Advanced features tutorial¶
The following tutorials highlight advanced functionality and provide in-depth material on ensemble APIs.
| Tutorial | Content |
|---|---|
| Propagating input features | Propagate feature input features through layers |
| to allow several layers to see the same input. | |
| Probabilistic ensemble learning | Build layers that output class probabilities from each base |
| learner so that the next layer or meta estimator learns | |
| from probability distributions. | |
| Advanced Subsemble techniques | Learn homogenous partitions of feature space |
| that maximize base learner’s performance on each partition. | |
| General multi-layer ensemble learning | How to build ensembles with different layer classes |
| Passing file paths as data input | Avoid loading data into the parent process by specifying a |
| file path to a memmaped array or a csv file. | |
| Ensemble model selection | Build transformers that replicate layers in ensembles for |
| model selection of higher-order layers and / or meta learners. |
We use the same preliminary settings as in the getting started section.
import numpy as np
from pandas import DataFrame
from sklearn.metrics import accuracy_score
from sklearn.datasets import load_iris
seed = 2017
np.random.seed(seed)
data = load_iris()
idx = np.random.permutation(150)
X = data.data[idx]
y = data.target[idx]
Propagating input features¶
When stacking several layers of base learners, the variance of the input
will typically get smaller as learners get better and better at predicting
the output and the remaining errors become increasingly difficult to correct
for. This multicolinearity can significantly limit the ability of the
ensemble to improve upon the best score of the subsequent layer as there is too
little variation in predictions for the ensemble to learn useful combinations.
One way to increase this variation is to propagate features from the original
input and / or earlier layers. To achieve this in ML-Ensemble, we use the
propagate_features
attribute. To see how this works, let’s compare
a three-layer ensemble with and without feature propagation.
from mlens.ensemble import SuperLearner
from sklearn.linear_model import LogisticRegression
from sklearn.ensemble import RandomForestClassifier
from sklearn.svm import SVC
def build_ensemble(incl_meta, propagate_features=None):
"""Return an ensemble."""
if propagate_features:
n = len(propagate_features)
propagate_features_1 = propagate_features
propagate_features_2 = [i for i in range(n)]
else:
propagate_features_1 = propagate_features_2 = None
estimators = [RandomForestClassifier(random_state=seed), SVC()]
ensemble = SuperLearner()
ensemble.add(estimators, propagate_features=propagate_features_1)
ensemble.add(estimators, propagate_features=propagate_features_2)
if incl_meta:
ensemble.add_meta(LogisticRegression())
return ensemble
Without feature propagation, the meta learner will learn from the predictions of the penultimate layers:
base = build_ensemble(False)
base.fit(X, y)
pred = base.predict(X)[:5]
print("Input to meta learner :\n %r" % pred)
Out:
Input to meta learner :
array([[2., 2.],
[2., 2.],
[2., 2.],
[1., 1.],
[1., 1.]], dtype=float32)
When we propagate features, some (or all) of the input seen by one layer is passed along to the next layer. For instance, we can propagate some or all of the input array through our two intermediate layers to the meta learner input of the meta learner:
base = build_ensemble(False, [1, 3])
base.fit(X, y)
pred = base.predict(X)[:5]
print("Input to meta learner :\n %r" % pred)
Out:
Input to meta learner :
array([[3.2, 2.3, 2. , 2. ],
[3.2, 2.3, 2. , 2. ],
[3. , 2.1, 2. , 2. ],
[3.2, 1.5, 1. , 1. ],
[2.8, 1.4, 1. , 1. ]], dtype=float32)
In this scenario, the meta learner will see noth the predictions made by the penultimate layer, as well as the second and fourth feature of the original input. By propagating features, the issue of multicolinearity in deep ensembles can be mitigated. In particular, it can give the meta learner greater opportunity to identify neighborhoods in the original feature space where base learners struggle. We can get an idea of how feature propagation works with our toy example. First, we need a simple ensemble evaluation routine. In our case, propagating the original features through two layers of the same library of base learners gives a dramatic increase in performance on the test set:
def evaluate_ensemble(propagate_features):
"""Wrapper for ensemble evaluation."""
ens = build_ensemble(True, propagate_features)
ens.fit(X[:75], y[:75])
pred = ens.predict(X[75:])
return accuracy_score(pred, y[75:])
score_no_prep = evaluate_ensemble(None)
score_prep = evaluate_ensemble([0, 1, 2, 3])
print("Test set score no feature propagation : %.3f" % score_no_prep)
print("Test set score with feature propagation: %.3f" % score_prep)
Out:
Test set score no feature propagation : 0.667
Test set score with feature propagation: 0.987
By combining feature propagation with the Subset
transformer, you can
propagate the feature through several layers without any of the base estimators
in those layers seeing the propagated features. This can be desirable if you
want to propagate the input features to the meta learner without intermediate
base learners always having access to the original input data. In this case,
we specify propagation as above, but add a preprocessing pipeline to
intermediate layers:
from mlens.preprocessing import Subset
estimators = [RandomForestClassifier(random_state=seed), SVC()]
ensemble = SuperLearner()
# Initial layer, propagate as before
ensemble.add(estimators, propagate_features=[0, 1])
# Intermediate layer, keep propagating, but add a preprocessing
# pipeline that selects a subset of the input
ensemble.add(estimators,
preprocessing=[Subset([2, 3])],
propagate_features=[0, 1])
In the above example, the two first features of the original input data will be propagated through both layers, but the second layer will not be trained on it. Instead, it will only see the predictions made by the base learners in the first layer.
ensemble.fit(X, y)
n = list(ensemble.layer_2.learners[0].learner)[0].estimator.feature_importances_.shape[0]
m = ensemble.predict(X).shape[1]
print("Num features seen by estimators in intermediate layer: %i" % n)
print("Num features in the output array of the intermediate layer: %i" % m)
Out:
Num features seen by estimators in intermediate layer: 2
Num features in the output array of the intermediate layer: 4
Probabilistic ensemble learning¶
When the target to predict is a class label, it can often be beneficial to let higher-order layers or the meta learner learn from class probabilities, as opposed to the predicted class. Scikit-learn classifiers can return a matrix that, for each observation in the test set, gives the probability that the observation belongs to the a given class. While we are ultimately interested in class membership, this information is much richer that just feeding the predicted class to the meta learner. In essence, using class probabilities allow the meta learner to weigh in not just the predicted class label (the highest probability), but also with what confidence each estimator makes the prediction, and how estimators consider the alternative. First, let us set a benchmark ensemble performance when learning is by predicted class membership.
from mlens.ensemble import BlendEnsemble
from sklearn.linear_model import LogisticRegression
from sklearn.ensemble import RandomForestClassifier
from sklearn.svm import SVC
def build_ensemble(proba, **kwargs):
"""Return an ensemble."""
estimators = [RandomForestClassifier(random_state=seed),
SVC(probability=proba)]
ensemble = BlendEnsemble(**kwargs)
ensemble.add(estimators, proba=proba) # Specify 'proba' here
ensemble.add_meta(LogisticRegression())
return ensemble
As in the ensemble guide, we fit on the first half, and test on the remainder.
ensemble = build_ensemble(proba=False)
ensemble.fit(X[:75], y[:75])
preds = ensemble.predict(X[75:])
print("Accuracy:\n%r" % accuracy_score(preds, y[75:]))
Out:
Accuracy:
0.6666666666666666
Now, to enable probabilistic learning, we set proba=True
in the add
method for all layers except the final meta learner layer.
ensemble = build_ensemble(proba=True)
ensemble.fit(X[:75], y[:75])
preds = ensemble.predict(X[75:])
print("\nAccuracy:\n%r" % accuracy_score(preds, y[75:]))
Out:
Accuracy:
0.96
In this case, using probabilities has a drastic effect on predictive performance, increasing some 40 percentage points. For an applied example see the ensemble used to beat the Scikit-learn MNIST benchmark.
Advanced Subsemble techniques¶
Subsembles leverages the idea that neighborhoods of feature space have a specific local structure. When we fit an estimator across all feature space, it is very hard to capture several such local properties. Subsembles partition the feature space and fits each base learner to each partitions, thereby allow base learners to optimize locally. Instead, the task of generalizing across neighborhoods is left to the meta learner. This strategy can be very powerful when the local structure first needs to be extracted, before an estimator can learn to generalize. Suppose you want to learn the probability distribution of some variable \(y\). Often, the true distribution is multi-modal, which is an extremely hard problem. In fact, most machine learning algorithms, especially with convex optimization objectives, are ill equipped to solve this problem. Subsembles can overcome this issue allowing base estimators to fit one mode of the distribution at a time, which yields a better representation of the distribution and greatly facilitates the learning problem of the meta learner.
By default, the Subsemble
class partitioning the dataset randomly.
Note however that partitions are created on the data “as is”, so if the ordering
of observations is not random, neither will the partitioning be. For this
reason, it is recommended to shuffle the data (e.g. via the shuffle
option at initialization). To build a subsemble with random partitions, the
only parameter needed is the number of partitions
when instantiating
the Subsemble
.
from mlens.ensemble import Subsemble
from sklearn.linear_model import LogisticRegression
from sklearn.svm import SVC
def build_subsemble():
"""Build a subsemble with random partitions"""
sub = Subsemble(partitions=3, folds=2)
sub.add([SVC(), LogisticRegression()])
return sub
sub= build_subsemble()
sub.fit(X, y)
s = sub.predict(X[:10]).shape[1]
print("No. prediction features: %i " % s)
Out:
No. prediction features: 6
During training, the base learners are copied to each partition, so the output of each layer gets multiplied by the number of partitions. In this case, we have 2 base learners for 3 partitions, giving 6 prediction features.
By creating partitions, subsembles scale significantly better
than the
SuperLearner
, but in contrast to BlendEnsemble
,
the full training data is leveraged during training. But randomly partitioning
the data does however not exploit the full advantage of locality, since it is
only by luck that we happen to create such partitions. A better way is to
learn how to best partition the data. We can either use
unsupervised algorithms to generate clusters, or supervised estimators and
create partitions based on their predictions. In ML-Ensemble, this is
achieved by passing an estimator as partition_estimator
. This estimator
can differ between layers.
Very few limitation are imposed on the estimator: you can specify whether
you want to fit it before generating partitions, whether to use
labels in the partitioning, and what method to call to generate the
partitions. See ClusteredSubsetIndex
for the full documentation.
This level of generality does impose some
responsibility on the user. In particular, it is up to the user to ensure that
sensible partitions are created. Problems to watch out for is too small
partitions (too many clusters, too uneven cluster sizes) and clusters with too
little variation: for instance with only a single class label in the entire
partition, base learners have nothing to learn. Let’s see how to do this in
practice. For instance, we can use an unsupervised K-Means
clustering estimator to partition the data, like so:
from sklearn.cluster import KMeans
def build_clustered_subsemble(estimator):
"""Build a subsemble with random partitions"""
sub = Subsemble(partitions=2,
partition_estimator=estimator,
folds=2, verbose=2)
sub.add([SVC(), LogisticRegression()])
sub.add_meta(SVC())
return sub
sub = build_clustered_subsemble(KMeans(2))
sub.fit(X[:, [0, 1]], y)
Out:
Fitting 2 layers
Processing layer-1 done | 00:00:00
Processing layer-2 done | 00:00:00
Fit complete | 00:00:00
The Iris dataset can actually separate the classes perfectly with a KMeans
estimator which leads to zero label variation in each partition. For that
reason the above code fits the KMeans estimator on only the first two
columns. But this approach is nota very good way of doing it since we loose
the rest of the data when fitting the estimators too. Instead, we could
customize the
partitioning estimator to make the subset selection itself. For instance,
we can use Scikit-learn’s sklearn.pipeline.Pipeline
class to put a dimensionality reduction transformer before the partitioning
estimator, such as a sklearn.decomposition.PCA
, or the
mlens.preprocessing.Subset
transformer to drop some features before
estimation. We then use this pipeline as a our partition estimator and fit
the subsemble on all features.
from mlens.preprocessing import Subset
from sklearn.pipeline import make_pipeline
# This partition estimator is equivalent to the one used above
pe = make_pipeline(Subset([0, 1]), KMeans(2))
sub = build_clustered_subsemble(pe)
sub.fit(X, y)
Out:
Fitting 2 layers
Processing layer-1 done | 00:00:00
Processing layer-2 done | 00:00:00
Fit complete | 00:00:00
In general, you may need to wrap an estimator around a custom class to modify it’s output to generate good partitions. For instance, in regression problems, the output of a supervised estimator needs to be binarized to give a discrete number of partitions. Here’s minimalist way of wrapping a Scikit-learn estimator:
from sklearn.linear_model import LinearRegression
class MyClass(LinearRegression):
def __init__(self, **kwargs):
super(MyClass, self).__init__(**kwargs)
def fit(self, X, y):
"""Fit estimator."""
super(MyClass, self).fit(X, y)
return self
def predict(self, X):
"""Generate partition"""
p = super(MyClass, self).predict(X)
return 1 * (p > p.mean())
Importantly, your partition estimator should implement a get_params
method to avoid unexpected errors. If you don’t, you may encounter
a NotFittedError
when calling predict
.
To summarize the functionality in one example,
let’s implement a simple (but rather useless) partition estimator that splits
the data in half based on the sum of the features.
class SimplePartitioner():
def __init__(self):
pass
def our_custom_function(self, X, y=None):
"""Split the data in half based on the sum of features"""
# Labels should be numerical
return 1 * (X.sum(axis=1) > X.sum(axis=1).mean())
def get_params(self, deep=False):
return {}
# Note that the number of partitions the estimator creates *must* match the
# ``partitions`` argument passed to the subsemble.
sub = Subsemble(partitions=2, folds=3, verbose=1)
sub.add([SVC(), LogisticRegression()],
partition_estimator=SimplePartitioner(),
fit_estimator=False,
attr="our_custom_function")
sub.fit(X, y)
Out:
Fitting 1 layers
Fit complete | 00:00:00
A final word of caution. When implementing custom estimators from scratch, some
care needs to be taken if you plan on copying the Subsemble. It is advised that
the estimator inherits the sklearn.base.BaseEstimator
class to
provide a Scikit-learn compatible interface. For further information,
see the API Reference documentation of the Subsemble
and mlens.base.indexer.ClusteredSubsetIndex
.
For an example of using clustered subsemble, see the subsemble used to beat the Scikit-learn MNIST benchmark.
General multi-layer ensemble learning¶
To alternate between the type of layer with each add
call,
the SequentialEnsemble
class can be used to specify what type of
layer (i.e. stacked, blended, subsamle-style) to add. This is particularly
powerful if facing a large dataset, as the first layer can use a fast approach
such as blending, while subsequent layers fitted on the remaining data can
use more computationally intensive approaches.
from mlens.ensemble import SequentialEnsemble
ensemble = SequentialEnsemble()
# The initial layer is a blended layer, same as a layer in the BlendEnsemble
ensemble.add('blend',
[SVC(), RandomForestClassifier(random_state=seed)])
# The second layer is a stacked layer, same as a layer of the SuperLearner
ensemble.add('stack', [SVC(), RandomForestClassifier(random_state=seed)])
# The third layer is a subsembled layer, same as a layer of the Subsemble
ensemble.add('subsemble', [SVC(), RandomForestClassifier(random_state=seed)])
# The meta estimator is added as in any other ensemble
ensemble.add_meta(SVC())
The below table maps the types of layers available in the SequentialEnsemble
with the corresponding ensemble.
| Ensemble equivalent | SequentialEnsemble parameter |
|---|---|
| ‘SuperLearner’ | ‘stack’ |
| ‘BlendEnsemble’ | ‘blend’ |
| ‘Subsemble’ | ‘subsemble’ |
Once instantiated, the SequentialEnsemble`
behaves as expect:
preds = ensemble.fit(X[:75], y[:75]).predict(X[75:])
accuracy_score(preds, y[75:])
In this case, the multi-layer SequentialEnsemble
with an initial
blended layer and second stacked layer achieves similar performance as the
BlendEnsemble
with probabilistic learning. Note that we could have
made any of the layers probabilistic by setting Proba=True
.
Passing file paths as data input¶
With large datasets, it can be expensive to load the full data into memory as
a numpy array. Since ML-Ensemle uses a memmaped cache, the need to keep the
full array in memory can be entirely circumvented by passing a file path as
entry to X
and y
. There are two important things to note when doing
this.
First, ML-Ensemble delpoys Scikit-learn’s array checks, and passing a
string will cause an error. To avoid this, the ensemble must be initialized
with array_check=0
, in which case there will be no checks on the array.
The user should make certain that the the data is approprate for esitmation,
by converting missing values and infinites to numerical representation,
ensuring that all features are numerical, and remove any headers,
index columns and footers.
Second, ML-Ensemble expects the file to be either a csv
,
an npy
or mmap
file and will treat these differently.
- If a path to a
csv
file is passed, the ensemble will first load the file into memory, then dump it into the cache, before discarding the file from memory by replacing it with a pointer to the memmaped file. The loading module used for thecsv
file is thenumpy.loadtxt()
function.- If a path to a
npy
file is passed, a memmaped pointer to it will be loaded.- If a path to a
mmap
file is passed, it will be used as the memmaped input array for estimation.
import os
import tempfile
# We create a temporary folder in the current working directory
temp = tempfile.TemporaryDirectory(dir=os.getcwd())
# Dump the X and y array in the temporary directory, here as csv files
fx = os.path.join(temp.name, 'X.csv')
fy = os.path.join(temp.name, 'y.csv')
np.savetxt(fx, X)
np.savetxt(fy, y)
# We can now fit any ensemble simply by passing the file pointers ``fx`` and
# ``fy``. Remember to set ``array_check=0``.
ensemble = build_ensemble(False, array_check=0)
ensemble.fit(fx, fy)
preds = ensemble.predict(fx)
print(preds[:10])
Out:
[2. 2. 2. 1. 1. 2. 2. 2. 2. 2.]
If you are following the examples on your machine, don’t forget to remove the temporary directory.
try:
temp.cleanup()
del temp
except OSError:
# This can fail on Windows
pass
Ensemble model selection¶
Ensembles benefit from a diversity of base learners, but often it is not clear how to parametrize the base learners. In fact, combining base learners with lower predictive power can often yield a superior ensemble. This hinges on the errors made by the base learners being relatively uncorrelated, thus allowing a meta estimator to learn how to overcome each model’s weakness. But with highly correlated errors, there is little for the ensemble to learn from.
To fully exploit the learning capacity in an ensemble, it is beneficial to conduct careful hyper parameter tuning, treating the base learner’s parameters as the parameters of the ensemble. By far the most critical part of the ensemble is the meta learner, but selecting an appropriate meta learner can be an ardous task if the entire ensemble has to be evaluated each time.
The task can be made considerably easier by treating the lower layers of an
ensemble as preprocessing pipeline, and performing model selection on
higher-order layers or meta learners. To use an ensemble for this purpose,
set the model_selection
parameter to True
before fitting. This will
modify how the transform
method behaves, to ensure predict
is called
on test folds.
Warning
Remember to turn model selection off when done.
from mlens.model_selection import Evaluator
from mlens.metrics import make_scorer
from scipy.stats import uniform, randint
# Set up two competing ensemble bases as preprocessing transformers:
# one stacked ensemble base with proba and one without
base_learners = [RandomForestClassifier(random_state=seed),
SVC(probability=True)]
proba_transformer = SequentialEnsemble(
model_selection=True, random_state=seed).add(
'blend', base_learners, proba=True)
class_transformer = SequentialEnsemble(
model_selection=True, random_state=seed).add(
'blend', base_learners, proba=False)
# Set up a preprocessing mapping
# Each pipeline in this map is fitted once on each fold before
# evaluating candidate meta learners.
preprocessing = {'proba': [('layer-1', proba_transformer)],
'class': [('layer-1', class_transformer)]}
# Set up candidate meta learners
# We can specify a dictionary if we wish to try different candidates on
# different cases, or a list if all estimators should be run on all
# preprocessing pipelines (as in this example)
meta_learners = [SVC(), ('rf', RandomForestClassifier(random_state=seed))]
# Set parameter mapping
# Here, we differentiate distributions between cases for the random forest
params = {'svc': {'C': uniform(0, 10)},
'class.rf': {'max_depth': randint(2, 10)},
'proba.rf': {'max_depth': randint(2, 10),
'max_features': uniform(0.5, 0.5)}
}
scorer = make_scorer(accuracy_score)
evaluator = Evaluator(scorer=scorer, random_state=seed, cv=2)
evaluator.fit(X, y, meta_learners, params, preprocessing=preprocessing, n_iter=2)
We can now compare the performance of the best fit for each candidate meta learner.
print("Results:\n%s" % evaluator.results)
Out:
Results:
test_score-m test_score-s train_score-m train_score-s fit_time-m fit_time-s pred_time-m pred_time-s params
class rf 0.947 0.013 0.946 0.000 0.724 0.019 0.032 0.030 {'max_depth': 5}
class svc 0.947 0.013 0.946 0.000 0.799 0.087 0.127 0.078 {'C': 0.20960225406117416}
proba rf 0.947 0.013 1.000 0.000 0.780 0.070 0.027 0.016 {'max_depth': 5, 'max_features': 0.5104801127030587}
proba svc 0.960 0.000 0.986 0.014 0.725 0.104 0.001 0.000 {'C': 7.670701646824877}
Total running time of the script: ( 0 minutes 17.055 seconds)


