Design under uncertainty¶
This module contains three app routes:
1: @app.route(‘/Design_under_uncertainty’) Displays an Input-Data page to define the parameters of the distribution of the properties of the three-storey building: mass, stiffness, damping and input force. The parameters of the Gaussian distribution are the mean value (mu) and standard deviation (sigma), whereas the corresponding parameters of the Gamma distribution are shape (kappa) and scale (theta) This page also allows to define the frequency of analysis, the number of Monte Carlo simulations and the output requested for the Fisher Information Matrix (the outputs are the displacements of each floor and the shear force between two floors, at least one output must be selected to proceed) The default values are given by experimental data gathered at Sheffield University The Frequency response function displayed is calculated with the mean values.
2: @app.route(‘/Design_under_uncertainty_output’) Displays the probability distribution of the selected outputs. The number of bins of the 1-D histogram can be modified. Additionally, it allows to define the number of bins of the n-dimentional joint pdf of the correlated outputs for the analysis of the sensitivities based on the Fisher Information Matrix
3: @app.route(‘/Design_under_uncertainty_fisher’) Plots the scaled form of the Fisher Information Matrix and the corresponding eigenvalues and eigenvectors
-
dtApp.dtCode.design.
EigenBar
(Y_listtext1, vIMnorm1, lamIMnorm1, II)¶
-
dtApp.dtCode.design.
Histo
(I, N_bins)¶
-
class
dtApp.dtCode.design.
InputForm
(*args, **kwargs)¶ Bases:
wtforms.form.Form
-
C1_type
= <UnboundField(SelectField, (), {'label': 'Type', 'validators': [<wtforms.validators.InputRequired object>], 'choices': [(1, 'Gaussian'), (2, 'Gamma')]})>¶
-
C2_type
= <UnboundField(SelectField, (), {'label': 'Type', 'validators': [<wtforms.validators.InputRequired object>], 'choices': [(1, 'Gaussian'), (2, 'Gamma')]})>¶
-
C3_type
= <UnboundField(SelectField, (), {'label': 'Type', 'validators': [<wtforms.validators.InputRequired object>], 'choices': [(1, 'Gaussian'), (2, 'Gamma')]})>¶
-
F1_type
= <UnboundField(SelectField, (), {'label': 'Type', 'validators': [<wtforms.validators.InputRequired object>], 'choices': [(1, 'Gaussian'), (2, 'Gamma')]})>¶
-
F2_type
= <UnboundField(SelectField, (), {'label': 'Type', 'validators': [<wtforms.validators.InputRequired object>], 'choices': [(1, 'Gaussian'), (2, 'Gamma')]})>¶
-
F3_type
= <UnboundField(SelectField, (), {'label': 'Type', 'validators': [<wtforms.validators.InputRequired object>], 'choices': [(1, 'Gaussian'), (2, 'Gamma')]})>¶
-
JPDFsize
= <UnboundField(FloatField, (), {'label': 'jpdf_size', 'validators': [<wtforms.validators.InputRequired object>]})>¶
-
K1_type
= <UnboundField(SelectField, (), {'label': 'Type', 'validators': [<wtforms.validators.InputRequired object>], 'choices': [(1, 'Gaussian'), (2, 'Gamma')]})>¶
-
K2_type
= <UnboundField(SelectField, (), {'label': 'Type', 'validators': [<wtforms.validators.InputRequired object>], 'choices': [(1, 'Gaussian'), (2, 'Gamma')]})>¶
-
K3_type
= <UnboundField(SelectField, (), {'label': 'Type', 'validators': [<wtforms.validators.InputRequired object>], 'choices': [(1, 'Gaussian'), (2, 'Gamma')]})>¶
-
M1_type
= <UnboundField(SelectField, (), {'label': 'Type', 'validators': [<wtforms.validators.InputRequired object>], 'choices': [(1, 'Gaussian'), (2, 'Gamma')]})>¶
-
M2_type
= <UnboundField(SelectField, (), {'label': 'Type', 'validators': [<wtforms.validators.InputRequired object>], 'choices': [(1, 'Gaussian'), (2, 'Gamma')]})>¶
-
M3_type
= <UnboundField(SelectField, (), {'label': 'Type', 'validators': [<wtforms.validators.InputRequired object>], 'choices': [(1, 'Gaussian'), (2, 'Gamma')]})>¶
-
Xsize
= <UnboundField(FloatField, (), {'label': 'x_size', 'default': 20, 'validators': [<wtforms.validators.InputRequired object>]})>¶
-
c1_mean
= <UnboundField(FloatField, (), {'label': 'c1 mean', 'validators': [<wtforms.validators.InputRequired object>]})>¶
-
c1_sigma
= <UnboundField(FloatField, (), {'label': 'c1 sigma', 'validators': [<wtforms.validators.InputRequired object>]})>¶
-
c2_mean
= <UnboundField(FloatField, (), {'label': 'c2 mean', 'validators': [<wtforms.validators.InputRequired object>]})>¶
-
c2_sigma
= <UnboundField(FloatField, (), {'label': 'c2 sigma', 'validators': [<wtforms.validators.InputRequired object>]})>¶
-
c3_mean
= <UnboundField(FloatField, (), {'label': 'c3 mean', 'validators': [<wtforms.validators.InputRequired object>]})>¶
-
c3_sigma
= <UnboundField(FloatField, (), {'label': 'c3 sigma', 'validators': [<wtforms.validators.InputRequired object>]})>¶
-
check_s1
= <UnboundField(BooleanField, (), {'label': 'Shear force s1'})>¶
-
check_s2
= <UnboundField(BooleanField, (), {'label': 'Shear force s2'})>¶
-
check_s3
= <UnboundField(BooleanField, (), {'label': 'Shear force s3'})>¶
-
check_x1
= <UnboundField(BooleanField, (), {'label': 'Displacement x1'})>¶
-
check_x2
= <UnboundField(BooleanField, (), {'label': 'Displacement x2'})>¶
-
check_x3
= <UnboundField(BooleanField, (), {'label': 'Displacement x3'})>¶
-
f1_mean
= <UnboundField(FloatField, (), {'label': 'f1 mean', 'validators': [<wtforms.validators.InputRequired object>]})>¶
-
f1_sigma
= <UnboundField(FloatField, (), {'label': 'f1 sigma', 'validators': [<wtforms.validators.InputRequired object>]})>¶
-
f2_mean
= <UnboundField(FloatField, (), {'label': 'f2 mean', 'validators': [<wtforms.validators.InputRequired object>]})>¶
-
f2_sigma
= <UnboundField(FloatField, (), {'label': 'f2 sigma', 'validators': [<wtforms.validators.InputRequired object>]})>¶
-
f3_mean
= <UnboundField(FloatField, (), {'label': 'f3 mean', 'validators': [<wtforms.validators.InputRequired object>]})>¶
-
f3_sigma
= <UnboundField(FloatField, (), {'label': 'f3 sigma', 'validators': [<wtforms.validators.InputRequired object>]})>¶
-
freq_analysis
= <UnboundField(FloatField, (), {'label': 'Frequency of Analysis', 'validators': [<wtforms.validators.InputRequired object>]})>¶
-
k1_mean
= <UnboundField(FloatField, (), {'label': 'k1 mean', 'validators': [<wtforms.validators.InputRequired object>]})>¶
-
k1_sigma
= <UnboundField(FloatField, (), {'label': 'k1 sigma', 'validators': [<wtforms.validators.InputRequired object>]})>¶
-
k2_mean
= <UnboundField(FloatField, (), {'label': 'k2 mean', 'validators': [<wtforms.validators.InputRequired object>]})>¶
-
k2_sigma
= <UnboundField(FloatField, (), {'label': 'k2 sigma', 'validators': [<wtforms.validators.InputRequired object>]})>¶
-
k3_mean
= <UnboundField(FloatField, (), {'label': 'k3 mean', 'validators': [<wtforms.validators.InputRequired object>]})>¶
-
k3_sigma
= <UnboundField(FloatField, (), {'label': 'k3 sigma', 'validators': [<wtforms.validators.InputRequired object>]})>¶
-
m1_mean
= <UnboundField(FloatField, (), {'label': 'm1 mean', 'validators': [<wtforms.validators.InputRequired object>]})>¶
-
m1_sigma
= <UnboundField(FloatField, (), {'label': 'm1 sigma', 'validators': [<wtforms.validators.InputRequired object>]})>¶
-
m2_mean
= <UnboundField(FloatField, (), {'label': 'm2 mean', 'validators': [<wtforms.validators.InputRequired object>]})>¶
-
m2_sigma
= <UnboundField(FloatField, (), {'label': 'm2 sigma', 'validators': [<wtforms.validators.InputRequired object>]})>¶
-
m3_mean
= <UnboundField(FloatField, (), {'label': 'm3 mean', 'validators': [<wtforms.validators.InputRequired object>]})>¶
-
m3_sigma
= <UnboundField(FloatField, (), {'label': 'm3 sigma', 'validators': [<wtforms.validators.InputRequired object>]})>¶
-
n_realisations
= <UnboundField(FloatField, (), {'label': 'N Monte Carlo', 'validators': [<wtforms.validators.InputRequired object>]})>¶
-
-
dtApp.dtCode.design.
OutDis
()¶
-
dtApp.dtCode.design.
dplot4M
()¶
-
dtApp.dtCode.design.
gamma
(shape, scale=1.0, size=None)¶ Draw samples from a Gamma distribution.
Samples are drawn from a Gamma distribution with specified parameters, shape (sometimes designated “k”) and scale (sometimes designated “theta”), where both parameters are > 0.
Note
New code should use the
gamma
method of adefault_rng()
instance instead; please see the random-quick-start.- Parameters
shape (float or array_like of floats) – The shape of the gamma distribution. Must be non-negative.
scale (float or array_like of floats, optional) – The scale of the gamma distribution. Must be non-negative. Default is equal to 1.
size (int or tuple of ints, optional) – Output shape. If the given shape is, e.g.,
(m, n, k)
, thenm * n * k
samples are drawn. If size isNone
(default), a single value is returned ifshape
andscale
are both scalars. Otherwise,np.broadcast(shape, scale).size
samples are drawn.
- Returns
out – Drawn samples from the parameterized gamma distribution.
- Return type
ndarray or scalar
See also
scipy.stats.gamma
probability density function, distribution or cumulative density function, etc.
Generator.gamma
which should be used for new code.
Notes
The probability density for the Gamma distribution is
\[p(x) = x^{k-1}\frac{e^{-x/\theta}}{\theta^k\Gamma(k)},\]where \(k\) is the shape and \(\theta\) the scale, and \(\Gamma\) is the Gamma function.
The Gamma distribution is often used to model the times to failure of electronic components, and arises naturally in processes for which the waiting times between Poisson distributed events are relevant.
References
- 1
Weisstein, Eric W. “Gamma Distribution.” From MathWorld–A Wolfram Web Resource. http://mathworld.wolfram.com/GammaDistribution.html
- 2
Wikipedia, “Gamma distribution”, https://en.wikipedia.org/wiki/Gamma_distribution
Examples
Draw samples from the distribution:
>>> shape, scale = 2., 2. # mean=4, std=2*sqrt(2) >>> s = np.random.gamma(shape, scale, 1000)
Display the histogram of the samples, along with the probability density function:
>>> import matplotlib.pyplot as plt >>> import scipy.special as sps >>> count, bins, ignored = plt.hist(s, 50, density=True) >>> y = bins**(shape-1)*(np.exp(-bins/scale) / ... (sps.gamma(shape)*scale**shape)) >>> plt.plot(bins, y, linewidth=2, color='r') >>> plt.show()
-
dtApp.dtCode.design.
normal
(loc=0.0, scale=1.0, size=None)¶ Draw random samples from a normal (Gaussian) distribution.
The probability density function of the normal distribution, first derived by De Moivre and 200 years later by both Gauss and Laplace independently [2]_, is often called the bell curve because of its characteristic shape (see the example below).
The normal distributions occurs often in nature. For example, it describes the commonly occurring distribution of samples influenced by a large number of tiny, random disturbances, each with its own unique distribution [2]_.
Note
New code should use the
normal
method of adefault_rng()
instance instead; please see the random-quick-start.- Parameters
loc (float or array_like of floats) – Mean (“centre”) of the distribution.
scale (float or array_like of floats) – Standard deviation (spread or “width”) of the distribution. Must be non-negative.
size (int or tuple of ints, optional) – Output shape. If the given shape is, e.g.,
(m, n, k)
, thenm * n * k
samples are drawn. If size isNone
(default), a single value is returned ifloc
andscale
are both scalars. Otherwise,np.broadcast(loc, scale).size
samples are drawn.
- Returns
out – Drawn samples from the parameterized normal distribution.
- Return type
ndarray or scalar
See also
scipy.stats.norm
probability density function, distribution or cumulative density function, etc.
Generator.normal
which should be used for new code.
Notes
The probability density for the Gaussian distribution is
\[p(x) = \frac{1}{\sqrt{ 2 \pi \sigma^2 }} e^{ - \frac{ (x - \mu)^2 } {2 \sigma^2} },\]where \(\mu\) is the mean and \(\sigma\) the standard deviation. The square of the standard deviation, \(\sigma^2\), is called the variance.
The function has its peak at the mean, and its “spread” increases with the standard deviation (the function reaches 0.607 times its maximum at \(x + \sigma\) and \(x - \sigma\) [2]_). This implies that normal is more likely to return samples lying close to the mean, rather than those far away.
References
- 1
Wikipedia, “Normal distribution”, https://en.wikipedia.org/wiki/Normal_distribution
- 2
P. R. Peebles Jr., “Central Limit Theorem” in “Probability, Random Variables and Random Signal Principles”, 4th ed., 2001, pp. 51, 51, 125.
Examples
Draw samples from the distribution:
>>> mu, sigma = 0, 0.1 # mean and standard deviation >>> s = np.random.normal(mu, sigma, 1000)
Verify the mean and the variance:
>>> abs(mu - np.mean(s)) 0.0 # may vary
>>> abs(sigma - np.std(s, ddof=1)) 0.1 # may vary
Display the histogram of the samples, along with the probability density function:
>>> import matplotlib.pyplot as plt >>> count, bins, ignored = plt.hist(s, 30, density=True) >>> plt.plot(bins, 1/(sigma * np.sqrt(2 * np.pi)) * ... np.exp( - (bins - mu)**2 / (2 * sigma**2) ), ... linewidth=2, color='r') >>> plt.show()
Two-by-four array of samples from N(3, 6.25):
>>> np.random.normal(3, 2.5, size=(2, 4)) array([[-4.49401501, 4.00950034, -1.81814867, 7.29718677], # random [ 0.39924804, 4.68456316, 4.99394529, 4.84057254]]) # random
-
dtApp.dtCode.design.
start_method
()¶
-
dtApp.dtCode.design.
testt
()¶


