<model-viewer>
Drop a
glTF or GLB
for the model =>
Drop an
HDR or JPG
for the skybox =>
src:
Kent State
Astronaut
Boom Box
Brain Stem
Corset
Damaged Helmet
Flight Helmet
Lantern
Monkey
Water Bottles
Robot Expressive
Transparency Test
Metal Rough Spheres
environment-image:
default
spruit sunrise HDR
spruit sunrise JPG
music hall
whipple creek
aircraft workshop
pillars
use as skybox:
exposure:
shadow-intensity:
shadow-softness:
auto-rotate:
To create a seamless poster:
Orient model and resize window to desired size (small is recommended), then click:
Create Poster
Then resize window to any aspect ratio and click:
Display Poster
Click the model to dismiss the poster and interact. To save your poster, click:
Download Poster
camera-orbit:
To calculate hotspot locations, click:
Add Hotspot
Then click on the model.
To remove a hotspot, select it, then click:
Remove Hotspot


WQMY
Thai Ridgeback puppies for sale - Dogs & Puppies for Sale
Find Thai Ridgeback puppies for sale by local Thai Ridgeback puppy breeders . Thai Ridgeback dog and puppy breeders may advertise their Thai Ridgeback .
http://www.wellbredpets.com/Thai-Ridgeback-puppies-for-sale.html
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| Owner | New Age Media, LLC Thai Ridgeback breed information. Get answers to questions about ... |
| First air date | December 30, 1988 |
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| design ideas group deal nj | WOLF-TV, flight victoria bc to phoenix arizona |
| timeline for the year 2000 | WILF (1988-2006) |
| Former channel number(s) | Thai Ridgeback - The Pedigree Database |
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| Transmitter coordinates | keith stevens rochester area band used pallet racks san diego county (WOLF-DT3) |
Regal Thai Ridgebacks - Breeder of quality Thai Ridgeback Dogs
elcome to Regal Thai Kennel. Home of the the royal dog of Thailand, the Thai Ridgeback. Thai Ridgebacks are a natural hunting dog. Identifiable by their .
http://www.thailandridgebacks.com/
Thai Ridgeback Puppies for Sale - AKC Breeders - Puppy Tips
American Kennel Club can help you find a puppy. AKC breeder classifieds have purebred puppies in your area and helpful puppy selection tips and info.
http://www.akc.org/classified/search/landing_puppy.cfm?breed_code=436
Contents |
[359 east gabilan plaza soledad ca] History
Thai Ridgeback Puppies Breeders Ridgebacks - Puppies for Sale ...
Thai Ridgeback puppies, breeders, puppies for sale, Thai Ridgeback dog breed profile, history, height, weight, care, training, grooming, pictures, Thai .
http://puppydogweb.com/caninebreeds/thairidgeback.htm
Thai Ridgeback - Dog Breed Information Center
Thai Ridgeback Puppies for Sale. Thai Ridgeback (Thai Ridgeback Dog) (Mah Thai). Thai Ridgeback dog. "Photo taken by James at Manape Thai Ridgeback: .
http://www.dogbreedinfo.com/thairidgeback.htm
Thai Ridgeback Puppies for Sale - Breeder
Dog Breed Info Center®, Puppies, Dogs, Dog Breeders, For Sale, . Manape Thai Ridgeback : Hobbyist Breeder of Quality Thai Ridgeback Contact: James .
http://www.breederinfocenter.com/index.php?c=Thai+Ridgeback
American-Thailand Ridgeback Association (ATRA) - First American ...
First American registry for the Thailand Ridgeback dog. . These were the first three Thai Ridgeback Dogs that started off this ATRA group. While in Bangkok in .
http://www.thaidog.org/
Thai Ridgeback Puppies for Sale - PuppyFind.com
Thai Ridgeback puppies for sale, Thai Ridgeback dogs for adoption and Thai Ridgeback dog breeders. Find the perfect Thai Ridgeback puppy at .
http://www.puppyfind.com/thai+ridgeback.php
ThaiDog.com
Your best source of the Thai Ridgeback puppies. . If you think this breed is right for you then call me for the "real ride" of your life... the Thai Ridgeback Dog .
http://www.thaidog.com/
Thai Ridgeback - Wikipedia, the free encyclopedia
The Thai Ridgeback (Thai: ??????????, RTGS: Thai Lang-An) is an ancient breed of dog. The breed was formerly unknown outside of Thailand, but is gaining .
http://en.wikipedia.org/wiki/Thai_Ridgeback
Show Quality Thai Ridgeback Dogs
We are the only international cooperative Thai Ridgeback breeders working with our own dogs in both Thailand and the US. Together we breed quality Thai .
http://www.manapethairidgeback.com/
Thai Ridgeback Puppies 4 Weeks Old - YouTube
May 4, 2010 . We lucked out when looking for a dog last year and found our Red Thai Ridgeback for sale on line. She is an amazing dog, shy with strangers .
http://www.youtube.com/watch?v=O1UQiXMBY7s
Thai Ridgeback Rescue and Adoption - Adopt a Thai ... - Adopt A Pet
Why find a Thai Ridgeback for sale when you can adopt? See photos of cute Thai Ridgebacks to rescue in your area. Search Thai Ridgebacks by size, color, .
http://www.adoptapet.com/s/adopt-a-thai-ridgeback
Thai Ridgeback Puppies for Sale
Records 1 - 6 of 6 . Thai Ridgeback puppies for sale and dogs for adoption. Find the perfect Thai Ridgeback puppy for sale at PuppyFind.com.
http://www.puppyfind.com/for_sale/?breed_id=154
[honda shadow starter circuit] Digital channels
| Channel | Name | gold coast real estate austraila | oreo and black stools | Programming |
|---|---|---|---|---|
| 53.1 | thai ridgebacks for sale DT | girls leopard print high heels | bravo good for you | Main thai ridgebacks for sale programming / MyNetworkTV |
| 53.2 | WOLF DT | flight victoria bc to phoenix arizona | journal of communication interpersonal | Simulcast of bo diddley home page |
| 53.3 | WSWB DT | 480i | 4:3 | Simulcast of low carb alcoholic beverages |
Association of Thai Ridgebacks Owners & Fanciers
Welcome to the temporary web site for the Association of Thai Ridgeback . We are the only club recognized by the AKC-FSS for the Thai Ridgeback Dog in the .
http://thaidog.us/_wsn/page2.html
[kadia water hand pump] References
[black box inc louisville] External links
- ristes 2010 phpbb group
- seattle festivals in july
- new computer viruses or hoaxes
- journal of communication interpersonal
- home automation com port
- .21 or .28 for truggy
|
|


In the previous chapter, you have learnt about the characteristics of a Computer System and its components. You have also learnt about System Software and Application Software.
In the current chapter, you will learn about a very important System Software viz. Operating System. We will use Windows to explain various aspects of an operating system. Microsoft has produced a number of operating systems. Windows 95/98/ME/XP/Vista, Windows NT, Windows 2000/2003, Windows 8, Windows 10 are some of them.


The charming adventures of an analyst and his solver.
Affine scaling for interior point optimization in R.
I recently stumbled across an implementation of the affine scaling interior point method for solving linear programs that I'd coded up in R once upon a time. I'm posting it here in case anyone else finds it useful. There's not a whole lot of thought given to efficiency or numerical stability, just a demonstration of the basic algorithm. Still, sometimes that's exactly what one wants.
solve.affine <- function(A, rc, x, tolerance=10^-7, R=0.999) {
# Affine scaling method
while (T) {
X_diag <- diag(x)
# Compute (A * X_diag^2 * A^t)-1 using Cholesky factorization.
# This is responsible for scaling the original problem matrix.
q <- A %*% X_diag**2 %*% t(A)
q_inv <- chol2inv(chol(q))
# lambda = q * A * X_diag^2 * c
lambda <- q_inv %*% A %*% X_diag^2 %*% rc
# c - A^t * lambda is used repeatedly
foo <- rc - t(A) %*% lambda
# We converge as s goes to zero
s <- sqrt(sum((X_diag %*% foo)^2))
# Compute new x
x <- (x + R * X_diag^2 %*% foo / s)[,]
# If s is within our tolerance, stop.
if (abs(s) < tolerance) break
}
x
}
This function accepts a matrix A which contains all technological coefficients for an LP, a vector rc containing its reduced costs, and an initial point x interior to the LP's feasible region. Optional arguments to the function include a tolerance, for detecting when the method is within an acceptable distance from the optimal point, and a value for R, which must be strictly between 0 and 1 and controls scaling.
The method works by rescaling the matrix A around the current solution x. It then computes a new x such that it remains feasible and interior, which is why R cannot be 0 or 1. It requires a feasible interior point to start and only projects to other feasible interior points, so the right hand side of the LP is not required (it is implicit from the starting point). The shadow prices for each iteration are captured in the vector lambda, so the gap between primal and dual solutions is easy to compute.
We run this function against a 3x3 LP with a known solution:
max z = 5x1 + 4x2 + 3x3 st 2x1 + 3x2 + x3 <= 5 4x1 + x2 + 2x3 <= 11 3x1 + 4x2 + 2x3 <= 8 x1, x2, x3 >= 0
The optimal solution to this LP is:
z = 13 x1 = 2 x2 = 0 x3 = 1
This problem can be run against the affine scaling function by defining A with all necessary slack variables, and using an arbitrary feasible interior point:
A <- matrix(c(
2,3,1,1,0,0,
4,1,2,0,1,0,
3,4,2,0,0,1
), nrow=3, byrow=T)
rc <- c(5, 4, 3, 0, 0, 0)
x <- c(0.5, 0.5, 0.5, 2, 7.5, 3.5)
solution <- solve.affine(A, rc, x)
print(solution)
print(sum(solution * rc))
This provides an output vector that is very close to the optimal primal solution shown above. Since interior point methods converge asymptotically to optimal solutions, it is important to note that we can only ever get (extremely) close to our final optimal objective and decision variable values.
print(solution)
[1] 1.999998e+00 4.268595e-07 1.000002e+00 1.280579e-06 1.000005e+00
[6] 1.280579e-06
print(sum(solution * rc))
[1] 13.00000