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| value | Current value of input (v-model). Array of strings or null | {{keywords}} |
|---|---|---|
| showDeleteButton | Show button (×) to delete keyword. Boolean | |
| separator |
Symbol between keywords. String or Object If object provided it can has following fields:
Tip: to put each keyword on its own line, set '\r\n' as the separator value |
Presets: |
| splittingRegexp |
RegExp to split entered or pasted text. RegExp Note: if null provided, entered text will be not splitted automatically, but you can provide your own splitting logic using `preprocessKeyword` property |
|
| preprocessKeyword | Allows to specify the function that will change the entered keywords before emitting new value. Function
Note: this affects only new entered/pasted text |
{{displayFunc(preprocessKeyword)}}
|
| getKeywordClasses | Allows to set CSS-classes for keywords. Function
|
{{displayFunc(getKeywordClasses)}}
|
| scrollY | Allows to control vertical scroll position. You can set and get value (using `scroll-y.sync`). Number |
|
| handleExceptions |
Allows to set handler for unhandled exceptions. Function
|
Will output to the console by default |
| emitValueEvent | Allow to change the name of event that will be emitted when user changes value Function | `input` by default |
| customizeMultiKeywordDraggingCanvas |
Allow to customize drag image for multi keyword dragging. Function |
- |
| historyController | Allow to customize undo/redo handling. HistoryController | - |
| focus() | Make component focused |
|---|---|
| selectAll() | Select all keywords |
| setCursorPosition(keyword_index, after) |
Set cursor position before or after specified keyword index
|
| deleteSelectedKeywords(emit = true) |
Delete selected keywords
|
| getSelectionsAsJoinedString() | Return selected keywords as joined string |