{% extends "layout.html" %} {% block title %} Todoes | Register {% endblock %} {% block content %} todoes Todoes help people manage their tasks. Register or Log In {% endblock %}
or


Assembly for the Google APIs Client Library for Java 1.7.0-beta
Overview
High-level details about this library can be found at http://code.google.com/p/google-api-java-client
Library Details
- google-api-client can be used for all platforms.
- google-api-client-android2 is for Android SDK 2.0 & higher.
- google-api-client-appengine is for Google App Engine.
- google-api-client-servlet is for the HTTP Servlet environment.
Dependencies and Licenses
The license can be found here.
Dependent jars can be found in the dependencies folder.
The dependency structure and licenses for the different libraries can be found here:


Songr: A sample app built on EveryBit.js
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Gecode::Int::Bool::Clause< VX, VY > Class Template Reference
[Integer propagators]
Boolean clause propagator (disjunctive). More...
#include <bool.hh>
Classes | |
| class | Tagged |
| Advisors for views (tagged whether for x or y) More... | |
Public Member Functions | |
| virtual Actor * | copy (Space &home) |
| Copy propagator during cloning. | |
| virtual ExecStatus | advise (Space &home, Advisor &a, const Delta &d) |
| Give advice to propagator. | |
| virtual PropCost | cost (const Space &home, const ModEventDelta &med) const |
| Cost function (defined as low unary). | |
| virtual void | reschedule (Space &home) |
| Schedule function. | |
| virtual ExecStatus | propagate (Space &home, const ModEventDelta &med) |
| Perform propagation. | |
| virtual size_t | dispose (Space &home) |
| Delete propagator and return its size. | |
Static Public Member Functions | |
| static ExecStatus | post (Home home, ViewArray< VX > &x, ViewArray< VY > &y, VX z) |
| Post propagator . | |
Protected Member Functions | |
| void | cancel (Space &home) |
| Cancel subscriptions. | |
| Clause (Home home, ViewArray< VX > &x, ViewArray< VY > &y, VX z) | |
| Constructor for posting. | |
| Clause (Space &home, Clause< VX, VY > &p) | |
| Constructor for cloning p. | |
Protected Attributes | |
| ViewArray< VX > | x |
| Positive views. | |
| ViewArray< VY > | y |
| Positive views (origin from negative variables). | |
| VX | z |
| Result. | |
| int | n_zero |
| The number of views assigned to zero in x and y. | |
| Council< Tagged > | c |
| The advisor council. |
Detailed Description
template<class VX, class VY>
class Gecode::Int::Bool::Clause< VX, VY >
Boolean clause propagator (disjunctive).
Requires
#include <gecode/int/bool.hh>
Definition at line 492 of file bool.hh.
Constructor & Destructor Documentation
| Gecode::Int::Bool::Clause< VX, VY >::Clause | ( | Home | home, | |
| ViewArray< VX > & | x, | |||
| ViewArray< VY > & | y, | |||
| VX | z | |||
| ) | [inline, protected] |
Constructor for posting.
Definition at line 204 of file clause.hpp.
| Gecode::Int::Bool::Clause< VX, VY >::Clause | ( | Space & | home, | |
| Clause< VX, VY > & | p | |||
| ) | [inline, protected] |
Constructor for cloning p.
Definition at line 214 of file clause.hpp.
Member Function Documentation
| void Gecode::Int::Bool::Clause< VX, VY >::cancel | ( | Space & | home | ) | [inline, protected] |
Cancel subscriptions.
Definition at line 293 of file clause.hpp.
| Actor * Gecode::Int::Bool::Clause< VX, VY >::copy | ( | Space & | home | ) | [inline, virtual] |
Copy propagator during cloning.
Implements Gecode::Actor.
Definition at line 238 of file clause.hpp.
| ExecStatus Gecode::Int::Bool::Clause< VX, VY >::advise | ( | Space & | home, | |
| Advisor & | a, | |||
| const Delta & | d | |||
| ) | [inline, virtual] |
Give advice to propagator.
Reimplemented from Gecode::Propagator.
Definition at line 316 of file clause.hpp.
| PropCost Gecode::Int::Bool::Clause< VX, VY >::cost | ( | const Space & | home, | |
| const ModEventDelta & | med | |||
| ) | const [inline, virtual] |
Cost function (defined as low unary).
Implements Gecode::Propagator.
Definition at line 287 of file clause.hpp.
| void Gecode::Int::Bool::Clause< VX, VY >::reschedule | ( | Space & | home | ) | [inline, virtual] |
| ExecStatus Gecode::Int::Bool::Clause< VX, VY >::propagate | ( | Space & | home, | |
| const ModEventDelta & | med | |||
| ) | [inline, virtual] |
| ExecStatus Gecode::Int::Bool::Clause< VX, VY >::post | ( | Home | home, | |
| ViewArray< VX > & | x, | |||
| ViewArray< VY > & | y, | |||
| VX | z | |||
| ) | [inline, static] |
Post propagator .
Definition at line 246 of file clause.hpp.
| size_t Gecode::Int::Bool::Clause< VX, VY >::dispose | ( | Space & | home | ) | [inline, virtual] |
Delete propagator and return its size.
Reimplemented from Gecode::Actor.
Definition at line 307 of file clause.hpp.
Member Data Documentation
ViewArray<VX> Gecode::Int::Bool::Clause< VX, VY >::x [protected] |
ViewArray<VY> Gecode::Int::Bool::Clause< VX, VY >::y [protected] |
VX Gecode::Int::Bool::Clause< VX, VY >::z [protected] |
int Gecode::Int::Bool::Clause< VX, VY >::n_zero [protected] |
Council<Tagged> Gecode::Int::Bool::Clause< VX, VY >::c [protected] |
The documentation for this class was generated from the following files:
- gecode/int/bool.hh
- gecode/int/bool/clause.hpp


HTC One launched in India for Rs. 42,990
HTC has launched their latest Android flagship device, the HTC One, in India for Rs.42,990, which is approximately $789, unlocked. The phone is expected to go on sale towards the end of this month. HTC has tied up with Reliance Communications to offer 1GB of free 3G data for three months to those who purchase the One.
The pricing of the HTC One in India is still fairly high but reasonable compared to some of their previous devices. The HTC Butterfly, for example, was priced at Rs.45,990 ($845) at launch and continues to be priced about the same since then. It's interesting to see the new flagship being priced lower than the existing one, although a price drop for Butterfly before the One hits the shelves cannot be ruled out.
The older One X and One X+, meanwhile, has seen a price drop in India, although the final price will vary by retailer.
Source


