public class Controller extends java.lang.Object implements ControllerInterface
Controller class for interfacing between the model and view.
This class creates an instance of the view (Frame) and model (Cecil) classes.
This class contains the main method.
| Constructor and Description |
|---|
Controller()
Constructor which creates a view (Frame) and model (Cecil) object.
|
| Modifier and Type | Method and Description |
|---|---|
boolean |
checkCorrectInput(java.util.ArrayList<java.util.ArrayList<java.lang.String>> code)
User input validation method before sending the input to the compiler to parse
|
void |
compileClicked(java.util.ArrayList<java.util.ArrayList<java.lang.String>> code)
Event listener for handling the compile button click event
|
void |
fileOpened(java.io.File file)
Loads the file into the input editor
|
void |
ioCheckClicked(boolean ioPortsEnabled)
Checks for
Hardware platform is Raspberry Pi
GPIO ports display checkbox is ticked
|
static void |
main(java.lang.String[] args)
Launches the application by creating a new CecilController.
|
void |
runClicked()
Event listener for handling the run button click event
|
void |
saveToFile(java.util.ArrayList<java.util.ArrayList<java.lang.String>> code,
java.lang.String filename)
Saves the Cecil Program in the file
|
void |
sendOutputToGPIO()
GPIO hook that enables the interactive LED Display by transmitting each bit of the
accumulator value as a binary sequence to the physical GPIO ports of the Raspberry Pi
|
void |
setButtonsEnabled(boolean isEnabled)
Setter method to enable or disable the run and step-through button
|
void |
stepThroughClicked(int line)
Event listener for handling the step-through button click event
|
public Controller()
public static void main(java.lang.String[] args)
String[]
- args : command line user input has not been allowedpublic void compileClicked(java.util.ArrayList<java.util.ArrayList<java.lang.String>> code)
compileClicked
in interface ControllerInterface
Program
- consisting of statementspublic void runClicked()
runClicked
in interface ControllerInterface
public void stepThroughClicked(int line)
stepThroughClicked
in interface ControllerInterface
int
- line : remembers the current line of program executionpublic void setButtonsEnabled(boolean isEnabled)
boolean
- isEnabledpublic void sendOutputToGPIO()
GPIO hook that enables the interactive LED Display by transmitting each bit of the accumulator value as a binary sequence to the physical GPIO ports of the Raspberry Pi
The ports in use are 1 to 10 on RPi
sendOutputToGPIO
in interface ControllerInterface
public boolean checkCorrectInput(java.util.ArrayList<java.util.ArrayList<java.lang.String>> code)
Program
- : Cecil program in where each line is in form of ArrayList of Strings(Columns)public void fileOpened(java.io.File file)
fileOpened
in interface ControllerInterface
File
- file : file to be opended in the input editorpublic void saveToFile(java.util.ArrayList<java.util.ArrayList<java.lang.String>> code, java.lang.String filename)
saveToFile
in interface ControllerInterface
Program
- : Cecil Program cString
- filename : Cecil program in where each line is in form of ArrayList of Strings(Columns)public void ioCheckClicked(boolean ioPortsEnabled)
Checks for
ioCheckClicked
in interface ControllerInterface
booleam
- ioPortsEnabled : state of the checkbox


R2-D2 is an R2 series astromech droid manufactured by Industrial Automation. It is a smart, spunky droid for the price of $1,000.
C-3PO is a 3PO unit protocol droid designed to interact with organics, programmed primarily for etiquette and protocol. He is fluent in over seven million forms of communication and is priced at $2,000.
The B2 super battle droid is an advanced battle droid used by the Confederacy of Independent Systems during the Clone Wars. They do no require a command system to operate and comes at a price of $3,000. Buy and use at your own risk. NO REFUNDS.


| Home | Trees | Indices | Help |
|---|
|
|
ZenUtils.Ext.DirectRouter --+ | TreeRouter --+ | TemplateRouter
A JSON/ExtDirect interface to operations on templates
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Inherited from Inherited from |
|
Abstract method for child classes to use to get their facade
|
Get all templates.
|
Get all templates by device class. This will return a tree where device classes are nodes, and templates are leaves.
|
Get a list of available device classes where new templates can be added.
|
Add a template to a device class.
|
Delete a template.
|
|
|
|
Get the thresholds for a template.
|
Get a threshold's details.
|
Get a list of available data points for a template.
|
Add a new data point to a data source.
|
Add a new data source to a template.
|
Get the data sources for a template.
|
Get a data source's details.
|
Get a data point's details.
|
Set attributes on an object. This method accepts any keyword argument for the property that you wish to set. The only required property is "uid".
|
Add a threshold.
|
Remove a threshold.
|
Get a list of available threshold types.
|
Get a list of available data source types.
|
Get the graph definitions for a template.
|
Add a data point to a graph.
|
Get a list of available device classes to copy a template to.
|
Copy a template to a device or device class.
|
Add a new graph definition to a template.
|
Delete a data source.
|
Delete a data point.
|
Delete a graph definition.
|
Delete a graph point.
|
Get a list of graph points for a graph definition.
|
Get the properties of an object.
|
Add a threshold to a graph definition.
|
Add a custom graph point to a graph definition.
|
Get a list of available instruction types for graph points.
|
Sets the sequence of graph points in a graph definition.
|
Get a graph definition.
|
Set attributes on an graph definition. This method accepts any keyword argument for the property that you wish to set. Properties are enumerated via getGraphDefinition(). The only required property is "uid".
|
Sets the sequence of graph definitions.
|
| Home | Trees | Indices | Help |
|---|
| Generated by Epydoc 3.0.1 on Mon Sep 26 13:47:56 2011 | http://epydoc.sourceforge.net |