Try out our Premium Member services: Virtual Legal Assistant, Query Alert Service and an ad-free experience. Free for one month and pay only if you like it.
1 IN THE HIGH COURT OF JUDICATURE AT MADRAS Dated : 04.10.2018 CORAM: THE HONOURABLE MR. JUSTICE P.VELMURUGAN Crl.R.C.No.212 of 2016 1. M/s.Arvind Remedies Limited, Rep. by its Managing Director, G28, 29, SIPCOT Industries Estate, Irungatukottai, Sriperumbudur, Kancheepuram – 602105. 2. Dr.B.Arvind Shah ... Petitioners Vs. 1. M/s.”Voice of Nature” Rep. by its President, Mr.R.Gokulraj, Regn.No.Adhigathur Raod, Manavalnagar, Thiruvallur District-602 002 2. CMK Reddy 3. R.Raja Mohan 4. V.R.Metha 5. Chandra Ravindran 6. Sudhir Chandra 7. G.Ramesh Kumar .... Respondents The Criminal Revision filed under Section 374 (2) of Code of Criminal Procedure to set aside the order dated 29.12.2015 made in C.M.P.No.1822 of 2015 in C.C.No.219 of 2015 by the learned District Munsif-cum-Judicial Magistrate, Sriperumbudur. For Petitioners : Mr.Gopalakrishna Lakshmana Raja, Senior Counsel for Mr.T.M.Madav Yadav For Respondents : Mr.C.S.Monica for R1 http://www.judis.nic.in 2 ORDER
The first respondent herein filed a complaint against the petitioners and the respondents 2 to 7 under Section 25 of Tamil Nadu Water (Prevention and Control of Pollution) Act, 1974 (herein after referred to as “the Act”), before the learned District Munsif-cum- Judicial Magistrate, Sriperumbudur, which was taken on file in C.C.No.219 of 2015. Pending the above case, the petitioners along with respondents 2 to 7 have filed a miscellaneous petition in C.M.P.No.1822 of 2015, seeking discharge. The learned Magistrate, after hearing both sides, had dismissed the petition by an order dated 29.12.2015, against which, the accused 1 & 2 have filed the present criminal revision before this Court.
2 The learned Senior Counsel appearing for the petitioners would submit that the first respondent has no locus standi to file a private complaint against the petitioners. The petitioner had obtained consent from the Tamil Nadu Pollution Control Board (herein after referred to as “the Board”) for establishment of the factory and operated the same. The Board has sent a notice for violation of certain conditions and soon after receiving the said notice, the http://www.judis.nic.in 3 petitioner had closed the factory and therefore they have not committed any offence. Since the petitioners had violated certain conditions, the Board had sent notice, for which, the petitioners had sent a proper reply, and hence it is between the Board and the petitioners . The first respondent, being a private person, has no locus standi to file a complaint against the petitioner Company. Further, the allegations made in the complaint does not constitute an offence under Section 25 of the Act. If there is any violation of the conditions by the petitioners, it is for the Board to take any action against the petitioners. The Board has not taken any criminal action against the petitioners and they only returned the application submitted by the petitioner and sent a notice and the petitioners also replied for the said notice. The trial Court failed to consider these aspects and erroneously dismissed the petition, seeking discharge, which warrants serious interference.
3 The learned counsel for the first respondent would submit that Section 49(b) of the Act enables any person to file a private complaint after effecting 60 days prior notice to the Board, which has been complied with by the first respondent and hence the contention of the learned Senior Counsel appearing for the petitioners that this http://www.judis.nic.in 4 first respondent has no locus standi to file a complaint against the petitioners is not acceptable. Further in the complaint, there are allegations, which clearly reveals that the petitioners had committed offence under Section 25 of the Act. The learned Magistrate had elaborately discussed all the contentions raised by the learned Senior Counsel and given reasons for dismissal of the petition, seeking discharge. Therefore, the petitioners have to face trial for the offence committed by them.
4 Heard the learned Senior Counsel appearing for the petitioners and the learned counsel appearing for the first respondent and perused the materials available on record.
5 Admittedly the petitioners had obtained consent from the Board to establish the Factory and subsequently, for the violation of certain conditions by the petitioners, the Board had issued a notice and as per Section 49 (b) of the Act, the Board or any person can file a private complaint, after compliance of 60 days notice. The first respondent had issued notice as contemplated under Section 49 (b) of the Act and therefore question of locus standi does not arise and the learned Magistrate also answered accordingly. Section 49 (b) of the http://www.judis.nic.in 5 Act reads as follows:
“(b) any person who has given notice of not less than sixty days, in the manner prescribed, of the alleged offence and of his intention to make a complaint, to the Board or officer authorised as aforesaid, and no court inferior to that of a Metropolitan Magistrate or a judicial Magistrate of the first class shall try any offence punishable under this Act.” 6 Therefore, the first respondent has locus standi to file a complaint against the petitioners. Further there are prima facie allegations against the petitioners and while deciding petition, seeking discharge, the Court has to see the allegations made in the complaint and the documents annexed with the complaint and not defence of the accused. The probative value of the materials need not be gone into at this stage and the same cane be done only after trial.
7 In the result, the criminal revision is dismissed as there is no illegality or infirmity in the order passed by the Court below, and the Court below is directed to proceed further, in accordance with law. Further, the personal appearance of the petitioner before the Court below is dispensed with. However, the petitioner is directed to appear before the Court below as and when required.
04.10.2018
Index : Yes/No
cgi
P.VELMURUGAN, J.,
http://www.judis.nic.in
6
cgi
To
The District Munsif-cum-Judicial Magistrate, Sriperumbudur.
Crl.R.C.No.212 of 2016 04.10.2018 http://www.judis.nic.in


--- layout: default title: challenges ---
{{ challenges.content | strip_html | truncate: 350 }} Read more {{ challenges.date | date_to_string }} - {% include read-time.html %}