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ReBootKamp offers a full-time, 18-week software engineering program in Amman, Jordan. The curriculum was developed by Hack Reactor and covers advanced full stack development including full stack JavaScript, MongoDB, React, Angular, NodeJS, and more with a success-based tuition model where graduates pay after they land a job. Students will experience code challenges, pair programming, real-world partner projects, lectures, guest speakers, and special hiring events.
*This bootCamp would cover:
• Blockchain Fundamentals.
• Introduction to Ethereum.
• Building DApps and deploying it on the Ethereum Network.
• Basics of Hyperledger Sawtooth.
• How to build permissioned blockchain networks using Sawtooth.
• How to make an in-class Sawtooth network
CEO & Founder
Some text that describes me lorem ipsum ipsum lorem.
<EMAIL_ADDRESS>Art Director
Some text that describes me lorem ipsum ipsum lorem.
<EMAIL_ADDRESS>Designer
Some text that describes me lorem ipsum ipsum lorem.
example@example.com


Classes
The following classes are available globally.
-
User-Agent
Example:
iOS Example/1.0 (io.off-piste.iOS-Example; build:1; iOS 10.0.0) TrolleyCore/10.5.10
See - https://tools.ietf.org/html/rfc7231#section-5.5.3
See moreDeclaration
Swift
@objc public final class UserAgent: NSObject
-
<#Description#>
See moreDeclaration
Swift
@objcMembers public final class TRLOptions: NSObject
-
A wrapper for UserDefaults
This allows for eaiser handling of items into and out of NSUserDefaults with just a key and object been needed and we do the rest.
See moreDeclaration
Swift
@objc public final class TRLDefaultsManager: NSObject
-
Responsible for handling all delegate callbacks for the underlying session.
See moreDeclaration
Swift
@objc open class TRLURLSessionDelegate: NSObject
-
The entry point for our Trolley SDKs.
Initialize and configure FIRApp using +Trolley configure or other customized ways as shown below.
The logging system
See moreDeclaration
Swift
@objcMembers public final class Trolley: NSObject


RIT Student Government takes your privacy seriously.
In order to provide a great evaluation experience, while respecting the needs of RIT's faculty, the following protections are in place for review data:
For the purposes of reminding students to evaluate their courses, OpenEvals automatically retrieves a list of all University-affiliated individuals and their course enrollment. OpenEvals does not take advantage of any privileged information sources. All of the information used by OpenEvals is available to any member of the RIT community.
In order to protect the integrity of our data, the enrollment status of each student is automatically verified by the system. Our system is only accessing publicly available enrollment information. Information automatically collected through this system is only used for the purposes of verifying enrollment, sending reminders and development of the system.


How long is processing & shipping?
All orders are shipped after 3-5 business days of processing.
When will I recieve a tracking number?
Once your order is processed you will recieve a tracking number.
What if my item is defective?
If you recieve a damaged item please send us an e-mail<EMAIL_ADDRESS>and return your items by using the return shipment label. Once we recieve the item
we will issue a refund or replacement.


Project - Holberton Simple Shell
23 Dec 2016Holberton Shell (hsh) is a simple command line shell for OS X and Linux. Hsh includes most basic features present in the bash shell. This shell was built as a project for Holberton School.
Quick Start
HSH works like any other shell, like bash or zsh. Detailed user documentation on specific commands is available by running help within hsh.
HSH builds successfully with a GCC 4.8.5 compiler or later. HSH can compile using gcc 4.8.4 -Wall -Werror -Wextra -pedantic *.c -o simple_shell
.
To start up HSH, simply type the following into your terminal:
./simple_shell
To view the source code or to download HSH, visit the holberton shell’s github page.
Supported Features
1) Syntax
Like other shells, hsh is used by given it commands. Every hsh command follows the same general syntax: command name {arguments}
. The command is executed by writing the name of the command followed by any arguments.
echo Hello Holberton
2) Commands
Any program in your computer can be used as a command in hsh. If the program is located in any of the directories in the PATH variable, then typing the filename is enough to run the command. Otherwise, a full path to the program is required.
Commands and any arguments are seperated by a
' '
. Commands end with either a newline (i.e Return Key) or a;
,&&
, or ||
.To enter more than one command in one line, a
;
can be used to seperate commands. Commands are read and executed left to right.
If commands are seperated by
&&
, the leftmost command is read and executed and if the execution failed, no other commands are executed, otherwise, the next command in line will be executed. If the commands is seperated by||
, regardless of execution failure or success of the leftmost command, all suceeded commands are executed.
2.2 Switches and Flags
Switches and flags arguements of commands are also supported by hsh. Most switches start with a hypen,
-
and always somehows affect the command in one way.
2.3 Comments
hsh provides support for comments added into standard input. Comments can be inputed by placing a
#
before any statement. Comments are ignored by hsh.
3) Builtins
There are several builtins programmed into the hsh. Below is a description and use for each builtin.
To print out a list of all the environemental variables, the builtin
env
can be used. Each environmental variable and its value is printed out with a syntax of key=value
. Each variable is seperated by a new line.
To set an environemental variable, the builtin is
setenv
. The syntax to use setenv
is setenv key value
. If one of the arguments is missing, an error is returned.
To remove an environemental variable, the builtin
unsetenv
can be used. The syntax to use unsetenv
is unsetenv key
. If no key
value is given, an error is returned.
3.2 cd
The
cd
command changes the shell working directory. The syntax to use the command is cd DIR
. If no DIR
is given, it is defaulted to the HOME shell variable. The full path of the directory is needed. To change the directory to its parent, the argument..
can be used.
3.3 history
To print out a list of previous commands used, the command
history
can be used. The previous 50 commands will be printed out. The history of all commands is written into the .simple_shell_history
file.
3.4 help
To receive a description and the syntax use of a specific command, you can use the
help
command.
3.5 exit
To exit out of the shell, the user may use the
exit
builtin. To use exit
, a status number,n
can be given. Ifn
is ommited, the exit status is that of the last command executed.
Exiting commands and hsh
To exit out of a process or command, ctrl c
is used. Control-C interrupts a process and causes it to abort.
To exit out of the hsh shell, the user can do one of the following, ctrl D
or exit n
. When exiting with ctrl D
, an exit status of 0 is given. Using exit
, you can input its exit status or it is defaulted to the status of the last command executed.