Welcome
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---
layout: api
title: "v3.3.0 JavaScript Library: L.TileLayer.Canvas"
categories: api
version: v3.3.0
permalink: /api/v3.3.0/l-tilelayer-canvas/
---
TileLayer.Canvas
Used to create Canvas-based tile layers where tiles get drawn on the browser side. Extends TileLayer.
Usage example
var canvasTiles = L.tileLayer.canvas();
canvasTiles.drawTile = function(canvas, tilePoint, zoom) {
var ctx = canvas.getContext('2d');
// draw something on the tile canvas
}
Creation
| Factory |
Description |
L.tileLayer.canvas(
<TileLayer options> options? )
|
Instantiates a Canvas tile layer object given an options object (optionally). |
Options
| Option |
Type |
Default |
Description |
async |
Boolean |
false |
Indicates that tiles will be drawn asynchronously. tileDrawn method should be called for each tile after drawing completion. |
Methods
| Method |
Returns |
Description |
drawTile(
<HTMLCanvasElement> canvas,
<Point> tilePoint,
<Number> zoom )
|
this |
You need to define this method after creating the instance to draw tiles; canvas is the actual canvas tile on which you can draw, tilePoint represents the tile numbers, and zoom is the current zoom. |
tileDrawn( <HTMLCanvasElement> canvas ) |
- |
If async option is defined, this function should be called for each tile after drawing completion. canvas is the same canvas element, that was passed to drawTile. |


SKIN DEEP A Film Review by David Alba Jr Copyright 1989 David Alba Jr
Through the years, Blake Edwards has made some outrageously funny films. Remember all the Pink Panther movies? More recently he has also made some very funny films like S.O.B., 10 and VICTOR/VICTORIA.
His latest directorial effort is entitled SKIN DEEP. This is a film about a married, successful writer and musician (John Ritter), who decides to engage in the art of infidelity. This new hobby of his succeeds until his wife unexpectedly catches him fooling around. He soon gets ousted from his happy home. He is later seen explaining all of his female encounters to his psychiatrist.
This motion picture features Vincent Gardenia (MOONSTRUCK) as his friend. It also features Julianne Phillips (the former Mrs. Bruce Springsteen) and Denise Crosby as two of his female conquests. He soon learns the meaning to the saying "Hell hath no fury like a woman scorned."
This film simply stated is a light romantic comedy. It is not really Blake Edwards' best work, but it does contain a few funny moments. This advertisement for this film promotes a scene that they say is too sexy to be mentioned on television. Well, it is too sexy for me to mention it here either. All I can say is that it is probably the funniest scene in the movie.
Rating: +1
David Alba Jr Naperville, IL att!ihlpa!alba
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


One of the functions React - it offered them the process of thinking, to create applications.
Why React?
Key features
Why is become so popular?
Most popular projects based on React
Multipurpose
Declarative approach
Components
JSX


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Suspect in Connecticut bar owner's death may have fled to Dominican Republic, police say
by Greg NormanA man wanted for questioning following the “suspicious” death of his Connecticut bar owner girlfriend was on the run Thursday — and may have fled to the Dominican Republic.
Alfredo Esmerli Peguero-Gomez, whose car was found at Newark Liberty International Airport in New Jersey, is being sought in the death of Janet Avalo-Alvarez, with whom he co-owned a bar with in Waterbury, Conn.
The 26-year-old's body was found earlier this week in nearby Wolcott. She was last seen at that establishment on Nov. 12.
"The family is going through a lot of pain and they don’t deserve [the] pain they are going through," Grisella Guerrero, who identified herself as a family friend, said in an interview with WFSB.
NEW YORK DOG CHEWED OFF OWN LEG WHILE LIVING IN CRATE OUTSIDE, POLICE SAY
DOMINICAN REPUBLIC POLICE ARREST 8 FOLLOWING DEATH OF AMERICAN TEACHER
Police have called the death “suspicious” and told WFSB they believe Avalo-Alvarez died from neck compression. She was found strangled to death a few feet from the road, Fox 61 reported.
U.S. marshals are now helping in the search for Peguero-Gomez.
The day after Avalo-Alvarez vanished, police went to the home the couple shared in Waterbury and Peguero-Gomez said he didn’t know where she had gone.
Investigators say he left the home at 1 a.m. the following morning.
CLICK HERE FOR THE FOX NEWS APP
Waterbury Police Chief Fernando Spagnolo says Peguero-Gomez has family in the Dominican Republic, but it’s unclear if he flew there.
The Associated Press contributed to this report.


All about me
By Kishan Sambhi
About Me
Hello. My Name is Kishan. This is a website about my hobbies and interests, which you can find a list of below.
NB: You may see a black footer at the bottom of some pages. This is not intentional and is related to how the website behaves on different screen sizes.


Frequently Asked Questions
What are the license terms?
H2O is licensed under the MIT license.
Portions of the software use following libraries that are also licensed under the MIT license: khash.h, PicoHTTPParser, libyaml.
Depending on how H2O is configured, the software links against OpenSSL or LibreSSL, both of which are dual-licensed under the OpenSSL License and the original SSLeay license.
Are there any design documents?
Please refer to the main developer's presentation slides at the HTTP/2 conference, and his weblog.
How do I use H2O as a library?
Aside from the standalone server, H2O can also be used as a software library.
The name of the library is libh2o
.
To build H2O as a library you will need to install the following dependencies:
In case the dependencies are installed under a non-standard path,PKG_CONFIG_PATH
configuration variable can be used for specifying their paths. For example, the following snippet builds libh2o
using the libraries installed in their respective paths.
% PKG_CONFIG_PATH=/usr/local/libuv-1.4/lib/pkgconfig:/usr/local/openssl-1.0.2a/lib/pkgconfig cmake .
% make libh2o
For more information, please refer to the GitHub issues tagged as libh2o.
I have a problem. Where should I look for answers?
Please refer to the GitHub issues tagged as FAQ.
Notes:
- libh2o cannot be linked against the bundled LibreSSL; see issue #290