Forecasting II: state space models¶
This tutorial covers state space modeling with the pyro.contrib.forecast module. This tutorial assumes the reader is already familiar with SVI, tensor shapes, and univariate forecasting.
See also:
Summary¶
Pyro’s ForecastingModel can combine regression, variational inference, and exact inference.
To model a linear-Gaussian dynamical system, use a GaussianHMM
noise_dist
.To model a heavy-tailed linear dynamical system, use LinearHMM with heavy-tailed distributions.
To enable inference with LinearHMM, use a LinearHMMReparam reparameterizer.
[1]:
import math
import torch
import pyro
import pyro.distributions as dist
import pyro.poutine as poutine
from pyro.contrib.examples.bart import load_bart_od
from pyro.contrib.forecast import ForecastingModel, Forecaster, eval_crps
from pyro.infer.reparam import LinearHMMReparam, StableReparam, SymmetricStableReparam
from pyro.ops.tensor_utils import periodic_repeat
from pyro.ops.stats import quantile
import matplotlib.pyplot as plt
%matplotlib inline
assert pyro.__version__.startswith('1.8.6')
pyro.set_rng_seed(20200305)
Intro to state space models¶
In the univariate tutorial we saw how to model time series as regression plus a local level model, using variational inference. This tutorial covers a different way to model time series: state space models and exact inference. Pyro’s forecasting module allows these two paradigms to be combined, for example modeling seasonality with regression, including a slow global trend, and using a state-space model for short-term local trend.
Pyro implements a few state space models, but the most important are the GaussianHMM distribution and its heavy-tailed generalization the LinearHMM distribution. Both of these model a linear dynamical system with hidden state; both are multivariate, and both allow learning of all process parameters. On top of these the
pyro.contrib.timeseries module implements a variety of multivariate Gaussian Process models that compile down to GaussianHMM
s.
Pyro’s inference for GaussianHMM
uses parallel-scan Kalman filtering, allowing fast analysis of very long time series. Similarly, Pyro’s inference for LinearHMM
uses entirely parallel auxiliary variable methods to reduce to a GaussianHMM
, which then permits parallel-scan inference. Thus both methods allow parallelization of long time series analysis, even for a single univariate time series.
Let’s again look at the BART train ridership dataset:
[2]:
dataset = load_bart_od()
print(dataset.keys())
print(dataset["counts"].shape)
print(" ".join(dataset["stations"]))
dict_keys(['stations', 'start_date', 'counts'])
torch.Size([78888, 50, 50])
12TH 16TH 19TH 24TH ANTC ASHB BALB BAYF BERY CAST CIVC COLM COLS CONC DALY DBRK DELN DUBL EMBR FRMT FTVL GLEN HAYW LAFY LAKE MCAR MLBR MLPT MONT NBRK NCON OAKL ORIN PCTR PHIL PITT PLZA POWL RICH ROCK SANL SBRN SFIA SHAY SSAN UCTY WARM WCRK WDUB WOAK
[3]:
data = dataset["counts"].sum([-1, -2]).unsqueeze(-1).log1p()
print(data.shape)
plt.figure(figsize=(9, 3))
plt.plot(data, 'b.', alpha=0.1, markeredgewidth=0)
plt.title("Total hourly ridership over nine years")
plt.ylabel("log(# rides)")
plt.xlabel("Hour after 2011-01-01")
plt.xlim(0, len(data));
torch.Size([78888, 1])
[4]:
plt.figure(figsize=(9, 3))
plt.plot(data)
plt.title("Total hourly ridership over one month")
plt.ylabel("log(# rides)")
plt.xlabel("Hour after 2011-01-01")
plt.xlim(len(data) - 24 * 30, len(data));
GaussianHMM¶
Let’s start by modeling hourly seasonality together with a local linear trend, where we model seasonality via regression and local linear trend via a GaussianHMM. This noise model includes a mean-reverting hidden state (an Ornstein-Uhlenbeck process) plus Gaussian observation noise.
[5]:
T0 = 0 # beginning
T2 = data.size(-2) # end
T1 = T2 - 24 * 7 * 2 # train/test split
means = data[:T1 // (24 * 7) * 24 * 7].reshape(-1, 24 * 7).mean(0)
[6]:
class Model1(ForecastingModel):
def model(self, zero_data, covariates):
duration = zero_data.size(-2)
# We'll hard-code the periodic part of this model, learning only the local model.
prediction = periodic_repeat(means, duration, dim=-1).unsqueeze(-1)
# On top of this mean prediction, we'll learn a linear dynamical system.
# This requires specifying five pieces of data, on which we will put structured priors.
init_dist = dist.Normal(0, 10).expand([1]).to_event(1)
timescale = pyro.sample("timescale", dist.LogNormal(math.log(24), 1))
# Note timescale is a scalar but we need a 1x1 transition matrix (hidden_dim=1),
# thus we unsqueeze twice using [..., None, None].
trans_matrix = torch.exp(-1 / timescale)[..., None, None]
trans_scale = pyro.sample("trans_scale", dist.LogNormal(-0.5 * math.log(24), 1))
trans_dist = dist.Normal(0, trans_scale.unsqueeze(-1)).to_event(1)
# Note the obs_matrix has shape hidden_dim x obs_dim = 1 x 1.
obs_matrix = torch.tensor([[1.]])
obs_scale = pyro.sample("obs_scale", dist.LogNormal(-2, 1))
obs_dist = dist.Normal(0, obs_scale.unsqueeze(-1)).to_event(1)
noise_dist = dist.GaussianHMM(
init_dist, trans_matrix, trans_dist, obs_matrix, obs_dist, duration=duration)
self.predict(noise_dist, prediction)
We can then train the model on many years of data. Note that because we are being variational about only time-global variables, and exactly integrating out time-local variables (via GaussianHMM
), stochastic gradients are very low variance; this allows us to use a large learning rate and few steps.
[7]:
%%time
pyro.set_rng_seed(1)
pyro.clear_param_store()
covariates = torch.zeros(len(data), 0) # empty
forecaster = Forecaster(Model1(), data[:T1], covariates[:T1], learning_rate=0.1, num_steps=400)
for name, value in forecaster.guide.median().items():
if value.numel() == 1:
print("{} = {:0.4g}".format(name, value.item()))
INFO step 0 loss = 0.878717
INFO step 100 loss = 0.650493
INFO step 200 loss = 0.650542
INFO step 300 loss = 0.650579
timescale = 4.461
trans_scale = 0.4563
obs_scale = 0.0593
CPU times: user 26.3 s, sys: 1.47 s, total: 27.8 s
Wall time: 27.8 s
Plotting forecasts of the next two weeks of data, we see mostly reasonable forecasts, but an anomaly on Christmas when rides were overpredicted. This is to be expected, as we have not modeled yearly seasonality or holidays.
[8]:
samples = forecaster(data[:T1], covariates, num_samples=100)
samples.clamp_(min=0) # apply domain knowledge: the samples must be positive
p10, p50, p90 = quantile(samples, (0.1, 0.5, 0.9)).squeeze(-1)
crps = eval_crps(samples, data[T1:])
print(samples.shape, p10.shape)
plt.figure(figsize=(9, 3))
plt.fill_between(torch.arange(T1, T2), p10, p90, color="red", alpha=0.3)
plt.plot(torch.arange(T1, T2), p50, 'r-', label='forecast')
plt.plot(torch.arange(T1 - 24 * 7, T2),
data[T1 - 24 * 7: T2], 'k-', label='truth')
plt.title("Total hourly ridership (CRPS = {:0.3g})".format(crps))
plt.ylabel("log(# rides)")
plt.xlabel("Hour after 2011-01-01")
plt.xlim(T1 - 24 * 7, T2)
plt.text(78732, 3.5, "Christmas", rotation=90, color="green")
plt.legend(loc="best");
torch.Size([100, 336, 1]) torch.Size([336])
Next let’s change the model to use heteroskedastic observation noise, depending on the hour of week.
[9]:
class Model2(ForecastingModel):
def model(self, zero_data, covariates):
duration = zero_data.size(-2)
prediction = periodic_repeat(means, duration, dim=-1).unsqueeze(-1)
init_dist = dist.Normal(0, 10).expand([1]).to_event(1)
timescale = pyro.sample("timescale", dist.LogNormal(math.log(24), 1))
trans_matrix = torch.exp(-1 / timescale)[..., None, None]
trans_scale = pyro.sample("trans_scale", dist.LogNormal(-0.5 * math.log(24), 1))
trans_dist = dist.Normal(0, trans_scale.unsqueeze(-1)).to_event(1)
obs_matrix = torch.tensor([[1.]])
# To model heteroskedastic observation noise, we'll sample obs_scale inside a plate,
# then repeat to full duration. This is the only change from Model1.
with pyro.plate("hour_of_week", 24 * 7, dim=-1):
obs_scale = pyro.sample("obs_scale", dist.LogNormal(-2, 1))
obs_scale = periodic_repeat(obs_scale, duration, dim=-1)
obs_dist = dist.Normal(0, obs_scale.unsqueeze(-1)).to_event(1)
noise_dist = dist.GaussianHMM(
init_dist, trans_matrix, trans_dist, obs_matrix, obs_dist, duration=duration)
self.predict(noise_dist, prediction)
[10]:
%%time
pyro.set_rng_seed(1)
pyro.clear_param_store()
covariates = torch.zeros(len(data), 0) # empty
forecaster = Forecaster(Model2(), data[:T1], covariates[:T1], learning_rate=0.1, num_steps=400)
for name, value in forecaster.guide.median().items():
if value.numel() == 1:
print("{} = {:0.4g}".format(name, value.item()))
INFO step 0 loss = 0.954783
INFO step 100 loss = -0.0344435
INFO step 200 loss = -0.0373581
INFO step 300 loss = -0.0376129
timescale = 61.41
trans_scale = 0.1082
CPU times: user 28.1 s, sys: 1.34 s, total: 29.5 s
Wall time: 29.6 s
Note this gives us a much longer timescale and thereby more accurate short-term predictions:
[11]:
samples = forecaster(data[:T1], covariates, num_samples=100)
samples.clamp_(min=0) # apply domain knowledge: the samples must be positive
p10, p50, p90 = quantile(samples, (0.1, 0.5, 0.9)).squeeze(-1)
crps = eval_crps(samples, data[T1:])
plt.figure(figsize=(9, 3))
plt.fill_between(torch.arange(T1, T2), p10, p90, color="red", alpha=0.3)
plt.plot(torch.arange(T1, T2), p50, 'r-', label='forecast')
plt.plot(torch.arange(T1 - 24 * 7, T2),
data[T1 - 24 * 7: T2], 'k-', label='truth')
plt.title("Total hourly ridership (CRPS = {:0.3g})".format(crps))
plt.ylabel("log(# rides)")
plt.xlabel("Hour after 2011-01-01")
plt.xlim(T1 - 24 * 7, T2)
plt.text(78732, 3.5, "Christmas", rotation=90, color="green")
plt.legend(loc="best");
[12]:
plt.figure(figsize=(9, 3))
plt.fill_between(torch.arange(T1, T2), p10, p90, color="red", alpha=0.3)
plt.plot(torch.arange(T1, T2), p50, 'r-', label='forecast')
plt.plot(torch.arange(T1 - 24 * 7, T2),
data[T1 - 24 * 7: T2], 'k-', label='truth')
plt.title("Total hourly ridership (CRPS = {:0.3g})".format(crps))
plt.ylabel("log(# rides)")
plt.xlabel("Hour after 2011-01-01")
plt.xlim(T1 - 24 * 2, T1 + 24 * 4)
plt.legend(loc="best");
Heavy-tailed modeling with LinearHMM¶
Next let’s change our model to a linear-Stable dynamical system, exhibiting learnable heavy tailed behavior in both the process noise and observation noise. As we’ve already seen in the univariate tutorial, this will require special handling of stable distributions by poutine.reparam(). For state space models, we combine LinearHMMReparam with other reparameterizers like StableReparam and SymmetricStableReparam. All reparameterizers preserve behavior of the generative model, and only serve to enable inference via auxiliary variable methods.
[13]:
class Model3(ForecastingModel):
def model(self, zero_data, covariates):
duration = zero_data.size(-2)
prediction = periodic_repeat(means, duration, dim=-1).unsqueeze(-1)
# First sample the Gaussian-like parameters as in previous models.
init_dist = dist.Normal(0, 10).expand([1]).to_event(1)
timescale = pyro.sample("timescale", dist.LogNormal(math.log(24), 1))
trans_matrix = torch.exp(-1 / timescale)[..., None, None]
trans_scale = pyro.sample("trans_scale", dist.LogNormal(-0.5 * math.log(24), 1))
obs_matrix = torch.tensor([[1.]])
with pyro.plate("hour_of_week", 24 * 7, dim=-1):
obs_scale = pyro.sample("obs_scale", dist.LogNormal(-2, 1))
obs_scale = periodic_repeat(obs_scale, duration, dim=-1)
# In addition to the Gaussian parameters, we will learn a global stability
# parameter to determine tail weights, and an observation skew parameter.
stability = pyro.sample("stability", dist.Uniform(1, 2).expand([1]).to_event(1))
skew = pyro.sample("skew", dist.Uniform(-1, 1).expand([1]).to_event(1))
# Next we construct stable distributions and a linear-stable HMM distribution.
trans_dist = dist.Stable(stability, 0, trans_scale.unsqueeze(-1)).to_event(1)
obs_dist = dist.Stable(stability, skew, obs_scale.unsqueeze(-1)).to_event(1)
noise_dist = dist.LinearHMM(
init_dist, trans_matrix, trans_dist, obs_matrix, obs_dist, duration=duration)
# Finally we use a reparameterizer to enable inference.
rep = LinearHMMReparam(None, # init_dist is already Gaussian.
SymmetricStableReparam(), # trans_dist is symmetric.
StableReparam()) # obs_dist is asymmetric.
with poutine.reparam(config={"residual": rep}):
self.predict(noise_dist, prediction)
Note that since this model introduces auxiliary variables that are learned by variational inference, gradients are higher variance and we need to train for longer.
[14]:
%%time
pyro.set_rng_seed(1)
pyro.clear_param_store()
covariates = torch.zeros(len(data), 0) # empty
forecaster = Forecaster(Model3(), data[:T1], covariates[:T1], learning_rate=0.1)
for name, value in forecaster.guide.median().items():
if value.numel() == 1:
print("{} = {:0.4g}".format(name, value.item()))
INFO step 0 loss = 42.9188
INFO step 100 loss = 0.243742
INFO step 200 loss = 0.112491
INFO step 300 loss = 0.0320302
INFO step 400 loss = -0.0424252
INFO step 500 loss = -0.0763611
INFO step 600 loss = -0.108585
INFO step 700 loss = -0.129246
INFO step 800 loss = -0.143037
INFO step 900 loss = -0.173499
INFO step 1000 loss = -0.172329
timescale = 11.29
trans_scale = 0.04193
stability = 1.68
skew = -0.0001891
CPU times: user 2min 57s, sys: 21.9 s, total: 3min 19s
Wall time: 3min 19s
[15]:
samples = forecaster(data[:T1], covariates, num_samples=100)
samples.clamp_(min=0) # apply domain knowledge: the samples must be positive
p10, p50, p90 = quantile(samples, (0.1, 0.5, 0.9)).squeeze(-1)
crps = eval_crps(samples, data[T1:])
plt.figure(figsize=(9, 3))
plt.fill_between(torch.arange(T1, T2), p10, p90, color="red", alpha=0.3)
plt.plot(torch.arange(T1, T2), p50, 'r-', label='forecast')
plt.plot(torch.arange(T1 - 24 * 7, T2),
data[T1 - 24 * 7: T2], 'k-', label='truth')
plt.title("Total hourly ridership (CRPS = {:0.3g})".format(crps))
plt.ylabel("log(# rides)")
plt.xlabel("Hour after 2011-01-01")
plt.xlim(T1 - 24 * 7, T2)
plt.text(78732, 3.5, "Christmas", rotation=90, color="green")
plt.legend(loc="best");
[16]:
plt.figure(figsize=(9, 3))
plt.fill_between(torch.arange(T1, T2), p10, p90, color="red", alpha=0.3)
plt.plot(torch.arange(T1, T2), p50, 'r-', label='forecast')
plt.plot(torch.arange(T1 - 24 * 7, T2),
data[T1 - 24 * 7: T2], 'k-', label='truth')
plt.title("Total hourly ridership (CRPS = {:0.3g})".format(crps))
plt.ylabel("log(# rides)")
plt.xlabel("Hour after 2011-01-01")
plt.xlim(T1 - 24 * 2, T1 + 24 * 4)
plt.legend(loc="best");
[ ]:


There are certain months that have a greater meaning than others
because of some special event that happens during the month. December
is treasured because it serves as the home to Christmas, July is
famous for the Fourth of July, and the month of April is heavily
anticipated because of the celebration of Easter. However, in the
minds of most anxious seniors in high school, there are only two
months in the universe that hold any meaning at all: November and
January. These are the months that
the Common
Application, an extremely popular online college application, closes
as thousands of aspiring scholars desperately try to put the finishing
touches on their application. While it may seem like an exaggeration,
the large financial obligations and life-altering potential make the
business of college a monumental staple of our society, and the
college application process truly serves as a gateway to the future
for the applicant.
Take it from someone who has vividly
experienced it firsthand, the process of applying to and accepting a
college can be stressful. The application process, in particular, can
be tedious and downright confusing, and by the time that I had
finished all of my applications I felt that I had discovered useless
personal information that I didn’t even know existed. The essay
prompts are generally vague to the point of obscurity as applicants
nervously try to portray the image of the student that they believe
the university wants to see. Keep in mind that all this preparation is
happening while on a strict deadline that is set in stone like the ten
commandments. Once all the work is finally done and the applications
are submitted (after paying a pricey application fee of course), then
the waiting game begins as the suspense of approval or rejection sets
in. Similar to the feeling of suffocation, the feeling of anxiousness
grows as more time passes and the period for acceptance/rejection
letters grows nearer. The entire process can be exhausting and takes a
young adult on a roller coaster of emotions that can completely shape
the outlook of their life.
However, if the situation is
right and the stars perfectly align, the thrill of college can make
the arduous application process and stress of waiting seem trivial and
worth the wait. For me personally, college has opened so many doors
both socially and academically that I will treasure for the rest of my
life, and the memories I have made here so far are priceless. Above
everything else, whether it be when applying to college or during
move-in day, the most important thing for someone involved in the
college system to remember is to just be yourself. While this may
sound simple and generic, a college essay will sound much more
compelling if it is coming from the emotions of someone who believes
what they are writing, and people naturally gravitate to individuals
who are genuine. College, like anything in life, has the possibility
to be a special four years that can be treasured forever.


