plt.subplot(2,1,1)# 两行一列,当前选中第一个子图 plt.plot(x,y1,label='Sin')plt.title('Sin Function')plt.legend() plt.subplot(2,1,2)# 两行一列,当前选中第二个子图 plt.plot(x,y2,label='Cos')plt.title('Cos Function')plt.legend() plt.tight_layout()# 调整子图布局,防止...
(outlier detection:当训练数据中包含离群点,模型训练时要匹配训练数据的中心样本,忽视训练样本的其他异常点) The Local Outlier Factor(LOF) algorithm is an unsupervised anomaly detection method which computes the local density deviation of a given data point with respect to its neighbors. It considers as...
points_num=len(points) x= [points[i][0]foriinrange(points_num)] y= [points[i][1]foriinrange(points_num)] xmin=min(x) xmax=max(x) ymin=min(y) ymax=max(y) label= shape["label"] node_obj= xml_doc.createElement("object") node_name= xml_doc.createElement("name") node_name...
The Local Outlier Factor(LOF) algorithm is an unsupervised anomaly detection method which computes the local density deviation of a given data point with respect to its neighbors. It considers as outliers the samples that have a substantially lower density than their neighbors. This example shows ho...
import matplotlib.pyplot as pltimport numpy as np# 创建示例数据x = np.linspace(0, 10, 100)y1 = np.sin(x)y2 = np.cos(x)# 绘制散点图plt.figure(figsize=(6, 6))plt.scatter(x, y1, label='Sine Data', color='green', marker='o', s=30)plt.scatter(x, y2, label='Cosine Data...
# Create the two figures and draw the datahide_fig = figure(**common_figure_kwargs, title='Click Legend to HIDE Data', y_axis_label='Rebounds')hide_fig.circle(**common_circle_kwargs, **common_lebron_kwargs)hide_fig.circle(**common_circle_kwargs, **common_durant_kwargs)mute_fig =...
后面后分享一个labelme标注的json或xml格式转二值图的源码(以备以后使用) 而我现在在研究显著性目标检测,需要的是边缘mask的二值图像。搜了很久,并没有人做过这种工作,只能得到如下的掩膜图 而我需要的图像为二值图,如下 说下 我的过程 并附上代码: ...
label_num[i['label']] = label_num[i['label']] + 1 else: label_num[i['label']] = 1 # 生成存储长和宽数据的列表 for i in json_data['shapes']: w = i['points'][1][0] - i['points'][0][0] h = i['points'][1][1] - i['points'][0][1] ...
Data points: [[-0.753] [ 2.704] [ 1.392] [ 0.592] [-2.064]] Bin membership for data points: [[ 4] [10] [ 8] [ 6] [ 2]] 我们在这里做的是将wave数据集中单个连续输入特征变换为一个分类特征,用于表示数据点所在的箱子。要想在这个数据上使用scikit-learn模型,我们利用preprocessing模块的One...
x=np.arange(0,4*np.pi,0.1)y_sin=np.sin(x)y_cos=np.cos(x)# Plot the points using matplotlib plt.plot(x,y_sin)plt.plot(x,y_cos)plt.xlabel('x axis label')plt.ylabel('y axis label')plt.title('Sine and Cosine')plt.legend(['Sine','Cosine'])plt.show() ...