Our team is working to develop statistical methods and tools that can improve real-time infectious disease forecasting efforts for a variety of diseases, including dengue fever and influenza. For example, in a collaboration with the Ministry of Public Health in Thailand, our team has built statistical forecast models to predict outbreaks of dengue fever in real-time since early 2014 for each of the 77 provinces in Thailand. These forecasts are based on over 2 million unique reported case records of dengue fever in Thailand since 1968.
View PublicationsIn multi-pathogen infectious disease systems, complex immunological interactions between multiple strains of disease govern the evolutionary and epidemiological dynamics of disease. Understanding these interactions plays a vital role in clinical and public health decision-making. Our work combines multiple data streams from complex disease systems with modern statistical and computational methodologies to find evidence of complex interactions within the system. For example, our research was the first to use population-level data to explicitly estimate the duration of temporary immunity experienced by individuals after an infection with one of the four serotypes of dengue fever.
View PublicationsCluster-randomized trials are a type of clinical trial where clusters of individuals are randomized instead of individuals. In our work on several high-profile cluster-randomized clinical trials -- including the ResPECT Study (funded by CDC and VA), the SCRUB Trial, and a telehealth collaborative care trial for HIV patients (funded by VA) -- we have developed simulation methods for calculating power for cluster-randomized and cluster-randomized crossover trials. These methods are available as an R software package, clusterPower.
View PublicationsThe incubation period -- the length of time between infection with a pathogen and the onset of symptoms -- plays a vital role in the prevention and control of infectious disease. Estimating the incubation period can be challenging because often both the time of infection and the time of onset are not observed exactly. Our work has developed robust statistical methods to estimate the full duration of the incubation period. Recent work focuses on characterizing the uncertainty in incubation period estimates in ways that could assist in creating evidence-based quarantine policies for infectious diseases, such as Ebola or influenza.
View Publications


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WATCH | Truck driver dies after crashing into home in KZN
by Kamva SomdyalaA truck driver who lost control of his vehicle that crashed into the wall of a house in Reservoir Hills, KwaZulu-Natal, on Thursday has died, the authorities say.
Six passengers who were also in the truck were stable, Rescue Care spokesperson Garrith Jamieson told News24.
According to Jamieson, the two passengers who were seated in front with the driver were trapped in the cab and the Jaws of Life was used to free them. The other four passengers were travelling on the back of the truck.
The accident happened at about 17:30, said Jamieson.
No one in the house was injured.
Jamieson said while the weather in the province - with rescue workers and disaster management teams on high alert as thunderstorms loom - remained bad, "it is calm".
Inclement weather in KwaZulu-Natal has seen two tornadoes form in the past two days - one in New Hanover on Tuesday and the other in Bergville on Thursday afternoon.
On Wednesday afternoon, the body of a seven-year-old boy, who was swept off the Soweto bridge in Darnell while walking home from school, was found.
The province has been warned to expect more bad weather in the coming days.
Meanwhile, the KwaZulu-Natal Department of Cooperative Governance and Traditional Affairs indicated that preparations were in place to assist with any incidents resulting from the stormy weather.
Cooperative Governance and Traditional Affairs MEC Sipho Hlomuka told the media a provincial joint operation centre in eThekwini would co-ordinate responses across the province.


subject
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purrge
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by cesar ferreira