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We know that we can use the service of LoadBalancer in the Kubernetes cluster to expose backend workloads externally. Cloud providers often offer cloud LoadBalancer plugins, which requires the cluster to be deployed on a specific IaaS platform. However, many enterprise users often deploy the Kubernetes cluster on bare metal, especially when it is used for the production environment. For the local bare metal cluster, Kubernetes does not provide LB implementation. Porter is an open source load balancer designed specifically for the bare metal Kubernetes cluster, which serves as an excellent solution to this problem.
Kubernetes Service Introduction
In the Kubernetes cluster, network represents a very basic and important part. For large-scale nodes and containers, it entails very complicated and delicate design if it is to ensure the connectivity and efficiency in the network. What’s more, IP addresses and ports need to be automatically assigned and managed in the network, with a user-friendly approach in place for the direct and quick access to applications in need.
Kubernetes has made great efforts in this connection. With CNI, Service, DNS and Ingress, it has solved the problem of service discovery and load balancing, providing an easier way in usage and configuration. Among them, Service underlies Kubernetes microservices. And services are made possible through kube-proxy in Kubernetes.
This component runs on each node, monitoring the change in the service object in API Server and achieving network forwarding by managing iptables. Users can create different forms of Services such as those based on Label Selector, Headless or ExternalName. Kube-proxy will create a virtual IP (or cluster IP) for the service for the internal access of the cluster.
Three Methods to Expose Services
If the access is required outside the cluster, or to expose the service to users, Kubernetes Service provides two methods: NodePort and LoadBalancer. Besides, Ingress is also a very common option to expose services.
NodePort
If the service type is set to NodePort, kube-proxy will apply for a port for the service which is above 3000 (by default). Iptables rules will be configured for all the hosts in the cluster. In this way, users can access the service through any node in the cluster with the assigned port. Please see the image below:
NodePort is the most convenient way to expose services while it also has obvious shortcomings:
- The real IP is not visible in Pod through the access based on SNAT.
- A host in the cluster is used as a jumper server to access the backend service, which means all the traffic will go to the server first. This can easily lead to performance bottlenecks and a single point of failure, making it difficult to be used in the production environment.
- Generally, NodePort uses large port numbers which are hard to remember.
Initially, NodePort is not designed for the exposure of services in the production environment which is why large port numbers are used by default.
LoadBalancer
LoadBalancer is a preferred solution by Kubernetes to service exposure. However, this cannot be done without the load balancer offered by cloud providers, which means the Kubernetes cluster has to be deployed in the cloud. Here is how LoadBalancer works:
The LoadBalancer service is achieved through the LB plugin offered by cloud providers. The package Kubernetes.io/cloud-provider will choose the appropriate backend service and expose it to the LB plugin, which creates a load balancer accordingly. That means network traffic will be distributed in the cloud service, avoiding a single point of failure and performance bottlenecks that may occur in NodePort. As mentioned above, LoadBalancer is a preferred solution by Kubernetes to service exposure, but it is only limited to the Kubernetes service offered by cloud providers. For the Kubernetes cluster that is deployed in a bare metal environment or in a non-cloud environment, this approach may not be applicable.
Ingress
Kubernetes itself does not provide the way to expose services through Ingress. Rather, Ingress exposes multiple services simultaneously with the help of applications just like a router. This plugin identifies different services through domains and uses annotations to control the way services are exposed externally. Here is how it works:
Ingress is the most used method in a business environment than NodePort and LoadBalancer. The reasons include:
- Compared with the load balancing way of kube-proxy, Ingress Controller is more capable (e.g. traffic control and security strategy).
- It is more direct to identify services through domains; large port numbers in NodePort are also not needed for Ingress.
Nevertheless, the following problems need to be solved for Ingress:
- Ingress is used more often for L7, with limited support for L4.
- All the traffic will go to Ingress Controller, which requires a LB to expose Ingress Controller.
For the first problem, Ingress can be used for L4 but the configuration of Ingress is too complicated for L4 applications. The best practice is to use LB directly for exposure. For the second problem, Ingress Controller can be exposed in a test environment with NodePort (or hostnetwork), while a single point of failure and performance bottlenecks may happen inevitably and the HA feature of Ingress-controller has not been properly used.
Porter Introduction
Porter is an open source cloud native load balancing plugin designed by the KubeSphere development team based on Border Gateway Protocol (BGP). It meanly features:
- ECMP routing load balancing
- BGP dynamic routing configuration
- VIP management
All Porter codes are open source and documents are available in GitHub. You are welcome to star and use it.
How to Install Porter
Porter has been deployed and tested in two environments so far as below. You can see more details in GitHub about the deployment, test and process by clicking the link below. It is recommended to have a try:
- Deploy Porter on Bare Metal Kubernetes Cluster
- Test in the QingCloud Platform Using a Simulated Router
Principle
ECMP
Equal-Cost Multi-Path (ECMP) means the package forwarding to a same destination can occur along multiple paths of equal cost. When the device supports ECMP, the three-layer traffic that is sent to the target IP or network segment can be distributed by different paths, achieving network load balancing. Besides, once a certain path malfunctions, other paths can finish the forwarding process instead, serving as the routing redundant backup. Please refer to the image below:
With the help of the virtual router, ECMP can select the next hop (Pod) according to Hash algorithm from the existing routing paths for a certain IP (the corresponding VIP of the service). This is how load balancing is achieved. As virtual routers support ECMP in general, Porter only needs to check the Kubernetes API server and deliver the corresponding information of backend Pod of a service to the router.
BGP
A Pod may be scheduled to other nodes in Kubernetes. For a router, the next hop of a service VIP is not fixed as the equal-cost routing information will often be updated. Calico, for example, uses BGP (Border Gateway Protocol) to advertise routes. BGP is a commonly used essential decentralized protocol to exchange routing information among autonomous systems on the Internet. Unlike other routing protocols, BGP uses L4 to ensure the update security of routing information. As BGP is decentralized, it is very easy to establish a routing layer of high availability to ensure network continuity.
The image above briefly demonstrates how BGP works in Porter. In the bottom-left corner, it is a two-node Kubernetes cluster with two routers (Leaf1 and Leaf2) above it. These two routers are connected to two kernel switches (Spine layer). Users are on the right side, whose routers are Border1 and Border2 (also connected to Spine).
The three layers of users and Kubernetes server are reachable. Services are created in the Kubernetes cluster and Porter is also used. A VIP (or other manually assigned IP) <IP_ADDRESS> is assigned by Porter, which sends the information to Leaf1 and Leaf2 through BGP. The next hop to access <IP_ADDRESS> can be Node1 or Node2. Meanwhile, the Leaf layer also sends the message to the Spine layer, which also knows the next hop to access <IP_ADDRESS> can be Leaf1 or Leaf2 based on its BGP.
According to the same logic, the routing information will also be updated on Border, meaning the path for users to access <IP_ADDRESS> is complete. At the same time, as each layer in the image features HA, a total of 16 (2*2*2*2
) paths are available to use for external access. Traffic can be distributed across the network and any downtime that occurs in the router in any layer will not affect user’s access.
Architecture
Porter has two components: a core controller and an agent deployed on each node. The main functions of the controller include:
- Monitor cluster Services and corresponding endpoints; acquire the Scheduling information of Pods
- VIP storage and assignment
- Establish BGP and advertise routes
The image above shows the working principle of Porter’s core controller.
Agent is a lightweight component to monitor VIP resources and add Iptables rules for external access to the VIP. By default, the kernel Forward table will drop any external access to VIP.
Designed for Cloud Natives
All resources in Porter are CRD, including VIP, BGPPeer and BGPConfig. Users who are used to Kubectl will find Porter very easy to use. For advanced users who want to customize Porter, Kubernetes API can be called directly for tailor-made development. The core controller of Porter will soon support high availability (HA).
Cautions
The VIP traffic of user access will go to a node in the Kubernetes cluster under BGP. This is because the routes advertised by Porter are also nodes instead of Pod IP which is inaccessible externally. The path from a node to a pod is maintained by kube-proxy as below:
The traffic will be sent to a pod randomly after the SNAT process. As Port will adjust routes based on the dynamic change of Service Endpoints to make sure a pod is available in a node for the next hop, we can change kube-proxy which is set by default. You can set ExternalTrafficPolicy=local in a Service and the result is shown as follows:
There are two advantages in this method:
- SourceIP will not go through the process of NAT
- Traffic will go locally, reducing a hop in the network
Future Plans
- Support of other simple routing protocols
- More convenient VIP management
- Policy support of BGP
- Integration into KubeSphere with UI provided


{{ end }} {{ .Content }}
{{ .Title }}
{{ if .Params.youtube }}
Corrections and suggestions for this page can be made on Github.
{{ partial "head.html" . }}
{{ end }} {{ .Content }}
Corrections and suggestions for this page can be made on Github.


Download anything you want from over 400,000 photos, vectors, and backgrounds! Start downloading now →
Hashtag cred air plant drinking vinegar.  Portland wolf mumblecore, synth cold-pressed polaroid poke cardigan gochujang farm-to-table photo booth.
Man bun banjo pop-up meh hammock. Skateboard hammock tousled retro, etsy taiyaki narwhal gentrify fixie food truck microdosing sustainable dreamcatcher.
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Drinking vinegar lumbersexual 90's flexitarian. Live-edge man bun air plant XOXO. Master cleanse vaporware keffiyeh.
Leggings green juice DIY, flannel tattooed selvage plaid yr sartorial chia. Scenester you probably haven't heard of them locavore.
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Celiac distillery man braid venmo, selfies you probably haven't heard of them tote bag forage fanny pack activated charcoal kale chips lo-fi before they sold out.
Mumblecore bespoke blog raw denim, authentic VHS sustainable +1 freegan neutra small batch paleo. Schlitz chicharrones pork belly palo santo.
Find this project on GitHub.
If you enjoyed this demo you might also like:
Guitar vector designed by Freepik.
Patterns by Pixel Buddha.


{{< layout}}
{{$propositionHeader}}{{/propositionHeader}}
{{$pageTitle}}Information about urgent passport{{/pageTitle}}
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You can still get a passport urgently. You need to apply with Paper Premium. {{/ content}} {{/ layout}}


<glossPartOfSpeech>
The <glossPartOfSpeech>
element identifies the part of speech for the
preferred and alternate terms. Alternate terms must have the same part of speech as the
preferred term, because all terms in the <glossentry>
topic designate
the same subject. If the part of speech is not specified, the default is a noun for the
standard enumeration.
Category: Glossentry elements


Instructions: Type a number in the text box beside a candidate's name.
Number at least 12 boxes consecutively in the order of your choice.
Showing {{filteredArray.length}} of {{vm.stateCandidatesCount}} candidates.
Last number placed: {{vm.mostRecentEnteredNumber}}
Total numbers placed: {{vm.totalEnteredNumbers}}


9/7/2018 - I got to sit in the jumpseat in the cockpit onboard a JetSuiteX flight from Concord, Northern California to Burbank, Southern California (this is a clip of the pilots doing a pre-flight check)
9/7/2018 - I got to sit in the jumpseat in the cockpit onboard a JetSuiteX flight from Concord, Northern California to Burbank, Southern California (this is a clip of the takeoff)
9/7/2018 - I got to sit in the jumpseat in the cockpit onboard a JetSuiteX flight from Concord, Northern California to Burbank, Southern California (this is a clip of the landing at night)
11/24/2018 - Victoria Secret supermodel Alessandra Ambroiso at the JetSuiteX terminal in Oakland, California about to board a flight
4/2/2019 - "Are You Dead Yet" by the Finnish melodic death metal band Children of Bodom (I recorded this live at the Observatory in Santa Ana, Orange County, California)
10/26/2019 - "Slaughter of the Soul" by the Swedish death metal band At the Gates (I recorded this live at the Hollywood Palladium in Hollywood, Los Angeles, California)
10/26/2019 - "Blinded by Fear" by the Swedish death metal band At the Gates (I recorded this live at the Hollywood Palladium in Hollywood, Los Angeles, California)
10/26/2019 - "Death in Fire" by the Swedish melodic death metal band Amon Amarth (I recorded this live at the Hollywood Palladium in Hollywood, Los Angeles, California)


Here you will find highly detailed reviews on professors. To write a review, email us at<EMAIL_ADDRESS>with
the name of the professor, the name of the course you
took, and the review. We look forward to hearing about your experiences and are thankful to all
the contributors in the ubc.io community!