| 1 | /* | |
| 2 | * Licensed to the Apache Software Foundation (ASF) under one or more | |
| 3 | * contributor license agreements. See the NOTICE file distributed with | |
| 4 | * this work for additional information regarding copyright ownership. | |
| 5 | * The ASF licenses this file to You under the Apache License, Version 2.0 | |
| 6 | * (the "License"); you may not use this file except in compliance with | |
| 7 | * the License. You may obtain a copy of the License at | |
| 8 | * | |
| 9 | * http://www.apache.org/licenses/LICENSE-2.0 | |
| 10 | * | |
| 11 | * Unless required by applicable law or agreed to in writing, software | |
| 12 | * distributed under the License is distributed on an "AS IS" BASIS, | |
| 13 | * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
| 14 | * See the License for the specific language governing permissions and | |
| 15 | * limitations under the License. | |
| 16 | */ | |
| 17 | ||
| 18 | package org.apache.commons.math3.geometry.euclidean.threed; | |
| 19 | ||
| 20 | import java.io.Serializable; | |
| 21 | ||
| 22 | import org.apache.commons.math3.geometry.Space; | |
| 23 | import org.apache.commons.math3.geometry.euclidean.twod.Euclidean2D; | |
| 24 | ||
| 25 | /** | |
| 26 | * This class implements a three-dimensional space. | |
| 27 | * @version $Id$ | |
| 28 | * @since 3.0 | |
| 29 | */ | |
| 30 | public class Euclidean3D implements Serializable, Space { | |
| 31 | ||
| 32 | /** Serializable version identifier. */ | |
| 33 | private static final long serialVersionUID =<PHONE_NUMBER>814886817L; | |
| 34 | ||
| 35 | /** Private constructor for the singleton. | |
| 36 | */ | |
| 37 | private Euclidean3D() { | |
| 38 | } | |
| 39 | ||
| 40 | /** Get the unique instance. | |
| 41 | * @return the unique instance | |
| 42 | */ | |
| 43 | public static Euclidean3D getInstance() { | |
| 44 |
1
1. getInstance : mutated return of Object value for org/apache/commons/math3/geometry/euclidean/threed/Euclidean3D::getInstance to ( if (x != null) null else throw new RuntimeException ) → KILLED |
return LazyHolder.INSTANCE; |
| 45 | } | |
| 46 | ||
| 47 | /** {@inheritDoc} */ | |
| 48 | public int getDimension() { | |
| 49 |
1
1. getDimension : replaced return of integer sized value with (x == 0 ? 1 : 0) → KILLED |
return 3; |
| 50 | } | |
| 51 | ||
| 52 | /** {@inheritDoc} */ | |
| 53 | public Euclidean2D getSubSpace() { | |
| 54 |
1
1. getSubSpace : mutated return of Object value for org/apache/commons/math3/geometry/euclidean/threed/Euclidean3D::getSubSpace to ( if (x != null) null else throw new RuntimeException ) → KILLED |
return Euclidean2D.getInstance(); |
| 55 | } | |
| 56 | ||
| 57 | // CHECKSTYLE: stop HideUtilityClassConstructor | |
| 58 | /** Holder for the instance. | |
| 59 | *
We use here the Initialization On Demand Holder Idiom.
 | |
| 60 | */ | |
| 61 | private static class LazyHolder { | |
| 62 | /** Cached field instance. */ | |
| 63 | private static final Euclidean3D INSTANCE = new Euclidean3D(); | |
| 64 | } | |
| 65 | // CHECKSTYLE: resume HideUtilityClassConstructor | |
| 66 | ||
| 67 | /** Handle deserialization of the singleton. | |
| 68 | * @return the singleton instance | |
| 69 | */ | |
| 70 | private Object readResolve() { | |
| 71 | // return the singleton instance | |
| 72 |
1
1. readResolve : mutated return of Object value for org/apache/commons/math3/geometry/euclidean/threed/Euclidean3D::readResolve to ( if (x != null) null else throw new RuntimeException ) → KILLED |
return LazyHolder.INSTANCE; |
| 73 | } | |
| 74 | ||
| 75 | } | |
Mutations | ||
| 44 |
1.1 |
|
| 49 |
1.1 |
|
| 54 |
1.1 |
|
| 72 |
1.1 |


AcceptUserResponseMerchantWhiteLabelApp Class Reference
| Inherits from | NSObject |
| Declared in | AcceptUserResponse.h |
Overview
@class AcceptUserResponseMerchantWhiteLabelApp
Basic merchant info as defined and received from backend
Tasks
-
name
property -
acquirerIdentifier
property -
locale
property -
createdAt
property -
countryId
property -
timeZone
property -
updatedAt
property -
amountUpperLimit
property -
amountLowerLimit
property -
customerSupportPhoneNumber
property -
id_
property -
identifier
property -
amountDailyLimit
property -
emailFrom
property -
serviceChargeAmount
property -
defaultCurrency
property
Properties
acquirerIdentifier
@property (nonatomic, strong) NSString *acquirerIdentifier
Declared In
AcceptUserResponse.h
customerSupportPhoneNumber
@property (nonatomic, strong) NSString *customerSupportPhoneNumber
Declared In
AcceptUserResponse.h
defaultCurrency
@property (nonatomic, strong) NSString *defaultCurrency
Declared In
AcceptUserResponse.h


Make your CE code easier to read. Paste your code in the input box on the left. The formatter will add linebreaks and indents to make it easier to read. It will also notify you if your code is missing certain elements.


COOL WATERS RESORT
We are so proud of our facility and staff below are some of the pictures
Below is our lovely staff


This plugin extends Openfire to support WebSocket. The implementation follows the XMPP WebSocket subprotocol (RFC 7395) specification, which is a standard extension of the WebSocket protocol specification (RFC 6455).
Note that the BOSH (http-bind) capabilities of Openfire must be enabled and correctly configured as a prerequisite before installing this plugin. The WebSocket servlet is installed within the same context as the BOSH component, and will reuse the same HTTP/S port(s) when establishing the WebSocket connection.
Copy websocket.jar into the plugins directory of your Openfire installation. The plugin will then be automatically deployed. To upgrade to a new version, copy the new websocket.jar file over the existing file.
Upon installation, the WebSocket URI path will be /ws/ on the same server/port as the BOSH connector. To establish a secure WebSocket, modify the following URL as appropriate:
wss://your.openfire.host:7443/ws/
The WebSocket plugin implements the Stream Management (XEP-0198) "ack" capabilities introduced with Openfire 4.0. This provides assurance for XMPP packet delivery by allowing the peers to agree on the number of stanzas exchanged. Two system properties are available to configure this feature:
stream.management.active
stream.management.unsolicitedAckFrequency
XEP-0198 allows either party (client or server) to send unsolicited ack/answer stanzas on a periodic basis. This implementation approximates BOSH ack behavior by sending unsolicited stanzas from the server to the client after a configurable number of stanzas have been received from the client.
Setting the system property to "1" would indicate that each client packet should be ack'd by the server when stream management is enabled for a particular stream. To disable unsolicited server acks, use the default value for system property "stream.management.unsolicitedAckFrequency" ("0"). This setting does not affect server responses to explicit ack requests from the client.


WELCOME TO GRAND HOTEL
In this project we make front end pages with using html ,css and javascript.
Main motive to make this project is help pepole
who come from another city and the do not know about the
where to stay that our website gives them right information about the hotel
from this project pepole can check here,hotel is available or not if hotel available then rooms is availble
then you can book the room easily form
our website with normal budget .
Project made by
NITESH KUMAR JAIN
K AKSHITH
YASHWANTH MANNER


[viewBag] title = "Forgot Password" url = "/forgot-password" layout = "forgot" is_hidden = 0 navigation_hidden = 0 meta_title = "Forgot Password" meta_description = "Forgot Password? Enter your details to reset your password." == {% put newPswdHeader %}
You've successfully changed your password.
{% endput %} {% put forgetOk %}A password reset link has been sent to your inbox. Please check this and follow the instructions. Thank you.
{% endput %} ==If you did not register with an email address, you must contact our Customer Support team to reset your password.


Buy Quality Houses From Housing
Home
Order
About
Name:
Phone:
Delivery Date:
Type:
Select type of House
Mansion
Bungalow
Igloo
Mini House
Fenced
Not Fenced
Submit


Apart from my univesity curriculum, I do spend time learning stuff online in my free time. However, there are countless resources which you can get online, be it online courses, videos, podcasts, blogs etc which’d help you to learn stuff. In this article, I’d mention the resources which I used along with their concise description. This article is indeed a live one, and would get timely updates after completing new milestones :). As of now, the list mainly consists of the data science related stuff.
Online Courses:-
-
Elements of AI: This was my first introduction to this new world where I came to know about Artificial Intelligence, Machine Learning, Deep Learning, Alan Turing, etc. It is a completely theoretical course and the main purpose is to get you acquainted with these buzz words. There are mentions about how this field evolved with time and contributions of some prominent researchers as well. There is a high level desciptions about Neural Networks and also about their cool applications. Do go for it if you don’t have in-depth idea about what these buzzwords mean and are curios to know them.
-
Machine Learning: Arguably one of the most famous course on Machine Learning out there on the internet. This can be attributed to the popularity of the instructor Andrew Ng, a world class AI researcher. This course is a subtle introduction to Machine Learning, and introduces about the algorithms which facilitate predictions. This course has assignments as well as quizzes which in my opinion are on the easier side, but nevertheless they are worth your time. This course is for those who aren’t familiar with Machine Learning algorithms as such. If you aren’t in this category than you may choose not to do it and proceed for the next one. Another reason for the popularity of this course is the pre-requisites is Introductory Linear Algebra and some knowledge about algorithms. However, this course uses Octave/Matlab as languages for Programming Assignments contrary to Python and R which are the languages which are mostly used.
-
CS109-Data Science: One of the not-so-popular online course but, believe me it is one of the best. The reasons why I am saying this is that it covers everything, you should have knowledge about if you want to become a data scientist someday. You’d learn Data Scraping, Data Processing, Data Visualization, Machine Learning, and Storytelling! The homework assignments are awesome projects on real datasets. However, the prereqs for this is Probability and Statistics which’d be covered in any intro-level course in your college. All the assignments use Python 2, and you’d have to figure out the Python3 equivalent, but it shouldn’t be too much of an issue. I highly recommend going for this if you satisfy the pre-reqs.
-
Introduction to Deep Learning: It’s an okayish course and it briefly introduces the various neural net architectures. The framework used for assignments is Tensorflow, and Keras. I found their explanation of backpropogation very cool. Their explanation of TensorFlow as a framework is also very lucid and intuitive. In rest of the course, they discuss CNNs, LSTMs, RNNs, Autoencoders, etc. Some of the assignments can be difficult for those who are using TensorFlow for the first time or using Neural Nets for the first time.
-
How to Win a Data Science Competition: This is an amazing course, targeted for those who know how to use Machine Learning in Python, but they want to hone their skills. What I mean is simply using model.fit() followed by model.predict() isn’t sufficient. There are several other aspects such as Feature Engineering, Hyperparameter tuning, Model Selection, Validation Strategies, etc which one needs to take care of while developing models which generalize well. This course covers all these other aspects. To be honest, this course is mainly for people who want to compete in data science competitions and get good scores but it’d certainly help you become a better data scientist at your workplace.
Blogs to Follow:
- Towards Data Science: If you’re a newbie in this field and don’t understand any particular concept related to probability, statistic, machine learning, etc. this is the place where you’d most probably find a blog post related to that. The writers also write about new cool applications and what it feels to work in the industry.
- Machine Learning Mastery - By Jason Brownlie Ph.D: Here you’d find many relevant blog posts for Machine Learning.
- Kaggle Discussion: Kaggle organizes data science competitions but apart from that it has an excellent forum for discussing the advances in this area. You’d be able to learn new tricks and hacks from the experts and also interact with them.