Inference with Discrete Latent Variables¶
This tutorial describes Pyro’s enumeration strategy for discrete latent variable models. This tutorial assumes the reader is already familiar with the Tensor Shapes Tutorial.
Summary¶
Pyro implements automatic enumeration over discrete latent variables.
This strategy can be used alone or inside SVI (via TraceEnum_ELBO), HMC, or NUTS.
The standalone infer_discrete can generate samples or MAP estimates.
Annotate a sample site
infer={"enumerate": "parallel"}
to trigger enumeration.If a sample site determines downstream structure, instead use
{"enumerate": "sequential"}
.Write your models to allow arbitrarily deep batching on the left, e.g. use broadcasting.
Inference cost is exponential in treewidth, so try to write models with narrow treewidth.
If you have trouble, ask for help on forum.pyro.ai!
Table of contents¶
[1]:
import os
import torch
import pyro
import pyro.distributions as dist
from torch.distributions import constraints
from pyro import poutine
from pyro.infer import SVI, Trace_ELBO, TraceEnum_ELBO, config_enumerate, infer_discrete
from pyro.infer.autoguide import AutoDiagonalNormal
from pyro.ops.indexing import Vindex
smoke_test = ('CI' in os.environ)
assert pyro.__version__.startswith('1.3.0')
pyro.enable_validation()
pyro.set_rng_seed(0)
Overview¶
Pyro’s enumeration strategy encompasses popular algorithms including variable elimination, exact message passing, forward-filter-backward-sample, inside-out, Baum-Welch, and many other special-case algorithms. Aside from enumeration, Pyro implements a number of inference strategies including variational inference (SVI) and monte carlo (HMC and NUTS). Enumeration can be used either as a stand-alone strategy via infer_discrete, or as a component of other strategies. Thus enumeration allows Pyro to marginalize out discrete latent variables in HMC and SVI models, and to use variational enumeration of discrete variables in SVI guides.
Mechanics of enumeration¶
The core idea of enumeration is to interpret discrete pyro.sample statements as full enumeration rather than random sampling. Other inference algorithms can then sum out the enumerated values. For example a sample statement might return a tensor of scalar shape under the standard “sample” interpretation (we’ll illustrate with trivial model and guide):
[2]:
def model():
z = pyro.sample("z", dist.Categorical(torch.ones(5)))
print('model z = {}'.format(z))
def guide():
z = pyro.sample("z", dist.Categorical(torch.ones(5)))
print('guide z = {}'.format(z))
elbo = Trace_ELBO()
elbo.loss(model, guide);
guide z = 4
model z = 4
However under the enumeration interpretation, the same sample site will return a fully enumerated set of values, based on its distribution’s .enumerate_support() method.
[3]:
elbo = TraceEnum_ELBO(max_plate_nesting=0)
elbo.loss(model, config_enumerate(guide, "parallel"));
guide z = tensor([0, 1, 2, 3, 4])
model z = tensor([0, 1, 2, 3, 4])
Note that we’ve used “parallel” enumeration to enumerate along a new tensor dimension. This is cheap and allows Pyro to parallelize computation, but requires downstream program structure to avoid branching on the value of z
. To support dynamic program structure, you can instead use “sequential” enumeration, which runs the entire model,guide pair once per sample value, but requires running the model multiple times.
[4]:
elbo = TraceEnum_ELBO(max_plate_nesting=0)
elbo.loss(model, config_enumerate(guide, "sequential"));
guide z = 4
model z = 4
guide z = 3
model z = 3
guide z = 2
model z = 2
guide z = 1
model z = 1
guide z = 0
model z = 0
Parallel enumeration is cheaper but more complex than sequential enumeration, so we’ll focus the rest of this tutorial on the parallel variant. Note that both forms can be interleaved.
Multiple latent variables¶
We just saw that a single discrete sample site can be enumerated via nonstandard interpretation. A model with a single discrete latent variable is a mixture model. Models with multiple discrete latent variables can be more complex, including HMMs, CRFs, DBNs, and other structured models. In models with multiple discrete latent variables, Pyro enumerates each variable in a different tensor dimension (counting from the right; see Tensor Shapes Tutorial). This allows Pyro to determine the dependency graph among variables and then perform cheap exact inference using variable elimination algorithms.
To understand enumeration dimension allocation, consider the following model, where here we collapse variables out of the model, rather than enumerate them in the guide.
[5]:
@config_enumerate
def model():
p = pyro.param("p", torch.randn(3, 3).exp(), constraint=constraints.simplex)
x = pyro.sample("x", dist.Categorical(p[0]))
y = pyro.sample("y", dist.Categorical(p[x]))
z = pyro.sample("z", dist.Categorical(p[y]))
print('model x.shape = {}'.format(x.shape))
print('model y.shape = {}'.format(y.shape))
print('model z.shape = {}'.format(z.shape))
return x, y, z
def guide():
pass
pyro.clear_param_store()
elbo = TraceEnum_ELBO(max_plate_nesting=0)
elbo.loss(model, guide);
model x.shape = torch.Size([3])
model y.shape = torch.Size([3, 1])
model z.shape = torch.Size([3, 1, 1])
Examining discrete latent states¶
While enumeration in SVI allows fast learning of parameters like p
above, it does not give access to predicted values of the discrete latent variables like x,y,z
above. We can access these using a standalone infer_discrete handler. In this case the guide was trivial, so we can simply wrap the model in infer_discrete
. We need to pass a first_available_dim
argument to tell infer_discrete
which dimensions are available for enumeration; this is related to the max_plate_nesting
arg of TraceEnum_ELBO
via
first_available_dim = -1 - max_plate_nesting
[6]:
serving_model = infer_discrete(model, first_available_dim=-1)
x, y, z = serving_model() # takes the same args as model(), here no args
print("x = {}".format(x))
print("y = {}".format(y))
print("z = {}".format(z))
model x.shape = torch.Size([3])
model y.shape = torch.Size([3, 1])
model z.shape = torch.Size([3, 1, 1])
model x.shape = torch.Size([])
model y.shape = torch.Size([])
model z.shape = torch.Size([])
x = 0
y = 2
z = 1
Notice that under the hood infer_discrete
runs the model twice: first in forward-filter mode where sites are enumerated, then in replay-backward-sample model where sites are sampled. infer_discrete
can also perform MAP inference by passing temperature=0
. Note that while infer_discrete
produces correct posterior samples, it does not currently produce correct logprobs, and should not be used in other gradient-based inference algorthms.
Indexing with enumerated variables¶
It can be tricky to use advanced indexing to select an element of a tensor using one or more enumerated variables. For example, suppose a plated random variable z
depends on two different random variables:
p = pyro.param("p", torch.randn(5, 4, 3, 2).exp(),
constraint=constraints.simplex)
x = pyro.sample("x", dist.Categorical(torch.ones(4)))
y = pyro.sample("y", dist.Categorical(torch.ones(3)))
with pyro.plate("z_plate", 5):
p_xy = p[..., x, y, :] # Not compatible with enumeration!
z = pyro.sample("z", dist.Categorical(p_xy)
Due to advanced indexing semantics, the expression p[..., x, y, :]
will work correctly without enumeration, but is incorrect when x
or y
is enumerated. Pyro provides a simple way to index correctly, but first let’s see how to correctly index using PyTorch’s advanced indexing without Pyro:
# Compatible with enumeration, but not recommended:
p_xy = p[torch.arange(5, device=p.device).reshape(5, 1),
x.unsqueeze(-1),
y.unsqueeze(-1),
torch.arange(2, device=p.device)]
Pyro provides a helper Vindex()[] to use enumeration-compatible advanced indexing semantics rather than PyTorch/NumPy semantics. Vindex()[]
makes the .__getitem__()
operator broadcast like other familiar operators +
, *
etc. Using Vindex()[]
we can write the same expression as if x
and y
were numbers (i.e. not enumerated):
# Recommended syntax compatible with enumeration:
p_xy = Vindex(p)[..., x, y, :]
Here is a complete example:
[7]:
@config_enumerate
def model():
p = pyro.param("p", torch.randn(5, 4, 3, 2).exp(), constraint=constraints.simplex)
x = pyro.sample("x", dist.Categorical(torch.ones(4)))
y = pyro.sample("y", dist.Categorical(torch.ones(3)))
with pyro.plate("z_plate", 5):
p_xy = Vindex(p)[..., x, y, :]
z = pyro.sample("z", dist.Categorical(p_xy))
print(' p.shape = {}'.format(p.shape))
print(' x.shape = {}'.format(x.shape))
print(' y.shape = {}'.format(y.shape))
print('p_xy.shape = {}'.format(p_xy.shape))
print(' z.shape = {}'.format(z.shape))
return x, y, z
def guide():
pass
pyro.clear_param_store()
elbo = TraceEnum_ELBO(max_plate_nesting=1)
elbo.loss(model, guide);
p.shape = torch.Size([5, 4, 3, 2])
x.shape = torch.Size([4, 1])
y.shape = torch.Size([3, 1, 1])
p_xy.shape = torch.Size([3, 4, 5, 2])
z.shape = torch.Size([2, 1, 1, 1])
Plates and enumeration¶
Pyro plates express conditional independence among random variables. Pyro’s enumeration strategy can take advantage of plates to reduce the high cost (exponential in the size of the plate) of enumerating a cartesian product down to a low cost (linear in the size of the plate) of enumerating conditionally independent random variables in lock-step. This is especially important for e.g. minibatched data.
To illustrate, consider a gaussian mixture model with shared variance and different mean.
[8]:
@config_enumerate
def model(data, num_components=3):
print('Running model with {} data points'.format(len(data)))
p = pyro.sample("p", dist.Dirichlet(0.5 * torch.ones(3)))
scale = pyro.sample("scale", dist.LogNormal(0, num_components))
with pyro.plate("components", num_components):
loc = pyro.sample("loc", dist.Normal(0, 10))
with pyro.plate("data", len(data)):
x = pyro.sample("x", dist.Categorical(p))
print("x.shape = {}".format(x.shape))
pyro.sample("obs", dist.Normal(loc[x], scale), obs=data)
print("dist.Normal(loc[x], scale).batch_shape = {}".format(
dist.Normal(loc[x], scale).batch_shape))
guide = AutoDiagonalNormal(poutine.block(model, hide=["x", "data"]))
data = torch.randn(10)
pyro.clear_param_store()
elbo = TraceEnum_ELBO(max_plate_nesting=1)
elbo.loss(model, guide, data);
Running model with 10 data points
x.shape = torch.Size([10])
dist.Normal(loc[x], scale).batch_shape = torch.Size([10])
Running model with 10 data points
x.shape = torch.Size([3, 1])
dist.Normal(loc[x], scale).batch_shape = torch.Size([3, 1])
Observe that the model is run twice, first by the AutoDiagonalNormal
to trace sample sites, and second by elbo
to compute loss. In the first run, x
has the standard interpretation of one sample per datum, hence shape (10,)
. In the second run enumeration can use the same three values (3,1)
for all data points, and relies on broadcasting for any dependent sample or observe sites that depend on data. For example, in the pyro.sample("obs",...)
statement, the distribution has
shape (3,1)
, the data has shape(10,)
, and the broadcasted log probability tensor has shape (3,10)
.
For a more in-depth treatment of enumeration in mixture models, see the Gaussian Mixture Model Tutorial and the HMM Example.
Dependencies among plates¶
The computational savings of enumerating in vectorized plates comes with restrictions on the dependency structure of models. These restrictions are in addition to the usual restrictions of conditional independence. The enumeration restrictions are checked by TraceEnum_ELBO
and will result in an error if violated (however the usual conditional independence restriction cannot be generally verified by Pyro). For completeness we list all three restrictions:
Restriction 1: conditional independence¶
Variables within a plate may not depend on each other (along the plate dimension). This applies to any variable, whether or not it is enumerated. This applies to both sequential plates and vectorized plates. For example the following model is invalid:
def invalid_model():
x = 0
for i in pyro.plate("invalid", 10):
x = pyro.sample("x_{}".format(i), dist.Normal(x, 1.))
Restriction 2: no downstream coupling¶
No variable outside of a vectorized plate can depend on an enumerated variable inside of that plate. This would violate Pyro’s exponential speedup assumption. For example the following model is invalid:
@config_enumerate
def invalid_model(data):
with pyro.plate("plate", 10): # <--- invalid vectorized plate
x = pyro.sample("x", dist.Bernoulli(0.5))
assert x.shape == (10,)
pyro.sample("obs", dist.Normal(x.sum(), 1.), data)
To work around this restriction, you can convert the vectorized plate to a sequential plate:
@config_enumerate
def valid_model(data):
x = []
for i in pyro.plate("plate", 10): # <--- valid sequential plate
x.append(pyro.sample("x_{}".format(i), dist.Bernoulli(0.5)))
assert len(x) == 10
pyro.sample("obs", dist.Normal(sum(x), 1.), data)
Restriction 3: single path leaving each plate¶
The final restriction is subtle, but is required to enable Pyro’s exponential speedup
For any enumerated variable
x
, the set of all enumerated variables on whichx
depends must be linearly orderable in their vectorized plate nesting.
This requirement only applies when there are at least two plates and at least three variables in different plate contexts. The simplest counterexample is a Boltzmann machine
@config_enumerate
def invalid_model(data):
plate_1 = pyro.plate("plate_1", 10, dim=-1) # vectorized
plate_2 = pyro.plate("plate_2", 10, dim=-2) # vectorized
with plate_1:
x = pyro.sample("y", dist.Bernoulli(0.5))
with plate_2:
y = pyro.sample("x", dist.Bernoulli(0.5))
with plate_1, plate2:
z = pyro.sample("z", dist.Bernoulli((1. + x + y) / 4.))
...
Here we see that the variable z
depends on variable x
(which is in plate_1
but not plate_2
) and depends on variable y
(which is in plate_2
but not plate_1
). This model is invalid because there is no way to linearly order x
and y
such that one’s plate nesting is less than the other.
To work around this restriction, you can convert one of the plates to a sequential plate:
@config_enumerate
def valid_model(data):
plate_1 = pyro.plate("plate_1", 10, dim=-1) # vectorized
plate_2 = pyro.plate("plate_2", 10) # sequential
with plate_1:
x = pyro.sample("y", dist.Bernoulli(0.5))
for i in plate_2:
y = pyro.sample("x_{}".format(i), dist.Bernoulli(0.5))
with plate_1:
z = pyro.sample("z_{}".format(i), dist.Bernoulli((1. + x + y) / 4.))
...
but beware that this increases the computational complexity, which may be exponential in the size of the sequential plate.
Time series example¶
Consider a discrete HMM with latent states \(x_t\) and observations \(y_t\). Suppose we want to learn the transition and emission probabilities.
[9]:
data_dim = 4
num_steps = 10
data = dist.Categorical(torch.ones(num_steps, data_dim)).sample()
def hmm_model(data, data_dim, hidden_dim=10):
print('Running for {} time steps'.format(len(data)))
# Sample global matrices wrt a Jeffreys prior.
with pyro.plate("hidden_state", hidden_dim):
transition = pyro.sample("transition", dist.Dirichlet(0.5 * torch.ones(hidden_dim)))
emission = pyro.sample("emission", dist.Dirichlet(0.5 * torch.ones(data_dim)))
x = 0 # initial state
for t, y in enumerate(data):
x = pyro.sample("x_{}".format(t), dist.Categorical(transition[x]),
infer={"enumerate": "parallel"})
pyro.sample("y_{}".format(t), dist.Categorical(emission[x]), obs=y)
print("x_{}.shape = {}".format(t, x.shape))
We can learn the global parameters using SVI with an autoguide.
[10]:
hmm_guide = AutoDiagonalNormal(poutine.block(hmm_model, expose=["transition", "emission"]))
pyro.clear_param_store()
elbo = TraceEnum_ELBO(max_plate_nesting=1)
elbo.loss(hmm_model, hmm_guide, data, data_dim=data_dim);
Running for 10 time steps
x_0.shape = torch.Size([])
x_1.shape = torch.Size([])
x_2.shape = torch.Size([])
x_3.shape = torch.Size([])
x_4.shape = torch.Size([])
x_5.shape = torch.Size([])
x_6.shape = torch.Size([])
x_7.shape = torch.Size([])
x_8.shape = torch.Size([])
x_9.shape = torch.Size([])
Running for 10 time steps
x_0.shape = torch.Size([10, 1])
x_1.shape = torch.Size([10, 1, 1])
x_2.shape = torch.Size([10, 1, 1, 1])
x_3.shape = torch.Size([10, 1, 1, 1, 1])
x_4.shape = torch.Size([10, 1, 1, 1, 1, 1])
x_5.shape = torch.Size([10, 1, 1, 1, 1, 1, 1])
x_6.shape = torch.Size([10, 1, 1, 1, 1, 1, 1, 1])
x_7.shape = torch.Size([10, 1, 1, 1, 1, 1, 1, 1, 1])
x_8.shape = torch.Size([10, 1, 1, 1, 1, 1, 1, 1, 1, 1])
x_9.shape = torch.Size([10, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1])
Notice that the model was run twice here: first it was run without enumeration by AutoDiagonalNormal
, so that the autoguide can record all sample sites; then second it is run by TraceEnum_ELBO
with enumeration enabled. We see in the first run that samples have the standard interpretation, whereas in the second run samples have the enumeration interpretation.
For more complex examples, including minibatching and multiple plates, see the HMM tutorial.
How to enumerate more than 25 variables¶
PyTorch tensors have a dimension limit of 25 in CUDA and 64 in CPU. By default Pyro enumerates each sample site in a new dimension. If you need more sample sites, you can annotate your model with pyro.markov to tell Pyro when it is safe to recycle tensor dimensions. Let’s see how that works with the HMM model from above. The only change we need is to annotate the for loop with pyro.markov
, informing Pyro that the variables in
each step of the loop depend only on variables outside of the loop and variables at this step and the previous step of the loop:
- for t, y in enumerate(data):
+ for t, y in pyro.markov(enumerate(data)):
[11]:
def hmm_model(data, data_dim, hidden_dim=10):
with pyro.plate("hidden_state", hidden_dim):
transition = pyro.sample("transition", dist.Dirichlet(0.5 * torch.ones(hidden_dim)))
emission = pyro.sample("emission", dist.Dirichlet(0.5 * torch.ones(data_dim)))
x = 0 # initial state
for t, y in pyro.markov(enumerate(data)):
x = pyro.sample("x_{}".format(t), dist.Categorical(transition[x]),
infer={"enumerate": "parallel"})
pyro.sample("y_{}".format(t), dist.Categorical(emission[x]), obs=y)
print("x_{}.shape = {}".format(t, x.shape))
# We'll reuse the same guide and elbo.
elbo.loss(hmm_model, hmm_guide, data, data_dim=data_dim);
x_0.shape = torch.Size([10, 1])
x_1.shape = torch.Size([10, 1, 1])
x_2.shape = torch.Size([10, 1])
x_3.shape = torch.Size([10, 1, 1])
x_4.shape = torch.Size([10, 1])
x_5.shape = torch.Size([10, 1, 1])
x_6.shape = torch.Size([10, 1])
x_7.shape = torch.Size([10, 1, 1])
x_8.shape = torch.Size([10, 1])
x_9.shape = torch.Size([10, 1, 1])
Notice that this model now only needs three tensor dimensions: one for the plate, one for even states, and one for odd states. For more complex examples, see the Dynamic Bayes Net model in the HMM example.
[ ]:


The awesomeness of git hooks
Automation is probably the word we developers love the most. We’re lazy, that’s why there’s so many Wordpress and Joomla websites. Oh wait I was talking about developers. Those are something different (let’s leave it at that).
So we version our code, and we obviously use git. BUt what if you wanted something to happen every-time we do make an action.
Well git-hooks got you covered.
The current definition of git hooks:
Hooks are little scripts you can place in $GIT_DIR/hooks directory to trigger action at certain points. When git init is run, a handful of example hooks are copied into the hooks directory of the new repository, but by default they are all disabled. To enable a hook, rename it by removing its .sample suffix.
Note: It is also a requirement for a given hook to be executable. However - in a freshly initialized repository - the .sample files are executable by default.
I’m not going to mention all the “actions” you can use with git-hooks. You can RTFM for that.
However, I can give you a real life example.
Imagine you’re using less with javascript:
<link rel="stylesheet/less" type="text/css" href="css/styles.less" />
<script src="js/lib/less-1.1.3.min.js" type="text/javascript"></script>
But now you want that to compiled every time you done editing and commit/push to your development server and the git post-recieve hook runs your build.sh and builds your css adn all that malarkey.
Post-receive
This hook is invoked by git-receive-pack on the remote repository, which happens when a git push is done on a local repository. It executes on the remote repository once after all the refs have been updated.
This hook executes once for the receive operation. It takes no arguments, but gets the same information as the pre-receive hook does on its standard input.
This hook does not affect the outcome of git-receive-pack, as it is called after the real work is done.
Net-Tuts tutorial on git-hooks