Implements imputation methods using EM and Data Augmentation for multinomial data following the work of Schafer 1997 <ISBN: 978-0-412-04061-0>.
| Version: | 0.6.4 |
| Depends: | R (≥ 3.2.1) |
| Imports: | gtools (≥ 3.3), methods, parallel, Rcpp, DBI (≥ 0.4), RSQLite (≥ 1.0) |
| LinkingTo: | Rcpp |
| Suggests: | testthat |
| Published: | 2017-02-12 |
| Author: | Alex Whitworth [aut, cre] |
| Maintainer: | Alex Whitworth <whitworth.alex at gmail.com> |
| License: | GPL-3 |
| NeedsCompilation: | yes |
| CRAN checks: | imputeMulti results |
| Reference manual: | imputeMulti.pdf |
| Package source: | imputeMulti_0.6.4.tar.gz |
| Windows binaries: | r-devel: imputeMulti_0.6.4.zip, r-release: imputeMulti_0.6.4.zip, r-oldrel: imputeMulti_0.6.4.zip |
| OS X binaries: | r-release: imputeMulti_0.6.4.tgz, r-oldrel: imputeMulti_0.6.4.tgz |
| Old sources: | imputeMulti archive |
Please use the canonical form https://CRAN.R-project.org/package=imputeMulti to link to this page.


{{/* Cloak an e-mail address (first argument) */ -}}
{{- $address_parts := split . "@" -}}
{{- if (eq (len $address_parts) 2) -}}
{{- $user := index $address_parts 0 -}}
{{- $domain := index $address_parts 1 -}}
{{- else -}}
INVALID E-MAIL ADDRESS
{{- end -}}
{{- /* Dummy comment to strip trailing newline */ -}}


We hope you are finding this website smooth enough for your use. If you face any issues or want to report any
grievances, please leave a feedback below. If you want to learn more about this website or how it works, leave a
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Detects usages of Gradle plugin
com.jfrog.bintray
. The plugin is used for publishing build results to Bintray. Bintray service is going to be disabled on May 1st, 2021. Publishing will not be possible after that.


This applications need Javascript to work.
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Remove
Created at {{currentNote.createdAt}}
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robeth: R Functions for Robust Statistics
Locations problems, M-estimates of coefficients and scale
in linear regression, Weights for bounded influence regression,
Covariance matrix of the coefficient estimates, Asymptotic
relative efficiency of regression M-estimates, Robust testing
in linear models, High breakdown point regression, M-estimates
of covariance matrices, M-estimates for discrete generalized
linear models.
| Version: |
2.7-4 |
| Depends: |
R (≥ 3.2.0) |
| Published: |
2018-08-24 |
| Author: |
Alfio Marazzi |
| Maintainer: |
A. Randriamiharisoa <Alex.Randriamiharisoa at chuv.ch> |
| License: |
GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| NeedsCompilation: |
yes |
| In views: |
Robust |
| CRAN checks: |
robeth results |
Downloads:
Reverse dependencies:
Linking:
Please use the canonical form
https://CRAN.R-project.org/package=robeth
to link to this page.


{% extends 'layouts/base.html' %} {% block content %}
This app for AShHabul Qur'an Payakumbuh, a school for learning Al-Quran. In this school you can learning about Tajweed, improve your Al-Qur'an reading, how to read Al-Qur'an correctly. Also we provide an Arabic language course. Let's join us now !
This app for AShHabul Qur'an Payakumbuh, a school for learning Al-Quran. In this school you can learning about Tajweed, improve your Al-Qur'an reading, how to read Al-Qur'an correctly. Also we provide an Arabic language course. Let's join us now !
This app for AShHabul Qur'an Payakumbuh, a school for learning Al-Quran. In this school you can learning about Tajweed, improve your Al-Qur'an reading, how to read Al-Qur'an correctly. Also we provide an Arabic language course. Let's join us now !
This app for AShHabul Qur'an Payakumbuh, a school for learning Al-Quran. In this school you can learning about Tajweed, improve your Al-Qur'an reading, how to read Al-Qur'an correctly. Also we provide an Arabic language course. Let's join us now !
This app for AShHabul Qur'an Payakumbuh, a school for learning Al-Quran. In this school you can learning about Tajweed, improve your Al-Qur'an reading, how to read Al-Qur'an correctly. Also we provide an Arabic language course. Let's join us now !


Tools for setting up ("design"), conducting, and evaluating
large-scale simulation studies with graphics and tables, including
parallel computations.
| Version: |
1.0-10 |
| Depends: |
R (≥ 3.1.0), graphics |
| Imports: |
stats, parallel, utils, grDevices, methods, grid, sfsmisc, gridBase (≥ 0.4-6), colorspace |
| Suggests: |
lattice, Rmpi, Hmisc, copula, foreach, doParallel, fGarch, robustbase |
| Published: |
2018-05-17 |
| Author: |
Marius Hofert and Martin Maechler |
| Maintainer: |
Marius Hofert <marius.hofert at uwaterloo.ca> |
| License: |
GPL-2 | GPL-3 |
| NeedsCompilation: |
no |
| Citation: |
simsalapar citation info |
| CRAN checks: |
simsalapar results |


Disclosure: Multicoin Capital is short LTC. Narratives are an immensely powerful tool, so much so, that experts generally attribute civilization’s rapid development to humans’ ability to efficiently tell stories. Due to their impact on human behavior, narratives can si...
1
1
snowdamn18-Sep-18 09:26 AM
n00bs never disappoint, constantly make you feel we haven't even started and will become billionaires
its been that way for months, doesnt seem to affect price in any way
Hollowman900018-Sep-18 10:25 AM
Experiencing wallet issues
Are you still staking it, @jl777c
jl777c18-Sep-18 10:26 AM
no
havent for a long time
burned most all of it
Hollowman900018-Sep-18 10:29 AM
Oh damn, thought you were keeping it around
Actually burned it eh
jl777c18-Sep-18 10:29 AM
I think I burned a millon or so
ComputerGenie 👴👴👴18-Sep-18 11:24 AM
@PHBA2061 I didn't "confound" anything. Perhaps you should do more reading of Ponzi's life than what some random person decided to note in "Wikipedia"...
ComputerGenie 👴👴👴18-Sep-18 11:31 AM
The creation of an intangible object (a "store of value" chain) that serves no purpose other than to sell it for more to the next guy than you paid for it would be right up his alley(edited)
actionslacks18-Sep-18 11:36 AM
remember all the btcd holders that didn't make the conversion window? well there you go. time to close those support tickets
jl777c18-Sep-18 11:37 AM
all the tickets are closed as far as I know
passcombo.com 🏴☠🏴18-Sep-18 11:40 AM
btw did you see poloniex impact on Qtum trades opening? 6 of September it was
it's new but actually it's really old and obsolete.  You have these Ico's spending millions to up their cmc rankings. and you got cast aside projects and
past associates making up a large percentage of this ecosystem.
is it just an open secret? Has intellectual curiosity really atrophied this much?
 I joined about a year ago, and am vaguely familiar with the story, but couldn't write anything super accurate.
Best is to go to the NXT forum and look through JL's history.
Deleted User18-Sep-18 12:35 PM
To buy kmd now or tomorrow
I’m nice and cozy in my warm bed rn
snowdamn18-Sep-18 12:35 PM
yesterday
Deleted User18-Sep-18 12:35 PM
But knowing my luck this shits gonna pump if I leave it til tomorrow
But if I buy now it’ll dump
Bryan_Beus18-Sep-18 12:35 PM
In short: NXT tried to bottleneck the developers, and JL and company left to start KMD, so that it wouldn't happen again.
BTCD: A project JL and a few others (ca ?) were involved in, and when they left, they kept their commitment to those folk by giving a swap opportunity.
ARDR: Nothing much in common. ARDR, I believe, is the next iteration of NXT.
jl777c18-Sep-18 12:38 PM
ARDR is a half coin
just does the staking part and the rest is split off into IGNIS, which is a 50% taxed airdrop of NXT
actionslacks18-Sep-18 12:39 PM
yeah ardor is crap, so is btcd, but its 70 mil marketcap just goes to show what kind of madness is still at play here. I just wanted to say that I am appreciative of the work you put in @Bryan_Beus and am glad you joined the cause.(edited)
1
jl777c18-Sep-18 12:40 PM
@CHMEX you have contacts at CMC, maybe you can let them know about the RD6 burned BTCD? but there probably isnt even a working explorer...
actionslacks18-Sep-18 12:43 PM
I also think a well referenced and linked timeline of projects and subsequent rankings/valuations would be instructional to newcomers and would serve as a boon to the kmd ecosystem in general.
to compare kmd to some eth token is a true disservice
Bryan_Beus18-Sep-18 12:44 PM
I think we did a blog post on this awhile ago. You'd need to ask @Daniel CP ?
I'm trying to focus on getting documentation into shape, so I wouldn't be able to work on anything else for awhile.
snowdamn18-Sep-18 12:44 PM
the masses won't know what hit them when KMD shows its teeth on CMC
With a proven history of innovation and development, Komodo Platform is recognized as one of the world's most advanced blockchain projects. Learn more here.
Haven't read the full convo but this might be the thing sidd is referencing ^
A bit of history and a basic KMD timeline
actionslacks18-Sep-18 12:50 PM
@snowdamn It kinda already is, but the teeth are well hidden
Daniel CP18-Sep-18 12:50 PM
And yeah, the marketing team is actually working on a longer piece now about the larger Komodo ecosystem
Will focus on all the projects using Komodo's tech and showcase the progress that's being made
snowdamn18-Sep-18 12:51 PM
@actionslacks yes, Top 50 will do for now
actionslacks18-Sep-18 12:57 PM
The best representation would be a web that fans out to all the projects/assets using the tech and that have had past associations. But yeah, like I said. I'm fine with the shadows and the ignorance of the masses. There was a lot of whining about lack of marketing in the past. Now that's gone, thankfully.
CHMEX18-Sep-18 01:05 PM
I asked CMC now to set BTCD as inactive I did not find a working explorer anymore.
Kitchen Table18-Sep-18 01:10 PM
I remember buying BTCD at 30k sats right about the same time James announced joining the dev group. The coin double up in only a few hours.
On Tuesday September 25th, at 12:00 ET, the following assets will be delisted from Poloniex: BTCD, BTM (Bitmark), EMC2, GRC, NEOS, POT, VRC, XBC.
BTCD
FishyGuts18-Sep-18 04:29 PM
omfg
unbeweaveable
bout time.
snowdamn18-Sep-18 04:31 PM
Markus18-Sep-18 04:34 PM
EMC2 gets delisted also FeelsBadMan
ComputerGenie 👴👴👴18-Sep-18 04:36 PM
is still throwing an internal party that NEOS is on that list
SHossain18-Sep-18 04:41 PM
never used any ETH/ERC20 tokens apart from BarterDEX testing
jl777c18-Sep-18 04:43 PM
what is wrong with NEOS?
CHMEX18-Sep-18 04:46 PM
Mcap of 1.8m
Its not the big chinese neo thing
PHBA206118-Sep-18 04:46 PM
@ComputerGenie 👴👴👴 my citation was taken on the same page as what you've cited on wikipedia.
Besides I was answering to your question why btc is a better store of value relative to eth or kmd (at least for now), or hyperinflated currencies . I never said it is a good store of value , compared to gold or any traditional store of value.
ComputerGenie 👴👴👴18-Sep-18 04:49 PM
@PHBA2061 I don't recall ever citing wiki for Ponzi, ever
and I must have missed where you were " answering to [my] question why btc is a better store of value relative to eth or kmd", could you please reiterate....
#BitcoinDark #BTCD definitely has strong potential. It has grown around 258.93% in the last 30 days, sitting now at $64.9832891512 - https://t.co/WRX5JXiMDP #cryptostare, #cryptocurrency, #bullish
5
1
3
8 Sep 2018
ComputerGenie 👴👴👴18-Sep-18 04:58 PM
As if to magically prove every point I've been trying to make about the perception of "value" in current markets


All queries must be ran against the "monsters" table in the database.
To execute a query using the keyboard, either hit "Alt+Q" or "F5".
To create the database, you must use the .csv version of the monster database from d20pfsrd.
After obtaining the file, click "Create Database...". Select the downloaded file and then select the spot where you would like to keep the database file.
Cheat sheet from cheat-sheets.org.