Gitboard aggregates your Github projects & issues into a responsive, filterable interface.
Get Started


model.world
- class World;
- This class describes a game world. A world contains all players and game objects (units'').
- immutable int tickIndex;
- Returns:
the current game tick.
- immutable int tickCount;
- Returns:
the base game duration in ticks. A real game duration may be lower. Equals to game.tickCount
.
- immutable double width;
- Returns:
the world width.
- immutable double height;
- Returns:
the world height.
- immutable Player[] players;
- Returns:
all players (in random order). After each tick the player objects are recreated.
- immutable Wizard[] wizards;
- Returns:
visible wizards (in random order). After each tick the wizard objects are recreated.
- immutable Minion[] minions;
- Returns:
visible minions (in random order). After each tick the minion objects are recreated.
- immutable Projectile[] projectiles;
- Returns:
visible projectiles (in random order). After each tick the projectile objects are recreated.
- immutable Bonus[] bonuses;
- Returns:
visible bonuses (in random order). After each tick the bonus objects are recreated.
- immutable Building[] buildings;
- Returns:
visible buildings (in random order). After each tick the building objects are recreated.
- immutable Tree[] trees;
- Returns:
visible trees (in random order). After each tick the tree objects are recreated.
- immutable pure nothrow @nogc @safe immutable(Player) getMyPlayer();
- Returns:
your player.
Page generated by Ddoc.


Generate charts from vmstat
output.
How to use: Save vmstat
output on a file and drop it in the box below
Drop file here...
Processes
- r: How many processes are waiting for CPU time.
- b: Wait Queue - Process which are waiting for I/O (disk, network, user input,etc..)
Memory
- free: Idle Memory
- buff: Memory used as buffers, like before/after I/O operations
- cache: Memory used as cache by the Operating System
- (Ignored)swpd: shows how many blocks are swapped out to disk (paged). Total Virtual memory usage.
Swap
- si: How many blocks per second the operating system is swapping in. i.e Memory swapped in from the disk (Read from swap area to Memory)
- so: How many blocks per second the operating system is swaped Out. i.e Memory swapped to the disk (Written to swap area and cleared from Memory)
IO
- bi: Blocks received from block device - Read (like a hard disk)
- bo: Blocks sent to a block device - Write
System
- in: The number of interrupts per second, including the clock.
- cs: The number of context switches per second.
CPU
- us: percentage of cpu used for running non-kernel code. (user time, including nice time)
- sy: percentage of cpu used for running kernel code. (system time - network, IO interrupts, etc)
- id: cpu idle time in percentage.
- wa: percentage of time spent by cpu for waiting to IO.


|
Name
|
Type
|
Description
|
| ioLoadBalanceAutomationMode | xsd:string |
Specifies the behavior of Storage DRS when it generates
recommendations for correcting I/O load imbalance in a datastore
cluster. See StorageDrsPodConfigInfo. If specified, this option
overrides the datastore cluster level automation behavior defined in the
StorageDrsPodConfigInfo.
|
| policyEnforcementAutomationMode | xsd:string |
Specifies the behavior of Storage DRS when it generates
recommendations for correcting storage and Vm policy violations
in a datastore cluster. See StorageDrsPodConfigInfoBehavior. If
specified, this option overrides the datastore cluster level
automation behavior defined in the StorageDrsPodConfigInfo for
recommendations aimed at fixing storage policy violations.
|
| ruleEnforcementAutomationMode | xsd:string |
Specifies the behavior of Storage DRS when it generates
recommendations for correcting affinity rule violations in a
datastore cluster. See StorageDrsPodConfigInfoBehavior. If
specified, this option overrides the datastore cluster level
automation behavior defined in the StorageDrsPodConfigInfo for
recommendations aimed at fixing rule violations.
|
| spaceLoadBalanceAutomationMode | xsd:string |
Specifies the behavior of Storage DRS when it generates
recommendations for correcting space load imbalance in a
datastore cluster. See StorageDrsPodConfigInfo. If specified, this option
overrides the datastore cluster level automation behavior defined in the
StorageDrsPodConfigInfo.
|
| vmEvacuationAutomationMode | xsd:string |
Specifies the behavior of Storage DRS when it generates
recommendations for datastore evacuations in a datastore
cluster. See StorageDrsPodConfigInfoBehavior. If specified, this
option overrides the datastore cluster level automation behavior
defined in the StorageDrsPodConfigInfo for recommendations aimed at
evacuating Vms from a datastore.
|
|
Properties inherited from DynamicData |
| None |