The simple class to store the options used to configure a fixation interaction technique.
More...
|
|
readonly double | Deviation |
| |
|
readonly long | FixationTime |
| |
|
readonly GameObject | GameObject |
| |
|
| bool | Running [get, set] |
| | Gets or sets a value indicating whether running. More...
|
| |
The simple class to store the options used to configure a fixation interaction technique.
◆ FixationTechniqueOptions()
| Fixations.FixationTechniqueOptions.FixationTechniqueOptions |
( |
GameObject |
gameObject, |
|
|
long |
fixationTime, |
|
|
double |
deviation |
|
) |
| |
|
inline |
Initializes a new instance of the FixationTechniqueOptions class.
- Parameters
-
| gameObject | The relevant GameObject for the fixation. If null is passed the interaction technique is game object independent, else the fixation is only triggered if the gameObject is fixated. |
| fixationTime | The minimum fixation time to trigger the fixation interaction technique. |
| deviation | The maximum deviation of consecutive gaze positions as a additional condition for a fixation. |
◆ Running
| bool Fixations.FixationTechniqueOptions.Running |
|
getset |
Gets or sets a value indicating whether running.
The documentation for this class was generated from the following file:
- C:/Users/Tim/Documents/informatik/master/pg/git/Code/Unity/PGBullsEye/PGBullsEye/Assets/Scripts/BullsEyeScripts/Model/Fixations/FixationTechniqueOptions.cs


