axis

In my project i am trying to create a rest client over apache axis 1.62 , It was working before over SSL . Due to server configuration change and proxy has been added when i tried to access the webservice now i am get the following error org.apache.axis2.AxisFault: Transport error: 403 Error: PortcullisNoMatch at org.apache.axis2.transport.http.HTTPSender.handleResponse(HTTPSender.java:310) at org.apache.axis2.transport.http.HTTPSender.sendViaPost(HTTPSender.java:194) at org.apache.axis2.transport.http.HTTPSender.send(HTTPSender.java:75) at org.apache.axis2.transport.http

So I'm very new to quaternions, but I understand the basics of how to manipulate stuff with them. What I'm currently trying to do is compare a known quaternion to two absolute points in space. I'm hoping what I can do is simply convert the points into a second quaternion, giving me an easy way to compare the two. What I've done so far is to turn the two points into a unit vector. From there I was hoping I could directly plug in the i j k into the imaginary portion of the quaternion with a scalar of zero. From there I could multiply one quaternion by the other's conjugate, resulting in a third

I would like to know how I can set the x and y axis limits that are displayed for a pyqtgraph.GraphicsWindow.addPlot object. I need to display a lot of data inside a loop (hence using pyqtgraph) but I would rather preallocate my axes as opposed to allowing autorange to potentially enhance speed. As an example, from pyqtgraph.Qt import QtGui, QtCore import numpy as np import pyqtgraph as pg app = QtGui.QApplication([]) win = pg.GraphicsWindow(title="My plotting examples") win.resize(1000,600) win.setWindowTitle('pyqtgraph example: Plotting') p1 = win.addPlot(title="plot1") p2 = win.addPlot

摘要：本文是TensorFlow基础知识的第二部分。 1、张量的典型应用 1.1 标量 一般用来进行误差值的表示、各种测量指标的表示，例如：准确率、精度、召回率等。下面举例： 1.2 向量 在神经网络当中，向量更是十分的常见，例如神经元的偏置值b一般就要用向量来表示，这里的向量就是 【b1,b2】 T 例如我们现在要创建一个输入为5个结点，输出层为4个结点的线性网络层，那么此时创建的网络偏置值应该就是4维的，并且在TensorFlow中默认初始值为0： 1.3 矩阵 很多情况下会用到矩阵，最典型的就是权重的表示。例如我们创建一个线性网络层： 输入：【2,4】，也就是输入特征的长度为4，样本数为2； 输出：【3,2】，也就是有三个输出，两个样本； 权值：【4,3】 具体如下： 再例如，我们可以通过Dence方法创建一个网络层，然后通过kernel属性可以直接查看权重矩阵的形式，我们设计如下的网络层：输入4个结点，输出3个结点： 1.4 三维张量和四维张量 一般来讲：语义信息用三维张量表示，第一位表示句子个数，第二位表示句子的长度，第三位表示句子中单词的编码；图像信息用四维张量表示：第一位表示图像的数目，第二位、第三位表示图像的宽和高、第四位表示图像的通道数。 2、索引和切片 2.1 索引 所谓索引就是获取某一个层面的张量的信息。有两种方式： 中括号法下标法【】【】【】...

I am trying to make a polar 'sypder' plot but I am having some problems with the axis labels. The xaxis tick lables always seem to end up on a layer below the y axis grid (the letters are covered by the grid line, as shown in the figure below), I would like them on top. I tried setting the zorders but with no success. If I set the zorder of the plotted lines above 2 they do go on top (in terms of layer) of the axis and grid... but I still want the labels to be visible on top of the plot. If I set them below 2 the lines go below the grid. Setting the zorder of the grid or the ticks labels does

I'm having an issue with highcharts where I have a number of different charts being generated by JSON calls. For the majority of these charts I need the minimum y-axis value to be set at 0, however there are a couple of occasions where negative values need to be shown. How can I tell highcharts to have a minimum y-axis value of 0 only if there are no negative values in the data, is this even possible? Thanks The option that you're looking for is called softMin and was introduced in version 5.0.1 . The docs describe it as follows: A soft minimum for the axis. If the series data minimum is

The following models are supported: SACMODEL_PLANE - used to determine plane models. The four coefficients of the plane are its Hessian Normal form : [ normal_x normal_y normal_z d ] SACMODEL_LINE - used to determine line models. The six coefficients of the line are given by a point on the line and the direction of the line as: [ point_on_line.x point_on_line.y point_on_line.z line_direction.x line_direction.y line_direction.z ] SACMODEL_CIRCLE2D - used to determine 2D circles in a plane. The circle's three coefficients are given by its center and radius as: [ center.x center.y radius ]

引言 Axis2 是下一代 Apache Axis。Axis2 虽然由 Axis 1.x 处理程序模型提供支持，但它具有更强的灵活性并可扩展到新的体系结构。Axis2 基于新的体系结构进行了全新编写，而且没有采用 Axis 1.x 的常用代码。支持开发 Axis2 的动力是探寻模块化更强、灵活性更高和更有效的体系结构，这种体系结构可以很容易地插入到其他相关 Web 服务标准和协议（如 WS-Security、WS-ReliableMessaging 等）的实现中。 Axis2 的特性包括： 采用名为 AXIOM（AXIs Object Model，Axis 对象模型）的新核心 XML 处理模型 支持 In-Only 和 In-Out 消息交换模式 (MEP) 阻塞和非阻塞客户端 API（应用程序编程接口） 支持内置的 Web 服务寻址 (WS-Addressing) 支持 XMLBeans 数据绑定 新部署模型 支持超文本传输协议 (HTTP)、简单邮件传输协议 (SMTP) 和传输控制协议 (TCP) 等传输协议 本系列文章以 Axis2 1.0 版本为基础。您可以在 Apache 网站获得 Axis2 的最新版本。 Axis 体系结构概述 图 1. Axis2 体系结构 Axis2 体系结构将逻辑与状态分离；这允许在并行线程中执行逻辑。服务和调用的静态状态和动态状态分别存储在

主要记录二维矩阵的append用法np.append(fea_neg,fea_pos,axis=0)#axis=0表示将两个矩阵上下堆叠，比如两个矩阵维度均为（3,4）则堆叠后为（6,4） np.append(fea_neg,fea_pos,axis=1)#axis=1表示左右拼接，比如两个矩阵维度均为（3,4）则堆叠后为（3,8） 来源： https://www.cnblogs.com/kjkj/p/11883169.html

# -*- coding: utf-8 -*- """ File Name： factor_factory_py Author : k0180110 Modify Date: 2019-11-13 """ import pandas as pd import numpy as np from sklearn.linear_model import LinearRegression class Func: def dtm(self, open, high): condition1 = open[open.diff(1) < 0] condition2 = open[open.diff(1) > 0] open = self.max_df((high * condition2 - open * condition2), (open) * condition2.diff(1)) return open def dbm(self, open, low): condition2 = open[open.diff(1) > 0] open = self.max_df((open * condition2 - low * condition2), (open) * condition2.diff(1)) return open def max_df(self, df1, df2): df3 =