# node-dateformat
A node.js package for Steven Levithan’s excellent [dateFormat()][dateformat] function.
## Modifications
- Removed the Date.prototype.format method. Sorry folks, but extending native prototypes is for suckers.
- Added a module.exports = dateFormat; statement at the bottom
## Usage
As taken from Steven’s post, modified to match the Modifications listed above:
var dateFormat = require(‘dateformat’); var now = new Date();
// Basic usage dateFormat(now, “dddd, mmmm dS, yyyy, h:MM:ss TT”); // Saturday, June 9th, 2007, 5:46:21 PM
// You can use one of several named masks dateFormat(now, “isoDateTime”); // 2007-06-09T17:46:21
// ...Or add your own dateFormat.masks.hammerTime = ‘HH:MM! “Can’t touch this!”’; dateFormat(now, “hammerTime”); // 17:46! Can’t touch this!
// When using the standalone dateFormat function, // you can also provide the date as a string dateFormat(“Jun 9 2007”, “fullDate”); // Saturday, June 9, 2007
// Note that if you don’t include the mask argument, // dateFormat.masks.default is used dateFormat(now); // Sat Jun 09 2007 17:46:21
// And if you don’t include the date argument, // the current date and time is used dateFormat(); // Sat Jun 09 2007 17:46:22
// You can also skip the date argument (as long as your mask doesn’t // contain any numbers), in which case the current date/time is used dateFormat(“longTime”); // 5:46:22 PM EST
// And finally, you can convert local time to UTC time. Simply pass in // true as an additional argument (no argument skipping allowed in this case): dateFormat(now, “longTime”, true); // 10:46:21 PM UTC
// ...Or add the prefix “UTC:” to your mask. dateFormat(now, “UTC:h:MM:ss TT Z”); // 10:46:21 PM UTC
// You can also get the ISO 8601 week of the year: dateFormat(now, “W”); // 42
## License
- 2007-2009 Steven Levithan [stevenlevithan.com][stevenlevithan], MIT license.
[dateformat]: http://blog.stevenlevithan.com/archives/date-time-format [stevenlevithan]: http://stevenlevithan.com/


Zig is the first language that I’ve seen which seems interested in seriously playing in the same space as Rust.
And it does it in a substantially different way, which I like! It feels (reading docs) kind of like a “doing the kinds of things Rust does but with C instead of C++ as its direct competitor.”


Default:
This example shows how to use the default configuration with div
boxes.
The param text
is the text inside each tab.
$('#tab').flapy({
headers: [{ text: 'Ruby' }, { text: 'Python' }, { text: 'Java' }]
});

Box 1

Box 2

Box 3

Tag:
You can choose some HTML tag to put inside the li
header:
$('selector').flapy({
headers: [{ tag: 'h3', text: 'Ruby' }, { tag: 'h3', text: 'Python' }, { tag: 'h3', text: 'Java' }]
});
Do not forget the style:
.flapy-header li h2
and.flapy-header li.active h2
.
Active:
By default the first tab is actived, but you can choose which tab will start activated with the active
option:
$('selector').flapy({
headers: [{ text: 'Ruby' }, { text: 'Python' }, { text: 'Java', active: true }]
});
Wrapper:
Normally we have a header and several boxes that hide and show up. When we want to add some fixed element outside the boxes, we need to add a wrapper:
Fixed element
$('selector').flapy({
headers: [{ text: 'Ruby' }, { text: 'Python' }, { text: 'Java' }]
wrapper: 'div'
});

Outside the box

Box 1

Box 1

Box 1

Border:
You can disable the auto borders to design your own tab style.
$('selector').flapy({
border : true,
headers: [{ tag: 'h3', text: 'Ruby' }, { tag: 'h3', text: 'Python' }, { tag: 'h3', text: 'Java' }]
});
Hide:
Hides the given tab. It still exist on DOM.
It is good when you have a logic to hide or show, without much restrictions.
If you hide an actived tab, the first available will be actived.
$('selector').flapy({
headers: [{ text: 'Ruby' }, { text: 'Python', hide: true }, { text: 'Java' } ]
});
Remove:
Different from hide it removes the element of the DOM.
Good to use when you have restrict data that will not be displayed on the screen and then want to its markup.
If you remove an actived tab, the first available will be actived.
$('selector').flapy({
headers: [{ text: 'Ruby' }, { text: 'Python', remove: true }, { text: 'Java' } ]
});
Changing the settings globally:
You can change any option mentioning the scope $.fn.flapy.defaults.
+ option_name. It must be called before you bind the plugin.
$.fn.flapy.defaults.box = 'div';
$.fn.flapy.defaults.wrapper = 'p';
Options:
Changes the tab borders automatic.
The children that will represent the boxes.
Array of object containing tag
, text
, active
, hide
and remove
attributes representing each tab.
An element that will keeps all boxes.
Tests:
This plugin is tested to work better. Check it out!


A Spry Validation Checkbox widget is a checkbox or group of checkboxes in an HTML code form that display valid or invalid states when the user selects or fails to select a checkbox. For example, you can add a Validation Checkbox widget to a form in which a user might be required to make three selections. If the user fails to make all three selections, the widget returns a message stating that the minimum number of selections was not met.
The following example shows a Validation Checkbox widget in various states.
The Validation Checkbox widget includes a number of states (for example, valid, invalid, required value, and so on). You can alter the properties of these states by editing the corresponding CSS file (SpryValidationCheckbox.css), depending on the desired validation results. A Validation Checkbox widget can validate at various points (for example, when the user clicks outside the widget, as the user makes selections, or when the user tries to submit the form).
Whenever a Validation Checkbox widget enters one of these states through user interaction, the Spry framework logic applies a specific CSS class to the HTML container for the widget at run time. For example, if a user tries to submit a form, but makes no selections, Spry applies a class to the widget that causes it to display the error message, “Please make a selection.” The rules that control the style and display states of error messages reside in the SpryValidationCheckbox.css file that accompanies the widget.
The default HTML code for the Validation Checkbox widget, usually inside a form, is made up of a container span tag that surrounds the input type="checkbox" tag of the checkbox. The HTML code for the Validation Checkbox widget also includes script tags in the head of the document and after the widget’s HTML code. The script tag in the head of the document defines all of the JavaScript functions related to the Checkbox widget. The script tag after the widget code creates a JavaScript object that makes the checkbox interactive.
Following is the HTML code for a Validation Checkbox widget:
 ...    <link href="SpryAssets/SpryValidationCheckbox.css" rel="stylesheet" type="text/css" />   <form id="form1" name="form1" method="post" action="">   <input type="checkbox" name="checkbox1" value="1"/> <input type="checkbox" name="checkbox2" value="2"/>  Please make a selection.  </form>
In the code, the new JavaScript operator initializes the Checkbox widget object, and transforms the span content with the ID of sprycheckbox1 from static HTML code into an interactive page element.
The span tag for the error message in the widget has a CSS class applied to it. This class (which is set to display:none; by default), controls the style and visibility of the error message, and exists in the accompanying SpryValidationCheckbox.css file. When the widget enters different states as a result of user interaction, Spry places different classes on the container for the widget, which in turn affects the error-message class.
You can add other error messages to a Validation Checkbox widget by creating a span tag (or any other type of tag) to hold the text of the error message. Then, by applying a CSS class to it, you can hide or show the message, depending on the widget state.
You can change the default appearance of the Validation Checkbox widget's states by editing the corresponding CSS rule in the SpryValidationCheckbox.css file. For example, to change the background color for a state, edit the corresponding rule or add a new rule (if it’s not already present) in the style sheet.
In the preceding example, span tags create the structure for the widget:
Container SPAN INPUT type="checkbox" Error message SPAN
You can, however, use almost any container tag to create the widget.
Container DIV INPUT type="checkbox" Error Message P
Spry uses the tag ID (not the tag itself) to create the widget. Spry also displays error messages using CSS code that is indifferent to the actual tag used to contain the error message.
The ID passed into the widget constructor identifies a specific HTML element. The constructor finds this element and looks inside the identified container for a corresponding input tag. If the ID passed to the constructor is the ID of the input tag (rather than a container tag), the constructor attaches validation triggers directly to the input tag. If no container tag is present, however, the widget cannot display error messages, and different validation states alter only the appearance of the input tag element (for example, its background color).


Choose a test and click "Run" to watch it on. You can edit the code with no fear of destroying the original examples!


SELECT_DESTROY
SELECT_DESTROY
When set it detroys the last selection result set and starts over a new selection.
$xml_query : \XDTNodeList
A set of the current matched elements.
connect(string $file_name, boolean $preserve_white_space = false, boolean $format_output = false) : boolean
Load XML from a file.
| string | $file_name | XML file to load. The file extension (.xml) is optional. |
| boolean | $preserve_white_space | [optional] Do not remove redundant white space. Default to false. |
| boolean | $format_output | [optional] Nicely formats output with indentation and extra space. Default to false. |
Returns true on success and false on failure.
load(string $xml, boolean $preserve_white_space = false, boolean $format_output = false) : boolean
Load a string containing XML structure.
| string | $xml | String containing XML structure. |
| boolean | $preserve_white_space | [optional] Do not remove redundant white space. Default to false. |
| boolean | $format_output | [optional] Nicely formats output with indentation and extra space. Default to false. |
Returns true on success and false on failure.
getDocumentRootElement() : \XDTNodeList
Get the document root element.
select(string $selector = '*', \DOMElement $context = null, integer $flag = \XDT::SELECT_DESTROY) : \XDTNodeList
Parse a string containing a CSS selector expression, match the selected elements and returns a XDTNodeList object containing the set of matched elements; XDTNodeList class implements arrayAccess and iteratorAgregate interfaces, so its objects can to be treated like an array; jQuery specific selectors (first, last, nth, eq, not) are also supported.
| string | $selector | [optional] A string containing the selector expression. You can provide multiples selectors by separating them with a comma (,). |
| \DOMElement | $context | [optional] DOM element used as the selection context. |
| integer | $flag | [optional]
Accepted flags are:
|
initListObject(\DOMElement $node) : \XDTNodeList
Cast DOMElement to XDTNodeList object.
| \DOMElement | $node |
createNewXMLFile(String $file_name, $root = null, $charset = 'UTF-8') : Boolean
Creates a new XML file