AO.com Black Friday deals: cheap Dyson, 4K TVs and LIVE NOW
Your guide to the best AO.com Black Friday deals: cheap coffee machines, games consoles, 4K TVs and more
by Paul DouglasWhat will you be looking for in the Black Friday deals this year? If you're planning to get a new electrical item – perhaps a coffee machine, a 4K TV or even a dishwasher or fridge – then AO.com is a retailer you should check out.
Black Friday lands on November 29 this year but AO.com hasn't waited until then to start bringing out the bargains. Read on to discover our pick of the AO.com Black Friday deals.
When is the AO.com Black Friday sale?
The AO.com Black Friday sale is on NOW. It kicked off on Wednesday 13 November and while we don't have an official end date for the sale, we assume it will run right the way through until AO.com's Cyber Monday deals, which will run on 2 December 2019, and probably beyond as AO.com says "Here at AO.com, we like to be generous, so we’ll be offering deals before and after Black Friday, too. Just in time to grab a Christmas bargain!"
In 2018 AO.com started its Black Friday sale a whole two weeks of Black Friday so this year's Black Friday sale follows a similar pattern.
What are the best AO.com Black Friday deals?
Ahead of starting its Black Friday sale, AO promised: "Whether it’s for life's essentials or you're Christmas shopping for someone special, you’ll find a little bit of everything at AO.com… TV & Entertainment, Computing, Phones, Smart Tech, Laundry, Dishwashers, Fridges & Freezers, Cooking, Small Appliances, Floorcare." Everything but the kitchen sink, then. (You'll need to head over to John Lewis for those.)
Here's our pick of the current AO.com Black Friday deals.
LG OLED55B9PLA 55" 4K OLED TV | Was £1,299 | Now £1,099 | Save £200 at AO.com
This fantastic 55" Smart 4K OLED TV features 4K Ultra HD with upscaling which delivers incredible picture quality while the TV supports dynamic HDR for more realistic colour. OLED Black provides more accurate tones and hues while surround sound comes courtesy of Dolby Atmos. It's a smart TV which you get all your favourite TV and movie viewing apps, catch-up TV and more.
VIEW DEAL
Samsung QLED QE55Q60RA 55" 4K TV | Was £869 | Now £779 | Save £90 at AO.com
Samsung's 55" QLED TV set brings 4K Ultra HD plus Dynamic HDR10+ for improved, vibrant colour. Non 4K content is upscaled by the Quantum 4K processor while the TV features a wide range of streaming apps including including Apple TV and BT Sport. A really cool feature is the ambient mode, which allows the TV to blend into your room when it’s turned off: it mimics the paint or wallpaper you’ve got behind the screen, showing it on the display – with family photos or things like weather reports over the top.
VIEW DEAL
KitchenAid Artisan Stand Mixer | Was: £449 | Now: £279 | Save: £170 at AO.com
A fantastic saving on what is a kitchen essential for anyone who likes to cook. Its 300 Watt power output makes short work of kneading dough, and because it comes equipped with four accessories, you’ll always have the right tool for the job. These include a mixing bowl, a wire whisk, a dough hook and a flat beater. The variable speeds give you more control, too.
VIEW DEAL
Dyson V7 Motorhead Extra Cordless Vacuum Cleaner | Was £299 | Now £199 | Save £100 at AO.com
The Dyson V7 offers up to 30 minutes run time from a single charge, the 2 in 1 tool makes switching between surfaces easy and the 2 Tier Radial cyclone technology captures more dust. Above floor cleaning is easy with the handheld option and when you're done you can hygienically empty the bin at the push of a button. A bargain at £199.
VIEW DEAL
Dyson V8 Animal Cordless Vacuum Cleaner | Was £399 | Now £299 | Save £100 at AO.com
Spend an extra £100 (and still save £100) and you can upgrade to the Dyson V8 Animal which provides up to 40 minutes of run time from a single charge. The cleaner comes with a special tool to remove embedded pet hair from carpets, a hard-bristled brush easily removes ground-in dirt and the cleaner also comes with mini motorised tool and dusting brush.
VIEW DEAL
Shark DuoClean with Flexology Cordless Vacuum Cleaner | Was £349 | Now £199 | Save £150 at AO.com
The Shark DuoClean offers up to 22 minutes run time from a single charge while the double brush design captures all types of dirt. it also comes with a handy tool for corners and crevices and dusting brush for cleaning delicate surfaces. Additionally, the flexible design makes it easy to clean awkward areas and £150 off makes it a great Black Friday vacuum cleaner buy.
VIEW DEAL
Save over £100 on vacuum cleaners
Deal highlight: Shark Bagless Upright Vacuum Cleaner | Was: £269 | Now: £189 | Save: £80
Save over 30% on coffee machines
Deal highlight: De’Longhi Autentica Bean to Cup Coffee Machine | Was: £789 | Now: £499 | Save: £290
Save over 20% on dishwashers
Deal highlight: Samsung Integrated Standard Dishwasher | Was: £499 | Now: £389 | Save: £110
Save up to 30% on TVs
Deal highlight: LG 55” Smart 4K Ultra HD TV | Was: £549 | Now: £449 | Save: £50
Save up to £120 on washer dryers
Deal highlight: Bosch 9kg Washing Machine | Was: £499 | Now: £379 | Save: £120
Save up to 50% on fridge freezers
Deal highlight: Bosch Frost Free Fridge Freezer | Was: £599 | Now: £499 | SAVE: £100
Save over £50 on food preparation appliances
Deal highlight: Ninja Soup Maker and Blender | Was: £149 | Now: £98 | Save: £51
Save up to 50% on bread makers
Deal highlight: Panasonic Croustina Bread Maker | Was: £199 | Now: £99 | Save: £100
Shop more AO.com Black Friday deals
You'll also find links to our other Black Friday roundups below. Happy shopping!
How can you get the best AO.com Black Friday deals
AO.com has supplied four tips to help you get the best Black Friday deals. These tips don't just apply to AO.com, though, they apply to all retailers. Here they are:
1: Do your research. To ensure you get the best possible deal on the day, make a list of the items you’d ideally like to buy, prioritise them and stick to your list.  Register for online accounts with retailers ahead of the event so you can be the first to know about the best deals around. Most retailers offer a sign-up service so that you can enter your email address and be notified when the Black Friday deals go live.
3: Get on the retailer’s apps and social media. Follow the retailers you want to hear about for their upcoming discounts and offers during Black Friday. Also, download their app and set up a notification so you are aware of any new deals that launch. You can follow AO.com on Twitter, and download the My AO app for iOS and Android.
4: Timing is everything. Due to the large volume of visitors some websites crashed before customers could grab their bargain last year. Be ahead of the game and shop at times that would be typically quieter to avoid this risk.
- Shop AO.com's pre Black Friday deals here
- Shop the AO.com Black Friday sale here
- Browse AO.com's Cyber Monday page
- Best Black Friday deals: sale dates and deals predictions
- Amazon Black Friday deals
- AO.com Black Friday deals
- Currys Black Friday deals
- Argos Black Friday deals
- John Lewis Black Friday deals
- Walmart Black Friday deals
- Best Buy Black Friday deals
Black Friday sales around the web
- Amazon.co.uk – massive savings every day
- Argos.co.uk – cheap Lego, toys, phones and more
- AO.com – huge sale on electrical appliances
- Currys.co.uk – Black Tag sale is live now
- Dell.co.uk – big savings on laptops and desktop PCs
- eBay.co.uk – knock-down prices and voucher codes
- Very.co.uk – deals on electricals, fashion and home
- Lovehoney.co.uk – up to 50% off sex toys and lingerie
- Goldsmiths.co.uk Black Friday sale – up to 50% off watches and jewellery
- John Lewis Black Friday sale – mega savings plus the John Lewis guarantee
- Le Creuset Black Friday sale – premium cookware gets tasty discounts
- Nike Black Friday sale – new sneakers at sneaky low prices
- Schuh Black Friday sale – save big on shoes, boots and trainers
- The White Company White Weekend Sale – quality bedding at reduced prices
- Amazon.com Black Friday Deals – new deals every day for a week
- Walmart.com Black Friday sale – cheap 4K TVs, games consoles and more
- eBay.com Black Friday sale– bargain prices and voucher codes
- Dell.com Black Friday sale – cut-price notebooks, monitors and more
- Target.com Black Friday sale – save on clothing, appliances and furniture
- Nordstrom.com Black Friday sale – refresh your winter wardrobe for not much money
- Lovehoney.com US Black Friday sale – up to 50% off sex toys and lingerie