| Country | 1982 | 2003 | 2004 | 2005 | 2006 | 2007 | 2008 | 2009 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | Total |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| USA | 2 | 45 | 77 | 167 | 174 | 324 | 531 | 643 | 599 | 597 | 757 | 905 | 1492 | 2627 | 920 | 9860 |
| India | 0 | 0 | 0 | 1 | 2 | 0 | 90 | 154 | 196 | 330 | 262 | 816 | 1423 | 1492 | 380 | 5146 |
| Philippines | 0 | 0 | 0 | 0 | 0 | 26 | 192 | 71 | 27 | 36 | 57 | 95 | 141 | 340 | 248 | 1233 |
| China | 0 | 0 | 1 | 0 | 2 | 107 | 271 | 760 | 830 | 686 | 655 | 1054 | 876 | 1243 | 184 | 6669 |
| Mexico | 0 | 0 | 1 | 0 | 0 | 3 | 29 | 4 | 37 | 107 | 97 | 56 | 292 | 244 | 150 | 1020 |
| Canada | 1 | 17 | 0 | 6 | 5 | 62 | 80 | 110 | 136 | 117 | 121 | 184 | 305 | 464 | 148 | 1756 |
| Spain | 0 | 0 | 20 | 16 | 8 | 43 | 93 | 134 | 90 | 58 | 42 | 80 | 194 | 239 | 99 | 1116 |
| Vietnam | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 122 | 53 | 35 | 30 | 92 | 81 | 81 | 497 |
| Korea | 0 | 0 | 0 | 2 | 51 | 90 | 140 | 7 | 43 | 49 | 51 | 83 | 152 | 242 | 76 | 986 |
| Singapore | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 67 | 12 | 11 | 11 | 21 | 36 | 103 | 76 | 337 |
| Peru | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 2 | 61 | 54 | 178 | 194 | 180 | 69 | 741 |
| Australia | 0 | 1 | 0 | 1 | 0 | 2 | 0 | 3 | 52 | 57 | 63 | 71 | 207 | 324 | 68 | 849 |
| Poland | 1 | 2 | 2 | 33 | 41 | 31 | 80 | 60 | 70 | 93 | 120 | 430 | 564 | 266 | 67 | 1860 |
| Colombia | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 4 | 60 | 139 | 82 | 228 | 115 | 64 | 694 |
| Chile | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 64 | 59 | 69 | 35 | 72 | 24 | 121 | 60 | 504 |
| Dominican Republic | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 25 | 101 | 70 | 58 | 255 |
| United Kingdom | 1 | 2 | 4 | 1 | 6 | 19 | 26 | 41 | 24 | 40 | 50 | 61 | 94 | 151 | 56 | 576 |
| Brazil | 0 | 0 | 0 | 0 | 0 | 29 | 6 | 89 | 85 | 151 | 260 | 321 | 437 | 703 | 55 | 2136 |
| Russia | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 50 | 69 | 96 | 112 | 108 | 136 | 277 | 52 | 902 |
| Nepal | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 51 | 52 |
| Indonesia | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 75 | 376 | 415 | 67 | 84 | 23 | 60 | 48 | 1150 |
| Ukraine | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 | 1 | 61 | 133 | 157 | 281 | 233 | 43 | 913 |
| Italy | 1 | 0 | 1 | 0 | 7 | 7 | 22 | 99 | 49 | 61 | 46 | 52 | 90 | 56 | 39 | 530 |
| Germany | 1 | 1 | 6 | 12 | 20 | 23 | 38 | 141 | 213 | 162 | 174 | 120 | 105 | 96 | 37 | 1149 |
| Malaysia | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 150 | 101 | 51 | 44 | 4 | 26 | 131 | 35 | 543 |
| Denmark | 0 | 1 | 0 | 0 | 2 | 1 | 15 | 19 | 50 | 28 | 19 | 23 | 28 | 33 | 34 | 253 |
| Switzerland | 0 | 1 | 0 | 0 | 2 | 0 | 4 | 4 | 10 | 3 | 10 | 7 | 41 | 71 | 33 | 186 |
| Bolivia | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 174 | 32 | 207 |
| Norway | 0 | 0 | 2 | 2 | 0 | 0 | 15 | 18 | 32 | 23 | 46 | 48 | 40 | 66 | 32 | 324 |
| Romania | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 4 | 42 | 18 | 52 | 57 | 62 | 42 | 32 | 311 |
| Iran | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 150 | 124 | 23 | 99 | 68 | 31 | 498 |
| Japan | 1 | 3 | 0 | 81 | 131 | 58 | 94 | 60 | 94 | 84 | 127 | 76 | 126 | 86 | 25 | 1046 |
| Portugal | 1 | 0 | 0 | 0 | 1 | 0 | 10 | 10 | 0 | 1 | 0 | 1 | 18 | 12 | 22 | 76 |
| Moldova | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 2 | 2 | 1 | 27 | 21 | 54 |
| Uruguay | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 89 | 42 | 16 | 149 |
| Czech Republic | 1 | 1 | 1 | 2 | 5 | 1 | 0 | 2 | 0 | 2 | 3 | 6 | 31 | 49 | 15 | 119 |
| Finland | 1 | 0 | 0 | 2 | 0 | 14 | 10 | 15 | 12 | 17 | 18 | 21 | 20 | 20 | 15 | 165 |
| France | 1 | 2 | 31 | 12 | 27 | 31 | 95 | 78 | 66 | 141 | 181 | 145 | 121 | 159 | 15 | 1105 |
| Sweden | 1 | 3 | 12 | 21 | 17 | 23 | 27 | 75 | 37 | 37 | 39 | 57 | 56 | 92 | 15 | 512 |
| Taiwan | 0 | 0 | 0 | 2 | 40 | 25 | 77 | 160 | 110 | 84 | 72 | 78 | 72 | 185 | 14 | 919 |
| Netherlands | 1 | 6 | 13 | 13 | 22 | 16 | 35 | 23 | 18 | 24 | 47 | 35 | 42 | 41 | 13 | 349 |
| Azerbaijan | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 21 | 11 | 34 |
| Belgium | 1 | 1 | 1 | 6 | 8 | 5 | 5 | 10 | 7 | 23 | 31 | 21 | 25 | 38 | 7 | 189 |
| Hong Kong | 0 | 0 | 1 | 4 | 1 | 4 | 80 | 39 | 12 | 3 | 23 | 7 | 7 | 57 | 7 | 245 |
| Venezuela | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 42 | 94 | 90 | 7 | 233 |
| Guatemala | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 27 | 151 | 95 | 6 | 279 |
| Slovakia | 0 | 0 | 0 | 1 | 0 | 0 | 3 | 9 | 0 | 1 | 7 | 3 | 17 | 14 | 6 | 61 |
| South Africa | 0 | 0 | 0 | 0 | 0 | 3 | 0 | 0 | 1 | 2 | 0 | 1 | 176 | 183 | 6 | 372 |
| Belarus | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 | 2 | 0 | 8 | 44 | 107 | 63 | 5 | 233 |
| Tunisia | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 29 | 53 | 5 | 89 |
| Argentina | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 2 | 0 | 31 | 14 | 33 | 20 | 127 | 4 | 233 |
| Bulgaria | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 6 | 1 | 0 | 7 | 4 | 20 |
| Croatia | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 5 | 23 | 4 | 4 | 5 | 13 | 3 | 58 |
| Estonia | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 12 | 2 | 5 | 2 | 3 | 3 | 14 | 3 | 45 |
| Hungary | 1 | 0 | 1 | 32 | 8 | 51 | 93 | 88 | 31 | 20 | 28 | 25 | 67 | 118 | 3 | 566 |
| Slovenia | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 20 | 2 | 14 | 13 | 5 | 9 | 3 | 68 |
| Ireland | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 3 | 1 | 1 | 7 | 4 | 7 | 16 | 2 | 42 |
| Israel | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 68 | 44 | 0 | 3 | 76 | 95 | 2 | 291 |
| Paraguay | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 2 | 2 | 5 |
| Thailand | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 172 | 80 | 72 | 16 | 10 | 12 | 5 | 2 | 370 |
| Algeria | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 6 | 1 | 1 | 9 |
| Austria | 1 | 0 | 0 | 1 | 0 | 3 | 1 | 5 | 25 | 19 | 13 | 19 | 13 | 6 | 1 | 107 |
| Bangladesh | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 1 | 1 | 4 |
| Honduras | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 |
| Kazakhstan | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 2 | 2 | 5 | 1 | 13 |
| Latvia | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 8 | 9 | 9 | 4 | 4 | 1 | 35 |
| Luxembourg | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 4 | 1 | 0 | 1 | 8 |
| Palestine | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 2 |
| Serbia | 1 | 0 | 0 | 0 | 0 | 1 | 2 | 2 | 2 | 0 | 19 | 38 | 2 | 15 | 1 | 83 |
| Sri Lanka | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 2 | 0 | 1 | 1 | 6 |
| Suriname | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 |
| United Arab Emirates | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 | 0 | 0 | 0 | 2 | 0 | 2 | 1 | 10 |
| Afghanistan | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 2 |
| Albania | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 |
| Andorra | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 1 | 0 | 4 |
| Angola | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 |
| Armenia | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 3 | 0 | 6 |
| Aruba | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 1 | 0 | 0 | 0 | 0 | 4 |
| Belize | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
| Bosnia and Herzegovina | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 5 | 2 | 0 | 9 |
| Costa Rica | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 |
| Cote d_Ivoire | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
| Cuba | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 2 |
| Cyprus | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 1 | 0 | 3 |
| Ecuador | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 2 |
| Egypt | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
| El Salvador | 0 | 0 | 0 | 0 | 0 | 1 | 2 | 0 | 1 | 0 | 0 | 3 | 0 | 54 | 0 | 61 |
| Georgia | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 30 | 0 | 14 | 0 | 44 |
| Greece | 0 | 0 | 0 | 0 | 0 | 2 | 1 | 2 | 12 | 1 | 4 | 21 | 0 | 31 | 0 | 74 |
| Haiti | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 |
| Iceland | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 15 | 0 | 5 | 1 | 0 | 23 |
| Jamaica | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 |
| Jordan | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 7 | 0 | 0 | 0 | 0 | 8 |
| Kosovo | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 |
| Lebanon | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 3 |
| Lithuania | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 2 | 3 | 10 | 1 | 5 | 0 | 22 |
| Macau | 0 | 0 | 0 | 0 | 0 | 1 | 2 | 8 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 12 |
| Macedonia | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
| Madagascar | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 |
| Malawi | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 |
| Mauritius | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
| Monaco | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 |
| Mongolia | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 8 | 64 | 0 | 0 | 0 | 73 |
| Montenegro | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 2 |
| Morocco | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 2 | 0 | 2 | 0 | 6 |
| New Zealand | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 28 | 41 | 7 | 11 | 6 | 18 | 58 | 0 | 169 |
| Nicaragua | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 |
| Nigeria | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 4 |
| Pakistan | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 2 | 1 | 2 | 0 | 6 |
| Puerto Rico | 0 | 0 | 1 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 4 |
| Samoa | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 |
| Saudi Arabia | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 2 | 0 | 4 |
| Senegal | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 |
| Sudan | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 |
| Trinidad and Tobago | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 |
| Turkey | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 3 | 3 | 2 | 35 | 65 | 28 | 0 | 138 |
| Zambia | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 |
| Zimbabwe | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 |


Buderim North
Score for Preschool and School Education:
9.5
Income
Median Employee Income:
$44,068
Unemployment Rate:
6.6%
Population
Total Population
50,788
Median Age
41
Accommodation
Median Mortgage:
$2,119
Median Rent:
$1,518
Median House Price:
$466,000
Industries
Industry Value ($) Per Employee


Snoop Dogg pays tribute as rapper Bad Azz dies at the age of 43
The rapper died in custody.
Snoop Dogg has led tributes for rapper Bad Azz, who has reportedly died in jail at the age of 43.
The rapper, real name Jamarr Antonio Stamps, is believed to have died in custody at the Southwest Detention Center in Riverside, California.
Paying tribute on Instagram, Snoop wrote: “Damn. R. I. P. To my young. D. P. G. L. L. G. L. B. C. Crew original @badazzlbc gone 2 soon [sic].”
Vince Staples was also among those paying their respects, writing on Instagram: “RIP BAD AZZ.”
Producer Daz Dillinger added: “RIP TO THE HOMEY BAD AZZ @badazzlbc JAMAR STAMPS DPGC MEMBER.”
Crook said: “RIP MY HOMIE BAD AZZ. LOW LIFE GANG FOREVER DPGC FOREVER LBC CREW FOREVER.
According to an official screenshot from Dillinger, Stamps was next due to appear in court tomorrow, November 13.
Although most widely known for his work with Snoop Dogg, Azz also worked with the late Tupac Shakur, Ice Cube, Busta Rhymes, Bizzy Bone, Warren G, Kurupt, MC Eight and Mistah F.A.B.
He released his debut solo album ‘Word On Tha Streets’ back in 1998 and was also part of the LBC Crew (Long Beach City Crew).
He was imprisoned for domestic violence charges earlier this month.


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Package net.sf.jasperreports.compilers
Compiling Report Templates
Source report templates, created either by using the API or by parsing JRXML files, are subject to the report compilation process before they are filled with data.This is necessary to make various consistency validations and to incorporate into these report templates data used to evaluate all report expressions at runtime.
The compilation process transforms
JasperDesign
objects into
JasperReport
objects. Both classes are
implementations of the same basic JRReport
interface. However, JasperReport
objects cannot be modified once they are produced,
while JasperDesign
objects can. This is because some modifications made on the
report template would probably require re-validation, or if a report expression is
modified, the compiler-associated data stored inside the report template would have to be
updated.
JasperDesign
objects are produced when parsing JRXML files using the
JRXmlLoader
or created directly by the parent
application if dynamic report templates are required. The GUI tools for editing
JasperReports templates also work with this class to make in-memory modifications to
the report templates before storing them on disk.
A JasperDesign
object must be subject to the report compilation process to produce a
JasperReport
object.
Central to this process is the JRCompiler
interface, which defines two methods, one being the following:
public JasperReport compileReport(JasperDesign design) throws JRException;There are several implementations for this compiler interface depending on the language used for the report expressions or the mechanism used for their runtime evaluation.
Expressions Scripting Language
The default language for the report expressions is Java, but report expressions can be written in Groovy, JavaScript or any other scripting language as long as a report compiler implementation that can evaluate them at runtime is available.JasperReports currently ships report compiler implementations for the Groovy scripting language (http://groovy.codehaus.org) and JavaScript (http://www.mozilla.org/rhino). The compiler implementation classes are:
Historically, these compiler implementations used to be shipped as separate samples, but now they are part of the core library.Related Documentation
JasperReports Tutorial-
Interface Summary Interface Description DirectExpressionEvaluation DirectExpressionEvaluator DirectExpressionEvaluators DirectExpressionValueFilter ReportClassWhitelist -
Enum Summary Enum Description DirectExpressionEvaluation.EvaluationType


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Flexbox lets you build flex-ible layouts!
By default flexbox positions elements within a single row. Note how all divs within the row are distributed equally and have the same width.
.flexbox-container{
display: flex;
}
.box{
width: 200px;
height: 200px;
margin: 10px;
}

1

2

3

4

5

You can specify the wrapping behavior of the elements within a flexbox container, that is, how the elements should be distributed when, in a normal situation, they wouldn't fit within a single row.
You use the flex-wrap CSS rule and set it to:
.flexbox-container{
display: flex;
}
.flexbox-container.wrap{
flex-wrap: wrap;
}
.box{
width: 200px;
height: 200px;
margin: 10px;
}

1

2

3

4

5

You can also specify the direction in which the different elements within a flexbox container are placed by using the flex-direction property:
Note that there is a shortcut property flex-flow that allows you to set both flex-direction and flex-wrap at the same time. For instance flex-flow: column wrap
.flexbox-container{
display: flex;
}
.fixed-height-500{
height: 500px;
}
.flexbox-container.wrap{
flex-wrap: wrap;
}
.flexbox-container.column{
flex-direction: column;
}
.box{
width: 200px;
height: 200px;
margin: 10px;
}

1

2

3

4

5

With the order CSS property you can specify the order in which each flexbox element appears explicitely:
.flexbox-container{
display: flex;
}
.first{
order: 1;
}
.second{
order: 2;
}
.third{
order: 3;
}
.fourth{
order: 4;
}
.fifth{
order: 5;
}
.box{
width: 200px;
height: 200px;
margin: 10px;
}