Pizza Time
What size pizza would you like?
X-Small
Small
Medium
Large
X-Large
Toppings
None
Pepperoni
Sausage
Mushrooms
Bell Peppers
Olives
Onions
-
Add Another Topping!
Place your Order!
Order Summary:


mod_dir.c
file, and
is compiled in by default. It provides for "trailing slash" redirects and
serving directory index files.
index.html
.
The DirectoryIndex directive sets
the name of this file.
This is controlled by mod_dir
.
mod_autoindex
.
A "trailing slash" redirect is issued when the server receives a
request for a URL http://servername/foo/dirname where
dirname is a directory. Directories require a trailing
slash, so mod_dir
issues a redirect to
http://servername/foo/dirname/.
DirectoryIndex index.html
The DirectoryIndex directive sets the list of resources to look for,
when the client requests an index of the directory by specifying a /
at the end of the a directory name. Local-url is the
(%-encoded) URL of a document on the server relative to the requested
directory; it is usually the name of a file in the directory. Several
URLs may be given, in which case the server will return the first one
that it finds. If none of the resources exist and the
Indexes
option is set, the server will generate its own
listing of the directory.
Example:
DirectoryIndex index.html
then a request for http://myserver/docs/
would return
http://myserver/docs/index.html
if it exists, or would list
the directory if it did not. Note that the documents do not need to be relative to the directory;
DirectoryIndex index.html index.txt /cgi-bin/index.pl
would cause the CGI script /cgi-bin/index.pl
to be executed
if neither index.html
or index.txt
existed in
a directory.


|
keigeki
1.0.0
傾撃
|
#import <CCNode.h>
Class Methods | |
| (id) | + node |
Protected Attributes | |
| float | rotation_ |
| float | scaleX_ |
| float | scaleY_ |
| float | vertexZ_ |
| CGPoint | position_ |
| float | skewX_ |
| float | skewY_ |
| CGPoint | anchorPointInPoints_ |
| CGPoint | anchorPoint_ |
| CGSize | contentSize_ |
| CGAffineTransform | transform_ |
| CGAffineTransform | inverse_ |
| CCCamera * | camera_ |
| CCGridBase * | grid_ |
| NSInteger | zOrder_ |
| CCArray * | children_ |
| CCNode * | parent_ |
| NSInteger | tag_ |
| void * | userData_ |
| id | userObject_ |
| CCGLProgram * | shaderProgram_ |
| ccGLServerState | glServerState_ |
| NSUInteger | orderOfArrival_ |
| CCScheduler * | scheduler_ |
| CCActionManager * | actionManager_ |
| BOOL | isRunning_ |
| BOOL | isTransformDirty_ |
| BOOL | isInverseDirty_ |
| BOOL | visible_ |
| BOOL | ignoreAnchorPointForPosition_ |
| BOOL | isReorderChildDirty_ |
Properties | |
| NSInteger | zOrder |
| float | vertexZ |
| float | skewX |
| float | skewY |
| float | rotation |
| float | scale |
| float | scaleX |
| float | scaleY |
| CGPoint | position |
| CCCamera * | camera |
| CCArray * | children |
| CCGridBase * | grid |
| BOOL | visible |
| CGPoint | anchorPoint |
| CGPoint | anchorPointInPoints |
| CGSize | contentSize |
| BOOL | isRunning |
| CCNode * | parent |
| BOOL | ignoreAnchorPointForPosition |
| NSInteger | tag |
| void * | userData |
| id | userObject |
| CCGLProgram * | shaderProgram |
| NSUInteger | orderOfArrival |
| ccGLServerState | glServerState |
| CCActionManager * | actionManager |
| CCScheduler * | scheduler |
CCNode is the main element. Anything thats gets drawn or contains things that get drawn is a CCNode. The most popular CCNodes are: CCScene, CCLayer, CCSprite, CCMenu.
The main features of a CCNode are:
Some CCNode nodes provide extra functionality for them or their children.
Subclassing a CCNode usually means (one/all) of:
Features of CCNode:
Default values:
Limitations:
Order in transformations with grid disabled
Order in transformations with grid enabled
Camera:
| - (void) addChild: | (CCNode*) | node |
Adds a child to the container with z-order as 0. If the child is added to a 'running' node, then 'onEnter' and 'onEnterTransitionDidFinish' will be called immediately.
Adds a child to the container with a z-order. If the child is added to a 'running' node, then 'onEnter' and 'onEnterTransitionDidFinish' will be called immediately.
Adds a child to the container with z order and tag. If the child is added to a 'running' node, then 'onEnter' and 'onEnterTransitionDidFinish' will be called immediately.
Reimplemented in CCTMXLayer.
| - (CGRect) boundingBox |
returns a "local" axis aligned bounding box of the node in points. The returned box is relative only to its parent. The returned box is in Points.
| - (void) cleanup |
Event that is called when the running node is no longer running (eg: its CCScene is being removed from the "stage" ). On cleanup you should break any possible circular references. CCNode's cleanup removes any possible scheduled timer and/or any possible action. If you override cleanup, you shall call [super cleanup]
Reimplemented in CCMenuItem.
| - (CGPoint) convertToNodeSpace: | (CGPoint) | worldPoint |
Converts a Point to node (local) space coordinates. The result is in Points.
| - (CGPoint) convertToNodeSpaceAR: | (CGPoint) | worldPoint |
Converts a Point to node (local) space coordinates. The result is in Points. treating the returned/received node point as anchor relative.
| - (CGPoint) convertToWorldSpace: | (CGPoint) | nodePoint |
Converts a Point to world space coordinates. The result is in Points.
| - (CGPoint) convertToWorldSpaceAR: | (CGPoint) | nodePoint |
Converts a local Point to world space coordinates.The result is in Points. treating the returned/received node point as anchor relative.
| - (void) draw |
Override this method to draw your own node. You should use cocos2d's GL API to enable/disable the GL state / shaders. For further info, please see ccGLstate.h. You shall NOT call [super draw];
| - (CCAction *) getActionByTag: | (NSInteger) | tag |
Gets an action from the running action list given its tag
| - (CCNode *) getChildByTag: | (NSInteger) | tag |
| - (id) init |
initializes the node
| + (id) node |
allocates and initializes a node. The node will be created as "autorelease".
| - (CGAffineTransform) nodeToParentTransform |
Returns the matrix that transform the node's (local) space coordinates into the parent's space coordinates. The matrix is in Pixels.
| - (CGAffineTransform) nodeToWorldTransform |
Retrusn the world affine transform matrix. The matrix is in Pixels.
| - (NSUInteger) numberOfRunningActions |
Returns the numbers of actions that are running plus the ones that are schedule to run (actions in actionsToAdd and actions arrays). Composable actions are counted as 1 action. Example: If you are running 1 Sequence of 7 actions, it will return 1. If you are running 7 Sequences of 2 actions, it will return 7.
| - (void) onEnter |
| - (void) onEnterTransitionDidFinish |
| - (void) onExit |
| - (void) onExitTransitionDidStart |
| - (CGAffineTransform) parentToNodeTransform |
Returns the matrix that transform parent's space coordinates to the node's (local) space coordinates. The matrix is in Pixels.
| - (void) pauseSchedulerAndActions |
pauses all scheduled selectors and actions. Called internally by onExit
| - (void) removeAllChildrenWithCleanup: | (BOOL) | cleanup |
Removes all children from the container and do a cleanup all running actions depending on the cleanup parameter.
| - (void) removeChild: | (CCNode*) | node | |
| cleanup: | (BOOL) | cleanup | |
Removes a child from the container. It will also cleanup all running actions depending on the cleanup parameter.
| - (void) removeChildByTag: | (NSInteger) | tag | |
| cleanup: | (BOOL) | cleanup | |
Removes a child from the container by tag value. It will also cleanup all running actions depending on the cleanup parameter
| - (void) removeFromParentAndCleanup: | (BOOL) | cleanup |
Remove itself from its parent node. If cleanup is YES, then also remove all actions and callbacks. If the node orphan, then nothing happens.
| - (void) reorderChild: | (CCNode*) | child | |
| z: | (NSInteger) | zOrder | |
Reorders a child according to a new z value. The child MUST be already added.
| - (void) resumeSchedulerAndActions |
resumes all scheduled selectors and actions. Called internally by onEnter
Executes an action, and returns the action that is executed. The node becomes the action's target.
| - (void) schedule: | (SEL) | s |
schedules a selector. The scheduled selector will be ticked every frame
schedules a custom selector with an interval time in seconds. If time is 0 it will be ticked every frame. If time is 0, it is recommended to use 'scheduleUpdate' instead.
If the selector is already scheduled, then the interval parameter will be updated without scheduling it again.
| - (void) schedule: | (SEL) | selector | |
| interval: | (ccTime) | interval | |
| repeat: | (uint) | repeat | |
| delay: | (ccTime) | delay | |
repeat will execute the action repeat + 1 times, for a continues action use kCCRepeatForever delay is the amount of time the action will wait before execution
| - (void) scheduleOnce: | (SEL) | selector | |
| delay: | (ccTime) | delay | |
Schedules a selector that runs only once, with a delay of 0 or larger
| - (void) scheduleUpdate |
check whether a selector is scheduled. schedules the "update" method. It will use the order number 0. This method will be called every frame. Scheduled methods with a lower order value will be called before the ones that have a higher order value. Only one "udpate" method could be scheduled per node.
| - (void) scheduleUpdateWithPriority: | (NSInteger) | priority |
schedules the "update" selector with a custom priority. This selector will be called every frame. Scheduled selectors with a lower priority will be called before the ones that have a higher value. Only one "udpate" selector could be scheduled per node (You can't have 2 'update' selectors).
| - (void) sortAllChildren |
performance improvement, Sort the children array once before drawing, instead of every time when a child is added or reordered don't call this manually unless a child added needs to be removed in the same frame
| - (void) stopAction: | (CCAction*) | action |
Removes an action from the running action list
| - (void) stopActionByTag: | (NSInteger) | tag |
Removes an action from the running action list given its tag
| - (void) stopAllActions |
Removes all actions from the running action list
| - (void) transform |
performs OpenGL view-matrix transformation based on position, scale, rotation and other attributes.
| - (void) transformAncestors |
performs OpenGL view-matrix transformation of its ancestors. Generally the ancestors are already transformed, but in certain cases (eg: attaching a FBO) it is necessary to transform the ancestors again.
| - (void) unschedule: | (SEL) | s |
unschedules a custom selector.
| - (void) unscheduleAllSelectors |
unschedule all scheduled selectors: custom selectors, and the 'update' selector.
Actions are not affected by this method.
| - (void) visit |
recursive method that visit its children and draw them
| - (CGAffineTransform) worldToNodeTransform |
Returns the inverse world affine transform matrix. The matrix is in Pixels.
|
readwritenonatomicretain |
CCActionManager used by all the actions. IMPORTANT: If you set a new CCActionManager, then previously created actions are going to be removed.
|
readwritenonatomicassign |
anchorPoint is the point around which all transformations and positioning manipulations take place. It's like a pin in the node where it is "attached" to its parent. The anchorPoint is normalized, like a percentage. (0,0) means the bottom-left corner and (1,1) means the top-right corner. But you can use values higher than (1,1) and lower than (0,0) too. The default anchorPoint is (0,0). It starts in the bottom-left corner. CCSprite and other subclasses have a different default anchorPoint.
|
readnonatomicassign |
The anchorPoint in absolute pixels. Since v0.8 you can only read it. If you wish to modify it, use anchorPoint instead
|
readnonatomicassign |
A CCCamera object that lets you move the node using a gluLookAt
|
readnonatomicassign |
Array of children
|
readwritenonatomicassign |
The untransformed size of the node in Points The contentSize remains the same no matter the node is scaled or rotated. All nodes has a size. Layer and Scene has the same size of the screen.
|
readwritenonatomicassign |
GL server side state
|
readwritenonatomicretain |
A CCGrid object that is used when applying effects
|
readwritenonatomicassign |
|
readnonatomicassign |
whether or not the node is running
|
readwritenonatomicassign |
used internally for zOrder sorting, don't change this manually
|
readwritenonatomicassign |
A weak reference to the parent
|
readwritenonatomicassign |
Position (x,y) of the node in points. (0,0) is the left-bottom corner.
|
readwritenonatomicassign |
The rotation (angle) of the node in degrees. 0 is the default rotation angle. Positive values rotate node CW.
|
readwritenonatomicassign |
The scale factor of the node. 1.0 is the default scale factor. It modifies the X and Y scale at the same time.
|
readwritenonatomicassign |
The scale factor of the node. 1.0 is the default scale factor. It only modifies the X scale factor.
|
readwritenonatomicassign |
The scale factor of the node. 1.0 is the default scale factor. It only modifies the Y scale factor.
|
readwritenonatomicretain |
CCScheduler used to schedule all "updates" and timers. IMPORTANT: If you set a new CCScheduler, then previously created timers/update are going to be removed.
|
readwritenonatomicretain |
Shader Program
|
readwritenonatomicassign |
The X skew angle of the node in degrees. This angle describes the shear distortion in the X direction. Thus, it is the angle between the Y axis and the left edge of the shape The default skewX angle is 0. Positive values distort the node in a CW direction.
|
readwritenonatomicassign |
The Y skew angle of the node in degrees. This angle describes the shear distortion in the Y direction. Thus, it is the angle between the X axis and the bottom edge of the shape The default skewY angle is 0. Positive values distort the node in a CCW direction.
|
readwritenonatomicassign |
A tag used to identify the node easily
|
readwritenonatomicassign |
A custom user data pointer
|
readwritenonatomicretain |
Similar to userData, but instead of holding a void* it holds an id
|
readwritenonatomicassign |
The real openGL Z vertex. Differences between openGL Z vertex and cocos2d Z order:
|
readwritenonatomicassign |
Whether of not the node is visible. Default is YES
|
readwritenonatomicassign |
The z order of the node relative to its "siblings": children of the same parent