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Our RESTful API comes with validations and dedicated error messages that help you as integrator to deal with any kind of exception. The following sections contain detailed information on how validation on API level work, what error messages are transmitted and in which format they are sent.
{
"status": 400,
"message": "Filtering /customers failed. Check 'errors' array for further details.",
"code": 6000,
"globalTrackId": "Request ID",
"errors": [
{
"code": 6001,
"field": "size",
"message": "size not valid. Expected: positive integer >= 1 ."
},
{
"code": 6002,
"field": "page",
"message": "page not valid. Expected: positive integer >= 1 ."
}
]
}


Problem Definition
In this app, the search box is not working. Your goal is to fix this search box so that typing the words should load the list of emojis.
Other Notes
Learn the Implementation and architecture of the given project code by reading the code files. Make the changes in the desired location and make it work.


Before Change
except ApiException:
_logger.error("Could not start tensorboard, please check your polyaxon spec.",
exc_info=True)
tensorboard.set_status(
JobLifeCycle.FAILED,
message="Could not start tensorboard, encountered a Kubernetes ApiException.")
return
except VolumeNotFoundError as e:
_logger.error("Could not start the tensorboard, please check your volume definitions.",
exc_info=True)
tensorboard.set_status(
JobLifeCycle.FAILED,
message="Could not start the tensorboard, "
"encountered a volume definition problem. %s" % e)
return False
except Exception as e:
_logger.error("Could not start tensorboard, please check your polyaxon spec.",
exc_info=True)
tensorboard.set_status(
JobLifeCycle.FAILED,
message="Could not start tensorboard encountered an {} exception.".format(
e.__class__.__name__
))
return
tensorboard.definition = get_job_definition(results)
tensorboard.save()
After Change
tolerations=tensorboard.tolerations)
tensorboard.definition = get_job_definition(results)
tensorboard.save()
return
except ApiException:
_logger.error("Could not start tensorboard, please check your polyaxon spec.",
exc_info=True)
error = {


These are mandala
paths
for wheelnav.js library. For more information please visit the
project page
YinYang
Flower
Flower 2
Coord
Spiral
Hexa
Hexa 2
Hexa 3