# 导入内置模块import mathprint(math.sin(math.pi / 2))# 导入自定义模块from mymodule import myfuncmyfunc()# 导入第三方库import numpy as npa = np.array([1, 2, 3])print(a)# 使用 pip 安装第三方库# pip install requestsimport requestsresponse = requests.get("https://www.example.com")prin...
double', 'ceil', 'cfloat', 'char', 'character', 'chararray', 'choose', 'clip', 'clongdouble', 'clongfloat', 'column_stack', 'common_type', 'compare_chararrays', 'compat', 'complex', 'complex128', 'complex64', 'complex_', 'complexfloating', 'compress', 'concatenate', 'conj...
We can grab the last element in an array by using negative indexes. The negative indexes count backward from the end of the array, but are most commonly used to reference the last element of an array. if crypt.crypt(guess,salt) == password: userInfo = { "user" : user, "pass" : ...
ArraySet在进行add和remove操作时,操作的是int[]类型的mHashes和Object[]类型的mArray,其中mHashes保存mArray每个元素的hash值,且mHashes和mArray相同下标的元素一一对应。 add 以上为add的流程,可以概括为以下几点: 判断元素是否存在,已存在则直接返回 indexOf函数判断待插入元素是否存在,如果存在,则直接返回元素在...
(precision=2) # 在混淆矩阵中每格的概率值 ind_array = np.arange(len(classes)+1) x, y = np.meshgrid(ind_array, ind_array)#生成坐标矩阵 diags = np.diag(cm)#对角TP值 TP_FNs, TP_FPs = [], [] for x_val, y_val in zip(x.flatten(), y.flatten()):#并行遍历 max_index = len...
y = np.array([1, 1, 2, 2]) scores = np.array([0.1, 0.4, 0.35, 0.8]) fpr, tpr, thresholds = roc_curve(y, scores, pos_label=2) # ***海明距离*** from sklearn.metrics import hamming_loss y_pred = [1, 2, 3, 4] ...
X_pred = model.predict(np.array(X_test)) X_pred = pd.DataFrame(X_pred, columns=X_test.columns) X_pred.index = X_test.index threshod =0.3 scored = pd.DataFrame(index=X_test.index) scored['Loss_mae'] = np.mean(np.abs(X...
6.IndexError: list index out of range 越界访问列表,下标超出了列表的范围。 a = [10, 20, 30] print(a[3]) # 由于下标是从0开始计数,此处最大下标为2,修改成a[2] 7.KeyError: 'xxx' 试图访问字典中不存在的键值。 d = {"name": "Tom", "age": 18} ...
(url) if response.status_code == 200: images = response.json() # Assuming the API returns a JSON array of image URLs for index, image_url in enumerate(images): image_response = requests.get(image_url) if image_response.status_code == 200: with open(f"{save_directory}/image_{index...
异常检测(Anomaly detection)是机器学习的常见应用,其目标是识别数据集中的异常或不寻常模式。尽管通常被归类为非监督学习问题,异常检测却具有与监督学习相似的特征。在异常检测中,我们通常处理的是未标记的数据,即没有明确的标签指示哪些样本是异常的。相反,算法需要根据数据本身的特征来确定异常。这使得异常检测成为一项...