Warning: If the file already exists, its content will be overwritten.
| String | $file_name |
|
| $root | ||
| $charset |
True on success, or False on error.
item(integer $index) : \DOMElement
Get an item at index n from the set of matched elements.
| integer | $index | Index of the element in the set of matched elements. |
Returns a DOM element or throws an OutOfRangeException if the element was not found.
merge(\XDTNodeList $list)
Merge the provided set of elements into the matched set of elements.
| \XDTNodeList | $list | Set of elements to be merged into the current set. |
remove() : \XDTNodeList
Remove the set of matched elements from the DOM.
Returns the set of matched elements that was removed.
emptyNode() : \XDTNodeList
Remove all child nodes of the set of matched elements from the DOM.
Returns the set of matched elements whose children were removed.
replaceWith(mixed $content) : \XDTNodeList
Replace each element in the set of matched elements with the provided new content and return the set of elements that was removed.
| mixed | $content | The content to insert may be an XML string, DOM element, array of DOM elements, or XDTNodeList object. When you pass a XDTNodeList collection containing more than one element, or a selector matching more than one element, the first element will be used. |
Returns the set of elements that was removed.
insertBefore(mixed $target) : \XDTNodeList
Insert the first element in the selection before the element passed in argument.
| mixed | $target | A selector, DOM element, XML string, or XDTNodeList object; the matched set of elements will be inserted before the element specified by this parameter. |
Returns the inserted elements on success, for chaining purpose, or &null on failure.
insertAfter(mixed $target) : \XDTNodeList
Insert the first element in the selection after the element passed in argument.
| mixed | $target | A selector, DOM element, XML string, or XDTNodeList object; the matched set of elements will be inserted after the element specified by this parameter. When you pass a XDTNodeList collection containing more than one element, or a selector matching more than one element, the first element will be used. |
Returns the inserted elements on success, for chaining purpose, or &null on failure.
append(mixed $content) : \XDTNodeList
Insert content, specified by the parameter, to the end of each element in the set of matched elements.
| mixed | $content | DOM element, text node, array of elements and text nodes, XML string, or XDTNodeList object to insert at the end of each element in the set of matched elements. You can provide any number of contents by separating them by a comma (,). |
Returns the set of inserted elements on success, for chaining purpose, or &null on failure.
prepend( $content) : \XDTNodeList
Insert content, specified by the parameter, to the begining of each element in the set of matched elements.
| $content |
Returns the set of inserted elements on success, for chaining purpose, or &null on failure.
appendTo(mixed $target) : \XDTNodeList
Insert every element in the set of matched elements to the end of the target.
| mixed | $target | A selector, element, XML string, array of elements, or XDTNodeList object; the matched set of elements will be inserted at the end of the element(s) specified by this parameter. |
Returns the set of inserted elements, for chaining purpose, on success or &null on failure.
prependTo(mixed $target) : \XDTNodeList
Insert every element in the set of matched elements to the begining of the target.
| mixed | $target | A selector, element, XML string, array of elements, or XDTNodeList object; the matched set of elements will be inserted at the begining of the element(s) specified by this parameter. |
Returns the set of inserted elements, for chaining purpose, on success or &null on failure.
wrap(mixed $selector) : \XDTNodeList
Wrap an HTML structure around each element in the set of matched elements.
| mixed | $selector | A selector, element, XML string, or XDTNodeList object specifying the structure to wrap around the matched elements. When you pass a XDTNodeList collection containing more than one element, or a selector matching more than one element, the first element will be used. |
Returns the set of matched elements for chaining purpose.
children(string $selector = null) : \XDTNodeList
Get the children of each element in the set of matched elements, optionally filtered by a selector.
| string | $selector | [optional] A string containing the selector expression to match against. |
hasChildren(mixed $selector = null) : boolean
Verify whether any element in the set of matched elements is a single parent or optionally verify whether the given element that match the selector is a child of any element in the set of matched elements.
| mixed | $selector | [optional] A string containing the selector expression or a DOMElement. |
Returns TRUE on success, or FALSE on error or failure.
parents(string $selector = null) : \XDTNodeList
Get the ancestors of each element in the current set of matched elements, optionally filtered by a selector.
| string | $selector | [optional] A string containing the selector expression. |
parent(string $selector = null) : \XDTNodeList
Get the parent of each element in the current set of matched elements, optionally filtered by a selector.
| string | $selector | [optional] A string containing the selector expression. |
find(string $selector = null) : \XDTNodeList
Get the descendences of each element in the current set of matched elements, filtered by a selector
| string | $selector | [optional] A string containing the selector expression. If omitted the current set of matched elements is returned. |
filter(string $selector = null) : \XDTNodeList
Reduce the set of matched elements to those that match the selector
| string | $selector | [optional] A string containing the selector expression. If omitted the current set of matched elements is returned. |
hasClass(string $class) : boolean
Verify whether any of the matched elements are assigned the given class; elements can have more than one class assigned to them, in html this is represented by separating the class names with space
| string | $class | The class name to search for. |
Returns boolean true on success, or false on failure.
data(array $data = null) : \DOMNamedNodeMap
Get attributes for the first matched element or set one or more attributes for every matched element.
| array | $data | [optional] |
object otherwise void
attr(mixed $name, mixed $value = null) : mixed
Get the value of an attribute for the first element in the set of matched elements or set one or more attributes for every matched element.
| mixed | $name | A string representing the attribute name which value is to be set or an array of attributes which values are to be set. |
| mixed | $value | [Optional] A string representing the new value of the first matched element or an array of values in the same order as the names array giving as the first argument to the method.
|
The current value of the named attribute of the first matched element when the method is used to get a value; otherwise void.
index(mixed $selector = null) : integer
Get the matched element index from among its siblings, or get a giving DOM element's index from the set of matched elements, or get a filtered element's index from among the set of matched elements
| mixed | $selector | [optional] A string representing a selector expression to filter the set of matched elements against, or a DOMElement representing the element which index is search for. |
Returns the matched element's index on success, or -1 on failure.
map(string $func_name) : \Returns
Translate all items in set of matched elements to a new set of elements by altering their value through a call to a callback function passed in argument

 Notice: This method alter directly the loaded xml file; an explicit call to the XDT::close method must follow the call of the method.
| string | $func_name | A function to process each item against. The first argument to the function is the element index and the second argument is a DOMElement object. The returned value of the function is used as the current value of the element. |
null on failure.
val(string $value = null) : string
Get the current value of the first element in the set of matched elements or optionally set the value of every matched element

 Notice: This method alter directly the loaded xml file; The content of the file should be explicitly saved after call to the method.
| string | $value | [optional] A string representing the current value of the first matched element. Otherwise void. |
A string representing the current value of the first element in the set of matched elements.
first() : \XDTNodeList
Get the first element in the set of matched elements.
last() : \XDTNodeList
Get the last element in the set of matched elements.
siblings(string $selector = null) : \XDTNodeList
Get the siblings of each element in the set of matched elements, optionally filtered by a selector.
| string | $selector | [optional] A string containing a selector expression to match against. When omitted, the siblings of each element in the set of matched elements are returned. |
Returns the sibling of each element in the set of matched elements, optionally filtered by a selector.
next(string $selector = null) : \XDTNodeList
Get the immediately following sibling of each element in the set of matched elements.
If a selector is provided, it retrieves the next sibling only if it matches that selector.
| string | $selector | [optional] A string containing a selector expression to match elements against. If selector is omitted, all the next siblings of each element in the matched set are returned. |
Returns the matched set.
prev(string $selector = null) : \XDTNodeList
Get the immediately preceding sibling of each element in the set of matched elements.
If a selector is provided, it retrieves the previous sibling only if it matches that selector.
| string | $selector | [optional] A string containing a selector expression to match elements against. If selector is omitted, all the previous siblings of each element in the matched set are returned. |
Returns the matched set.
eq(integer $index) : \DOMElement
Select the element at index n within the matched set.
| integer | $index | Zero-based index at which to select the element.
|
Returns the selected element object.
nth(string $selector) : \XDTNodeList
Select all elements that are nth-child of their parent.
| string | $selector | A string containing a selector expression to match against. |
Returns the set of matched elements.
not(mixed $selector = null) : \XDTNodeList
Remove elements from the set of matched elements.
| mixed | $selector | [optional] A string containing a selector expression, a DOM element, or an array of elements to match against the set. If selector is omitted, the filtering operation is canceled and the previous set of matched elements is returned. |
Returns the filtered elements.
end() : \XDTNodeList
End the recent filtering operation in the current chain and return the set of matched element to its initial state.
Returns the current matched elements.
html(string $html = null) : \HTML
Get the html of the first element in the set of matched elements or set the html element content of every matched element.
| string | $html | [optional] when set, the html content of every matched element will be set to that value. Otherwise the html content of the first element in the set of matched elements is returned. |
Returns the html content of the first element in the set of matched elements. Otherwise the elements in the set of matched elements which html content is set are returned.


{% extends 'lunch/base.html' %} {% load staticfiles %} {% block content %}
Meal options at this place:
Want to add another one?
There are no meal options for this place yet. Why not add one:


The driver supports the standard Modbus communication protocol and works as a slave. Using the driver, the Communicator waits for incoming requests and commands from a third-party device or application that works as a master. The following communication channels are supported: serial port, TCP server, and UDP. The driver can work in Modbus RTU or Modbus TCP mode.
Modbus Slave Driver features:
Modbus Slave Driver is installed in accordance with the general sequence of installing Communicator drivers. The driver library file is KpModbusSlave.dll.
First of all, create a new communication line and a new device in the configuration database, as well as in the Communicator settings. For this purpose, it is suggested to use wizards that are opened by the and buttons. The device address matters, because this is the unit ID that the Communicator validates to respond. Then open the main parameters of the communication line, select the communication channel type and configure its properties. Examples of communication channel properties are shown below.
After creating a device in Communicator, open the the device properties form and configure the device.
Input data validity period allows to automatically set the undefined status of the device input tags, if no new data has been received from the device within a specified time.
Device template defines the Modbus register map. Device templates of the KpModbus.dll and KpModbusSlave.dll drivers are fully compatible.
Data source device is set to non-zero to transmit the input channels values received from another device into a third-party system. If the driver is used to interact with a real device, this parameter should be 0.
The following figure shows the Modbus template editor:
Modbus Slave Driver requires registration. After completing the settings, upload the project to the server using the button. Then open the Drivers page in the Administrator application, select the KpModbusSlave.dll driver, open the driver properties and register it. After registration, upload the project to the server again.