Information is given for the southern Republic of Cyprus, which lies approximately south of parallel 35°N.
Cyprus Radio maintains continuous watch on VHF Channels 16 and 26.
The Cypriot courtesy flag must be flown when entering Cypriot waters. Everyone should remain on board until clearance formalities are completed. Ship's papers will be required by the port authority on arrival. Evidence of insurance may also be requested. A total of five crew lists will be needed.
As soon as you land, you must obtain a Marina pass. Submit two passport size photos to the Marina Office, and write in block letters, behind one of the two photos, your name, surname, passport no., nationality, yacht's name and your profession. Your pass must be left at the marina office on departure, but otherwise it must be carried at all times. Without it you will have difficulty entering and leaving the Marina. Visitors to the boat must also have identification and be met at the gate.
All yachts (EU or not) must clear with Customs.
Departure
Port Police and Immigration work 24/7 but Customs Office is only staffed 7am to 3pm Monday to Friday. It is possible to clear out them ahead of time is leaving at a weekend.
If Planning to Visit the North
The captains of boats which have stopped in Northern Cyprus are liable to incur heavy penalties and possibly imprisonment. This is because the authorities in the Republic of Cyprus consider it an illegal act to stop in an area of the island, which is under foreign occupation. This is a very serious matter and mariners are therefore advised to avoid at all cost being caught in the politics of the area.
If wishing to visit the south after having cleared out of the north, then it is advisable to visit a Turkish or Greek island first to avoid having any evidence of having been in the north.
Last updated November 2015.
Although a member of the EU, Cyprus is not part of the Schengen Area and so is a useful place to wait to 're-set' your Schengen visit limit.
Visas are not required by citizens of the European Economic Area, and many other counties, for stays of up to 3 months. See www.mfa.gov.cy for full list of nationalities do and do not require a visa.
Cyprus does operate a policy of allowing visits of 90 days in a 180 day period.
A permit to stay more than three months may be issued by the Chief Immigration Officer, Nicosia. In this circumstance, Immigration normally retains passports and each person is issued with a landing permit.
Passports of must be valid for 3 months beyond the period of intended stay. Visitors who need to obtain a visa are advised to have a passport valid at least 6 months beyond the period of intended stay.
Admission will be refused to holders of "passports" from the so-called "Turkish Republic of Northern Cyprus".
Visitors having stamps or visas of the so-called "Turkish Republic of Northern Cyprus" in their passports are only allowed to enter Cyprus after the above-mentioned visas/stamps are cancelled by the Immigration Authorities of the Republic of Cyprus.
Last updated November 2015.
All boats must report to Customs. EU boats need proof of VAT paid status, boat registration, Bill of Sale and, if possible, a builder's certificate and evidence of conformity with EU regulations and build standards. Many older boats may not have these documents in which case Customs will do an inspection.
Non-EU boats, non-VAT paid are issued with a 6 month import licence which can then be extended. Proof that the skipper is not an EU citizen (i.e. passport) is required. The import licence can be suspended if the skipper leaves the country as long as Customs is notified of departure and return.
If wishing to import a yacht (or motorboat) into the EU, the Cyprus applies the lowest rate of VAT (ranging from 3.4% to 11.7%).
Firearms must be declared and surrendered to customs on arrival. Sporting guns require a temporary permit which has to be obtained from the Ministry of the Interior.
Antiques and gold coins may not be exported.
Duty-free stores should be declared on entry and may be removed and retained by Customs until departure. Duty-free stores are available 24 hours before departure, spirits being limited to one case per person. Duty-free may be obtained in all ports where there are bonded stores.
Also prohibited are flick knives, daggers and swords, obscene books, photographs, films and articles, goods bearing a forged trade marks, pirated or counterfeit goods (e.g.computer programmes, films video and audio cassettes, CD, etc).
Uncooked meat and fish and products thereof, milk or dairy products will need to be inspected.
In order to protect the agriculture of the country from the danger of the introduction of new pests and diseases, the importation of agricultural products such as fruit, vegetables, cut-flowers, dry nuts, seeds, bulbs, bulb-wood sticks, cuttings, etc., is prohibited.
Further details can be obtained from The Customs website
Last updated November 2015.
Customs overtime fee for clearance outside office hours (07:30-14:30), Sundays, or holidays.
There are military restricted areas along the coasts and yachts should maintain a distance of 1640 ft (500 m) offshore by day and 3280 ft (1000 m) by night unless approaching a port.
Spearfishing is prohibited within bathing areas, which are marked by red buoys. A special licence is required for spearfishing with scuba gear. This is issued only to certified divers and is easily obtained from the District Fisheries Departments, on presentation of a certified diving licence. There is no charge.
It is forbidden to take antiquities and sponges from the sea.
All expired flares must be submitted immediately for disposal to the Marina Personnel.
Cyprus (but not Northern (Turkish) Cyprus)comes under the European Regulations on non-commercial movement of pet animals - Pet Travel Scheme (PETS) and usual rules apply requiring passport, microchip, current Rabies Vacination Certificate and Health Certificate.
The Authorities must be advised in advance that the yacht has a pet on board.
Parrots and budgerigars are prohibited.
The following breeds of dogsare not allowed into the Republic of Cyprus regardless of their country of origin:-
American Pitbull Terrier; Pitbull Terrier
Japanese Tosa; Tosa Inu
Dogo Argentino; Argentinean Mastiff
Fila Brasileiro; Brazilian Mastiff
Further information can be obtained from the Veterinary Services of the Ministry of Agriculture, Natural Resources and Environment.


