axis

部署web service的方式一般有两种： 1. 编写java源文件，将文件的java后缀改为jws，然后部署到webapps/axis目录下，axia引擎可以直接执行源文件中定义的java方法为web service；另外，要让你的web应用服务器识别jws文件需要确定已在web.xml中配置servlet（如果你依照本文方法采用拷贝axis目录为工作起点的话，可以不考虑这步）： <servlet> <servlet-name>AxisServlet</servlet-name> <display-name>Apache-Axis Servlet</display-name> <servlet-class> org.apache.axis.transport.http.AxisServlet </servlet-class> </servlet> <servlet-mapping> <servlet-name>AxisServlet</servlet-name> <url-pattern>*.jws</url-pattern> </servlet-mapping> <servlet-mapping> <servlet-name>AxisServlet</servlet-name> <url-pattern>/services/*</url-pattern> </servlet

refinedet只预测4个层,并且只有conv6_1、conv6_2,没有ssd中的conv7、8、9 refinedet的4个层都只有1个aspect ratio和1个min_size,所以每层每个点只有3个anchor,arm中做location的conv4_3_norm_mbox_loc等层都是3*4个channel,做confidence的conv4_3_norm_mbox_conf都是6个channel,因为这里变成了2分类,每个anchor必须要有negative和positive的概率 refinedet是两步都要回归bounding box的框,refinedet中的odm_loss就相当于ssd中的mbox_loss,mbox_loss获得了anchor的坐标后会加上回归再进行训练,odm_loss获得anchor的坐标后先要加上arm_loc的回归,再加odm_loc的回归,这样再去进行loss计算. name: "vgg_1/8" layer { name: "data" type: "AnnotatedData" top: "data" top: "label" include { phase: TRAIN } transform_param { mirror: true mean_value: 104.0 mean_value: 117.0 mean

NumPy 是 Python 数值计算非常重要的一个包。很多科学计算包都是以 NumPy 的数组对象为基础开发的。 本文用代码快速过了一遍 NumPy 的基本操作，对 NumPy 整体有一个把握。希望能对读者有帮助。 NumPy ndarray：多维数组对象 ndarray 是 NumPy 很重要的特性，它是快速而灵活的数据集容器。ndarray 可以在整个数据集上进行类似于标量的运算，既快速又方便。 在 Jupyter Notebook 上，首先引入 NumPy 包： 1 import numpy as np 创建 ndarray，其中包含随机值（ np.random.randn ）： 1 np.random.seed(666) # 设置随机种子 2 data = np.random.randn(2, 3) 3 data array([[ 0.82418808, 0.479966 , 1.17346801], [ 0.90904807, -0.57172145, -0.10949727]]) 进行一些运算（乘法和加法）： 1 data * 10 2 data + data array([[ 8.24188083, 4.79966003, 11.73468012], [ 9.09048069, -5.71721452, -1.09497268]]) array([[ 1

In R, how do I make a (bar)plot's y axis labels parallel to the X axis instead of parallel to the Y axis? Shane Not sure if this is what you mean, but try setting las=1 . Here's an example: require(grDevices) tN <- table(Ni <- stats::rpois(100, lambda=5)) r <- barplot(tN, col=rainbow(20), las=1) That represents the style of axis labels. (0=parallel, 1=all horizontal, 2=all perpendicular to axis, 3=all vertical) Use par(las=1) . See ?par : las numeric in {0,1,2,3}; the style of axis labels. 0: always parallel to the axis [default], 1: always horizontal, 2: always perpendicular to the axis, 3:

What are the advantages of using Apache CXF over Apache Axis and vice versa? Keep in mind, I'm completely biased (PMC Chair of CXF), but my thoughts: From a strictly "can the project do what I need it to do" perspective, both are pretty equivalent. There some "edge case" things that CXF can do that Axis 2 cannot and vice versa. But for 90% of the use cases, either will work fine. Thus, it comes down to a bunch of other things other than "check box features". API - CXF pushes "standards based" API's (JAX-WS compliant) whereas Axis2 general goes toward proprietary things. That said, even CXF may

Just like is being done in the following example, I want the tick mark labels on the domain of chart to be rotated 45 degrees as the are in this chart: http://left.subtree.org/2007/08/14/rotate-labels-jfreechart/ The difference is, I want to do this on a scatter plot with a numeric axis. I can't find an equivalent to setCategoryLabelPositions() in the class NumberAxis. trashgod The method setVerticalTickLabels() may be an alternative. If not, I don't see any choice but to override refreshTicksHorizontal() . See also this example . import java.awt.Color; import java.awt.Dimension; import java