Let us always meet each other with smile, for the smile is the beginning of love.
Mother Teres
A flower cannot blossom without sunshine, and man cannot live without love.
Max Muller
Immature love says: 'I love you because I need you.' Mature love says 'I need you because I love you.'
Erich Fromm
Love isn't something you find. Love is something that finds you.
Loretta Young
All sources come from: www.brainyquote.com


Docker Engine or “Docker” creates and runs Docker containers. Install Docker on Ubuntu, Mac OS X, or Windows. Or use the Install menu to choose from others.
Docker Machine automates container provisioning on your network or in the cloud. Install machine on Windows, Mac OS X, or Linux.
You can check all docker documentation here


{{ partial "header.html" . }} {{ partial "nav_links.html" . }}
Sorry, the page you requested does not exist.
{{ partial "footer.html" . }} {{ partial "scripts.html" . }}


{%if not is_dev%} {%endif%}
{{msg}}
Details: {{technical}}
Please post a message to the discussion group if you have any questions.


kt group
my
news
4
log out
This div is 12-columns wide on all screen sizes
create
open
friend
menu
cloud
edit
editor
Alvin
Alan
Jonathan
First
Second
Third


CCONJ
: coordinating conjunction
The English CCONJ
corresponds to PTB CC.
Treebank Statistics (UD_English)
There are 25 CCONJ
lemmas (0%), 26 CCONJ
types (0%) and 7487 CCONJ
tokens (3%).
Out of 17 observed tags, the rank of CCONJ
is: 16 in number of lemmas, 16 in number of types and 11 in number of tokens.
The 10 most frequent CCONJ
lemmas: and, but, or, &, both, either, yet, plus, nor, not
The 10 most frequent CCONJ
types: and, but, or, &, both, either, yet, plus, nor, not
The 10 most frequent ambiguous lemmas: and (CCONJ 5549, X 6, DET 5, ADP 2), but (CCONJ 822, ADP 11, ADV 2, SCONJ 2, NOUN 1), or (CCONJ 802, ADP 2, INTJ 1), & (CCONJ 121, X 4), both (CCONJ 58, DET 53, ADV 12), either (CCONJ 32, ADV 11, DET 8), yet (ADV 47, CCONJ 22), plus (CCONJ 19, NOUN 3, ADV 1), not (PART 1791, ADV 173, CCONJ 15), for (ADP 1906, SCONJ 164, CCONJ 7, X 4, ADV 2)
The 10 most frequent ambiguous types: and (CCONJ 5384, X 6, DET 5, ADP 2), but (CCONJ 682, ADP 10, SCONJ 2, ADV 2, NOUN 1), or (CCONJ 786, ADP 2), & (CCONJ 121, X 4), both (CCONJ 51, DET 42, ADV 12), either (CCONJ 29, ADV 11, DET 6), yet (ADV 46, CCONJ 17), plus (CCONJ 12, NOUN 3, ADV 1), not (PART 891, ADV 148, CCONJ 13), for (ADP 1846, SCONJ 163, CCONJ 6, X 3, ADV 2)
- and
- CCONJ 5384: Right now that seems to be the US , EU , and IAEA .
- X 6: « Alberta Transmission Access and Pricing Analysis_0712 .doc »
- DET 5: it s your cat you can pick and name you want
- ADP 2: The people there attempt to come across and professional and nice , but I was disappointed with their customer service .
- but
- CCONJ 682: The Army is unlikely to forgive or forget ; but who provoked it and why ?
- ADP 10: I have nothing but fantastic things to say .
- SCONJ 2: We had no choice but to stay but will take this as far as we can .
- ADV 2: 50 = Approximate number of policy changes and regulation rollbacks injurious to the environment that have been announced by the Bush administration on Fridays after 5 pm , a time that makes it all but impossible for news organisations to relay the information to the widest possible audience .
- NOUN 1: Lope her to the left in circles , keep her head to the inside of the circle and push her but out , keep doing this if she breaks to a jog go a little bit bigger circle , till she can get enough balance to go small and small . circles
- or
- &
- both
- either
- yet
- plus
- not
- for
- ADP 1846: Yet we did n’t charge them for the evacuation .
- SCONJ 163: Thanks for thinking of me to send it to .
- CCONJ 6: Please make this as simple as you can for my dad does nt really not that good .
- X 3: « File : 220b - dg - Agreement for Recruiting Services.doc » « File : 220a DG - Agreement for Contract Services.DOC »
- ADV 2: So they see the pictures flicker slower and there for it seems choppy to them .
Morphology
The form / lemma ratio of CCONJ
is 1.040000 (the average of all parts of speech is 1.181137).
The 1st highest number of forms (2) was observed with the lemma “and”: afnd, and.
The 2nd highest number of forms (1) was observed with the lemma “&”: &.
The 3rd highest number of forms (1) was observed with the lemma “’n”: ‘n.
CCONJ
does not occur with any features.
Relations
CCONJ
nodes are attached to their parents using 11 different relations: en-dep/cc (7365; 98% instances), en-dep/cc:preconj (94; 1% instances), en-dep/fixed (9; 0% instances), en-dep/conj (6; 0% instances), en-dep/advmod (5; 0% instances), en-dep/mark (3; 0% instances), en-dep/compound (1; 0% instances), en-dep/obl (1; 0% instances), en-dep/orphan (1; 0% instances), en-dep/reparandum (1; 0% instances), en-dep/root (1; 0% instances)
Parents of CCONJ
nodes belong to 18 different parts of speech: VERB (3278; 44% instances), NOUN (2140; 29% instances), ADJ (880; 12% instances), PROPN (644; 9% instances), ADV (166; 2% instances), PRON (155; 2% instances), NUM (77; 1% instances), DET (35; 0% instances), CCONJ (32; 0% instances), PART (20; 0% instances), SYM (14; 0% instances), ADP (13; 0% instances), SCONJ (10; 0% instances), AUX (9; 0% instances), X (6; 0% instances), PUNCT (4; 0% instances), INTJ (3; 0% instances), ROOT (1; 0% instances)
7438 (99%) CCONJ
nodes are leaves.
22 (0%) CCONJ
nodes have one child.
24 (0%) CCONJ
nodes have two children.
3 (0%) CCONJ
nodes have three or more children.
The highest child degree of a CCONJ
node is 4.
Children of CCONJ
nodes are attached using 11 different relations: en-dep/punct (32; 40% instances), en-dep/cc (29; 36% instances), en-dep/conj (7; 9% instances), en-dep/advmod (3; 4% instances), en-dep/case (3; 4% instances), en-dep/advcl (1; 1% instances), en-dep/amod (1; 1% instances), en-dep/cc:preconj (1; 1% instances), en-dep/discourse (1; 1% instances), en-dep/obl (1; 1% instances), en-dep/orphan (1; 1% instances)
Children of CCONJ
nodes belong to 11 different parts of speech: PUNCT (36; 45% instances), CCONJ (30; 38% instances), ADP (3; 4% instances), ADV (2; 3% instances), SYM (2; 3% instances), VERB (2; 3% instances), ADJ (1; 1% instances), INTJ (1; 1% instances), PART (1; 1% instances), PROPN (1; 1% instances), X (1; 1% instances)
Treebank Statistics (UD_English-LinES)
There are 1 CCONJ
lemmas (6%), 12 CCONJ
types (0%) and 2081 CCONJ
tokens (3%).
Out of 17 observed tags, the rank of CCONJ
is: 5 in number of lemmas, 15 in number of types and 11 in number of tokens.
The 10 most frequent CCONJ
lemmas: _
The 10 most frequent CCONJ
types: and, but, or, both, nor, as, either, for, neither, &
The 10 most frequent ambiguous lemmas: _ (NOUN 12161, PUNCT 8085, VERB 8020, ADP 6788, DET 6429, PRON 6303, ADJ 4270, ADV 3700, AUX 3539, PROPN 2257, CCONJ 2081, PART 1703, SCONJ 1231, NUM 462, INTJ 122, X 41, SYM 5)
The 10 most frequent ambiguous types: and (CCONJ 1507, ADV 5), but (CCONJ 169, ADP 11), both (CCONJ 11, PRON 4, DET 2), as (ADP 170, SCONJ 152, ADV 60, CCONJ 8), either (ADV 6, CCONJ 3, DET 2, PRON 1), for (ADP 424, SCONJ 12, ADV 5, CCONJ 1), neither (CCONJ 4, ADV 2), only (ADV 67, ADJ 14, CCONJ 3), let (VERB 21, CCONJ 1)
- and
- but
- both
- as
- ADP 170: You can group in intervals such as days , weeks , and quarters .
- SCONJ 152: So that , as they say in Chicago , is where the smart money is .
- ADV 60: He smiled , as if at some quiet joke .
- CCONJ 8: What is most needed in this , as well as in other areas , is a serious in depth analysis of the problems .
- either
- ADV 6: There ‘s no initiation either into such mysteries .
- CCONJ 3: Note that you are not required to link either a CSS file or an XSL style sheet to an XML document in order for Internet Explorer 5 ( and later versions ) to display the document .
- DET 2: His small nose showed unexpectedly beaky now that the skin had sunk on either side .
- PRON 1: Compared with my own objectives , I do not think the demands made by either of them are tough enough .
- for
- ADP 424: The current Windows NT user must be an Administrator for the computer .
- SCONJ 12: Such a suspicion made one pause – for out there there were no external checks .
- ADV 5: No one is left out ; there are clear references to women , for example .
- CCONJ 1: In the immutability of their surroundings the foreign shores , the foreign faces , the changing immensity of life , glide past , veiled not by a sense of mystery but by a slightly disdainful ignorance ; for there is nothing mysterious to a seaman unless it be the sea itself , which is the mistress of his existence and as inscrutable as Destiny .
- neither
- only
- let
Morphology
The form / lemma ratio of CCONJ
is 12.000000 (the average of all parts of speech is 527.705882).
The 1st highest number of forms (12) was observed with the lemma “_”: &, and, as, both, but, either, for, let, neither, nor, only, or.
CCONJ
does not occur with any features.
Relations
CCONJ
nodes are attached to their parents using 6 different relations: en-dep/cc (2060; 99% instances), en-dep/fixed (7; 0% instances), en-dep/mark (7; 0% instances), en-dep/advmod (5; 0% instances), en-dep/amod (1; 0% instances), en-dep/root (1; 0% instances)
Parents of CCONJ
nodes belong to 14 different parts of speech: VERB (1028; 49% instances), NOUN (628; 30% instances), ADJ (183; 9% instances), PROPN (86; 4% instances), ADV (63; 3% instances), PRON (38; 2% instances), NUM (26; 1% instances), AUX (15; 1% instances), PART (7; 0% instances), ADP (2; 0% instances), INTJ (2; 0% instances), DET (1; 0% instances), ROOT (1; 0% instances), SCONJ (1; 0% instances)
2048 (98%) CCONJ
nodes are leaves.
14 (1%) CCONJ
nodes have one child.
19 (1%) CCONJ
nodes have two children.
The highest child degree of a CCONJ
node is 2.
Children of CCONJ
nodes are attached using 5 different relations: en-dep/punct (30; 58% instances), en-dep/fixed (19; 37% instances), en-dep/advmod (1; 2% instances), en-dep/conj (1; 2% instances), en-dep/nmod (1; 2% instances)
Children of CCONJ
nodes belong to 6 different parts of speech: PUNCT (30; 58% instances), ADV (10; 19% instances), ADP (9; 17% instances), NOUN (1; 2% instances), PART (1; 2% instances), VERB (1; 2% instances)
Treebank Statistics (UD_English-ParTUT)
There are 13 CCONJ
lemmas (0%), 13 CCONJ
types (0%) and 1285 CCONJ
tokens (3%).
Out of 17 observed tags, the rank of CCONJ
is: 13 in number of lemmas, 14 in number of types and 11 in number of tokens.
The 10 most frequent CCONJ
lemmas: and, or, but, both, nor, neither, either, yet, as, et
The 10 most frequent CCONJ
types: and, or, but, both, nor, neither, either, yet, as, et
The 10 most frequent ambiguous lemmas: or (CCONJ 139, X 1), but (CCONJ 123, ADV 2, ADP 2), both (CCONJ 11, DET 7, ADJ 1, PRON 1), neither (CCONJ 5, DET 1), either (ADV 4, CCONJ 2), yet (ADV 7, CCONJ 2, SCONJ 1), as (ADP 205, SCONJ 41, ADV 29, CCONJ 1), namely (ADV 2, CCONJ 1), save (VERB 3, CCONJ 1)
The 10 most frequent ambiguous types: but (CCONJ 77, ADV 2, ADP 2), both (CCONJ 11, DET 7, PRON 1), neither (CCONJ 4, DET 1), either (ADV 4, CCONJ 2), yet (ADV 6, CCONJ 2, SCONJ 1), as (ADP 191, SCONJ 29, ADV 29, CCONJ 1), namely (ADV 2, CCONJ 1), save (CCONJ 1, VERB 1)
- but
- CCONJ 77: ” This is a great scientific discovery “ , but it was so awful .
- ADV 2: Londoners flocked there to see the first part of Henry IV , Leonard Digges recording , “ Let but Falstaff come , Hal , Poins , the rest … and you scarce shall have a room “ .
- ADP 2: There ‘s no mechanism in this game for them to go anywhere but into a bigger and bigger pile of indigestible Styrofoam peanuts .
- both
- CCONJ 11: similarly , human beings may behave both as individual particles and as components of a larger wave .
- DET 7: it must have good relations with both sides .
- PRON 1: In the case of King Lear , however , while most modern editions do conflate them , the 1623 folio version is so different from the 1608 quarto , that the Oxford Shakespeare prints them both , arguing that they can not be conflated without confusion .
- neither
- CCONJ 4: Balzac sought to present his characters as real people , neither fully good nor fully evil , but fully human .
- DET 1: Except for the limited purpose of indicating to the public that the Work is licensed under the CCPL , neither party will use the trademark “ Creative Commons “ or any related trademark or logo of Creative Commons without the prior written consent of Creative Commons .
- either
- yet
- ADV 6: As long as this Agreement is not yet concluded , insofar as it applies provisionally .
- CCONJ 2: The writing is simple , yet the individuals ( especially the bourgeois title character ) are dynamic and complex .
- SCONJ 1: These countries ‘ global economic and financial significance has grown rapidly , yet they have relatively little representation at the Fund .
- as
- ADP 191: Inward FDI could increase by as much as 83 % annually by 2020 ;
- SCONJ 29: Europe should not be completely absent , as the states tend to want .
- ADV 29: Inward FDI could increase by as much as 83 % annually by 2020 ;
- CCONJ 1: ” Balzac has received high praise from critics as diverse as Walter Benjamin and Camille Paglia .
- namely
- ADV 2: - train / inform new staff and extend the training to sectors that have less benefited from the Pericles programme , namely financial agents , prosecutors and technical staff ;
- CCONJ 1: Mr President , it is particularly pleasing for me to make my first speech in the European Parliament on what is regarded as the most important issue within that part of the United Kingdom that I represent in this Parliament , namely Wales .
- save
- CCONJ 1: Le Père Goriot ( Old Father Goriot , 1835 ) was his next success , in which Balzac transposes the story of King Lear to 1820 ‘s Paris in order to rage at a society bereft of all love save the love of money .
- VERB 1: Now , after the Ukrainian people ‘s commitment to closer ties with Europe fueled a successful popular insurrection , the EU , along with the International Monetary Fund , is putting together a multibillion - dollar rescue package to save the country from financial collapse .
Morphology
The form / lemma ratio of CCONJ
is 1.000000 (the average of all parts of speech is 1.187751).
The 1st highest number of forms (1) was observed with the lemma “and”: and.
The 2nd highest number of forms (1) was observed with the lemma “as”: as.
The 3rd highest number of forms (1) was observed with the lemma “both”: both.
CCONJ
does not occur with any features.
Relations
CCONJ
nodes are attached to their parents using 4 different relations: en-dep/cc (1279; 100% instances), en-dep/root (3; 0% instances), en-dep/conj (2; 0% instances), en-dep/fixed (1; 0% instances)
Parents of CCONJ
nodes belong to 13 different parts of speech: NOUN (589; 46% instances), VERB (391; 30% instances), ADJ (152; 12% instances), PROPN (84; 7% instances), NUM (18; 1% instances), ADV (17; 1% instances), PRON (11; 1% instances), ADP (9; 1% instances), AUX (5; 0% instances), ROOT (3; 0% instances), CCONJ (2; 0% instances), SYM (2; 0% instances), X (2; 0% instances)
1278 (99%) CCONJ
nodes are leaves.
4 (0%) CCONJ
nodes have one child.
3 (0%) CCONJ
nodes have two children.
The highest child degree of a CCONJ
node is 2.
Children of CCONJ
nodes are attached using 3 different relations: en-dep/punct (5; 50% instances), en-dep/conj (3; 30% instances), en-dep/advcl (2; 20% instances)
Children of CCONJ
nodes belong to 3 different parts of speech: PUNCT (5; 50% instances), VERB (3; 30% instances), CCONJ (2; 20% instances)
CCONJ in other languages: [u]