1

2

3

4

5

The flex CSS property lets you specify how the space should be distributed amongst the different flexbox items in relative terms. For instance, if you have two elements and set flex: 2; for the first one and flex: 1; for the second one, the first element will take double the amount of space (width or height based on whether flexbox direction is row or column).
If no flex property is set then the flexbox elements will just take their normal space. (Their specific width/height based on width or height or their contents)
The flex property is actually a short-hand syntax for flex-grow, flex-shrink and flex-basis which determines how flex elements can grow, shrink if allowed and their default size respectively.
.flexbox-container{
display: flex;
}
.fill{
flex: 1;
}
.fillx2{
flex: 2;
}
.box{
width: 200px;
height: 200px;
margin: 10px;
}

1

2

3

Flexbox is very flexible when it comes to the alignment of its elements and allows you to align elements in the direction items flow (i.e row - left/right), in the direction perpendicular to how items flow (i.e. row - up/down) and also configure how multiple flexbox rows/columns should align with respect to each other
You can align the items within your flexbox container by using the justify-content rule. This will let you align the different elements:
.flexbox-container{
display: flex;
}
.flexbox-container.flow-direction-centered{
justify-content: center;
}
.box{
width: 200px;
height: 200px;
margin: 10px;
}
.box.smaller{
width: 100px;
height: 100px;
}

1

2

3

4

5

The align-items CSS rule you can determine whether elements align:
This finally provides a good way to achieve centering an element in height.
.flexbox-container{
display: flex;
}
.flexbox-container.centered{
align-items: center;
}
.box{
width: 200px;
height: 200px;
margin: 10px;
}

1

2

3

4

5

When you have flexbox elements distributed in multiple rows or columns you can use the align-content CSS rule to determine how these multiple rows/columns are aligned in relation to each other. The options available are the same that with the justify-content rule.
.flexbox-container{
display: flex;
}
.flexbox-container.align-content-start{
align-content: flex-start;
}
.box{
width: 200px;
height: 200px;
margin: 10px;
}
.box.smaller{
width: 100px;
height: 100px;
}

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

Flexbox excels at helping you create flexible layouts that gracefully adapt to the viewport of whichever device you're using. Check these example layouts and the description of what I did in each one of them below:


Welcome to Victorious Arena
The most furious arena on the web
You will find many strong enemies but only the best will survive
Login
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Register
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Lost in Love
Title:
Lost in Love
TV Show:
Lost (Season 3)
Music:
Crystal Castles - Not In Love ft. Robert Smith
Release:
July 2014


| Browser: Firefox 35.0.0 (Windows 7 0.0.0) | ||
| Timestamp: 3/13/2017, 2:41:43 PM | ||
| 3 tests / 0 errors / 0 failures / 0 skipped / runtime: 0.098s | ||
| Status | Spec | Suite / Results |
| Passed in 0.082s | Should call get all() | staffapp » controller: staffController |
| Passed in 0.012s | Should call create | staffapp » controller: staffController |
| Passed in 0.004s | should call update | staffapp » controller: staffController |
| System output: Firefox 35.0.0 (Windows 7 0.0.0) LOG: '[{"id":1,"name":"aravind","depart":"dev","subject":"os","age":"30"}]' Firefox 35.0.0 (Windows 7 0.0.0) LOG: Object{name: 'reka', depart: 'Dev', subject: 'daa', age: '57'} Firefox 35.0.0 (Windows 7 0.0.0) LOG: '[{"id":1,"name":"aravind","depart":"dev","subject":"os","age":"30"}]' |


- Here is where you sanitize, manipulate and check for request parameters and craft the final response.
- The exported model function is available under the reserved process keyword
- The request object is available under the request keyword
- The response object is available under the response keyword
- In order to terminate the execution of the code you should use the return keyword


Text for holding purposes
A Twitter-esque networking site
Launch
A store for local goods
Designed in the studio
Website Completely Designed with CSS
A game to test knowledge
Everyone has one


Class SpeechEventData
Describes an input event that involves keyword recognition.
Inheritance
UnityEngine.EventSystems.AbstractEventData
UnityEngine.EventSystems.BaseEventData
SpeechEventData
Inherited Members
UnityEngine.EventSystems.BaseEventData.currentInputModule
UnityEngine.EventSystems.BaseEventData.selectedObject
UnityEngine.EventSystems.AbstractEventData.m_Used
UnityEngine.EventSystems.AbstractEventData.Reset()
UnityEngine.EventSystems.AbstractEventData.Use()
UnityEngine.EventSystems.AbstractEventData.used
Namespace: XRTK.EventDatum.Input
Assembly: XRTK.dll
Syntax
public class SpeechEventData : BaseInputEventData
Constructors
| Improve this Doc View SourceSpeechEventData(EventSystem)
Constructor.
Declaration
public SpeechEventData(EventSystem eventSystem)
Parameters
| Type | Name | Description |
|---|---|---|
| UnityEngine.EventSystems.EventSystem | eventSystem | Typically will be UnityEngine.EventSystems.EventSystem.current |
Properties
| Improve this Doc View SourceConfidence
A measure of correct recognition certainty.
Declaration
public RecognitionConfidenceLevel Confidence { get; }
Property Value
| Type | Description |
|---|---|
| RecognitionConfidenceLevel |
PhraseDuration
The time it took for the phrase to be uttered.
Declaration
public TimeSpan PhraseDuration { get; }
Property Value
| Type | Description |
|---|---|
| TimeSpan |
PhraseStartTime
The moment in time when uttering of the phrase began.
Declaration
public DateTime PhraseStartTime { get; }
Property Value
| Type | Description |
|---|---|
| DateTime |
RecognizedText
The text that was recognized.
Declaration
public string RecognizedText { get; }
Property Value
| Type | Description |
|---|---|
| String |
Methods
| Improve this Doc View SourceInitialize(IMixedRealityInputSource, MixedRealityInputAction, RecognitionConfidenceLevel, TimeSpan, DateTime, String)
Populates the event with data.
Declaration
public void Initialize(IMixedRealityInputSource inputSource, MixedRealityInputAction inputAction, RecognitionConfidenceLevel confidence, TimeSpan phraseDuration, DateTime phraseStartTime, string recognizedText)
Parameters
| Type | Name | Description |
|---|---|---|
| IMixedRealityInputSource | inputSource | |
| MixedRealityInputAction | inputAction | |
| RecognitionConfidenceLevel | confidence | |
| TimeSpan | phraseDuration | |
| DateTime | phraseStartTime | |
| String | recognizedText |


Watir 6.0 Beta 4 is now available on rubygems! Watir 6.0 depends on Selenium 3.0, and it will have its production release once Selenium 3 is released. We’re continuing to update and simplify the code in the meantime. This release adds two new methods and removes two outdated feature toggles.
To install:
or in your Gemfile:
New Methods
Element#stale?
- Returns true if a previously located element is no longer attached to DOM
Element#wait_until_stale
- Waits until an element has gone stale
Always Locate
The in depth details of the history and reasoning for this change are discussed in this Watirtight Testing post.
If you currently override the default settings and use
Watir.always_locate = false
, know that #exist?
and #present?
will
no longer return false for stale elements. Please update your code to
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At one point XPath was considered significantly slower (especially on Windows / IE)
than CSS as a means of locating elements.
Recent benchmarks in Windows,
and our own Watirspecs on Linux
have shown that this is no longer the case. The Watir#prefer_css
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has been deprecated and all elements that can not be passed directly as
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Genesis 30:42
But when the cattle were feeble, he put them not in: so the feebler were Laban's, and the stronger Jacob's.


All-Course Dashboard
{% if is_staff %} {% include "collections_selector.html" %} {% endif %} {% if mode=="mooc" %}Geographic Distribution
Show Map » {% endif %}Course Listings: Total {{ncourses}}
{% if mode=="mooc" %}Number of courses offered versus time:
(click and drag to zoom) {% endif %}Courses which have opened for registration on {{orgname}}:
{% autoescape off %}{{ table }}{% endautoescape %}Enrollment
Enrollment by day:
(data from student_enrollment table) {% if mode=="mooc" %}ID Verified by day:
{% endif %} {% autoescape off %}{{ custom_report["cross-course-enrollment-comparison"] }}{% endautoescape %} {% autoescape off %}{{ custom_report["cross-course-forum-usage-comparison"] }}{% endautoescape %}{% autoescape off %} {{custom_report['extra-cross-course-reports']}} {% endautoescape %}


Tesla's S-3-X-Y Lineup Is About To Get A Lot Smaller
With a new diecast collection.
by Jared RosenholtzLast week Tesla announced some big news that it would finally begin selling its long-anticipated second-generation Roadster model. Well, not really. The real car still has a ways to go before it is ready for production but just in time for the holidays, Tesla will begin selling a 1:18 scale diecast model for $250. This may be a bit expensive for a toy car but the diecast Roadster includes some amazing attention to detail such as a realistic interior, opening doors, removable roof, carpeted trunk and interior, and fabric seat belts.
We think the model would make an excellent gift for someone waiting to receive their real-life car but Tesla's diecast collection doesn't stop at the Roadster. In fact, Tesla now offers nearly its entire S-3-X-Y lineup as diecast models available for purchase on its website.
Of the current Tesla models, you can now buy diecast versions of the Model S, Model 3, and Model X in various colors, though the Model Y remains absent from the lineup. The Model 3 is offered in white, black, grey, blue, and red; the Model S comes in grey, black, white, blue, and red; while the Model X is only available in white or grey. Tesla says the Model 3 diecast models represent the Performance variant while the Model S and X represent the P100D variants (which now carry the 'Performance' moniker too).
In addition to the 1:18 scale versions of the existing cars, Tesla will also offer a 1:24 scale version of its upcoming Semi truck. It is available for the same $250 amount as the other models and is only available in silver.
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As with the other models, Tesla says the diecast model of the Semi truck was made using 3D CAD data from the real version to replicate every detail, curve, and surface. The model features reverse-opening suicide doors just like the real truck and a detailed interior with a center-seating position. Tesla says it measures 292 mm long x 124 mm wide x 172 mm high and weighs 3.3 pounds, which is significantly more than the Roadster diecast.


