- Sort Score
- Num 10 results
- Language All
- Labels All
Results 2371 - 2380 of over 10,000 for 1 (1.64 seconds)
Filter
-
归档文档
关于 Docker 镜像 步骤 1: 获取 Docker Compose 文件 方法 1: 单独下载文件 方法 2: 使用...确认版本兼容性 计划停机时间 步骤 1: 数据备份 备份配置数据 备份索引数据 方法 1: 使用快照功能（推荐） 方法 2:...fess.codelibs.org/zh-cn/archives.html -
plot_hgbt_regression.rst.txt
"nswdemand": 1, "nswprice": 1, "vicdemand": -1, "vicprice": -1, } hgbt_no_cst...Normalized between 0 and 1; - day: day of week (1-7); - period: half...scikit-learn.org/stable/_sources/auto_examples/ensemble/plot_hgbt_regression.rst.txt -
test.tiff
+00:00 2026 3 1 Top, left side (Horizontal / normal) 1 Adobe Deflate...Deflate 0 1 RGB 5368709/8388608 11072963/33554432 5033165/16777216...raw.githubusercontent.com/codelibs/fess-testdata/master/files/images/test.tiff -
Linux - Series - IBM Developer
roadmap for LPIC-1 certification Prepare for LPIC-1 Exam 102 and...roadmap for LPIC-1 certification Your guide to LPIC-1 exam-preparation...developer.ibm.com/technologies/linux/series/ -
plot_pca_iris.py
figure(1, figsize=(8, 6)) ax = fig.add_subplot(111,...X_reduced[:, 0], X_reduced[:, 1], X_reduced[:, 2], c=iris.target,...scikit-learn.org/stable/_downloads/1168f82083b3e70f31672e7c33738f8d/plot_pca_iris.py -
API de WebConfig
"numOfThread" : 1 , "intervalTime" : 1000 , "boost" : 1.0 , "available"..."numOfThread" : 1 , "intervalTime" : 1000 , "boost" : 1.0 , "available"...fess.codelibs.org/es/15.5/api/admin/api-admin-webconfig.html -
plot_kmeans_digits.ipynb
1].min() - 1, reduced_data[:, 1].max() + 1\nxx, yy =...reduced_data[:, 0].min() - 1, reduced_data[:, 0].max() + 1\ny_min, y_max =...scikit-learn.org/stable/_downloads/6bf322ce1724c13e6e0f8f719ebd253c/plot_kmeans_digits.ipynb -
Netezza Performance Server - IBM Developer
10 20 30 1–6 of 6 items Page number, of 1 pages 1 of 1 page Previous...models for geospatial data, Part 1 Explore how to use the geospatial...developer.ibm.com/components/netezza-performance-server -
clustering.rst.txt
1, 1, 1] >>> labels_pred = [0, 0, 1, 1, 2, 2] >>>...= [0, 0, 0, 1, 1, 1] >>> labels_pred = [0, 0, 1, 1, 2, 2] >>>...scikit-learn.org/stable/_sources/modules/clustering.rst.txt -
linear_model.rst.txt
array([[1, 0, 0, 0], [1, 0, 1, 0], [1, 1, 0, 0], [1, 1, 1, 1]]) >>>...1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02, 1.e+03,...scikit-learn.org/stable/_sources/modules/linear_model.rst.txt