hello
♥
I am Jose, a 1st year M.S. student studying Computer Engineering. I have a B.S. in Computer Science with highest honors from UC Santa Cruz.
Interests
My academic interets include computer vision, networks, machine learning, containerization, natural language processing, and distributed systems. In graduate school, I plan to specialize in a field that lets me intersect a few of these topics.
Experience
I've held several industry internships as well as participated in undergraduate research in the field of non-volatile memory, computational linguisitics, and computer networks. To see more of my academic/professional experience, please see my linkedin, linked below.
Hobbies
When I'm not spending all day at school or work, I love playing video games, working on my car, or lifting weights.
Projects
►Vehicle traffic congestion Survey Paper◄
►CVscan◄
►DigiGuess◄


Prepare data and train machine learning models.
machine_learn(d, ..., outcome, models, tune = TRUE, positive_class, n_folds = 5, tune_depth = 10, impute = TRUE)
| d | A data frame |
|---|---|
| ... | Columns to be ignored in model training, e.g. ID columns, unquoted. |
| outcome | Name of the target column, i.e. what you want to predict.
Unquoted. Must be named, i.e. you must specify |
| models | Models to be trained, k-nearest neighbors and random forest by
default. See |
| tune | If TRUE (default) models will be tuned via
|
| positive_class | For classification only, which outcome level is the "yes" case, i.e. should be associated with high probabilities? Defaults to "Y" or "yes" if present, otherwise is the first level of the outcome variable (first alphabetically if the training data outcome was not already a factor). |
| n_folds | How many folds to use to assess out-of-fold accuracy? Default = 5. Models are evaluated on out-of-fold predictions whether tune is TRUE or FALSE. |
| tune_depth | How many hyperparameter combinations to try? Defualt = 10. Ignored if tune is FALSE. |
| impute | Logical, if TRUE (default) missing values will be filled by
|
model_list object ready to make predictions via
predict.model_list
This is a high-level wrapper function. For finer control of data
cleaning and preparation use prep_data
or the functions it
wraps. For finer control of model tuning use tune_models
.
# Split the data into training and test sets, using just 100 rows for speed d <- split_train_test(d = pima_diabetes[1:100, ], outcome = diabetes, percent_train = .9) ### Classification ### # Clean and prep the training data, specifying that patient_id is an ID column, # and tune algorithms over hyperparameter values to predict diabetes diabetes_models <- machine_learn(d$train, patient_id, outcome = diabetes)#>#>#>#>#># Inspect model specification and performance diabetes_models#> Algorithms Trained: Random Forest, k-Nearest Neighbors #> Target: diabetes #> Class: Classification #> Performance Metric: AUROC #> Number of Observations: 91 #> Number of Features: 12 #> Models Trained: 2018-04-12 00:16:11 #> #> Models tuned via 5-fold cross validation over 10 combinations of hyperparameter values. #> Best model: Random Forest #> AUPR = 0.55, AUROC = 0.77 #> Optimal hyperparameter values: #> mtry = 4 #> splitrule = gini #> min.node.size = 9# Make predictions (predicted probability of diabetes) on test data predict(diabetes_models, d$test)#>#>#>#> # A tibble: 9 x 11 #> diabetes predicted_diabet… patient_id pregnancies plasma_glucose diastolic_bp #> * <chr> <dbl> <int> <int> <int> <int> #> 1 Y 0.196 7 3 78 50 #> 2 Y 0.567 17 0 118 84 #> 3 N 0.581 31 5 109 75 #> 4 N 0.478 36 4 103 60 #> 5 N 0.335 47 1 146 56 #> 6 N 0.189 70 4 146 85 #> 7 Y 0.379 73 13 126 90 #> 8 N 0.377 78 5 95 72 #> 9 N 0.176 83 7 83 78 #> # ... with 5 more variables: skinfold <int>, insulin <int>, weight_class <chr>, #> # pedigree <dbl>, age <int>### Regression ### # If the outcome variable is numeric, regression models will be trained age_model <- machine_learn(d$train, patient_id, outcome = age)#>#>#>#>#># If new data isn't specifed, get predictions on training data predict(age_model)#>#>#> # A tibble: 91 x 16 #> age predicted_age pregnancies plasma_glucose diastolic_bp skinfold insulin #> * <int> <dbl> <int> <dbl> <dbl> <dbl> <dbl> #> 1 50 38.4 6 148. 72.0 35.0 169. #> 2 31 23.6 1 85. 66.0 29.0 169. #> 3 32 48.0 8 183. 64.0 29.0 169. #> 4 21 25.9 1 89. 66.0 23.0 94.0 #> 5 33 34.6 0 137. 40.0 35.0 168. #> 6 30 31.7 5 116. 74.0 29.0 169. #> 7 29 39.4 10 115. 72.4 29.0 169. #> 8 53 43.1 2 197. 70.0 45.0 543. #> 9 54 36.5 8 125. 96.0 29.0 169. #> 10 30 35.9 4 110. 92.0 29.0 169. #> # ... with 81 more rows, and 9 more variables: pedigree <dbl>, #> # weight_class_normal <dbl>, weight_class_obese <dbl>, #> # weight_class_overweight <dbl>, weight_class_missing <dbl>, #> # weight_class_other <dbl>, diabetes_Y <dbl>, diabetes_other <dbl>, #> # diabetes_missing <dbl>### Faster model training without tuning hyperparameters ### # Train models at set hyperparameter values by setting tune to FALSE. This is # faster (especially on larger datasets), but produces models with less # predictive accuracy. machine_learn(d$train, patient_id, outcome = diabetes, tune = FALSE)#>#>#>#> Algorithms Trained: Random Forest, k-Nearest Neighbors #> Target: diabetes #> Class: Classification #> Performance Metric: AUROC #> Number of Observations: 91 #> Number of Features: 12 #> Models Trained: 2018-04-12 00:16:16 #> #> Models have not been tuned. Performance estimated via 5-fold cross validation at fixed hyperparameter values. #> Best model: Random Forest #> AUPR = 0.52, AUROC = 0.77 #> User-selected hyperparameter values: #> mtry = 5 #> splitrule = extratrees #> min.node.size = 10


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Trading in Binary Options is VERY SPECULATIVE AND HIGHLY RISKY and is not suitable for all members of the general public, but only for those investors who:
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It is understood that when it comes to trading in currencies, there may be situations, movements and/or conditions occurring at weekend, in the beginning of week or intra-day after release of significant macroeconomic figures, economic or political news that make currency markets to open with price levels that may substantially differ from previous prices.
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