--- layout: page title: Online Applets header: applets ---
Although almost anything I do is about desktop development. I have some tiny applets that works on the web. Many of them are the ports of the command line apps I used to have and all are very simple


Notebooks
Story: Words and sentences: plants and animals
Words and sentences: plants and animals
Title
Words and sentences: plants and animals
Collection
Summary
!nanni and|or Tamme give the names of plants and animals found in their country, as well as other words and sentences from their daily life.
Comments
1) There is a drawing dated 17 March 1881 inserted between pp.9799v and 9800. It is by !nanni (!) as well as Tamme (T.) and features what appear to be plants called !korubo, 2) This story is found in Book XI & XII-10
Contributors
Date
3 & 4 May 1881
Categories
Words and sentences, Custom and daily life
Keywords
words and sentences (given by !nanni and Tamme) , words and sentences (plants and animals) , vocabulary (!kun) , names (of plants and animals) , vocabulary (!kun) , vocabulary (given by !nanni and Tamme) , plants (!kun names of) , animals (!kun names of)
Story Pages
9800-9800v


echo doesn't read from stdin, are you sure you should be piping to it?
find . | echo
find .
You are piping command output to echo
, but echo
ignores all piped input.
In particular, echo
is not responsible for putting output on screen. Commands already output data, and with no further actions that will end up on screen.
None.


|
G24Lib
A set of convenient classes, functions and other utilities mainly for quickly writing numerical code. Provides relatively easy integration with CUDA, general Runge-Kutta methods, linear equation solvers and more.
|
Defines g24_lib::split_member_array
, that stores each member of a struct as a separate array.
More...
Go to the source code of this file.
Classes | |
| struct | g24_lib::split_member_array< Type, indexer, memory_manager_type, default_is_copy >::const_split_member_accessor |
An interface for the object held in the g24_lib::split_member_array so they can be changed through an operator= . More... | |
| struct | g24_lib::split_member_array< Type, indexer, memory_manager_type, default_is_copy >::split_member_accessor |
An interface for the object held in the g24_lib::split_member_array so they can be changed through an operator= . More... | |
| struct | g24_lib::split_member_array< Type, indexer, memory_manager_type, default_is_copy > |
A container that stores the different members of a struct that can undergo compile time reflection through g24_lib::class_info as separate arrays. In short, translates an array of structs into a struct of arrays in a more or less automatic way. More... | |
Namespaces | |
| g24_lib | |
| The namespace where all the library resides. | |
| g24_lib::internals | |
| A namespace where several implementation details are stored. When possibly relevant, they are documented here, but, being implementation details, they should not be used by the end users! | |
Typedefs | |
| template<class Type , class num = g24_lib::Definitions::default_unsigned_indexer> | |
| using | g24_lib::cpu_split_array = split_member_array< Type, num, default_memory_manager, true > |
A g24_lib::split_member_array that holds the array in the usual, CPU-addressable memory. More... | |
| template<class Type , class num = g24_lib::Definitions::default_unsigned_indexer> | |
| using | g24_lib::cuda_kernel_split_array = split_member_array< Type, num, CUDA_memory_manager, false > |
The appropriate way to pass a g24_lib::split_member_array into a CUDA kernel. Will copy the pointer if the memory already resides in the GPU, so the original array will be addressed and will experience any changes dictated by the kernel. More... | |
| template<class Type , class num = g24_lib::Definitions::default_unsigned_indexer> | |
| using | g24_lib::cuda_split_array = split_member_array< Type, num, CUDA_memory_manager, true > |
A g24_lib::split_member_array that holds the array in the memory of a CUDA-enabled GPU. More... | |
| template<class Type , class num = g24_lib::Definitions::default_unsigned_indexer> | |
| using | g24_lib::split_array = split_member_array< Type, num, MemoryContext::current_manager, true > |
A g24_lib::split_member_array that holds the array in the memory appropriate for the current context. More... | |
| template<class parallel , class Type , class num = g24_lib::Definitions::default_unsigned_indexer> | |
| using | g24_lib::split_array_parallel = split_member_array< Type, num, parallel::template memory_manager, true > |
A g24_lib::split_member_array that holds the array in a way that is appropriate for a specific type of parallelism. More... | |
Defines g24_lib::split_member_array
, that stores each member of a struct as a separate array.
Definition in file split_array.h.


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THE UMBRELLAS OF CHERBOURG A film review by Bryant Frazer Copyright 1996 Bryant Frazer
Directed by Jacques Demy Music by Michel Legrand Lyrics by Demy Cinematography by Jean Rabier Starring Catherine Deneuve and Nino Castelnuovo French with English subtitles France, 1964 U.S. reissue: April-June 1996
GRADE: A-
One of these days, somebody may come along and make another great musical. I'm not talking about PENNIES FROM HEAVEN, the British miniseries (and Hollywood remake) that defined the impossibility of making another great musical. I'm not talking about likely embarrassments like the forthcoming EVITA, either. (If you ask me, the best thing that could happen to Broadway musicals right now might be the announcement of Andrew Lloyd Webber's imminent retirement.) I want an intelligent, tuneful extravaganza along the lines of something Stanley Donen might have made -- SINGIN' IN THE RAIN or even FUNNY FACE.
You can hold your breath waiting for that to happen -- when you get frustrated, but before you've quite turned blue, visit your local art house for the revival of THE UMBRELLAS OF CHERBOURG. This singular little production was first exported from France in 1964, and while it's not quite as intoxicated on its own production values as the liveliest MGM musicals, it probably benefits from its perspective on the self-aware trailing edge of the vaunted Nouvelle Vague. Director Jacques Demy and composer Michel Legrand tread confidently through dangerous territory, concocting the film as a "pure" musical -- almost an operetta, with no spoken lines of dialogue at all, only song. In an oddly prescient casting coup, the relative ingenue Catherine Deneuve was given the lead role of Genevieve, the young daughter of an umbrella shop owner. She is in love somewhat beneath her station, with Guy, an auto mechanic played sweetly by Nino Castelnuevo. Naturally, Genevieve's mother registers gentle but firm disapproval of this relationship, and it's with a mixture of pragmatism and relief that she consoles her daughter on the news that her sweetheart has been drafted into two years of military service.
The film is divided into three sections, and for my money, the first is at once the most melodramatic and the most affecting. Titled "Departure," this act chronicles the headlong affection of the two lovers and the lush, nearly intolerable torture exacted on them when they learn of their impending separation. It culminates in a wrenching, tear-soaked railway platform farewell that's no less powerful for being a cinematic hand-me-down. A tremendous asset is Legrand's love theme, which has already intimated itself into the film's texture, but here is sweeping and thunderous in full Dolby reverb.
I'm usually wary of remixed soundtracks, but it does seem to make sense with material as musical as this (or the reissued TAXI DRIVER, which rated similar treatment for Bernard Herrmann's fine score). As remixed by Legrand himself, THE UMBRELLAS OF CHERBOURG sounds fine and looks even better, although some of the more colorful shots bear artifacts of the technicolor restoration process -- the original Eastmancolor release negative has been unusable for most of the film's life.
Even so, it might have been nice if the color scheme weren't quite so insistent. While the relentless mirroring of loud wallpaper with equally shrill, matching costumes helps to establish a level of ironic detachment that may be key to the film, it certainly breaks the spell of the flatly gorgeous song and earnest performances. And after the nearly perfect first segment, the second of three acts is a little tiresome, plodding toward a foregone conclusion.
I hope I'm not spoiling anything if I tell you that the second act is titled "Absence" and the third "Return." While Guy is away, Genevieve's mother is given the opportunity to quietly encourage her to marry her new suitor, a world-traveling jewelry dealer. Genevieve remains fond of Guy, but finally relents and agrees to be married (viewers may note that, even in this relatively fresh-faced persona, Deneuve was acquiring her reputation as aloof and untouchable.) In the third act, Guy returns and finds that -- well, he finds that everything has changed.
It's perhaps a characteristic of Demy's New Wave sensibility (he had recently married Agnes Varda, after all) that a film this delirious in its visuals and rapturous emoting can be, at the same time, so distressingly sober. Demy's film doesn't give Guy the chance to woo and win back his beloved, and it barely allows him his own sadness. The real sadness, interestingly, is our own. These lives have not turned out the way they had once seemed predestined, nor does the film turn out the way we had hoped it might. The final scene, which at last reunites the two, ever so briefly, is perhaps unavoidable, but this story is an inexorably sorrowful one. Denied our escapist pleasure, we can only watch, misty-eyed, as the cinematic dream falls apart -- as the musical, presumably, deconstructs itself.
+ + + Visit the NYC Film Forum's CHERBOURG site (http://www.filmforum.com/cherbourg/) for a list of cities where the film will be playing. + + +
-- DEEP FOCUS (Movie Reviews) http://www.panix.com/~bfrazer/flicker/
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
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public class UareUException
extends java.lang.Exception
| Modifier and Type | Field and Description |
|---|---|
static int |
DPFJ_E_ENROLLMENT_INVALID_SET |
static int |
URU_E_COMPRESSION_IN_PROGRESS |
static int |
URU_E_COMPRESSION_INVALID_WSQ_PARAMETER |
static int |
URU_E_COMPRESSION_NOT_STARTED |
static int |
URU_E_COMPRESSION_WSQ_FAILURE |
static int |
URU_E_COMPRESSION_WSQ_LIB_NOT_FOUND |
static int |
URU_E_DEVICE_BUSY |
static int |
URU_E_DEVICE_FAILURE |
static int |
URU_E_ENROLLMENT_IN_PROGRESS |
static int |
URU_E_ENROLLMENT_INVALID_SET |
static int |
URU_E_ENROLLMENT_NOT_READY |
static int |
URU_E_ENROLLMENT_NOT_STARTED |
static int |
URU_E_FAILURE |
static int |
URU_E_INVALID_DEVICE |
static int |
URU_E_INVALID_FID |
static int |
URU_E_INVALID_FMD |
static int |
URU_E_INVALID_PARAMETER |
static int |
URU_E_MORE_DATA |
static int |
URU_E_NO_DATA |
static int |
URU_E_NOT_IMPLEMENTED |
static int |
URU_E_QUALITY_FAILURE |
static int |
URU_E_QUALITY_LIB_NOT_FOUND |
static int |
URU_E_QUALITY_NO_IMAGE |
static int |
URU_E_QUALITY_TOO_FEW_MINUTIA |
static int |
URU_E_SUCCESS |
static int |
URU_E_TOO_SMALL_AREA |
| Constructor and Description |
|---|
UareUException(int n)
Constructs the exception object.
|
| Modifier and Type | Method and Description |
|---|---|
int |
getCode()
Returns U.are.U error code associated with the exception object.
|
java.lang.String |
toString()
Returns text string associated with the exception object.
|
public static final int URU_E_SUCCESS
public static final int URU_E_NOT_IMPLEMENTED
public static final int URU_E_FAILURE
public static final int URU_E_NO_DATA
public static final int URU_E_MORE_DATA
public static final int URU_E_INVALID_PARAMETER
public static final int URU_E_INVALID_DEVICE
public static final int URU_E_DEVICE_BUSY
public static final int URU_E_DEVICE_FAILURE
public static final int URU_E_INVALID_FID
public static final int URU_E_TOO_SMALL_AREA
public static final int URU_E_INVALID_FMD
public static final int URU_E_ENROLLMENT_IN_PROGRESS
public static final int URU_E_ENROLLMENT_NOT_STARTED
public static final int URU_E_ENROLLMENT_NOT_READY
public static final int DPFJ_E_ENROLLMENT_INVALID_SET
public static final int URU_E_ENROLLMENT_INVALID_SET
public static final int URU_E_COMPRESSION_IN_PROGRESS
public static final int URU_E_COMPRESSION_NOT_STARTED
public static final int URU_E_COMPRESSION_INVALID_WSQ_PARAMETER
public static final int URU_E_COMPRESSION_WSQ_FAILURE
public static final int URU_E_COMPRESSION_WSQ_LIB_NOT_FOUND
public static final int URU_E_QUALITY_NO_IMAGE
public static final int URU_E_QUALITY_TOO_FEW_MINUTIA
public static final int URU_E_QUALITY_FAILURE
public static final int URU_E_QUALITY_LIB_NOT_FOUND
public UareUException(int n)
n
- U.are.U error code.public int getCode()
public java.lang.String toString()
toString
in class java.lang.Throwable