{% extends "base.html" %} {% load bootstrap3 %} {% load customfilters %} {% block title %}results{% endblock title%} {% block content %}
The following table contains the items, and the number of times each choice has been selected for that item.
| Item | {% for choice in vote.choices.all %}{{ choice.choice }} | {% endfor %}
|---|---|
| {{ item.item }} | {% for choice in vote.choices.all %}{{ item|getcount:choice }} | {% endfor %}


research articles mining literature summarization available keyword text dataset wise article deepMINE user Download Open mined new summary using "CORONAVIRUS" coronavirus keywords language quick <;/div>;" ;<;span Fig. View abstract automatic country data-hide-link-title=""> data-icon-position="" figure human interested keyword(s) natural pandemic tab virus xmlns=";http://www.w.org//xhtml"; Coronavirus articles. efficient generate information openly pp. process words ,,, CORD- COVID- Research based brief deep existing fast given huge learning nearly novel number objective processing returns scanning showing shown technical title various view


Star
Fork
Watch
MP4Box.js / ISOBMFF Box Structure Viewer (see
other demos
)
Client-side tool: files are processed locally in your browser and not uploaded to any server
File Overview
Box View
Sample View
Item View
Entity Group View
Segment View
Tree View
Partition View
Treemap View
Box Tree View
Box Property View
View type:
Sample Table
Sample Graph
Sample Map
Sample Timeline
Track ID:
Codec:
Sample range:
View type:
Segment Table
Segment Graph
Segment range:


Proximal Policy Optimization¶
Actions space: Discrete | Continuous
References: Proximal Policy Optimization Algorithms
Network Structure¶
Algorithm Description¶
Choosing an action - Continuous actions¶
Run the observation through the policy network, and get the mean and standard deviation vectors for this observation. While in training phase, sample from a multi-dimensional Gaussian distribution with these mean and standard deviation values. When testing, just take the mean values predicted by the network.
Training the network¶
Collect a big chunk of experience (in the order of thousands of transitions, sampled from multiple episodes).
Calculate the advantages for each transition, using the Generalized Advantage Estimation method (Schulman ‘2015).
Run a single training iteration of the value network using an L-BFGS optimizer. Unlike first order optimizers, the L-BFGS optimizer runs on the entire dataset at once, without batching. It continues running until some low loss threshold is reached. To prevent overfitting to the current dataset, the value targets are updated in a soft manner, using an Exponentially Weighted Moving Average, based on the total discounted returns of each state in each episode.
Run several training iterations of the policy network. This is done by using the previously calculated advantages as targets. The loss function penalizes policies that deviate too far from the old policy (the policy that was used before starting to run the current set of training iterations) using a regularization term.
After training is done, the last sampled KL divergence value will be compared with the target KL divergence value, in order to adapt the penalty coefficient used in the policy loss. If the KL divergence went too high, increase the penalty, if it went too low, reduce it. Otherwise, leave it unchanged.
-
class
rl_coach.agents.ppo_agent.
PPOAlgorithmParameters
[source]¶ - Parameters
policy_gradient_rescaler – (PolicyGradientRescaler) This represents how the critic will be used to update the actor. The critic value function is typically used to rescale the gradients calculated by the actor. There are several ways for doing this, such as using the advantage of the action, or the generalized advantage estimation (GAE) value.
gae_lambda – (float) The \(\lambda\) value is used within the GAE function in order to weight different bootstrap length estimations. Typical values are in the range 0.9-1, and define an exponential decay over the different n-step estimations.
target_kl_divergence – (float) The target kl divergence between the current policy distribution and the new policy. PPO uses a heuristic to bring the KL divergence to this value, by adding a penalty if the kl divergence is higher.
initial_kl_coefficient – (float) The initial weight that will be given to the KL divergence between the current and the new policy in the regularization factor.
high_kl_penalty_coefficient – (float) The penalty that will be given for KL divergence values which are highes than what was defined as the target.
clip_likelihood_ratio_using_epsilon – (float) If not None, the likelihood ratio between the current and new policy in the PPO loss function will be clipped to the range [1-clip_likelihood_ratio_using_epsilon, 1+clip_likelihood_ratio_using_epsilon]. This is typically used in the Clipped PPO version of PPO, and should be set to None in regular PPO implementations.
value_targets_mix_fraction – (float) The targets for the value network are an exponential weighted moving average which uses this mix fraction to define how much of the new targets will be taken into account when calculating the loss. This value should be set to the range (0,1], where 1 means that only the new targets will be taken into account.
estimate_state_value_using_gae – (bool) If set to True, the state value will be estimated using the GAE technique.
use_kl_regularization – (bool) If set to True, the loss function will be regularized using the KL diveregence between the current and new policy, to bound the change of the policy during the network update.
beta_entropy – (float) An entropy regulaization term can be added to the loss function in order to control exploration. This term is weighted using the \(eta\) value defined by beta_entropy.


Communication link between RobotData and the user.
More...
#include <robot_frontend.hpp>
|
|
typedef time_series::Timestamp | TimeStamp |
| |
|
|
std::shared_ptr< RobotData< Action, Observation > > | robot_data_ |
| |
template<typename Action, typename Observation>
class robot_interfaces::RobotFrontend< Action, Observation >
Communication link between RobotData and the user.
Takes care of communication between the RobotData and the user. It is just a thin wrapper around RobotData to facilitate interaction and also to make sure the user cannot use RobotData in incorrect ways.
- Template Parameters
-
- Examples:
- demo.cpp, and demo_multiprocess_frontend.cpp.
◆ append_desired_action()
template<typename Action , typename Observation >
Append a desired action to the action time series.
This will append an action to the "desired actions" time series. Note that this does not block until the action is actually executed. The time series acts like a queue from which the RobotBackend takes the actions one by one to send them to the actual robot. It is possible to call this method multiple times in a row to already provide actions for the next time steps.
The time step at which the given action will be applied is returned by this method.
- Parameters
-
| desired_action | The action that shall be applied on the robot. Note that the actually applied action might be different depending on the implementation of the RobotDriver (see get_applied_action). |
- Returns
- Time step at which the action will be applied.
◆ get_applied_action()
template<typename Action , typename Observation >
Get the applied action of time step t.
The applied action is the one that was actually applied to the robot based on the desired action of that time step. It may differ from the desired one e.g. due to some safety checks which limit the maximum torque. If and how the action is modified depends on the implementation of the RobotDriver.
- Parameters
-
| t | Index of the time step. If t is in the future, this method will block and wait. |
- Returns
- The applied action of time step t.
- Exceptions
-
| std::invalid_argument | if t is too old and not in the time series buffer anymore. |
◆ get_current_timeindex()
template<typename Action , typename Observation >
Get the current time index.
- Returns
- The latest time index for which observations are available.
◆ get_desired_action()
template<typename Action , typename Observation >
Get the desired action of time step t.
The desired action is the action as it is passed by the user in append_desired_action.
- Parameters
-
| t | Index of the time step. If t is in the future, this method will block and wait. |
- Returns
- The desired action of time step t.
- Exceptions
-
| std::invalid_argument | if t is too old and not in the time series buffer anymore. |
◆ get_observation()
template<typename Action , typename Observation >
Get observation of time step t.
- Parameters
-
| t | Index of the time step. If t is in the future, this method will block and wait. |
- Returns
- The observation of time step t.
- Exceptions
-
| std::invalid_argument | if t is too old and not in the time series buffer anymore. |
◆ get_time_stamp_ms()
template<typename Action , typename Observation >
◆ get_timestamp_ms()
template<typename Action , typename Observation >
Get the timestamp of time step t.
- Parameters
-
| t | Index of the time step. If t is in the future, this method will block and wait. |
- Returns
- Timestamp of time step t.
- Exceptions
-
| std::invalid_argument | if t is too old and not in the time series buffer anymore. |
◆ wait_until_timeindex()
template<typename Action , typename Observation >
Wait until the specified time step is reached.
- Parameters
-
| t | Time step until which is waited. |
- Exceptions
-
| std::invalid_argument | if t is too old and not in the time series buffer anymore. |
The documentation for this class was generated from the following file:


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#include <sparse_ops.h>
Applies softmax to a batched N-D SparseTensor
.
The inputs represent an N-D SparseTensor with logical shape [..., B, C]
(where N >= 2
), and with indices sorted in the canonical lexicographic order.
This op is equivalent to applying the normal tf.nn.softmax()
to each innermost logical submatrix with shape [B, C]
, but with the catch that the implicitly zero elements do not participate. Specifically, the algorithm is equivalent to the following:
(1) Applies tf.nn.softmax()
to a densified view of each innermost submatrix with shape [B, C]
, along the size-C dimension; (2) Masks out the original implicitly-zero locations; (3) Renormalizes the remaining elements.
Hence, the SparseTensor
result has exactly the same non-zero indices and shape.
Arguments:
NNZ x R
matrix with the indices of non-empty values in a SparseTensor, in canonical ordering.NNZ
non-empty values corresponding to sp_indices
.Returns:
Output
: 1-D. The NNZ
values for the result SparseTensor
.
Constructors and Destructors |
|
|---|---|
SparseSoftmax(const ::tensorflow::Scope & scope, ::tensorflow::Input sp_indices, ::tensorflow::Input sp_values, ::tensorflow::Input sp_shape)
|
Public attributes |
|
|---|---|
operation
|
|
output
|
Public functions |
|
|---|---|
node() const
|
::tensorflow::Node *
|
operator::tensorflow::Input() const
|
|
operator::tensorflow::Output() const
|
|
Operation operation
::tensorflow::Output output
SparseSoftmax( const ::tensorflow::Scope & scope, ::tensorflow::Input sp_indices, ::tensorflow::Input sp_values, ::tensorflow::Input sp_shape )
::tensorflow::Node * node() const
operator::tensorflow::Input() const
operator::tensorflow::Output() const


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Page 1 of 1
Textures
Posted:
Sun Jan 07, 2007 2:14 pm
by Vitriol
Most people have lots of RAM/VideoRAM these days. I think we can use much higher resolution textures.
Posted:
Sun Jan 07, 2007 2:19 pm
by Karl G.
Yeah memory isn't a problem, only rendering speed and texture quality are (high-res textures viewed from a distance tend to be "shimmery" unless mipmapping is enabled, which causes textures to bleed into each other).
I'd like to upgrade all of Xen's stuff anyway.
Posted:
Sun Jan 07, 2007 3:26 pm
by secretrk1313
i found this forum through gamedev and i have been wondering what this "XEN" is can someone show me a screenshot, tell me what its like, and explain the story of whats going on with this game?
Posted:
Sun Jan 07, 2007 3:28 pm
by Vitriol
Its a recreation of Xenimus (google it) We intend to make the game feel like Xenimus did a long time ago before Xenimus got ruined.
Posted:
Sun Jan 07, 2007 3:52 pm
by thadiusofx3
wait you've had no idea what we were talking about this whole time?
Posted:
Sun Jan 07, 2007 9:02 pm
by secretrk1313
nope not at all, i saw a game that needed 3D models and i came to help.
looks like a fun though, shoulld i be downloading/playing it? whats wrong with it?
Posted:
Sun Jan 07, 2007 9:07 pm
by thadiusofx3
no don't download or play it lol
it's created by one man who sucks at programming and doesn't listen to anyone but himself, won't take any help with the game at all, and has crappy customer service.
Posted:
Sun Jan 07, 2007 9:11 pm
by 9sam1
Yet we where all addicted to it
.
Posted:
Sun Jan 07, 2007 10:15 pm
by Vitriol
9sam1 wrote:Yet we where all addicted to it
.
How anaologous to CRACK COCAINE.
In any case, lets get some snow textures so we can start making the snowy north section of the map.
Posted:
Sun Jan 07, 2007 10:18 pm
by Serenity
Wold I be able to like get a picture of snow and use it as a texture?
If so I can do that tomorrow If I still have any
Posted:
Sun Jan 07, 2007 10:21 pm
by thadiusofx3
We can see if the person who made the 2005 christmas update will let us use their textures, they were really good.


