa[i][a], b = fa[i][b];",
|
||||
" return fa[0][a];",
|
||||
" }",
|
||||
"};",
|
||||
""
|
||||
]
|
||||
},
|
||||
"Class_Graph_erfen": {
|
||||
"prefix": "Class_Graph_erfen",
|
||||
"body": [
|
||||
"",
|
||||
"class erfen_graph {",
|
||||
" public:",
|
||||
" ll res, n;",
|
||||
" vi match, st;",
|
||||
" Graph<ll> g;",
|
||||
" erfen_graph(Graph<ll> g) {",
|
||||
" this->g = g, this->n = g.n;",
|
||||
" st = vector<int>(n + 1), match = vector<int>(n + 1);",
|
||||
" res = 0;",
|
||||
" for (int i = 1; i <= n; i++) {",
|
||||
" st = vector<int>(n + 1);",
|
||||
" if (find(i)) res++;",
|
||||
" }",
|
||||
" }",
|
||||
"",
|
||||
" bool find(int u) {",
|
||||
" for (int i = head[u]; ~i; i = e[i].next) {",
|
||||
" if (!st[e[i].to]) {",
|
||||
" st[e[i].to] = true;",
|
||||
" if (!match[e[i].to] || find(match[e[i].to])) {",
|
||||
" match[e[i].to] = u;",
|
||||
" return true;",
|
||||
" }",
|
||||
" }",
|
||||
" }",
|
||||
" return false;",
|
||||
" }",
|
||||
"};",
|
||||
""
|
||||
]
|
||||
}
|
||||
}
|
||||
|
|
@ -0,0 +1,108 @@
|
|||
{
|
||||
"Math_QuickPow": {
|
||||
"prefix": "Math_QuickPow",
|
||||
"body": [
|
||||
"",
|
||||
"ll quick_Pow(ll a, ll b, ll mod) {",
|
||||
" // a的b次方模mod",
|
||||
" ll res = 1, t = a;",
|
||||
" while (b) {",
|
||||
" if (b & 1) res = (res * t) % mod;",
|
||||
" t = t * t % mod;",
|
||||
" b >>= 1;",
|
||||
" }",
|
||||
" return res;",
|
||||
"}",
|
||||
"",
|
||||
],
|
||||
},
|
||||
"Math_Fm": {
|
||||
"prefix": "Math_Fm",
|
||||
"body": [
|
||||
"",
|
||||
"ll quick_Pow(ll a, ll b, ll mod) {",
|
||||
" // a的b次方模mod",
|
||||
" ll res = 1, t = a;",
|
||||
" while (b) {",
|
||||
" if (b & 1) res = (res * t) % mod;",
|
||||
" t = t * t % mod;",
|
||||
" b >>= 1;",
|
||||
" }",
|
||||
" return res;",
|
||||
"}",
|
||||
"",
|
||||
"ll Fm(ll a, ll mod) {",
|
||||
" //费马小定理求逆元",
|
||||
" return quick_Pow(a, mod - 2, mod);",
|
||||
"}",
|
||||
"",
|
||||
],
|
||||
},
|
||||
"Math_C": {
|
||||
"prefix": "Math_C",
|
||||
"body": [
|
||||
"",
|
||||
"ll quick_Pow(ll a, ll b, ll mod) {",
|
||||
" // a的b次方模mod",
|
||||
" ll res = 1, t = a;",
|
||||
" while (b) {",
|
||||
" if (b & 1) res = (res * t) % mod;",
|
||||
" t = t * t % mod;",
|
||||
" b >>= 1;",
|
||||
" }",
|
||||
" return res;",
|
||||
"}",
|
||||
"",
|
||||
"ll Fm(ll a, ll mod) {",
|
||||
" //费马小定理求逆元",
|
||||
" return quick_Pow(a, mod - 2, mod);",
|
||||
"}",
|
||||
"",
|
||||
"ll C(ll n, ll m, ll mod) {",
|
||||
" ll fz = 1, fm = 1;",
|
||||
" for (ll i = n; i >= n - m + 1; i--) fz = fz * i % mod;",
|
||||
" for (ll i = 1; i <= m; i++) fm = fm * i % mod;",
|
||||
" return (fz * Fm(fm, mod)) % mod;",
|
||||
"}",
|
||||
"",
|
||||
],
|
||||
},
|
||||
"Class_Math_Bignum": {
|
||||
"prefix": "Class_Math_Bignum",
|
||||
"body": [
|
||||
"",
|
||||
"class Math_Bignum {",
|
||||
" public:",
|
||||
" string Bignum;",
|
||||
"",
|
||||
" vll num;",
|
||||
"",
|
||||
" Math_Bignum(string s) {",
|
||||
" Bignum = s;",
|
||||
" for (auto i : s) num.push_back(i - '0');",
|
||||
" }",
|
||||
"",
|
||||
" Math_Bignum(vll v) {",
|
||||
" string s;",
|
||||
" num = v;",
|
||||
" for (auto i : v) s.push_back(i + '0');",
|
||||
" }",
|
||||
"",
|
||||
" Math_Bignum(ll l) {",
|
||||
" string s;",
|
||||
" while (l) s.push_back(l % 10 + '0'), num.push_back(l % 10), l /= 10;",
|
||||
" reverse(num.begin(), num.end());",
|
||||
" reverse(s.begin(), s.end());",
|
||||
" Bignum = s;",
|
||||
" }",
|
||||
"",
|
||||
" ll get(ll l, ll r) {",
|
||||
" ll res = 0;",
|
||||
" for (int i = l - 1; i <= r - 1; i++) res = res * 10 + num[i];",
|
||||
" return res;",
|
||||
" }",
|
||||
"};",
|
||||
"",
|
||||
],
|
||||
},
|
||||
}
|
||||
|
|
@ -0,0 +1,43 @@
|
|||
{
|
||||
"class": {
|
||||
"prefix": "class",
|
||||
"body": "type $1 struct{$2}\n$0"
|
||||
},
|
||||
"errnil": {
|
||||
"prefix": "errnil",
|
||||
"body": [
|
||||
"if err != nil {",
|
||||
" $1",
|
||||
"}"
|
||||
]
|
||||
},
|
||||
"init": {
|
||||
"prefix": "init",
|
||||
"body": [
|
||||
"func init() {",
|
||||
" $1",
|
||||
"}"
|
||||
]
|
||||
},
|
||||
"ctx": {
|
||||
"prefix": "ctx",
|
||||
"body": [
|
||||
"ctx := context.Background()"
|
||||
]
|
||||
},
|
||||
"cok": {
|
||||
"prefix": "cok",
|
||||
"body": [
|
||||
"c.JSON(http.StatusOK, vo.Success($1))"
|
||||
]
|
||||
},
|
||||
"cbad": {
|
||||
"prefix": "cbad",
|
||||
"body": [
|
||||
"if err != nil {",
|
||||
" c.JSON(http.StatusBadRequest, vo.Fail(err))",
|
||||
" return",
|
||||
"}"
|
||||
]
|
||||
}
|
||||
}
|
||||
|
|
@ -0,0 +1,13 @@
|
|||
{
|
||||
"mod": {
|
||||
"prefix": "mod",
|
||||
"body": [
|
||||
" \"$1\": {",
|
||||
" \"prefix\": \"$1\",",
|
||||
" \"body\": [",
|
||||
" $2",
|
||||
" ]",
|
||||
" }"
|
||||
]
|
||||
}
|
||||
}
|
||||
|
|
@ -0,0 +1,3 @@
|
|||
{
|
||||
|
||||
}
|
||||
Loading…
Reference in New Issue