Heartbreaking deleted scene from 'Avengers: Endgame' released
Cape Town – Katherine Langford's deleted scene in Avengers: Endgame can now finally be seen.
Although the 13 Reasons Why actress was cast in the film fans were confused when she didn't appear in the theatrically released version earlier this year as her scene never made the final cut.
On Tuesday the film was released on Disney's streaming service with the addition of the deleted scene.
According to CoverMedia, it is a devastating goodbye between Tony Stark (Robert Downey Jr) and his daughter, Morgan (Langford).
The scene titled Tony at the Way Station takes place in a realm, not unlike the one in which Thanos finds himself during the end of Avengers Infinity War, explains CNN.
What follows is a conversation between Tony and Morgan, who is now an adult.
He questions whether he made the right decision to sacrifice himself; she reassures him, telling him she is proud of him.
WATCH THE CLIP HERE:
Compiled by Leandra Engelbrecht (Sources: CoverMedia, CNN ).


Download Statistics
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Specifying the Contents
The contents for your package must be organized into a single directory, called a root. The directory structure within this root will be installed to the install location.
The root directory for your package is specified in the “Contents” tab, and can be an absolute or relative path.
If there are files in your root that you do not wish to be included in your package (e.g. CVS or Subversion directories), you can filter them with regular expression “File Filters”.
More Info…
If you are installing items to multiple locations, the affected directory structure must be duplicated within your root. For example, if you were installing an application to /Applications
and a font to /Library/Fonts
, you'd have a root similar to the following:
- Root - Applications My Great App.app - Library - Fonts - My Great Font.dfont
In this example, you would make your install location /. However, it is recommended that in cases like this, you make separate packages for the application and for the font.
It is also critical in situations like this to ensure that the permissions and ownership of files in your root are correct.
More Info…
An Install_Resources
directory within your root will not be installed, but rather used by Installer.app to configure its interface during your install.
More Info…


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Quick Sketches of people waiting in line.


Luke Kwerneland
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First Block isn't actually too hard. It's quite similar to planning a cross, you just need to get used to doing it if you're coming from a CFOP background.
If you're trying to switch from CFOP, or maybe just to improve your first block, here are a few videos that are extremely useful.


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查看更多The core idea of preventative maintenance is the performance of proactive actions against unscheduled downtime in order to identify potential defects before they evolve into something more severe.
The planned maintenance percentage is the total planned maintenance time in your CMMS software over a date range over the total number of hours of a service performed by the technicians.
Calculating the critical percentage of planned maintenance will help you prioritize the most important operations that should be performed immediately.
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The number of impressions
Display Result


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Maintainer: The Unix Shell


When pressed, a floating action button can display multiple primary actions that can be performed on a page.
When pressed, a floating action button can display multiple primary actions that can be performed on a page. It has a collection of additional options defined by the model property. SpeedDial's position is calculated according to the container element with the position type style.
SpeedDial uses the common MenuModel API to define the items, visit MenuModel API for details.
SpeedDial has 4 types; linear, circle, semi-circle and quarter-circle.
Specifies the opening direction of actions. For the linear and semi-circle types; up, down, left and right. For the quarter-circle type; up-left, up-right, down-left and down-right.
Any property as style and class are passed to the main container element. Following are the additional properties to configure the component.
| Name | Type | Default | Description |
|---|---|---|---|
| id | string | null | Unique identifier of the element. |
| model | object | null | MenuModel instance to define the action items. |
| visible | boolean | false | Specifies the visibility of the overlay. |
| className | string | null | Style class of the element. |
| style | object | null | Inline style of the element. |
| direction | string | up | Specifies the opening direction of actions. Valid values are 'up', 'down', 'left', 'right', 'up-left', 'up-right', 'down-left' and 'down-right' |
| transitionDelay | number | 30 | Transition delay step for each action item. |
| type | string | linear | Specifies the opening type of actions. |
| radius | number | 0 | Radius for *circle types. |
| mask | boolean | false | Whether to show a mask element behind the speeddial |
| disabled | boolean | false | Whether the component is disabled. |
| hideOnClickOutside | boolean | true | Whether the actions close when clicked outside. |
| buttonClassName | string | null | Style class of the button element. |
| buttonStyle | object | null | Inline style of the button element. |
| buttonTemplate | any | null | Template of button element. |
| maskClassName | string | null | Style class of the mask element. |
| maskStyle | object | null | Inline style of the mask element. |
| showIcon | string | pi pi-plus | Show icon of the button element. |
| hideIcon | string | null | Hide icon of the button element. |
| rotateAnimation | boolean | true | Defined to rotate showIcon when hideIcon is not present. |
| Name | Parameters |
|---|---|
| button | - |
| Name | Parameters | Description |
|---|---|---|
| onVisibleChange | visible: Whether the actions are visible. | Fired when the visibility of element changed. |
| onClick | event: Browser event. | Fired when the button element clicked. |
| onShow | - | Fired when the actions are visible. |
| onHide | - | Fired when the actions are hidden. |
Following is the list of structural style classes, for theming classes visit theming page.
| Name | Element |
|---|---|
| p-speeddial | Container element. |
| p-speeddial-button | Button element of speeddial. |
| p-speeddial-mask | Mask element of speeddial. |
| p-speeddial-list | List of the actions. |
| p-speeddial-item | Each action item of list. |
None.


Trump Jumps Into UK Election, Pushing Brexit & Nigel Farage
by CAIN BURDEAU(CN) — In a tag-team effort, President Donald Trump and Nigel Farage, a nationalist British politician supported by the far right, joined forces to push Prime Minister Boris Johnson into breaking off ties with the European Union and backing an even more hardcore Brexit strategy before the December elections.
Ignoring the diplomatic protocol of not meddling in elections in foreign countries, Trump spent 28 minutes Thursday evening talking with Farage on his LBC radio show in London about what he’d like to happen in the United Kingdom.
Farage and Trump are friends and both push nationalist views. Farage’s political career is based around the U.K.’s withdrawal from the EU and he was a key figure backing the referendum to leave the EU. Trump also favors Brexit.
Trump praised Johnson in the conversation, and urged the prime minister to join forces with Farage’s Brexit Party and also scrap a withdrawal deal Johnson and the EU have come up with to keep trading and political ties somewhat close after Britain leaves the EU.
That deal, though, was blocked by a deeply divided Parliament and months of deadlock over Brexit forced the House of Commons last week to call a rare snap election for December.
Trump said the deal Johnson is pursuing with the EU would make it impossible for the U.K. and U.S. to enter into a free trade deal.
By saying that, he undermined one of Johnson’s core arguments in favor of his Brexit plans. Johnson and his allies in the Conservative Party say that leaving the EU will make the U.K. wealthier by allowing it to strike new trade deals around the globe. Many economists question that assumption.
Trump’s remarkable call into Farage’s radio show teed up what came next on Friday. At a news conference to launch the Brexit Party’s election campaign, Farage challenged Johnson to drop his EU deal or face his Brexit Party fielding candidates throughout Great Britain in the election.
It was a one-two punch that could make Johnson’s election campaign much more difficult and, ironically, improve the chances of those opposing Brexit.
Regardless, Trump’s intentions appeared clear: Back Johnson, back Brexit, back Farage and slam the opposition leader, Labour’s Jeremy Corbyn.
“He’s a fantastic man,” Trump said about Johnson. “I think he’s the exact right guy for the times.”
Trump added that Farage and Johnson should form an alliance to ensure the U.K. leaves the EU. He said the two would become “an unstoppable force.”
“I’d like you and Boris get together because you would really have some numbers,” Trump said of their chances of winning the election. “He [Johnson] respects you a lot, I can tell you that,” Trump said, referring to conversations he apparently has had with Johnson about the matter. “I wish you two guys could get together.”
But the Conservatives are unlikely to align themselves with Farage.
Robert Jenrick, the Tory housing secretary, said on a Friday morning television show that the Conservatives “are not interested in doing any pacts with the Brexit Party, or, indeed with anybody else.”
Trump laid into Corbyn, the opposition leader. Corbyn is pushing Labour further to the left and has advocated renationalizing parts of the British economy, such as utilities.
“Corbyn would be so bad for your country. He’d take you into such bad places,” Trump said.
He said Corbyn was “of a different persuasion, to put it mildly.”
Corbyn responded in a Tweet that Trump was “trying to interfere in U.K. election to get his friend Boris Johnson elected.”
Despite Trump’s criticism of Corbyn, Trump’s comments about Johnson’s deal with the EU could end up being damaging for Johnson.
Trump called Brexit a “good concept” but he questioned the deal Johnson negotiated with the EU.
“To be honest with you, this deal, under certain aspects of the deal, you can’t do it, you can’t trade,” Trump said.
Trump claimed the U.K. would reap huge benefits by opening its market to the United States. He said the U.K. was being “held back” in trade by its membership in the EU.
“I think we can do many times the numbers [on trade] that we’re doing right now,” Trump said. “And certainly much bigger numbers than you’re doing with the EU.”
British news media cited government officials who dismissed Trump’s claims and said Johnson’s deal would allow the U.K. to leave the EU’s customs regime and “strike our own free trade deals.”
On Friday, Farage took Trump’s comments and turned them into campaign fodder.
“There will not be trade deals with the U.S. or I think with any other part of the world,” Farage said, citing what Trump had said on his show. Farage said the deal leaves the U.K. too closely tied to the EU.
Farage threatened the Tories with an electoral fight unless Johnson agreed to scrap his EU deal and promised to leave the EU without a deal.
“I am going to say to Johnson, ‘Drop the deal because it is not Brexit,’” he said.
Farage gave Johnson two weeks to ditch his EU deal and agree to a “nonaggression pact” or face the Brexit Party challenging Tories for their seats. In the event they forge an alliance, Farage said the Brexit Party would focus on challenging Labour seats.
Quoting Trump, Farage said an alliance would make them “an unstoppable force.”
Tories pushed back against Farage.
“A vote for Farage risks letting Jeremy Corbyn into Downing Street via the back door,” said James Cleverly, the chairman of the Tory party. “It will not get Brexit done, and it will create another gridlocked Parliament that doesn’t work.”
(Courthouse News reporter Cain Burdeau is based in the European Union.)
Copyright © 2019 courthousenews.com


Programming languages
Python, C#, Mathematica and Rust
Version control
Computer-aided design
Autodesk Inventor
Data analysis
numpy, scipy, pandas, classical statistics, machine learning (scikit-learn and TensorFlow)
Visualization
matplotlib, Adobe Illustrator and PowerPoint
Hardware
Optical systems design and prototyping, machining, analog electronics, PID control and vacuum systems
Languages
English (Fluent), Serbian (Native)


{{Db-g3}}
{{Infobox concert tour
| concert_tour_name = McFly World Domination Tour
| image =
| image_size =
| image_caption =
| artist = [[McFly]]
| album =
| start_date = 7 March 2015
| end_date = 21 April 2015
| number_of_legs = 1
| number_of_shows = 30
| last_tour = [[McBusted Tour]]
(2014)
| this_tour = '''McFly World Domination Tour'''
(2015)
| next_tour =
}}
The "'''McFly World Domination Tour'''" is the third theatre tour undertaken by British pop-rock band [[McFly]]. The tour will start on 7 March 2015 and is due to conclude on 21 April.
Nothing has been confirmed and this is presumed to be a hoax.
==Setlist==
:1.Love is on the radio
:2.Star Girl
:3.Transylvania
:4.Five Colours In Her Hair
:5.Falling In Love
:6.Room On The 3rd Floor
:7.Obviously
:8.Corrupted
:9.Nowhere Left To Run
:10.Lies
:11.I'll Be OK
:12.Bubblewrap
:13.Smile
:14.Shine A Light
:15.One For The Radio
:16.Memory Lane
: A. "[[Livin' on a Prayer]]"
: B. "[[We Found Love]]"
: C. "[[What Makes You Beautiful]]"
: D. "[[Year 3000]]"
: E. "[[Somebody That I Used To Know]]"
: F. "[[I Wanna Dance With Somebody (Who Loves Me)]]"
Encore
:17. Love Is Easy
:18. All About You
:19. The Heart Never Lies
==Tour Dates==
{| class="wikitable" style="text-align:center;"
! style="width:90px;"| Date
! style="width:100px;"| City
! style="width:180px;"| Venue
|-
|March 7
|[[Portsmouth]]
|Portsmouth Guildhall
|-
|March 9
|[[Sheffield]]
|City Hall
|-
|March 10
|[[Manchester]]
|02 Manchester Apollo
|-
|March 11
|[[Oxford]]
|New Theatre
|-
|March 13
|[[Margate]]
|Winter Gardens
|-
|March 14
|[[Folkestone]]
|Leas Cliff Hall
|-
|March 16
|[[Nottingham]]
|Royal Concert Hall
|-
|March 17
|[[Leeds]]
|02 Academy Leeds
|-
|March 18
|[[Stoke-upon-Trent|Stoke]]
|Hanley Victoria Hall
|-
|March 21
|[[Southampton]]
|Southampton Guildhall
|-
|March 22
|[[Swindon]]
|Swindon Oasis
|-
|March 24
|[[Plymouth]]
|Plymouth Pavilions
|-
|March 25
|[[Torquay]]
|Torquay Riviera
|-
|March 26
|[[Bristol]]
|Colston Hall
|-
|March 28
|[[Ipswich]]
|Regent Theatre
|-
|March 30
|[[Birmingham]]
|02 Academy Birmingham
|-
|April 1
|[[Lincoln, England|Lincoln]]
|Engine Shed
|-
|April 3
|[[Norwich]]
|Norwich UEA
|-
|April 4
|[[Southend]]
|Southend Cliffs Pavilion
|-
|April 6
|[[Reading, Berkshire|Reading]]
|Hexagon Theatre
|-
|April 7
|[[London]]
|HMV Hammersmith Apollo
|-
|April 10
|[[Cambridge]]
|Cambridge Corn Exchange
|-
|April 11
|[[Preston, Lancashire|Preston]]
|Preston Guild Hall
|-
|April 13
|[[Glasgow]]
|SECC Clyde Auditorium
|-
|April 14
|[[Newcastle upon Tyne|Newcastle]]
|02 Academy Newcastle
|-
|April 15
|[[Leicester]]
|De Montfort Hall
|-
|April 17
|[[York]]
|York Barbican
|-
|April 19
|[[Birmingham]]
|02 Academy Birmingham
|-
|April 20
|[[London]]
|HMV Hammersmith Apollo
|-
|April 21
|[[Manchester]]
|02 Apollo Manchester
|}
==References==
{{reflist}}
{{McFly}}
{{DEFAULTSORT:World Domination}}
{{wikipedia-deleted|Milowent}}


IWAVE defines a framework for time-stepping simulation based on regular grids. While any sort of time-dependent problem might be attacked with IWAVE tools, the target application is wave simulation by explicit finite difference or finite element methods. The guiding principle of the IWAVE project is to abstract from all simulators of the type just described the common attributes, including job control, memory allocation, and i/o, and provide code which realizes these common computations without requiring the user to redefine them. In the end, only six main functions are required, each of which defines a common interface to a model-defined computation. Generally, the user will also need to supply model-dependent functions to which the six main functions will delegate, but that is merely a matter of convenience and good code organization - in principle, six functions plus a driver are all that is required.
Obtaining IWAVE:
See the TRIP software page for current download and installation instructions and release notes.
Generic Subpackages:
- IWAVE core classes. -
- classes for regularly sampled gridded data. -
- SEGY/SU based classes for trace data. -
- general facilities for out of core data containers, used in grid and trace. -
- utility classes and functions - MPI interface, useful data structures, command line parameter parser, file management system
Model Subpackages:
- Acoustic constant density finite difference modeling, uses (2,2k) schemes with cross stencil for 2nd order wave equation, pressure-free boundaries
- Acoustic variable density staggered grid finite difference modeling, uses (2,2k) schemes with cross stencil, pressure-free and PML boundaries
- Elastic staggered grid finite difference modeling, uses (2,2k) schemes with cross stencil
struct paper, also 08/09/10 annual report papers
Credits
The initial design of IWAVE, including basic data structures and computed
domain decomposition software, is due to Igor Terentyev [REF: Igor's
MA thesis]. He also created the first acoustic staggered grid
application within IWAVE, used to QC the Phase I SEAM acoustic model
data (ref to Fehler). Tetyana Vdovina added and tested the PML
implementation for the acoustic staggered grid code. William Symes,
Dong Sun, Xin Wang, Lei Fu, and Yin Huang have all contributed to
the development of IWAVE.
Maintained by William W<EMAIL_ADDRESS>Photo courtesy Ivy Gonzalez
Last modified: Wed Dec 3 11:34:52 PST 2014


"Welcome to my Repository"
"Hope you find it interesting"
BACKGROUND I graduated from The George Washington University with a degree in Civil Engineering and from Hampton University with a degree in general science. I have over ten years of work experience as a Life Cycle Manager with the Department of the Navy and as a Product Engineer with an automotive supplier. Moving several times with my husband's career I've had to re-invent and adapt myself with each move. Part of my career has been as a stay-at-home mom with a few part time jobs to keep my sanity. Ending with a position at the University of Pittsburgh, Katz Graduate School of Business as an Employment Coordinator for the MBA students. TOUGH: The last few years have been tough as an in-home caregiver for my spouse and caring for my father along with Covid. MOVING FORWARD: I have taken a Project Management course through the Virginia Tech continuing education system and now I am currently enrolled in the MIT xPro full stack curriculum program. The program is very challenging and I will be completing the JavaScript, CSS, HTML and front end segment of the course. As arduous as this course has been, I have learned a lot and been inspired by my female cohorts. I've also had a few firsts by taking this course: first online class, first time coding, first YouTube video and my first GitHub repository! PROJECT: Feel free to view my 3 projects by clicking on the navigation bar above. Press the "Home" button to go to my ccgarrison.github.io repository. Each project repository is linked under the Projects title.


Washington Park is the oldest park in Burlingame. The park is a year round center for community recreation. The Burlingame Recreation Division provides hundreds of classes and activities each year in the park and the Recreation Center. Tennis courts, a children’s playground, picnic areas, and baseball facilities are all in constant use. The park also has historical significance. It was once home to the Gunst Estate. Many of the magnificent large trees in the park date back to the estate. The baseball grandstand in the park dates back to the ‘30s. It was renovated in 1989. It still retains its original character and enjoys heavy use.
850 Burlingame Ave Burlingame, CA 94010 850 Burlingame Ave Burlingame, CA 94010


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mosquittoChat Docs!¶
An MQTT protocol based Chat-Server/Chat-System using Mosquitto Broker, tornado as web server, sockjs in client(browser) side javascript library, sockjs-tornado as sockjs implementation on server side and paho-mqtt (mqtt python client).
Home Page : https://pypi.python.org/pypi/mosquittoChat
Details¶
| Author: | Anirban Roy Das |
|---|---|
| Email: |<EMAIL_ADDRESS>|
| Copyright(C): | 2016, Anirban Roy Das<EMAIL_ADDRESS>|
Check rabbitChat/LICENSE
file for full Copyright notice.